essay

research articles on job performance

Share on FacebookShare on Twitter

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 09 May 2024

Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies

• Nan Wang 1   na1 ,
• Yuxiang Luan 2   na1 &

Humanities and Social Sciences Communications volume  11 , Article number:  595 ( 2024 ) Cite this article

852 Accesses

Metrics details

• Business and management

Given that competing hypotheses about the causal relationship between work motivation and job performance exist, the current research utilized meta-analytic structural equation modeling (MASEM) methodology to detect the causal relationships between work motivation and job performance. In particular, completing hypotheses were checked by applying longitudinal data that include 84 correlations ( n  = 4389) from 11 independent studies measuring both work motivation and job performance over two waves. We find that the effect of motivation (T1) on performance (T2), with performance (T1) controlled, was positive and significant ( β  = 0.143). However, the effect of performance (T1) on motivation (T2), with motivation (T1) controlled, was not significant. These findings remain stable and robust across different measures of job performance (task performance versus organizational citizenship behavior), different measures of work motivation (engagement versus other motivations), and different time lags (1–6 months versus 7–12 months), suggesting that work motivation is more likely to cause job performance than vice versa. Practical and theoretical contributions are discussed.

Similar content being viewed by others

Psychosocial work environment as a dynamic network: a multi-wave cohort study

Predicting organizational performance from motivation in Oromia Seed Enterprise Bale branch

Role of job mobility frequency in job satisfaction changes: the mediation mechanism of job-related social capital and person‒job match

Introduction.

Job performance is defined as “scalable actions, behavior, and outcomes that employees engage in or bring about that are linked with and contribute to organizational goals” (Viswesvaran and Ones, 2000 , p. 216), is a core concept in the applied psychological field (Campbell and Wiernik, 2015 ; Choi et al., 2022 ; Giancaspro et al., 2022 ; Hermanto and Srimulyani, 2022 ; Motowidlo, 2003 ). Employees’ job performance is important for both organization and the employee. For an organization, job performance is the vital antecedent of organizational performance (Almatrooshi et al., 2016 ); for an employee, job performance is a predictor of turnover (Bycio et al., 1990 ; Martin et al., 1981 ), and wellbeing (Bakker and Oerlemans, 2011 ; Ford et al., 2011 ). Considering the importance of job performance in the applied psychological field, it is not surprising that researchers have devoted significant effort to researching job performance, especially its antecedents.

Prior meta-analyses identified a series of antecedents of job performance, such as job satisfaction (Iaffaldano and Muchinsky, 1985 ; Judge et al., 2001 ; LePine et al., 2002 ), organizational commitment (Jaramillo et al., 2005 ; Mathieu and Zajac, 1990 ), and work motivation (Cerasoli et al., 2014 ; Van den Broeck et al., 2021 ; Van Iddekinge et al., 2018 ). Among these factors, motivation, which refers to the force that drives the direction, intensity, and persistence of employee behavior (Pinder, 2014 ), is a medium to strong predictor of performance (Cerasoli et al. 2014 ). Although the early meta-analyses (e.g., Cerasoli et al., 2014 ; Van Iddekinge et al., 2018 ) confirmed the significant correlations between work motivation and job performance, the accurate causal relationship between work motivation and job performance remains unclear. Does work motivation cause job performance? Does reverse causality exist? Or there is a reciprocal relationship between them? Unfortunately, previous meta-analyses (e.g., Cerasoli et al., 2014 ; Van Iddekinge et al., 2018 ), which are based on cross-temporal data rather than longitudinal cross-lagged panel data, could not address this research gap.

We propose four competing hypotheses to explain the causal relationship between them. First, work motivation causes job performance. Second, job performance causes work motivation. Third, work motivation causes job performance and vice versa (reciprocal model). Finally, work motivation and job performance are causally unrelated. In the Theory and Hypotheses part, we will describe these hypotheses in detail.

By checking all four hypotheses, the current study aims to reveal the causal relationship between work motivation and job performance. A single primary study could not accomplish our research goal due to the distorting of statistical artifacts (e.g., sampling error and measurement error; Hunter and Schmidt, 2004 ). For instance, the relationships of interest may vary when sampling from different organizations because of sampling error, which would harm the accuracy of the results. Fortunately, the meta-analysis methodology could help us to correct the statistical artifacts and thereby provide solid and reliable empirical evidence for the theory. As such, we utilize a meta-analysis methodology that allows us to aggregate cross-lagged panel data to test the four hypotheses.

This article provides the first meta-analysis that estimates the longitudinal effects between work motivation and job performance, contributing to both theory and practice. In terms of theory, this study will provide solid evidence for the causal relationship between work motivation and job performance, contributing to motivation and performance literature. In relation to practice, the results of our study will provide guidance for human resource management. For instance, if we find that motivation causes performance, using human resource practice (e.g., performance appraisal and training) that will influence motivation to improve performance will be reasonable; whereas if other results were found, perhaps we will reconsider the effectiveness of the current human resource practices.

Theory and hypotheses

In this part, we will review work motivation and job performance and their measurements. Then, we will develop the hypotheses between them. Finally, as a meta-analysis, we will propose a research question about the moderators that might influence the relationships between motivation and performance.

Before the 1970s, organizational psychologists primarily directed their attention toward job satisfaction, often sidelining the exploration of work performance (Organ, 2018 ). However, the tide turned in the 1980s, when scholars began conceptualizing individual job performance as a distinct construct (Campbell and Wiernik, 2015 ). Job performance is commonly characterized by two key forms: task performance and organizational citizenship behavior (OCB), providing a structured framework for evaluating employee contributions (Hoffman et al., 2007 ; Sidorenkov and Borokhovski, 2021 ; Young et al., 2021 ). Notably, performance should not be conflated with efficiency and productivity. While performance encompasses a broader term, often associated with achieving various levels or outcomes potentially under myriad conditions, both efficiency and productivity are intricately tied to the concept of optimizing resource utilization and maximizing output production (Campbell and Wiernik, 2015 ).

Task performance refers to the effectiveness with which job incumbents perform activities that contribute to the organization’s technical core (Borman and Motowidlo, 1997 , p. 99). Notably, this concept is also identified as “in-role performance/behavior” in the literature (Koopmans et al., 2011 ; Raja and Johns, 2010 ). In-role performance essentially encapsulates behaviors aimed at fulfilling formal tasks, duties, and responsibilities, often detailed in job descriptions (Becker and Kernan, 2003 ; Williams and Anderson, 1991 ). Contrarily, early meta-analyses have amalgamated related concepts, acknowledging their overlapping domains (Riketta, 2008 ; Young et al., 2021 ). OCB is delineated as “individual behavior that is discretionary, not directly or explicitly recognized by the formal reward system, and that in the aggregate promotes the effective functioning of the organization” (Organ, 1988 , p. 4). Contextual performance, reflecting actions extending beyond formal job descriptions and enhancing organizational effectiveness (MacKenzie et al., 1991 ), is frequently paralleled with OCB in meta-analytic practices (Riketta, 2008 ; Young et al., 2021 ). A noteworthy correlation between task performance and OCB (ρ = 0.74) is illuminated through a meta-analysis by Hoffman et al. ( 2007 ). While some scholars propose that performance can exhibit counterproductive facets (Campbell and Wiernik, 2015 ), meta-analysis unveils only a moderate relationship between OCB and counterproductive work behavior and reveals somewhat disparate relationship patterns with their antecedents (Dalal, 2005 ). Therefore, in this study, we study two fundamental dimensions of job performance: task performance and OCB.

Motivation reflects why people do something. It is widely researched in the work and educational psychological field (Anesukanjanakul et al., 2019 ; Christenson et al., 2012 ; Fishbach and Woolley, 2022 ; Hartinah et al., 2020 ; Muawanah et al., 2020 ). Work motivation stands distinct amidst a spectrum of related concepts. Firstly, it is imperative to differentiate motivation from personality. Personality, defined as a construct embodying a set of “traits and styles displayed by an individual, represents (a) dispositions, that is, natural tendencies or personal inclinations of the person, and (b) aspects wherein the individual deviates from the ‘standard normal person’ in their society” (Bergner, 2020 , p.4). Personality acts as a distal antecedent to performance, influencing it indirectly through the medium of motivation (Judge and Ilies, 2002 ; Kanfer et al., 2017 ). Secondly, while interrelated, goal pursuit and motivation are distinctive concepts. For example, if employees aim to earn money, their motivations are characterized as external. Conversely, intrinsically motivated employees engage in work for the enjoyment derived from the process itself, potentially without being driven by explicit work goals (Deci et al., 2017 ). Thirdly, motivation is different from attitude. Job attitudes (e.g., job satisfaction) reflect the evaluations of one’s job (Judge and Kammeyer-Mueller, 2012 ). Motivation may not necessarily include the evaluation of the job. For instance, engaged people, who usually put a great deal of effort into their work (Bakker et al., 2014 ), may not include the evaluation of the job. Actually, attitudes may likely be influenced by motivations, indicating they are different concepts (Judge and Kammeyer-Mueller, 2012 ).

As work motivation is a very grand concept, many psychological and organizational theories try to measure motivation by using different scales. For instance, in the perspective of the Job Demands–Resources (JD-R) Theory (Bakker, 2011 ; Bakker and Demerouti, 2017 ), work engagement is regarded as the motivation factor that links job resources and job performance; in the perspective of the Self-determination Theory (SDT), motivation (e.g., intrinsic motivation and extrinsic motivation) is the antecedent of job performance (Deci et al., 2017 ; Deci and Ryan, 2000 ). In the review process, we notice that work engagement is one of the most widely-used measurements of motivation when researching the work motivation-job performance linkage.

Hypotheses between motivation and performance

The first potential causal relationship is that work motivation causes job performance. This argument is shown in Fig. 1 . This Argument is supported by many well-established theories and empirical evidence. To start, in the JD-R theory (Bakker, 2011 ; Bakker and Demerouti, 2007 ; Bakker and Demerouti, 2017 ), engaged (well-motivated) people will accomplish job performance because they will experience more positive emotions which may increase the creation of new ideas and resources and they will be healthy and be energetic at work. The correlational relationship was confirmed by a prior meta-analysis as it found a medium correlation ( ρ  = 0.48) between engagement and job performance (Neuber et al., 2021 ). Then, from the perspective of SDT (Deci et al., 2017 ; Deci and Ryan, 2000 ; Gagné and Deci, 2005 ), motivation also influences performance. In particular, intrinsically motivated employees will be creative and productive, increasing their job performance. An early meta-analysis finds a moderate correlation between intrinsic motivation and performance ( ρ  = 0.28) (Cerasoli et al., 2014 ). Finally, motivation may influence performance directly by determining the level of effort and persistence an individual will exert in the face of obstacles (Kanfer, 1990 ). Motivation may also influence performance indirectly, as motivated individuals are more likely to set challenging goals and commit to achieving them, leading to higher performance (Locke and Latham, 2006 ). Together, it seems obvious that work motivation will cause subsequent job performance. When using the cross-lagged panel research design to test this hypothesis, the subsequent performance will be predicted by the previous motivation after controlling the auto-correlation effect. As such, the following hypothesis is proposed:

An illustration of arguments for a “motivation-causing-performance” process. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 1 : Work motivation causes job performance. In particular, work motivation (T1) is the significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1).

As illustrated in Fig. 2 , the second potential causal relationship is that performance causes motivation. As SDT suggested, feedback will influence motivation (Deci et al., 1999 ). Employees who achieve job performance may receive positive feedback (e.g., pay and recognition) from their organizations and leaders (Riketta, 2008 ), increasing their work motivation. Applying longitudinal data, Presbitero ( 2017 ) provided indirect evidence that improvements in reward management yielded a positive change in the level of motivation (measured by engagement). Therefore, we hypothesize the following:

An illustration of arguments for a “performance-causing-motivation” process. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 2 : Job performance causes work motivation. In particular, job performance (T1) is the significant predictor of work motivation (T2) after controlling the auto-correlation effect of work motivation (T1).

According to Fig. 3 , the third hypothesis is that motivation causes performance and performance causes motivation simultaneously. Combining Hypotheses 1 and 2, we could conclude this reciprocal hypothesis. Utilizing cross-lagged panel data, early studies found reciprocal relationships between (a) self-efficacy and academic performance (Talsma et al., 2018 ) and (b) job characteristics and emotional exhaustion (Konze et al., 2017 ). That is to say, there might be a reciprocal relationship between variables. Thus, we derive the following hypotheses:

An illustration of arguments for a simultaneous reciprocity between work motivation and job performance. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 3 : There is a reciprocal causal relationship between work motivation and job performance. In particular, work motivation (T1) is the significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1) and vice versa.

As presented in Fig. 4 , the final potential causal relationship is that performance and motivation are causally unrelated. Performance and motivation may be causally unrelated due to cross-temporal research design and common method bias (Podsakoff et al., 2003 ). For instance, when work motivation and job performance are measured at the same time point and rated by one person, their correlation may inflate due to common method bias and thereby draw inaccurate causality. Therefore, we put the following hypothesis:

An illustration of arguments for a causally unrelated relationship between work motivation and job performance. This figure is covered by the Creative Commons Attribution 4.0 International License.

Hypothesis 4 : Work motivation and job performance are causally unrelated. In particular, work motivation (T1) is not a significant predictor of job performance (T2) after controlling the auto-correlation effect of job performance (T1), whereas job performance (T1) is also not the significant predictor of work motivation (T2) after controlling the auto-correlation effect of work motivation (T1).

We also propose a research question about the potential moderators that may influence the relationship of interest. Following early longitudinal meta-analyses (Riketta, 2008 ; Talsma et al., 2018 ), three moderators are considered, namely, performance measurements, motivation measurements, and length of time lag (shorter vs. longer time lags between two waves).

Firstly, as we illustrated in the Introduction part, there are two measurements of work performance, namely, task performance and OCB. We would like to explore the potential moderating role of job performance measurements (task performance versus OCB). This exploration is pivotal. Theoretically, performance should envelop two dimensions: task performance and OCB (Koopmans et al., 2011 ). However, a disparity exists in organizational recognition and reward systems, wherein task performance is formally acknowledged, while OCB is not (Organ, 2018 ). The impact of such discrepancies on their respective relationships with performance remains nebulous. Undertaking a meta-analysis to probe into these moderating variables will not only deepen our understanding of the nexus between motivation and performance but also furnish supplementary evidence to buttress their interconnection.

Secondly, the motivation measurement is taken into consideration. In particular, many longitudinal studies (e.g., Shimazu et al., 2018 ; Nawrocka et al., 2021 ) use work engagement to measure motivation. Although theoretical frameworks suggest that these measures might reflect motivation, various measures of motivation may exhibit distinct relationships with performance. Despite the absence of cross-lagged meta-analyses, insights can potentially be derived from cross-temporal meta-analyses. For example, Cerasoli et al. ( 2014 ) identified a correlation of 0.26 between intrinsic motivation and performance, while Corbeanu and Iliescu ( 2023 ) observed a correlation of 0.37 between work engagement and performance. Consequently, we question whether the measurement of motivation exerts a significant moderating effect. Given that work engagement is the most prevalently utilized measure, we draw comparisons between the results pertaining to work engagement and those associated with other forms of motivation.

Finally, it is unclear how long the time lag process (i.e., the length of time between two measurement waves) will influence the relationship of interest. In the present study, time lags varied from 1 to 12 months (refer to the coding information for details). On the one hand, the relationship between motivation and performance may depend on time. For instance, even with strong motivation, employees may require time to learn and adapt to new tasks, affecting performance enhancement. Furthermore, the delay in receiving feedback or recognition, especially in long-term projects, may decelerate the positive influence of performance on motivation.

On the other hand, there may exist an optimal time lag interval in cross-lagged analysis, as suggested by Dormann and Griffin ( 2015 ). When the time lag falls short of this optimal point, the cross-lagged effect size diminishes sharply; inversely, if the time lag exceeds it, the effect size likewise declines. Aligning with prior meta-analysis efforts (Riketta, 2008 ), we categorize the time lag into two groups, namely, 1–6 months and 7–12 months, to explore the possible moderating influence of the time lag. The efficacy of a 6-month time lag design remains uncertain. Nevertheless, a design that maintains a 6-month interval at each end—presenting a symmetrical six-month span—prompts a subgroup analysis within the meta-analysis, increasing the likelihood of discerning potential moderating impacts. To sum up, we seek to answer the following research question:

Research Question 1 : Do the causal relationship between work motivation and job performance vary due to (a) job performance measurement (task performance versus OCB), (b) work motivation measurement (work commitment versus other motivations), and (c) time lag (1–6 months versus 7–12 months)?

Literature search

To locate the studies that might include the cross-lagged data about work motivation and job performance, following early meta-analyses (Neuber et al., 2021 ; Riketta, 2008 ; Van Iddekinge et al., 2018 ), the authors searched the following keywords: (a) motivation ( motivation or engagement ), (b) performance ( performance , job performance , task performance , or organization citizenship behavior ), and (c) cross-lagged ( longitudinal or cross-lagged) utilizing Web of Science and Google Scholar databases. The authors (W and L) seek to include studies published from 2000 to 2022. The search was conducted in January 2023 and encompassed English-language research materials. We did not restrict the types of research sources, including journal articles, book chapters, and dissertations. Authors W and L performed the search using the Title, Abstract, and Keywords. After removing duplicates, the authors initially obtained 120 potential articles that used longitudinal data.

Inclusion criteria and coding

After reviewing some early published longitudinal meta-analyses (Maricuțoiu et al., 2017 ; Riketta, 2008 ; Talsma et al., 2018 ), the authors made the following inclusion criteria. First, samples should come from organizations because the current study focuses on work motivation and job performance. As such, students’ or athletes’ samples were removed.

Second, studies should provide a full correlation matrix that includes six correlations and measure motivation and performance at two (or more) measurement waves. Six correlations are two synchronous correlations, the two cross-lagged correlations, and the two stabilities correlations (Kenny, 1975 ). In particular, two synchronous correlations are correlations (a) between motivation (T1) and performance (T1) and (b) between motivation (T2) and performance (T2). Two cross-lagged correlations are correlations (a) between motivation (T1) and performance (T2) and (b) between performance (T1) and motivation (T2). Two stabilities correlations are correlations (a) between motivation (T1) and motivation (T2) and (b) between performance (T1) and performance (T2).

After reading all potential studies ( k  = 120) and excluding studies that were not able to meet the inclusion criteria, the final database contained 11 studies that included 84 correlations ( n  = 4389). Considering the challenges in obtaining samples and findings from early meta-analyses (Riketta, 2008 , with 16 studies; Talsma et al., 2018 , with 11 studies), a sample of 11 studies is likely sufficient for conducting a cross-lagged meta-analysis. Two authors coded the following information: bibliographic references (authors and publication year), sample description (sample size and country), research design (interval between two measurement waves), effect sizes, and the reliabilities (i.e., Cronbach’s α) of all scales. The authors discussed the differences in the coding information until the intercoder agreement was researched 100%. Among the examined studies, 8 utilized a self-reported method for measuring performance, 2 adopted a leader-reported method, and 1 study employed an objective indicator, specifically the results of performance appraisals. The majority of these studies ( k  = 10) originated from companies, with only one emanating from an educational organization. The samples in the 11 studies encompass a wide range of industries, including banking, auditing, and social services. The diversity in this study stems from the primary authors’ intentional strategy to collect data from a variety of industries. This approach enables a comprehensive insight into the nature of professional settings and employee motivation across different sectors. Geographically, most samples were drawn from Europe (k = 9), while the remaining were from East Asia (k = 2). A PRISMA flowchart (see Fig. 5 ) presents the process of literature search.

An illustrative demonstration of literature search procedures and inclusion criteria. This figure is covered by the Creative Commons Attribution 4.0 International License.

Before analyzing, publication bias is taken into consideration. We used the Trim-and-Fill method and Eggs’ Regression method to detect potential publication bias. This analysis was conducted utilizing metafor package (Viechtbauer, 2010 ) in R. The results were shown in Table 1 .

Generally speaking, there are two steps in a meta-analytic structural equation modeling analysis (Bergh et al., 2016 ; Viswesvaran and Ones, 1995 ). The first one is to build a meta-analytic correlation matrix. The second one is to use this matrix to conduct path analysis. In the current study, to build a meta-analytic correlation matrix, we employed the Hunter-Schmidt methods’ meta-analysis technology to aggregate effect sizes (Hunter and Schmidt, 2004 ). In particular, reliabilities (i.e., Cronbach’s α) were used to correct measurement errors. The random effect meta-analysis method was utilized to correct sampling errors. This analysis was accomplished using the psychmeta package (Dahlke and Wiernik, 2019 ) in R. The results of the meta-analytic correlation matrix for path analysis were shown in Table 2 . To answer research question 1, Table 2 also includes correlations that are grouped by performance measurements, motivation measurements, and time lags.

Then, this meta-analytic correlation matrix was used to conduct path analysis, the results were shown in Table 3 . This analysis was accomplished using MPLUS software (Muthén and Muthén, 2017 ). Specifically, to conduct path analysis, the maximum likelihood estimation (MLE) was used. Besides, the sum of the sample sizes was employed as the inputted sample size (Riketta, 2008 ).

As Table 1 shows, the results suggest there is not a significant publication bias. First, using the Trim-and-Fill method, only one asymmetric effect size was located (i.e., the correlation between performance T1 and performance T2). After inputting this “missed” correlation, the averaged correlation only decreased by 0.02, suggesting the publication bias is not serious. Second, utilizing the Eggs’ Regression method, all the p-values are bigger than 0.05, confirming the publication bias is not significant. Together, the overall publication bias is not serious.

Table 2 depicts the averaged correlation (r) and true score correlation (ρ) of interest. For instance, the ρ between motivation (T1) and motivation (T2) is 0.80, whereas the ρ between performance (T1) and performance (T2) is 0.54.

As Table 3 presents, overall, work motivation appears to be a predictor of job performance, whereas job performance appears to be a predictor of work motivation. In particular, the path coefficient (i.e., M1 → P2) from motivation (T1) to performance (P2) is positive and significant ( β  = 0.143, p  < 0.001). However, the path coefficient (i.e., P1 → M2) from performance (T1) to motivation (P2) is not significant ( β  = −0.014, p  > 0.050). As such, H1 was supported, whereas H2, H3, and H4 were rejected. We draw Fig. 6 to explain the causal relationship between work motivation and job performance.

An illustration of estimated causal relationship between work motivation and job performance following MASEM analysis. This figure is covered by the Creative Commons Attribution 4.0 International License.

To answer research question 1, as Table 3 shows, neither the performance measure, motivation measure, nor time lag influence the causal relationship between motivation and performance. In particular, all the path coefficients (i.e., M1 → P2) from motivation (T1) to performance (P2) are positive and significant. However, the path coefficients (i.e., P1 → M2) from performance (T1) to motivation (P2) are negative or insignificant, supporting H1. The moderating effect was determined using z-tests to compare the two effect sizes. For example, when examining the moderating role of the performance measure, there was no significant difference in path coefficients for M1 → P2 (β1 = 0.129, β2 = 0.085; z = 1.4, p  = 0.08). Similarly, for path coefficients P1 → M2, no significant difference was observed (β1 = −0.016, β2 = −0.052; z  = 1.14, p  = 0.13). Additionally, we did not observe any significant moderating effect for either motivation measures or time lag. Together, the causal relationship is motivation causes subsequent performance rather than vice versa. Besides, this relationship is not influenced by the three potential moderators.

In this part, we will first discuss our findings. Then, we will discuss the theoretical and practical implications. Finally, the limitations and future directions will be discussed.

To start, we will discuss the magnitude of correlations. Cohen ( 2013 ) suggested that a correlation at 0.1 is small, at 0.3 is medium, whereas at 0.5 is large. Applying this standard, we find that the magnitudes of correlations of interest are from medium to large. For instance, the ρ between motivation (T1) and motivation (T2) is 0.80 which is large, whereas the ρ between performance (T1) and performance (T2) is 0.54 which is medium. Besides, the correlation ( ρ  = 0.34) between motivation (T1) and performance (T1) is bigger than the correlation ( ρ  = 0.31) between motivation (T1) and performance (T2). One plausible explanation is that the former is measured at the same time point whereas the latter is measured at different time points. Two constructs measuring at the same time point may suffer from common method bias and their correlation may inflate (Podsakoff et al., 2003 ). Besides, early meta-analyses also found the correlations between motivation and performance are medium. For instance, Cerasoli et al. ( 2014 ) found a correlation between intrinsic motivation and performance is 0.26. Similarly, Borst et al. ( 2019 ) found medium correlations between engagement and in-role performance and ex-role performance (range from 0.31 to 0.46). To sum up, the overall correlations between motivation and performance are medium.

Then, we found that work motivation causes job performance rather than vice versa. This finding rejects the reciprocal and causally unrelated model. This finding is in line with many experiment studies (e.g., Amabile, 1985 ; Hendijani et al., 2016 ; Kovjanic et al., 2013 ) which found that motivation influenced performance. Combining the findings of both longitudinal and experimental studies, evidence suggests that work motivation appears to be a predictor of job performance.

However, what makes us surprised is that job performance cannot predict work motivation based on cross-lagged data. One possible explanation is there might be mediators that fully mediate the relationship between job performance and subsequent work motivation. For instance, in the perspective of SDT (Deci et al., 2017 ; Deci and Ryan, 2000 ), basic psychological needs (i.e., competence, autonomy, and relatedness) are the antecedents of motivation. Employees who accomplished their job performance are likely to fulfill the need for competence and thereby influence motivation. Thus, job performance (T1) may not directly influence work motivation (T2) but through the mediating role of basic psychological needs. In the JD-R theory (Bakker, 2011 ; Bakker and Demerouti, 2017 ), there could also have mediators between performance and motivation. These mediators are job resources (e.g., leader support). Employees who achieve performance may influence job resources (e.g., leader support) and thereby influence their motivation. In the current cross-lagged panel meta-analysis, these potential mediators (e.g., basic psychological needs and leader support) could not be tested. Therefore, we do not find job performance (T1) causes work motivation (T2).

Finally, three moderators (i.e., performance measure, motivation measure, and time lag) do not influence the causal relationship between motivation and performance. First, for performance measures, one explanation is that both task performance and OCB captured the nature of job performance. Second, for motivation measures, one explanation is that different measures of motivation both reflect the definition of motivation (Pinder, 2014 ). For instance, employees could work hard by being driven by both work engagement (Bakker, 2011 ) and intrinsic motivation (Deci et al., 2017 ). In other words, despite different measures of motivation being used, these concepts all capture the characteristics of motivation, indicating a consensus conclusion.

It’s important to acknowledge that various studies have employed distinct measures to gauge motivation, including psychological capital and self-efficacy, among others. Psychological capital can indeed serve as a reflection of motivation. Comprising four subdimensions—self-efficacy, hope, resilience, and optimism—psychological capital embodies the internal forces (motivation) that drive individuals to confront challenges (Newman et al., 2014 ). These components collectively capture the essence of motivation by epitomizing the underlying reasons that initiate and direct behavior. Therefore, they are integral in understanding the multifaceted nature of motivation. Additionally, our moderation analysis contributes further insights, suggesting that despite the nuanced complexities of motivation measures, they didn’t exhibit a substantial moderating impact on the outcomes. This finding underscores the importance of considering these motivational aspects not just as isolated factors but as integral components that interact with other elements in human behavior and response mechanisms.

For time lag, an early meta-analysis study finds a significant moderating role in the length of time lag (Riketta, 2008 ) which is different from the current study. In the current study, we noticed that the length of time lag is between 1 month and 12 months. However, we still lack the knowledge of whether this causal relationship will change over a longer period of time (e.g., more than 12 months). Together, three moderators do not influence the causal relationship between work motivation and job performance, strengthening the confidence in our findings.

Theoretical and practical implications

The current study is the first meta-analysis that uses longitudinal data to test the causal relationship between work motivation and job performance, making some theoretical implications. First, utilizing meta-analysis methodology, we reconciled four competing hypotheses about the causal relationship between work motivation and job performance, contributing to work motivation and job performance literature. Second, the current study contributes to SDT literature. SDT suggests that work motivation will influence human behavior and job performance (Deci et al., 2017 ). The current study provides solid evidence for the argument of SDT by using longitudinal data. Besides, the current study collected data from multiple organizations, making the findings have high external validity. Finally, the current study provided evidence for the JD-R theory, as we found engagement causes job performance rather than vice versa using a cross-lagged research design. Drawing on this finding, some results (e.g., Yu et al., 2020 ; Almawali et al., 2021 ), in JD-R literature using a cross-temporal research design, should be explained with caution.

The current study is also essential to practice. First, as the current study provides solid causal evidence for the motivation-performance linkage, it provides knowledge for human performance management. That is, human performance practices (e.g., compensation management and performance management) that influence employee motivation, will influence employee performance. Second, our knowledge suggests that some motivation-based leadership (e.g., empowering leadership) is useful as motivation predicts job performance in the long run. Finally, since we do not find job performance could predict subsequent work performance, practitioners should try to find some try practices to strengthen feedback mechanisms between them, making employees increase their performance continuously.

Limitations and future directions

There are some limitations in the current study. First, in the current study, both motivation and performance are measured by self-reported scales, which may trigger common method bias (Podsakoff et al., 2003 ). This effect is stronger when two constructs are measured at the same time point. For instance, the ρ between job performance (T1) and work motivation (T1) may inflate due to common method bias. Future studies could try to measure performance utilizing more objective indicators. Second, due to the cross-lagged research design, it allows for only tentative causal conclusions and cannot rule out some alternative causal explanations (Riketta, 2008 ). Future studies could try to use instrumental variables to rule out alternative causal explanations (Saridakis et al., 2020 ). Third, the present study employed the MASEM method to carry out path analyses. However, the generalizability of this method to other populations may be limited when dealing with heterogeneous correlation matrices (Cheung, 2018 ). Upon the accumulation of more homogeneous evidence, future research could replicate this study. Fourth, during our search process, we did not impose geographical constraints on the origin of primary studies. However, we observed that the majority of the samples predominantly come from Europe ( k  = 9). This brings to light the potential influence of culture on the relationship between motivation and performance. In countries characterized by high individualism, values such as personal achievement and autonomy are emphasized (Hofstede et al. 2010 ). In such cultures, motivation is frequently linked to personal goals and achievements, which may intensify the association between personal-focused motivation and performance. Nonetheless, our current dataset limits our ability to definitively assess these cultural effects. Future research should aim to explore the impact of cultural factors on the motivation-performance dynamics. Finally, our study faced certain constraints regarding data availability, particularly concerning specific motivation metrics such as extrinsic motivation, which were not obtainable from the primary studies. Future research could enhance and validate the findings of this study by employing a broader range of motivation measures. This expanded approach will not only reinforce the comprehensiveness and reliability of the results but also provide a more nuanced understanding of motivational dynamics.

Conclusions

This meta-analysis is the first one to detect the accurate causal relationship between work motivation and job performance using longitudinal data. The evidence supports the effects of work motivation on job performance and does not support the reverse effects. The reciprocal model and causally unrelated model are also not supported. The results appear reasonably robust, as the finding that work motivation predicts job performance was consistent across the examined moderators of job performance measure, motivation measure, and time lag length. This study contributes to motivation and performance literature. Besides, our findings are important for human resource management and leadership. Future studies could try to use instrumental variables to get a more accurate causal relationship.

Data availability

All data used to conduct the meta-analytic review are included in the supplemental file.

Almatrooshi B, Singh S.K, Farouk S (2016) Determinants of organizational performance: a proposed framework. Int J Product Perform Manag 65(6):844–859. https://doi.org/10.1108/IJPPM-02-2016-0038

Article   Google Scholar  

Almawali H, Hafit NIA, Hassan N (2021) Motivational factors and job performance: the mediating roles of employee engagement. Int J. Hum. Resour. Stud. 11(3):6782–6782

Amabile TM (1985) Motivation and creativity: effects of motivational orientation on creative writers. J. Personal. Soc. Psychol. 48(2):393–399

Anesukanjanakul J, Banpot K, Jermsittiparsert K (2019) Factors that influence job performance of agricultural workers. Int J. Innov. Creativ Change 7(2):71–86

Google Scholar  

Bakker AB (2011) An evidence-based model of work engagement. Curr. Directions Psychol. Sci. 20(4):265–269. https://doi.org/10.1177/0963721411414534

Bakker AB, Demerouti E (2007) The job demands‐resources model: state of the art. J. Manag Psychol. 22(3):309–328. https://doi.org/10.1108/02683940710733115

Bakker AB, Oerlemans W (2011) Subjective well-being in organizations. Oxf. Handb. Posit. Organ Scholarsh. 49:178–189

Bakker AB, Demerouti E (2017) Job demands-resources theory: taking stock and looking forward. J. Occup. Health Psychol. 22(3):273–285. https://doi.org/10.1037/ocp0000056

Article   PubMed   Google Scholar  

Bakker AB, Demerouti E, Sanz-Vergel AI (2014) Burnout and work engagement: the JD–R approach. Annu Rev. Organ Psychol. Organ Behav. 1(1):389–411. https://doi.org/10.1146/annurev-orgpsych-031413-091235

Becker TE, Kernan MC (2003) Matching commitment to supervisors and organizations to in-role and extra-role performance. Hum. Perform. 16(4):327–348

Bergh DD, Aguinis H, Heavey C, Ketchen DJ, Boyd BK, Su P, Lau CL, Joo H (2016) Using meta‐analytic structural equation modeling to advance strategic management research: guidelines and an empirical illustration via the strategic leadership‐performance relationship. Strategic Manag J. 37(3):477–497

Bergner, RM (2020) What is personality? Two myths and a definition. New Ideas Psychol 57 . https://doi.org/10.1016/j.newideapsych.2019.100759

Borman WC, Motowidlo SJ (1997) Task performance and contextual performance: The meaning for personnel selection research. Hum. Perform. 10(2):99–109

Borst RT, Kruyen PM, Lako CJ, de Vries MS (2019) The attitudinal, behavioral, and performance outcomes of work engagement: a comparative meta-analysis across the public, semipublic, and private sector. Rev. Public Pers. Adm. 40(4):613–640. https://doi.org/10.1177/0734371x19840399

Bycio P, Hackett RD, Alvares KM (1990) Job performance and turnover: a review and meta‐analysis. Appl Psychol. 39(1):47–76

Campbell JP, Wiernik BM (2015) The modeling and assessment of work performance. Annu Rev. Organ Psychol. Organ Behav. 2(1):47–74

Cerasoli CP, Nicklin JM, Ford MT (2014) Intrinsic motivation and extrinsic incentives jointly predict performance: a 40-year meta-analysis. Psychol. Bull. 140(4):980–1008. https://doi.org/10.1037/a0035661

Cheung MW-L (2018) Computing multivariate effect sizes and their sampling covariance matrices with structural equation modeling: theory, examples, and computer simulations. Fron Psychol 9:1387. https://doi.org/10.3389/fpsyg.2018.01387

Choi Y, Ha S-B, Choi D (2022) Leader Humor and followers’ change-oriented organizational citizenship behavior: the role of leader machiavellianism. Behav Sci 12(2):22 https://www.mdpi.com/2076-328X/12/2/22

Christenson, SL, Reschly, AL, Wylie, C (eds) (2012) Handbook of Research on Student Engagement. Boston, MA: Springer US. https://doi.org/10.1007/978-1-4614-2018-7

Cohen J (2013) Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587

Corbeanu A, Iliescu D (2023) The link between work engagement and job performance. J Personnel Psychol 22(3). https://doi.org/10.1027/1866-5888/a000316

Dahlke JA, Wiernik BM (2019) psychmeta: an R package for psychometric meta-analysis. Appl Psychol. Meas. 43(5):415–416

Dalal RS (2005) A meta-analysis of the relationship between organizational citizenship behavior and counterproductive work behavior. J. Appl Psychol. 90(6):1241–1255. https://doi.org/10.1037/0021-9010.90.6.1241

Deci EL, Ryan RM (2000) The” what” and” why” of goal pursuits: Human needs and the self-determination of behavior. Psychol. Inq. 11(4):227–268

Deci EL, Koestner R, Ryan RM (1999) A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychol. Bull. 125(6):627–668

Article   CAS   PubMed   Google Scholar  

Deci EL, Olafsen AH, Ryan RM (2017) Self-determination theory in work organizations: the state of a science. Annu. Rev. Organ Psychol. Organ Behav. 4(1):19–43. https://doi.org/10.1146/annurev-orgpsych-032516-113108

Dormann C, Griffin MA (2015) Optimal time lags in panel studies. Psychol. Methods 20(4):489–505. https://doi.org/10.1037/met0000041

Fishbach A, Woolley K (2022) The structure of intrinsic motivation. Annu Rev. Organ Psychol. Organ Behav. 9:339–363

Ford MT, Cerasoli CP, Higgins JA, Decesare AL (2011) Relationships between psychological, physical, and behavioural health and work performance: a review and meta-analysis. Work Stress 25(3):185–204. https://doi.org/10.1080/02678373.2011.609035

Gagné M, Deci EL (2005) Self‐determination theory and work motivation. J. Organ Behav. 26(4):331–362. https://doi.org/10.1002/job.322

Giancaspro ML, De Simone S, Manuti A (2022) Employees’ perception of HRM practices and organizational citizenship behaviour: the mediating role of the Work–Family Interface. Behav. Sci. 12(9):301

Article   PubMed Central   PubMed   Google Scholar  

Hartinah S, Suharso P, Umam R, Syazali M, Lestari B, Roslina R, Jermsittiparsert K (2020) Retracted: Teacher’s performance management: the role of principal’s leadership, work environment and motivation in Tegal City, Indonesia. Manag Sci. Lett. 10(1):235–246

Hendijani R, Bischak DP, Arvai J, Dugar S (2016) Intrinsic motivation, external reward, and their effect on overall motivation and performance. Hum. Perform. 29(4):251–274

Hermanto YB, Srimulyani VA (2022) The effects of organizational justice on employee performance using dimension of organizational citizenship behavior as mediation. Sustainability 14(20):13322

Hoffman BJ, Blair CA, Meriac JP, Woehr DJ (2007) Expanding the criterion domain? A quantitative review of the OCB literature. J. Appl Psychol. 92(2):555–566. https://doi.org/10.1037/0021-9010.92.2.555

Hofstede G, Garibaldi De Hilal AV, Malvezzi S, Tanure B, Vinken H (2010) Comparing Regional Cultures Within a Country: Lessons From Brazil. J Cross-Cult Psychol 41(3):336–352. https://doi.org/10.1177/0022022109359696

Hunter, JE, Schmidt, FL (2004) Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks, CA: Sage. https://doi.org/10.4135/9781483398105

Iaffaldano MT, Muchinsky PM (1985) Job satisfaction and job performance: a meta-analysis. Psychol. Bull. 97(2):251–273

Jaramillo F, Mulki JP, Marshall GW (2005) A meta-analysis of the relationship between organizational commitment and salesperson job performance: 25 years of research. J. Bus. Res 58(6):705–714

Judge TA, Ilies R (2002) Relationship of personality to performance motivation: a meta-analytic review. J. Appl Psychol. 87(4):797–807. https://doi.org/10.1037/0021-9010.87.4.797

Judge TA, Kammeyer-Mueller JD (2012) Job attitudes. Annu Rev. Psychol. 63:341–367. https://doi.org/10.1146/annurev-psych-120710-100511

Judge TA, Thoresen CJ, Bono JE, Patton GK (2001) The job satisfaction–job performance relationship: a qualitative and quantitative review. Psycho Bull. 127(3):376–407

Article   CAS   Google Scholar  

Kanfer R (1990) Motivation theory and industrial and organizational psychology. Handb. Ind. Organ Psychol. 1(2):75–130

Kanfer R, Frese M, Johnson RE (2017) Motivation related to work: a century of progress. J. Appl Psychol. 102(3):338–355. https://doi.org/10.1037/apl0000133

Kenny DA (1975) Cross-lagged panel correlation: a test for spuriousness. Psychol. Bull. 82(6):887–903

Konze A-K, Rivkin W, Schmidt K-H (2017) Is Job Control a Double-Edged Sword? A cross-lagged panel study on the interplay of quantitative workload, emotional dissonance, and job control on emotional exhaustion. Int J Environ Res Public Health 14(12). https://doi.org/10.3390/ijerph14121608

Koopmans L, Bernaards CM, Hildebrandt VH, Schaufeli WB, de Vet Henrica CW, van der Beek AJ (2011) Conceptual frameworks of individual work performance: a systematic review. J. Occup. Environ. Med 53(8):856–866. https://doi.org/10.1097/JOM.0b013e318226a763

Kovjanic S, Schuh SC, Jonas K (2013) Transformational leadership and performance: an experimental investigation of the mediating effects of basic needs satisfaction and work engagement. J. Occup. Organ Psychol. 86(4):543–555

LePine JA, Erez A, Johnson DE (2002) The nature and dimensionality of organizational citizenship behavior: a critical review and meta-analysis. J. Appl Psychol. 87(1):52–65. https://doi.org/10.1037//0021-9010.87.1.52

Locke EA, Latham GP (2006) New directions in goal-setting theory. Curr. Directions Psychol. Sci. 15(5):265–268

MacKenzie SB, Podsakoff PM, Fetter R (1991) Organizational citizenship behavior and objective productivity as determinants of managerial evaluations of salespersons' performance. Organ Behav Hum Decis Process 50(1):123–150. https://doi.org/10.1016/0749-5978(91)90037-T

Maricuțoiu LP, Sulea C, Iancu A (2017) Work engagement or burnout: which comes first? A meta-analysis of longitudinal evidence. Burnout Res 5:35–43. https://doi.org/10.1016/j.burn.2017.05.001

Martin TN, Price J, Mueller CW (1981) Job performance and turnover. J. Appl Psychol. 66(1):116–119

Mathieu JE, Zajac DM (1990) A review and meta-analysis of the antecedents, correlates, and consequences of organizational commitment. Psychol. Bull. 108(2):171–194

Motowidlo SJ (2003) Job performance. Handb. Psychol. Ind. Organ Psychol. 12(4):39–53

Muawanah M, Muhamad Y, Syamsul H, Iskandar T, Muhamad S, Rofiqul U, Kittisak J (2020) Career management policy, career development, and career information as antecedents of employee satisfaction and job performance. Int J. Innov. Creativity Change 11(6):458–482

Muthén B, Muthén L (2017) Mplus. In Handbook of item response theory, Chapman and Hall/CRC, (pp. 507-518)

Nawrocka S, De Witte H, Brondino M, Pasini M (2021) On the reciprocal relationship between quantitative and qualitative job insecurity and outcomes. testing a cross-lagged longitudinal mediation model. Int J Environ Res Public Health 18(12). https://doi.org/10.3390/ijerph18126392

Neuber L, Englitz C, Schulte N, Forthmann B, Holling H (2021) How work engagement relates to performance and absenteeism: a meta-analysis. Eur. J. Work Organ Psychol. 31(2):292–315. https://doi.org/10.1080/1359432x.2021.1953989

Newman A, Ucbasaran D, Zhu F, Hirst G (2014) Psychological capital: a review and synthesis. J. Organ Behav. 35(S1):S120–S138. https://doi.org/10.1002/job.1916

Organ DW (2018) Organizational citizenship behavior: recent trends and developments. Annu Rev. Organ Psychol. Organ Behav. 80:295–306

Organ, DW (1988) Organizational citizenship behaviour: The good soldier syndrome. Lexington, MA: Lexington

Pinder CC (2014) Work motivation in organizational behavior. New York: Psychology Press

Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl Psychol. 88(5):879–903. https://doi.org/10.1037/0021-9010.88.5.879

Presbitero A (2017) How do changes in human resource management practices influence employee engagement? A longitudinal study in a hotel chain in the Philippines. J. Hum. Resour. Hospitality Tour. 16(1):56–70

Raja U, Johns G (2010) The joint effects of personality and job scope on in-role performance, citizenship behaviors, and creativity. Hum. Relat. 63(7):981–1005

Riketta M (2008) The causal relation between job attitudes and performance: a meta-analysis of panel studies. J. Appl Psychol. 93(2):472–481. https://doi.org/10.1037/0021-9010.93.2.472

Saridakis G, Lai Y, Muñoz Torres RI, Gourlay S (2020) Exploring the relationship between job satisfaction and organizational commitment: an instrumental variable approach. Int J. Hum. Resour. Manag 31(13):1739–1769

Shimazu A, Schaufeli WB, Kubota K, Watanabe K, Kawakami N (2018) Is too much work engagement detrimental? Linear or curvilinear effects on mental health and job performance. PLoS One 13(12):e0208684. https://doi.org/10.1371/journal.pone.0208684

Article   CAS   PubMed Central   PubMed   Google Scholar  

Sidorenkov AV, Borokhovski EF (2021) Relationships between Employees’ identifications and citizenship behavior in work groups: the role of the regularity and intensity of interactions. Behav. Sci. 11(7):92. https://www.mdpi.com/2076-328X/11/7/92

Talsma K, Schüz B, Schwarzer R, Norris K (2018) I believe, therefore I achieve (and vice versa): a meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learn Individ Differ. 61:136–150. https://doi.org/10.1016/j.lindif.2017.11.015

Van den Broeck A, Howard JL, Van Vaerenbergh Y, Leroy H, Gagné M (2021) Beyond intrinsic and extrinsic motivation: a meta-analysis on self-determination theory’s multidimensional conceptualization of work motivation. Organ Psychol. Rev. 11(3):240–273

Van Iddekinge CH, Aguinis H, Mackey JD, DeOrtentiis PS (2018) A meta-analysis of the interactive, additive, and relative effects of cognitive ability and motivation on performance. J. Manag 44(1):249–279

Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36(3):1–48

Viswesvaran C, Ones DS (1995) Theory testing: combining psychometric meta‐analysis and structural equations modeling. Pers. Psychol. 48(4):865–885

Viswesvaran C, Ones DS (2000) Perspectives on models of job performance. Int J. Sel. Assess. 8(4):216–226

Williams LJ, Anderson SE (1991) Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors. J. Manag 17(3):601–617

Young HR, Glerum DR, Joseph DL, McCord MA (2021) A meta-analysis of transactional leadership and follower performance: double-edged effects of LMX and empowerment. J. Manag 47(5):1255–1280

Yu J, Ariza-Montes A, Giorgi G, Lee A, Han H (2020) Sustainable relationship development between hotel company and its employees: linking job embeddedness, job satisfaction, self-efficacy, job performance, work engagement, and turnover. Sustainability 12(17). https://doi.org/10.3390/su12177168

Download references

Author information

These authors contributed equally: Nan Wang, Yuxiang Luan.

Authors and Affiliations

School of Business, North Minzu University, Yinchuan, Ningxia, China

School of Labor and Human Resources, Renmin University of China, Beijing, China

Yuxiang Luan

School of Management, North Minzu University, Yinchuan, Ningxia, China

You can also search for this author in PubMed   Google Scholar

Contributions

NW and YL: idea and design; NW and YL: introduction, hypotheses, and coding; YL and RM: method and results; NW, YL and RM: discussion and conclusion. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Yuxiang Luan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This study does not contain any interaction with human participants performed by any of the authors.

Informed consent

No human subjects are involved in this study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Raw data and analysis codes, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, N., Luan, Y. & Ma, R. Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies. Humanit Soc Sci Commun 11 , 595 (2024). https://doi.org/10.1057/s41599-024-03038-w

Download citation

Received : 19 May 2023

Accepted : 16 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1057/s41599-024-03038-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

The relationships between job performance, job burnout, and psychological counselling: a perspective on sustainable development goals (sdgs).

1. Introduction

1.1. sdgs and job burnout in higher education, 1.2. job performance and job burnout in higher education, 1.3. research aim, questions, and hypothesis development, 2. materials and methods, 2.1. research design and participants, 2.2. population and sampling techniques, 2.3. measures, 2.3.1. control variables, 2.3.2. job performance (kpi), 2.3.3. burnout level, 2.3.4. psychological counselling, 2.4. ethical considerations, 2.5. data analysis, 3.1. job performance predicts job burnout in higher education, 3.2. job burnout: the moderating role of psychological counselling, 4. discussion, 4.1. job-performance vs. job burnout, 4.2. psychological counselling to alleviate treating burnout: post-cautionary measurement vs. pre-cautionary measurement, 4.3. reducing job burnout for sdgs in higher education, 5. limitations and future studies, 6. conclusions and implications, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Alam, G.M. Has Secondary Science Education Become an Elite Product in Emerging Nations?—A Perspective of Sustainable Education in the Era of MDGs and SDGs. Sustainability 2023 , 15 , 1596. [ Google Scholar ] [ CrossRef ]
• Allen, C.; Metternicht, G.; Wiedmann, T. Initial progress in implementing the Sustainable Development Goals (SDGs): A review of evidence from countries. Sustain. Sci. 2018 , 13 , 1453–1467. [ Google Scholar ] [ CrossRef ]
• United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development ; United Nations: New York, NY, USA, 2015. [ Google Scholar ]
• Leal Filho, W.; Wu, Y.C.J.; Brandli, L.L.; Avila, L.V.; Azeiteiro, U.M.; Caeiro, S.; Madruga, L.R.D.R.G. Identifying and overcoming obstacles to the implementation of sustainable development at universities. J. Integr. Environ. Sci. 2017 , 14 , 93–108. [ Google Scholar ] [ CrossRef ]
• Blasco, N.; Brusca, I.; Labrador, M. Drivers for Universities’ Contribution to the Sustainable Development Goals: An Analysis of Spanish Public Universities. Sustainability 2021 , 13 , 89. [ Google Scholar ] [ CrossRef ]
• Boni, A.; Lopez-Fogues, A.; Walker, M. Higher Education and The Post-2015 Agenda: A Contribution from the Human Development Approach. J. Glob. Ethics 2016 , 12 , 17–28. [ Google Scholar ] [ CrossRef ]
• Sonetti, G.; Sarrica, M.; Norton, L.S. Conceptualization of Sustainability among Students, Administrative and Teaching Staff of A University Community: An Exploratory Study in Italy. J. Clean. Prod. 2021 , 316 , 128292. [ Google Scholar ] [ CrossRef ]
• Mohiuddin, M.; Hosseini, E.; Faradonbeh, S.B.; Sabokro, M. Achieving Human Resource Management Sustainability in Universities. Int. J. Environ. Res. Public Health 2022 , 19 , 928. [ Google Scholar ] [ CrossRef ]
• Lei, M.; Alam, G.M.; Hassan, A.B. Job Burnout amongst University Administrative Staff Members in China—A Perspective on Sustainable Development Goals (SDGs). Sustainability 2023 , 15 , 8873. [ Google Scholar ] [ CrossRef ]
• Lizano, E.L. Examining the Impact of Job Burnout on the Health and Well-Being of Human Service Workers: A Systematic Review and Synthesis. Human Service Organizations: Management. Leadersh. Gov. 2015 , 39 , 167–181. [ Google Scholar ]
• Hirsig, N.; Rogovsky, N.; Elkin, M. Enterprise Sustainability and HRM in Small and Medium-Sized Enterprises. In Sustainability and Human Resource Management. CSR, Sustainability, Ethics & Governance ; Ehnert, I., Harry, W., Zink, K., Eds.; Springer: Berlin/Heidelberg, Germany, 2014. [ Google Scholar ]
• Lambert, E.G.; Hogan, N.L.; Altheimer, I. An Exploratory Examination of the Consequences of Burnout in Terms of Life Satisfaction, Turnover Intent, and Absenteeism Among Private Correctional Staff. Prison. J. 2010 , 90 , 94–114. [ Google Scholar ] [ CrossRef ]
• Tomislav, K. The Concept of Sustainable Development: From its Beginning to the Contemporary Issues. Zagreb Int. Rev. Econ. Bus. 2018 , 21 , 67–94. [ Google Scholar ]
• Schaufeli, W.B.; Bakker, A.B. Job Demands, Job Resources, and Their Relationship with Burnout and Engagement: A Multi-Sample Study. J. Organ. Behav. 2004 , 25 , 293–315. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Li, Y.; Castaño, G. The Impact of Teaching-Research Conflict on Job Burnout among University Teachers: An Integrated Model. Int. J. Confl. Manag. 2020 , 31 , 76–90. [ Google Scholar ] [ CrossRef ]
• Greere, A. Training for Quality Assurance in Higher Education: Practical Insights for Effective Design and Successful Delivery. Qual. High. Educ. 2023 , 29 , 165–191. [ Google Scholar ] [ CrossRef ]
• Govindaras, B.; Wern, T.S.; Kaur, S.; Haslin, I.A.; Ramasamy, R.K. Sustainable Environment to Prevent Burnout and Attrition in Project Management. Sustainability 2023 , 15 , 2364. [ Google Scholar ] [ CrossRef ]
• Usán Supervía, P.; Salavera Bordás, C. Burnout Syndrome, Engagement and Goal Orientation in Teachers from Different Educational Stages. Sustainability 2020 , 12 , 6882. [ Google Scholar ] [ CrossRef ]
• Mijakoski, D.; Cheptea, D.; Marca, S.C.; Shoman, Y.; Caglayan, C.; Bugge, M.D.; Gnesi, M.; Godderis, L.; Kiran, S.; McElvenny, D.M.; et al. Determinants of Burnout among Teachers: A Systematic Review of Longitudinal Studies. Int. J. Environ. Res. Public Health 2022 , 19 , 5776. [ Google Scholar ] [ CrossRef ]
• Boamah, S.A.; Hamadi, H.Y.; Havaei, F.; Smith, H.; Webb, F. Striking a Balance between Work and Play: The Effects of Work–Life Interference and Burnout on Faculty Turnover Intentions and Career Satisfaction. Int. J. Environ. Res. Public Health 2022 , 19 , 809. [ Google Scholar ] [ CrossRef ]
• Meng, H.; Luo, Y.; Huang, L.; Wen, J.; Ma, J.; Xi, J. On the Relationships of Resilience with Organizational Commitment and Burnout: A Social Exchange Perspective. Int. J. Hum. Resour. Manag. 2017 , 30 , 2231–2250. [ Google Scholar ] [ CrossRef ]
• López-Núñez, M.I.; Rubio-Valdehita, S.; Diaz-Ramiro, E.M.; Aparicio-García, M.E. Psychological Capital, Workload, and Burnout: What’s New? The Impact of Personal Accomplishment to Promote Sustainable Working Conditions. Sustainability 2020 , 12 , 8124. [ Google Scholar ] [ CrossRef ]
• Leiter, M.P.; Maslach, C. Motivation, Competence, and Job Burnout. In Handbook of Competence and Motivation: Theory and Application , 2nd ed.; Elliot, A.J., Dweck, C.S., Yeager, D.S., Eds.; The Guilford Press: New York City, NY, USA, 2017; pp. 370–384. [ Google Scholar ]
• Liu, Y.; Song, Y.; Jiang, Y.; Guo, C.; Zhou, Y.; Li, T.; Ge, W.; An, N. Burnout and Its Association with Competence among Dental Interns in China. Front. Psychol. 2022 , 13 , 832606. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fazey, D.M.A.; Fazey, J.A. The Potential for Autonomy in Learning: Perceptions of Competence, Motivation and Locus of Control in First-Year Undergraduate Students. Stud. High. Educ. 2001 , 26 , 345–361. [ Google Scholar ] [ CrossRef ]
• Blaskova, M.; Blasko, R.; Figurska, I.; Sokol, A. Motivation and Development of The University Teachers’ Motivational Competence. Procedia Soc. Behav. Sci. 2015 , 182 , 116–126. [ Google Scholar ] [ CrossRef ]
• UNESCO. Teachers in Tertiary Education Programmes, Both Sexes (Number) in World ; UNESCO: Washington, DC, USA, 2019. [ Google Scholar ]
• Watts, J.; Robertson, N. Burnout in University Teaching Staff: A Systematic Literature Review. Educ. Res. 2011 , 53 , 33–50. [ Google Scholar ] [ CrossRef ]
• National Bureau of Statistics of China. 2022. Available online: http://www.stats.gov.cn/sj/ndsj/2022/indexch.htm (accessed on 1 January 2023).
• Alam, G.M.; Giacosa, E.; Mazzoleni, A. Does MBA’s Paradigm Transformation Follow Business Education’s Philosophy—A Comparison of Academic and Job-Performance and SES among Five Types of MBAian. J. Bus. Res. 2022 , 139 , 881–892. [ Google Scholar ] [ CrossRef ]
• Paudel, K.P. Level of Academic Performance Among Faculty Members in the Context of Nepali Higher Educational Institution. J. Comp. Int. High. Educ. 2021 , 13 , 98–111. [ Google Scholar ] [ CrossRef ]
• Davidescu, A.A.; Apostu, S.A.; Paul, A.; Casuneanu, I. Work Flexibility, Job Satisfaction, and Job Performance among Romanian Employees—Implications for Sustainable Human Resource Management. Sustainability 2020 , 12 , 6086. [ Google Scholar ] [ CrossRef ]
• Musah, M.B.; Tahir, L.M.; Ali, H.M.; Al-Hudawi, S.V.H.; Issah, M.; Farah, A.M.; Abdallah, A.K.; Kamil, N.M. Testing the Validity of Academic Staff Performance Predictors and Their Effects on Workforce Performance. Int. J. Eval. Res. Educ. (IJERE) 2023 , 12 , 941–955. [ Google Scholar ] [ CrossRef ]
• Purohit, B.; Martineau, T. Is the Annual Confidential Report System Effective? A Study of the Government Appraisal System in Gujarat, India. Hum. Resour. Health 2016 , 14 , 33–44. [ Google Scholar ] [ CrossRef ]
• Koopmans, L.; Bernaards, C.M.; Hildebrandt, V.H.; de Vet, H.C.W.; van der Beek, A.J. Construct Validity of the Individual Work Performance Questionnaire. J. Occup. Environ. Med. 2014 , 56 , 331–337. [ Google Scholar ] [ CrossRef ]
• Jahangirian, M.; Taylor, S.J.E.; Young, T.; Robinson, S. Key Performance Indicators for Successful Simulation Projects. J. Oper. Res. Soc. 2017 , 68 , 747–765. [ Google Scholar ] [ CrossRef ]
• Karkoulian, S.; Assaker, G.; Hallak, R. An Empirical Study of 360-Degree Feedback, Organizational Justice, and Firm Sustainability. J. Bus. Res. 2016 , 69 , 1862–1867. [ Google Scholar ] [ CrossRef ]
• Maslach, C.; Schaufeli, W.B.; Leiter, M.P. Job Burnout. Annu. Rev. Psychol. 2001 , 52 , 397–422. [ Google Scholar ] [ CrossRef ]
• Teymoori, E.; Zareiyan, A.; Babajani-Vafsi, S.; Laripour, R. Viewpoint of Operating Room Nurses about Factors Associated with the Occupational Burnout: A Qualitative Study. Front. Psychol. 2022 , 1 , 947189. [ Google Scholar ] [ CrossRef ]
• Martínez-López, J.Á.; Lázaro-Pérez, C.; Gómez-Galán, J. Burnout among Direct-Care Workers in Nursing Homes during the COVID-19 Pandemic in Spain: A Preventive and Educational Focus for Sustainable Workplaces. Sustainability 2021 , 13 , 2782. [ Google Scholar ] [ CrossRef ]
• Oosterholt, B.G.; Maes, J.H.R.; Van der Linden, D.; Verbraak, M.J.P.M.; Kompier, M.A.J. Cognitive Performance in Both Clinical and Non-Clinical Burnout. Stress 2014 , 17 , 400–409. [ Google Scholar ] [ CrossRef ]
• Lei, M.; Alam, G.M.; Bashir, K.; Pingping, G. Whether Academics’ Job Performance Makes A Difference to Burnout and The Effect of Psychological Counselling—Comparison of Four Types of Performers. PLoS ONE 2024 , 19 , e0305493. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Taris, T.W. Is There A Relationship between Burnout and Objective Performance? A Critical Review of 16 Studies. Work. Stress 2006 , 20 , 316–334. [ Google Scholar ] [ CrossRef ]
• Luceño-Moreno, L.; Talavera-Velasco, B.; García-Albuerne, Y.; Martín-García, J. Symptoms of Posttraumatic Stress, Anxiety, Depression, Levels of Resilience and Burnout in Spanish Health Personnel during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2020 , 17 , 5514. [ Google Scholar ] [ CrossRef ]
• WHO. Mental Health at Work. 28 September 2022. Available online: https://www.who.int/news-room/fact-sheets/detail/mental-health-at-work (accessed on 1 January 2023).
• National Institutes of Health. Theory at A Glance: A Guide for Health Promotion Practice ; Bethesda: Rockville, MD, USA, 2005. [ Google Scholar ]
• Lei, M.; Alam, G.M.; Bashir, K.; Pingping, G. Does the Job Performance of Academics’ Influence Burnout and Psychological Counselling? A Comparative Analysis amongst High-, Average-, Low-, and Non-performers. BMC Public Health 2024 , 24 , 1708. [ Google Scholar ] [ CrossRef ]
• Allison, P.D. Fixed Effects Regression Models ; SAGE Publications: London, UK, 2009. [ Google Scholar ]
• Hsiao, C. Analysis of Panel Data ; Cambridge University Press: Cambridge, UK, 2022. [ Google Scholar ]
• Creswell, J.W. Research Design in Qualitative, Quantitative, and Mixed Methods Approaches , 4th ed.; Sage: Thousand Oaks, CA, USA, 2014. [ Google Scholar ]
• National Bureau of Statistics of China. The Method of Dividing the Eastern, Central, Western, and Northeastern Regions ; National Bureau of Statistics of China: Beijing, China, 2011.
• Ministry of Education of China. Several Opinions on Deepening the Promotion of the Construction of World-Class Universities and First-Class Disciplines ; Higher Education Press: Beijing, China, 2022.
• Cochran, W.G. Sampling Techniques , 3rd ed.; Wiley: New York, NY, USA, 1977. [ Google Scholar ]
• Wang, L.; Chen, Y. Success or Growth? Distinctive Roles of Extrinsic and Intrinsic Career Goals in High-Performance Work Systems, Job Crafting, and Job Performance. J. Vocat. Behav. 2022 , 135 , 103714. [ Google Scholar ] [ CrossRef ]
• Ministry of Education of China. The Guiding Opinions on Deepening the Reform of the Assessment and Evaluation System for Universities Teachers ; Higher Education Press: Beijing, China, 2016.
• Organization Department of the CPC Central Committee. Regulations on the Assessment of Staff in Public Institutions ; Organization Department of the CPC Central Committee: Beijing, China, 2023. [ Google Scholar ]
• National Health Commission. The Guiding Opinions on Strengthening Psychological Health Services ; National Health Commission: Beijing, China, 2017. [ Google Scholar ]
• Nizami, N.; Prasad, N. Decent Work ; Springer: Singapore, 2017. [ Google Scholar ]
• Pijpker, R.; Vaandrager, L.; Veen, E.J.; Koelen, M.A. Combined Interventions to Reduce Burnout Complaints and Promote Return to Work: A Systematic Review of Effectiveness and Mediators of Change. Int. J. Environ. Res. Public Health 2020 , 17 , 55. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kennedy, F. Beyond “Prevention is Better than Cure”: Understanding Prevention and Early Intervention as An Approach to Public Policy. Policy Des. Pract. 2020 , 3 , 351–369. [ Google Scholar ] [ CrossRef ]
• Anwar, N.; Nik Mahmood, N.H.; Yusliza, M.Y.; Ramayah, T.; Noor Faezah, J.; Khalid, W. Green Human Resource Management for Organisational Citizenship Behaviour Towards the Environment and Environmental Performance on A University Campus. J. Clean. Prod. 2020 , 256 , 120401. [ Google Scholar ] [ CrossRef ]
• Žydžiūnaitė, V.; Arce, A. Being An Innovative and Creative Teacher: Passion-Driven Professional Duty. Creat. Stud. 2021 , 14 , 125–144. [ Google Scholar ] [ CrossRef ]
• Ferraro, T.; dos Santos, N.R.; Moreira, J.M. Decent work, Work Motivation, Work Engagement and Burnout in Physicians. Int. J. Appl. Posit. Psychol. 2020 , 5 , 13–35. [ Google Scholar ] [ CrossRef ]
• Barthauer, L.; Kaucher, P.; Spurk, D.; Kauffeld, S. Burnout and Career (un) Sustainability: Looking into the Blackbox of Burnout Triggered Career Turnover Intentions. J. Vocat. Behav. 2020 , 117 , 103334. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

District	University Type	Total A.	Sample A.	High P.O.	Average P.O.	Low P.O.	Non-P.O.	Sample A.O.
Eastern	Double World-Class university	2156	164	83	171	81	157	492
	Regular university	1849	141	70	146	72	135	423
	Vocational college	643	49	27	45	32	43	147
Central	Double World-Class university	3512	215	101	198	105	241	645
	Regular university	1712	105	66	102	48	99	315
	Vocational college	658	40	19	47	21	33	120
Western	Double World-Class university	2683	198	91	201	97	205	594
	Regular university	1599	118	49	110	50	145	354
	Vocational college	521	39	21	39	20	37	117
Northeastern	Double World-Class university	2068	177	82	200	83	166	531
	Regular university	1532	131	57	144	69	123	393
	Vocational college	501	43	18	37	29	45	129
Total		19,434	1420	684	1440	707	1429	4260

Control Variables	Number	Percentage %
Gender
Male	706	49.72
Female	714	50.28
Marital status
Married	878	61.83
Unmarried	542	38.17
Age
35 or below	312	21.97
36–45	373	26.27
46–55	360	25.35
56 or above	375	26.41
Majors
Social science	757	53.31
Natural science	663	46.69
Professional titles
Teaching assistant	305	21.48
Lecturer	399	28.1
Associate Professor	436	30.7
Professor	280	19.72
Years in service
10 or less	448	31.55
11–20	400	28.17
21–30	387	27.25
31 or above	185	13.03

Research Questions	Methodology	Hypotheses
Does job performance influence job burnout among academic staff? How can a mechanism be developed to address job burnout crises among academics? Does reducing job burnout play an important role in supporting the achievement of SDGs?	Quantitative Linear regression, frequency trends Results from RQ1 and RQ2	Ha1: Academics’ “job performance” has a substantially detrimental impact on “job burnout” when all predictor variables are considered. Ha2: “Psychological counseling” has different moderating effects on academic performance and burnout when all predictor variables are considered.

Variables	Overall
Gender	−0.043
	(0.030)
Marital status	−0.040
	(0.031)
Age	0.018
	(0.014)
Majors	−0.018
	(0.030)
Professional titles	0.049
	(0.044)
Years in service	0.001
	(0.008)
Job performance	−0.011 ***
	(0.001)
Constant	2.062 ***
	(0.101)
Observations	4260
R-squared	0.039
Individual fixed effect	YES
Time fixed effect	YES

	High P.O.	Average P.O.	Low P.O.	Non-P.O.
Gender	−0.029	−0.044	−0.074	0.042
	(0.061)	(0.041)	(0.060)	(0.041)
Marital status	−0.114	0.014	0.021	0.032
	(0.063)	(0.042)	(0.063)	(0.043)
Age	0.044	0.020	0.016	0.008
	(0.028)	(0.019)	(0.027)	(0.019)
Majors	−0.103	0.011	−0.035	0.044
	(0.062)	(0.042)	(0.060)	(0.042)
Professional titles	0.088	0.029	0.074	−0.011
	(0.089)	(0.059)	(0.087)	(0.060)
Years in service	0.008	−0.005	−0.017	0.003
	(0.015)	(0.010)	(0.015)	(0.010)
Job performance	−0.214 ***	−0.110 ***	−0.230 ***	−0.108 ***
	(0.011)	(0.004)	(0.011)	(0.004)
Constant	21.477 ***	9.877 ***	16.102 ***	6.778 ***
	(0.999)	(0.298)	(0.708)	(0.212)
Observations	684	1440	707	1429
R-squared	0.404	0.408	0.423	0.399
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

	Block 1	Block 2	Block 3	Block 4
Gender	−0.060	−0.059	−0.046	−0.044
	(0.029)	(0.028)	(0.027)	(0.027)
Marital status	−0.037	−0.043	−0.027	−0.029
	(0.030)	(0.029)	(0.028)	(0.028)
Age	0.009	0.008	0.008	0.009
	(0.013)	(0.013)	(0.012)	(0.012)
Majors	−0.040	−0.048	−0.031	−0.037
	(0.029)	(0.029)	(0.027)	(0.027)
Professional titles	−0.001	−0.001	−0.007	−0.011
	(0.041)	(0.041)	(0.039)	(0.039)
Years in service	−0.007	−0.006	−0.007	−0.006
	(0.007)	(0.007)	(0.007)	(0.007)
Job performance		−0.006 ***		−0.004 ***
		(0.001)		(0.001)
Psychological counselling			−0.577 ***	−0.583 ***
			(0.029)	(0.029)
Job performance * psychological counseling				−0.008 ***
				(0.002)
Constant	1.768 ***	2.185 ***	2.146 ***	2.417 ***
	(0.075)	(0.093)	(0.073)	(0.089)
Observations	3248	3248	3248	3248
R-squared	0.008	0.025	0.118	0.133
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

	High P.O.	Average P.O.	Low P.O.	Non-P.O.
Gender	0.054	−0.077	−0.041	0.072
	(0.066)	(0.047)	(0.068)	(0.046)
Marital status	−0.146 *	−0.021	−0.009	−0.035
	(0.067)	(0.047)	(0.071)	(0.048)
Age	0.042	0.019	−0.014	0.015
	(0.030)	(0.021)	(0.031)	(0.020)
Majors	−0.160 *	0.032	0.071	0.066
	(0.067)	(0.047)	(0.068)	(0.046)
Professional titles	0.091	0.033	0.034	0.034
	(0.094)	(0.065)	(0.098)	(0.066)
Years in service	0.011	0.002	−0.027	0.015
	(0.017)	(0.012)	(0.017)	(0.011)
Job performance	−0.186 ***	−0.112 ***	−0.103 ***	−0.047 ***
	(0.031)	(0.011)	(0.028)	(0.010)
Psychological counselling	−0.155	−0.732	−0.077 **	−0.357 *
	(1.319)	(0.414)	(0.995)	(0.259)
Job performance * psychological counseling	0.002	0.009	−0.065 ***	−0.030 ***
	(0.014)	(0.005)	(0.015)	(0.005)
Constant	18.534 ***	10.079 ***	8.225 ***	3.683 ***
	(2.932)	(0.914)	(1.845)	(0.526)
Observations	460	944	433	925
R-squared	0.411	0.379	0.453	0.412
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

Lei, M.; Alam, G.M.; Bashir, K. The Relationships between Job Performance, Job Burnout, and Psychological Counselling: A Perspective on Sustainable Development Goals (SDGs). Sustainability 2024 , 16 , 7569. https://doi.org/10.3390/su16177569

Lei M, Alam GM, Bashir K. The Relationships between Job Performance, Job Burnout, and Psychological Counselling: A Perspective on Sustainable Development Goals (SDGs). Sustainability . 2024; 16(17):7569. https://doi.org/10.3390/su16177569

Lei, Miao, Gazi Mahabubul Alam, and Karima Bashir. 2024. "The Relationships between Job Performance, Job Burnout, and Psychological Counselling: A Perspective on Sustainable Development Goals (SDGs)" Sustainability 16, no. 17: 7569. https://doi.org/10.3390/su16177569

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

ORIGINAL RESEARCH article

The relationship between work engagement and job performance: psychological capital as a moderating factor.

• 1 School of Education Science, Huaiyin Normal University, Huai’an, China
• 2 School of Education, Huzhou University, Huzhou, China
• 3 School of Psychology, Nanjing Normal University, Nanjing, China

Based on the job demands-resources model, this study explored the relationships of work engagement, job performance and psychological capital in industry employees. A total of 399 IT programmers were recruited and completed the work engagement scale, knowledge employee job performance scale and psychological capital questionnaire. The results showed that: (1) There is a relationship between work engagement and job performance, which may not be linear but inverted U-shaped, and (2) psychological capital plays a moderating role in the inverted U-shaped relationship between work engagement and job performance.

Introduction

Traditionally, enterprises have developed internally and externally to maintain a competitive advantage in industry. By purchasing high technology and other resources, the entry threshold to industry could be improved while the cost of similar enterprises could be increased and their competitiveness reduced. The continuous development of human resources could also improve work efficiency and the potential for innovation. However, in the current era of “Internet Plus” which is the integration of the internet and traditional industries through online platforms and information technology (IT). Therefore, external resources and technology can reach a common state through the Internet. As such, the mechanisms for improving the threshold of entry do not now bring more competitive advantages to enterprises. In contrast, the development of internal human resources has become an important source of competitive advantage and innovation for enterprises today ( Yao and Yang, 2016 ). In the past, internal human resource development measures mainly included: (1) actively carrying out knowledge and skills training and improving the professional quality of employees to improve work efficiency and innovation ability, and (2) increasing the engagement of employees in work and increasing the total amount of completed business to maintain the performance of the whole enterprise ( Tang and Sun, 2011 ).

Although these measures previously achieved some positive results, they may not be able to do so in these “Internet Plus” times and may even be a hindrance. The main reason for this is that the knowledge and technology represented by information technology have increased greatly. At the same time, the speed of updating is also very fast, and the cost of knowledge and skills training has been improved. In addition, increasing the work engagement of employees also increases the pressure placed on them, which is likely to lead to them falling into cycles of excessive fatigue and burnout ( Rycroft and Kash, 2016 ). In the new era, performance improvement brought by the training of knowledge and skills and the increase of work engagement has been found to be lacking, which makes researchers and practitioners doubt that the cost of increasing investment can achieve the expected benefits. However, at present, the emphasis of enterprises on increasing the work Engagement of employees to achieve the growth of human capital remains unchanged ( Tang and Sun, 2011 ), in order to solve this contradiction, it is necessary to find a new sustainable development of internal resources to promote the improvement of job performance. In the process of finding such resources, many researchers have paid attention to the role of psychological capital. Psychological capital refers to an individual’s positive state of psychological development, which is manifested as: (1) when facing challenging work, having confidence (self-efficacy/self-confidence) and making the necessary efforts to achieve success, (2) having a positive attribution (optimism) to present and future successes, (3) persevering in goals and adjusting the approach (hope) to achieve goals and successes, and (4) when faced with adversity and problems, persevering, recovering quickly and surpassing difficulties (resilience) to achieve success ( Luthans et al., 2008 ).

Psychological capital can maintain employee working motivation and alleviate job burnout. However, employees with higher psychological capital will actively connect with other resources, learn new skills related to work, and promote individual growth, development, and performance improvement ( Wu et al., 2012 ). Psychological capital has a strong role in promoting job performance. In this context, what role can psychological capital play in the contradiction between input and output? Therefore, under the background of “Internet plus,” this paper explores the relationship between employee’s job involvement and job performance and the effect of psychological capital, which can provide a way to solve the contradiction between employee’s input and output.

The theoretical basis of this study are job demands-resources model. According to the job demands-resources model, when the job requirements and work resources match, the employee’s work efficiency is higher, on the contrary, the work efficiency is low ( Liu et al., 2020 ). To some extent, the two theories reveal the relationship between work engagement and job performance, and the role of psychological capital in it, the details are as follows:

The Relationship Between Work Engagement and Job Performance

Work engagement is a positive and complete emotional and cognitive state related to work, associated with the characteristics of persistence and dispersion ( Li and Ling, 2007 ; Aldabbas et al., 2021 ). Based on findings from previous studies, there remains debate regarding the relationship between job involvement and job performance. Some researchers have proposed that with an increase of work engagement, employee emotional, cognitive and forward-looking behaviors will positively improve, which will also lead to an increase in job performance ( Wang and Chen, 2020 ).

However, some other researchers argue that an increase in work engagement does not necessarily lead to the continuous growth of job performance, which may reflect an inverted U-shaped relationship ( Bouckenooghe et al., 2021 ). For example, the job demands-resources model (JD-R) proposed by Demerouti et al. (2001) proposes that the factors that affect the job performance of employees are due to two aspects: work requirements and work resources. Work requirements refer to the physical, psychological, social and organizational requirements of employees, which draw on their continuous physical and/or psychological (cognitive and emotional) efforts and/or skills including their ability to deal with work pressure, work engagement, emotional exhaustion, work-life conflict and so on. Work resources refer to the physical, psychological, social and organizational resources that can be used by employees to achieve work objectives, including the resources owned by individuals themselves, as well as the social and organizational resources that can be obtained. These include workers’ cognitive styles, self-confidence and behavior models, leadership, support from colleagues, family and friends, promotion opportunities, salary, working atmosphere, diversity of tasks, and so on ( Demerouti et al., 2001 ; Qi and Wu, 2018 ).

When work requirements match an individual’s work resources, increasing work engagement will improve job performance. However, if the work requirements exceed an individual’s work resources and increase work engagement, this will fail to bring about an improvement in job performance and will also result in the loss of an individual’s mental and physical resources, leading to energy exhaustion, anxiety, burnout, disappointment and other negative emotions, further reducing their job performance and leading to turnover and health problems ( Lu and Tu, 2015 ). The empirical research confirms this view. For example, Adler and Koch (2017) and others found that employees undertake two kinds of countermeasures when work requirements exceed the work resources. One is coping with fatigue. Employees rely on their own subjective efforts to mobilize all the resources they can to maintain or meet work requirements. Such excessive efforts will cause fatigue. The second is a negative response in which employees are not willing to make full use of their resources to maintain or meet work requirements, and will actively reduce their awareness of work requirements, leading to performance degradation and other unprofessional behaviors. Therefore, when employees face higher work requirements and their available work resources are unable to meet this, there will be a negative impact on job performance. In the IT industry, the resources required by jobs often exceed the resources that employees can provide. The main reason is that the IT industry knowledge update speed is fast, and the staff’s learning intensity and work intensity are usually high, which may lead to fatigue coping and negative coping ( Kun and Gadanecz, 2019 ). From this, we made the hypothesis H1, that the relationship between work engagement and job performance is an inverted U-shape.

The Role of Psychological Capital in the Relationship Between Work Engagement and Job Performance

The JD-R model also proposes that work resources will buffer the physical and/or psychological consumption of work requirements, and regulate the relationship between work engagement and job performance. In the case of greater work resource support, job performance will increase accordingly. For example, Wang et al. (2012) found that social support and job development opportunities have a positive impact on job performance.

However, in the recent development of the information technology industry in terms of internal resources, the focus is on developing and utilizing the existing knowledge and experience of employees. That is, paying attention to the development of human capital and relatively ignoring the importance of psychological capital of programmers to the development of individuals and enterprises. Psychological capital, more so than human capital, can predict the job performance and positive work attitude and behavior of employees ( Tian and Xie, 2010 ; Yin et al., 2018 ), and is more likely to be an adjustment variable on the relationship between work engagement and job performance. Therefore, when considering the JD-R theoretical model, many researchers have proposed taking psychological capital into account ( Zhao et al., 2013 ). For example, Sun et al. (2014) , when studying the JR-D theoretical model, considered psychological capital to be an internal resource for development that helps practitioners respond to various work requirements with a positive psychological state, and one that can effectively prevent and improve job burnout and finally, improve job performance. Psychological capital has increasingly been found to play a positive role in the relationship between work engagement and job performance ( Qi and Wu, 2018 ). Psychological capital is usually regarded as an individual’s internal resources, which plays a positive role in individuals’ work efficiency ( Luthans et al., 2008 ). In the IT industry, the positive role played by psychological capital is also being concerned by researchers ( Sihag and Sarikwal, 2015 ). Therefore, we made the hypothesis H2, that psychological capital plays a moderate role in the relationship between work engagement and job performance.

Materials and Methods

Participants.

Participants were programmers from 3 well-known IT companies in Nanjing. The reason for choosing them is that IT industry has a relatively fast updating knowledge, and programmers can best represent the working status of employees in “Internet plus” era. A total of 420 questionnaires were sent out and 399 valid questionnaires were collected. The response rate is 95%. Participants were aged 20–48 ( M = 26.84, S = 5.82), of whom 271 were male and 128 were female. In total, 122 (30.6%) had worked for less than 1 year, 171 (42.9%) for 1–3 years and 106 (26.5%) for more than 3 years.

Work Engagement

The Chinese version of the work engagement scale, developed by Schaufeli and Bakker (2004) and revised by Zhang and Gan (2005) , was used to assess the level of employee work engagement from physical, emotional and cognitive perspectives. The scale consists of 15 items in total, for example, “I feel myself bursting with energy in my work,” “I am immersed in my work.” Each item was scored on a 6-point scale, ranging from 1 (never) to 6 (Always). The reliability of this scale is greater than 0.70, in this study, Cronbach’s α coefficient was 0.72. Confirmatory factor analysis showed that the fit indexes for χ 2 /df = 1.36, Tucker–Lewis index (TLI) = 0.99, comparative fit index (CFI) = 0.99, and root mean square error of approximation (RMSEA) = 0.030. The indicators of the model fit were accepted.

Job Performance

The measure of employee job performance was developed by Han et al. (2007) and it is suitable for the measurement of domestic knowledge workers’ job performance. There are 39 items, for example, “I complete my work in accordance with the requirements of the formal performance appraisal,” “I volunteer for duties that are not my own.” Each item was scored on a 5-point scale, ranging from 1 (Strongly disagree) to 5 (Strongly agree), the scale assess employee job performance across the four dimensions of innovation performance, relationship performance, learning performance and task performance. Part of the reason for choosing this scale is that enterprise programmers also belong to the category of knowledge employees. The other reason is that the four dimensions of this scale are closely matched to the work content of IT enterprise programmers. The Cronbach’s α coefficient of the job performance scale was 0.88, in this study, Cronbach’s α coefficient was 0.87. Confirmatory factor analysis showed that the fit indexes for χ 2 /df = 1.04, TLI = 0.99, CFI = 0.99, and RMSEA = 0.010. The indicators of the model fit were accepted.

Psychological Capital

The Chinese version of the psychological capital scale, developed by Luthans et al. (2008) and revised by Zhong et al. (2013) . There are 24 items which are measured across the four dimensions of self-efficacy, hope, optimism and resilience on a six-point scale. for example, “I believe I can analyze long-term problems and find solutions,” “Currently, I am working energetically to accomplish my goals.” Each item was scored on a 6-point scale, ranging from 1 (Strongly disagree) to 6 (Strongly agree). The Cronbach’s α coefficient of Psychological capital scale was 0.89. In this study, the Cronbach’s α coefficient was 0.90. Confirmatory factor analysis showed that the fit indexes for χ 2 /df = 2.09, TLI = 0.90, CFI = 0.92, and RMSEA = 0.052. The indicators of the model fit were accepted.

Control Variables

We control some variables that can influence the research results, such as gender, working years and other demographic variables to maintain a balance. Environmental variables such as noise in the measurement process are excluded, and the experimenters and assistants are strictly trained to ensure that there is no error caused by human factors.

This study was approved by the ethical review boards of the authors’ institutions. Written informed consent was obtained from all participants before their enrollment in the study. They were informed that they could withdraw from the study at any time. Participants from three IT companies in Nanjing were gathered in a quiet place. After reading the instructions provided by the experimenter, they completed the questionnaire according to their recent job performance. After completing the task, they received ¥30 for taking part in the survey. Before the formal survey, we conducted a pilot test with about 100 IT employees, and found significant correlation among the three variables.

Common Method Bias Control

The data of this study were collected by self-report, the could have been affected by common method bias, which might, in turn, decrease the validity of the results. So we used “process control” and “statistical control” for controlling for common method bias. Process control refers to control measures incorporated into the process of a study’s design and measurement by researchers ( Yao and Yang, 2017 ). In this study, we kept strict principles of confidentiality and voluntarism, and asked participants to truthful answer each question. We used random sampling method to get participants and collect data in a closed environment, and recycled the questionnaires immediately after each survey was completed. These methods can effectively control the common method bias. In addition, statistical control involves “a statistical test that is applied after data collection” ( Yao and Yang, 2017 ), and we used the Harman single factor test to test for common method bias. The results showed that eight factors had an eigenvalue greater than 1, and the first factor accounted for 25.42% of the variance, which is less than the critical standard of 40%. This shows that common method bias was not apparent.

Descriptive Statistics

Before testing the hypothesis model, we conduct confirmatory factor analysis to evaluate the suitability of the research model, the result the fit indexes for χ 2 /df = 1.58, TLI = 0.91, CFI = 0.91, and RMSEA = 0.038. The indicators of the model fit were accepted.

The means, standard deviations, and correlation coefficients for each variable were calculated and presented in Table 1 . Work engagement, job performance and psychological capital were all positively correlated.

Table 1. Means, standard deviations, and intercorrelations for the key study variables.

The Test of the Relationship Between Work Engagement and Job Performance

Regression analysis was used to assess the relationship between work engagement and job performance, and to compare the advantages and disadvantages of the linear and quadratic models. Results are shown in Table 2 where it can be seen that the linear and quadratic relationships between work engagement and job performance were both significant. In the linear model, work engagement could only explain 26% ( R 2 = 0.26) of the variation in job performance but in the quadratic model, work engagement could explain 72% ( R 2 = 0.72) of the variation of job performance, showing that the quadratic model was better than the linear model. The relationship between work engagement and job performance was an inverted U-shape (see Figure 1 ).

Table 2. Linear and curvilinear estimation of work engagement and job performance.

Figure 1. Plot models and the relationship between work engagement and job performance.

The Moderating Effect of Psychological Capital

In this study, psychological capital, work engagement and job performance were all continuous variables, and the relationship between work engagement and job performance was an inverted U-shape. We adopted the regulatory analysis method of non-linear relationships described by Luo and Jiang (2014) relating to the questionnaire research method by regulating the high (one standard deviation higher than the average) and low (one standard deviation lower than the average) values for psychological capital, and the high (one standard deviation higher than the average), medium (average), and low (one standard deviation lower than the average) values for work engagement. The confidence intervals of the dependent variable (job performance) corresponding to the independent variable (work engagement) were calculated, respectively ( p = 0.05). We used Mplus 6.0 to analyze the regulatory effect of the inverted U-shape relationship and the results are shown in Table 3 . These show that when psychological capital is low, the 95% confidence interval for the job performance of participants with high, medium, and low work engagement almost overlapped. This shows that different levels of work engagement do not cause significant differences in job performance. However, when the psychological capital is high, the middle point of the confidence interval is higher than the other two points, showing an inverted U-shaped relationship. That is to say, only when psychological capital is high, do work engagement and job performance show a significant inverted U-shaped relationship, confirming a regulatory effect.

Table 3. The moderating effect of psychological capital.

SPSS was used to draw the curve estimation model of the relationship between work engagement and job performance under the condition of high mental capital (the highest 27%) and low mental capital (the lowest 27%), and put the two models into the same coordinate axis. The results are shown in Figure 2 , which more clearly shows the regulatory effect of mental capital on the relationship between work engagement and job performance, that is, for low mental capital. In the case of adjustment, the job performance of participants only slightly increased with the increase of work engagement and then decreased. In the case of high psychological capital adjustment, this took place before the work engagement reached the critical value. The job performance of IT enterprise programmers significantly increased with the increase of work engagement, and after exceeding the critical value of work engagement, the job performance decreased with the increase of work engagement.

Figure 2. The moderating effect of psychological capital.

In this study, programmers in the IT industry were selected as research participants to explore the relationship between work engagement and job performance, as well as the regulatory role of psychological capital. The results of the correlation analysis showed that there were significant positive correlations between work engagement, job performance, and psychological capital, and this indicated that there may also be positive relationships between work engagement, job performance and psychological capital. Through the analysis of the relationship between work engagement and job performance, we found that an inverted U-shaped relationship was more suitable for the data distribution than a linear relationship, meaning that the relationship between work engagement and job performance is not simply positive correlation. Appropriate work engagement is very important to job performance. This result is consistent with findings from some previous studies. For example, Demerouti et al. (2001) found that if the level of work engagement is too high, the relationship between work engagement and job performance will not be positive. Macey and Schneider (2010) also pointed out that to maintain long-term and stable job performance, employees cannot be in a high engagement state in a short period of time.

Through the analysis of the moderating role of psychological capital, we also found that there is a significant inverted U-shaped relationship between work engagement and job performance for individuals with high psychological capital. This shows that when individuals have a certain amount of psychological capital, higher job performance was associated with appropriate work engagement. However, for individuals with low psychological capital, job performance is always at a low level, and has a weak association with work engagement. The reasons for this may be twofold. First, psychological capital plays an important role in improving job performance, which will stimulate individuals to invest more efforts to participate in individual work ( Qi and Wu, 2018 ). At the same time, psychological capital may produce more organizational citizenship behavior and promote performance. Second, when an individual has certain resources, particularly internal resources such as psychological capital, it can effectively buffer the adverse effects of work engagement on the individual, including anxiety, psychological exhaustion or burnout. We also found that under the same level of work engagement, individuals with higher psychological capital will have better job performance.

This research has made contributions in both theory and practice. In theory, it confirms the inverted “U” relationship between work engagement and job performance, and verifies the applicability of job requirement-resource model on Chinese cultural groups. At the same time, it makes a useful exploration on the theoretical model of IT employees’ job performance. The study also verified the moderating role played by psychological capital in the relationship between work engagement and work performance, which implies that individual work performance is not only related to the provision of good working conditions, but also closely related to the state of the individual. Psychological capital can be used as a resource to enhance performance.

In practice, job performance can be improved in three ways: first, by providing suitable working conditions to meet their needs so that they can devote more time and energy to their work; second, a reasonable match between people and jobs can improve performance. Thirdly, the psychological capital of employees is developed from within, thus improving the individual state.

The value of this article is the discovery that for individuals to achieve the highest job performance, a moderate level of work engagement is optimal, while individuals with higher psychological capital will have higher performance with the same work engagement. The disadvantage of this study is that performance is measured by self-report. Although it is more suitable for this study, it is different from the real situation of employee performance. In order to offset this limitation, we controlled the social desirability and possible memory bias of the participants, and asked them to evaluate their own situation in the last week and answer truthfully. Although it cannot completely eliminate the influence of social approval and memory bias, it can reduce the reaction bias to a certain extent. In the future, we can measure performance from the perspective of a third party to reduce errors. In addition, the results obtained from cross-sectional data are essentially a correlation, not a causal relationship. Therefore, this study only makes a possible inference on causality on the basis of correlation, and will use longitudinal data to reveal causality in the future.

The future research can be expanded on the following three aspects: first, explore the relationship between work engagement and job performance in the context of group, and consider the influence of group characteristics, such as collective psychological capital. Secondly, longitudinal research can be used to confirm the causal effect on the development of employee psychological capital and the improvement on employee performance. Finally, qualitative research can be used to explore the theoretical model of the impact process on how psychological capital can buffer the negative impact of excessive work engagement and how to improve job performance, and lay a foundation for future research in this field.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

This study was carried out following approval by the Ethics Committee of the Psychological Experiment Teaching Centre of Nanjing Normal University. All procedures performed in this study were in accordance with the ethical standards of authors’ institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Written informed consent was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JY participated in the design, data collection, data analysis, data interpretation, and drafting the early version of the article. XQ and LY participated in the design and revising the article critically for better intrinsic logicality. XH participated in the design and drafting the early version of the article. YL participated in data analysis. All authors contributed to the article and approved the submitted version.

This study was supported in part by the Humanities and Social Sciences Foundation of Ministry of Education of P. R. China (No. 19YJC880070) and the Collaborative Education Project of the Ministry of Education of P. R. China (No. 202002234058).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Adler, M., and Koch, A. K. (2017). Expanding the job demands-resources model to classify innovation-predicting working conditions. Manag. Rev. 28, 175–203. doi: 10.5771/0935-9915-2017-2-175

PubMed Abstract | CrossRef Full Text | Google Scholar

Aldabbas, H., Pinnington, A., and Lahrech, A. (2021). The influence of perceived organizational support on employee creativity: the mediating role of work engagement. Curr. Psychol. 136, 280–287. doi: 10.1007/s12144-021-01992-1

CrossRef Full Text | Google Scholar

Bouckenooghe, D., Clercq, D. D., Naseer, S., and Syed, F. (2021). A curvilinear relationship between work engagement and job performance: the roles of feedback-seeking behavior and personal resources. J. Bus. Psychol. [Epub ahead of print]. doi: 10.1007/s10869-021-09750-7

Demerouti, E., Nachreiner, F., Bakker, A. B., and Schaufeli, W. B. (2001). The job demands-resources model of burnout. J. Appl. Psychol. 86, 499–512. doi: 10.1037/0021-9010.86.3.499

Han, Y., Liao, J., and Long, L. (2007). Model of development and empirical study on employee job performance construct. J. Manag. Sci. China 10, 62–77. doi: 10.3321/j.issn:1007-9807.2007.05.007

Kun, A., and Gadanecz, P. (2019). Workplace happiness, well-being and their relationship with psychological capital: a study of hungarian teachers. Curr. Psychol. 41, 185–199. doi: 10.1007/s12144-019-00550-0

Li, R., and Ling, W. (2007). A review of the research on work/job engagement. Adv. Psychol. Sci. 15, 366–372. doi: 10.3969/j.issn.1671-3710.2007.02.028

Liu, R., Zhu, F., and Pan, P. (2020). Study on the grass-roots civil servants’ burnout based on job demands-resources model:The mediation and moderation effects of self-efficacy. Hubei Agric. Sci. 59, 182–189. doi: 10.14088/j.cnki.issn0439-8114.2020.12.041

Lu, X., and Tu, Y. (2015). The short-term fluctuation of work engagement. Adv. Psychol. Sci. 23, 268–279. doi: 10.3724/SP.J.1042.2015.00268

Luo, S., and Jiang, Y. (2014). Management Survey Research Methodology. Chongqing: Chongqing University Press.

Google Scholar

Luthans, F., Youssef, C., and Avolio, B. J. (2008). Psychological Capital: Developing the Human Competitive Edge. Beijing: China Light Industry Press.

Macey, W. H., and Schneider, B. (2010). Engaged in engagement: we are delighted we did it. Ind. Organ. Psychol. 1, 76–83. doi: 10.1111/j.1754-9434.2007.00016.x

Qi, Y., and Wu, X. (2018). Job demands-resources model: the development of theoretical and empirical research. J. Beijing Norm. Univ. Soc. Sci. Ed. 06, 28–36.

Rycroft, R. W., and Kash, D. E. (2016). Complexity Challenge: Technological Innovation for the 21st Century. Beijing: Peking University Press.

Schaufeli, W. B., and Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: a multisample study. J. Organ. Behav. 25, 293–315. doi: 10.1002/job.248

Sihag, P., and Sarikwal, L. (2015). Effect of perceived organizational support on psychological capital - a study of it industries in indian framework. Electron. J. Bus. Ethics Organ. Stud. 20, 19–26.

Sun, K., Yin, W., Huang, D., Yu, Q., and Zhao, Y. (2014). Effect of psychological capital on the relationship between job requirement and job burnout of physicians in governmental hospitals. Chin. J. Health Psychol. 22, 1527–1529. doi: 10.13342/j.cnki.cjhp.2014.10.036

Tang, S., and Sun, Q. (2011). Study on motivating factor of IT knowledge workers in China. J. Univ. Sci. Technol. Liaon. 34, 521–525. doi: 10.3969/j.issn.1674-1048.2011.05.016

Tian, X., and Xie, J. (2010). The influence of POS on working behaviours of employees: empirical research on mediating role of psychological capital. Nankai Bus. Rev. 13, 23–29. doi: 10.3969/j.issn.1008-3448.2010.01.004

Wang, C., and Chen, H. (2020). Relationships among workplace incivility, work engagement and job performance National Taichung University of Education. J. Hosp. Tour. 3, 415–429. doi: 10.1108/JHTI-09-2019-0105

Wang, Y., Wei, Z., and Wang, L. (2012). An empirical research of impact of work resources and work engagement on knowledge sharing. Sci. Technol. Manag. Res. 32, 150–153. doi: 10.3969/j.issn.1000-7695.2012.24.034

Wu, W., Liu, Y., Lu, H., and Xie, X. (2012). The chinese indigenous psychological capital and career well-being. Acta Psychol. Sin. 44, 1349–1370. doi: 10.3724/SP.J.1041.2012.01349

Yao, J., and Yang, L. (2016). The “internet+” model of mental health service in community: opportunities and challenges. J. Soochow Univ. Educ. Sci. Ed. 4, 24–31.

Yao, J., and Yang, L. (2017). Perceived prejudice and the mental health of chinese ethnic minority college students: the chain mediating effect of ethnic identity and hope. Front. Psychol. 8:1167. doi: 10.3389/fpsyg.2017.01167

Yin, H., Wang, W., Huang, S., and Li, H. (2018). Psychological capital, emotional labor and exhaustion: examining mediating and moderating models. Curr. Psychol. 37, 343–356. doi: 10.1007/s12144-016-9518-z

Zhang, Y., and Gan, Y. (2005). The chinese version of utrecht work engagement scale: an examination of reliability and validity. Chinese J. Clin. Psychol. 13, 268–270. doi: 10.3969/j.issn.1005-3611.2005.03.005

Zhao, J., Sun, J., and Zhang, X. (2013). The impact of job demands, job resources, psychological capital on work family relationship. J. Psychol. Sci. 36, 170–174. doi: 10.3390/ijerph16112011

Zhong, L., Wang, Z., Li, M., and Li, C. (2013). Transformational leadership, psychological capital and employee job performance. Chinese J. Manag. 10:536. doi: 10.3969/j.issn.1672-884x.2013.04.009

Keywords : work engagement, job performance, psychological capital, moderating, U-shaped relationship

Citation: Yao J, Qiu X, Yang L, Han X and Li Y (2022) The Relationship Between Work Engagement and Job Performance: Psychological Capital as a Moderating Factor. Front. Psychol. 13:729131. doi: 10.3389/fpsyg.2022.729131

Received: 22 June 2021; Accepted: 12 January 2022; Published: 17 February 2022.

Reviewed by:

Copyright © 2022 Yao, Qiu, Yang, Han and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiangbin Qiu, [email protected] ; Liping Yang, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Employee psychological well-being and job performance: exploring mediating and moderating mechanisms

International Journal of Organizational Analysis

ISSN : 1934-8835

Article publication date: 12 August 2020

Issue publication date: 7 May 2021

Given the importance of employee psychological well-being to job performance, this study aims to investigate the mediating role of affective commitment between psychological well-being and job performance while considering the moderating role of job insecurity on psychological well-being and affective commitment relationship.

Design/methodology/approach

The data were gathered from employees working in cellular companies of Pakistan using paper-and-pencil surveys. A total of 280 responses were received. Hypotheses were tested using structural equation modeling technique and Hayes’s Model 1.

Findings suggest that affective commitment mediates the association between psychological well-being (hedonic and eudaimonic) and employee job performance. In addition, perceived job insecurity buffers the association of psychological well-being (hedonic and eudaimonic) and affective commitment.

Practical implications

The study results suggest that fostering employee psychological well-being may be advantageous for the organization. However, if interventions aimed at ensuring job security are not made, it may result in adverse employee work-related attitudes and behaviors.

Originality/value

The study extends the current literature on employee well-being in two ways. First, by examining psychological well-being in terms of hedonic and eudaimonic well-being with employee work-related attitude and behavior. Second, by highlighting the prominent role played by perceived job insecurity in explaining some of these relationships.

• Psychological well-being
• Affective commitment
• Job insecurity
• Job performance
• Eudaimonic wellbeing
• Hedonic wellbeing

Kundi, Y.M. , Aboramadan, M. , Elhamalawi, E.M.I. and Shahid, S. (2021), "Employee psychological well-being and job performance: exploring mediating and moderating mechanisms", International Journal of Organizational Analysis , Vol. 29 No. 3, pp. 736-754. https://doi.org/10.1108/IJOA-05-2020-2204

Emerald Publishing Limited

Copyright © 2020, Yasir Mansoor Kundi, Mohammed Aboramadan, Eissa M.I. Elhamalawi and Subhan Shahid.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Does the employee well-being have important implications both at work and for other aspects of an employees’ life? Of course! For years, we have known that they impact life at work and a plethora of research has examined the impact of employee well-being on work outcomes (Karapinar et al. , 2019 ; Turban and Yan, 2016 ). What is less understood is how employee well-being impacts job performance. Evidence suggests that employee health and well-being are among the most critical factors for organizational success and performance (Bakker et al. , 2019 ; Turban and Yan, 2016 ). Several studies have documented that employee well-being leads to various individual and organizational outcomes such as increased organizational performance and productivity (Hewett et al. , 2018 ), customer satisfaction (Sharma et al. , 2016 ), employee engagement (Tisu et al. , 2020 ) and organizational citizenship behavior (OCB; Mousa et al. , 2020 ).

The organizations’ performance and productivity are tied to the performance of its employees (Shin and Konrad, 2017 ). Much evidence has shown the value of employee job performance (i.e. the measurable actions, behaviors and outcomes that employee engages in or bring about which are linked with and contribute to organizational goals; Viswesvaran and Ones, 2017 ) for organizational outcomes and success (Al Hammadi and Hussain, 2019 ; Shin and Konrad, 2017 ), which, in turn, has led scholars to seek to understand what drives employee performance. Personality traits (Tisu et al. , 2020 ), job conditions and organizational characteristics (Diamantidis and Chatzoglou, 2019 ) have all been identified as critical antecedents of employee job performance.

However, one important gap remains in current job performance research – namely, the role of psychological well-being in job performance (Hewett et al. , 2018 ). Although previous research has found happy workers to be more productive than less happy or unhappy workers (DiMaria et al. , 2020 ), a search of the literature revealed few studies on psychological well-being and job performance relationship (Salgado et al. , 2019 ; Turban and Yan, 2016 ). Also, very little is known about the processes that link psychological well-being to job performance. Only a narrow spectrum of well-being related antecedents of employee performance has been considered, especially in terms of psychological well-being. Enriching our understanding of the consequences and processes of psychological well-being in the workplace, the present study examines the relationship between psychological well-being and job performance in the workplace setting. Such knowledge will not only help managers to attain higher organizational performance during the uncertain times but will uncover how to keep employees happy and satisfied (DiMaria et al. , 2020 ).

Crucially, to advance job performance research, more work is needed to examine the relationship between employees’ psychological well-being and their job performance (Ismail et al. , 2019 ). As Salgado et al. (2019) elaborated, we need to consider how an employees’ well-being affects ones’ performance at work. In an attempt to fill this gap in the literature, the present study seeks to advance job performance research by linking ones’ psychological well-being in terms of hedonic and eudaimonic well-being to ones’ job performance. Hedonic well-being refers to the happiness achieved through experiences of pleasure and enjoyment, while eudaimonic well-being refers to the happiness achieved through experiences of meaning and purpose (Huta, 2016 ; Rahmani et al. , 2018 ). We argue that employees with high levels of psychological well-being will perform well as compared to those having lower levels of psychological well-being. We connect this psychological well-being-job performance process through an employee affective commitment (employees’ perceptions of their emotional attachment to or identification with their organization; Allen and Meyer, 1996 ) – by treating it as a mediating variable between well-being-performance relationship.

Additionally, we also examine the moderating role of perceived job insecurity in the well-being-performance relationship. Perceived job insecurity refers to has been defined as the perception of being threatened by job loss or an overall concern about the continued existence of the job in the future (De Witte et al. , 2015 ). There is evidence that perceived job insecurity diminishes employees’ level of satisfaction and happiness and may lead to adverse job-related outcomes such as decreased work engagement (Karatepe et al. , 2020 ), deviant behavior (Soomro et al. , 2020 ) and reduced employee performance (Piccoli et al. , 2017 ). Thus, addressing the gap mentioned above, this study has two-fold objectives; First, to examine how the path between psychological well-being and job performance is mediated through employee affective commitment. The reason to inquire about this path is that well-being is associated with an employees’ happiness, pleasure and personal growth (Ismail et al. , 2019 ). Therefore, higher the well-being, higher will be the employees’ affective commitment, which, in turn, will lead to enhanced job performance. The second objective is to empirically test the moderating effects of perceived job insecurity on employees’ emotional attachment with their organizations. Thus, we propose that higher job insecurity may reduce the well-being of employees and their interaction may result in lowering employees’ emotional attachment with their organization.

The present study brings together employee well-being and performance literature and contributes to these research areas in two ways. First, we contribute to this line of inquiry by investigating the direct and indirect crossover from hedonic well-being and eudaimonic well-being to employees’ job performance. We propose that psychological well-being (hedonic and eudaimonic) influence job performance through employee affective commitment. Second, prior research shows that the effect of well-being varies across individuals indicating the presence of possible moderators influencing the relationship between employee well-being and job outcomes (Lee, 2019 ). We, therefore, extend the previous literature by proposing and demonstrating the general possibility that perceived job insecurity might moderate the relationship of psychological well-being (hedonic and eudaimonic) and affective commitment. While there is evidence that perceived job insecurity influence employees’ affective commitment (Schumacher et al. , 2016 ), what is not yet clear is the impact of perceived job insecurity on psychological well-being − affective commitment relationship. The proposed research model is depicted in Figure 1 .

2. Hypotheses development

2.1 psychological well-being and affective commitment.

Well-being is a broad concept that refers to individuals’ valued experience (Bandura, 1986 ) in which they become more effective in their work and other activities (Huang et al. , 2016 ). According to Diener (2009) , well-being as a subjective term, which describes people’s happiness, the fulfillment of wishes, satisfaction, abilities and task accomplishments. Employee well-being is further categorized into two types, namely, hedonic well-being and eudaimonic well-being (Ballesteros-Leiva et al. , 2017 ). Compton et al. (1996) investigated 18 scales that assess employee well-being and found that all the scales are categorized into two broad categories, namely, subjective well-being and personal growth. The former is referred to as hedonic well-being (Ryan and Deci, 2000 ) whereas, the latter is referred to as eudaimonic well-being (Waterman, 1993 ).

Hedonic well-being is based on people’s cognitive component (i.e. people’s conscious assessment of all aspects of their life; Diener et al. , 1985 ) and affective component (i.e. people’s feelings that resulted because of experiencing positive or negative emotions in reaction to life; Ballesteros-Leiva et al. , 2017 ). In contrast, eudaimonic well-being describes people’s true nature and realization of their actual potential (Waterman, 1993 ). Eudaimonic well-being corresponds to happy life based upon ones’ self-reliance and self-truth (Ballesteros-Leiva et al. , 2017 ). Diener et al. (1985) argued that hedonic well-being focuses on happiness and has a more positive affect and greater life satisfaction, and focuses on pleasure, happiness and positive emotions (Ryan and Deci, 2000 ; Ryff, 2018 ). Contrarily, eudaimonic well-being is different from hedonic well-being as it focuses on true self and personal growth (Waterman, 1993 ), recognition for ones’ optimal ability and mastery ( Ryff, 2018 ). In the past, it has been found that hedonic well-being and eudaimonic well-being are relatively correlated with each other but are distinct concepts (Sheldon et al. , 2018 ).

To date, previous research has measured employee psychological well-being with different indicators such as thriving at work (Bakker et al. , 2019 ), life satisfaction (Clark et al. , 2019 ) and social support (Cai et al. , 2020 ) or general physical or psychological health (Grey et al. , 2018 ). Very limited studies have measured psychological well-being with hedonic and eudaimonic well-being, which warrants further exploration (Ballesteros-Leiva et al. , 2017 ). Therefore, this study assesses employee psychological well-being based upon two validated measures, namely, hedonic well-being (people’s satisfaction with life in general) and eudaimonic well-being (people’s personal accomplishment feelings).

Employee well-being has received some attention in organization studies (Huang et al. , 2016 ). Prior research has argued that happier and healthier employees increase their effort, performance and productivity (Huang et al. , 2016 ). Similarly, research has documented that employee well-being has a positive influence on employee work-related attitudes and behaviors such as, increasing OCB (Mousa et al. , 2020 ), as well as job performance (Magnier-Watanabe et al. , 2017 ) and decreasing employees’ work-family conflict (Karapinar et al. , 2019 ) and absenteeism (Schaumberg and Flynn, 2017 ). Although there is evidence that employee well-being positively influences employee work-related attitudes, less is known about the relationship between psychological well-being (hedonic and eudaimonic) and employee affective commitment (Pan et al. , 2018 ; Semedo et al. , 2019 ). Moreover, the existing literature indicated that employee affective commitment is either used as an antecedent or an outcome variable of employee well-being (Semedo et al. , 2019 ; Ryff, 2018 ). However, affective commitment as an outcome variable of employee well-being has gained less scholarly attention, which warrants further investigation. Therefore, in the present study, we seek to examine employee affective commitment as an outcome variable of employee psychological well-being because employees who are happy and satisfied in their lives are more likely to be attached to their organizations (Semedo et al. , 2019 ).

Hedonic well-being positively predicts employee affective commitment.

Eudaimonic well-being positively predicts employee affective commitment.

2.2 Affective commitment and job performance

The concept of organizational commitment was first initiated by sit-bet theory in the early 1960s (Becker, 1960 ). Organizational commitment is defined as the psychological connection of employees to the organization and involvement in it (Cooper-Hakim and Viswesvaran, 2005 ). It is also defined as the belief of an individual in his or her organizational norms (Hackett et al. , 2001 ); the loyalty of an employee toward the organization (Cooper-Hakim and Viswesvaran, 2005 ) and willingness of an employee to participate in organizational duties (Williams and Anderson, 1991 ).

Organizational commitment is further categorized into three correlated but distinct categories (Meyer et al. , 1993 ), known as affective, normative and continuance. In affective commitment, employees are emotionally attached to their organization. In normative commitment, employees remain committed to their organizations due to the sense of obligation to serve. While in continuance commitment, employees remain committed to their organization because of the costs associated with leaving the organization (Allen and Meyer, 1990 , p. 2). Among the dimensions of organizational commitment, affective commitment has been found to have the most substantial influence on organizational outcomes (Meyer and Herscovitch, 2001 ). It is a better predictor of OCB (Paul et al. , 2019 ), low turnover intention (Kundi et al. , 2018 ) and job performance (Jain and Sullivan, 2019 ).

Affective commitment positively predict employee job performance.

2.3 Affective commitment as a mediator

Many studies had used the construct of affective commitment as an independent variable, mediator and moderating variable because of its importance as an effective determinant of work outcomes such as low turnover intention, job satisfaction and job performance (Jain and Sullivan, 2019 ; Kundi et al. , 2018 ). There is very little published research on employee well-being and affective commitment relationship. Surprisingly, the effects of employee psychological well-being in terms of hedonic well-being and eudaimonic well-being have not been closely examined.

Affective commitment mediates the association between hedonic well-being and job performance.

Affective commitment mediates the association between eudaimonic well-being and job performance.

2.4 The moderating role of job insecurity

Job insecurity is gaining importance because of the change in organizational structure as it is becoming flattered, change in the nature of the job as it requires a diverse skill set and change in human resource (HR) practices as more temporary workers are hired nowadays (Piccoli et al. , 2017 ; Kundi et al. , 2018 ). Such changes have caused several adverse outcomes such as job dissatisfaction (Bouzari and Karatepe, 2018 ), unethical pro-organizational behavior (Ghosh, 2017 ), poor performance (Piccoli et al. , 2017 ), anxiety and lack of commitment (Wang et al. , 2018 ).

Lack of harmony on the definition of job insecurity can be found among the researchers. However, a majority of them acknowledge that job insecurity is subjective and can be referred to as a subjective perception (Wang et al. , 2018 ). Furthermore, job insecurity is described as the perception of an employee regarding the menace of losing a job in the near future (De Witte et al. , 2015 ). When there is job insecurity, employees experience a sense of threat to the continuance and stability of their jobs (Shoss, 2017 ).

Although job insecurity has been found to influence employee work-related attitudes, less is known about its effects on behavioral outcomes (Piccoli et al. , 2017 ). As maintained by the social exchange theory, behaviors are the result of an exchange process (Blau, 1964 ). Furthermore, these exchanges can be either tangible or socio-emotional aspects of the exchange process (Kundi et al. , 2018 ). Employees who perceive and feel that their organization is providing them job security and taking care of their well-being will turn to be more committed to their organization (Kundi et al. , 2018 ; Wang et al. , 2018 ). Much research has found that employees who feel job security are happier and satisfied with their lives (Shoss, 2017 ; De Witte et al. , 2015 ) and are more committed to their work and organization (Bouzari and Karatepe, 2018 ; Wang et al. , 2018 ). Shoss (2017) conducted a thorough study on job insecurity and found that job insecurity can cause severe adverse consequences for both the employees and organizations.

Employees who are uncertain about their jobs (i.e. high level of perceived job insecurity) are less committed with their organizations.

Employees with temporary job contracts were found to have low organizational committed as compared to the employees with permanent job contracts.

Such a difference between temporary and permanent job contract holders was mainly due to the perceived job insecurity by the temporary job contract holders.

Job insecurity will moderate the relationship between hedonic well-being, eudaimonic well-being and affective organizational commitment.

3.1 Sample and procedure

The data for this study came from a survey of Pakistani employees, who worked in five private telecommunication organizations (Mobilink, Telenor, Ufone, Zong and Warid). These five companies were targeted because they are the largest and highly competitive companies in Pakistan. Moreover, the telecom sector is a private sector where jobs are temporary or contractual (Kundi et al. , 2018 ). Hence, the investigation of how employees’ perceptions of job insecurity influence their psychological well-being and its outcomes is highly relevant in this context. Studies exploring such a phenomenon are needed, particularly in the Pakistani context, to have a better insight and thereby strengthen the employee well-being and job performance literature.

Two of the authors had personal and professional contacts to gain access to these organizations. The paper-and-pencil method was used to gather the data. Questionnaires were distributed among 570 participants with a cover letter explaining the purpose of the study, noted that participation was voluntary, and provided assurances that their responses would be kept confidential and anonymous. After completion of the questionnaires, the surveys were collected the surveys on-site by one of the authors. As self-reported data often render itself to common method bias (CMB; Podsakoff et al. , 2012 ), we applied several procedural remedies such as reducing the ambiguity in the questions, ensuring respondent anonymity and confidentiality, separating of the predictor and criterion variable and randomizing the item order to limit this bias.

Of the 570 surveys distributed initially, 280 employees completed the survey form (response rate = 49%). According to Baruch and Holtom (2008) , the average response rate for studies at the individual level is 52.6% (SD = 19.7). Hence, our response rate meets the standard for a minimum acceptable response rate, which is 49%. Of the 280 respondents, 39% were female, their mean age was 35.6 years (SD = 5.22) and the average organizational tenure was 8.61 years (SD  =  4.21). The majority of the respondents had at least a bachelors’ degree (83 %). Respondents represented a variety of departments, including marketing (29%), customer services (26%), finance (20%), IT (13%) and HR (12%).

3.2 Measures

The survey was administered to the participants in English. English is the official language of correspondence for professional organizations in Pakistan (De Clercq et al. , 2019 ). All the constructs came from previous research and anchored on a five-point Likert scale ranging from 1 = Strongly disagree to 5 = Strongly agree.

Psychological well-being. We measured employee psychological well-being with two sub-dimensions, namely, hedonic well-being and eudaimonic well-being. Hedonic well-being was measured using five items (Diener et al. , 1985 ). A sample item is “my life conditions are excellent” ( α = 0.86). Eudaimonic well-being was measured using 21 items (Waterman et al. , 2010 ), of which seven items were reverse-scored due to its negative nature. Sample items are “I feel that I understand what I was meant to do in my life” and “my life is centered around a set of core beliefs that give meaning to my life” ( α = 0.81).

Affective commitment. The affective commitment was measured using a six-item inventory developed by Allen and Meyer (1990) . The sample items are “my organization inspires me to put forth my best effort” and “I think that I will be able to continue working here” ( α = 0.91).

Job insecurity. Job insecurity was measured using a five-item inventory developed by Chirumbolo et al. (2015) . The sample item is “I fear I will lose my job” ( α = 0.87).

Job performance . We measured employee job performance with the seven-item inventory developed by Williams and Anderson (1991) . The sample items are “I do fulfill my responsibilities, which are mentioned in the job description” and “I try to work as hard as possible” ( α = 0.87).

Controls. We controlled for respondents’ age (assessed in years), gender (1 = male, 2 = female) and organizational tenure (assessed in years) because prior research (Alessandri et al. , 2019 ; Edgar et al. , 2020 ) has found significant effects of these variables on employees’ job performance.

4.1 Descriptive statistics

Table 1 presents the means, standard deviations and correlations among study variables.

4.2 Construct validity

Before testing hypotheses, we conducted a series of confirmatory factor analyzes (CFAs) using AMOS 22.0 to examine the distinctiveness of our study variables. Following the guidelines of Hu and Bentler (1999) , model fitness was assessed with following fit indices; comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR). We used a parceling technique (Little et al. , 2002 ) to ensure item to sample size ratio. According to Williams and O’Boyle (2008) , the item-parceling approach is widely used in HRM research, which allows estimation of fewer model parameters and subsequently leads to the optimal variable to sample size ratio and stable parameter estimates (Wang and Wang, 2019 ). Based on preliminary CFAs, we combined the highest item loading with the lowest item loading to create parcels that were equally balanced in terms of their difficulty and discrimination. Item-parceling was done only for the construct of eudaimonic well-being as it entailed a large number of items (i.e. 21 items). Accordingly, we made five parcels for the eudaimonic well-being construct (Waterman et al. , 2010 ).

As shown in Table 2 , the CFA results revealed that the baseline five‐factor model (hedonic well-being, eudaimonic well-being, job insecurity, affective commitment and job performance) was significant ( χ 2 = 377.11, df = 199, CFI = 0.971, RMSEA = 0.034 and SRMR = 0.044) and better than the alternate models, including a four‐factor model in which hedonic well-being and eudaimonic well-being were considered as one construct (Δ χ 2 = 203.056, Δdf = 6), a three-factor model in which hedonic well-being, eudaimonic well-being and affective commitment were loaded on one construct (Δ χ 2 = 308.99, Δdf = 8) and a one‐factor model in which all items loaded on one construct (Δ χ 2 = 560.77, Δdf = 11). The results, therefore, provided support for the distinctive nature of our study variables.

To ensure the validity of our measures, we first examined the convergent validity through the average variance extracted (AVE). We found AVE scores higher than the threshold value of 0.5 ( Table 1 ; Fornell and Larcker, 1981 ), supporting the convergent validity of our constructs. We also estimated discriminant validity by comparing the AVE of each construct with the average shared variance (ASV), i.e. mean of the squared correlations among constructs ( Hair et al. , 2010 ). As expected, all the values of AVE were higher than the ASV constructs, thereby supporting discriminant validity ( Table 1 ).

4.3 Common method variance

Harman’s one-factor test.

CFA ( Podsakoff et al. , 2012 ).

Harman’s one-factor test showed five factors with eigenvalues of greater than 1.0 accounted for 69.12% of the variance in the exogenous and endogenous variables. The results of CFA showed that the single-factor model did not fit the data well ( χ 2 = 937.88, df = 210, CFI = 0.642, RMSEA = 0.136, SRMR = 0.122). These tests showed that CMV was not a major issue in this study.

4.4 Hypotheses testing

The hypotheses pertaining to mediation were tested using a structural model in AMOS 22.0 ( Figure 2 ), which had an acceptable goodness of fit ( χ 2 = 298.01, df = 175, CFI = 0.97, RMSEA = 0.04 and SRMR = 0.04). Hypotheses about moderation were tested in SPSS (25 th edition) using PROCESS Model I ( Hayes, 2017 ; Table 3 ).

H1a and H1b suggested that hedonic well-being and eudaimonic well-being positively relate to employee affective commitment. According to Figure 2 , the results indicate that hedonic well-being ( β = 0.26, p < 0.01) and eudaimonic well-being ( β = 0.32, p < 0.01) are positively related to employee affective commitment. Taken together, these two findings provide support for H1a and H1b . In H2 , we predicted that employee affective commitment would positively associate with employee job performance. As seen in Figure 2 , employee affective commitment positively predicted employee job performance ( β = 0.41, p < 0.01), supporting H2 .

H3a and H3b suggested that employee affective commitment mediates the relationship between hedonic and eudaimonic well-being and employee job performance. According to Figure 2 , the results indicate that hedonic well-being is positively related to employee job performance via employee affective commitment ( β = 0.11, 95% CI = 0.09; 0.23). Similarly, eudaimonic well-being is positively related to employee job performance via employee affective commitment ( β = 0.15, 95% CI = 0.12; 0.35), supporting H3a and H3b .

Hedonic well-being.

Eudaimonic well-being and employee affective commitment.

In support of H4a , our results ( Table 3 ) revealed a negative and significant interaction effect between hedonic well-being and job insecurity on employee affective commitment ( β = −0.12, p < 0.05). The pattern of this interaction was consistent with our hypothesized direction; the positive relationship between hedonic well-being and employee affective commitment was weaker in the presence of high versus low job insecurity ( Figure 3 ). Likewise, the interaction effect between eudaimonic well-being and job insecurity on employee affective commitment was negatively significant ( β = −0.28, p < 0.01). The pattern of this interaction was consistent with our hypothesized direction; the positive relationship between eudaimonic well-being and employee affective commitment was weaker in the presence of high versus low job insecuritay ( Figure 4 ). Thus, H4a and H4b were supported. The pattern of these interactions was consistent with our hypothesized direction; the positive relationship of hedonic well-being and eudaimonic well-being with an employee affective commitment were weaker in the presence of high versus low perceived job insecurity.

5. Discussion

The present research examined the direct and indirect crossover from psychological well-being (hedonic and eudaimonic) to job performance through employee affective commitment and the moderating role of job insecurity between psychological well-being and affective commitment relationship. The results revealed that both hedonic well-being and eudaimonic well-being has a direct and indirect effect on employee job performance. Employee affective commitment was found to be a potential mediating mechanism (explaining partial variance) in the relationship between psychological well-being and job performance. Findings regarding the buffering role of job insecurity revealed that job insecurity buffers the positive relationship between psychological well-being and employee affective commitment such that higher the job insecurity, lower will be employee affective commitment. The findings generally highlight and reinforce that perceived job insecurity can be detrimental for both employees’ well-being and job-related behaviors (Soomro et al. , 2020 ).

5.1 Theoretical implications

The present study offers several contributions to employee well-being and job performance literature. First, the present research extends the employee well-being literature by investigating employee affective commitment as a key mechanism through which psychological well-being (hedonic and eudaimonic) influences employees’ job performance. In line with SDT, we found that both hedonic well-being and eudaimonic well-being enhanced employees’ affective commitment, which, in turn, led them to perform better in their jobs. Our study addresses recent calls for research to understand better how psychological well-being influence employees’ performance at work (Huang et al. , 2016 ), and adds to a growing body of work, which confirms the importance of psychological well-being in promoting work-related attitudes and behaviors (Devonish, 2016 ; Hewett et al. , 2018 ; Ismail et al. , 2019 ). Further, we have extended the literature on employee affective commitment, highlighting that psychological well-being is an important antecedent of employee’ affective commitment and thereby confirming previous research by Aboramadan et al. (2020) on the links between affective commitment and job performance.

Second, our results provide empirical support for the efficacy of examining the different dimensions of employee well-being, i.e. hedonic well-being and eudaimonic well-being as opposed to an overall index of well-being at work. Specifically, our results revealed that both hedonic well-being and eudaimonic well-being boost both employees’ attachment with his or her organization and job performance (Hewett et al. , 2018 ; Luu, 2019 ). Among the indicators of psychological well-being, eudaimonic well-being (i.e. realization and fulfillment of ones’ true nature) was found to have more influence on employee affective commitment and job performance as compared to hedonic well-being (i.e. state of happiness and sense of flourishing in life). Therefore, employees who experience high levels of psychological well-being are likely to be more attached to their employer, which, in turn, boosts their job performance.

Third, job insecurity is considered as an important work-related stressor (Schumacher et al. , 2016 ). However, the moderating role of job insecurity on the relationship between psychological well-being and affective commitment has not been considered by the previous research. Based on social exchange theory (Blau, 1964 ), we expected job insecurity to buffer the positive relationship between the psychological well-being and affective commitment. The results showed that employees with high levels of perceived job insecurity reduce the positive relationship of psychological well-being (hedonic and eudaimonic) and affective commitment. This finding is consistent with previous empirical evidence supporting the adverse role of perceived job insecurity in reducing employees’ belongingness with their organization (Jiang and Lavaysse, 2018 ). There is strong empirical evidence (Qian et al. , 2019 ; Schumacher et al. , 2016 ) that employee attitudes and health are negatively affected by increasing levels of job insecurity. Schumacher et al. (2016) suggested in an elaborate explanation of the social exchange theory that the constant worrying about the possibility of losing ones’ job promotes psychological stress and feelings of unfairness, which, in turn, affects employees’ affective commitment. Hence, employees’ psychological well-being and affective commitment are heavily influenced by the experience of high job insecurity.

5.2 Practical implications

Our study has several implications. First and foremost, this study will help managers in understanding the importance of employees’ psychological well-being for work-related attitudes and behavior. Based on our findings, managers need to understand how important psychological well-being is for employees’ organizational commitment and job performance. According to Hosie and Sevastos (2009) , several human resource-based interventions could foster employees’ psychological well-being, such as selecting and placing employees into appropriate positions, ensuring a friendly work environment and providing training that improves employees’ mental health and help them to manage their perceptions positively.

Besides, managers should provide their employees with opportunities to use their full potential, which will increase employees’ sense of autonomy and overall well-being (Sharma et al. , 2017 ). By promoting employee well-being in the workplace, managers can contribute to developing a workforce, which will be committed to their organizations and will have better job performance. However, based on our findings, in the presence of job insecurity, organizations spending on interventions to improve employees’ psychological well-being, organizational commitment and job performance might go in vain. In other words, organizations should ensure that employees feel a sense of job security or else the returns on such interventions could be nullified.

Finally, as organizations operate in a volatile and highly competitive environment, it is and will be difficult for them to provide high levels of job security to their employees, especially in developing countries such as Pakistan (Soomro et al. , 2020 ). Given the fact that job insecurity leads to cause adverse employee psychological well-being and affective commitment, managers must be attentive to subordinates’ perceptions of job insecurity and adverse psychological well-being and take action to prevent harmful consequences (Ma et al. , 2019 ). Organizations should try to avoid downsizings, layoffs and other types of structural changes, respectively, and find ways to boost employees’ perceptions of job security despite those changes. If this is not possible, i.e. the organization not able to provide job security, this should be communicated to employees honestly and early.

5.3 Limitations and future studies

There are several limitations to this study. First, we measured our research variables by using a self-report survey at a single point of time, which may result in CMB. We used various procedural remedies to mitigate the potential for CMB and conducted CFA as per the guidelines of Podsakoff et al. (2012) to ensure that CMV was unlikely to be an issue in our study. However, future research may rely on supervisors rated employees’ job performance or collect data at different time points to avoid the threat of such bias.

Second, the sample of this study consisted of employees working in cellular companies of Pakistan with different demographic characteristics and occupational backgrounds; thus, the generalizability of our findings to other industries or sectors is yet to be established. Future research should test our research model in various industries and cultures.

A final limitation pertains to the selection of a moderating variable. As this study was conducted in Pakistan, contextual factors such as the perceived threat to terrorism, law and order situation or perceived organizational injustice might also influence the psychological well-being of employees working in Pakistan (Jahanzeb et al. , 2020 ; Sarwar et al. , 2020 ). Future studies could consider the moderating role of such external factors in the relationship between employee psychological well-being, affective commitment and job performance.

6. Conclusion

This study proposed a framework to understand the relationship between employee psychological well-being, affective commitment and job performance. It also described how psychological well-being influences job performance. Additionally, this study examined the moderating role of perceived job insecurity on psychological well-being and affective commitment relationship. The results revealed that employee psychological well-being (hedonic and eudaimonic) has beneficial effects on employee affective commitment, which, in turn, enhance their job performance. Moreover, the results indicated that perceived job insecurity has ill effects on employee affective commitment, especially when the employee has high levels of perceived job insecurity.

Research model

Structural model with standardized coefficients; N = 280

Interactive effect of hedonic well-being and job insecurity on employee affective commitment

Interactive effect of eudaimonic well-being and job insecurity on employee affective commitment

Descriptive statistics and correlations among of variables

Variables	Mean	SD	AVE	ASV	1	2	3	4	5
1. Hedonic well-being	3.82	0.64	0.67	0.06	(0.86)
2. Eudaimonic well-being	3.66	0.72	0.53	0.09	0.35**	(0.81)
3. Affective commitment	3.91	0.87	0.64	0.12	0.31**	0.42**	(0.91)
4. Job insecurity	2.88	1.01	0.63	0.04	−0.19**	−0.25**	−0.26**	(0.87)
5. Job performance	4.01	0.69	0.61	0.08	0.21**	0.34**	0.49**	−0.15*	(0.87)

Notes: = 280. AVE = average variance extracted, ASV = average shared variance, reliability coefficients (shown in diagonal position of table in parentheses).

< 0.05,

* p < 0.05,

** p < 0.01; Unstandardized coefficients and average bootstrap estimates are stated; demographic variables are controlled; bootstrapping procedure [5,000 iterations, bias-corrected, 95% CI]

Aboramadan , M. , Dahleez , K. and Hamad , M.H. ( 2020 ), “ Servant leadership and academics outcomes in higher education: the role of job satisfaction ”, International Journal of Organizational Analysis , Vol. 1 .

Alessandri , G. , Truxillo , D.M. , Tisak , J. , Fagnani , C. and Borgogni , L. ( 2019 ), “ Within-individual age-related trends, cycles, and event-driven changes in job performance: a career-span perspective ”, Journal of Business and Psychology , Vol. 1 , pp. 1 - 20 .

Allen , N.J. and Meyer , J.P. ( 1990 ), “ The measurement and antecedents of affective, continuance and normative commitment to the organization ”, Journal of Occupational Psychology , Vol. 63 No. 1 , pp. 1 - 18 .

Allen , N.J. and Meyer , J.P. ( 1996 ), “ Affective, continuance, and normative commitment to the organization: an examination of construct validity ”, Journal of Vocational Behavior , Vol. 49 No. 3 , pp. 252 - 276 .

Al Hammadi , F. and Hussain , M. ( 2019 ), “ Sustainable organizational performance: a study of health-care organizations in the United Arab Emirates ”, International Journal of Organizational Analysis , Vol. 27 No. 1 , pp. 169 - 186 .

Bakker , A.B. , Hetland , J. , Olsen , O.K. and Espevik , R. ( 2019 ), “ Daily strengths use and employee wellbeing: the moderating role of personality ”, Journal of Occupational and Organizational Psychology , Vol. 92 No. 1 , pp. 144 - 168 .

Ballesteros-Leiva , F. , Poilpot-Rocaboy , G. and St-Onge , S. ( 2017 ), “ The relationship between life-domain interactions and the wellbeing of internationally mobile employees ”, Personnel Review , Vol. 46 No. 2 , pp. 237 - 254 .

Bandura , A. ( 1986 ), Social Foundations of Thought and Action: A Social-Cognitive View , Prentice-Hall , Englewood Cliffs, NJ .

Baruch , Y. and Holtom , B.C. ( 2008 ), “ Survey response rate levels and trends in organizational research ”, Human Relations , Vol. 61 No. 8 , pp. 1139 - 1160 .

Blau , P.M. ( 1964 ), Exchange and Power in Social Life , Wiley , New York, NY .

Becker , H.S. ( 1960 ), “ Notes on the concept of commitment ”, American Journal of Sociology , Vol. 66 No. 1 , pp. 32 - 40 .

Bouzari , M. and Karatepe , O.M. ( 2018 ), “ Antecedents and outcomes of job insecurity among salespeople ”, Marketing Intelligence and Planning , Vol. 36 No. 2 , pp. 290 - 302 .

Cai , L. , Wang , S. and Zhang , Y. ( 2020 ), “ Vacation travel, marital satisfaction, and subjective wellbeing: a chinese perspective ”, Journal of China Tourism Research , Vol. 16 No. 1 , pp. 118 - 139 .

Chirumbolo , A. , Hellgren , J. , De Witte , H. , Goslinga , S. , NäSwall , K. and Sverke , M. ( 2015 ), “ Psychometrical properties of a short measure of job insecurity: a European cross-cultural study ”, Rassegna di Psicologia , Vol. 3 , pp. 83 - 98 .

Clark , B. Chatterjee , K. Martin , A. and Davis , A. ( 2019 ), “ How commuting affects subjective wellbeing ”, Transportation .

Compton , W.C. , Smith , M.L. , Cornish , K.A. and Qualls , D.L. ( 1996 ), “ Factor structure of mental health measures ”, Journal of Personality and Social Psychology , Vol. 71 No. 2 , pp. 406 - 413 .

Cooper-Hakim , A. and Viswesvaran , C. ( 2005 ), “ The construct of work commitment: testing an integrative framework ”, Psychological Bulletin , Vol. 131 No. 2 , pp. 241 - 259 .

De Clercq , D. , Haq , I.U. and Azeem , M.U. ( 2019 ), “ Perceived contract violation and job satisfaction: buffering roles of emotion regulation skills and work-related self-efficacy ”, International Journal of Organizational Analysis , Vol. 28 No. 2 , pp. 383 - 398 .

De Witte , H. and Näswall , K. ( 2003 ), “ Objective’ vs subjective’ job insecurity: consequences of temporary work for job satisfaction and organizational commitment in four European countries ”, Economic and Industrial Democracy , Vol. 24 No. 2 , pp. 149 - 188 .

De Witte , H. Vander Elst , T. and De Cuyper , N. ( 2015 ), “ Job insecurity, health and well-being ”, Sustainable Working Lives , pp. 109 - 128 .

Deci , E.L. and Ryan , R.M. ( 1985 ), Intrinsic Motivation and Self-Determination in Human Behavior , Springer Science and Business Media New York, NY .

Devonish , D. ( 2016 ), “ Emotional intelligence and job performance: the role of psychological well-being ”, International Journal of Workplace Health Management , Vol. 9 No. 4 , pp. 428 - 442 .

Diamantidis , A.D. and Chatzoglou , P. ( 2019 ), “ Factors affecting employee performance: an empirical approach ”, International Journal of Productivity and Performance Management , Vol. 68 No. 1 , pp. 171 - 193 .

Diener , E. ( 2009 ), “ Subjective well-being ”, In The Science of Wellbeing , Springer , Dordrecht , pp. 11 - 58 .

Diener , E. , Emmons , R.A. , Larsen , R.J. and Griffin , S. ( 1985 ), “ The satisfaction with life scale ”, Journal of Personality Assessment , Vol. 49 No. 1 , pp. 71 - 75 .

DiMaria , C.H. , Peroni , C. and Sarracino , F. ( 2020 ), “ Happiness matters: productivity gains from subjective well-being ”, Journal of Happiness Studies , Vol. 21 No. 1 , pp. 139 - 160 .

Edgar , F. , Blaker , N.M. and Everett , A.M. ( 2020 ), “ Gender and job performance: linking the high performance work system with the ability–motivation–opportunity framework ”, Personnel Review , Vol. 1

Fornell , C. and Larcker , D.F. ( 1981 ), “ Evaluating structural equation models with unobservable variables and measurement error ”, Journal of Marketing Research , Vol. 18 No. 1 , pp. 39 - 50 .

Ghosh , S.K. ( 2017 ), “ The direct and interactive effects of job insecurity and job embeddedness on unethical pro-organizational behavior ”, Personnel Review , Vol. 46 No. 6 , pp. 1182 - 1198 .

Grey , J.M. , Totsika , V. and Hastings , R.P. ( 2018 ), “ Physical and psychological health of family carers co-residing with an adult relative with an intellectual disability ”, Journal of Applied Research in Intellectual Disabilities , Vol. 31 , pp. 191 - 202 .

Hackett , R.D. , Lapierre , L.M. and Hausdorf , P.A. ( 2001 ), “ Understanding the links between work commitment constructs ”, Journal of Vocational Behavior , Vol. 58 No. 3 , pp. 392 - 413 .

Hair , J.F. , Black , W.C. , Babin , B.J. and Anderson , R.E. ( 2010 ), Multivariate Data Analysis: A Global Perspective 7e , Pearson , Upper Saddle River, NJ .

Hayes , A.F. ( 2017 ), Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach , Guilford publications , New York .

Hewett , R. , Liefooghe , A. , Visockaite , G. and Roongrerngsuke , S. ( 2018 ), “ Bullying at work: cognitive appraisal of negative acts, coping, well-being, and performance ”, Journal of Occupational Health Psychology , Vol. 23 No. 1 , pp. 71 .

Hosie , P.J. and Sevastos , P. ( 2009 ), “ Does the “happy‐productive worker” thesis apply to managers? ”, International Journal of Workplace Health Management , Vol. 2 No. 2 , pp. 131 - 160 .

Hu , L. and Bentler , P.M. ( 1999 ), “ Cut-off criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives ”, Structural Equation Modeling: A Multidisciplinary Journal , Vol. 6 No. 1 , pp. 1 - 55 .

Huang , L.-C. , Ahlstrom , D. , Lee , A.Y.-P. , Chen , S.-Y. and Hsieh , M.-J. ( 2016 ), “ High performance work systems, employee wellbeing, and job involvement: an empirical study ”, Personnel Review , Vol. 45 No. 2 , pp. 296 - 314 .

Huta , V. ( 2016 ), “ An overview of hedonic and eudaimonic well-being concepts ”, Handbook of Media Use and Wellbeing: International Perspectives on Theory and Research on Positive Media Effects , Routldge London pp. 14 - 33 .

Ismail , H.N. , Karkoulian , S. and Kertechian , S.K. ( 2019 ), “ Which personal values matter most? job performance and job satisfaction across job categories ”, International Journal of Organizational Analysis , Vol. 27 No. 1 , pp. 109 - 124 .

Jahanzeb , S. , De Clercq , D. and Fatima , T. ( 2020 ), “ Organizational injustice and knowledge hiding: the roles of organizational dis-identification and benevolence ”, Management Decision , Vol. 1 .

Jain , A.K. and Sullivan , S. ( 2019 ), “ An examination of the relationship between careerism and organizational commitment, satisfaction, and performance ”, Personnel Review , Vol. 1 .

Jiang , L. and Lavaysse , L.M. ( 2018 ), “ Cognitive and affective job insecurity: a meta-analysis and a primary study ”, Journal of Management , Vol. 44 No. 6 , pp. 2307 - 2342 .

Karapinar , P.B. , Camgoz , S.M. and Ekmekci , O.T. ( 2019 ), “ Employee well-being, workaholism, work–family conflict and instrumental spousal support: a moderated mediation model ”, Journal of Happiness Studies , Vol. 1 , pp. 1 - 21 .

Karatepe , O.M. , Rezapouraghdam , H. and Hassannia , R. ( 2020 ), “ Job insecurity, work engagement and their effects on hotel employees’ non-green and nonattendance behaviors ”, International Journal of Hospitality Management , Vol. 87 , p. 102472 .

Kundi , M. , Ikramullah , M. , Iqbal , M.Z. and Ul-Hassan , F.S. ( 2018 ), “ Affective commitment as mechanism behind perceived career opportunity and turnover intentions with conditional effect of organizational prestige ”, Journal of Managerial Sciences , Vol. 1 .

Lee , Y. ( 2019 ), “ JD-R model on psychological wellbeing and the moderating effect of job discrimination in the model: findings from the MIDUS ”, European Journal of Training and Development , Vol. 43 No. 3/4 , pp. 232 - 249 .

Little , T.D. , Cunningham , W.A. , Shahar , G. and Widaman , K.F. ( 2002 ), “ To parcel or not to parcel: exploring the question, weighing the merits ”, Structural Equation Modeling: A Multidisciplinary Journal , Vol. 9 No. 2 , pp. 151 - 173 .

Luu , T.T. ( 2019 ), “ Discretionary HR practices and employee well-being: the roles of job crafting and abusive supervision ”, Personnel Review , Vol. 49 No. 1 , pp. 43 - 66 .

Ma , B. , Liu , S. , Lassleben , H. and Ma , G. ( 2019 ), “ The relationships between job insecurity, psychological contract breach and counterproductive workplace behavior: does employment status matter? ”, Personnel Review , Vol. 48 No. 2 , pp. 595 - 610 .

Magnier-Watanabe , R. , Uchida , T. , Orsini , P. and Benton , C. ( 2017 ), “ Organizational virtuousness and job performance in Japan: does happiness matter? ”, International Journal of Organizational Analysis , Vol. 25 No. 4 , pp. 628 - 646 .

Meyer , J.P. and Herscovitch , L. ( 2001 ), “ Commitment in the workplace: toward a general model ”, Human Resource Management Review , Vol. 11 No. 3 , pp. 299 - 326 .

Meyer , J.P. , Allen , N.J. and Smith , C.A. ( 1993 ), “ Commitment to organizations and occupations: extension and test of a three-component conceptualization ”, Journal of Applied Psychology , Vol. 78 No. 4 , pp. 538 - 551 .

Mousa , M. , Massoud , H.K. and Ayoubi , R.M. ( 2020 ), “ Gender, diversity management perceptions, workplace happiness and organisational citizenship behaviour ”, Employee Relations: The International Journal , Vol. 1 .

Pan , S.-L. , Wu , H. , Morrison , A. , Huang , M.-T. and Huang , W.-S. ( 2018 ), “ The relationships among leisure involvement, organizational commitment and well-being: viewpoints from sport fans in Asia ”, Sustainability , Vol. 10 No. 3 , p. 740 .

Paul , H. , Bamel , U. , Ashta , A. and Stokes , P. ( 2019 ), “ Examining an integrative model of resilience, subjective well-being and commitment as predictors of organizational citizenship behaviours ”, International Journal of Organizational Analysis , Vol. 27 No. 5 , pp. 1274 - 1297 .

Piccoli , B. , Callea , A. , Urbini , F. , Chirumbolo , A. , Ingusci , E. and De Witte , H. ( 2017 ), “ Job insecurity and performance: the mediating role of organizational identification ”, Personnel Review , Vol. 46 No. 8 , pp. 1508 - 1522 .

Podsakoff , P.M. , MacKenzie , S.B. and Podsakoff , N.P. ( 2012 ), “ Sources of method bias in social science research and recommendations on how to control it ”, Annual Review of Psychology , Vol. 63 No. 1 , pp. 539 - 569 .

Qian , S. , Yuan , Q. , Niu , W. and Liu , Z. ( 2019 ), “ Is job insecurity always bad? The moderating role of job embeddedness in the relationship between job insecurity and job performance ”, Journal of Management and Organization , Vol. 1 , pp. 1 - 17 .

Rahmani , K. , Gnoth , J. and Mather , D. ( 2018 ), “ Hedonic and eudaimonic well-being: a psycholinguistic view ”, Tourism Management , Vol. 69 , pp. 155 - 166 .

Ryan , R.M. and Deci , E.L. ( 2000 ), “ Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being ”, American Psychologist , Vol. 55 No. 1 , pp. 68 - 78 .

Ryff , C.D. ( 2018 ), “ Eudaimonic well-being: highlights from 25 years of inquiry ”, in Shigemasu , K. , Kuwano , S. , Sato , T. and Matsuzawa , T. (Eds),  Diversity in Harmony – Inghts from Psychology: Proceedings of the 31st International Congress of Psychology , John Wiley & Sons , pp. 375 - 395 .

Salgado , J.F. , Blanco , S. and Moscoso , S. ( 2019 ), “ Subjective well-being and job performance: Testing of a suppressor effect ”, Revista de Psicología Del Trabajo y de Las Organizaciones , Vol. 35 No. 2 , pp. 93 - 102 .

Sarwar , F. , Panatik , S.A. and Jameel , H.T. ( 2020 ), “ Does fear of terrorism influence psychological adjustment of academic sojourners in Pakistan? Role of state negative affect and emotional support ”, International Journal of Intercultural Relations , Vol. 75 , pp. 34 - 47 .

Schaumberg , R.L. and Flynn , F.J. ( 2017 ), “ Clarifying the link between job satisfaction and absenteeism: the role of guilt proneness ”, Journal of Applied Psychology , Vol. 102 No. 6 , p. 982 .

Schoemmel , K. and Jønsson , T.S. ( 2014 ), “ Multiple affective commitments: quitting intentions and job performance ”, Employee Relations , Vol. 36 No. 5 , pp. 516 - 534 .

Schumacher , D. , Schreurs , B. , Van Emmerik , H. and De Witte , H. ( 2016 ), “ Explaining the relation between job insecurity and employee outcomes during organizational change: a multiple group comparison ”, Human Resource Management , Vol. 55 No. 5 , pp. 809 - 827 .

Semedo , A.S. , Coelho , A. and Ribeiro , N. ( 2019 ), “ Authentic leadership, happiness at work and affective commitment: an empirical study in Cape Verde ”, European Business Review , Vol. 31 No. 3 , pp. 337 - 351 .

Sharma , S. , Conduit , J. and Rao Hill , S. ( 2017 ), “ Hedonic and eudaimonic well-being outcomes from co-creation roles: a study of vulnerable customers ”, Journal of Services Marketing , Vol. 31 Nos 4/5 , pp. 397 - 411 .

Sharma , P. , Kong , T.T.C. and Kingshott , R.P.J. ( 2016 ), “ Internal service quality as a driver of employee satisfaction, commitment and performance: exploring the focal role of employee well-being ”, Journal of Service Management , Vol. 27 No. 5 , pp. 773 - 797 .

Sheldon , K.M. , Corcoran , M. and Prentice , M. ( 2018 ), “ Pursuing eudaimonic functioning versus pursuing hedonic well-being: the first goal succeeds in its aim, whereas the second does not ”, Journal of Happiness Studies , Vol. 20 No. 3 , pp. 1 - 15 .

Shin , D. and Konrad , A.M. ( 2017 ), “ Causality between high-performance work systems and organizational performance ”, Journal of Management , Vol. 43 No. 4 , pp. 973 - 997 .

Shoss , M.K. ( 2017 ), “ Job insecurity: an integrative review and agenda for future research ”, Journal of Management , Vol. 43 No. 6 , pp. 1911 - 1939 .

Soomro , S.A. , Kundi , Y.M. and Kamran , M. ( 2020 ), “ Antecedents of workplace deviance: role of job insecurity, work stress, and ethical work climate ”, Problemy Zarzadzania , Vol. 17 No. 6 .

Staw , B.M. and Barsade , S.G. ( 1993 ), “ Affect and managerial perfornnance: a test of the sadder-but-Wiser hypotheses ”, Administrative Science Quarterly , Vol. 38 No. 2 , pp. 304 - 331 .

Thoresen , C.J. , Kaplan , S.A. , Barsky , A.P. , Warren , C.R. and de Chermont , K. ( 2003 ), “ The affective underpinnings of job perceptions and attitudes ”, Psychological Bulletin , Vol. 129 No. 6 , pp. 914 - 945 .

Tisu , L. , Lupșa , D. , Vîrgă , D. and Rusu , A. ( 2020 ), “ Personality characteristics, job performance and mental health the mediating role of work engagement ”, Personality and Individual Differences , Vol. 153 .

Turban , D.B. and Yan , W. ( 2016 ), “ Relationship of eudaimonia and hedonia with work outcomes ”, Journal of Managerial Psychology , Vol. 31 No. 6 , pp. 1006 - 1020 .

Viswesvaran , C. and Ones , D.S. ( 2017 ), “ Job performance: assessment issues in personnel selection ”, The Blackwell Handbook of Personnel Selection , Blackwell London , pp. 354 - 375 .

Wang , J. and Wang , X. ( 2019 ), Structural Equation Modeling: Applications Using Mplus , John Wiley and Sons New York, NY .

Wang , W. , Mather , K. and Seifert , R. ( 2018 ), “ Job insecurity, employee anxiety, and commitment: the moderating role of collective trust in management ”, Journal of Trust Research , Vol. 8 No. 2 , pp. 220 - 237 .

Waterman , A.S. ( 1993 ), “ Two conceptions of happiness: contrasts of personal expressiveness (eudaimonia) and hedonic enjoyment ”, Journal of Personality and Social Psychology , Vol. 64 No. 4 , p. 678 .

Waterman , A.S. , Schwartz , S.J. , Zamboanga , B.L. , Ravert , R.D. , Williams , M.K. , Bede Agocha , V. and Yeong Kim , S. ( 2010 ), “ The questionnaire for eudaimonic well-being: psychometric properties, demographic comparisons, and evidence of validity ”, The Journal of Positive Psychology , Vol. 5 No. 1 , pp. 41 - 61 .

Williams , L.J. and Anderson , S.E. ( 1991 ), “ Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors ”, Journal of Management , Vol. 17 No. 3 , pp. 601 - 617 .

Williams , L.J. and O’Boyle , E.H. Jr ( 2008 ), “ Measurement models for linking latent variables and indicators: a review of human resource management research using parcels ”, Human Resource Management Review , Vol. 18 No. 4 , pp. 233 - 242 .

Further reading

Sabella , A.R. , El-Far , M.T. and Eid , N.L. ( 2016 ), “ The effects of organizational and job characteristics on employees' organizational commitment in arts-and-culture organizations ”, International Journal of Organizational Analysis , Vol. 24 No. 5 , pp. 1002 - 1024 .

Acknowledgements

Funding and Support statement : The authors did not receive any external funding or additional support from third parties for this work.

Corresponding author

Related articles, all feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

The Relationship Between “Job Satisfaction” and “Job Performance”: A Meta-analysis

• Original Research
• Published: 24 August 2021
• Volume 23 , pages 21–42, ( 2022 )

Cite this article

• Ali Katebi 1 ,
• Mohammad Hossain HajiZadeh 1 ,
• Ali Bordbar 1 &
• Amir Masoud Salehi 1  

7780 Accesses

34 Citations

Explore all metrics

The purpose of this meta-analytic research is to obtain a clear and unified result for the relationship between job satisfaction and job performance, as previous research has shown contradictions in this regard. A total of 913 articles in both English and Persian languages were obtained from four databases, and finally, 113 articles with 123 independent data were selected and analyzed. The random-effects model was adopted based on results, and the analysis resulted a medium, positive, and significant relationship between job performance and job satisfaction ( r  = 0.339; 95% CI = 0.303 to 0.374; P  = 0.000). Finally, the country of India was identified as a moderator variable. The publication, language, selection, and citation biases have been examined in this study. Increasing and improving the job performance of employees have always been an important issue for organizations. The results of this study can be useful for managers in different industries, especially for Indian professionals in both public and private sectors, to better plan and manage the satisfaction and the performance of their employees. Also, Indian scholars can use these results to localize the global research in this regard.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

The Mediating Effects of Job Satisfaction and Propensity to Leave on Role Stress-Job Performance Relationships: Combining Meta-Analysis and Structural Equation Modeling

Detecting causal relationships between work motivation and job performance: a meta-analytic review of cross-lagged studies

Development and validation of a self-reported measure of job performance.

Abbas, M., Raja, U., Anjum, M., & Bouckenooghe, D. (2019). Perceived competence and impression management: Testing the mediating and moderating mechanisms. International Journal of Psychology, 54 (5), 668–677. https://doi.org/10.1002/ijop.12515

Article   Google Scholar  

Abbas, M., Raja, U., Darr, W., & Bouckenooghe, D. (2014). Combined effects of perceived politics and psychological capital on job satisfaction, turnover intentions, and performance. Journal of Management, 40 (7), 1813–1830. https://doi.org/10.1177/0149206312455243

Adams, J. S. (1965). Inequity in social exchange. In Leonard Berkowitz (Ed.), Advances in experimental social psychology (Vol. 2, pp. 267–299). Elsevier.

Ahn, N., & García, J. R. (2004). Job satisfaction in Europe. Documento de Trabajo, 16 (September), 29.

Google Scholar  

Alessandri, G., Borgogni, L., & Latham, G. P. (2017). A Dynamic model of the longitudinal relationship between job satisfaction and supervisor-rated job performance. Applied Psychology, 66 (2), 207–232. https://doi.org/10.1111/apps.12091

Ambrose, S. C., Rutherford, B. N., Shepherd, C. D., & Tashchian, A. (2014). Boundary spanner multi-faceted role ambiguity and burnout: An exploratory study. Industrial Marketing Management, 43 (6), 1070–1078. https://doi.org/10.1016/j.indmarman.2014.05.020

Arab, H. R., & Atan, T. (2018). Organizational justice and work outcomes in the Kurdistan Region of Iraq. Management Decision, 56 (4), 808–827. https://doi.org/10.1108/MD-04-2017-0405

Bal, P. M., De Lange, A. H., Jansen, P. G. W., & Van Der Velde, M. E. G. (2013). A longitudinal study of age-related differences in reactions to psychological contract breach. Applied Psychology, 62 (1), 157–181. https://doi.org/10.1111/j.1464-0597.2012.00524.x

Barakat, L. L., Lorenz, M. P., Ramsey, J. R., & Cretoiu, S. L. (2015). Global managers: An analysis of the impact of cultural intelligence on job satisfaction and performance. International Journal of Emerging Markets, 10 (4), 781–800. https://doi.org/10.1108/IJoEM-01-2014-0011

Bhatti, M. A., Alshagawi, M., Zakariya, A., & Juhari, A. S. (2019). Do multicultural faculty members perform well in higher educational institutions?: Examining the roles of psychological diversity climate, HRM practices and personality traits (Big Five). European Journal of Training and Development, 43 (1/2), 166–187. https://doi.org/10.1108/EJTD-08-2018-0081

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1 (2), 97–111.

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2011). Introduction to meta-analysis . John Wiley & Sons.

Bouckenooghe, D., Raja, U., & Butt, A. N. (2013). Combined effects of positive and negative affectivity and job satisfaction on job performance and turnover intentions. Journal of Psychology: Interdisciplinary and Applied, 147 (2), 105–123. https://doi.org/10.1080/00223980.2012.678411

Bowling, N. A., Khazon, S., Meyer, R. D., & Burrus, C. J. (2015). Situational strength as a moderator of the relationship between job satisfaction and job performance: a meta-analytic examination. Journal of Business and Psychology, 30 (1), 89–104. https://doi.org/10.1007/s10869-013-9340-7

Brief, A. P. (1998). Attitudes in and around organizations (Vol. 9). Sage.

Bukhari, I., & Kamal, A. (2017). Perceived organizational support, its behavioral and attitudinal work outcomes: Moderating role of perceived organizational politics. Pakistan Journal of Psychological Research, 32 (2), 581–602.

Campbell, J. P., McCloy, R. A., Oppler, S. H., & Sager, C. E. (1993). A theory of performance. Personnel Selection in Organizations, 3570 , 35–70.

Carlson, R. E. (1969). Degree of job fit as a moderator of the relationship between job performance and job satisfaction. Personnel Psychology, 22 (2), 159–170.

Chao, M. C., Jou, R. C., Liao, C. C., & Kuo, C. W. (2015). Workplace stress, job satisfaction, job performance, and turnover intention of health care workers in rural Taiwan. Asia-Pacific Journal of Public Health, 27 (2), NP1827–NP1836. https://doi.org/10.1177/1010539513506604

Charoensukmongkol, P. (2014). Effects of support and job demands on social media use and work outcomes. Computers in Human Behavior, 36 (July 2014), 340–349. https://doi.org/10.1016/j.chb.2014.03.061

Chatzoudes, D., Chatzoglou, P., & Vraimaki, E. (2015). The central role of knowledge management in business operations. Business Process Management Journal, 21 (5), 1117–1139.

Chen, J., & Silverthorne, C. (2008). The impact of locus of control on job stress, job performance and job satisfaction in Taiwan. Leadership & Organization Development Journal, 29 (7), 572–582.

Chen, L., & Muthitacharoen, A. (2016). An empirical investigation of the consequences of technostress: Evidence from China. Information Resources Management Journal, 29 (2), 14–36. https://doi.org/10.4018/IRMJ.2016040102

Cheng, J. C., Chen, C. Y., Teng, H. Y., & Yen, C. H. (2016). Tour leaders’ job crafting and job outcomes: The moderating role of perceived organizational support. Tourism Management Perspectives, 20 (October 2016), 19–29. https://doi.org/10.1016/j.tmp.2016.06.001

Chinomona, R., & Sandada, M. (2014). Organisational support and its influence on teachers job satisfaction and job performance in limpopo province of South Africa. Mediterranean Journal of Social Sciences, 5 (9), 208–214. https://doi.org/10.5901/mjss.2014.v5n9p208

Choi, Y., Jung, H., & Kim, T. (2012). Work-family conflict, work-family facilitation, and job outcomes in the Korean hotel. International Journal of Contemporary Hospitality Management, 24 (7), 1011–1028.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112 (1), 155–159.

Cortini, M., Converso, D., Galanti, T., Di Fiore, T., Di Domenico, A., & Fantinelli, S. (2019). Gratitude at work works! A mix-method study on different dimensions of gratitude, job satisfaction, and job performance. Sustainability (switzerland), 11 (14), 3902. https://doi.org/10.3390/su11143902

Dabić, M., Vlačić, B., Paul, J., Dana, L. P., Sahasranamam, S., & Glinka, B. (2020). Immigrant entrepreneurship: A review and research agenda. Journal of Business Research, 113 (November 2019), 25–38. https://doi.org/10.1016/j.jbusres.2020.03.013

Dello Russo, S., Vecchione, M., & Borgogni, L. (2013). Commitment profiles, job satisfaction, and behavioral outcomes. Applied Psychology, 62 (4), 701–719. https://doi.org/10.1111/j.1464-0597.2012.00512.x

Derakhshide, H., & Ansari, M. (2012). Investigating the impact of managerial competence and management commitment on employee empowerment on their job performance. Journal of Management and Development Process, 27 (1), 73–93.

Derakhshide, H., & Kazemi, A. (2013). The impact of job participation and organizational commitment on employee satisfaction and job performance in mashhad hotel industry using structural equation model. Journal of Applied Sociology, 25 (3), 89–101.

Dinc, M. S., Kuzey, C., & Steta, N. (2018). Nurses’ job satisfaction as a mediator of the relationship between organizational commitment components and job performance. Journal of Workplace Behavioral Health, 33 (2), 75–95. https://doi.org/10.1080/15555240.2018.1464930

Ding, Z., Ng, F., Wang, J., & Zou, L. (2012). Distinction between team-based self-esteem and company-based self-esteem in the construction industry. Journal of Construction Engineering and Management, 138 (10), 1212–1219. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000534

Doargajudhur, M. S., & Dell, P. (2019). Impact of BYOD on organizational commitment: An empirical investigation. Information Technology and People, 32 (2), 246–268. https://doi.org/10.1108/ITP-11-2017-0378

Durrah, O., Alhamoud, A., & Khan, K. (2016). Positive psychological capital and job performance: The mediating role of job satisfaction. Ponte, 72 (7), 214–225. https://doi.org/10.21506/j.ponte.2016.7.17

Edwards, B. D., Bell, S. T., Arthur Winfred, J., & Decuir, A. D. (2008). Relationships between facets of job satisfaction and task and contextual performance. Applied Psychology, 57 (3), 441–465.

Egger, M., & Smith, G. D. (1998). Meta-Analysis Bias in Location and Selection of Studies. BMJ, 316 (7124), 61–66.

Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315 (7109), 629–634.

Ersen, Ö., & Bilgiç, R. (2018). The effect of proactive and preventive coping styles on personal and organizational outcomes: Be proactive if you want good outcomes. Cogent Psychology, 5 (1), 1–14. https://doi.org/10.1080/23311908.2018.1492865

Esmaieli, M., & Seydzadeh, H. (2016). The effect of job satisfaction on performance with the mediating role of organizational loyalty. Journal of Management Studies (improvement and Transformation), 25 (83), 51–68.

EU Statistics Center report. (2019). isna.ir/news/98122619604/

Ewen, R. B. (1973). Pressure for production, task difficulty, and the correlation between job satisfaction and job performance. Journal of Applied Psychology, 58 (3), 378–380.

Fisher, R. T. (2001). Role stress, the type A behavior pattern, and external auditor job satisfaction and performance. Behavioral Research in Accounting, 13 (1), 143–170.

Freeman, R. B. (1978). Job Satisfaction as an Economic Variable. American Economic Review, 68 (2), 135–141.

Fu, W., & Deshpande, S. P. (2014). The impact of caring climate, job satisfaction, and organizational commitment on job performance of employees in a China’s insurance company. Journal of Business Ethics, 124 (2), 339–349. https://doi.org/10.1007/s10551-013-1876-y

Geddes, J., & Carney, S. (2003). Systematic reviews and meta-analyses. Evidence in Mental Health Care . Oxford University Press. https://doi.org/10.1016/b978-0-443-06367-1.50015-6

Book   Google Scholar  

Gerlach, G. I. (2019). Linking justice perceptions, workplace relationship quality and job performance: The differential roles of vertical and horizontal workplace relationships. German Journal of Human Resource Management, 33 (4), 337–362. https://doi.org/10.1177/2397002218824320

Ghosh, K., & Sahney, S. (2010). Organizational sociotechnical diagnosis of managerial retention: SAP-LAP framework. Global Journal of Flexible Systems Management, 11 (1–2), 75–88. https://doi.org/10.1007/bf03396580

Gibbs, T., & Ashill, N. J. (2013). The effects of high performance work practices on job outcomes: Evidence from frontline employees in Russia. International Journal of Bank Marketing, 31 (4), 305–326. https://doi.org/10.1108/IJBM-10-2012-0096

Gilal, F. G., Zhang, J., Paul, J., & Gilal, N. G. (2019). The role of self-determination theory in marketing science: An integrative review and agenda for research. European Management Journal, 37 (1), 29–44. https://doi.org/10.1016/j.emj.2018.10.004

Giri, V. N., & Pavan Kumar, B. (2010). Assessing the impact of organizational communication on job satisfaction and job performance. Psychological Studies, 55 (2), 137–143. https://doi.org/10.1007/s12646-010-0013-6

Godarzi, H. (2017). Investigating the effect of work-family conflict and work-family support on job satisfaction and job performance of employees of National Iranian Drilling Company. Journal of Human Resource Management in the Oil Industry, 9 (33), 111–132.

Goldsmith, R. E., McNeilly, K. M., & Russ, F. A. (1989). Similarity of sales representatives’ and supervisors’ problem-solving styles and the satisfaction-performance relationship. Psychological Reports, 64 (3), 827–832.

Grissom, R. J., & Kim, J. J. (2005). Effect sizes for research: A broad practical approach . Lawrence Erlbaum Associates Publishers.

Guan, X., Sun, T., Hou, Y., Zhao, L., Luan, Y. Z., & Fan, L. H. (2014). The relationship between job performance and perceived organizational support in faculty members at Chinese universities: A questionnaire survey. BMC Medical Education, 14 (1), 1–10. https://doi.org/10.1186/1472-6920-14-50

Gul, H., Usman, M., Liu, Y., Rehman, Z., & Jebran, K. (2018). Does the effect of power distance moderate the relation between person environment fit and job satisfaction leading to job performance? Evidence from Afghanistan and Pakistan. Future Business Journal, 4 (1), 68–83. https://doi.org/10.1016/j.fbj.2017.12.001

Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327 (7414), 557–560. https://doi.org/10.1136/bmj.327.7414.557

Hill, N. S., Kang, J. H., & Seo, M. G. (2014). The interactive effect of leader-member exchange and electronic communication on employee psychological empowerment and work outcomes. Leadership Quarterly, 25 (4), 772–783. https://doi.org/10.1016/j.leaqua.2014.04.006

Hsieh, J. Y. (2016). Spurious or true? An exploration of antecedents and simultaneity of job performance and job satisfaction across the sectors. Public Personnel Management, 45 (1), 90–118. https://doi.org/10.1177/0091026015624714

Huang, L. V., & Liu, P. L. (2017). Ties that work: Investigating the relationships among coworker connections, work-related Facebook utility, online social capital, and employee outcomes. Computers in Human Behavior, 72 (July 2017), 512–524. https://doi.org/10.1016/j.chb.2017.02.054

Hur, W. M., Han, S. J., Yoo, J. J., & Moon, T. W. (2015b). The moderating role of perceived organizational support on the relationship between emotional labor and job-related outcomes. Management Decision, 53 (3), 605–624. https://doi.org/10.1108/MD-07-2013-0379

Hur, W., Kim, B., & Park, S. (2015a). The relationship between coworker incivility, emotional exhaustion, and organizational outcomes: the mediating role of emotional exhaustion. Medicina (argentina), 75 (5), 303–306. https://doi.org/10.1002/hfm

Iaffaldano, M. T., & Muchinsky, P. M. (1985). Job satisfaction and job performance: A meta-analysis. Psychological Bulletin, 97 (2), 251–273.

Ieong, C. Y., & Lam, D. (2016). Role of Internal Marketing on Employees’ Perceived Job Performance in an Asian Integrated Resort. Journal of Hospitality Marketing and Management, 25 (5), 589–612. https://doi.org/10.1080/19368623.2015.1067664

Iyer, R., & Johlke, M. C. (2015). The role of external customer mind-set among service employees. Journal of Services Marketing, 29 (1), 38–48. https://doi.org/10.1108/JSM-09-2013-0237

Jabri, M. M. (1992). Job satisfaction and job performance among R&D scientists: The moderating influence of perceived appropriateness of task allocation decisions. Australian Journal of Psychology, 44 (2), 95–99.

Jahangiri, A., & Abaspor, H. (2017). The impact of talent management on job performance: with the mediating role of job effort and job satisfaction. Journal of Management and Development Process, 30 (1), 29–50.

Jain, A. (2016). the mediating role of job satisfaction in the realationship of vertical trust and distributed leadership in health care context. Journal of Modelling in Management, 11 (2), 722–738.

Jannot, A. S., Agoritsas, T., Gayet-Ageron, A., & Perneger, T. V. (2013). Citation bias favoring statistically significant studies was present in medical research. Journal of Clinical Epidemiology, 66 (3), 296–301. https://doi.org/10.1016/j.jclinepi.2012.09.015

Jeong, M., Lee, M., & Nagesvaran, B. (2016). Employees’ use of mobile devices and their perceived outcomes in the workplace: A case of luxury hotel. International Journal of Hospitality Management, 57 (August 2016), 40–51. https://doi.org/10.1016/j.ijhm.2016.05.003

Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., Mamonov, S., & Hill, C. (2021). Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy.  Global Journal of Flexible Systems Management , 22 (3), 197–217.

Jia, L., Hall, D., Yan, Z., Liu, J., & Byrd, T. (2018). The impact of relationship between IT staff and users on employee outcomes of IT users. Information Technology and People, 31 (5), 986–1007. https://doi.org/10.1108/ITP-03-2017-0075

Jing, F. F. (2018). Leadership paradigms and performance in small service firms. Journal of Management and Organization, 24 (3), 339–358. https://doi.org/10.1017/jmo.2017.44

Johlke, M. C., & Iyer, R. (2017). Customer orientation as a psychological construct: evidence from Indian B-B salespeople. Asia Pacific Journal of Marketing and Logistics, 29 (4), 704–720.

Jones, A., Guthrie, C. P., & Iyer, V. M. (2012). Role stress and job outcomes in public accounting: Have the gender experiences converged? In Advances in Accounting Behavioral Research (Vol. 15, pp. 53–84). Emerald Group Publishing Ltd. doi: https://doi.org/10.1108/S1475-1488(2012)0000015007

Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001). The job satisfaction–job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127 (3), 376–407.

Kammeyer-Mueller, J. D., Rubenstein, A. L., Long, D. M., Odio, M. A., Buckman, B. R., Zhang, Y., & Halvorsen-Ganepola, M. D. K. (2013). A meta-analytic structural model of dispositonal affectivity and emotional labor. Personnel Psychology, 66 (1), 47–90. https://doi.org/10.1111/peps.12009

Karadağ, E., Bektaş, F., Çoğaltay, N., & Yalçin, M. (2017). The effect of educational leadership on students’ achievement. In The Factors Effecting Student Achievement (Vol. 16, pp. 11–33). Springer. doi: https://doi.org/10.1007/978-3-319-56083-0_2

Karatepe, O. M., & Agbaim, I. M. (2012). Perceived ethical climate and hotel employee outcomes: an empirical investigation in Nigeria. Journal of Quality Assurance in Hospitality and Tourism, 13 (4), 286–315. https://doi.org/10.1080/1528008X.2012.692291

Kašpárková, L., Vaculík, M., Procházka, J., & Schaufeli, W. B. (2018). Why resilient workers perform better: The roles of job satisfaction and work engagement. Journal of Workplace Behavioral Health, 33 (1), 43–62. https://doi.org/10.1080/15555240.2018.1441719

Katzell, R. A., Barrett, R. S., & Parker, T. C. (1961). Job satisfaction, job performance, and situational characteristics. Journal of Applied Psychology, 45 (2), 65–72.

Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17 (2), 137–152. https://doi.org/10.1037/a0028086

Kim, S. (2005). Individual-level factors and organizational performance in government organizations. Journal of Public Administration Research and Theory, 15 (2), 245–261. https://doi.org/10.1093/jopart/mui013

Kim, T. Y., Aryee, S., Loi, R., & Kim, S. P. (2013). Person-organization fit and employee outcomes: Test of a social exchange model. International Journal of Human Resource Management, 24 (19), 3719–3737. https://doi.org/10.1080/09585192.2013.781522

Kim, T. Y., Gilbreath, B., David, E. M., & Kim, S. P. (2019). Self-verification striving and employee outcomes: The mediating effects of emotional labor of South Korean employees. International Journal of Contemporary Hospitality Management, 31 (7), 2845–2861. https://doi.org/10.1108/IJCHM-08-2018-0620

Kim, T. Y., Liden, R. C., Kim, S. P., & Lee, D. R. (2015). The interplay between follower core self-evaluation and transformational leadership: effects on employee outcomes. Journal of Business and Psychology, 30 (2), 345–355. https://doi.org/10.1007/s10869-014-9364-7

Kim, T. Y., & Liu, Z. (2017). Taking charge and employee outcomes: The moderating effect of emotional competence. International Journal of Human Resource Management, 28 (5), 775–793. https://doi.org/10.1080/09585192.2015.1109537

Knoll, J., & Matthes, J. (2017). The effectiveness of celebrity endorsements: A meta-analysis. Journal of the Academy of Marketing Science, 45 (1), 55–75. https://doi.org/10.1007/s11747-016-0503-8

Kock, N., & Moqbel, M. (2019). Social Networking Site Use, Positive Emotions, And Job Performance. Journal of Computer Information Systems, 00 (00), 1–11. https://doi.org/10.1080/08874417.2019.1571457

Kolbadinejad, M., Ganjouei, F. A., & Anzehaei, Z. H. (2018). Performance evaluation model according to performance improvement and satisfaction of the staff in the individual sports federations and federations with historical aspect. Annals of Applied Sport Science, 6 (4), 59–67. https://doi.org/10.29252/aassjournal.6.4.59

Koo, B., Yu, J., Chua, B. L., Lee, S., & Han, H. (2020). Relationships among emotional and material rewards, job satisfaction, burnout, affective commitment, job performance, and turnover intention in the hotel industry. Journal of Quality Assurance in Hospitality and Tourism, 21 (4), 371–401. https://doi.org/10.1080/1528008X.2019.1663572

Kumar, A., Paul, J., & Unnithan, A. B. (2020). ‘Masstige’ marketing: A review, synthesis and research agenda. Journal of Business Research, 113 (September), 384–398. https://doi.org/10.1016/j.jbusres.2019.09.030

Kuo, C. W., Jou, R. C., & Lin, S. W. (2012). Turnover intention of air traffic controllers in Taiwan: A note. Journal of Air Transport Management, 25 (December 2012), 50–52. https://doi.org/10.1016/j.jairtraman.2012.08.003

Kuzey, C. (2018). Impact of health care employees’ job satisfaction on organizational performance support vector machine approach. Journal of Economics and Financial Analysis, 2 (1), 45–68. https://doi.org/10.1991/jefa.v2i1.a12

Laurence, G. A., Fried, Y., & Raub, S. (2016). Evidence for the need to distinguish between self-initiated and organizationally imposed overload in studies of work stress. Work and Stress, 30 (4), 337–355. https://doi.org/10.1080/02678373.2016.1253045

Lauring, J., & Selmer, J. (2018). Person-environment fit and emotional control: Assigned expatriates vs. self-initiated expatriates. International Business Review, 27 (5), 982–992. https://doi.org/10.1016/j.ibusrev.2018.02.010

Lee, M., Mayfield, C. O., Hinojosa, A. S., & Im, Y. (2018). A dyadic approach to examining the emotional intelligence-work outcome relationship: the mediating role of LMX. Organization Management Journal, 15 (1), 1–16. https://doi.org/10.1080/15416518.2018.1427539

Liao, P. Y. (2015). The role of self-concept in the mechanism linking proactive personality to employee work outcomes. Applied Psychology, 64 (2), 421–443. https://doi.org/10.1111/apps.12003

Lin, S., Lamond, D., Yang, C.-L., & Hwang, M. (2014). Personality traits and simultaneous reciprocal influences between job performance and job satisfaction. Chinese Management Studies, 8 (1), 6–26.

Lipsey, M. W. (2003). Those confounded moderators in meta-analysis: Good, bad, and ugly. Annals of the American Academy of Political and Social Science, 587 (1), 69–81. https://doi.org/10.1177/0002716202250791

Liu, F., Chow, I. H. S., Xiao, D., & Huang, M. (2017). Cross-level effects of HRM bundle on employee well-being and job performance: The mediating role of psychological ownership. Chinese Management Studies, 11 (3), 520–537. https://doi.org/10.1108/CMS-03-2017-0065

Lu, C., Wang, B., Siu, O., Lu, L., & Du, D. (2015). Work-home interference and work values in Greater China. Journal of Managerial Psychology, 30 (7), 801–814.

Lu, L., Lin, H. Y., & Cooper, C. L. (2013). Unhealthy and present: Motives and consequences of the act of presenteeism among taiwanese employees. Journal of Occupational Health Psychology, 18 (4), 406–416. https://doi.org/10.1037/a0034331

Luna-Arocas, R., & Morley, M. J. (2015). Talent management, talent mindset competency and job performance: The mediating role of job satisfaction. European Journal of International Management, 9 (1), 28–51. https://doi.org/10.1504/EJIM.2015.066670

Mathies, C., & Ngo, L. V. (2014). New insights into the climate-attitudes-outcome framework: Empirical evidence from the Australian service sector. Australian Journal of Management, 39 (3), 473–491. https://doi.org/10.1177/0312896213495054

Melian, S. (2016). An extended model of the interaction between work-related attitudes and job performance. International Journal of Productivity and Performance Management, 65 (1), 42–57.

Mikkelsen, A., & Espen, O. (2018). The influence of change-oriented leadership on work performance and job satisfaction in hospitals – the mediating roles of learning demands and job involvement. Leadership in Health Services, 32 (1), 37–53.

Mittal, A., & Jain, P. K. (2012). Mergers and acquisitions performance system: Integrated framework for strategy formulation and execution using flexible strategy game-card. Global Journal of Flexible Systems Management, 13 (1), 41–56. https://doi.org/10.1007/s40171-012-0004-7

Mohammadi, J., Bagheri, M., Safaryan, S., & Alavi, A. (2015). Explain the role of party play in employee job satisfaction and performance. Journal of Human Resource Management Research, 6 (1), 229–249.

Monavarian, A., Fateh, O., & Fateh, A. (2017). The effect of Islamic work ethic on individual job performance considering the mediating role of organizational commitment and job satisfaction. Journal of Management and Development Process, 31 (1), 57–82.

Moqbel, M., Nevo, S., & Kock, N. (2013). Organizational members’ use of social networking sites and job performance: An exploratory study. Information Technology & People, 26 (3), 240–264. https://doi.org/10.1108/ITP-10-2012-0110

Mosuin, E., Mat, T. Z. T., Ghani, E. K., Alzeban, A., & Gunardi, A. (2019). Accountants’ acceptance of accrual accounting systems in the public sector and its influence on motivation, satisfaction and performance. Management Science Letters, 9 (5), 695–712. https://doi.org/10.5267/j.msl.2019.2.002

Motowidlo, S. J., & Kell, H. J. (2012). Job performance. Handbook of Psychology, Second Edition, 12 , 91–130.

Mount, M., Ilies, R., & Johnson, E. (2006). Relationship of personality traits and counterproductive work behaviors: The mediating effects of job satisfaction. Personnel Psychology, 59 (3), 591–622.

Naidoo, R. (2018). Role stress and turnover intentions among information technology personnel in South Africa: The role of supervisor support. SA Journal of Human Resource Management, 16 (1), 1–10. https://doi.org/10.4102/sajhrm.v16i0.936

Ng, T. W. H., Sorensen, K. L., & Yim, F. H. K. (2009). Does the job satisfaction-job performance relationship vary across cultures? Journal of Cross-Cultural Psychology, 40 (5), 761–796. https://doi.org/10.1177/0022022109339208

Ning, B., Omar, R., Ye, Y., Ting, H., & Ning, M. (2020). The role of Zhong-Yong thinking in business and management research: A review and future research agenda. Asia Pacific Business Review, 27 (2), 150–179. https://doi.org/10.1080/13602381.2021.1857956

Noh, M., Johnson, K. K. P., & Koo, J. (2015). Building an exploratory model for part-time sales associates’ turnover intentions. Family and Consumer Sciences Research Journal, 44 (2), 184–200. https://doi.org/10.1111/fcsr.12135

Oh, J. H., Rutherford, B. N., & Park, J. (2014). The interplay of salesperson’s job performance and satisfaction in the financial services industry. Journal of Financial Services Marketing, 19 (2), 104–117. https://doi.org/10.1057/fsm.2014.7

Olsen, E., Bjaalid, G., & Mikkelsen, A. (2017). Work climate and the mediating role of workplace bullying related to job performance, job satisfaction, and work ability: A study among hospital nurses. Journal of Advanced Nursing, 73 (11), 2709–2719. https://doi.org/10.1111/jan.13337

Oluwatayo, A. A., & Adetoro, O. (2020). Influence of Employee Attributes, Work Context and Human Resource Management Practices on Employee Job Engagement. Global Journal of Flexible Systems Management, 21 (4), 295–308. https://doi.org/10.1007/s40171-020-00249-3

Paggi, M. E., & Jopp, D. S. (2015). Outcomes of occupational self-efficacy in older workers. International Journal of Aging and Human Development, 80 (4), 357–378. https://doi.org/10.1177/0091415015607640

Panthee, B., Shimazu, A., & Kawakami, N. (2014). Validation of Nepalese version of Utrecht work engagement scale. Journal of Occupational Health, 56 (6), 421–429. https://doi.org/10.1539/joh.14-0041-OA

Park, J., Kim, S., Lim, M., & Sohn, Y. W. (2019). Having a calling on board: Effects of calling on job satisfaction and job performance among South Korean newcomers. Frontiers in Psychology, 10 (JULY), 1584. https://doi.org/10.3389/fpsyg.2019.01584

Paul, J., & Benito, G. R. G. (2018). A review of research on outward foreign direct investment from emerging countries, including China: What do we know, how do we know and where should we be heading? Asia Pacific Business Review, 24 (1), 90–115. https://doi.org/10.1080/13602381.2017.1357316

Paul, J., & Criado, A. R. (2020). The art of writing literature review: What do we know and what do we need to know? International Business Review, 29 (4), 101717. https://doi.org/10.1016/j.ibusrev.2020.101717

Paul, J., & Feliciano-Cestero, M. M. (2021). Five decades of research on foreign direct investment by MNEs: An overview and research agenda. Journal of Business Research, 124 (February), 800–812. https://doi.org/10.1016/j.jbusres.2020.04.017

Paul, J., & Mas, E. (2020). Toward a 7-P framework for international marketing. Journal of Strategic Marketing, 28 (8), 681–701. https://doi.org/10.1080/0965254X.2019.1569111

Paul, J., & Singh, G. (2017). The 45 years of foreign direct investment research: Approaches, advances and analytical areas. World Economy, 40 (11), 2512–2527. https://doi.org/10.1111/twec.12502

Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90 (1), 175–181. https://doi.org/10.1037/0021-9010.90.1.175

Petitti, D. B. (2000). Meta-analysis, decision analysis, and cost-effectiveness analysis: Methods for quantitative synthesis in medicine . OUP USA.

Piansoongnern, O. (2013). Flexible leadership for managing talented employees in the securities industry: A case study of Thailand. Global Journal of Flexible Systems Management, 14 (2), 107–113. https://doi.org/10.1007/s40171-013-0036-7

Piansoongnern, O., & Anurit, P. (2007). A global competitiveness study of Thai securities industry: A case study of factors influencing investors’ loyalty to securities companies in Bangkok. Global Journal of Flexible Systems Management, 8 (1–2), 1–16. https://doi.org/10.1007/BF03396516

Porter, C. M., Woo, S. E., Allen, D. G., & Keith, M. G. (2019). How do instrumental and expressive network positions relate to turnover? A meta-analytic investigation. Journal of Applied Psychology, 104 (4), 511–536. https://doi.org/10.1037/apl0000351

Qureshi, M. A., Qureshi, J. A., Thebo, J. A., Shaikh, G. M., Brohi, N. A., & Qaiser, S. (2019). The nexus of employee’s commitment, job satisfaction, and job performance: An analysis of FMCG industries of Pakistan. Cogent Business and Management, 6 (1), 1654189. https://doi.org/10.1080/23311975.2019.1654189

Rai, A., & Hornyak, R. (2013). The impact of sourcing enterprise system use and work process interdependence on sourcing professionals’ job outcomes. Journal of Operations Management, 31 (6), 474–488. https://doi.org/10.1016/j.jom.2013.07.005

Raja, U., Haq, I. U., De Clercq, D., & Azeem, M. U. (2019). When ethics create misfit: Combined effects of despotic leadership and Islamic work ethic on job performance, job satisfaction, and psychological well-being. International Journal of Psychology, 55 (3), 332–341. https://doi.org/10.1002/ijop.12606

Ramezani, Y., Mashhadi, A., Chahak, A., & Hosseinpor, M. (2018). Job performance in the university: Explain the role of job satisfaction, work orientation and organizational commitment. Journal of Transformation Management, 9 (18), 142–159. https://doi.org/10.22067/pmt.v9i18.60445

Rana, J., & Paul, J. (2020). Health motive and the purchase of organic food: A meta-analytic review. International Journal of Consumer Studies, 44 (2), 162–171. https://doi.org/10.1111/ijcs.12556

Regts, G., & Molleman, E. (2016). The moderating influence of personality on individual outcomes of social networks. Journal of Occupational and Organizational Psychology, 89 (3), 656–682. https://doi.org/10.1111/joop.12147

Rietzschel, E. F., Slijkhuis, M., & Van Yperen, N. W. (2014). Close monitoring as a contextual stimulator: How need for structure affects the relation between close monitoring and work outcomes. European Journal of Work and Organizational Psychology, 23 (3), 394–404. https://doi.org/10.1080/1359432X.2012.752897

Robledo, E., Zappalà, S., & Topa, G. (2019). Job crafting as a mediator between work engagement and wellbeing outcomes: A time-lagged study. International Journal of Environmental Research and Public Health, 16 (8), 1376. https://doi.org/10.3390/ijerph16081376

Rosenberg, M. S. (2005). The file-drawer problem revisited: A general weighted method for calculating fail-safe numbers in meta-analysis. Evolution, 59 (2), 464–468. https://doi.org/10.1111/j.0014-3820.2005.tb01004.x

Rosenbusch, N., Brinckmann, J., & Bausch, A. (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs. Journal of Business Venturing, 26 (4), 441–457. https://doi.org/10.1016/j.jbusvent.2009.12.002

Rosenthal, R. (1986). Meta-analytic procedures for social science research. Educational Researcher, 15 (8), 18–20. https://doi.org/10.3102/0013189x015008018

Rousseau, M. B., Mathias, B. D., Madden, L. T., & Crook, T. R. (2016). Innovation, firm performance, and appropriation: a meta-analysis. International Journal of Innovation Management, 20 (3), 1650033. https://doi.org/10.1142/S136391961650033X

Rowley, C., & Paul, J. (2021). Introduction: The role and relevance of literature reviews and research in the Asia Pacific. Asia Pacific Business Review, 27 (2), 145–149. https://doi.org/10.1080/13602381.2021.1894839

Rutherford, B., Wei, Y., Park, J., & Hur, W. M. (2012). Increasing job performance and reducing turnover: An examination of female Chinese salespeople. Journal of Marketing Theory and Practice, 20 (4), 423–436. https://doi.org/10.2753/MTP1069-6679200405

Sánchez-Beaskoetxea, J., & Coca García, C. (2015). Media image of seafarers in the Spanish printed press. Maritime Policy & Management, 42 (2), 97–110.

Shahnawaz Adil, M. (2015). Strategic Human Resource Management Practices and Competitive Priorities of the Manufacturing Performance in Karachi. Global Journal of Flexible Systems Management, 16 (1), 37–61. https://doi.org/10.1007/s40171-014-0084-7

Shaik, A. S., & Dhir, S. (2020). A meta-analytical review of factors affecting the strategic thinking of an organization. Foresight, 22 (2), 144–177. https://doi.org/10.1108/FS-08-2019-0076

Shayan, A., Danaie, H., & Andami, M. (2017). The effect of using social media on the job performance of Tarbiat Modares University staff. Journal of Human Resource Management Research, 7 (3), 135–155.

Shin, I., Hur, W. M., & Kang, S. (2016). Employees’ perceptions of corporate social responsibility and job performance: A sequential mediation model. Sustainability (switzerland), 8 (5), 1–12. https://doi.org/10.3390/su8050493

Shujaat, A., Rashid, A., & Muzaffar, A. (2019). Exploring the effects of social media use on employee performance: Role of commitment and satisfaction. International Journal of Human Capital and Information Technology Professionals, 10 (3), 1–19. https://doi.org/10.4018/IJHCITP.2019070101

Singh, S., Akbani, I., & Dhir, S. (2020a). Service innovation implementation: A systematic review and research agenda. Service Industries Journal, 40 (7–8), 491–517. https://doi.org/10.1080/02642069.2020.1731477

Singh, S., & Dhir, S. (2019). Structured review using TCCM and bibliometric analysis of international cause-related marketing, social marketing, and innovation of the firm. International Review on Public and Nonprofit Marketing, 16 (2–4), 335–347. https://doi.org/10.1007/s12208-019-00233-3

Singh, S., Dhir, S., Das, V. M., & Sharma, A. (2020b). Bibliometric overview of the technological forecasting and social change journal: analysis from 1970 to 2018. Technological Forecasting and Social Change, 154 (May), 119963. https://doi.org/10.1016/j.techfore.2020.119963

Singh, S., Dhir, S., Gupta, A., Das, V. M., & Sharma, A. (2020). Antecedents of innovation implementation: a review of literature with meta-analysis. Foresight, 23 (3), 273–298.

Singh, S., & Vidyarthi, P. R. (2018). Idiosyncratic deals to employee outcomes: mediating role of social exchange relationships. Journal of Leadership and Organizational Studies, 25 (4), 443–455. https://doi.org/10.1177/1548051818762338

Sobaih, A. E. E., Ibrahim, Y., & Gabry, G. (2019). Unlocking the black box: Psychological contract fulfillment as a mediator between HRM practices and job performance. Tourism Management Perspectives, 30 (April), 171–181.

Sony, M., & Mekoth, N. (2017a). Workplace spirituality, frontline employee adaptability and job outcomes: An empirical study. International Journal of Process Management and Benchmarking, 7 (4), 437–465. https://doi.org/10.1504/ijpmb.2017.10006820

Sony, M., & Mekoth, N. (2017b). The mediation role of frontline employee adaptability between service orientation and job outcomes: Evidence from Indian power sector. International Journal of Business Excellence, 11 (3), 357–380. https://doi.org/10.1504/IJBEX.2017.081931

Sony, M., & Mekoth, N. (2019). The relationship between workplace spirituality, job satisfaction and job performance. International Journal of Process Management and Benchmarking, 9 (1), 27–46. https://doi.org/10.1504/IJPMB.2019.097819

Springer, G. J. (2011). A study of job motivation, satisfaction, and performance among bank employees. Journal of Global Business Issues, 5 (1), 29–42.

Srivastava, S., Singh, S., & Dhir, S. (2020). Culture and International business research: A review and research agenda. International Business Review, 29 (4), 101709. https://doi.org/10.1016/j.ibusrev.2020.101709

Steele, J. P., Rupayana, D. D., Mills, M. J., Smith, M. R., Wefald, A., & Downey, R. G. (2012). Relative importance and utility of positive worker states: a review and empirical examination. Journal of Psychology: Interdisciplinary and Applied, 146 (6), 617–650. https://doi.org/10.1080/00223980.2012.665100

Steers, R. M. (1975). Effects of need for achievement on the job performance-job attitude relationship. Journal of Applied Psychology, 60 (6), 678–682.

Stock, R. M., Strecker, M. M., & Bieling, G. I. (2016). Organizational work–family support as universal remedy? A cross-cultural comparison of China, India and the USA. International Journal of Human Resource Management, 27 (11), 1192–1216. https://doi.org/10.1080/09585192.2015.1062039

Stumpf, S. A., & Rabinowitz, S. (1981). Career stage as a moderator of performance relationships with facets of job satisfaction and role perceptions. Journal of Vocational Behavior, 18 (2), 202–218.

Surana, S. J., & Singh, A. K. (2012). The effect of job burnout on job outcomes among call centre customer service representatives in India. International Journal of Intelligent Enterprise, 1 (3–4), 270–289. https://doi.org/10.1504/IJIE.2012.052557

Talukder, A., Vickers, M., & Khan, A. (2018). Supervisor support and work-life balance: Impacts on job performance in the Australian financial sector. Personnel Review, 47 (3), 727–744.

Tong, J., & Wang, L. (2012). Work locus of control and its relationship to stress perception, related affections, attitudes and behaviours from a domain-specific perspective. Stress and Health, 28 (3), 202–210. https://doi.org/10.1002/smi.1423

Torlak, N. G., & Kuzey, C. (2019). Leadership, job satisfaction and performance links in private education institutes of Pakistan. International Journal of Productivity and Performance Management, 68 (2), 276–295. https://doi.org/10.1108/IJPPM-05-2018-0182

Tsui, P. L., Lin, Y. S., & Yu, T. H. (2013). The influence of psychological contract and organizational commitment on hospitality employee performance. Social Behavior and Personality, 41 (3), 443–452. https://doi.org/10.2224/sbp.2013.41.3.443

Tufail, M., Sultan, F., & Anum. (2019). Examining the effect of challenge-hindrance stressors on work attitude and behavior. FWU Journal of Social Sciences, 13 (1), 90–104.

van Beek, I., Taris, T. W., Schaufeli, W. B., & Brenninkmeijer, V. (2014). Heavy work investment: Its motivational make-up and outcomes. Journal of Managerial Psychology, 29 (1), 46–62. https://doi.org/10.1108/JMP-06-2013-0166

Walker, A. G. (2013). The relationship between the integration of faith and work with life and job outcomes. Journal of Business Ethics, 112 (3), 453–461. https://doi.org/10.1007/s10551-012-1271-0

Wampold, B. E., Ahn, H., & Kim, D. (2000). Meta-analysis in the social sciences. Asia Pacific Education Review, 1 (1), 67–74.

Yalabik, Z. Y., Popaitoon, P., Chowne, J. A., & Rayton, B. A. (2013). Work engagement as a mediator between employee attitudes and outcomes. International Journal of Human Resource Management, 24 (14), 2799–2823. https://doi.org/10.1080/09585192.2013.763844

Yuan, B., Li, J., & Zeng, G. (2018). Trapped as a good worker: the influence of coercive acquaintance advertising on work outcomes. Cornell Hospitality Quarterly, 59 (4), 428–441. https://doi.org/10.1177/1938965518777212

Yuen, K. F., Loh, H. S., Zhou, Q., & Wong, Y. D. (2018). Determinants of job satisfaction and performance of seafarers. Transportation Research Part A: Policy and Practice, 110 (November 2017), 1–12. https://doi.org/10.1016/j.tra.2018.02.006

Yustina, A. I., & Valerina, T. (2018). Does work-family conflict affect auditor’s performance? Examining the mediating roles of emotional exhaustion and job satisfaction. Gadjah Mada International Journal of Business, 20 (1), 89–111. https://doi.org/10.22146/gamaijb.26302

Zhang, J., Akhtar, M. N., Bal, P. M., Zhang, Y., & Talat, U. (2018). How do high-performance work systems affect individual outcomes: A multilevel perspective. Frontiers in Psychology, 9 (APR), 1–13. https://doi.org/10.3389/fpsyg.2018.00586

Zhang, Z., Wang, M., & Shi, J. (2012). Leader-follower congruence in proactive personality and work outcomes: The mediating role of leader-member exchange. Academy of Management Journal, 55 (1), 111–130. https://doi.org/10.5465/amj.2009.0865

Zhou, H., Ye, L., & Gong, D. (2016). Mental workload ’ s influence on job performance for the high-speed railway drivers – job satisfaction as mediator Hong Zhou *, Long Ye and Daqing Gong. School of Economics and Management, 22 (July 2015), 27–29.

Zhou, L., Wang, M., Chen, G., & Shi, J. (2012). Supervisors’ upward exchange relationships and subordinate outcomes: Testing the multilevel mediation role of empowerment. Journal of Applied Psychology, 97 (3), 668–680. https://doi.org/10.1037/a0026305

Ziegler, R., Hagen, B., & Diehl, M. (2012). Relationship Between Job Satisfaction and Job Performance: Job Ambivalence as a Moderator. Journal of Applied Social Psychology, 42 (8), 2019–2040. https://doi.org/10.1111/j.1559-1816.2012.00929.x

Download references

Acknowledgements

We would like to express our special thanks and gratitude to prof. Gholamreza Asadollahfardi for sharing his pearls of wisdom and experience with us to improve this research. We are immensely grateful to him for his comments on an earlier version of the manuscript. His insightful comments and suggestions have truly shined a light on our path and helped us to achieve better and brighter intuition.

There is no research grant support for this study.

Author information

Authors and affiliations.

Faculty of Engineering, Kharazmi University, Tehran, Iran

Ali Katebi, Mohammad Hossain HajiZadeh, Ali Bordbar & Amir Masoud Salehi

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ali Katebi .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Katebi, A., HajiZadeh, M.H., Bordbar, A. et al. The Relationship Between “Job Satisfaction” and “Job Performance”: A Meta-analysis. Glob J Flex Syst Manag 23 , 21–42 (2022). https://doi.org/10.1007/s40171-021-00280-y

Download citation

Received : 22 September 2020

Accepted : 25 June 2021

Published : 24 August 2021

Issue Date : March 2022

DOI : https://doi.org/10.1007/s40171-021-00280-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Job Performance
• Job Satisfaction
• Meta-Analysis
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 04 September 2021

Employee motivation and job performance: a study of basic school teachers in Ghana

• Joseph Ato Forson   ORCID: orcid.org/0000-0002-5997-5713 1 ,
• Eric Ofosu-Dwamena 2 ,
• Rosemary Afrakomah Opoku 3 &
• Samuel Evergreen Adjavon   ORCID: orcid.org/0000-0002-2713-3327 4  

Future Business Journal volume  7 , Article number:  30 ( 2021 ) Cite this article

84k Accesses

16 Citations

Metrics details

Motivation as a meaningful construct is a desire to satisfy a certain want and is a central pillar at the workplace. Thus, motivating employees adequately is a challenge as it has what it takes to define employee satisfaction at the workplace. In this study, we examine the relationship between job motivation factors and performance among teachers of basic schools in Ghana. The study employs a quantitative approach on a sample of 254 teachers from a population of 678 in the Effutu Municipality of Ghana, of which 159 questionnaires were duly answered and returned (representing 62.6% return rate). Using multiple regression and ANOVA, the study finds compensation package, job design and environment and performance management system as significant factors in determining teacher’s motivation in the municipality. Thus, these motivation factors were significant predictors on performance when regressed at a decomposed and aggregated levels. These findings support the self-determination theory, more specifically on the explanations advanced under the controlled and autonomous motivation factors. Significant differences were also observed in teachers’ performance among one of the age cohorts. The study urges the municipal directorate of education to make more room for young teacher trainees and interns who are at the formative stage of their careers to be engaged to augment the experienced staff strength. More should be done to make the profession attain some level of autonomy in the discharge of duty to breed the next genre of innovative educators in the municipality.

Introduction

Motivation as a meaningful construct is a central pillar at the workplace. Thus, motivating employees adequately is a challenge as it has what it takes to define employee satisfaction at the workplace. Quite a number of studies have been devoted to the link between motivation and its constituent factors and employee performance in different organizations [ 7 , 46 ]. Our study draws inspiration from the self-determination theory (SDT) advanced by Deci et al. [ 14 ] as a framework that can be applied to teachers motivation and performance in basic schools in Ghana. It is worth noting that SDT differentiates between controlled motivation and autonomous motivation. The latter is evident when individuals are faced with pressure and control. The former on the other hand emphasizes on the volitional nature of the behavior of individuals. The SDT provides evidence that suggests that motivation fuels performance [ 14 , 57 ].

In Ghana, the subject of motivation has always been at the apex of national agenda and is evident in the number of strike actions in the public service. In the early part of the 2000s, teachers were part of the public servants whose agitation for improved condition of service did not go unnoticed. Forson and Opoku [ 16 ] had stated that teachers’ emolument accounted for less than 35% of the public service wage bill although teachers were perceived to be in the majority in terms of numbers. This phenomenon did spark a wave of attrition of trained teachers to other sectors of the Ghanaian economy. The teaching profession as a matter of fact became a launched pad for the youth. It should be said that the nature of the school setting is basically a function of internal management and leadership. The head teacher or director of education as the Chief Executive needs to appreciate and recognize that results can be obtained through people. In today’s world, organizations are concerned with what should be done to achieve sustained high level of performance through people who are innovative thinkers [ 4 , 17 , 41 ]. These include paying more attention to how individuals can best be motivated and provision of an atmosphere that helps individuals to deliver on their mandates in accordance with the expectations of management [ 25 ]. This means that an educational manager or an individual engaged as a teacher cannot do this job without knowing what motivates people. The building of motivating factors into organizational roles and the entire process of leading people should be contingent on knowledge of motivation. Koontz and Weinrich [ 25 ] agree that the educational managers’ job is not to manipulate people but rather to recognize what motivates people.

A national debate ensued on the significant role played by teachers in nation building and the need to address the shortfall in the condition of service of teachers to motivate them to perform. Wider consultative meetings were held with stakeholders in the teaching fraternity and the outcome and the panacea was the introduction of a uniform pay structure based on qualification. The legislative arm of government passed Act 737 in 2007 that saw the birth of the Fair Wages Salary Commission (FWSC). The mandate of the commission was to ensure a fair and systematic implementation of government pay policy [ 18 ]. Although this has stabilized the teaching profession in terms of the level of attrition, concerns on how this inducement translate into teacher’s performance seem to dominate national discourse especially in the face of fallen standard of education in Ghana. Such concerns have raised questions such as the following: (1) Does pay rise correlate with performance? (2) Are there other factors that ought to be considered in the nexus between motivation and performance? (3) Are there any significant differences in the level of performance among various age cohorts (4) Do educational background motivate teachers to perform better? These and other questions are addressed in this study.

The objective of this paper is to examine the link between job motivation factors and performance among basic school teachers in Ghana. This is against the backdrop that teachers have for some time now complained about condition of service and with the passage of FWSC bill, one would have thought that would have impacted on performance of teachers as it has been proven that motivation leads to satisfaction and ultimately to high performance. The standard of education continues to be a major concern in the educational setup of Ghana.

We organize the paper as follows: section one is the introduction that sets the tone for the paper. The problem is defined in this section, and the necessary questions that warrant redress are asked. We continue with a brief literature review on the concept of motivation, leading to the development of a conceptual framework and hypothesis based on the self-determination theory (SDT). Section two focuses on the method deployed, with emphasis on the aim, design and setting of the study. The theoretical equation for the multiple regression is brought to the fore here. Section three is the results and discussion, and section four concludes with policy implications.

The concept of motivation and self-determination theory (SDT)

Maslow [ 33 ] is credited for being part of the early contributors of human motivation concept. Maslow classifies human needs that motivate them into two: (1) homeostasis and (2) finding that appetites (preferential choices among foods). The former refers to the body’s automatic efforts to maintain a constant, normal state of the blood stream. The latter concept, on the other hand, is of the view that if the body lacks some chemical, the individual will tend (in an imperfect way) to develop a specific appetite or partial hunger for that missing food element. Thus, Maslow was of the view that any of the physiological needs and the consummatory behavior involved with them serve as channels for all sorts of other needs. Relating this assertion to teachers and the need for a salary pay rise, it should be pointed out that a person who thinks he is hungry may actually be seeking more for comfort, or dependence and managers in the educational sector ought to know this. Contemporary researches have expanded on the theory of motivation as advanced by Maslow [ 33 , 34 ]. For an organization to thrive and be efficient, certain conditions ought to be available in order for managers to get the best out of its human resources (workers/employees). Employees of an organization are the greatest asset in a dynamic and competitive environment [ 49 ]. In the words of Martin [ 32 ], if an organization wants to be effective and aims to sustain the success for a longer period of time, it is important for it to have a motivated workforce made up of employees ready to learn. The last three decades have witnessed an avalanche of studies that emphasizes on the point that employee motivation is essential for the success of a business [ 2 ].

In exploring further on this connection, Mifflin [ 35 ] delved into the fundamental meaning of the word “motivation” and pointed out that it is a Latin word which means to move. Therefore, it is near impossible to move peoples’ behavior in an organization unless such move is triggered by certain incentives. Robins and Coulter [ 49 ] explained the term motivation as the desire and willingness to exert high level of inspiration to reach organizational goals, conditioned by the efforts ability to satisfy some individual need. In this study, we define motivation simply as the act of moving people triggered by the provision of some incentives to achieve a desired goal.

In the words of Deci and Ryan [ 13 ], the SDT focuses on human beings inherent desire to bring change and progress as they advance to their fullest potential. Several studies have applied the SDT in various research areas that includes education, medicine and other organizational context. The SDT is of the view that individuals are by nature active entities who will do everything possible to be integrated into the wider social environment in an attempt to be responsive to the behavior consistent with existing self. The theory according to Trépanier et al. [ 57 ] defines social context as the workplace which facilitate or frustrate ones striving toward self-determination.

The SDT theory has two major forms of motivation which may be differentiated on the basis of its nature and quality according to Howard et al. [ 22 ]. When employees engage in interesting activities or in pursuance of their needs, such a form of motivation is ascribed as autonomous motivation. Such a form of motivation facilitates employees’ vitality and energy including satisfaction and well-being [ 14 ]. When employees engage in activities out of pressure as a result of external factors such as attaining rewards including threat of being punished, or even endogenous sources of such pressure as maintaining self-esteem, want of approval, image management or avoiding guilt, such a form of motivation can be ascribed as controlled motivation. Gillet et al. [ 20 ] explain that people with controlled motivational behavior do so out of reason as long as these contingencies exist and thus it predicts maladaptive work outcomes (e.g., exhaustion of personal energy) and turnover intentions.

SDT and job performance

According to Motowildo et al. [ 38 ], job performance is a construct that elicits behavior related to achievement with evaluative components. Most studies on this relationship have emphasized on the role of autonomous and intrinsic motivation on performance with the argument that individuals autonomously motivated have certain inherent values and behaviors and thus give off optimal performance. The theory of self-determination explains that autonomous motivation should be the necessary ingredient for better performance. That is, when individuals are better informed about the purpose of their job and have a sense of ownership and the degree of freedom to operate (autonomy), the possibility of they performing better at work may be high. The source of such motivation according to Deci et al. [ 14 ] may be from one’s interest and values. It is purpose-driven, amplifies energy, enjoyable and provides enough rationalization for tasks to be accomplished effectively. Moreover, the intrinsic component of autonomous motivation has been linked with job performance in related literature and types of performance [ 7 ].

Empirically, there are evidence to suggest that autonomous motivation is linked with performance. Evidence pertaining to controlled motivation is less dispositive. Proponents of the SDT have argued that controlled motivation (e.g., performance management systems) could reduce employee functioning because action derived from personal values and interest may be disconnected, therefore leading to negative effects on performance [ 48 ]. Counter argument posits that controlled motivation may foster employee willingness to complete tasks in an attempt to avoid guilt or punishment or to earn external reward which may come in the form of compensation package [ 27 ]. In this study, we focus on both the controlled and autonomous motivational factors. More specifically, we focus on Herzberg et al. [ 21 ] motivators validated by Harvard Business Review in 2003 which were made up of two motivators: (1) intrinsic factors such as achievement, recognition for achievement, the work itself, growth, responsibility and advancement, and (2) extrinsic factors such as supervision, working conditions, payment, interpersonal relationship, appreciation and company policy. Therefore, the bundle of motivators used in this study are similar to the aforementioned ones and may include performance management systems, external rewards that come in the form of compensation packages, job environment and training and development [ 30 ]. We explain these constructs further with the empirical evidence leading to the development of the conceptual framework.

• Compensation package

Rasheed et al. [ 44 ] posit that package of compensation offered to teachers in institutions of higher learning has to be made based on several factors that may include the experience that underpins the abilities of the teacher, qualifications and perhaps market rates. This is supported by Bohlander et al. [ 6 ] who argued that teachers compensation ought to be the most central concern for managers and administrators of schools in stimulating them. Most of these research studies are premised on the fact that compensation should be designed to meet the needs of teachers and has be fashioned in the form of tangible rewards. In corroborating this assertion, Marlow et al. [ 31 ] observed that low condition of service defined by salary creates stress among teachers in schools. Thus, teachers’ condition of service should be market competitive in order to get higher motivation and to maintain them. Other studies have found that salary levels have been the main challenge for education managers and are the reason for the high attrition and that education planners and managers should pay attention to the design of compensation packages.

Job design and working environment

The needs of teachers on the job ought to be planned properly. The workload on teachers should not be such that it will de-motivate [ 44 ], p. 103. Teachers at all levels should have a learning environment, and educational administrators should make a point to treat existing human resource (teachers) with maximum respect devoid of any discrimination.

Nowadays, job design is the central focus of managers and human resource researchers. Thus, a well-designed job has what it takes in getting interest of employees. On the contrary, poorly designed job breeds boredom among employees. Davidson [ 12 ] makes an important observation and remarked in his research that when teachers are overloaded and burdened with so many non-teaching activities, it portends as a hindrance in the job design. Other scholars such as Clarke and Keating [ 9 ] have argued that the working environment of an educational institution affects teachers’ motivation. Clarke and Keating [ 9 ] found students to be the main reason why teachers are motivated in schools. His emphasis was on talented and hardworking students who boost the morale of teachers. Students who do not produce the desired results, on the other hand, de-motivate teachers. Moreover, class size is another important consideration in motivating teachers. Other variants of the job design and environment are captured in Ofoegbu [ 39 ] research in which he argued that institutions provide support in the form of resources to the teachers in the form of computers with Internet connections. Moreover, other factors such as the provision of e-libraries and research equipment, and other logistics for students may also serve as an effective motivator for teachers.

Performance management system

Management of teachers and educational administrators in all levels of education should focus on implementing basic performance management systems to continually appraise teachers’ accomplishments. For instance, the use of a so-called 360-degree feedback system is important where students’ feedback is attended to with the attention it deserves.

Stafyarakis [ 53 ] corroborated this and asserted that ‘Annual Confidential Reports’ have become obsolete. Yet there has been an emergence of a scientific approach on the field of performance management as time goes on. In discussing this further, Milliman [ 37 ] is of the view that although there are many practices available in this field, but a performance management system based on 360-degree feedback approach is the most effective.

Contrary to the norm that teachers are most motivated by the intrinsic factors and least motivated by the monetary aspects of teaching, Rao [ 43 ] demonstrates that poor appraisal systems, lack of recognition and lack of respect from the head and other co-workers are some common reasons of distress and de-motivation among teachers in educational institutions. The lack of recognition from supervisors is one of the many reasons why teachers would want to leave the teaching profession Stafyarakis [ 53 ].

Moreover, Rasheed et al. [ 45 ] points out that teachers are much concerned about students’ feedback; hence, feedback from the students should be given a proper weightage and in appraising and managing teachers’ performance in the institutions of higher education. Jordan [ 23 ] stressed that the feedback of students is a major issue of that motivates teachers and therefore teachers should be given feedback from their students in scientific manners.

Training and development

It is of significance that educational administrators focus on training activities as an essential means of both motivating employees and sustaining the survival of that organization according to Photanan [ 42 ] and Bohlander et al. [ 6 ]. Leslie [ 28 ] identified professional growth as basic motivator for teachers. He stressed that the professional learning platform available to a teacher is the basic path of his/her career development [ 29 ].

Conceptual framework and hypothesis development

In this section, the study harmonizes the components of the SDT theory into a conceptual framework on motivation and performance connection. The framework developed in this research may be useful as a guide by academicians and practitioners in understanding the mechanisms through which motivational factors affect job performance among teachers in the Effutu Municipality of Ghana. On elucidating on what a framework is, Chinn and Kramer [ 8 ] explained that a framework can be seen as a complex mental formulation of experience. Further clarification was given to distinguish conceptual framework from a theoretical framework. They assert that while theoretical framework is the theory on which the study is based, the conceptual framework deals with the operationalization of the theory. Put in another way, it represents the position of the researcher on the problem at hand and at the same time gives direction to the study. It may be entirely new, or an adoption of, or adaptation of, a model used in previous research with modification to fit the context of the inquiry [ 8 ].

The framework developed in this research has three components: the first component looks at the factors necessary to induce motivation among teachers. The second component focuses on motivation as a concept. The last component which is on job performance looks at the link between the aggregate motivational factors and performance. The extant literature survey on motivational factors and performance provides all the necessary ingredients for the construction of the framework. First, the extant literature shows that motivation as a concept is simply the act of moving people triggered by the provision of some incentives to achieve a desired goal. The triggers of motivation may include such factors such as compensation packages, job design and working environment, performance management system and training and development which are controlled and autonomous factors as crucial elements for motivation.

The second component of the framework is the aggregate motivation, which is the interaction of the controlled and autonomous factors of motivation. Motivation according to Reeve (2001) refers to the excitement level, the determination and the way a person works hard at his work setting. Ricks et al. [ 47 ] explicating on the thesis of motivation was of the view that motivation is an internal aspiration of a man that compels him to reach an objective or the goal set for him.

The third component of the framework is performance. According to Culture IQ [ 11 ] and Motowildo et al. [ 38 ], job performance is the assessment of whether an employee has done their job well. It is an individual evaluation (one measured based on a single person’s effort). In the words of Viswesvaran and Ones [ 58 ], p. 216, the term job performance is used in reference to actions that are scalable, behavior and outcomes that employees engage in or bring about that are linked with and contribute to the goals of an organization. It is linked to both employee- and organizational-level outcomes. A distinctive feature of the framework developed in this research is that it shows the interaction between autonomous and controlled factors and motivation and how it affects the performance of teachers in Fig.  1 .

Source : Created by the authors

A Conceptual model of the relationship between Motivation and Teachers’ Performance.

It can be visibly seen from the framework that teachers motivation may be defined by both controlled and intrinsic motivational factors that may include those that fall under compensation packages, working environment, performance management system and training and development of teachers [ 44 ]. Yet the performance of teachers in itself motivates management and policy makers to institute compensation packages, improved psychological aura through enhanced working environment and job design and implementing appropriate performance management policy for a continued performance enhancement. It should also be emphasized here that these job satisfaction factors may pass as job motivational factors and theorize that a highly motivated teacher may be related to the level of satisfaction.

Scholars such as Thus Milda et al. [ 36 ] and Spector [ 52 ] collectively share the opinion that teachers differ from typical employees in various ways. Therefore, instruments that usually measure such job satisfaction and motivation dimensions as appreciation, communication, coworkers, fringe benefits, job conditions, nature of work, organization itself, organizations’ policies and procedures, pay, personal growth, promotion opportunities, recognition, security, supervision may not always match with teachers’ motivation aspects on the teaching field. However, some of these factors according to some researchers can be used in understanding motivation and performance among teachers. The consensus on these dimensions is especially on supervision, work itself, promotion and recognition being important dimensions of teachers’ motivation at work [ 50 , 51 , 56 ]. In addition, several researchers have used the same measurement or dimension but with different wording (synonym). For instance, Kreitner and Kinici [ 26 ] define job satisfaction with the synonym “motivation” which they argue contains “those psychological processes that cause the arousal, direction and persistence of voluntary actions that are goal directed” Motivation depends on certain intrinsic, as well as extrinsic factors which in collaboration results in fully committed employees. Based on this relationship, we hypothesize that:

Hypothesis 1

Teachers’ compensation package, job environment and design, performance management systems, training and development significantly affect teachers’ motivation.

In a similar manner, Board [ 5 ] asserted that tangible incentives are effective in increasing performance for task not done before, to encourage “thinking smarter” and to support both quality and quantity to achieve goals. Incentives, rewards and recognitions are the prime factors that impact on employee motivation. Aarabi et al. [ 1 ] confirmed this assertion by making use of factors such as payment, job security, promotion, freedom, friendly environment, and training and employee job performance to measure the term organizational motivation with positive relationship found on these factors. On rewards (which comes in various forms, e.g., income/pay, bonus, fringe benefits among others ) and recognition/appreciation, according to other researchers keep high spirit among employees which boost employee’s morale which may have a direct impact on performance and output. The study hypothesizes that:

Hypothesis 2

Teacher’s motivation positively affects their performance.

The aim, design and setting of the study

The paper aims to examine the link between motivation factors and performance among basic school teachers in Ghana. Data for this study were collected from primary. Primary data were sourced from the field of study through questionnaire administration. The researchers sought for permission from the municipal directorate of education to engage with teachers within the municipality. A written permission was granted, and questionnaires were administered to all basic schools’ teachers in the municipality.

At the preparatory stage, the questionnaires designed were tested to make sure participants understood the demands of the questions in the questionnaires. Informal interviews method has been adopted to make sure that additional information that could not have been gathered through the use of questionnaires was captured. The formal interviews using questionnaires ensured that we stayed focused on the background objective that formed the basis of the study.

Sampling technique and data analysis

On the determination of the sample size, different authors have differing views, but in most cases, the recommendation is that it should be large. Stevens [ 54 ] recommends at least 15 participants per predictor for reliable equation in the case of factor analysis. Tabachnick and Fidel [ 55 ] provides a formula for calculating sample size requirements, taking into consideration the number of independent variables that one wish to use: N  > 50 + 8  m (where m  = number of independent variables). In line with these and other requirements like Yamane [ 60 ], the exact sample size will be determined and questionnaires distributed accordingly to the selected public and private schools in the Effutu Municipality.

The human resource unit of the educational directorate of education in the municipality has indicated that there are over 678 teachers teaching at various levels in the municipality [ 15 ]. Thus, the 678 teachers become the population in the municipality. Using Yamane [ 60 ] and validating with other sampling size technique, a sample size of 254 has been adopted with a 0.5 level of precision. Thus, 254 questionnaires were distributed among the various schools, but 159 were filled and returned (representing 62.6% return rate).

Quantitative data are analyzed by means of a software called Statistical Package for Social Sciences (SPSS version 20). This is necessitated by the fact that the analyzed quantitative data ought to be presented by graphs to give quick visual impression of what it entails.

The scale measurement of the questionnaires included nominal scale, ordinal and intervals. Questionnaires used were segmented to capture the demographic characteristics of the respondents and the constructs that feeds into the multi-level latent variables using a five-point Likert scale (see [ 19 , 24 ]). A verification was done to assess the suitability of the data for factor analysis with the expectation that Kaiser–Meyer–Olkin Measure of Sampling Adequacy ( \({\mathrm{i.e}}., {\rm KMo}\ge 0.6)\) and Bartlett’s Test of Sphericity value are significant ( p  = 0.05), which was the case for our sample data. In measuring some of the latent variables, the study developed a 9-scale item on compensation package with the following loadings (e.g., how high is your qualification and pay ( \(\alpha =0.72)\) , “is your experience linked to your current pay?” ( \(\alpha =0.80)\) , “are you satisfied with the market premium” ( \(\alpha =0.75)\) etc.). All items were rated on a five-point Likert scale ranging from 1 = “not important” to 5 = “very important.” A confirmatory factor analysis (CFA) indicates that the hypothesized correlated 3-factor structure fits well with the responds of the participants ( \({\chi }^{2}/df = 2.01, {\rm RMR}=0.05,{\rm RMSEA}=0.06,{\rm TLC}=0.94,{\rm CFI}=0.94)\) .

Job design and working environment was measured by a 7-item scale based on questions such as “how do you perceive your workload” ( \(\alpha =0.88)\) , “does your work type offer learning environment?” ( \(\alpha =0.83),\) “Are you inspired by your working environment?” ( \(\alpha =0.87)\) , “Talented student boost morale” ( \(\alpha =0.84)\) etc. Similarly, all items were rated on a five-point Likert scale ranging from 1 = “not important” to 5 = “very important.” A confirmatory factor analysis reveals that the hypothesized one-factor structure fits well with the data ( \({\chi }^{2}/df = 3.06, {\rm RMR}=0.05,{\rm RMSEA}=0.06,{\rm TLC}=0.94,{\rm CFI}=0.94)\) .

Performance management system was assessed using a 9-item scale based on these inferences (e.g., “number of times supervisor visits” ( \(\alpha =0.69)\) , “how often are you visited by the municipal director of education” ( \(\alpha =0.78)\) , “work recognition” ( \(\alpha =0.72)\) , etc.). All constructs were rated as 1 = “not important” to 5 = “very important.” A confirmatory factor analysis reveals that the hypothesized two-factor structure was in line with the data ( \({\chi }^{2}/df=2.86, {\rm RMR}=0.05,{\rm RMSEA}=0.06,{\rm TLC}=0.94,{\rm CFI}=0.94)\) .

The last but not the least concept explored was job performance. It was assessed on a 12-item scale based on the inferences such as (e.g., “are pupils treated with respect?” ( \(\alpha\) =0.77), “do you help pupils work on their social-emotional skills?” ( \(\alpha\) = 0.69), “are you fair and consistent with pupils” ( \(\alpha\) = 0.87), etc.). All items were rated on a five-point Likert scale ranging from 1 = “not important” to 5 = “very important.” A confirmatory factor analysis reveals that the hypothesized two-factor structure was in line with the data ( \({\chi }^{2}/df = 2.06, {\rm RMR} = 0.05,{\rm RMSEA} = 0.06,{\rm TLC} = 0.94,{\rm CFI} = 0.93)\) . The study proceeds to make use of the proposed measurement models to assess the relationship outlined in the conceptual model in Fig.  1 .

Hypothesized theoretical equation

Based on the conceptual model in Fig.  1 , the study makes a number of hypothesis on the relation between motivational factors and motivation itself and subsequently the link between motivation and performance. Consequently, the study model leads to two structural equations as presented below:

where JM = job motivation, CP = compensation package, JDWE = job design and working environment, PMS = performance management system, TD = training and development, JP = job performance.

Results and discussion

The study begins with a frequency distribution and descriptive statistics to capture the responses of teachers regarding the itemized construct identified in the conceptual model. Beginning with these two is borne out of the fact that the data category used in the study included categorical, ordinal and nominal variables which may be difficult to have a summary descriptive statistic.

With the understanding that every statistical approach is guided by certain principles or in most cases what has come to be known as assumptions, a diagnostic check was undertaken. Multicollinearity and singularity, for instance, look at the relationship among the independent variables. Thus, multicollinearity exists when the independent variables are highly correlated (r = 0.5 and above). The study was particular about these assumptions because multiple regression abhors them (singularity and multicollinearity). Issues concerning outliers (i.e., very high and low scores) was dealt with given the fact that multiple regression is sensitive to them. On normality, the results of the Kolmogorov–Smirnov statics were used to assess the distribution of scores. The test result was insignificant (i.e., sig. value of more than 0.05), which pointed to normality. Having done these, the study was sure there were no errors in the data and that the descriptive phase of the data used can begin.

Consistent with the general distribution of gender in the demographic characteristics of Ghana, about 63 of the teachers were female (39.6%) with 59.1% made up of male and 1.3% being transgender. The transgender teachers were foreign teachers who were here on an exchange program. Most of the teachers in the sample taught at the primary level (46.5%), followed by junior high level (43.4%) and kindergarten (8.8%), respectively. About 34.6% of the respondent responded they have taught between 6 and 10 years and 22.0% had spent between 11 and 20 years teaching. In terms of educational background, about 50.3% of the respondent have had first degree, with the remaining 49.7% being holders of teachers Cert. A or Diploma from the training colleges, and master’s degree of the returned samples. The average number of years participants have taught was observed to be 2.34 years with a corresponding standard deviation of 1.010. We present the demographic characteristics of our participants in Table 1 .

As shown in Table 2 , the compensation package scale has good internal consistency, with a Cronbach alpha coefficient reported to be around 0.725. According to Pallant [ 40 ], Cronbach alpha values above 0.7 are considered acceptable; however, values above 0.8 are preferable. Therefore, the threshold value of 0.725 means our scale is internally consistent and acceptable. Similarly, the job design and working environment scale recorded a Cronbach alpha coefficient of 0.793.

Performance management on the other hand had a Cronbach alpha coefficient of 0.70, yet training and development recorded a lower Cronbach alpha of 0.53, which meant it lacked internal consistency. The study had to drop training and development as factor for job motivation and proceed with the others. Job performance, however conspicuously recorded a Cronbach alpha of 0.83. In terms of the output from the correlation matrix, it can be visibly seen that the scales computed were not highly correlated and fallen below the threshold of 0.8 as recommended (see [ 40 ], p. 56). Both the assumption of singularity and multicollinearity by extension have not been violated (see Durbin Watson results) and thus the study can proceed to run the regression as per the set objectives and the conceptual model.

We go further to examine the causal effect of the factors identified as triggers of motivation on teachers’ level of motivation using ordinary least square method with multiple regression as the exact approach. Having gained credence from the test of reliability and validity, examining the causal effect becomes imperative. Using the baseline model in Eq. ( 1 ), the study concurrently runs the regression with the output shown in Tables 2 , 3 and 4 .

In model one, the study regresses compensation package with the dependent variable without controlling for other related factors. By implication what the results in model (1) seeks to explain is that, as the value of compensation package for teachers increases by 73 percentage points in the municipality, the mean of job motivation increases by that same margin. The high compensation is evidenced by government of Ghana reform in salary structure and bolstered by the effort of the Member of Parliament (MP) through the sharing of teaching and learning materials (TLMs) in the municipality. By this gesture by the MP, teachers feel appreciated and derive high motivation. Moreover, the presence of a university (University of Education, Winneba) has helped to deepen the level of motivation. The model has cross-variable variance of 52 percentage and with close to about 48 percentage unexplained as inferred from the coefficients of both coefficient of determination ( R 2 ) and adjusted coefficient of determination. Generally, the model is jointly significant ( F  = 170, p  < 0.01) with a corresponding tolerance and variable inflationary factor (VIF) of 1.

In model (2), the study varies the variables used with the inclusion of job design and working environment to examine how well the model can be through it cross-variable variance. Controlling for job design and environment shows a significant drop in the coefficient of compensation package from 0.73 to 0.53 although highly significant. Job design and environment recorded a coefficient of 0.49 which meant this indicator increases teachers’ satisfaction and thus motivation by 49 percentage points. In explaining this phenomenon, one would say that jobs that are rich in positive behavioral essentials such as autonomy, task significance and identity and feedback contribute to employees’ motivation. Government has since the introduction of its flagship program on free senior high education emphasized the significance of education across all the strata. The autonomy of heads of unit was by this directive curtailed. Heads of unit were barred from initiating policies to ease their operations. This finding is supported in the literature [ 7 , 30 , 46 ] and is aligned with the SDT. For example, head teachers who had levied pupils with printing fees were sanctioned for such initiative. Thus, by this gesture, the autonomy of the profession was in doubt and this explains why the level of motivation when this parameter is mentioned is low. With this addition, model (2) marginally sees an improvement of 0.73 in the cross-variable variance which is a significant. Model (2) was jointly significant ( F  = 170, p  < 0.01).

All the identified job motivation variables are concurrently used in model (3) to infer whether there was going to be a significant increase in the coefficient of determination and a drop in the residue. As a confirmation to the priori assumption, there was a marginal improvement of the explanatory strength of the model (R 2  = 0.88). However, the model witnessed significant drop in the coefficients. Thus, compensation package dropped further from 0.53 to 0.42 and job design and environment from 0.49 to 0.34.

It is important to note that the value of Durbin Watson test results when all the identified factors are brought together in model (3) indicate a no autocorrelation in the model which validates the earlier point of having dealt with critical assumptions that borders on autocorrelation. Moreover, both our VIF and tolerance were within the acceptable level. For instance, models (1)–(3) had a VIF score less than or equal to 1, which meant there were no issues concerning a possibility of high multicollinearity. For tolerance, there are no clear-cut cut-off point, but there is a suggestion of a tolerance greater than 0.40 according to Allison [ 3 ]. Yet Weisburd and Britt [ 59 ] are of the view that anything below 0.2 is an indication of serious multicollinearity. Inferring from these, it therefore goes to suggest that the tolerance levels of above 1 meant no multicollinearity.

In examining the relationship between the aggregated motivational factors and performance, the study brings to the fore the following findings as shown in Table 3 . The study presents four (4) different models on the relationship between motivation and performance. Model (1) regresses the aggregate motivational factors on job performance, and the findings are quite interesting to note. The job performance indicator is increased by 46% for every unit increase in motivation. This relationship can further be explained to mean a teacher within the municipality with a sense of satisfaction with his/her teaching job may feel more inclined to be at post no matter what the prevailing circumstances are. The snowball effect of this phenomenon is the reduction in absenteeism with a corresponding curb on teachers’ turnover. Although the coefficient of determination which explains the cross-variable variance is by far lower than expected ( R 2  = 0.214), the model is jointly significant ( F  = 41.44, p  < 0.01). The VIF and tolerance levels are within acceptable threshold with a Durbin Watson of 2.04 that signals a no concern of autocorrelation in the model.

Models (2)–(4) regress the decomposed job motivation factors on performance to ascertain their level of significance, and indeed, as theorized, these factors were positively significant except with lower coefficient of determinations ( R 2 ). To explain the relation in model (2), it is important to note that compensation is the output and the benefit that a teacher within the municipality receives in the form of pay, or even any form of exchanges (in kind or in cash) to increase performance. The Member of Parliament for the area as part of effort to ensure teachers are well compensated shared over 700 laptops to teachers within the municipality for effective teaching and learning. This certainly explains why the attrition rate in the municipality is low vis-à-vis high morale of teachers which largely explains the level of motivation and satisfaction.

Model (3) touches on the psychological state the teacher finds him or herself owed to the nature and state of the job. This may include the job environment and the degree of specialization. Yet in model (4), there is an exponential increase in the coefficient of performance management systems as it increases job performance within the municipality by 51 percentage point. It should be noted that performance management sets expectations for teachers’ performance and thus motivates them to work harder in ways expected by the municipal directorate of education under GES. The results in model (5) confirm earlier ones, but the inclusion of the other variables as control seems to have increased the coefficients of the various motivational factors. This partly explains the performance of the municipality in the central region in successive BECE.

Further investigation is made to understand which of the age groups is responsible for the ensuing level of performance in the municipality. To do this, the study relies on one-way analysis of variance (ANOVA). Here, the mean scores of more than two groups are compared using a continuous variable as the dependent variable. Having transformed the ordinal variables to continuous, it makes it quite straightforward to do this. Using the categorical independent variable of age which has more than three categories and the job performance variable which we have transformed to be continuous variable, the study undertakes a one-way between groups ANOVA with post hoc tests. Teachers were divided into four groups according to their ages (group 1: 20–30 yrs.; group 2: 31–40 yrs.; group 3: 41–50 yrs.; group 4: above 51 yrs.). There was a statistically significant difference at the \(p<0.10\) level in job performance scores for the four age groups: F (4, 159) = 0.042, p  = 0.10. Despite reaching statistical significance for one of the groups, the actual difference in mean scores between the groups was quite small. The effect size was calculated using eta squared (eta squared = 179.1/8513 = 0.02) which in Cohen’s ([ 10 ], pp. 248–7) terms is considered far too small a size. Note should be taking that Cohen categorizes 0.01 as a small effect, 0.06 as a medium effect and 0.14 as a large effect. Post hoc comparisons using the Tukey HSD test indicated that the mean score for group 1 (56.12, SD = 4.26) is significantly different from the other three groups which were insignificant. The result has theoretical soundness. Group 1 was made up of young teachers who had either returned from training colleges after completion or on internship and thus had cause to perform for a possibility of being retained or given a very good report since internship supervision forms part of the trainees’ assessment.

In this study, we examined among a host of autonomous and controlled motivational factors and their relationship to performance among basic schools’ teachers in the Effutu Municipality of Ghana. A conceptual model was developed with the necessary hypotheses formulated. Using multiple regression and one-way analysis of variance (ANOVA), the causal effect as shown in the model is tested.

The study finds compensation package, job design and environment and performance management system to be positively significant factors in explaining teacher’s motivation in the municipality. These job motivation factors were significant predictors on job performance. The aggregated job motivation indicator when regressed on job performance reveals a positive and significant effect. Based on the results from the ANOVA, the study recommends the municipal directorate of education to make more room for young teacher trainees who are at the formative stage of their career to be engaged to augment the experienced staff strength. More should be done to make the profession attain some level of autonomy in the discharge of duty to breed the next genre of innovative educators in the municipality. A limitation of the study is its inability to treat job motivation as a mediatory variable as captured in the framework. The study recommends future research to explore this connection.

Availability of data and material

Not applicable.

Abbreviations

Analysis of variance

Self-determination theory

Single spine salary structure

Fair wages salary commission

Teaching and learning materials

Member of parliament

Job motivation

Job performance

Kaiser–Meyer–Olkin

Confirmatory factor analysis

Standardized root mean square residual

Root mean square error of approximation

Statistical package for social science

Variable inflationary factor

Aarabi MS, Subramaniam IV, Akeel AB (2013) Relationship between motivational factors and job performance of employees in Malaysian Service Industry. Asian Soc Sci 9(9):301–310. https://doi.org/10.5539/ass.v9n9p301

Article   Google Scholar  

Al-Alawi AI (2005) Motivating factors on information technology employees in Bahrain Hotel Industry.

Allison P (1999) Multiple regression: a primer. Pine Forge Press

Armstrong M (2003) Handbook of management and leadership: a guide to management for results. Kogan

Board LM (2007) Coaching a stockholder on performance improvement option. In: ASTD international conference

Bohlander G, Snell S, Sherman A (2001) Managing human resources. South-Western College

Cerasoli CP, Nicklin JM, Ford MT (2014) Intrinsic motivation and extrinsic incentives jointly predict performance: a 40-year meta-analysis. Psychol Bull 140(4):980–1008

Chinn PL, Kramer MK (1999) Theory and nursing: integrated knowledge development, 5th edn. Mosby Inc

Clarke R, Keating WF (1995) A fresh look at teacher job satisfaction (ED 391; 795).

Cohen JW (1988) Statistical power analysis for the behavorial sciences, 2nd edn. Lawrence Erlbaum Associates

Culture IQ (2018) Understanding job performance in your company. Job Performance.

Davidson E (2005) Understanding and improving quality in Tanzanian primary schooling. University of East Anglia

Deci EL, Ryan RM (2000) The ‘what’ and ‘why’ of goal pursuits: human needs and the self-determination of behavior. Psychol Inq 11(4):227–268

Deci EL, Olafsen AH, Ryan RM (2017) Self-determination theory in work organizations: the state of a science. Annu Rev Organ Psych Organ Behav 4(1):19–43

EMA (2019) Effutu Municipal Directorate

Forson JA, Opoku RA (2014) Government’s restructuring pay policy and job satisfaction: the case of teachers in the Ga West Municipal Assembly of Ghana. Int J Manag Knowl Learn 3(1):79–99. https://doi.org/10.2139/ssrn.2457629

Forson JA, Opoku RA, Appiah MO, Kyeremeh E, Ahmed IA, Addo-quaye R, Peng Z, Acheampong EY, Bekuni B, Bingab B, Bosomtwe E (2020) Innovation, institutions and economic growth in sub-Saharan Africa—an IV estimation of a panel threshold model. J Econ Admin Sci. https://doi.org/10.1108/JEAS-11-2019-0127

FWSC (2013) Is performance management in Ghana’s public service a mirage? The fair wages and salaries commission’s role in public service performance management. http://www.fairwages.gov.gh/index.php/Slideshow-News/is-performance-management-in-ghanas-public-service-a-mirage/The-Fair-Wages-and-Salaries-Commissions-Role-in-Public-Service-Performance-Management.html

Gall P, Gall D, Borg W (2007) Applying educational research. Pearson Education Inc

Gillet N, Vallerand RJ, Lafreniere MAK, Bureau JS (2013) The mediating role of positive and negative affect in the situational motivation-performance relationship. Motiv Emot 37(3):465–479

Herzberg F, Mausner B, Synderman B (1959) Motivation to work. Wiley

Google Scholar  

Howard JL, Gagné M, Bureau JS (2017) Testing a continuum structure of self-determined motivation: a meta-analysis. Psychol Bull 143(12):1346–1377

Jordan JL (1992) Performance appraisal satisfaction and supervisor’s traits. Psychol Rep 66(1):1337–1338

Kerlinger FN (1978) Foundation of behavioral research. Holt, Rinehart and Winston, New York

Koontz H, Weinrich H (1998) Essentials of management, 5th edn. McGraw-Hill, London

Kreitner R, Kinici A (2004) Organizational behavior. McGraw-Hill, Irwin

Kuvaas B, Buch R, Weibel A, Dysvik A, Nerstad CGL (2017) Do intrinsic and extrinsic motivation relate differently to employee outcomes? J Econ Psychol 61(1):244–258

Leslie K (1989) Administrators must consider and improve teacher satisfaction. NASSP Bull 73(1):19–22

Lynn S (2002) The winding path: understanding the career cycle of teachers. Clearing House J Educ Strat Issues Ideas 75(4):179–182

Manolopoulos D (2008) An evaluation of employee motivation in the extended public sector in Greece. Empl Relat 30(1):63–85

Marlow L, Inman D, Bentancourt-Smith M (1996) Teacher job satisfaction (ED 393; 802)

Martin AJ (2003) The student motivation scale: further testing of an instrument that measures school students motivation. Aust J Educ 47(1):88–106

Maslow AH (1943) A theory of human motivation. Psychol Rev 50:370–396. https://doi.org/10.1037/h0054346

Maslow AH (1954) Motivation and personality. Harper & Row

Mifflin (1995) Management and organization. South-Western Publishing Co

Milda A, Raimundas V, Aidas P (2011) Job satisfaction survey: a confirmatory factor analysis based on secondary school teachers’ sample. Int J Bus Manag 6(5):41–50

Milliman JZ (1994) Companies evaluate employees from all perspectives. Pers J 73(11):99–103

Motowildo SJ, Borman WC, Schmit MJ (1997) A theory of individual differences in task and contextual performance. Hum Perform 10(2):71–83

Ofoegbu FI (2004) Teacher motivation as an essential factor for classroom effectiveness and school improvement. Coll Stud J 3(1):54–61

Pallant J (2011) SPSS survival manual. Allen & Unwin, Australia

Peng Z, Lian Y, Forson JA (2020) Peer effects in R&D investment policy: Evidence from China. Int J Finance Econ. https://doi.org/10.1002/ijfe.2028

Photanan T (2004) Human resource focus. Innographics Ltd

Rao TV (2004) Performance management and appraisal systems: HR tools for global competitiveness. Sage Publications Inc

Rasheed MI, Humayon AA, Awan U, Din Ahmed A (2016) Factors affecting teachers’ motivation. Norwich 30(1):101–114. https://doi.org/10.1108/IJEM-04-2014-0057

Rasheed MI, Sarwar S, Aslam HD (2010) Motivational issues for teachers in higher education: a critical case of IUB. J Manag Res 2(2):1–23

Reizer A, Brender-Ilan Y, Sheaffer Z (2019) Employee motivation, emotions and performance: a longitudinal diary study. J Manag Pschol 34(6):415–428

Ricks BR, Glinn ML, Daughtrey AS (1995) Contemporary supervision, managing people and technology. McGraw-Hill Inc, New York

Rigby CS, Ryan RM (2018) Self-determination theory in human resource development: new directions and practical considerations. Adv Dev Hum Resour 20(2):133–147

Robins SP, Coulter M (2005) Management, 7th edn. A Pearson Education Company

Rosser VJ (2005) Measuring the change in faculty perceptions over time: An examination of their work life and satisfaction. Res High Educ 46(1):81–107. https://doi.org/10.1007/s11162-004-6290-y

Sharma RD, Jyoti J (2009) Job satisfaction of university teachers: an empirical study. J Serv Res 9(2)

Spector PE (1997) Job satisfaction: application, assessment, causes, and consequences. Sage Publications Ltd

Stafyarakis M (2002) HRD and performance management. University of Manchester

Stevens J (1996) Applied multivariate statistics for the social sciences. Lawrence Erlbaum

Tabachnick BG, Fidel S (2013) Using multivariate statistics. New Jersey Education Inc

Tillman WR, Tillman CJ (2008) And you thought it was the apple: a study of job satisfaction among teachers. Acad Educ Leadersh J 12(3):1–18

Trépanier SG, Forest J, Fernet C, Austin S (2015) On the psychological and motivational processes linking job characteristics to employee functioning: insights from self-determination theory. Work Stress 29(3):286–305

Viswesvaran C, Ones DS (2000) Perspectives on models of job performance. Int J Sel Assess 8(4):216–226

Weisburd D, Britt C (2013) Statistics in criminal justice, 4th edn. Springer, Berlin

Yamane T (1967) Statistics, an introductory analysis, 2nd edn. New York Harper and Row, New York

Download references

Acknowledgements

The authors would like to acknowledge the support of the Effutu Directorate of Education, particularly the Municipal Director of Education for the support during the data collection stage. We thank all the basic school teachers in the municipality who devoted time to fill and return questionnaires sent to them. We are also grateful to the Directorate for the secondary materials given to the team.

Author information

Authors and affiliations.

Department of Applied Finance and Policy Management, University of Education, Winneba, P.O. Box 25, Winneba, Ghana

Joseph Ato Forson

Department of Educational Foundations, University of Education, Winneba, Winneba, Ghana

Eric Ofosu-Dwamena

Department of Educational Administration and Management, University of Education, Winneba, Winneba, Ghana

Rosemary Afrakomah Opoku

Department of Applied Finance and Policy Management, University of Education, Winneba, Winneba, Ghana

Samuel Evergreen Adjavon

You can also search for this author in PubMed   Google Scholar

Contributions

JAF contributed 50%, EOD contributed 25%, RAO contributed 20%, and SEA contributed 5%, respectively. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Joseph Ato Forson .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Forson, J.A., Ofosu-Dwamena, E., Opoku, R.A. et al. Employee motivation and job performance: a study of basic school teachers in Ghana. Futur Bus J 7 , 30 (2021). https://doi.org/10.1186/s43093-021-00077-6

Download citation

Received : 21 February 2021

Accepted : 11 June 2021

Published : 04 September 2021

DOI : https://doi.org/10.1186/s43093-021-00077-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Performance
• Performance management systems
• Single spine salary structure (SSSS)

JEL Classification

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Taylor & Francis Open Select

Does IQ Really Predict Job Performance?

Ken richardson.

a Independent Researcher

Sarah H. Norgate

b University of Salford

IQ has played a prominent part in developmental and adult psychology for decades. In the absence of a clear theoretical model of internal cognitive functions, however, construct validity for IQ tests has always been difficult to establish. Test validity, therefore, has always been indirect, by correlating individual differences in test scores with what are assumed to be other criteria of intelligence. Job performance has, for several reasons, been one such criterion. Correlations of around 0.5 have been regularly cited as evidence of test validity, and as justification for the use of the tests in developmental studies, in educational and occupational selection and in research programs on sources of individual differences. Here, those correlations are examined together with the quality of the original data and the many corrections needed to arrive at them. It is concluded that considerable caution needs to be exercised in citing such correlations for test validation purposes.

IQ has now been used as a measure of cognitive functioning for over a century. It has played a prominent part in developmental studies in many ways: as an index of normal development; for clinical diagnostics; as a descriptor of individual differences in cognitive ability; as explanation for differences in achievement or success in the world; as a predictor of future success as in school, training and occupational selection; and as an index for exploring causes of individual differences in cognitive ability. For example, it is argued that the current search for associations between molecular genetic variations and IQ “will transform both developmental psychology and developmental psychopathology” (Plomin & Rutter, 1998 , p. 1223; see also Plomin, 2013 ). Likewise, Kovas, Haworth, Dale, and Plomin ( 2007 ) say that their conclusions on the heritability of IQ “have far-reaching implications for education and child development as well as molecular genetics and neuroscience” (p. vii). Clearly, a lot hinges on the validity of the test, especially as such studies are very expensive.

The validity of an IQ test—or what it actually measures—on the other hand, has always been a difficult subject. Since Galton in the 1880's ( 1883 ) and Spearman ( 1927 ) a little later, it has been widely assumed that the test measures “intelligence,” commonly referred to as “general cognitive ability,” or g . The identity of that ability, however has never been agreed; its function has only been characterized metaphorically as a kind of pervasive cognitive energy, power or capacity, by analogy with physical strength. In consequence, measuring it has always been indirect, creating perpetual debate and controversy about the validity of the tests. This article is about such validity.

In scientific method, generally, we accept external, observable, differences as a valid measure of an unseen function when we can mechanistically relate differences in one to differences in the other (e.g., height of a column of mercury and blood pressure; white cell count and internal infection; erythrocyte sedimentation rate (ESR) and internal levels of inflammation; breath alcohol and level of consumption). Such measures are valid because they rely on detailed, and widely accepted, theoretical models of the functions in question. There is no such theory for cognitive ability nor, therefore, of the true nature of individual differences in cognitive functions. A number of analyses of the inter-correlations of aspects of test scores have produced theories of the statistical structure of score patterns, as in the Cattell-Horn-Carroll theory (see McGrew, 2005 ); but this is not the same thing as detailed characterization of the function itself. Accordingly, as Deary ( 2001 ) put it, “There is no such thing as a theory of human intelligence differences—not in the way that grown-up sciences like physics or chemistry have theories” (p. ix).

The alternative strategy has been to attempt to establish test validity indirectly, by comparison of a proposed measure with what is considered to be some other expression of intelligence. Galton ( 1883 ) chose differences in social esteem; subsequently, scholastic performance and age-related differences were chosen. Typically, in constructing a test, cognitive problems or items thought to engage aspects of intelligence are devised for presentation to testees in trials. Those items on which differences in performance agree with differences in the criterion are put together to make up an intelligence test. There are many other technical aspects of test construction, but this remains the essential rationale. Thus, nearly all contemporary tests, such as the Stanford-Binet or the Woodcock-Johnson tests, rely on correlations of scores with those from other IQ or achievement tests as evidence of validity.

However, the question of whether such procedure measures the fundamental cognitive ability (or g) assumed has continued to haunt the field. Measuring what we think is being measured is known as the construct validity of the test—something that cannot, by definition, be measured indirectly. Generally, a test is valid for measuring a function if (a) the function exists and is well characterized; and (b) variations in the function demonstrably cause variation in the measurement outcomes. Validation research should be directed at the latter, not merely at the relation between what are, in effect, assumed to be independent tests of that function (Borsboom, Mellenberg, & van Heerden, 2005 ).

It is true to say that various attempts have been made to correlate test scores with some cortical/physiological measures in order to identify cerebral “efficiency” as the core of intelligence. However, as Nisbett et al. ( 2012 ), in their review for the American Psychological Association, point out, such studies have been inconsistent:

Patterns of activation in response to various fluid reasoning tasks are diverse, and brain regions activated in response to ostensibly similar types of reasoning (inductive, deductive) appear to be closely associated with task content and context. The evidence is not consistent with the view that there is a unitary reasoning neural substrate. (p. 145)

Haier et al. ( 2009 ) likewise conclude after similar inconsistent results that “identifying a ‘neuro- g ’ will be difficult” (p. 136). Associations have also been sought between various elementary tasks such as reaction time and IQ test scores. These have been difficult to interpret because the correlations are (a) small (leaving considerable variance, as well as true causes, unexplained) and (b) subject to a variety of other factors such as anxiety, motivation, experience with equipment, and training or experience of various kinds such as video game playing (e.g., Green & Bavelier, 2012 ).

Accordingly, validation of IQ tests has continued to rely on correlation with other tests. That is, test validity has been forced to rely, not on calibration with known internal processes, but on correlation with other assumed expressions, or criteria, of intelligence. This is usually referred to as “predictive” or “criterion” validity. In almost all validity claims for IQ those criteria have been educational achievement, occupational level and job performance.

Predictive Validity of IQ

It is undoubtedly true that moderate correlations between IQ and those criteria have been reported. For example, in their recent review Nisbett et al. ( 2012 ) say “the measurement of intelligence—which has been done primarily by IQ tests—has utilitarian value because it is a reasonably good predictor of grades at school, performance at work, and many other aspects of success in life” (p. 2). But how accurate and meaningful are such correlations?

It is widely accepted that test scores predict school achievement moderately well, with correlations of around 0.5 (Mackintosh, 2011 ). The problem lies in the possible self-fulfilment of this prediction because the measures are not independent. Rather they are merely different versions of the same test. Since the first test designers such as Binet, Terman, and others, test items have been devised, either with an eye on the kinds of knowledge and reasoning taught to, and required from, children in schools, or from an attempt to match an impression of the cognitive processes required in schools. This matching is an intuitively-, rather than a theoretically-guided, process, even with nonverbal items such as those in the Raven's Matrices. As Carpenter, Just, and Shell ( 1990 ) explained after examining John Raven's personal notes, “ … the description of the abilities that Raven intended to measure are primarily characteristics of the problems, not specifications of the requisite cognitive processes” (p. 408).

In other words, a correlation between IQ and school achievement may emerge because the test items demand the very kinds of (learned) linguistic and cognitive structures that are also the currency of schooling (Olson, 2005 ). As Thorndike and Hagen ( 1969 ) explained, “From the very way in which the tests were assembled [such correlation] could hardly be otherwise” (p. 325). Evidence for this is that correlations between IQ and school achievement tests tend to increase with age (Sternberg, Grigorenko, & Bundy, 2001 ). And this is why parental drive and encouragement with their children's school learning improves the children's IQ, as numerous results confirm (Nisbett, 2009 ; Nisbett et al., 2012 ).

Similar doubts arise around the use of occupational level, salary, and so on, as validatory criteria. Because school achievement is a strong determinant of level of entry to the job market, the frequently reported correlation ( r  ∼ 0.5) between IQ and occupational level, and, therefore, income, may also be, at least partly, self-fulfilling (Neisser et al., 1996 ). Again, the measures may not be independent.

The really critical issue, therefore, surrounds the question of whether IQ scores predict individual differences in the seemingly more independent measure of job performance. Indeed, correlation of IQ scores with job performance is regularly cited as underpinning the validity of IQ tests. Furnam ( 2008 ) probably reflects most views when he says that “there is a large and compelling literature showing that intelligence is a good predictor of both job performance and training proficiency at work” (p. 204). In another strong commentary, Kuncel and Hezlett ( 2010 ) refer to “this robust literature” as “facts” (p. 342). Ones, Viswesvaran, and Dilchert ( 2005 ) say that “Data are resoundingly clear: [measured cognitive ability] is the most powerful individual differences trait that predicts job performance … Not relying on it for personnel selection would have serious implications for productivity. There is no getting away from or wishing away this fact” (p. 450; see also Ones, Dilchert, & Viswesvaran, 2012 ). Drasgow ( 2012 ) describes the correlation as “incontrovertible.” Hunter and Schmidt ( 1983 ) even attached dollar value to it when they claimed that the U.S. economy (even then) would save $80 billion per year if job selection were to be universally based on IQ testing.

Unfortunately, nearly all authors merely offer uncritical citations of the primary sources in support of their statements (for exceptions see, for example, Wagner, 1994 , and in the following sections). Instead of scrutiny of the true nature of the evidence, a conviction regarding a “large and compelling literature” seems to have developed from a relatively small number of meta-analyses over a cumulative trail of secondary citations (Furnham, 2008, p. 204). It seems important, therefore, to take a closer look at the quality of data and method behind the much-cited associations between IQ and job performance, and how they have been interpreted. The aim, here, is not to do an exhaustive review of such studies, nor to offer a sweeping critique of meta-analyses, which have many legitimate uses. Indeed, the approach devised by Schmidt and Hunter ( 1998 ), which we go on to discuss, brought a great deal of focus and discipline to the area and we agree with Guion ( 2011 ) that it must be recognized as a major methodological advance. Rather our aim is to emphasize the care needed in interpretation of correlations when based on corrections to original data of uncertain quality and then invoked as evidence of IQ test validity.

In contrast with the confidence found in secondary reports, even a cursory inspection of the primary sources shows that they are highly varied in terms of data quality and integrity, involving often-small samples and disparate measures usually obtained under difficult practical constraints in single companies or institutions. Their collective effect has mainly arisen from their combination in a few well-known meta-analyses. Hundreds of studies prior to the 1970s reported that correlations between IQ tests and job performance were low (approximately 0.2–0.3) and variable (reviewed by Ghiselli, 1973 ). These results were widely accepted as representative of the disparate contexts in which people actually work. Then, Schmidt and Hunter ( 2003 , for an historical account) quite reasonably considered the possibility that the large quantity of results were attenuated by various statistical artifacts, including sampling error, unreliability of measuring instruments, and restriction of range. They devised methods for correcting these artifacts and incorporating the studies into meta-analyses. The corrections doubled the correlations to approximately 0.5. Nearly all studies cited in favor of IQ validity are either drawn from the Schmidt and Hunter meta-analyses or from others using the correction methods developed for them.

The Schmidt and Hunter approach ( 1998 ), as first devised, seemed relatively straightforward. First, the results were collated from as many studies as were available. Then, the variance due to sampling error in the reported (observed) correlations was estimated. Then, the mean of the observed correlations was computed and corrected for measurement unreliability in the criterion (i.e., job performance) and for restriction of range in predictor and criterion measures. This produced the results now so widely cited in vindication of IQ test validity (Hunter, Schmidt, & Jackson, 1982 ; Schmidt & Hunter, 1977 , 1998 ).

Hunter and Hunter ( 1984 ) first reported the application of these methods—usually referred to as “validity generalization,” or VG—to the hundreds of studies reviewed by Ghiselli ( 1973 ). In addition, they reported a further meta-analysis of 515 studies carried out by the U.S. Employment Service using the General Aptitude Test Battery (GATB). This produced corrected correlations in the range 0.5–0.6. Similar results have been reported from application of the same methods in more recent studies. For example, in meta-analyses of European and British studies, Salgado et al. ( 2003 ) and Bertua, Anderson, and Salgado ( 2005 ) found raw correlations between 0.12 and 0.34, depending on job category. However, all correlations virtually doubled under correction. Lang, Kersting, Hülsheger, and Lang ( 2010 ) report similar results from meta-analysis of 50 studies in Germany.

It is these corrected correlations from meta-analyses that are almost universally cited in favor of IQ as a predictor of job performance (and, by implication, that IQ really does measure something that can be called intelligence or general ability). But many doubts have been expressed regarding those methods, and results have been subject to continual criticism. Generally, meta-analyses are rarely straightforward and, at times, have been controversial. Although undoubtedly useful in many subject areas, as Murphy ( 2003 ) says, they are often viewed with distaste because they mix good and bad studies, and encourage the drawing of strong conclusions from often-weak data. In the IQ-job performance studies in question, quality checks are often difficult because the original reports were unpublished, sometimes with parts of original data lost. In addition, the corrections themselves involve many assumptions, for example about normality of distributions and randomness of effects, which are rarely articulated in primary reports (Murphy, 2003 ). Landy ( 2003 ) described them as the “psychometric equivalent of alchemy” (p. 157). The criticisms here will focus on both the quality of the primary data and the surety of the meta-analytic corrections to them. First, let us consider the measuring instruments used.

However well-intentioned, most studies have been done under difficult circumstances so that study design, including choice of test, has often been based on convenience rather than principles of empirical precision. Accordingly, a wide variety of vaguely mental tests has been adopted across individual studies, and incorporated into meta-analyses, on the assumption that they measure essentially the same thing (by implication “general intelligence” or g ). Apart from the traditional psychometrically validated instruments (e.g., Wechsler's Adult Intelligence Scale, Raven's Progressive Matrices, or the U.S. Employment Service's General Aptitude Test Battery), studies have included working memory tests, reading tests, scholastic aptitude tests (SATS) and university admission tests, all taken in meta-analyses as surrogate measures of IQ. Sometimes, a “general” factor has been deduced as a composite of “special ability” tests (e.g., perceptual speed, memory; Lang et al., 2010 ), or by renaming the construct “general mental ability” (GMA) as “another name for g ” (James & Carretta, 2002 , p. 13).

Illustrative of the variety of tests used in meta-analysis are those listed in the European study of Salgado et al. ( 2003 ). They include “(a) Batteries: DAT, GATB, T2, ASDIC, Intelligence Structure Test (IST-70), Wilde Intelligence Test (WIT), GVK, PMA, and Aptitudes Mentales Primarias (AMPE); (b) g tests: Raven's Progressive Matrices, Cattell's Culture Fair Tests, Otis Employment Test, Alpha Test, Logique Intelligence, CERP, Domino, D-48, NIIP-33” (Salgado et al., 2003 , p. 1070). This categorization implies that “batteries” and “ g tests” measure something different from each other—if so, what? More importantly, the studies using them cover a vast range of dates, some from the 1920s, while the majority are pre-1970s. These will not, of course, take any account of the “Flynn effect”—the substantial cross-generational rise in average IQ scores—which affects different tests differently and affects variances and distributions as well as means (Flynn, 2007 ; Wai & Ptallaz, 2011 ). Likewise, with the Bertua et al. ( 2005 ) meta-analysis of 60 UK studies: studies date from the 1920s to the 1980s, and utilized an equally wide range of disparate tests.

Further uncertainty is added by the high proportion of original studies involving men and women serving in the armed forces. These used a wide range of specialist and multi-purpose tests, such as the Armed Forces Qualification test, the Australian Army Intelligence Test, and the Armed Service Vocational Aptitude Battery. Sometimes, measures have been statistically reduced to a single component of variance, or primary factor, before meta-analysis (e.g., Olea & Ree, 1994 ). The usual justification for doing so is that any general factor condensed from inter-correlated scores can be assumed to represent g , and, therefore, that the tests are genuine tests of intelligence (even though a general factor typically covers only around 50% of the score variance). It is always a possibility, of course, that different correlates, even though resolving as a statistical “common factor,” may well not be the same “thing,” or even the thing it is thought to be, so that mischaracterization can occur. In the case of mental test performances, the general factor may not even be cognitive in origin (Richardson, 2002 ; see the following sections).

As Murphy ( 2003 ) says, the assumption that these measures, with disparate properties, distributions, and so on, can be combined as if a single uniform variable can lead to serious problems in meta-analysis including “lack of clarity in what population parameter is being estimated” (p. 31). Murphy and Newman ( 2003 ) add that, “if several hundred studies each claim to measure ability and performance, but they use wildly different measures of one or both constructs, the average ability-performance correlation across those studies might be hard to interpret” (p.414). Burke and Landis ( 2003 ) also complain about the “cavalier” treatment of construct issues in meta-analyses.

In contrast to the vast diversity of predictor tests, the measure of job performance has almost always consisted of supervisors’ ratings. These, of course, should be reliable, valid, and free from bias of whatever source. Unfortunately, as with ability testing, the strict requirements are often overlooked (Guion, 2006 ). It turns out that there are a number of problems with such ratings (Woerh, 2011 ).

The main problem is that supervisors tend to be subjective, and use inconsistent criteria, in making their judgments of performance. This is hardly surprising, given the difficulty of defining good or poor performance. As Gottfredson ( 1991 ) noted, “One need only ask a group of workers in the same job to suggest specific criterion measures for that job in order to appreciate how difficult it is to reach consensus about what constitutes good performance and how it can be measured fairly” (p. 76). In addition, a variety of systematic biases are evident: age effects and “halo” effects have been reported (e.g., Murphy & Balzer, 1986 ). Subjects’ height (Judge & Cable, 2004 ); facial attractiveness (Hosoda, Stone-Romero, & Coats, 2003 ); and unconscious ethnic bias (Berry, Clark, & McClure, 2011 ; Jencks, 1998 ; Stauffer & Buckley, 2005 ), have all been shown to influence supervisor ratings of work performance. In describing the difficulties, in his own experience, of seeking objective supervisor ratings across a wide range of jobs, Guion ( 2006 ) says, “Perhaps, indeed, we should abandon the pretence about ‘objective’, ‘true’, or ‘hard’ criteria of proficiency in performance” (pp. 268–269).

Perhaps it is hardly surprising, therefore, that supervisor ratings have rather low correlations with more objective criteria such as work samples or work output (Bommer, Johnson, Rich, Podsakoff, & Mackenzie 1995 ; Cook, 2009 ; Heneman, 1986 ). Schmidt, Hunter, and Outerbridge ( 1986 ) put it at virtually zero. In a study of salespersons, Vinchur, Schippmann, Switzer, and Roth ( 1998 ) found that “general cognitive ability” showed a correlation of .40 with supervisor ratings but only .04 with objective sales. Roth, Bobko, and McFarland ( 2005 ) found a mean observed correlation between work sample tests and measures of job performance (mostly supervisor ratings) of only 0.26, and a correlation between work sample tests and “general cognitive ability” of only 0.33. It is somewhat strange, therefore that Hunter ( 1986 ) reported that IQ/GMA predicted work sample ratings even better than it predicted supervisor ratings suggesting, perhaps, that they are measuring different things.

Another problem is the difficulty investigators have experienced in establishing reliabilities for supervisor ratings. Accurate reliabilities are needed, of course, in order to achieve the corrections to correlations. But they tend to be available for only a minority of the studies incorporated in the commonly cited meta-analyses. The strategy of Schmidt and Hunter and other meta-analysts has been to simply extrapolate from the average of those actually available. That strategy, of course, involves many assumptions about representativeness, randomness, uniformity across disparate samples, and so on. Using such a strategy, Hunter and Hunter ( 1984 ) assumed a reliability of 0.6 for their corrections, which some investigators have considered to be too low (Hartigan & Wigdor, 1989 ). Bertua et al. ( 2005 ) used the same figure for their meta-analysis of British studies. Moreover, that estimate was based on inter-rater reliability. Murphy and DeShon ( 2000 ) pointed out that differences between raters should not be considered error to be corrected because different raters may be looking for different things in a worker. Instead, intra -rater reliabilities should be used. However these tend to be much higher: 0.86 rather than 0.6. according to the meta-analysis carried out by Viswesvaran, Ones, and Schmidt ( 1996 ). The lower the value adopted, of course, the bigger the inflation to raw correlations. Using the reliability of 0.6, for example, inflates the correlations by 29%. By comparison, distinguished statistician John Hartigan, and colleague Alexandra Wigdor, favour the 0.8 estimate which only inflates the correlation by 12% (Hartigan & Wigdor, 1989 ) As Murphy ( 2003 ) says, evidence of error is so pervasive that many commentators urge caution in using supervisor ratings as criterion of job performance.

In meta-analyses the reported correlation between IQ and job performance is a mean of observed correlations (usually weighted by sample size, if known). It could be that the low correlations from early studies are the true correlations for the general population of employees across their myriad jobs and contexts. Hunter and Schmidt (1977) argued, conversely, that the diverse correlations are artefacts of data collection. They devised a number of formulae for making corrections to them that have been refined over the years but remain essentially the same.

First, sampling error arises because the observed (primary study) correlations are being estimated from sub-samples of the general population as well as sub-samples of the universe of jobs. The correlations, that is, will deviate from the (unknown) population correlation by an unknown degree, affecting the overall estimate as well as its confidence intervals. The mean of the observed correlations—as used in meta-analysis—will also have an inflated variance. Therefore, the sampling error variance has to be subtracted from the overall variance to arrive at the variance for the true correlation and it's statistical significance. Estimates for all these values need to be computed from the data. In using their methods and assumptions Schmidt and Hunter ( 1998 ) estimated that approximately 70% of the apparent variance consisted of sampling error variance.

A number of issues surround corrections for sampling errors. The Schmidt and Hunter approach ( 2003 ) assumes that all specific study samples are from essentially the same reference population with a single underlying IQ/job-performance correlation having close to zero variance. This assumption, together with the distribution of sampling errors, is used to indicate how close the average observed correlation is likely to be to the “true” correlation.

However, this maneuver is based on the further assumption that the primary studies are random samples from the (hypothetical) general population. This cannot be checked in samples where a number of details are missing. Rather than being carefully planned as random designs, particular studies are conducted on an as available basis, as Murphy ( 2003 ) puts it. After all, recruitment of participants is based on finding an employer willing to have employees tested and finding supervisors willing to rate them, which will be more likely to occur with some jobs than others. Hartigan and Wigdor ( 1989 ) provide evidence of such bias. Moreover, effects of systematic moderator variables are rarely taken into account (Schmitt, Gooding, Noe, & Kirsch, 1984 ). These can only be eliminated through primary research with appropriate controls (Russell & Gilliland, 1995 ).

When the corrections to sampling errors are done is also an issue. The estimated true mean correlation is computed as an average of observed correlations, as previously mentioned. Ideally, the sample means should be individually corrected for sampling error, measurement unreliability and range restriction before the averaging occurs; that is, meta-analysis should be done on the fully corrected samples. However, as most of that information is not available in the individual studies, the Schmidt and Hunter method ( 2003 ) corrects for them after the averaging, which can introduce further inaccuracies including reduction of observed variance and exaggerated sampling error variance (Davar, 2004 ; Oswald & McCloy, 2003 ). Hartigan and Wigdor ( 1989 ), in their meta-analysis of more recent studies, estimated sampling error to be about half the observed variability (compared with the 70% suggested by Schmidt & Hunter [ 1998 ]). In other studies (e.g., Burke & Landis, 2003 ; Lang et al., 2010 ) corrections have been based on the weighted mean of available estimates from other meta-analyses, or “hypothetical estimates” (Lang et al., p. 612).

The sample means may also deviate from the hypothetical true mean because of unreliability of measurement, or measurement error, in both ability test and job performance assessment. A correlation between IQ and job performance in a specific study may be depressed because of such error. That also needs to be corrected. The main effect of correcting for measurement error is to increase the observed correlations usually in proportion to the unreliability of the measure: the greater the unreliability the bigger the upward correction to the correlation.

The desirable technique for measurement error correction consists of adjusting each coefficient included in the meta-analysis individually using reliability information provided for the specific predictor and criterion measures in the study report. In the most-used and reputable standardized tests reliability is well established and the attenuation can be corrected in advance of the meta-analysis. However, the reliabilities of the measures actually used in the meta-analyses in question were “only sporadically available” (Hunter & Schmidt, 1990 , p. 79). They recommended basing them on the subset of the studies for which information happened to be available.

Using that strategy, Schmidt and Hunter ( 1977 ) arrived at a reliability of .60 for job performance. As Hartigan and Wigdor ( 1989 ) explained, this figure “has met with some scepticism among industrial/organizational psychologists many of whom believe that the .60 value is too low” (p. 166). The overall effect of using the .60 value is to increase the estimate of the population correlation by 30%. This too has remained an area of controversy (Sackett, 2003 ).

More generally, although correcting for measurement error seems straightforward and desirable, it is theoretically more complicated and may not always be consistent with psychometric principles (Murphy & DeShon, 2000 ). DeShon ( 2003 ) says, “there are numerous theoretical reasons for urging caution when correcting the magnitude of the correlation coefficients for measurement error”, and it “is of dubious merit in many situations” (p. 382). One of these is that, although correcting for measurement error will often increase the correlation coefficient, it also increases its standard error with larger confidence intervals not differentiating it from zero. Reliabilities of job performance ratings are computed from estimates on different occasions. However, differences in estimates may be due to genuine differences in performance rather than measurement error. Most individuals create a difference between their maximum and their typical performances such that these indices are not highly correlated and have different correlates (Marcus, Goffin, Johntson, & Rothstein, 2007 ). Stewart and Nandkeolyar ( 2006 ) found that intra-individual variation was greater than inter -individual variation in job performance. Again, correction becomes, to some extent, guesswork, yet the adjusted correlations depend upon it.

The statistical model used for meta-analysis and its corrections may also be an issue here. Correction of measurement error is based on a random effects model, but the unreliability of (in this case) supervisor ratings may stem in part from a number of systematic (i.e., non-random) effects (Murphy & DeShon, 2000 ). For example, different job contexts may involve different kinds of disagreement among raters about what should be measured or about how the rating scales should be used. Also, there may be systematic differences among testees related to, for example, gender, ethnic background and social class background and the effects of these on such variables as self-confidence and ability expression (see subsequent sections). A variety of studies indicate that “macrosocial differences in the distribution of economic goods are linked to microsocial processes of perceiving the self” (Loughnan et al., 2011 , p. 1254). Such perceptions can impinge on correlations between test and job performances. These are non-random errors that complicate inferences from particular samples used in particular times and places (DeShon, 2003 ).

Correcting for measurement error also has complex effects on the variances of the observed correlation coefficients. As implied above, corrections for measurement error made after, rather than before, averaging in meta-analyses, may exaggerate sampling error variance and reduces the variance of the estimated correlation. Much more statistical evaluation of the combination of known and unknown measurement unreliabilities is called for “before this procedure could be recommended as general practice” (DeShon, 2003 , p. 397).

More generally, measurement error may also arise on account of the lack of construct validity (the proof that it is measuring the function intended). It is, of course, the acknowledged lack of construct validity in IQ testing that has led to such reliance on predictive validity in the first place. Lack of it, nevertheless, has implications for corrections for unreliability in meta-analyses. Schmidt and Hunter's approach ( 1977 ) insists that correcting for measurement error provide an estimate of the “true” correlation between the underlying constructs. Borsboom and Mellenbergh ( 2002 ), on the basis of classical test theory, have vehemently disagreed with this because it also assumes what it is trying to prove, namely the validity of that construct being revealed through the test-criterion correlation. As Burke and Landis ( 2003 ) explain:

Meta-analytic research … is sometimes cavalier in its treatment of construct-related issues. In particular, there sometimes is an apparent assumption that superficially similar studies, or those that claim to be dealing with the same set of constructs, can be easily combined to draw meaningful construct-level inferences. This is not true. Rather, careful thought needs to go into decisions about how to link study outcomes with constructs. (p. 298)

The third common problem is that sample correlations may vary because of range restriction in the samples, compared with the general population. The main reason it arises is that job performance ratings can only be provided for those who are actually in the job, and have been IQ tested, not for all possible workers (including applicants who did not get the job). An unmeasured complication is that those who might even apply for a job will be self-selecting to some extent, reflecting self-perceptions of a variety of other attributes such as experience, ability, self-confidence, experience, paper qualifications, and so on. The statistic needed to correct for range restriction is the ratio of the observed standard deviation ( SD ) in the restricted sample to that in the unrestricted population. For example, if the ratio is 0.5 the effect of correction is to double the sample correlation. Legitimate correction depends, of course, on having accurate estimates of both sample and population variances. As with measurement unreliabilities, however, few primary studies have reported range restrictions, so that extrapolation is again necessary, and again with all the dangers entailed.

The main problem is that of identifying the variance for the appropriate reference population. In the present case the true reference population is all applicants for a job—all of which should have been IQ tested—from which a limited proportion are recruited for the job and assessed for job performance. However, the standard deviation ( SD ) of the observed (job applicants’) test results is rarely available. So the strategy has been to deduce it from that of actual workers’ scores, the only ones available. In the Schmidt and Hunter methods it is simply assumed that the reference population SD could be represented by the “entire US workforce” which could, in turn, be adequately represented by the 515 jobs in the (then) GATB database. The SD s for those samples available were then compared with this overall SD as the basis for correction of range restriction for all the samples. Schmidt and Hunter ( 1977 ) thus arrived at a restriction ratio of 0.6.

The review of these studies by Hartigan and Wigdor ( 1989 , p. 167) says that the assumption that the applicant pool for each and every job can be approximated by the GATB workforce is “troubling.” As previously mentioned, it is rarely clear to what degree a particular sample may be restricted, in relation to the reference, because people tend to be self-sorting in the jobs they seek rather than belonging to a random applicant pool. In other words, it is likely that employee samples will display inhomogeneity, and not be representative of normative data (Lang et al., 2010 ). This inhomogeneity is more likely with smaller samples. Hunter and Hunter ( 1984 ) cite earlier studies as having average sample sizes of just 68, which means some must have been even smaller than that. Schmidt and Hunter ( 1998 ) say that n 's were usually in the 40–70 range. This is also important in that there are certain situations, such as non-normal data with outliers, in which the correction can actually decrease rather than increase the correlation (Zimmerman & Williams, 2000 ).

In sum, there is a danger that adjustments for any of these parameters will over-correct, making the validity coefficients spuriously large (Wagner, 1994 ). As Hartigan and Wigdor ( 1989 ) stress, the device of using an average figure for population variance could lead to inflated corrections for restriction of range, and argue that, in the absence of clear information for each group, the safest thing is to apply no corrections.

As it is, Schmidt and Hunter's ( 1998 ) corrections inflate the correlations in their samples by 61% when combined with their correction for measurement unreliability. Hartigan and Wigdor's (1998) own estimates increased the correlation by only 12%, to 0.22, compared with Schmidt and Hunter's ( 1998 ) 0.51. Their critique has been taken up by other critical reviews in, for example, Cook ( 2009 ), McDaniel ( 2007 ), and Jencks ( 1998 ), reiterating their cautionary notes. There have been attempts to refine these correction methods (e.g., Le & Schmidt, 2006 ), albeit with further assumptions and approximations for missing data, and, therefore, the debate continues.

It needs to be emphasized, again, that the meta-analytic approach used in this area has been generally well accepted and even critics tend to urge cautions and further questions rather than complete dismissals. We now review these, try to add a few more, and stress the dangers of drawing strong conclusions. As Murphy ( 2003 ) says the “long and bitter controversy” over the use of these corrections in validity studies is partly due to the way that strong claims have been made from mixed primary data. Pointing to a number of statistical issues, Bobko and Roth ( 2003 ) similarly suggest that proponents of meta-analysis “may be a bit over-zealous in claims about what meta-analysis could or could not accomplish’, and that “caveats … are in order” (p. 68). The main problems stem from weaknesses and uncertainties in the primary data. Schmitt, Arnold, and Nieminen ( 2010 ) suggest boldly that the absence of data in most primary studies simply does not allow “for sample-based corrections for criterion unreliability or range restriction” (p. 66). Kaufman and Lichtenberger ( 2006 , p. 18) also warn against “incautious and, perhaps, overzealous corrections” of primary correlations.

Moreover, biases may have arisen from the fact that statistically significant findings, or ones that conform to previous results, are more likely to have been published than nonsignificant, or low effect, findings (known as the “file-drawer” problem; Field, 2007 ; Murphy, 2003 ). McDaniel, Rothstein, and Whetzel ( 2006 ) analyzed the validity claims in the technical manuals of four test providers that used supervisor ratings as criterion. They noted that two of the publishers tended to report only statistically significant correlations. We can only guess the extent to which this problem has affected results of meta-analyses.

More important, perhaps, is the problem of how to interpret the corrected correlations. Most uncritical readers have accepted corrected correlations as the “true” correlations. It is probably more prudent, however, to interpret them as theoretical maximum correlations given such weak samples and unreliable test instruments: “elevated idealized correlations rather than actual correlations” (Sternberg et al., 2001 , p. 10). Or, as Kaufman and Lichtenberger ( 2006 ) put it, “these corrections inflate the correlations by estimating their magnitudes in ‘what-if’ situations” (p. 18); for example, what the correlation might be in ideal conditions with perfectly reliable testing instruments, which do not exist.

Finally, nearly all studies are concurrent in design: instead of testing predictor at one age/time and then the criterion some time later the measures of both are usually taken more or less together. As Banks and McDaniel ( 2014 ) discovered, this may overestimate the validity “perhaps substantially.”

Note that similar claims have been made about correlations between IQ and training success in various occupations. Schmidt and Hunter ( 1998 ) indicate a correlation of 0.54, and that figure has been widely accepted (Bertua et al., 2005 ; James & Carretta, 2002 ). But they are subject to the same objections as those for job performance: the raw correlations are very low (around 0.2), doubled or more in the meta-analyses through estimated corrections. The most quoted results are from training in Forces personnel, whereas all meta-analyses include dozens of different tests, of varying psychometric standards, and many very old studies, dating as far back as the 1920s (e.g., Bertua et al., 2005 ).

As mentioned already, most of the studies incorporated into meta-analyses, from which the corrected correlations are widely cited, are pre-1970s. Some of the issues arising are illustrated in the report, already previously mentioned, by Hartigan and Wigdor ( 1989 ). This is the report of a Committee set up by the U.S. National Academy of Science to consider whether the U.S. Employment Service might promote the use of the GABT routinely throughout the country. Though broadly supportive, the committee's report critically commented on all the corrections reported in Hunter and Hunter ( 1984 ), based on the GATB, especially those based on assumptions not supported by available data.

As stated in Hartigan and Wigdor ( 1989 ), the 515 studies of Hunter and Hunter ( 1984 ) were conducted in the period 1945–1970: 10% in the 1940s; 40% each in the 1950s and 60 s; and 10% in the 1970s. However, a further 264 studies around the GATB were conducted after that and analyzed in the same report. As Hartigan and Wigdor ( 1989 ) note “The most striking finding … is a distinct diminution of validities in the newer, post 1972 set” (p. 150). These are described as “puzzling and obviously somewhat worrisome” (p. 160), and, therefore, other factors were considered. For example, the 264 newer studies have much larger average sample sizes (146 c.w. 75). It was shown how the larger samples produced much lower sampling error, requiring less correction. They also produced much lower variation with job family (or level of job complexity, see the following section). The more recent studies also exhibited less range restriction, also requiring less correction (with much less possibility of a false boost to observed correlations). These findings were supported by Jencks ( 1998 ) who noted that “GATB scores do not predict job performance very well”, and that “for reasons nobody understands, the GATB's ability to predict job performance has been falling” (p. 75).

Another explanation for the lower IQ/job performance correlations in the more recent years may lie in the general skill-upgrading of jobs, with reduced differences in the cognitive demands of occupations. This is, of course, also an explanation sometimes offered for the so-called “Flynn effect” concerning the substantial rise of average IQ scores over time (Flynn, 2007 ). The effects of reduced variance of output together with rising “inputs” may be inter-twined such as to also reduce the IQ/job-performance correlation over time. In addition, it may be that higher IQ test performance and more favorable job supervisor ratings both reflect a variety of mediating non-cognitive factors such as self-confidence (see further the following section).

Finally, it seems that even the weak IQ-job performance correlations usually reported in the United States and Europe are not universal. For example, Byington and Felps ( 2010 ) found that IQ correlations with job performance are “substantially weaker” in other parts of the world, including China and the Middle East, where performances in school and work are more attributed to motivation and effort than cognitive ability.

Based on their meta-analyses of studies using the GATB, Hunter and Hunter ( 1984 ) categorized jobs based on impressions of the complexity of cognition demanded. They claimed that the correlation between IQ and job performance is stronger in the more complex jobs. Much has been made of that claim in the many subsequent citations of it. Thus, Gottfredson ( 1997 ) said that, “An especially important observation is that predictive validities vary systematically according to the overall complexity of the work involved” (p. 82). On the basis of the same meta-analyses, Ones et al. ( 2012 , p. 189) reiterated that “relationships … are strongest for highly complex jobs (e.g., attornies, medical doctors, pilots). The validities in medium complex jobs are somewhat lower … (mostly in 0.50 s). Even for low-complexity jobs, criterion correlations are in the useful range (0.20 s)”. But how true is this inference? Is this what the data unequivocally show?

First of all, as already mentioned, the meta-analyses include studies that are very old with much missing data. The association may itself be an artefact of “corrections for artefacts” in such compromised empirical circumstances. Table ​ Table1 1 compares those correlations with others from the newer studies reported by Hartigan and Wigdor ( 1989 ). Although following a similar correction protocol as Hunter and Hunter, the newer correlations are remarkably uniform (and small) across all job complexity categories. When Hartigan and Wigdor corrected the newer 264 studies for only sampling error (because they were suspicious of the empirical justification for other corrections) the correlations were very low (0.06–0.07) and virtually identical across job families.

Note. UC = uncorrected; C = corrected.

Data from Hunter and Hunter ( 1984 ) and Hartigan and Wigdor ( 1989 ). In Hunter's two classification schemes I is “precision setup” group (e.g., machinist, cabinetmaker, metal fabricator); II is feeding-offbearing group (e.g., shrimp picker, corn-husking machine operator, cannery worker); III is “high complexity” (e.g., retail food manager, fish and game warden, biologist, city circulation manager); IV is “medium complexity” (e.g., automotive mechanic, radiologic technician, automotive parts counterman, high school teacher); V is “low complexity” (e.g., assembler, insulating machine operator, forklift truck operator).

Where the correlations do vary, however slightly, that may be attributed to other systematic effects across job categories. As previously noted, people do not, generally, perform as well as they could in most situations and supervisor ratings are likely to report typical rather than maximal performances, perhaps depending on working conditions. More complex jobs will usually offer more congenial working conditions and more equal relationships between managers and employees (indeed, many of them will be managers), thus ameliorating many of the psycho-situational variables such as stress and anxiety that can interfere with both test performance and job performance (see the following section). That is, workers are more likely to perform asymptotically in more congenial (i.e., higher class) jobs than in less congenial jobs, boosting the correlation between IQ and job performance. Jobs of different complexity will also vary systematically with other psychological attributes of testees and job situations. Testees are from distinct social class backgrounds associated with different levels of preparedness for both test and job performance. For example, higher-class jobs will usually be associated with important psychological attributes of testees, such as abundant self-esteem and self-efficacy beliefs (Bandura, 1997 ; Dweck, 2008 ). At those levels, testees are more likely to be from the same social class as their performance raters (with the bias effects described earlier). Conversely, it is observed that those in lower complexity/lower class jobs are likely to have less frequent and less skillful communication with supervisors (Guion, 1983 ).

Perhaps the biggest problem throughout this validation history has been the readiness with which correlations have been accepted as causes: that is, the inference that individual differences in IQ test performances really are differences in a general mental ability because they are associated with individual differences in job performance. Correlations are repeatedly described in terms of “the effects” (of whatever an IQ test measures on job performance), instead of mere statistical covariation that does not, of itself, reveal the source(s) of that covariation.

That the causes may be more complex than a unitary cognitive factor is indicated by a number of anomalies in the findings. Further analyses of inter-correlations between factors surrounding correlations between IQ and job performance (i.e., path analyses) have led to the suggestion that any causal effect of cognitive ability on job performance is indeed indirect. For example, Schmidt, Hunter, and Outerbridge ( 1986 ) found that supervisor ratings had virtually zero correlations with actual samples of work performance, as previously mentioned. However, they exhibited a correlation of 0.3 with subjects’ job knowledge. In an experimental study, using regression analyses, Palumbo et al. ( 2005 ) found that cognitive ability accounted for 12% of variance in performance, but this was completely mediated by the association between cognitive ability and job knowledge. They thus recommend replacing IQ tests with job knowledge, or job understanding, tests as better predictors of job performance.

As Wagner ( 1994 ) says, disentangling causal effects from these associations requires additional constructs. It could be, as Schmidt and Hunter ( 2004 ) argue, that “general mental ability” (GMA) is related to job performance because it determines speed of acquisition of job knowledge, as well as its complexity; that is, simply another expression of g . But, however plausible that argument, it means accepting that an already small (∼0.3) correlation between job performance and performance on a pencil and paper test of job knowledge is entirely determined by an uncharacterized construct ( g ), the test of which is still lacking in acceptable construct validity. This is what led Wagner to complain that “we appear to have been blinded by what we have termed the ‘g-eocentric’ view” (p. 137).

The danger is that of viewing job knowledge as, itself, a pure variable, when its acquisition is probably affected by a range of other variables, known and unknown. For example, individual job knowledge is likely to be a function of prior experience, irrespective of level of the hypothetical g , and degree of experience can influence both IQ test performance and supervisor ratings of performance. Indeed, organizations tend to look carefully at previous experience in selecting candidates for a job. Research suggests that prior experience, as expected, tends to have a positive effect on job performance; however, it can also, in some individuals, have a negative effect on performance via behavioral and cognitive rigidity (Dokko, Wilk, & Rothbard, 2008 ). There is, of course, much evidence that IQ test performance can be boosted by—presumably knowledge-based—experience with compatible cognitive tasks (e.g., Mackey, Hill, Stone, & Bunge, 2011 ; Moreno et al., 2011 ).

It is because of such doubts that alternative, or additional, causal pathways in the correlations between IQ (or job knowledge) and job performance have been explored. The possible role of motivation was mentioned above. But other affective and contextual factors have been considered in recent years. In his studies, Working with Emotional Intelligence, Goleman ( 2000 ) found that “67 percent - two out of three - of the abilities deemed essential for effective performance were emotional competencies. Compared to IQ and expertise, emotional competence mattered twice as much. This held true across all categories of jobs, and in all kinds of organizations” (p. 31) (however, see Landy, 2005 , for difficulties of testing).

According to Arthur and Villado ( 2008 ), the focus of personnel selection research is increasingly taking the “applicant perspective,” including “applicant reactions to selection systems, processes, methods, and decisions and the relationships of these reactions to outcomes, such as perceptions of fairness, face validity, test-taking motivation, test performance, and self-withdrawal from the selection process” (p. 435). These, too, may vary systematically, as previously noted. Similarly, the importance of work context on performance, as a crucial source of variance, has recently been studied, and shows the relationship between apparent ability and job performance to be remarkably labile. For example, Groysberg ( 2010 ), after examining the careers of more than a 1000 high performers (“star” analysts) on Wall Street, showed that those who change firms tend to suffer an immediate and lasting decline in performance. Performance seems to have depended more on their former firms’ organizational support, networks, and colleagues than the intellectual attributes of the individuals. This may partly explain why even the weak IQ-job performance correlations reported do not pertain outside of the United States and Europe, as previously mentioned.

Other factors can depress performance in both IQ tests and jobs below true ability. For social structural reasons, low-income parents “face a tax on their psychic resources” (Mullainathan, 2012 ). Testees/employees overwhelmed with worries about rent, feeding and clothing children, paying household bills, and reduced sense of control over circumstances, can suffer from a reduced “mental bandwidth” equivalent to a 13-point loss in IQ test performance (Mullainathan). They will also tend to have reduced motivation and self-confidence, and increased anxiety in both test and work situations. Ackerman and Heggestad ( 1997 ) reported a correlation of r  = −0.33 between test anxiety and performance. Raven, Raven, and Court ( 1993 , p. G14) note how fatigue, ill health and stress affect speed and accuracy on the RPM. In a meta-analysis Duckworth, Quinn, Lynamc, Loeberd, and Stouthamer-Loeberd ( 2010 ) showed that, after adjusting for test motivation the predictive validity of intelligence for life outcomes was significantly diminished, particularly for nonacademic out-comes. This means that those study participants will tend to perform below their best, or more erratically, on both predictor and criterion measures, thus lowering the correlation between them. Such considerations ought at least to moderate the strong claims usually made about the predictive validity of IQ tests drawn from correlations with job performance.

We have urged caution in using IQ-job performance correlations for supporting the validity of IQ tests. The vast bulk of that reliance is based on the results of meta-analyses combining studies of variable quality involving corrections and estimates that many have criticized. However, meta-analyses are generally well-respected techniques with many supporters. It may, therefore, be appropriate to consider the specific contentious topics arising within the context of this particular article (for wider discussion of “matters still at issue” see Guion, 2011 ).

The Diversity of Tests: Does It Matter?

Primary studies have often chosen the most convenient rather than the most appropriate tests, from simple reading or memory tests to the highly respected Raven. This diversity has been viewed in two ways. On the one hand, is the view that we cannot be sure what is being measured with such a variety of tests and with what psychometric properties, especially when combined in meta-analyses. On the other hand is the view that the emergence of significant predictive correlations, across a wide variety of tests demonstrates the robustness of the effect and, therefore, of conclusions from it.

Of course, it has to be remembered that many if not most of the primary studies report small and/or non-significant correlations, anyway: it is only their corrected composites in meta-analyses that can be called robust. In our view, there are two answers to the question. On the assumption of genuine and substantial correlations it can be said that the diversity of tests does not really matter, as long as the aim is mere prediction (after all a vast variety of other non-psychometric indices of job performance exist, including track record, interest inventories, language dialect, self-presentation, and so on). It certainly does matter, however, if the correlations are to be used for theoretical explanations of what is actually creating individual differences, as in developmental or career selection purposes or expensive genetic association studies—or for justifying the validity of IQ tests as measuring what we believe them to measure.

Such justification is based on the claim that, because scores on different tests inter-correlate to some extent, each test, however specialized, is also a measure of a general factor, g . Schmitt and Fandre ( 2008 ) suggest that “all are equally representative of a general factor g ” (p. 169). The psychological identity of that factor is, however, another matter. The inter-correlation between tests may be due to something different from what we think it is, especially when there is so much disagreement about the identity of g and human intelligence.

Identity of the Predictor Variable: g or Not g ?

Almost any glance at the literature confirms the level of such disagreements. Take, for example, recent contributions to the Cambridge Handbook of Intelligence (2011; see Sternberg & Kaufman, 2011 ). Davidson and Kemp ( 2011 ) note that “Few constructs are as mysterious and controversial as human intelligence”, and that “there is little consensus on what exactly it means … for one person to be more intelligent than another” (p. 59). They also suggest that this heterogeneity of views has increased in recent times. Urbina ( 2011 ) reviews some of the “excessive and unjustified meanings that the IQ label has acquired” (p. 22). Sternberg and Kaufman ( 2011 ) simply say “there has never been much agreement on what intelligence is” (p. xv).

Charles Spearman, the originator of the term g , believed that it reflected differences in ability for the “eduction of relations and correlates” (Spearman, 1927 ). Schmidt and Hunter ( 2003 ) define it as learning ability. This is consistent with Gottfredson's ( 2007 ) view that g reflects differences in “general capacity to learn and reason” and that “all mental tests measure mostly g , whatever their content” (para. 2). Mackintosh ( 2004 ) on the other hand, reminds us that “ g reflects no more (and no less) than the indisputable fact that scores on all IQ tests are positively correlated. Equally indisputably, however, we have little idea of the reason(s) for this positive manifold” (p. 217).

The problem with the “ g is learning ability” argument is that it cannot be measured independently of an instrument that requires rather specific learning that is or has been more available to some social classes or (sub-) cultures than others. Trying to distinguish the “learned” from the “learning potential” is impossible (Kaufman & Lichtenberger, 2006 ). Simply introducing nonverbal items is not enough. Indeed, analysis of the content of items like those in the Raven (supposedly the most g -loaded test) suggests that they are the most , not the least, dependent on specific learning (Richardson & Norgate, In press ). Because specific cultural tools (language, work, technologies, cultural methods, and practices) are the medium of all human transaction and learning, the very idea of a culture-free test is “a contradiction in terms … by its very nature, IQ testing is culture bound” (Cole, 1999 , p. 646).

In other words, testees can be more or less prepared for the test by having acquired knowledge and cognitive styles in cultural formats more or less distant from the specific format of most tests. We cannot distinguish cognitive “strength” from cognitive “distance.” Additionally, there is abundant evidence of ability for cognitive activity much more complex than that in IQ test items, verbal or nonverbal, in everyday activities of most people (Richardson & Norgate, In press ).

Identity of the Criterion Variable

Job performance may seem, superficially, to be a perfectly unambiguous and stable criterion of intelligence. More recent research has shown that notion to be too simple: job performance is a much more complex entity that varies with a host of tangible and intangible factors. According to Sackett and Lievens ( 2008 ) the recent trend is an emerging new view of job performance beyond a single unitary concept to a more differentiated model. We noted earlier the suggestion of Guion ( 2006 , pp. 268–269) for abandoning the pretense about objective or “hard” criteria of proficiency in performance. This is one reason why simple ratings, as in nearly all the IQ-job performance literature, need to be treated with skepticism.

Status of Meta-Analysis

As already mentioned, our concern is not meta-analysis per se , which, together with the innovations of Schmidt and Hunter, have become respected techniques, but with its more narrow application to IQ test validity. We simply draw attention to problems surrounding the quality of primary data, the legitimacy of corrections, and the strength of conclusions drawn from them, urging caution about questions where high precision is needed. The main issue surrounding the Schmidt and Hunter approach ( 1998 ; the main source of alleged IQ test validity) is the validity of the corrections. A number of those were previously mentioned in this article. Here, we can only emphasize how even strong supporters demur. McDaniel ( 2007 ) constructively reviews the many detailed demands of an adequate meta-analysis. It is clear that they are not fully met in the case of IQ and job performance. Banks and McDaniel ( 2014 ) note that data analysis techniques cannot overcome poor reporting practices in primary studies. Guion ( 2011 ) expresses serious doubts about using very old primary studies (which he refers to as “rancid data”). He emphasizes that validities (IQ-job performance correlations) can change over time; and suggests that “the early computer warning (garbage in, garbage out) seems equally applicable to meta-analysis” (p. 265). Sackett ( 2003 ) notes continuing controversy about the appropriate use of some reliability estimates in meta-analytic corrections. In spite of increased confidence with meta-analysis, generally, as Schmitt and Fandre ( 2008 ) say, it is obvious that “important gaps in our understanding of ability-performance relationships remain” (p. 167). Strangely, Schmidt and Hunter ( 1998 ) did not respond to the fundamental critique of Borsboom and Mellenbergh ( 2002 ) which has attracted much support in the literature. Humphreys ( 1986 ), perhaps, put it more simply: “Given the heterogeneity among the many studies to be aggregated, corrections … are rough estimates at best” (p. 427).

A “Large and Compelling Literature”

The impression of Furnham (previously cited) of “a large and compelling literature,” reporting essentially the same finding, is widely shared among readers of secondary reports. As a number of commentators have noted, at least some of the impression has been created by the fact that “Proponents of validity generalization have not been shy about making sweeping claims about the implications of their findings” (Murphy & Newman, 2003 , pp. 417–418). It may be unfortunate that such over-zealousness appears to have been carried over into IQ advocacy by psychologists.

The reality is of a handful of meta-analyses pooling hundreds of studies of variable quality (many very old, with missing data, and so on) corrected with many assumptions and estimates. A multiplicity of studies of variable standard is no substitute for properly conducted primary studies, with larger representative samples, clearer measures, and so on. Until they are done, we suggest the validity of IQ tests remains an open question, especially when there are alternative explanations.

Alternative Explanations

So what else could the correlations (such as they are), and the “positive manifold” among test performances be attributable to? One possibility is that both IQ and job performance reflect specific culturally-related learning, or cognitive preparedness, as already mentioned. Another is that the correlations could be entirely or partially non-cognitive in origin. Remember that a correlation is simply a measure of covariation of scores/ratings as reflected in degrees of deviation from respective means, without identifying the source of the covariation. Even covariations that are slight in relation to the respective measurement ranges can yield substantial correlations.

Many non-cognitive factors are known to jointly influence test performance and job performance such as to possibly yield such correlations. Levels of self-confidence, stress, motivation, and anxiety, and general physical and mental vigor, all affect cognitive test and job performances that will, therefore, tend to correlate (Derakshan & Eysenck, 2009 ; Dweck, 2008 ; Richardson & Norgate, In press ). In addition, “macrosocial differences in the distribution of economic goods are linked to microsocial processes of perceiving the self” (Loughnan et al., 2011 , p. 1254).

Turning the usual argument on its head, we suggest that inter-correlation of scores among such a diversity of tests actually suggests common noncognitive factors in operation. In other words, the “general factor” is (at least partly) an affective rather than a cognitive one. Factors of cognitive and affective preparedness could also explain the enigmatic Flynn effect (of rise of average IQ scores across generations), which cannot be explained by a general cognitive factor (Nisbett et al., 2012 ). However, the effect is readily explained by the demographic swelling of the middle classes in developing societies and the joint effects of better cognitive and affective preparedness (self-confidence, motivation, etc.).

So What Do We Get?

To supporters of IQ testing (as cited earlier) the picture seems crystal clear. Job performance must be a good test of individual differences in intelligence. IQ test scores (or their surrogates) correlate significantly with ratings of job performance. As a result, IQ tests must be a valid test of intelligence.

What we actually have are scores from a predictor of nebulous identity correlated with ratings for a seemingly discrete construct that is turning out to be equally slippery. In other words, very strong conclusions are seemingly being drawn from correlations between two under-specified constructs. This makes interpretation of the (modest) correlations extremely difficult. In primary studies such correlations have generally left over 95% of the variance unexplained (Kaufman & Lichtenberger, 2006 ). Even the typical meta-analytic correlation of 0.5 still leaves 75% of the variance unexplained. This does not seem to us to constitute grounds for asserting test validation so strongly.

SUMMARY AND CONCLUSION

Supporters of IQ testing have been quick to point to correlations between IQ and job performance as evidence of test validity. A closer look at the data and results, however, suggests a rather murkier picture. Here we have acknowledged the methodological advances in meta-analyses from which such evidence has been drawn, while drawing attention to the problems surrounding them in this particular area. We conclude with a summary of the main points:

• Much in developmental theory, and psychology in general, depends upon the validity of IQ tests.
• In the absence of agreed construct validity this has weighed heavily on indirect validity using correlations with criterion outcomes among which job performance has a special status.
• Hundreds of studies prior to the 1970s reported low and/or inconsistent correlations between IQ and job performance.
• These correlations have been approximately doubled using corrections for supposed errors in primary results and combining them in meta-analyses. Such corrections have many strengths, theoretically, but are compromised in these cases by the often uncertain quality of the primary studies.
• The corrections to sampling errors, measurement errors, and to range restriction have required making a number of assumptions that may not be valid and have created a number of persistently contentious issues.
• The claim that the IQ-job performance correlation increases with job complexity is not born out in more recent studies.
• A range of other—including noncognitive—factors could explain a correlation between IQ and job performance, and even constitute part or all of the enigmatic “general factor.”
• There remains great uncertainty about the interpretation of IQ-job performance correlations and great caution needs to be exercised in using them as a basis for the validity of IQ tests and associated concepts.

As others have pointed out, statistical corrections are no magical compensation for weak data and that it is risky to reach conclusions about test validities from those currently available (Oswald & McCloy, 2003 ; Russell & Gilliland, 1995 ). The only solution is properly conducted primary studies, with larger representative samples, better measures, and so on. Until they are available, investigators should be extremely cautious about disseminating conclusions about IQ test validities, from correlations between IQ and job performance.

• Ackerman P. L., Heggestad E. D. Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin. 1997; 121 :219–245. doi: 10.1037//0033-2909.121.2.219. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arthur W., Jr., Villado A. J. The importance of distinguishing between constructs and methods when comparing predictors in personnel selection research and practice. Journal of Applied Psychology. 2008; 93 :435–442. doi: 10.1037/0021-9010.93.2.435. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bandura A. Self-efficacy: The exercise of control. New York, NY: Freeman; 1997. [ Google Scholar ]
• Banks G. C., McDaniel M. A. Meta-analysis as a validity summary tool. In: Schmitt N., editor. The Oxford handbook of personnel assessment and selection. Oxford, England: Oxford University Press; 2014. pp. 156–175. [ Google Scholar ]
• Berry C. M., Clark M. A., McClure T. K. Racial/ethnic differences in the criterion-related validity of cognitive ability tests: A qualitative and quantitative review. Journal of Applied Psychology. 2011; 96 :881–906. doi: 10.1037/a0023222. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bertua C., Anderson N., Salgado J. F. The predictive validity of cognitive ability tests: A UK meta-analysis. Journal of Occupational and Organizational Psychology. 2005; 78 :387–409. doi: 10.1348/096317905X26994. [ CrossRef ] [ Google Scholar ]
• Bobko P., Roth P. L. Meta-analysis and validity generalization as research tools: Issues of sample bias and degrees of mis-specification. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 67–90. [ Google Scholar ]
• Bommer W. H., Johnson J. L., Rich G. A., Podsakoff P. M., Mackenzie S. B. On the interchangeability of objective and subjective measures of employee performance: A meta-analysis. Personnel Psychology. 1995; 48 :587–605. doi: 10.1111/j.1744-6570.1995.tb01772.x. [ CrossRef ] [ Google Scholar ]
• Borsboom D., Mellenbergh G. J. True scores, latent variables, and constructs: A commentary on Schmidt and Hunter. Intelligence. 2002; 30 :505–514. doi: 10.1016/s0160-2896(02)00082-x. [ CrossRef ] [ Google Scholar ]
• Borsboom D., Mellenbergh G. J., van Heerden J. The concept of validity. Psychological Review. 2005; 111 :1061–1071. [ PubMed ] [ Google Scholar ]
• Burke M. J., Landis R. S. Methodological and conceptual challenges in conducting and interpreting meta-analyses. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 287–310. [ Google Scholar ]
• Byington E., Felps W. Why do IQ scores predict job performance? An alternative, sociological explanation. Research in Organizational Behavior. 2010; 30 :175–202. doi: 10.1016/j.riob.2010.08.003. [ CrossRef ] [ Google Scholar ]
• Carpenter P. A., Just M. A., Shell P. What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review. 1990; 97 :404–431. doi: 10.1037//0033-295x.97.3.404. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cole M. Culture-free versus culture-based measures of cognition. In: Sternberg R. J., editor. The nature of cognition. Cambridge, MA: MIT Press; 1999. [ Google Scholar ]
• Cook M. Personnel selection: Adding value through people. London: Wiley; 2009. [ Google Scholar ]
• Davar S. C. Meta-analysis: An improved version of Hunter, Schmidt and Jackson framework. Decision. 2004; 31 :1–10. [ Google Scholar ]
• Davidson J. E., Kemp I. A. In: The Cambridge handbook of intelligence. Sternberg R. J., editor; Kaufman S. B., editor. Cambridge, UK: Cambridge University Press; 2011. pp. 58–83. [ Google Scholar ]
• Deary I. J. Intelligence: A very short introduction. Oxford, England: Oxford University Press; 2001. [ Google Scholar ]
• Derakshan N., Eysenck M. W. Anxiety, processing efficiency, and cognitive performance: New developments from attentional control theory. European Psychologist. 2009; 14 :168–176. doi: 10.1027/1016-9040.14.2.168. [ CrossRef ] [ Google Scholar ]
• DeShon R. P. A generalizability theory perspective on measurement error corrections in validity generalization. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 365–402. [ Google Scholar ]
• Dokko G., Wilk S. L., Rothbard N. P. Unpacking prior experience: How career history affects job performance. Organization Science. 2008; 20 :51–68. doi: 10.1287/orsc.1080.0357. (published online June 20, 2008) [ CrossRef ] [ Google Scholar ]
• Drasgow F. Intelligence and the workplace. In: Weiner I. B., Schmitt N. W., Highouse S., editors. Handbook of psychology, industrial and organizational psychology. London, England: Wiley; 2012. [ Google Scholar ]
• Duckworth A. L., Quinn P. B., Lynam D. R., Loeber R., Stouthamer-Loeber M. Role of test motivation in intelligence testing. Proceedings of the National Academy of Sciences United States of America. 2010; 108 :7716–7720. doi: 10.1073/pnas.1018601108. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dweck C. Mindset: The new psychology of success. New York, NY: Random House; 2008. [ Google Scholar ]
• Field A. P. Can meta-analysis be trusted? The Psychologist. 2007; 16 :642–645. [ Google Scholar ]
• Flynn J. R. What is intelligence? Cambridge, UK: Cambridge University Press; 2007. pp. 7–36. [ Google Scholar ]
• Furnam A. Intelligence and cognitive abiloities at work. In: Cartwright S., Cooper C. L., editors. Oxford handbook of personnel psychology. Oxford, England: Oxford University Press.; 2008. pp. 7–36. [ Google Scholar ]
• Galton F. Inquiry into human faculty and its development. London, England: Macmillan; 1883. [ Google Scholar ]
• Ghiselli E. E. The validity of aptitude tests in personnel selection. Personnel Psychology. 1973; 26 :461–477. doi: 10.1111/j.1744-6570.1973.tb01150.x. [ CrossRef ] [ Google Scholar ]
• Goleman D. Working with emotional intelligence. New York, NY: Random House; 2000. [ Google Scholar ]
• Gottfredson L. S. Performance assessment for the workplace, vol II: Technical issues. New York, NY: National Academy Press.; 1991. The evaluation of alternative measures of job performance. In Commission on Behavioral and Social Sciences and Education (Ed.) pp. 75–126. [ Google Scholar ]
• Gottfredson L. S. Why g matters: The complexity of everyday life. Intelligence. 1997; 24 :79–132. [ Google Scholar ]
• Gottfredson L. S. Shattering logic to explain the Flynn effect. Cato Unbound. 2007 Retrieved from http://www.cato-unbound.org/2007/11/08/linda-s-gottfredson/shattering-logic-to-explain-the-flynn-effect/ [ Google Scholar ]
• Green C. S., Bavelier D. Learning, attentional control, and action video games. Current Biology. 2012; 22 :R197–R206. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Groysberg B. Chasing stars: The myth of talent and the portability of performance. Princeton, NJ: Princeton University Press; 2010. [ Google Scholar ]
• Guion R. M. Comments on Hunter. In: Landy F., Zedeck S., Cleveland J., editors. Performance measurement and theory. Hillsdale, NJ: Lawrence Erlbaum; 1983. pp. 267–275. [ Google Scholar ]
• Guion R. M. Assessment, measurement, and prediction for personnel decisions. Hillsdale, New Jersey: Lawrence Erlbaum Publishers; 2006. [ Google Scholar ]
• Guion R. M. Assessment, measurement, and prediction for personnel decisions. Hillsdale, NJ: Lawrence Erlbaum; 2011. [ Google Scholar ]
• Haier R. J., Colom R., Schroeder D. H., Condon C. A., Tang C., Eaves E., Head K. Gray matter and intelligence factors: Is there a neuro-g? Intelligence. 2009; 37 :136–144. doi: 10.1016/j.intell.2008.10.011. [ CrossRef ] [ Google Scholar ]
• Hartigan J. A., Wigdor A. K. Fairness in employment testing: Validity generalization, minority issues and the General Aptitude Test Battery. Washington, DC: National Academic Press; 1989. [ Google Scholar ]
• Heneman R. L. The relationship between supervisory ratings and results-oriented measures of performance: A meta-analysis. Personnel Psychology. 1986; 39 :811–826. doi: 10.1111/j.1744-6570.1986.tb00596.x. [ CrossRef ] [ Google Scholar ]
• Hosoda M., Stone-Romero E. F., Coats G. The effects of physical attractiveness on job-related outcomes: A meta-analysis of experimental studies. Personnel Psychology. 2003; 56 :431–462. doi: 10.1111/j.1744-6570.2003.tb00157.x. [ CrossRef ] [ Google Scholar ]
• Humphreys L. G. Commentary. Journal of Vocational Behavior. 1986; 29 :421–437. doi: 10.1016/0001-8791(86)90018-7. [ CrossRef ] [ Google Scholar ]
• Hunter J. E. Cognitive ability, cognitive aptitude, job knowledge, and job performance. Journal of Vocational Behavior. 1986; 29 :340–362. [ Google Scholar ]
• Hunter J. E., Hunter R. F. Validity and utility of alternative predictors of job performance. Psychological Bulletin. 1984; 96 :72–98. doi: 10.1037/0033-2909.96.1.72. [ CrossRef ] [ Google Scholar ]
• Hunter J. E., Schmidt F. L. The economic benefits of personnel selection using psychological ability tests. American Psychologist. 1983; 38 :473–478. [ Google Scholar ]
• Hunter J. E., Schmidt F. L. Methods of meta-analysis: Correcting error and bias in research findings. Beverly Hills, CA: Sage; 1990. [ Google Scholar ]
• Hunter J. E., Schmidt F. L., Jackson G. Meta-analysis: Cumulating research findings across studies. Beverly Hills, CA: Sage; 1982. [ Google Scholar ]
• James M., Carretta T. R. g2 K. Human Performance. 2002; 15 :3–23. doi: 10.1080/08959285.2002.9668081. [ CrossRef ] [ Google Scholar ]
• Jencks C. Racial bias in testing. In: Jencks C., Phillips M., editors. The black-white test score gap. New York, NY: Brookings Institution Press.; 1998. pp. 55–85. [ Google Scholar ]
• Judge T. A., Cable D. M. The effect of physical height on workplace success and income: Preliminary test of a theoretical model. Journal of Applied Psychology. 2004; 89 :428–441. doi: 10.1037/0021-9010.89.3.428. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaufman A. S., Lichtenberger E. O. Assessing adolescent and adult intelligence. (3rd ed.) London, England: Wiley; 2006. [ Google Scholar ]
• Kovas Y., Haworth C. M., Dale P. S., Plomin R. The genetic and environmental origins of learning abilities and disabilities in the early school years. Monographs of the Society for Research in Child Development. 2007; 72 :vii, 1–144.. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kuncel N. R., Hezlett S. A. Fact and fiction in cognitive ability testing for admissions and hiring decisions. Current Directions in Psychological Science. 2010; 19 :339–345. doi: 10.1177/0963721410389459. [ CrossRef ] [ Google Scholar ]
• Landy F. J. Validity generalization: Then and now. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 155–196. [ Google Scholar ]
• Landy F. J. Some historical and scientific issues related to research on emotional intelligence. Journal of Organizational Behavior. 2005; 26 :411–424. doi: 10.1002/job.317. [ CrossRef ] [ Google Scholar ]
• Lang J. W. B., Kersting M., Hülsheger U. R., Lang J. General mental ability, narrower cognitive abilities, and job performance: The perspective of the nested-factors model of abilities. Personnel Psychology. 2010; 63 :595–640. doi: 10.1111/j.1744-6570.2010.01182.x. [ CrossRef ] [ Google Scholar ]
• Le H., Schmidt F. L. Correcting for indirect range restriction in meta-analysis: Testing a new meta-analytic procedure. Psychological Methods. 2006; 11 :416–438. doi: 10.1037/1082-989x.11.4.416. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Loughnan S., Kuppens P., Allik J., Balazs K., de Lemus S., Dumont K., … Haslam N. Economic inequality is linked to biased self-perception. Psychological Science. 2011; 22 :1254–1258. doi: 10.1177/0956797611417003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mackintosh N. J. IQ and human intelligence. Oxford, UK: Blackwell; 2004. [ Google Scholar ]
• Mackintosh N. J. Intelligence and its measurement: 1. History of theories and measurement of intelligence. In: Sternberg R. J., Kaufman S. B., editors. The Cambridge handbook of intelligence. Cambridge, England: Cambridge University Press.; 2011. pp. 1–19. [ Google Scholar ]
• Mackey A. P., Hill S. S., Stone S. I., Bunge S. A. Differential effects of reasoning and speed training in children. Developmental Science. 2011; 14 :582–590. doi: 10.1111/j.1467-7687.2010.01005.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Marcus B., Goffin R. D., Johnston N. G., Rothstein M. G. Personality and cognitive ability as predictors of typical and maximum managerial performance. Human Performance. 2007; 20 :275–285. doi: 10.1080/08959280701333362. [ CrossRef ] [ Google Scholar ]
• McDaniel M. A. Validity generalization as a test validation approach. In: McPhail S. M., editor. Alternative validation strategies. San Francisco, CA: Jossey-Bass; 2007. pp. 159–180. [ Google Scholar ]
• McDaniel M. A., Rothstein H. R., Whetzel D. L. Publication bias: A case study of four test vendors. Personnel Psychology. 2006; 59 :927–953. doi: 10.1111/j.1744-6570.2006.00059.x. [ CrossRef ] [ Google Scholar ]
• McGrew K. S. The Cattell-Horn-Carroll theory of cognitive abilities: Past, present, and future. In: Flanagan D. P., Genshaft J. L., Harrison P. L., editors. Contemporary intellectual assessment: Theories, tests, and issues. New York, NY: Guilford; 2005. pp. 136–182. [ Google Scholar ]
• Moreno S., Bialystok E., Barac R., Schellenberg E. G., Cepeda N. J., Chau T. Short-term music training enhances verbal intelligence and executive function. Psychological Science. 2011; 22 :1425–1433. doi: 10.1177/0956797611416999. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mullainathan S. Stress impacts on good parenting. 2012. W. K. Kellogg Foundation Annual Report. Cambridge, MA: Kellogg Foundation. [ Google Scholar ]
• Murphy K. R. The logic of validity generalization. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum.; 2003. pp. 1–30. [ Google Scholar ]
• Murphy K. R., Balzer W. K. Systematic distortions in memory-based behavior ratings and performance evaluations: Consequences for rating accuracy. Journal of Applied Psychology. 1986; 71 :39–44. doi: 10.1037//0021-9010.71.1.39. [ CrossRef ] [ Google Scholar ]
• Murphy K. R., DeShon R. Interrater correlations do not estimate the reliability of job performance ratings. Personnel Psychology. 2000; 53 :873–900. doi: 10.1111/j.1744-6570.2000.tb02421.x. [ CrossRef ] [ Google Scholar ]
• Murphy K. R., Newman D. A. The past, present and future of validity generalization. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 403–424. [ Google Scholar ]
• Neisser U., Boodoo G., Bouchard T. J., Jr., Boykin A. W., Brody N., Ceci S. J., … Urbina S. Intelligence: Knowns and unknowns. American Psychologist. 1996; 51 :77–101. doi: 10.1037/0003-066X.51.2.77. [ CrossRef ] [ Google Scholar ]
• Nisbett R. E. Intelligence and how to get it: Why schools and cultures count. New York, NY: Norton; 2009. [ Google Scholar ]
• Nisbett R. E., Aronson J., Blair C., Dickens W., Flynn J., Halpern D. F., Turkheimer E. Intelligence: New findings and theoretical developments. American Psychologist. 2012; 67 :130–159. doi: 10.1037/a0026699. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Olea M., Ree M. J. Predicting pilot and navigational error: Little more than g . Journal of Applied Psychology. 1994; 79 :845–849. [ Google Scholar ]
• Olson D. R. Technology and intelligence in a literate society. In: Sternberg R. J., Preiss D., editors. Intelligence and technology: The impact of tools on the nature and development of human abilities. Hillsdale, NJ: Erlbaum.; 2005. pp. 3–67. [ Google Scholar ]
• Ones D. S., Dilchert S., Viswesvaran C. Cognitive ability. In: Schmitt N., editor. Oxford handbook of personnel assessment and selection. Oxford, England: Oxford University Press.; 2012. pp. 179–224. [ Google Scholar ]
• Ones D. S., Viswesvaran C., Dilchert S. Cognitive ability in personnel selection decisions. In: Evers A., Anderson N., Voskuijl O., editors. The Blackwell handbook of personnel selection. Oxford, England: Blackwell Publishing.; 2005. pp. 331–353. [ Google Scholar ]
• Oswald F. L., McCloy R. A. Meta-analysis and the art of the average. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum.; 2003. [ Google Scholar ]
• Palumbo M. V., Miller C. E., Shalin V. L., Steele-Johnson D. The impact of job knowledge in the cognitive ability-performance relationship. Applied Human Resource Management. 2005; 10 :13–20. [ Google Scholar ]
• Plomin R. Child development and molecular genetics: 14 years later. Child Development. 2013; 84 :104–120. doi: 10.1111/j.1467-8624.2012.01757.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Plomin R., Rutter M. Child development, molecular genetics, and what to do with genes once they are found. Child Development. 1998; 69 :1223–1242. doi: 10.2307/1132371. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Raven J., Raven J. C., Court J. H. Manual for Raven's progressive matrices and vocabulary scales, section 1. Oxford, England: Oxford Psychologists Press; 1993. [ Google Scholar ]
• Richardson K. What IQ tests test. Theory and Psychology. 2002; 12 :283–314. [ Google Scholar ]
• Richardson K., Norgate S. H. In press. Does IQ measure ability for complex cognition? Theory and Psychology . Retrieved from http://tap.sagepub.com/content/early/2014/10/09/0959354314551163 . [ Google Scholar ]
• Roth P. L., Bobko P., McFarland L. A. A meta-analysis of work sample test validity: Updating and integrating some classic literature. Personnel Psychology. 2005; 58 :1009–1037. doi: 10.1111/j.1744-6570.2005.00714.x. [ CrossRef ] [ Google Scholar ]
• Russell C. J., Gilliland S. W. Why meta-analysis doesn't tell us what the data really mean: Distinguishing between moderator effects and moderator processes. Journal of Management. 1995; 21 :813–831. doi: 10.1016/0149-2063(95)90012-8. [ CrossRef ] [ Google Scholar ]
• Sackett P. R. The status of validity generalization research: Key issues in drawing inferences from cumulative research findings. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. pp. 91–114. [ Google Scholar ]
• Sackett P. R., Lievens F. Personnel selection. Annual Review of Psychology. 2008; 59 :419–450. doi: 10.1146/annurev.psych.59.103006.093716. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Salgado J. F., Anderson N., Moscoso S., Bertua C., de Fruyt F., Rolland J. P. A meta-analytic study of general mental ability validity for different occupations in the European community. Journal of Applied Psychology. 2003; 88 :1068–1081. doi: 10.1037/0021-9010.88.6.1068. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schmidt F. L., Hunter J. E. Development of a general solution to the problem of validity generalization. Journal of Applied Psychology. 1977; 62 :529–540. [ Google Scholar ]
• Schmidt F. L., Hunter J. E. The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin. 1998; 124 :262–274. doi: 10.1037//0033-2909.124.2.262. [ CrossRef ] [ Google Scholar ]
• Schmidt F. L., Hunter J. E. History, development, evolution, and impact of validity generalization and meta-analysis methods. 1975–2001. In: Murphy K. R., editor. Validity generalization: A critical review. Hove, England: Erlbaum; 2003. [ Google Scholar ]
• Schmidt F. L., Hunter J. E. General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology. 2004; 86 :162–173. [ PubMed ] [ Google Scholar ]
• Schmidt F. L., Hunter J. E., Outerbridge A. N. Impact of job experience and ability on job knowledge, work sample performance and supervisory ratings of job performance. Journal of Applied Psychology. 1986; 71 :432–439. doi: 10.1037//0021-9010.71.3.432. [ CrossRef ] [ Google Scholar ]
• Schmitt N., Arnold J. D., Nieminen L. Validation strategies for primary studies. In: Farr J. L., Tippins N. T., editors. Handbook of employee selection. London, England: Routledge; 2010. pp. 51–68. [ Google Scholar ]
• Schmitt N., Fandre J. Validity of selection methods. In: Cartwright S., Cooper C. L., editors. Oxford handbook of personnel psychology. Oxford, England: Oxford University Press.; 2008. [ Google Scholar ]
• Schmitt N., Gooding R. Z., Noe R. A., Kirsch M. Metaanalyses of validity studies published between 1964 and 1982 and the investigation of study characteristics. Personnel Psychology. 1984; 37 :407–422. doi: 10.1111/j.1744-6570.1984.tb00519.x. [ CrossRef ] [ Google Scholar ]
• Spearman C. The abilities of man. London, UK: Macmillan; 1927. [ Google Scholar ]
• Stauffer J. M., Buckley M. R. The existence and nature of racial bias in supervisory ratings. Journal of Applied Psychology. 2005; 90 :586–591. doi: 10.1037/0021-9010.90.3.586. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sternberg R. J., Grigorenko E., Bundy D. A. The predictive value of IQ. Merrill-Palmer Quarterly. 2001; 47 :1–41. doi: 10.1353/mpq.2001.0005. [ CrossRef ] [ Google Scholar ]
• Sternberg R. J., Kaufman S. B. Preface. In: Sternberg R. J., Kaufman S. B., editors. The Cambridge handbook of intelligence. Cambridge, UK: Cambridge University Press; 2011. pp. xv–xix. [ Google Scholar ]
• Stewart G. L., Nadkeolyar A. K. Adaptation and intraindividual variation in sales outcomes: Exploring the interactive effects of personality and environmental opportunity. Personnel Psychology. 2006; 59 :307–332. doi: 10.1111/j.1744-6570.2006.00038.x. [ CrossRef ] [ Google Scholar ]
• Thorndike R. L., Hagen E. P. Measurement and evaluation in psychology and education. New York, NY: Wiley; 1969. [ Google Scholar ]
• Urbina S. Tests of intelligence: Their whye and wherefores. In: Sternberg R. J., Kaufman S. B., editors. The Cambridge handbook of intelligence. Cambridge, UK: Cambridge University Press.; 2011. [ Google Scholar ]
• Vinchur A. J., Schippmann J. S., Switzer F. S., Roth P. L. A meta-analytic review of predictors of job performance for salespeople. Journal of Applied Psychology. 1998; 83 :586–597. doi: 10.1037//0021-9010.83.4.586. [ CrossRef ] [ Google Scholar ]
• Viswesvaran C., Ones D. S., Schmidt F. L. Comparative analysis of the reliability of job performance ratings. Journal of Applied Psychology. 1996; 81 :557–574. doi: 10.1037//0021-9010.81.5.557. [ CrossRef ] [ Google Scholar ]
• Wagner R. K. Context counts: The case of cognitive ability testing for job selection. In: Sternberg R. J., Wagner R. K., editors. Mind in context: Interactionist perspectives on human intelligence. Cambridge, England: Cambridge University Press.; 1994. pp. 133–151. [ Google Scholar ]
• Wai J., Ptallaz M. The Flynn effect puzzle: A 30-year examination from the right tail of the ability distribution provides some missing pieces. Intelligence. 2011; 39 :443–455. doi: 10.1016/j.intell.2011.07.006. [ CrossRef ] [ Google Scholar ]
• Zimmerman D. W., Williams R. H. Restriction of range and correlation in outlier prone distributions. Applied Psychological Measurement. 2000; 24 :267–280. doi: 10.1177/01466210022031741. [ CrossRef ] [ Google Scholar ]

• Announcements
• About AOSIS

SA Journal of Human Resource Management

• Editorial Team
• Submission Procedures
• Submission Guidelines
• Submit and Track Manuscript
• Publication fees
• Make a payment
• Journal Information
• Journal Policies
• Frequently Asked Questions
• Reviewer Guidelines
• Article RSS
• Support Enquiry (Login required)
• Aosis Newsletter

Department of Industrial Psychology and People Management.

Open Journal Systems

Mohammed Al-Haziazi Arab Open University, Muscat, Oman Al-Haziazi, M. (2024). Critical analysis of drivers of employee engagement and their impact on job performance. (0), a2633. 14 Apr. 2024; 09 July 2024; 30 Aug. 2024 © 2024. The Author(s). Licensee: AOSIS. This is an Open Access article distributed under the terms of the , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study examines the impact of drivers of employee engagement on job performance and investigates the relationship between employee engagement and job performance. The purpose of this study is to assess how various factors, such as job characteristics, organisational support, support from superiors, rewards, recognition, and organisational justice, influence employee engagement and subsequently affect job performance. The researcher is motivated by the need to understand the drivers of employee engagement and their implications for job performance in organisations, particularly in the context of the Sultanate of Oman. The study was conducted based on a closed-ended questionnaire across various industries in the Sultanate of Oman, focusing on three levels of management: junior, middle, and senior. Non-probability convenience sampling was utilised. The study employed models of drivers leading to employee engagement and assessed their impact on job performance. The study reveals that drivers of employee engagement significantly affect job performance across all levels of management. Job characteristics and rewards and recognition emerged as strong predictors of job performance. Organisations are encouraged to prioritise the development and nurturing of employee engagement, fostering a two-way relationship between employers and employees. Engaged employees contribute to higher retention rates, increased productivity, profitability, growth, and customer satisfaction. This study provides valuable insights into the relationship between employee engagement and job performance in the Sultanate of Oman, offering guidance for the development of effective employee engagement strategies aimed at improving organisational outcomes. employee engagement; job performance; productivity; relationship; rewards. In today’s competitive business environment, organisations recognise the fact that their most valuable asset is their workforce. The strategic management of human resources has emerged as a key differentiator, enabling companies to adapt, innovate, and excel (Barney & Wright, ). As markets become increasingly complex, investing in employee engagement is crucial for sustaining high performance and achieving long-term success (Alam et al., ; Barreiro & Treglown, ; Saks, ). Human resources bring a competitive advantage by contributing knowledge, skills, and capabilities to an organisation (Hafiza et al., ). Employee engagement is an important predictor of job performance (Christian et al., ). Engaged employees are more likely to contribute to a high-performance organisation (Mishra et al., ). Organisations constantly seek solutions to motivate their employees to be more engaged in their work (Cole et al., ). Engaged employees are more efficient and productive, add to the top line, and are more likely to stay with the company (Dabke & Patole, ). Engagement refers to the extent of emotional and intellectual dedication an employee demonstrates towards their organisation and its achievements. Engaged employees are inclined to speak favourably about the organisation, exhibit greater retention rates, and contribute to its daily effectiveness (Mishra et al., ). Their profound commitment to their employers precipitates significant enhancements in business outcomes, such as decreased absenteeism, turnover, shrinkage, safety incidents, and product defects. Employee engagement is continuous and highly specific to each organisation (Gupta & Sharma, ). Mohapatra and Sharma ( ) believed that an organisation and its staff have a synergetic bond in which they depend on each other to achieve their desires. Engagement must then be an ongoing process rather than an individual event. Employee engagement can also contribute to organisational success. Having satisfied employees who perform well, are in the right jobs, and are present and committed helps foster engagement (Bin & Shmailan, ). Employee engagement and performance outcomes are interconnected; heightened levels of employee engagement correspond to increased feelings of belongingness, enthusiasm, passion, and work knowledge. Consequently, this fosters improved employer–employee relations, resulting in reduced confusion, fewer conflicts, decreased absenteeism, lower turnover rates, and enhanced role comprehension. This role of knowledge increases effectiveness and efficiency and leads employees to take up extra work or duties to further the organisation’s performance and reputation, expediting its process of advancement (Tanwar, ). According to Shuck and Wollard ( ), employee engagement is an ‘emergent working condition and a positive cognitive, emotional and behavioural state directed toward organisational outcomes’. Studies on employee engagement have become important in recent academic research because organisations face challenges in improving the performance and productivity of employees from different generations (Douglas & Roberts, ). This situation poses a significant challenge for both academic researchers in organisational studies and professionals in the field concerning how to improve employee engagement, which is believed to influence organisational performance and outcomes (Harter et al., ). Therefore, the purpose of this study is to assess the impact of employee engagement factors on job performance, investigate the relationship between employee engagement and job performance, and suggest practices to improve employee engagement in the Sultanate of Oman. Many researchers have tried to identify drivers of employee engagement and developed models to draw implications for managers. In this study, the author has developed a conceptual model consisting of job characteristics, organisational support, rewards and recognition, and organisational justice, all of which lead to employee engagement and contribute to job performance (see ). Drivers of employees’ engagement and contribution to job performance. Gallup characterises employee engagement as the active participation in and passion for one’s work (Markos & Sridevi, 2010 ; Turner & Turner, 2020 ). Employee engagement entails a favourable disposition exhibited by the employee towards the organisation and its principles. A fully engaged employee comprehends the business environment and collaborates with peers to enhance job efficacy for the organisation’s advancement. Fostering engagement demands concerted efforts from the organisation, necessitating a two-way relationship between employers and employees (Robinson et al. 2004). The connection between employee engagement and important business results is considerable. Studies have revealed a positive link between employee engagement and organisational performance outcomes (Markos & Sridevi, 2010 ). Job characteristics are regarded as the ‘system factors’ that can impact employees’ behavioural outcomes (Williams, 2002 ). This is because of the influence of job attractiveness on the level of effort that employees are willing to invest in their job responsibilities (Johari & Yahya, 2016 ). Empirical evidence (Christen et al. 2006 ; Grant, 2008 ; Wood et al., 2012 ) has demonstrated a significant and direct influence of job characteristics on job performance. According to Organisational Support Theory, employees develop overall perceptions concerning the extent to which their organisations furnish sufficient resources and appreciate them as individuals, encompassing the probability of the organisation rewarding their performance and assisting them during difficult circumstances (Cullen et al., 2014 ). A positive perception of the support employees receive from an organisation contributes to beneficial outcomes for both the employees and the organisation itself. Additionally, organisational support is associated with increased levels of job satisfaction and enhanced performance. It increases performance in standard work-related activities, helps surpass the predetermined standards, and increases organisational identification significantly (Köse, 2016 ; Turunç & Çelik, 2010 ). Research has shown that frontline supervision plays a pivotal role in fostering employee engagement, underscoring the significance of proficient communication and managerial support (Mishra et al., 2014 ). Sparrowe and Liden ( 2005 ) acknowledged the fact that the quality of the relationship between supervisors and subordinates correlates with engagement. Similarly, Brunetto et al. ( 2013 ) proposed that the supervisor–subordinate relationship affects teamwork quality, which in turn positively influences engagement levels. Hafiza et al. ( 2011 ) found that reward systems increase employee satisfaction, which directly influences performance. According to San et al. ( 2012 ), if an organisation fails to reward employees, employee performance will decrease; furthermore, an efficient reward system can be a good motivator, but an inefficient reward system demotivates employees and causes low productivity, internal conflicts, absenteeism, high turnover, a lack of commitment and loyalty, lateness, and grievances. Therefore, organisations must develop strategic reward systems to retain competent employees and maintain a competitive advantage (Edirisooriya, 2014 ). Ajila and Abiola ( 2004 ) concluded that reward systems increase employee performance by enhancing skills, knowledge, and abilities to achieve organisational objectives. Conversely, the impact of organisational justice may be contingent upon cultural context and could have a diminished role in fostering employee engagement within Eastern cultures, where leaders tend to adopt a more directive approach in decision-making processes (He et al., 2014 ). Organisational justice refers to an employee’s perceptions of their organisation’s policies and procedures (Loi et al., 2012 ). According to research by Brebels et al. ( 2011 ), fairness in the workplace is a significant factor that encourages cooperative behaviour and improves job performance. Conversely, as noted by Skarlicki et al. ( 2008 ), a perceived lack of fairness can result in harmful and unethical behaviours like retaliation. Research design Research approach. This research was conducted within various industries in the Sultanate of Oman. Data collection, processing, and analysis were carried out from April 2023 to June 2023. Primary and secondary data are used in this research. The primary data were collected through a closed-ended questionnaire, and the secondary data were obtained through relevant literature. Research method The primary method utilised in this study was a closed-ended questionnaire. This questionnaire focused on evaluating several drivers of employee engagement, including job characteristics, organisational support, support from superiors, rewards and recognition, and organisational justice. Respondents were asked to rate their agreement with each statement on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire included 35 statements, and it was pretested. Cronbach’s alpha was found to be 0.839, meeting the reliability condition. Sample definition and selection The study’s sample was drawn from several sectors in Oman, including oil, gas, and energy; manufacturing; retail; education; information technology; construction; banking and insurance; and services. The focus was on employees across three management levels: junior, middle, and senior. Using a non-probability convenience sampling method, participants were selected based on accessibility and willingness to partake in the study. This approach was deemed appropriate given the exploratory nature of the research and the aim to capture diverse perspectives across industries. Research procedure The research procedure commenced with the researcher providing a detailed explanation of the study’s purpose prior to data collection, ensuring transparency and ethical compliance. Ethical clearance was obtained from the Arab Open University, Oman, adhering to all stipulated ethical requirements, including confidentiality assurances. Subsequently, a validity test was conducted involving academicians, practitioners, and an English proofreading expert to assess the appropriateness of formulated objectives and statements. Following this, a reliability test was performed on the questionnaire, which consisted of 35 statements, resulting in a Cronbach’s alpha of 0.839, meeting reliability standards. As this falls within the range from 0.60 to 0.90, one might suggest that all the scales met the reliability condition (Hair et al., 1998 ). The questionnaire was then distributed directly to the targeted population encompassing the oil, gas, and energy; manufacturing; retail; education; information technology; construction; banking and insurance; and service sectors of the Sultanate of Oman, with data collection and analysis conducted using SPSS software. Out of 151 initial samples, 133 were deemed valid after excluding instances of missing or duplicate information. Furthermore, rigorous evaluation led to the identification and removal of 12 erroneous samples, resulting in a final dataset of 121 samples for subsequent analysis and hypothesis testing. Throughout the process, meticulous attention was paid to maintain accuracy and integrity in data collection and analysis procedures. Statistical analysis The study conducted a comprehensive statistical analysis to derive insights from the collected data. Descriptive statistics summarised the variables, while inferential techniques such as ANOVA, Chi-square tests, Pearson correlation analysis, and multiple regression were employed to explore relationships and test hypotheses. This rigorous analysis facilitated the identification of patterns and predictive factors related to employee engagement and job performance, enabling evidence-based recommendations for enhancing engagement practices in Oman. Survey results The demographic analysis provided insights into the respondents’ composition based on gender, age, management level, and type of organisation, accompanied by their corresponding frequencies and percentages. Notably, 64% of respondents identified as male, whereas 36% were female. Age-wise, 10% were under 25 years old, representing recent graduates, while 45% fell within the 26–40 years old age range, and 35% were aged between 41 years and 55 years. Moreover, 10% of respondents were above 55 years old. In terms of management hierarchy, 15% held lower-level positions, 48% occupied middle-management roles, and 37% were part of upper management. The distribution across various organisational sectors indicated 17.2% in oil, gas, and energy and 26.4% in education, with the remaining percentages dispersed across sectors such as manufacturing, retail, construction, banking and insurance, information technology, and service. Multivariate test on management levels and drivers of employee engagement H0: There is no significant difference in the drivers of employee engagement between management levels. One can infer from Table 1 that the test yielded a significant result (Wilk’s A = 0.934, F [10, 228] = 2.791, p = 0.001). A separate ANOVA was conducted for each dependent variable, with each ANOVA evaluated at an alpha level of 0.05.  Multivariate tests on management levels and drivers of employee engagement. Table 2 shows that a significant difference was found in organisational support between management levels: F (2,118) = 1.425, p = 0.045.  Tests of between-subjects effects on management levels and drivers of employee engagement. The estimated marginal means across management levels reveal distinct values for key factors impacting employee engagement. For organisational support, middle management demonstrates the highest mean score of 19.8889, followed by senior management with 19.1556 and junior management with 19.5517. Similarly, middle management leads in supervisor support with a mean of 20.0862, while senior management follows with 19.1111 and junior management with 20.4444. In rewards and recognition, middle management scores the highest (17.5345), followed by senior management (16.5556) and junior management (16.5000). Lastly, for organisational justice, middle management achieves the highest mean score (18.8448), with senior management at 17.9333 and junior management at 18.7778. These findings underscore the substantial influence of management levels on various aspects of employee engagement, suggesting a partial rejection of the null hypothesis (H0). Association between management levels and employee engagement levels (H1) A Chi-square analysis was carried out to find the significant association between management levels and employee engagement levels: H0: There is no significant association between management levels and employee engagement levels. H1: There is a significant association between management levels and employee engagement levels. Table 3 shows a significant association between management levels and employee engagement levels at 5%. Hence, the null hypothesis is rejected. The table shows that employee engagement is high for middle management.  Association between management levels and employee engagement levels. Relationship between drivers of employee engagement and employee engagement levels (H2) Pearson correlation analysis was carried out to find the relationship between drivers of employee engagement and employee engagement levels: H0: There is no significant relationship between drivers of employee engagement and employee engagement levels. H2: There is a significant relationship between drivers of employee engagement and employee engagement levels. Table 4 shows a significant relationship between drivers of employee engagement and employee engagement levels at 1%. Hence, the null hypothesis is rejected. The table shows that all drivers of employee engagement are positive and highly correlated with employee engagement levels.  Relationship between drivers of employee engagement and employee engagement levels. Association between employee engagement levels and job performance levels (H3) A Chi-square analysis was carried out to find the significant association between employee engagement levels and job performance levels: H0: There is no significant association between employee engagement levels and job performance levels. H3: There is a significant association between employee engagement levels and job performance levels. Table 5 shows a significant association between employee engagement levels and job performance levels at 5%. Hence, the null hypothesis is rejected. The table shows that high employee engagement levels result in high job performance levels.  Association between employee engagement levels and job performance levels. Relationship between employee engagement levels and job performance levels (H4) Pearson correlation analysis was carried out to find the relationship between employee engagement levels and job performance levels: H0: There is no significant relationship between employee engagement levels and job performance levels. H4: There is a significant relationship between employee engagement levels and job performance levels. The analysis indicates a significant relationship between employee engagement levels and job performance levels, with a correlation coefficient ( R ) of 0.295 and a p -value of 0.001. This suggests a positive correlation between employee engagement and job performance, leading to the rejection of the null hypothesis. Impact of drivers of employee engagement on job performance (H5) Multiple regression was used to predict the impact of drivers of employee engagement on job performance. Table 6 displays the unstandardised regression coefficient (B), the unstandardised standard error of regression coefficients (SE B), the standardised regression coefficient (β), R 2 , and F for changes in R 2 .  Multiple regression for job performance based on drivers of employee engagement. Table 6 shows that the drivers of employee engagement together explain 38.5% of the variation in job performance. The adjusted R 2 (0.35) for the overall study on the five factors shown in Table 6 suggests a moderate effect on job performance. The F value (9.182; degree of freedom [ df ] 5.115) is significant, which indicates that the model fits well. The table shows that job characteristics and rewards and recognition significantly impact job performance. The independent variable with a higher level of β has a stronger impact on the dependent variable. This study’s results reveal that rewards and recognition (β = 0.406, p < 0.01) are the most influential factors impacting on job performance, followed by job characteristics (β = 0.248, p < 0.05); both show significant and positive influences. The Standardised Coefficients Beta column gives the coefficients of significant independent variables in the regression equation Y = 0.248 (Job Characteristics) + 0.406 (Rewards and Recognition). This suggests that Job Characteristics and Rewards and Recognition are significant predictors and play significant roles in job performance. Outline of the results The results reveal a significant effect of the drivers of employee engagement across the levels of management. Employee engagement was measured and found to be highest in middle management. Pearson correlation analysis was carried out to find the relationship between drivers of employee engagement and employee engagement levels. The findings show that all drivers of employee engagement are positively and highly correlated with employee engagement levels. Multiple regression was performed for job performance based on drivers of employee engagement. This comprehensive approach highlights that all drivers of employee engagement are positively and significantly correlated with both subjective and objective performance measures. The results underscore the importance of addressing specific drivers of engagement, such as job characteristics and recognition and reward systems, to enhance organisational performance. These findings emphasise the need for tailored engagement strategies across different management levels to maximise employee engagement and ultimately improve job performance. Practical implications This research has several practical implications. Firstly, the findings suggest that managers should be aware of the positive impact of various drivers of employee engagement. Secondly, this study further enhances our comprehension of the significance accorded by top management to their responsibility in preserving and enhancing a firm’s reputation (Chetty & Price, 2024 ). Employee engagement should not be a one-time exercise, but it should instead be integrated into the company culture (Bedarkar & Pandita, 2014 ). Prior research has shown that organisations that invest in employees are viewed as better employers by external audiences (Gill, 2010 ). This study emphasises the roles that job characteristics, organisational support, support from superiors, rewards and recognition, organisational justice, and employee engagement play in job performance. By incorporating both subjective and objective measures of job performance, our findings suggest that organisations benefit from a holistic approach to performance assessment. This includes leveraging objective metrics alongside employee self-assessments and peer reviews to capture a complete picture of job performance. Organisations characterised by high levels of employee engagement experience enhanced employee retention because of decreased turnover rates and reduced intentions to leave the company. Moreover, they exhibit heightened levels of productivity, profitability, growth, and customer satisfaction. Conversely, enterprises with disengaged employees encounter inefficiencies, talent attrition, diminished employee commitment, and elevated absenteeism. They demonstrate diminished customer orientation, reduced productivity, and lower operating and net profit margins (Markos & Sridevi, 2010 ). Robertson-Smith and Markwick ( 2009 ) underscored the role of engagement in allowing employees to invest themselves in their work and fostering a sense of self-efficacy. Research suggests that engaged employees may experience improved health and harbour positive attitudes towards their work and the organisation. Additionally, engaged employees demonstrate superior task performance with fewer errors compared to their disengaged counterparts (Gonring, 2008 ). Organisations are encouraged to adopt ‘radical transparency’, prioritising communication with employees as a fundamental practice. By disseminating information widely, companies foster a sense of inclusion among employees and cultivate a shared commitment to the organisation’s mission. This engenders a foundation of trust between the organisation and its employees, thereby promoting employee engagement (Mishra et al., 2014 ). Additionally, top management should ensure that employees have access to necessary resources, provide adequate training to enhance their competencies, implement reward systems, cultivate a unique corporate culture that values diligence and preserves success narratives, and establish robust performance management mechanisms. Limitations and recommendations The study has some limitations. Firstly, this research employed convenience sampling to accomplish its objectives. Therefore, the limitations associated with convenience sampling apply to this study. Secondly, the sample size is another limitation, as it is insufficient to represent all industries. To foster engagement, companies are advised to practise transparency, starting with open communication with employees. Providing resources, training, establishing reward mechanisms, fostering a corporate culture that values hard work, and developing a robust performance management system are essential strategies for top management. Organisations in the Sultanate of Oman must develop effective employee engagement strategies that include value-added activities to generate future improvement in job performance. This study significantly advances the field of organisational behaviour by identifying key drivers of employee engagement and assessing their impact on job performance across management levels. Through rigorous statistical analysis, it provides empirical evidence supporting the theoretical link between employee engagement and organisational outcomes, such as productivity and profitability. The study enriches theoretical understanding by emphasising the importance of integrating employee engagement into organisational culture, highlighting its role as a fundamental aspect of organisational functioning. Additionally, it offers practical insights for managers, bridging the gap between theory and practice and guiding the development of effective engagement strategies. By identifying areas for future research, the study contributes to ongoing discourse, paving the way for further empirical investigations and theoretical development in this field. The purpose of this study was to assess the impact of employee engagement factors on job performance, investigate the relationship between employee engagement and job performance, and suggest practices to improve employee engagement in the Sultanate of Oman. The analysis of the data involved the utilisation of various statistical tools, revealing a significant impact of employee engagement drivers across management levels. Particularly, engagement was found to be highest among middle management. Pearson correlation analysis was conducted to ascertain the relationship between these drivers and engagement levels, indicating a positive and highly correlated association. Additionally, multiple regression was performed to assess job performance based on engagement drivers, with results highlighting the significant predictive roles of job characteristics and rewards and recognition. Acknowledgements Competing interests. The author declares that he has no financial or personal relationships that may have inappropriately influenced him in writing this article. Author’s contributions M.A.-H. contributed to the conceptualisation, design, and implementation of the research, analysis of the results, and writing of the article. Ethical considerations Ethical clearance to conduct this study was obtained from the Arab Open University Ethical Research Committee (no. 105/23). Funding information This research did not receive funding from any public, commercial, or not-for-profit sectors. Data availability The data supporting the findings of this study are available from the corresponding author, M.A-H., upon reasonable request. The views and opinions expressed in this article are those of the author and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The author is responsible for this article’s results, findings, and content. Alam, J., Mendelson, M., Ibn Boamah, M., & Gauthier, M. (2023). Exploring the antecedents of employee engagement. International Journal of Organizational Analysis , 31 (6), 2017–2030. https://doi.org/10.1108/IJOA-09-2020-2433 Ajila, C., & Abiola, A. (2004). Influence of rewards on work performance in an organization. Journal of Social Sciences , 8 (1), 7–12. https://doi.org/10.1080/09718923.2004.11892397 Barney, J.B., & Wright, P.M. (1998). On becoming a strategic partner: The role of human resources in gaining competitive advantage. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in Alliance with the Society of Human Resources Management , 37 (1), 31–46. https://doi.org/10.1002/(SICI)1099-050X(199821)37:1%3C31::AID-HRM4%3E3.0.CO;2-W Barreiro, C.A., & Treglown, L. (2020). What makes an engaged employee? A facet-level approach to trait emotional intelligence as a predictor of employee engagement. Personality and Individual Differences , 159 , 109892. https://doi.org/10.1016/j.paid.2020.109892 Bedarkar, M., & Pandita, D. (2014). A study on the drivers of employee engagement impacting employee performance. Procedia—Social and Behavioral Sciences , 133 , 106–115. https://doi.org/10.1016/j.sbspro.2014.04.174 Bin, A.S., & Shmailan, A. (2015). The relationship between job satisfaction, job performance and employee engagement: An explorative study. Issues in Business Management and Economics , 4 (1), 1–8. Brebels, L., De Cremer, D., & Van Dijke, M. (2011). Using self-definition to predict the influence of procedural justice on organizational-, interpersonal-, and job/task-oriented citizenship behavior. Journal of Management , 40 (3), 731–763. https://doi.org/10.1177/0149206311410605 Brunetto, Y., Xerri, M., Shriberg, A., Farr-Wharton, R., Shacklock, K., Newman, S., & Dienger, J. (2013). The impact of workplace relationships on engagement, well-being, commitment and turnover for nurses in Australia and the USA. Journal of Advanced Nursing , 69 (12), 2786–2799. https://doi.org/10.1111/jan.12165 Chetty, K., & Price, G. (2024). Ubuntu leadership as a predictor of employee engagement: A South African study. SA Journal of Human Resource Management , 22 (1), 1–11. https://doi.org/10.4102/sajhrm.v22i0.2462 Christen, M., Iyer, G., & Soberman, D. (2006), Job satisfaction, job performance, and effort: A re-examination using agency theory. Journal of Marketing , 70 (1), 137–150. https://doi.org/10.1509/jmkg.70.1.137.qxd Christian, M.S., Garza, A.S., & Slaughter, J.E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology , 64 (1), 89–136. https://doi.org/10.1111/j.1744-6570.2010.01203.x Cole, M.S., Walter, F., Bedeian, A.G., & O’Boyle, E.H. (2012). Job burnout and employee engagement: A meta-analytic examination of construct proliferation. Journal of Management , 38 (5), 1550–1581. https://doi.org/10.1177/0149206311415252 Cullen, K.L., Edwards, B.D., Casper, W.C., & Gue, K.R. (2014). Employees’ adaptability and perceptions of change-related uncertainty: Implications for perceived organizational support, job satisfaction, and performance. Journal of Business and Psychology , 29 , 269–280. https://doi.org/10.1007/s10869-013-9312-y Dabke, D., & Patole, S. (2014). Predicting employee engagement: Role of perceived organizational support and perceived support from superiors. Tactful Management Research Journal , 3 (1), 1–8. Douglas, S., & Roberts, R. (2020). Employee age and the impact on work engagement. Strategic HR Review , 19 (5), 209–213. https://doi.org/10.1108/SHR-05-2020-0049 Edirisooriya, W.A. (2014). The impact of rewards on employee performance: With special reference to ElectriCo . Retrieved from https://www.researchgate.net/publication/323747331_The_Impact_of_Reward_on_Employee_Performance_with_Special_Reference_to_ElectriCo Gill, R. (2010). Employer of choice: Using computers to enhance employee engagement in Australia. Global Business and Organizational Excellence , 29 (3), 44–63. https://doi.org/10.1002/joe.20318 Gonring, M.P. (2008). Customer loyalty and employee engagement: An alignment for value. Journal of Business Strategy , 29 (4), 29–40. https://doi.org/10.1108/02756660810887060 Grant, A.M. (2008). The significance of task significance: Job performance effects, relational mechanisms, and boundary conditions. Journal of Applied Psychology , 93 (1), 108–124. https://doi.org/10.1037/0021-9010.93.1.108 Gupta, N., & Sharma, V. (2016). Exploring employee engagement—A way to better business performance. Global Business Review , 17 (3_suppl), 45S–63S. https://doi.org/10.1177/0972150916631082 Hafiza, N.S., Shah, S.S., Jamsheed, H., & Zaman, K. (2011). Relationship between rewards and employee’s motivation in the non-profit organizations of Pakistan. Business Intelligence Journal , 4 (2), 327–334. Hair, J.F., Tatham, R.L., Anderson, R.E., & Black, W. (1998). Multivariate data analysis (4th ed.). Prentice Hall. Harter, J.K., Schmidt, F.L., & Hayes, T.L. (2002). Business-unit level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology , 87 (2), 268–279. https://doi.org/10.1037/0021-9010.87.2.268 He, H., Zhu, W., & Zheng, X. (2014). Procedural justice and employee engagement: Roles of organizational identification and moral identity centrality. Journal of Business Ethics , 122 (4), 681–695. https://doi.org/10.1007/s10551-013-1774-3 Johari, J., & Yahya, K.K. (2016). Job characteristics, work involvement, and job performance of public servants. European Journal of Training and Development , 40 (7), 554–575. https://doi.org/10.1108/EJTD-07-2015-0051 Köse, A. (2016). The relationship between work engagement behavior and perceived organizational support and organizational climate. Journal of Education and Practice , 7 (27), 42–52. Loi, R., Lam, L.W., & Chan, K.W. (2012). Coping with job insecurity: The role of procedural justice, ethical leadership and power distance orientation. Journal of Business Ethics , 108 (1), 361–372. https://doi.org/10.1007/s10551-011-1095-3 Markos, S., & Sridevi, M.S. (2010). Employee engagement: The key to improving performance. International Journal of Business and Management , 5 (12), 89. https://doi.org/10.5539/ijbm.v5n12p89 Mohapatra, M., & Sharma, B.R. (2010). Study of employee engagement and its predictors in an Indian public sector undertaking. Global Business Review , 11 (2), 281–301. https://doi.org/10.1177/097215091001100210 Mishra, K., Boynton, L., & Mishra, A. (2014). Driving employee engagement: The expanded role of internal communications. Journal of Business Communication , 51 (2), 183–202. https://doi.org/10.1177/2329488414525399 Robinson, S.L., Wang, W., & Kiewitz, C. (2014). Coworkers behaving badly: The impact of coworker deviant behavior upon individual employees. Annual Review of Organizational Psychology and Organizational Behavior , 1 (1), 123–143. https://doi.org/10.1146/annurev-orgpsych-031413-091225 Robertson-Smith, G., & Markwick, C. (2009). Employee engagement: A review of current thinking . Institute for Employment Studies. Saks, A.M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology , 21 (7), 600–619. https://doi.org/10.1108/02683940610690169 San, O.T., Theen, Y.M., & Heng, T.B. (2012). The reward strategy and performance measurement (evidence from Malaysian insurance companies). International Journal of Business, Humanities and Technology , 2 (1), 211–223. Shuck, B., & Wollard, K. (2010). Employee engagement and HRD: A seminal review of the foundations. Human Resource Development Review , 9 (1), 89–110. https://doi.org/10.1177/1534484309353560 Skarlicki, D.P., Van Jaarsveld, D.D., & Walker, D.D. (2008). Getting even for customer mistreatment: The role of moral identity in the relationship between customer interpersonal injustice and employee sabotage. Journal of Applied Psychology , 93 , 1335–1347. https://doi.org/10.1037/a0012704 Sparrowe, T., & Liden, C. (2005). Two routes to influence: Integrating leader-member exchange and social network perspectives. Administrative Science Quarterly , 50 (4), 505–535. https://doi.org/10.2189/asqu.50.4.505 Tanwar, A. (2017). Impact of employee engagement on performance. International Journal of Advanced Engineering, Management and Science , 3 (5), 239845. https://doi.org/10.24001/ijaems.3.5.16 Turner, P., & Turner, P. (2020). What is employee engagement? In P. Turner (Ed.), Employee engagement in contemporary organizations: Maintaining high productivity and sustained competitiveness (pp. 27–56). Palgrave Macmillan. Turunç, Ö., & Çelik, M. (2010). Effect of perceived organizational value on work-family/family-work conflicts, organizational identification and the intention to resign: A study in defense sector. Atatürk University Journal of Social Sciences Institute , 14 (1), 209–232. Williams, R.S. (2002), Managing employee performance: Design and implementation in organizations . Thompson Learning. Wood, S., Van Veldhoven, M., Croon, M., & De Menezes, L.M. (2012). Enriched job design, high involvement management and organizational performance: The mediating roles of job satisfaction and well-being. Human Relations , 65 (4), 419–445. https://doi.org/10.1177/0018726711432476 Crossref Citations Subscribe to our newsletter Get specific, domain-collection newsletters detailing the latest CPD courses, scholarly research and call-for-papers in your field. SA Journal of Human Resource Management    |    ISSN: 1683-7584 (PRINT)    |    ISSN: 2071-078X (ONLINE) ISSN: 2071-078X SUGGESTED TOPICS The Magazine Newsletters Managing Yourself Managing Teams Work-life Balance The Big Idea Data & Visuals Reading Lists Case Selections HBR Learning Topic Feeds Account Settings Email Preferences Research: When Bonuses Backfire Dirk Sliwka Timo Vogelsang How to rethink your incentive strategy and reward employees in ways that actually motivate them. Why do bonuses sometimes backfire? It’s because each incentive design choice both signals information about your own beliefs and intentions as an employer and shapes the signaling value of employee behavior within the organization. If you don’t think through these signals carefully, you may end up approving a bonus scheme with results that are the opposite of what you intend. This article offers a way to help you align the signals your incentive scheme sends with your performance goals. More than 30 years ago, author and lecturer Alfie Kohn, in a rather controversial but often cited HBR article , claimed that “rewards typically undermine the very processes they are intended to enhance.” Yet until recently, nearly all scientific studies that have documented such “backfiring” effects have been confined to laboratory experiments or field settings outside of the firm. This may cause some to question whether these effects are really present in commercial contexts. Our new research, which consists of two large field experiments in retail organizations, demonstrates that they do indeed occur. One of the studies showed unequivocally that the provision of a monetary attendance bonus increased absence days. The other revealed that the added value of performance review conversations was wiped out when they were combined with a monetary bonus. In both cases, a well-intentioned financial reward ultimately had crucial and costly unintended effects, largely because of signals about expected behavior given by the incentives. To understand what was going on, let’s look at the two studies in turn. DS Dirk Sliwka is a professor of management in the Faculty of Management, Economics, and Social Sciences at the University of Cologne in Germany TV Timo Vogelsang is an Associate Professor of Managerial Accounting at the Frankfurt School of Finance and Management in Germany Partner Center Open access Published: 02 September 2024 Leadership support and satisfaction of healthcare professionals in China’s leading hospitals: a cross-sectional study Jinhong Zhao 1 , 2 , Tingfang Liu 2 & Yuanli Liu 2   BMC Health Services Research volume  24 , Article number:  1016 ( 2024 ) Cite this article Metrics details Healthcare professionals’ job satisfaction is a critical indicator of healthcare performance, pivotal in addressing challenges such as hospital quality outcomes, patient satisfaction, and staff retention rates. Existing evidence underscores the significant influence of healthcare leadership on job satisfaction. Our study aims to assess the impact of leadership support on the satisfaction of healthcare professionals, including physicians, nurses, and administrative staff, in China’s leading hospitals. A cross-sectional survey study was conducted on healthcare professionals in three leading hospitals in China from July to December 2021. These hospitals represent three regions in China with varying levels of social and economic development, one in the eastern region, one in the central region, and the third in the western region. Within each hospital, we employed a convenience sampling method to conduct a questionnaire survey involving 487 healthcare professionals. We assessed perceived leadership support across five dimensions: resource support, environmental support, decision support, research support, and innovation encouragement. Simultaneously, we measured satisfaction using the MSQ among healthcare professionals. The overall satisfaction rate among surveyed healthcare professionals was 74.33%. Our study revealed significant support from senior leadership in hospitals for encouraging research (96.92%), inspiring innovation (96.30%), and fostering a positive work environment (93.63%). However, lower levels of support were perceived in decision-making (81.72%) and resource allocation (80.08%). Using binary logistic regression with satisfaction as the dependent variable and healthcare professionals’ perceived leadership support, hospital origin, job role, department, gender, age, education level, and professional designation as independent variables, the results indicated that support in resource provision (OR: 4.312, 95% CI: 2.412  ∼  7.710) and environmental facilitation (OR: 4.052, 95% CI: 1.134  ∼  14.471) significantly enhances healthcare personnel satisfaction. The findings underscore the critical role of leadership support in enhancing job satisfaction among healthcare professionals. For hospital administrators and policymakers, the study highlights the need to focus on three key dimensions: providing adequate resources, creating a supportive environment, and involving healthcare professionals in decision-making processes. Peer Review reports Introduction In the era of accelerated globalization, the investigation of global leadership has assumed heightened significance [ 1 ]. Leadership, as a dynamic and evolving process, holds the potential to cultivate both the personal and professional growth of followers [ 2 ]. Effective healthcare leadership can enhance medical service quality, patient safety, and staff job satisfaction through skill development, vision establishment, and clear direction-setting [ 3 , 4 , 5 ]. Moreover, leadership support can effectively enhance staff well-being and work efficiency [ 6 , 7 ]. For example, Mendes et al. found that the quality of healthcare is significantly influenced by four dimensions of leadership: communication, recognition, development, and innovation [ 8 ]. Additionally, Shanafelt et al. discovered that leaders can effectively reduce employee burnout and subsequently improve the quality of medical services by formulating and implementing targeted work interventions and motivating employees [ 9 ]. Job satisfaction among healthcare professionals is a crucial indicator of healthcare performance, playing a vital role in addressing challenges related to hospital quality outcomes, patient satisfaction, and nurse retention rates [ 10 , 11 , 12 , 13 ]. Researchers from different national backgrounds have conducted studies on the job satisfaction of healthcare workers across various disciplines. For example, Balasubramanian et al. examined the satisfaction of immigrant dentists in Australia [ 14 ], Mascari et al. studied physicians and hospital researchers in the United States [ 15 ], and Rosta et al. investigated the satisfaction of doctors in Norway [ 12 ]. Research has demonstrated that characteristics of the work environment, balanced workloads, relationships with colleagues, career opportunities, and leadership support all influence job satisfaction [ 16 ]. Several instruments are commonly used to measure job satisfaction, each relevant depending on the context and discipline. For instance, the Job Descriptive Index (JDI) focuses on different facets of job satisfaction such as work, pay, promotion, supervision, and co-workers [ 17 ]. The Job Satisfaction Survey (JSS) covers similar dimensions and is particularly useful in public sector organizations due to its comprehensive nature and ease of use [ 18 ]. The Minnesota Satisfaction Questionnaire (MSQ) is a comprehensive tool that assesses employee satisfaction across multiple dimensions including intrinsic and extrinsic satisfaction, and is commonly used for evaluating job satisfaction in the healthcare field [ 19 ]. Recent studies have linked leadership to healthcare professionals’ job satisfaction, highlighting the pivotal role of leadership in guiding, coordinating, and motivating employees [ 5 ]. For instance, the Mayo Clinic found that leadership from immediate supervisors could alleviate burnout and increase job satisfaction [ 20 ]. Choi’s research indicated that leadership empowerment significantly enhances nursing staff’s job satisfaction [ 21 ]. Additionally, Liu discovered that the support provided by hospital senior leadership is closely associated with employee satisfaction [ 22 ]. In China, while leadership research has gained some traction in areas such as business and education, it remains relatively scarce within healthcare institutions. Existing studies primarily focus on the nursing sector, and comprehensive assessments of leadership at the leading public hospitals (top 10% of Chinese hospitals) have not been extensively conducted [ 23 , 24 ]. Research on leadership and healthcare professionals’ satisfaction often relies on single indicators to measure job satisfaction, such as overall job satisfaction or specific aspects like compensation satisfaction and burnout levels [ 25 ]. This narrow focus may fail to fully capture the multidimensional nature of employee satisfaction, which includes aspects such as workload, ability utilization, sense of achievement, initiative, training and self-development, and interpersonal communication [ 26 ]. Additionally, most existing studies focus on the job satisfaction of nurses or physicians in isolation, lacking comparative research across different groups within healthcare institutions, such as doctors, nurses, and administrative personnel [ 27 , 28 , 29 ]. Therefore, this study utilized the MSQ to conduct a thorough assessment of employee satisfaction and assess the impact of leadership support on the satisfaction of healthcare personnel in China’s leading public hospitals. Through this research, we aim to enhance the core competitiveness of hospitals and provide valuable data to support leadership assessments in developing countries’ healthcare institutions. Moreover, this study seeks to contribute to the broader international understanding of effective leadership practices in China’s leading public hospitals, with implications for global health management strategies. Study design and participants From July to December 2021, a cross-sectional survey study was conducted on healthcare professionals in China’s 3 leading hospitals. The 3 leading hospitals represent three regions in China with different levels of social and economic development, one in the eastern, one in the central, and one in the western. In each hospital, a convenience sampling method was used to conduct a questionnaire survey among physicians, nurses, and administrative staff. Criteria for inclusion of healthcare professionals: (1) employed at the hospital for at least 1 year or more; (2) formal employees of the hospital (full-time staff); (3) possessing cognitive clarity and the ability to independently understand and respond to electronic questionnaires, as assessed by their leaders. Exclusion criteria: (1) diagnosed with mental health disorders that impair their ability to participate, as identified by the hospital’s mental health professionals; (2) unable to communicate effectively due to severe language barriers, hearing impairments, or other communication disorders, as determined by their direct supervisors or relevant medical evaluations; (3) visiting scholars, interns, or graduate students currently enrolled in a degree program. Instrument development Leadership support. In reference to the Malcolm Baldrige National Quality Award (MBNQA) framework and Supporting Relationship Theory [ 6 , 30 , 31 ], we determined the survey scale after three expert discussions involving 5–7 individuals. These experts included personnel from health administrative departments, leading public hospital leaders, middle management, and researchers specializing in hospital management. Their collective expertise ensured that the survey comprehensively assessed leadership support within hospitals from the perspective of healthcare personnel. The Leadership Support Scale consists of 5 items: Environmental Support: ‘My leaders provide a work environment that helps me perform my job,’ Resource Support: ‘My leaders provide the resources needed to improve my work,’ Decision Support: ‘My leaders support my decisions to satisfy patients,’ Research Support: ‘My leaders support my application for scientific research projects,’ and Innovation Encouragement: ‘My leaders encourage me to innovate actively and think about problems in new ways‘ (Supplementary material). All questionnaire items are rated on a 5-point Likert scale, ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The Cronbach’s alpha coefficient for the 5-item scale is 0.753. Job satisfaction The measurement of job satisfaction was carried out using the Minnesota Satisfaction Questionnaire (MSQ) [ 32 , 33 ], which has been widely used and has been shown by scholars to have good reliability and validity in China [ 34 , 35 ]. The questionnaire consists of 20 items that measure healthcare personnel’s satisfaction with various aspects of their job, including individual job load, ability utilization, achievement, initiative, hospital training and self-development, authority, hospital policies and practices, compensation, teamwork, creativity, independence, moral standards, hospital rewards and punishments, personal responsibility, job security, social service contribution, social status, employee relations and communication, and hospital working conditions and environment. Responses to these items were balanced and rated on a scale from 1 to 5, with 1 = Very Dissatisfied, 2 = Dissatisfied, 3 = Neither Dissatisfied nor Satisfied, 4 = Satisfied, and 5 = Very Satisfied. Scores range from 20 to 100, with higher scores indicating higher satisfaction. In this study, a comprehensive assessment of healthcare personnel’s job satisfaction was made using a score of 80 and above [ 32 ], where a score of ≥ 80 was considered satisfied, and below 80 was considered dissatisfied. The Cronbach’s alpha coefficient for the questionnaire in this survey was 0.983. Investigation process The survey was administered through an online platform “Wenjuanxing”, and distributed by department heads to healthcare professionals within their respective departments. The selection of departments and potential participants followed a structured process: (1) Potential participants were identified based on the inclusion criteria, which were communicated to the department heads. (2) Department heads received a digital link to the survey, which they forwarded to eligible staff members via email or internal communication platforms. (3) The informed consent form was integrated into the survey link, detailing the research objectives, ensuring anonymity, and emphasizing voluntary participation. At the beginning of the online survey, participants were asked if they agreed to participate. Those who consented continued with the survey, while those who did not agree were directed to end the survey immediately. According to Kendall’s experience and methodology, the sample size can be 5–10 times the number of independent variables (40 items) [ 36 , 37 ]. Our sample size is ten times the number of independent variables. Considering potentially disqualified questionnaires, the sample size was increased by 10%, resulting in a minimum total sample size of 460. Therefore, we distributed 500 survey questionnaires. Data analysis We summarized the sociodemographic characteristics of healthcare personnel survey samples using descriptive statistical methods. For all variables, we calculated the frequencies and percentages of categorical variables. Different sociodemographic characteristics in relation to healthcare personnel’s perception of leadership support and satisfaction were analyzed using the Pearson χ² test. We employed a binary logistic regression model to estimate the risk ratio of healthcare personnel satisfaction under different levels of leadership support. Estimates from three sequentially adjusted models were reported to transparently demonstrate the impact of various adjustments: (1) unadjusted; (2) adjusted for hospital of origin; (3) adjusted for hospital of origin, gender, age, education level, job type, and department. For the binary logistic regression model, we employed a backward stepwise regression approach, with inclusion at P  < 0.05 and exclusion at P  > 0.10 criteria. In all analyses, a two-tailed p -value of < 0.05 was considered significant, and all analyses were conducted using SPSS 26.0 (IBM Corp., Armonk, NY, USA). Demographic characteristics and job satisfaction This study recruited a total of 500 healthcare personnel from hospitals to participate in the survey, with 487 valid questionnaires collected, resulting in an effective response rate of 97.4%. The majority of participants were female (77.21%), with ages concentrated between 30 and 49 years old (73.71%). The predominant job titles were mid-level (45.17%) and junior-level (27.31%), and educational backgrounds were mostly at the undergraduate (45.17%) and graduate (48.25%) levels. The marital status of most participants was married (79.88%), and their primary departments were surgery (38.19%) and internal medicine (24.85%). The overall satisfaction rate among the sampled healthcare personnel was 74.33%. Differences in satisfaction were statistically significant among healthcare personnel of different genders, ages, educational levels, job types, hospitals, and departments ( P  < 0.05). Table  1 displays the participants’ demographic characteristics and job satisfaction. By analyzed the satisfaction level of healthcare personnel in different dimensions, the results show that “Social service” (94.3%) and “Moral values” (92.0%) have the highest satisfaction. “Activity” (66.8%) and “Compensation” (71.9%) were the least satisfied. Table  2 shows participants’ job satisfaction in different dimensions. Perception of different types of leadership support among healthcare professionals Overall, surveyed healthcare personnel perceived significant levels of support from hospital leadership for research encouragement (96.92%), innovation inspiration (96.30%), and the work environment (93.63%), while perceiving lower levels of support for decision-making (81.72%) and resource allocation (80.08%). Female healthcare personnel perceived significantly higher levels of resource support compared to males ( P  < 0.05). Healthcare personnel in the 30–39 age group perceived significantly higher levels of resource, environmental, and research support compared to other age groups ( P  < 0.05). Healthcare personnel with senior-level job titles perceived significantly lower levels of resource and decision-making support compared to associate-level and lower job titles, and those with doctoral degrees perceived significantly lower levels of resource support compared to other educational backgrounds ( P  < 0.05). Clinical doctors perceived significantly lower levels of resource and environmental support compared to administrative personnel and clinical nurses, while administrative personnel perceived significantly lower levels of decision-making support compared to clinical doctors and clinical nurses ( P  < 0.05). Among healthcare personnel in internal medicine, perceptions of resource, environmental, research, and innovation support were significantly lower than those in surgery, administration, and other departments, whereas perceptions of decision-making support in administrative departments were significantly lower than in internal medicine, surgery, and other departments ( P  < 0.05). Figure  1 displays the perception of leadership support among healthcare personnel with different demographic characteristics. Perception of leadership support among healthcare professionals with different demographic characteristics in China’s leading public hospitals (* indicates P  < 0.05, ** indicates P  < 0.01, and *** indicates P  < 0.001.) The impact of leadership support on job satisfaction among healthcare professionals The study results indicate that healthcare personnel who perceive that their leaders provide sufficient resource, environmental, and decision-making support have significantly higher job satisfaction than those who feel that leaders have not provided enough support ( P  < 0.05). Similarly, healthcare personnel who perceive that their leaders provide sufficient research and innovation inspiration have significantly higher job satisfaction than those who believe leaders have not provided enough inspiration ( P  < 0.05). Table  3 displays the univariate analysis of leadership support on healthcare professional satisfaction. With healthcare personnel satisfaction as the dependent variable, leadership resource support, environmental support, decision-making support, research support, and innovation inspiration were included in the binary logistic regression model. After adjusting for hospital, gender, age, education level, job type, and department, leadership’s increased resource support (OR: 4.312, 95% CI: 2.412  ∼  7.710) and environmental support (OR: 4.052, 95% CI: 1.134  ∼  14.471) were found to enhance the satisfaction levels of healthcare personnel significantly. Additionally, healthcare professionals in Hospital 2 (OR: 3.654, 95% CI: 1.796 to 7.435) and Hospital 3 (OR: 2.354, 95% CI: 1.099 to 5.038) exhibited higher levels of satisfaction compared to those in Hospital 1. Table 4 displays the binary Logistic regression analysis of leadership support on satisfaction among healthcare professionals. This study aimed to determine the impact of support from hospital senior leadership on the job satisfaction of healthcare personnel and to explore the effects of demographic and different types of support on the job satisfaction of healthcare personnel in China. The research indicates that hospital leadership’s resource support, environmental support, and decision-making support have a significantly positive impact on the job satisfaction of healthcare personnel. These forms of support can assist healthcare personnel in better adapting to the constantly changing work environment and demands, thereby enhancing their job satisfaction, and ultimately, positively influencing the overall performance of the hospital and the quality of patient care. Our research indicates that, using the same MSQ to measure job satisfaction, the job satisfaction among healthcare personnel in China’s top-tier hospitals is at 74.33%, which is higher than the results of a nationwide survey in 2016 (48.22%) [ 38 ] and a survey among doctors in Shanghai in 2013 (35.2%) in China [ 39 ]. This improvement is likely due to the Chinese government’s recent focus on healthcare personnel’s compensation and benefits, along with corresponding improvement measures, which have increased their job satisfaction. It’s worth noting that while job satisfaction among healthcare personnel in China’s top-tier hospitals is higher than the national average in China, it is slightly lower than the job satisfaction of doctors in the United States, as measured by the MSQ (81.73%) [ 40 ]. However, when compared to the job satisfaction by the MSQ of doctors in Southern Nigeria (26.7%) [ 32 ], nurses in South Korea (65.89%) [ 41 ], and nurses in Iran (59.7%) [ 42 ], the level of job satisfaction among healthcare personnel in China’s top-tier hospitals is significantly higher. This suggests that China has achieved some level of success in improving healthcare personnel’s job satisfaction. Studies have shown that for healthcare professionals, job satisfaction is influenced by work conditions, compensation, and opportunities for promotion, with varying levels of satisfaction observed across different cultural backgrounds and specialties [ 29 , 43 ]. Furthermore, the observed differences in job satisfaction levels can be influenced by cultural factors unique to China, including hierarchical workplace structures and the emphasis on collective well-being over individual recognition. Leadership support can influence employees’ work attitudes and emotions. Effective leaders can establish a positive work environment, and provide constructive feedback, thereby enhancing employee job satisfaction [ 44 , 45 ]. Our research results show that clinical physicians perceive significantly lower levels of resource and environmental support compared to administrative staff and clinical nurses, while administrative staff perceive significantly lower levels of decision-making support compared to clinical physicians and clinical nurses. This difference can be attributed to their different roles and job nature within the healthcare team [ 9 ]. Nurses typically have direct patient care responsibilities, performing medical procedures, providing care, and monitoring patient conditions, making them in greater need of resource and environmental support to efficiently deliver high-quality care [ 46 ]. Doctors usually have responsibilities for clinical diagnosis and treatment, requiring better healthcare environments and resources due to their serious commitment to patients’ lives. Administrative staff often oversee the hospital’s day-to-day operations and management, including budgeting, resource allocation, and personnel management. Their work may be more organizationally oriented, involving strategic planning and management decisions. Therefore, they may require more decision-making support to succeed at the managerial level [ 47 ]. The job satisfaction of healthcare personnel is influenced by various factors, including the work environment, workload, career development, and leadership support [ 48 , 49 ]. When healthcare personnel are satisfied with their work, their job enthusiasm increases, contributing to higher patient satisfaction. Healthcare organizations should assess the leadership and management qualities of each hospital to enhance their leadership capabilities. This will directly impact employee satisfaction, retention rates, and patient satisfaction [ 50 ]. Resource support provided by leaders, such as data, human resources, financial resources, equipment resources, supplies (such as medications), and training opportunities, significantly influences the job satisfaction of healthcare personnel [ 51 ]. From a theoretical perspective, researchers believe that leaders’ behavior, by providing resources to followers, is one of the primary ways to influence employee satisfaction [ 7 ]. These resources can assist healthcare personnel in better fulfilling their job responsibilities, improving work efficiency, and thereby enhancing their job satisfaction. In hospital organizations, leaders play a crucial role in shaping the work environment for healthcare personnel and providing decision-making support [ 52 , 53 ]. Hospital leaders are committed to ensuring the safety of the work environment for their employees by formulating and promoting policies and regulations. They also play a key role in actively identifying and addressing issues in the work environment, including conflicts among employees and resource shortages. These initiatives are aimed at continuously improving working conditions, enabling healthcare personnel to better fulfill their duties [ 54 ]. The actions of these leaders not only contribute to improving the job satisfaction of healthcare personnel but also create the necessary foundation for providing high-quality healthcare services. It is worth noting that our research results show that in the context of leading public hospitals in China, leadership support for research, encouragement of innovation, and decision-making do not appear to significantly enhance the job satisfaction of healthcare personnel, which differs from some international literature [ 23 , 55 , 56 ]. International studies often suggest that fostering innovation is particularly important in influencing healthcare personnel’s job satisfaction [ 57 , 58 ]. Inspiring a shared vision is particularly important in motivating nursing staff and enhancing their job satisfaction and organizational commitment [ 59 ]. This may reflect the Chinese healthcare personnel’s perception of leadership’s innovation encouragement, scientific research encouragement, and decision support, but it does not significantly improve their job satisfaction. However, material support (resources and environment) can significantly increase their satisfaction. Strengths and limitations of this study For the first time, we analyzed the role of perceived leadership support in enhancing healthcare providers in China’s leading public hospitals. We assessed the impact of perceived leadership on healthcare professional satisfaction across five dimensions: resources, environment, decision-making, research, and innovation. The sample includes physicians, nurses, and administrative staff, providing a comprehensive understanding of leadership support’s impact on diverse positions and professional groups. However, it’s important to note that this study exclusively recruited healthcare professionals from three leading public hospitals in China, limiting the generalizability of the research findings. Additionally, the cross-sectional nature of the study means that causality cannot be established. There is also a potential for response bias as the data were collected through self-reported questionnaires. Furthermore, the use of convenience sampling may introduce selection bias, and the reliance on electronic questionnaires may exclude those less comfortable with digital technology. Implications for research and practice The results of this study provide important empirical evidence supporting the significance of leadership assessment in the context of Chinese hospitals. Specifically, the findings underscore the critical role of leadership support in enhancing job satisfaction among healthcare professionals, which has implications for hospital operational efficiency and the quality of patient care. For hospital administrators and policymakers, the study highlights the need to prioritize leadership development programs that focus on the three dimensions of leadership support: resources, environment, and decision-making. Implementing targeted interventions in these areas can lead to improved job satisfaction. Moreover, this study serves as a foundation for comparative research across different cultural and organizational contexts, contributing to a deeper understanding of how leadership practices can be optimized to meet the unique needs of healthcare professionals in various regions. Our study found a close positive correlation between leadership support in Chinese leading public hospitals and employee job satisfaction. They achieve this by providing ample resources to ensure employees can effectively fulfill their job responsibilities. Furthermore, they create a comfortable work environment and encourage active employee participation. By nurturing outstanding leadership and support, hospitals can enhance employee job satisfaction, leading to improved overall performance and service quality. This is crucial for providing high-quality healthcare and meeting patient needs. Data availability Data are available upon reasonable request. Kempster S, Parry KW. Grounded theory and leadership research: a critical realist perspective. Leadersh Q. 2011;22(1):106–20. Article   Google Scholar   Northouse PG. Leadership: Theory and Practice: Leadership: Theory and Practice; 2014. Mosadeghrad AM. Factors affecting medical service quality. Iran J Public Health. 2014;43(2):210. PubMed   PubMed Central   Google Scholar   de Vries JM, Curtis EA. Nursing leadership in Ireland: experiences and obstacles. Leadersh Health Serv. 2019;32(3):348–63. Boamah SA, Laschinger HKS, Wong C, Clarke S. Effect of transformational leadership on job satisfaction and patient safety outcomes. Nurs Outlook. 2018;66(2):180–9. Article   PubMed   Google Scholar   Likert R. The human organization: its management and values. 1967. Inceoglu I, Thomas G, Chu C, Plans D, Gerbasi A. Leadership behavior and employee well-being: an integrated review and a future research agenda. Leadersh Q. 2018;29(1):179–202. Mendes L, Fradique MJJG. Influence of leadership on quality nursing care. Int J Health Care Qual Assur. 2014;27(5):439–50. Shanafelt TD, Noseworthy JH, editors. Executive leadership and physician well-being: nine organizational strategies to promote engagement and reduce burnout. Mayo Clinic Proceedings; 2017: Elsevier. Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002;288(16):1987–93. Cicolini G, Comparcini D, Simonetti V. Workplace empowerment and nurses’ job satisfaction: a systematic literature review. J Nurs Manag. 2014;22(7):855–71. Rosta J, Aasland OG, Nylenna M. Changes in job satisfaction among doctors in Norway from 2010 to 2017: a study based on repeated surveys. BMJ open. 2019;9(9):e027891. Article   PubMed   PubMed Central   Google Scholar   Zhang Z, Shi G, Li L, Bian Y. Job satisfaction among primary care physicians in western China. BMC Fam Pract. 2020;21:1–10. Balasubramanian M, Spencer AJ, Short SD, Watkins K, Chrisopoulos S, Brennan DS. Job satisfaction among ‘migrant dentists’ in Australia: implications for dentist migration and workforce policy. Aust Dent J. 2016;61(2):174–82. Article   CAS   PubMed   Google Scholar   Mascari C. Job satisfaction of doctors vs. researchers in the US University Hospital Environment: a comparative case study. Northcentral University; 2020. Friedberg MW, Chen PG, Van Busum KR, Aunon F, Pham C, Caloyeras J et al. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q. 2014;3(4). Nhung DTH, Linh TM. Identifying work-related factors influencing job satisfaction using job descriptive index questionnaire: a study of IT companies in Hanoi. J Int Econ Manage. 2021;21(1):63–85. Gomez Garcia R, Alonso Sangregorio M, Lucía Llamazares Sánchez M. Evaluation of job satisfaction in a sample of Spanish social workers through the ‘Job satisfaction survey’scale. Eur J Social Work. 2018;21(1):140–54. Walkowiak D, Staszewski R. The job satisfaction of Polish nurses as measured with the Minnesota satisfaction questionnaire. J Public Health Nurs Med Rescue. 2019;4:34–40. Google Scholar   Dyrbye LN, Major-Elechi B, Hays JT, Fraser CH, Buskirk SJ, West CP, editors. Relationship between organizational leadership and health care employee burnout and satisfaction. Mayo Clinic Proceedings; 2020: Elsevier. Choi SL, Goh CF, Adam MBH, Tan OK. Transformational leadership, empowerment, and job satisfaction: the mediating role of employee empowerment. Hum Resour Health. 2016;14:1–14. Liu W, Zhao S, Shi L, Zhang Z, Liu X, Li L, et al. Workplace violence, job satisfaction, burnout, perceived organisational support and their effects on turnover intention among Chinese nurses in tertiary hospitals: a cross-sectional study. BMJ open. 2018;8(6):e019525. Wang X, Chontawan R, Nantsupawat R. Transformational leadership: effect on the job satisfaction of registered nurses in a hospital in China. J Adv Nurs. 2012;68(2):444–51. Wang L, Tao H, Bowers BJ, Brown R, Zhang Y. When nurse emotional intelligence matters: how transformational leadership influences intent to stay. J Nurs Manag. 2018;26(4):358–65. Adamopoulos IP. Job satisfaction in public health care sector, measures scales and theoretical background. Eur J Environ Public Health. 2022;6(2):em0116. Montano D, Reeske A, Franke F, Hüffmeier J. Leadership, followers’ mental health and job performance in organizations: a comprehensive meta-analysis from an occupational health perspective. J Organizational Behav. 2017;38(3):327–50. Carlson MA, Morris S, Day F, Dadich A, Ryan A, Fradgley EA, Paul C. Psychometric properties of leadership scales for health professionals: a systematic review. Implement Sci. 2021;16(1):85. Aiken LH, Sermeus W, Van den Heede K, Sloane DM, Busse R, McKee M et al. Patient safety, satisfaction, and quality of hospital care: cross sectional surveys of nurses and patients in 12 countries in Europe and the United States. BMJ. 2012;344. Cunningham R, Westover J, Harvey J. Drivers of job satisfaction among healthcare professionals: a quantitative review. Int J Healthc Manag. 2023;16(4):534–42. Foster TC, Johnson JK, Nelson EC, Batalden PB. Using a Malcolm Baldrige framework to understand high-performing clinical microsystems. BMJ Qual Saf. 2007;16(5):334–41. Shields JA, Jennings JL. Using the Malcolm Baldrige are we making progress survey for organizational self-assessment and performance improvement. J Healthc Qual. 2013;35(4):5–15. Bello S, Adewole DA, Afolabi RF. Work facets predicting overall job satisfaction among resident doctors in selected teaching hospitals in Southern Nigeria: a Minnesota satisfaction Questionnaire Survey. J Occup Health Epidemiol. 2020;9(1):52–60. Ozyurt A, Hayran O, Sur H. Predictors of burnout and job satisfaction among Turkish physicians. J Association Physicians. 2006;99(3):161–9. Article   CAS   Google Scholar   Wang YY, Xiong Y, Zhang Y, Li CY, Fu LL, Luo HL, Sun Y. Compassion fatigue among haemodialysis nurses in public and private hospitals in China. Int J Nurs Pract. 2022;28(1):e13011. Jiang F, Hu L, Rakofsky J, Liu T, Wu S, Zhao P, et al. Sociodemographic characteristics and job satisfaction of psychiatrists in China: results from the first nationwide survey. Psychiatric Serv. 2018;69(12):1245–51. Kendall MG. Note on bias in the estimation of autocorrelation. Biometrika. 1954;41(3–4):403–4. Hinkle DE, Wiersma W, Jurs SG. Applied statistics for the behavioral sciences. Houghton Mifflin college division; 2003. Zhou H, Han X, Zhang J, Sun J, Hu L, Hu G et al. Job satisfaction and Associated Factors among medical staff in Tertiary Public hospitals: results from a National Cross-sectional Survey in China. Int J Environ Res Public Health. 2018;15(7). Liu J, Yu W, Ding T, Li M, Zhang L. Cross-sectional survey on job satisfaction and its associated factors among doctors in tertiary public hospitals in Shanghai, China. BMJ Open. 2019;9(3):e023823. Ritter B. Senior healthcare leaders: exploring the relationship between the rates of job satisfaction and person-job value congruence. Int J Healthc Manag. 2021;14(1):85–90. Shin S, Oh SJ, Kim J, Lee I, Bae SH. Impact of nurse staffing on intent to leave, job satisfaction, and occupational injuries in Korean hospitals: a cross-sectional study. Nurs Health Sci. 2020;22(3):658–66. Shahrbabaki PM, Abolghaseminejad P, Lari LA, Zeidabadinejad S, Dehghan M. The relationship between nurses’ psychological resilience and job satisfaction during the COVID-19 pandemic: a descriptive-analytical cross-sectional study in Iran. BMC Nurs. 2023;22(1):137. Shanafelt TD, Hasan O, Dyrbye LN, Sinsky C, Satele D, Sloan J, West CP. Changes in Burnout and Satisfaction With Work-Life Balance in Physicians and the General US Working Population Between 2011 and 2014. Mayo Clin Proc. 2015;90(12):1600-13. Laschinger HKS, Wong CA, Grau AL. The influence of authentic leadership on newly graduated nurses’ experiences of workplace bullying, burnout and retention outcomes: a cross-sectional study. Int J Nurs Stud. 2012;49(10):1266–76. Chang C-S. Moderating effects of nurses’ organizational support on the relationship between job satisfaction and organizational commitment. West J Nurs Res. 2015;37(6):724–45. Lake ET, Friese CR. Variations in nursing practice environments: relation to staffing and hospital characteristics. Nurs Res. 2006;55(1):1–9. Bååthe F, Erik Norbäck L. Engaging physicians in organisational improvement work. J Health Organ Manag. 2013;27(4):479–97. Zhang M, Zhu CJ, Dowling PJ, Bartram T. Exploring the effects of high-performance work systems (HPWS) on the work-related well-being of Chinese hospital employees. Int J Hum Resource Manage. 2013;24(16):3196–212. Baek H, Han K, Ryu E. Authentic leadership, job satisfaction and organizational commitment: the moderating effect of nurse tenure. J Nurs Adm Manag. 2019;27(8):1655–63. Robbins B, Davidhizar R. Transformational leadership in health care today. Health Care Manag. 2020;39(3):117–21. Hussain MK, Khayat RAM. The impact of transformational leadership on job satisfaction and organisational commitment among hospital staff: a systematic review. J Health Manage. 2021;23(4):614–30. Mete M, Goldman C, Shanafelt T, Marchalik D. Impact of leadership behaviour on physician well-being, burnout, professional fulfilment and intent to leave: a multicentre cross-sectional survey study. BMJ open. 2022;12(6):e057554. Avolio BJ, Walumbwa FO, Weber TJ, Leadership. Current theories, research, and future directions. Ann Rev Psychol. 2009;60:421–49. Zhang L-f, You L-m, Liu K, Zheng J, Fang J-b, Lu M-m, et al. The association of Chinese hospital work environment with nurse burnout, job satisfaction, and intention to leave. Nurs Outlook. 2014;62(2):128–37. Cummings G, Estabrooks CA. The effects of hospital restructuring that included layoffs on individual nurses who remained employed: a systematic review of impact. Int J Sociol Soc Policy. 2003;23(8/9):8–53. Laschinger HKS, Finegan J, Shamian J. The impact of workplace empowerment, organizational trust on staff nurses’ work satisfaction and organizational commitment. Health Care Manage Rev. 2001:7–23. Wong CA, Laschinger HKS. The influence of frontline manager job strain on burnout, commitment and turnover intention: a cross-sectional study. Int J Nurs Stud. 2015;52(12):1824–33. Alrowwad Aa, Abualoush SH. Masa’deh re. Innovation and intellectual capital as intermediary variables among transformational leadership, transactional leadership, and organizational performance. J Manage Dev. 2020;39(2):196–222. Chiok Foong Loke J. Leadership behaviours: effects on job satisfaction, productivity and organizational commitment. J Nurs Adm Manag. 2001;9(4):191–204. Download references This study was funded by the Fundamental Research Funds for the Central Universities (2020-RC630-001), the Fundamental Research Funds for the Central Universities (3332022166), and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M-1-046). Author information Authors and affiliations. Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China Jinhong Zhao School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China Jinhong Zhao, Tingfang Liu & Yuanli Liu You can also search for this author in PubMed   Google Scholar Contributions JZ, TL, and YL designed the study. JZ collected the original data in China, reviewed the literature, performed the analyses, and wrote the first draft of the manuscript. TL and YL critically revised the manuscript. All authors contributed to the interpretation of data and the final approved version. Corresponding authors Correspondence to Tingfang Liu or Yuanli Liu . Ethics declarations Ethics approval. This study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Chinese Academy of Medical Sciences & Peking Union Medical College Institutional Review Board (CAMS & PUMC-IRC-2020-026). The survey was distributed by department heads and included informed consent and survey materials. The informed consent form described the research objectives, assured anonymity, emphasized voluntary participation, and instructed participants to complete the questionnaire through the online system. The statement ‘No signature is required, completing the survey implies consent to participate in the study’ implies implied consent. Patient and public involvement Statement Patients or the public were not involved in the design, or conduct of our study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s note. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 Rights and permissions. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . Reprints and permissions About this article Cite this article. Zhao, J., Liu, T. & Liu, Y. Leadership support and satisfaction of healthcare professionals in China’s leading hospitals: a cross-sectional study. BMC Health Serv Res 24 , 1016 (2024). https://doi.org/10.1186/s12913-024-11449-3 Download citation Received : 07 January 2024 Accepted : 16 August 2024 Published : 02 September 2024 DOI : https://doi.org/10.1186/s12913-024-11449-3 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Hospital leadership Satisfaction Healthcare professional BMC Health Services Research ISSN: 1472-6963 General enquiries: [email protected] 188 IMAGES (PDF) The Impact of Job Satisfaction on Employee Performance: A Case at (PDF) INTERNATIONAL JOURNAL OF CREATIVE RESEARCH AND STUDIES THE EFFECT (PDF) The impact of job stress on job performance: A case study on (PDF) Impact of Job Related Stress on Employee Performance: A Review (PDF) Factors Affecting on Job Performance (PDF) The Employee Performance: Career Development, Work Motivation VIDEO Hive Systems on WWBT How Does Performance Management Impact Employee Motivation? JOB SATISFACTION HUMAN RESOURCE MANAGEMENT Effectively Managing Employee Performance and Conduct HR Strategy Course: Performance Metrics for Employee Productivity and Efficiency Measurement 12 06 Research Report: Employees’ Perception of Job Satisfaction in the Banking Sector Part 1 COMMENTS An approach to employees' job performance through work environmental 1. Introduction. Job performance is probably the most important and studied variable in industrial management and organizational behaviour (Carpini, Parker, & Griffin, 2017).It can be defined as individual behaviour-something that people do and can be observed-that generates value for the organization (Campbell, McCloy, Oppler, & Sager, 1993) and contributes to the organization's goals ... The future of feedback: Motivating performance improvement through Assessing subsequent behavior change and job performance is both important and complicated for evaluating feedback ... the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial ... (PDF) Job Performance Abstract. This chapter presents an overview of job performance as it is conceptualized in the Industrial/Organizational Psychology literature. It includes a definition of job performance that ... Employee Performance at Workplace: Conceptual Model and Empirical Exploratory factor analysis revealed three distinct factors of employee performance that constitute the new scale: task performance, adaptive performance, and contextual performance (TAC). Reliability study on the sample reported significant internal consistency on the total scale ( a = 0.80) along with the three subscales ( a ranging from 0.80 ... Employee Performance at Workplace: Conceptual Model and Empirical The present study explores the concomitant areas for extending the scope of employee performance as a major domain of human resource (HR) effectiveness. We have interviewed researchers and ... Factors affecting employee performance: an empirical approach A good organizational culture is an important factor affecting employee performance. According to Denison [3], a strong organizational culture can provide clear values, enhance employees' sense of ... The Impact of Incentives on Job Performance, Business Cycle, and Job performance has received research attention in the last few decades . Effectiveness of job tasks involves evaluating the results of employee performance (i.e., financial value of sales). In comparison, productivity is defined as the ratio of effectiveness to the cost of attaining the outcome. For example, the ratio of hours of work that an ... Transformational Leadership and Job Performance: The Mediating Role of Although prior research examined the underlying processes of the relationships between transformational leadership and beneficial outcomes, few attempted to address how transformational leaders motivate their members (Shamir et al., 1993) to help them achieve in-role task requests and exceed expectations ().Understanding the underlying motivation process is important, because motivation is ... Detecting causal relationships between work motivation and job Job performance is defined as "scalable actions, behavior, and outcomes that employees engage in or bring about that are linked with and contribute to organizational goals" (Viswesvaran and ... Job Performance Analysis: Scientific Studies in the Main Journals of Our study analyzed 175 articles that investigated the theme job performance. The results showed the importance of performance in organizational studies, especially in understanding which factors (or variables) contribute to an increase in job performance. Therefore, it is recommended that more research be conducted to further develop this field ... Job performance in healthcare: a systematic review The first research question examines which of the four dimensions of work performance (i.e., task, context, and adaptive performance and counterproductive work behaviour) are described in studies of work performance in healthcare. The results show that these dimensions are applicable to work performance in healthcare. Job Performance In subject area: Social Sciences. Job performance is defined as the total expected value to the organization of the discrete behavioral episodes that an individual carries out over a specified time period. It encompasses both mean performance and performance variation, which are key indicators in pay-for-performance research. Performance Improvement: Articles, Research, & Case Studies on Read Articles about Performance Improvement- HBS Working Knowledge: The latest business management research and ideas from HBS faculty. ... New research on performance improvement from Harvard Business School faculty on issues including the importance of reflection on improving job performance, how to counteract the harmful effects of anxiety ... Factors affecting employee performance: an empirical approach The purpose of this paper is to examine the interrelations between firm/environment-related factors (training culture, management support, environmental dynamism and organizational climate), job-related factors (job environment, job autonomy, job communication) and employee-related factors (intrinsic motivation, skill flexibility, skill level ... The Relationships between Job Performance, Job Burnout, and ... Job burnout (as an independent variable) and job performance (as a dependent variable) are often correlated, and one of the main arguments is that psychological counseling can mitigate job burnout, which in turn influences job performance. This article aims to introduce a new perspective on the subject by establishing a new paradigm in the field. It also explores the role of psychological ... Full article: The impact of high-performance work practices on employee This research measured these levels and sought evidence of whether high-performance work practices (HPWPs) moderate the user's level of burnout. ... Cherrie Jiuhua Zhu, Charmine E.J. Härtel, and Chris Nyland. 2014. "Influence of High Performance Work Systems on Employee Subjective Well-Being and Job Burnout: Empirical Evidence from the ... The Relationship Between Work Engagement and Job Performance 1 School of Education Science, Huaiyin Normal University, Huai'an, China; 2 School of Education, Huzhou University, Huzhou, China; 3 School of Psychology, Nanjing Normal University, Nanjing, China; Based on the job demands-resources model, this study explored the relationships of work engagement, job performance and psychological capital in industry employees. Employee psychological well-being and job performance: exploring 2.3 Affective commitment as a mediator. Many studies had used the construct of affective commitment as an independent variable, mediator and moderating variable because of its importance as an effective determinant of work outcomes such as low turnover intention, job satisfaction and job performance (Jain and Sullivan, 2019; Kundi et al., 2018).There is very little published research on ... Full article: Impact of training on employees performance: A case study Employee performance demonstrated the improvement in production by perfect use of new technology with the help of highly aggravated employees (Al-Omari et al., Citation 2020). Manger used to set high standards for individual in order to measure the performance of employees for the betterment of organization (Buchanan. & Badham, Citation 2020). Job performance in healthcare: a systematic review As healthcare is mainly people work, change and improvement in organisational performance will be closely linked to the performance (i.e., the actions and behaviours) of employees [6]. In other words, the job performance of healthcare professionals is of crucial importance to achieve organisational goals [6 - 8]. Full article: Work conditions and job performance: An indirect The current research firstly, establishes that work place conditions have a positive influence on job performance of the employees. Secondly, work place conditions actually increase the satisfaction of employees because of which, they become productive and efficient. The current research goes a step further from the previous studies and ... The Relationship Between "Job Satisfaction" and "Job Performance": A The purpose of this meta-analytic research is to obtain a clear and unified result for the relationship between job satisfaction and job performance, as previous research has shown contradictions in this regard. A total of 913 articles in both English and Persian languages were obtained from four databases, and finally, 113 articles with 123 independent data were selected and analyzed. The ... (PDF) Job Satisfaction: Understanding the Meaning, Importance, and The results also show that there is a significant relationship between employees' involvement in decision making and job satisfaction (V = 0.294, p<0.05); non-monetary rewards and job retention ... Employee motivation and job performance: a study of basic school Motivation as a meaningful construct is a desire to satisfy a certain want and is a central pillar at the workplace. Thus, motivating employees adequately is a challenge as it has what it takes to define employee satisfaction at the workplace. In this study, we examine the relationship between job motivation factors and performance among teachers of basic schools in Ghana. The study employs a ... Relationship between job satisfaction and organisational performance Besides the relationship between job satisfaction and organisational performance, this conceptual model predicts the existence of a reverse connection or the connection between organisational performance and job satisfaction. Based on the above presented model the main research hypothesis of this article was identified. Research hypothesis. Influence of Digitalisation and Personality on Job Performance Among This research attempted to examine the influence of digitalisation and well-being as aspects of personality traits on job performance among medical doctors in a government hospital in Malaysia. This study collected responses through quantitative close-ended questionnaires from 239 medical doctors, ranging from specialists, general medical ... Does IQ Really Predict Job Performance? Job performance has, for several reasons, been one such criterion. Correlations of around 0.5 have been regularly cited as evidence of test validity, and as justification for the use of the tests in developmental studies, in educational and occupational selection and in research programs on sources of individual differences. Here, those ... Critical analysis of drivers of employee engagement and their impact on Orientation: This study examines the impact of drivers of employee engagement on job performance and investigates the relationship between employee engagement and job performance. Research purpose: The purpose of this study is to assess how various factors, such as job characteristics, organisational support, support from superiors, rewards ... Research: When Bonuses Backfire It's because each incentive design choice both signals information about your own beliefs and intentions as an employer and shapes the signaling value of employee behavior within the organization. Leadership support and satisfaction of healthcare professionals in Healthcare professionals' job satisfaction is a critical indicator of healthcare performance, pivotal in addressing challenges such as hospital quality outcomes, patient satisfaction, and staff retention rates. Existing evidence underscores the significant influence of healthcare leadership on job satisfaction. Our study aims to assess the impact of leadership support on the satisfaction of ... assignment essay homework paper phd report resume review speech thesis