work values research paper

ORIGINAL RESEARCH article

Work values across generations: development of the new work values scale (nwvs) and examination of generational differences.

• Department of Work, Institute of Education and Psychology, Organizational, and Media Psychology, Johannes Kepler University Linz, Linz, Austria

The “battle for talent” requires organizations to more strongly focus on employer branding strategies, and, thus, on work values or work orientations of potential candidates. We therefore developed and validated the New Work Values Scale (Study 1; n  = 316), a brief, 28-item, rating scale that covers a broad set of both, instrumental and symbolic, values, relevant for the appraisal of an employers’ attractiveness. We also applied the scale to a sample representative to the German online population, to explore the controversially discussed generational differences in work values (Study 2; n  = 956). Results revealed that work values associated with sustainable organizational development or basic needs were highly similar across generations. Younger and older generations only differed significantly with regard to how much they valued clarity, money, career, development, stimulation, and relating, all of which are highly plausible from a lifecycle perspective.

Introduction

According to a survey by Manpower from the year 2020 ( https://go.manpowergroup.com/talent-shortage ), the labor market suffers from a dramatic shortage of talents with 75% of all companies reporting hiring difficulties. This represents a sharp increase compared to the last 16 years. The destabilization of the labor market, caused by the pandemic situation due to COVID-19, increases organizational difficulties to recruit well-matching personnel – or even personnel at all. Several sectors currently suffer from high vacancy rates, and new jobs seem to pop up faster than they can be filled ( Ramskogler, 2022 ). This points to the need for organizations to reconsider their recruitment strategies: it is no longer just the job candidate who has to sell him- or herself best to the organization but also the organization which has to sell itself to the candidate. Those kinds of mindsets are nothing new, neither is the awareness of a widespread talent shortage, often termed as “battle for talent” ( Beechler and Woodward, 2009 ). But with the battle for talent expected to further increase in the near future, even those companies that are not having hiring difficulties right now should rethink their strategies for attracting and retaining suitable employees.

Consequently, ‘employer branding’ has been increasingly gaining attention. Even if used in mixed ways, in a very broad definition, employer branding can be understood as all efforts an organization takes to appear as an attractive employer and make people come and stay. Those kinds of efforts might result in a certain image and reputation of an organization (for overview see Theurer et al., 2018 ). Image and reputation are expected to influence the recruitment process insofar as potential job candidates who agree with a certain image will have higher intentions to apply for a job, accept a job if offered by that organization, or also stay at that organization ( Gatewood et al., 1993 ).

From a potential job candidate’s perspective, a certain organizational image or ‘brand’ is supposed to be the sum of ideas about the organization as an employer or place to work. Those ideas are known to be based on instrumental attributes that serve utilitarian functions (e.g., money, job location, job security, or promotion perspectives). Instrumental attributes are suggested to be the primary constituents of employer branding that serve for the discrimination between a better and a less good employer ( Reis et al., 2021 ). Research further points to the importance of symbolic attributes that serve less utilitarian but rather self-expressive reasons, like innovativeness or prestige. Symbolic attributes are intangible and can be understood as a set of characteristics that form the moral and spirit of an organization ( Theurer et al., 2018 ). Those symbolic attributes appear likewise powerful in predicting applicants’ initial attraction to an organization as place to work and should therefore always be considered ( Lievens and Highhouse, 2003 ).

Instruments measuring instrumental and symbolic attributes within the employer branding framework traditionally focus on the one or the other ( Lievens and Slaughter, 2016 ), suggesting that there is a clear distinction between attributes that serve utilitarian reasons only and those that transport the intangible mindset of the organization as an employer. Lievens (2007) developed an instrument to assess both categories within one scale, but with the very specific focus on the army as an employer. This instrument was further adapted to another very specific occupational context (maritime industry; Rai, 2020 ). Contradictory with the tradition of the instrumental-symbolic framework, we argue that there is no clear distinction between instrumental and symbolic attributes forming the ‘brand’. We assume that there might be a spill-over from one classification to the other, suggesting the need for an instrument that treats utilitarian and symbolic organizational benefits based on one shared concept, like basic needs and values. One might consider for instance family-supportive arrangements that an organization offers to its employees, such as work time and work location flexibility. Those attributes are utilitarian on the one hand but also symbolic on the other, as they stand for the symbolic organizational attribute to be family friendly. Or, to name a second example, innovation as a symbolic attribute might have instrumental consequences such as to orient oneself in a fast-changing environment.

The aim of the presented work was therefore to develop and validate a context-free, brief rating scale to assess how much a person values those attributes that form the organizational image as an employer or place to work (i.e., New Work Values Scale , Study 1). Referring to propositions indicating generational differences in employer branding strategies ( Reis and Braga, 2016 ), we then applied the newly developed scale to a sample representative to the German population, to explore potential generational differences in those values (Study 2).

Valuable work attributes

Due to the seminal conceptualization by Backhaus and Tikoo (2004) , employer branding leads to an organizational image that further influences the perception of the employers’ attractiveness, an important antecedent for the successful recruitment of talents ( Backhaus and Tikoo, 2004 ). A positive employer brand is therefore suggested to be essential to attract and retain the best among available talents ( Reis et al., 2021 ). At the core is the question of those main drivers that motivate people to work for a certain organization. What are the benefits, goods, and mindsets that an organization needs to offer to attract and keep talents? What kind of attributes serve for a differentiation between more and less attractive employers?

Several concepts have been suggested to explain work motivators outside of the employer branding framework, such as work values or work orientations.

Work values describe a relatively broad concept of evaluative standards relating to work or the work environment, which can include individual preferences as well as moral standards and social norms ( Dose, 1997 ). This broad definition led to a variety of work value taxonomies and measurement instruments. To overcome the inconsistencies in the work value concept, Consiglio et al. (2017) developed a measure of work values which is grounded in the well-established framework of general life values, the theory of basic personal values by Schwartz (1992) . The resulting work values scale is suggested to capture those very fundamental goals people seek in their work life: (1) Achievement , (2) Power , (3) Benevolence , (4) Universalism , (5) Security , (6) Tradition , (7) Conformity , (8) Self-Direction , (9) Stimulation , and (10) Hedonism ( Consiglio et al., 2017 ). We consider the allocation of those fundamental goals not only essential for the motivation to work but also an essential basis for the differentiation between a more and a less desired employer. Certainly, with regard to the employer branding framework, more specific aspects regarding a certain job or organization should be considered along with these very fundamental goals. Those job- or organization-specific attributes are more comprehensively considered within the conception of work orientations.

In line with Fossen and Vredenburgh (2014) , work orientations are work values defined as fundamental purposes paid work serves in the context of one’s life. They understand work orientations as those values an individual seeks to find meaning in a certain job ( Fossen and Vredenburgh, 2014 ). Work orientations are considered as stable traits, conceptualized as tripartite classification of the perception of a job: (1) Job , (2) Career , and (3) Calling ( Wrzesniewski et al., 1997 ). Each one of these work orientations reflects certain feelings and behaviors within the organizational context ( Pitacho et al., 2019 ). Individuals who see their work as a Job are more oriented on instrumental attributes, such as monetary rewards. Individuals who face their work as Career likewise seek for instrumental, but also symbolic benefits, for instance prestige and status. An attractive employer for individuals who are career-oriented would therefore support career development and advancement. Finally, individuals who hold a Calling orientation are expected to work for the pure intrinsic motivation of fulfilment trough work ( Pitacho et al., 2019 ). Willner et al. (2019) extended the established tripartite model by two additional work orientations: (4) Social-Embeddedness and (5) Busyness . Social-embeddedness-oriented individuals are expected to work mainly for being part of a group or organization, whereas busyness-oriented individuals work to occupy their time ( Willner et al., 2019 ). Interestingly, these work orientations mirror the benefits of work identified in the famous Marienthal study by Marie Jahoda (1981) . According to this research, employment not only offers financial benefits ( cf. Job orientation), but also fulfills basic psychological needs by providing status and identity ( cf. Career orientation), collective purpose ( cf. Calling orientation), social contacts ( cf. Social-embeddedness orientation), as well as activity and time structure ( cf. Busyness orientation).

Another reevaluation of values in the sense of work orientations was introduced by Höge (2011) , who aimed to expand the concept of work orientations with the focus on rather new work realities, namely the work reality of a so called entreployee . Entreployees are suggested to work with increased organizational flexibility, self-organization and self-control, reduced hierarchy levels and with high amount of team and project work. Work orientations regarding the entreployee-concept were classified by nine different orientations: the need for (1) Efficiency , (2) Challenge , (3) Role Clarity , (4) Opportunity Optimizing Career Development , (5) Autonomy , (6) Security , (7) Spatial Flexibility , (8) Temporal Flexibility , and (9) Segmentation of the Work-Life-Domain ( Höge, 2011 ). Thus, the entreployee work orientations strongly tap into two of the three basic psychological needs postulated by the Self-Determination Theory (SDT; for overview see Deci et al., 2017 ): the need for autonomy and the need for competence. 1

We suggest that all of the reported concepts describing values individuals seek for in the context of work are relevant for the employer branding framework, even if they were seldom considered as theoretical ground in that context. Understanding the values and needs that guide people’s behaviors can be considered as essential for the employer’s brand, as employers will be rated most attractive, when they provide the maximum congruence or fit to those values and needs. Furthermore, person environment fit (PE fit) research has shown that poor person-job and/or person-organization fit is a major predictor of employee withdrawal (e.g., Tak, 2011 ). Thus, in order to keep their best employees, managers need to know what their employees’ needs and values are ( Mitchell et al., 2001 ). Otherwise, they will leave it to chance whether or not complementary (i.e., employees’ needs are satisfied by what the job/organization is offering) and supplementary fit (i.e., employees and the organization share similar values) will be achieved ( cf. , van Vianen, 2018 ).

Standardized and validated measurements are available for all reported concepts. However, to our knowledge, no measure exists that integrates the different approaches. Therefore, the first aim of the present research was to provide a brief measurement that combines all above reviewed aspects into one brief rating scale that can be economically administered in the context of employer branding.

Generational differences

The question about generational differences in the workplace is a controversially discussed topic. The controversy starts with the definition of a generation . In its roots, the idea of a generation was the reference to individuals born within the same historical and socio-cultural context who made comparable formative experiences within a set of historical events they experienced with more or less the same age. As a result, a generation was suggested as a set of individuals who hold to some extent “collective memories” ( Lyons and Kuron, 2014 ). Quantitative research predominantly treats generations at the level of birth-cohorts, with currently four different generations that are represented in the working population. Those cohorts are defined as (1) Baby Boomers (born between 1950 and mid-1960s), (2) Generation X (born between the early 60s and early or mid-80s), (3) Generation Y / Millennials (born between mid-80s and late 90s), and (4) Generation Z (born between late 80s and late 90s ( Lyons and Kuron, 2014 ; Parry and Urwin, 2011 ; Pop and Pop, 2019 ). 2

Although this “generational approach” has become quite popular, it bears the age-period-cohort problem, as underlying effects might represent either a process of biological aging, specifics of the period when the cohort was observed, or influences associated with a person’s date of birth. In other words, different age groups are at different stages of life, often referred to as “life cycle effect.” Further, individuals are exposed to different events with population-wide effects that vary with the timeframe of assessment. Also, different birth cohorts experience different histories, institutions, and peer-group socialization ( Browning et al., 2012 ). Thus, even if generational differences are observed, their driving forces remain unclear.

Different generations are frequently expected to hold different values and attitudes toward work. This is why generational diversity in organizations might bear the risk for conflict in the work environment and has become an increasing concern at managerial level ( Joshi et al., 2011 ). The issue of generational differences also has been addressed within the scope of employer branding, albeit to a lesser extent. Reis and Braga (2016) for instance reported that economic values were rated with more importance in the context of employer attractiveness with every consecutive generation. In a final consideration they recommend different branding strategies for the respective generation (e.g., positive workplace which encourages creativity for Baby Boomers, development opportunities and good compensation packages for Generation X, and rewards package, development opportunities and positive workplace design for Generation Y).

Besides a general criticism of the research concept of different generations, holding different generational identities ( Joshi et al., 2010 ), there is controversial empirical support for the notion that generations differ in their work values. Reviewing available results regarding this issue, Twenge (2010) for instance comes to the conclusion that work ethic and work centrality (the importance of work in relation to other life domains) declines with every consecutive birth cohort. Intrinsic values, such as finding meaning and interest in work, on the contrary, were relatively consistent across generations. Further, a critical review by Lyons and Kuron (2014) came to the careful conclusion that generations differ in aspects of their work values and attitudes, as well as in leadership and teamwork preferences. Younger generations seem to put more importance on monetary rewards and leisure and are more extroverted, neurotic, and narcissistic. On the other hand, a review addressing generational differences in work values by Parry and Urwin (2011) sums up that empirical evidence is highly inconsistent: many studies fail to find differences and others contradict the popular stereotypes. Similarly, a rather recent review by Cucina et al. (2018) concludes that even if there are significant differences, effect sizes are pretty small.

Within Study 2 we aim to shed more light on this controversial topic by examining generational differences in a broad set of work values within a representative sample of the German population.

Study 1: Construction of the New Work Values Scale (NWVS)

In line with common recommendations for scale construction ( Clark and Watson, 2019 ; Kyriazos and Stalikas, 2018 ; Simms, 2008 ), a literature review on work values and the respective measurement instruments was conducted in a first step ( cf. Valuable work attributes). Based on this, 15 distinct work values were defined (see Table 1 ). They cover organizational/cultural attributes which are relevant for supplementary fit ( cf. Table 1 , focus on sustainable organizational development) as well as basic needs and individual motivators which are most important in terms of complementary fit. For the 15 work values an initial item pool was generated by nine individuals (researchers in the field of work and organizational psychology, HR practitioners, employees, and students). Next, a team of four experts reviewed the items, taking conventional criteria regarding content relevancy and formulation into account. The best rated items (six to eight for each defined work value) were included in the validation study and then analyzed in terms of structural characteristics (distributions, exploratory factor analyses EFA, and reliability analyses). This, finally, led to the selection of two items for every work value for the final measurement instrument. Their construct validity was further analyzed by examining the correlations with well-established scales for (entreployee) work orientations ( Höge, 2011 ; Willner et al., 2019 ) and Schwartz’s theory of basic values ( Schmidt et al., 2007 ).

Table 1 . Work Values Covered by the New Work Values Scale.

Materials and methods

Participants and procedure.

Three hundred and thirty individuals completed an online questionnaire including questions regarding demographic and employment-related characteristics, the newly developed items, and three well-established scales for the construct validity analyses. They were randomly selected from the German population of Respondi ’s ( www.respondi.com ) online access panel members and received bonus points for their participation that they could eventually swap for products. Excluding respondents with unreasonable response times led to a final sample of n  = 316 individuals (57.6% female; 41.1% male; 0.3% other), aged 18 to 65 years ( M  = 45.62, SD  = 13.78) with levels of education ranging from compulsory (14.9%) to university (23.4%) levels (37.0% vocational and 23.7% high school diploma). The majority (66.8%) was employed, 9.2% were in education, and the remaining participants were unemployed/out of the labor force.

The 97 newly developed work values items (six to eight per work value) that were included in this study are reported in Table A2 in the Supplementary material (please note that the items were developed in German language; however, English translations can be found in Table A2 as well). To examine construct validity of the newly developed scale, three well-established measures were included (their reliability estimates are reported in Table A3 in the Supplementary material ): First, Willner et al.’ (2019) Work Orientation Questionnaire assesses the orientations Job , Career , Calling , Social-Embeddedness , and Busyness with five items each on a 7-point response scale (1 =  not at all ; 7 very much ). Second, the Entreployee Work Orientation Scales ( Höge, 2011 ) assess the nine needs relevant in the entreployee context defined by Höge (2011) with 25 items in total using a 6-point response format with 1 =  unimportant and 6 =  very important . Third, the German 21-item version of the Portraits Value Questionnaire (PVQ; Schmidt et al., 2007 ) was used to measure the ten basic values of Schwartz’s theory with two items each (and three items for universalism) on a 6-point response scale (1 = very much like me ; 6 =  not like me at all ).

The factor loadings of the EFA including the most relevant two items for every work value are shown in Table 2 . The work value Comfort and its associated items were excluded due to substantial overlap with other work values. Variance explained by the 14 factors was 62.74%. The items loaded on the expected factor, and most items demonstrated negligible cross-loadings. Reliability estimates were mostly acceptable to good ( cf. Table 2 ).

Table 2 . Item Loadings (EFA, Promax Rotation, Structure Matrix) and Spearman Brown Reliability Estimates for the 14 Work Values.

Correlations of the 14 work values provided by the NWVS with the constructs of the three other well-established measurement instruments (Work Orientation Questionnaire, WOQ, Willner et al. 2019 ; Entreployee Work Orientation Scale, EWOS, Höge, 2011 ; and the Portraits Value Questionnaire, PVQ, Schmidt et al., 2007 ) were largely as expected (see Table A1 and A3 in the Supplementary material for construct definitions and detailed results) and support construct validity of the NWVS: Corporate Social Responsibility (CSR) and Inclusion correlated most strongly with Universalism (PVQ); Job Security related most strongly to the value Security (PVQ) and the Need for Security (EWOS). Clarity and Flexibility demonstrated the highest correlations with the Need for Clarity (EWOS) and the Needs for Spatial or Temporal Flexibility and Segmentation (EWOS), respectively. Money related most strongly with Power (PVQ) and Job Orientation (WOQ) . Career revealed the highest relationships with Achievement and Power (PVQ), Career Orientation (WOQ) and the Need for Career Development (EWOS). Development most strongly related to Career Orientation (WOQ) as well as the Needs for Efficiency (EWOS) and Achievement (PVQ) . Stimulation / Autonomy had their highest correlations with the respective values ( Stimulation / Self-Direction; PVQ) and needs ( Challenge / Autonomy; EWOS). Also, in line with expectations, Meaning correlated highly with the Calling Orientation (WOQ), while Relating correlated most strongly with Social-Embeddedness Orientation (WOQ). Additionally, there were significant, but not as high correlations between Participation and the values Self-Direction , Universalism , and Benevolence (PVQ). Readiness for Change was expected to most strongly relate to low levels of the value of Tradition (PVQ), which, however, could not be confirmed by the results. Thus, this work value seems to capture aspects other than traditional values.

Study 2: Generational differences in work values

In a next step, the NWVS was administered to a sample representative to the German online population with the aim to examine potential generational differences in work values.

The representative German online sample was recruited with the help of the panel provider Respondi ( www.respondi.com ). As in Study 1, respondents ( N  = 1.115) received bonus points for completing the short online questionnaire that included socio-demographics and employment-related characteristics as well as the newly developed work values scale. Participants with very short response times and who were no longer part of the working population (i.e., retirees) were excluded, resulting in a final sample of n  = 956 (47.3% female; 52.3% male; 0.4% other), aged 16 to 65 years ( M  = 41.92, SD  = 13.51) and with different levels of education (24.8% compulsory, 26.3% vocational, 20.6% high school, and 28.3% university). Three quarters (75.2%) were employed (full-time, part-time, or self-employed), 11.0% were in education, and 13.8% unemployed/out of the labor force.

Work values were measured with the newly developed NWVS ( Table A4 in the Supplementary material reports the final scale). Responses were scored on a 5-point scale with 1 =  do not agree at all , 3 =  part-part , and 5 =  fully agree . A Confirmatory Factor Analysis (CFA) with the 14 work values modelled as latent constructs indicated by the respective two items provided good support for the proposed factor structure, Χ 2 (259) = 734.18, p  < 0.001, Comparative Fit Index (CFI) = 0.957, Tucker Lewis Index (TLI) = 0.932, and Root Mean Square Error of Approximation (RSMEA) = 0.044. Factor loadings ranged from 0.63 to 0.89, and Spearman Brown reliability estimates from 0.67 to 0.87 (see Table A5 in the Supplementary material for detailed results).

Generations were defined based on respondents’ age, relying on conventional classifications ( cf. Eberhardt, 2021 ) of Babyboomers comprising the birth cohorts 1950 to 1964 (19.8%), Generation X the cohorts 1965 to 1979 (30.2%), Generation Y the cohorts 1980 to 1994 (32.8%), and Generation Z the cohorts 1995 to 2009 (17.2%).

To examine generational differences in work values based on the NWVS, a multivariate analysis of covariances (MANCOVA) was conducted with the 14 work values as dependent variables and generation as the independent variable. To control for potential confounding, gender (two dummy variables representing female and divers ), education (continuous variable from 1 representing the lowest level of education, i.e., compulsory, to 4 representing the highest level, i.e., university), and employment status (three dummy variables indicating unemployed , out of the labor force , and in education ) were included as covariates ( cf. Table A6 in the Supplementary material , which reports the bivariate correlations of the work values with sociodemographic characteristics).

Results showed a significant multivariate effect of generation, revealing that, overall, younger cohorts hold stronger work values, F (42, 2,805) = 3.66, p  < 0.001, part. η 2  = 0.052. Significant univariate effects were found for six out of the 14 work values: clarity, money, career, development, stimulation, and relating ( cf. Table 3 ). Post-hoc, Bonferroni corrected, multiple comparisons ( cf. Table 3 ) revealed that the differences were primarily between the two older cohorts (putting higher emphasis on Clarity ) and the two younger cohorts (holding stronger values towards Money , Career , and Stimulation ). Generation Z also hold significantly stronger values towards Development and Relating than Generation X. Figure 1 illustrates the mean work values for each generation.

Table 3 . Generational Differences in Work Values (Univariate Effects).

Figure 1 . Mean Work Values of the Generations. RFC = Readiness for Change, CSR = Corporate Social Responsibility, INC = Inclusion, SEC = Security, PAR = Participation, CLA = Clarity, FLE = Flexibility, MON = Money, CAR = Career, DEV = Development, STI = Stimulation, AUT = Autonomy, MEA = Meaning, REL = Relating.

The present research was conducted within a context of dramatic deterioration in talent shortage and the resulting need for organizations to optimize their branding strategies, summarized by the term “employer branding.” Traditionally, employer branding has focused either on so called instrumental (e.g., pay and benefits) or symbolic (e.g., maintaining self-identity, enhancing someone’s self-image) attributes associated with the attractiveness of an organization as place to work. Consequently, scales measuring attributes in the context of employer branding rather focused on the one or the other ( Lievens, 2007 ; Lievens and Slaughter, 2016 ). With Study 1, we aimed to provide a brief and validated measurement for the rating of a broad set of both, instrumental and symbolic, values, associated with the appraisal of an employers’ attractiveness, the New Work Values Scale (NWVS). With Study 2, we further investigated whether different generations hold different work values and should therefore be addressed in different ways within employer branding strategies.

Construction of the NWVS

The theoretical ground for our selection of the respective set of work values are well established need and value theories, namely the theory of basic personal work values ( Consiglio et al., 2017 ; Schwartz, 1992 ), the seminal concept of work orientations ( Wrzesniewski et al., 1997 ) including current extensions and work trends by Willner et al. (2019) and Höge (2011) , and the framework of self-determination theory ( Deci et al., 2017 ; van den Broeck et al., 2016 ). The NWVS is therefore unique in the embracement of different value theories, assessed with a convenient number of items (28 items encompassing 14 dimensions) and brief processing time (response times of five to ten minutes). Abessolo et al. (2021) recently used a similar approach by combining common frameworks regarding work attributes (work values, work orientations, and career anchors) into one validated measurement. However, the resulting questionnaire claims to measure career choices across the life span and not work-related values in general. Furthermore, Abessolo et al. (2021) looked for overlaps between the selected frameworks, whereas our claim was to integrate the different, additional aspects of several sound value concepts. As a consequence, the NWVS shows a broader set of underlying dimensions. Furthermore, the 15 work values covered by the NWVS include attributes relevant for both supplementary and complementary person-job/organization fit ( van Vianen, 2018 ).

Reliability analyses and exploratory as well as confirmatory factor analyses provide strong support for the 14-factor structure of the NWVS. All 28 items loaded on the expected factors and showed negligible cross-loadings as well as acceptable to good reliability estimates. Construct validity was also supported by correlational analyses between the NWVS and well-established measures. Taken together, the NWVS can be considered a sound instrument to briefly assess a wide range of different work values.

During the last decade there has been an increase in literature discussing generational differences in work values, mainly with the focus on managerial concerns. Currently, the entrance of post-millennials, also called Generation Z, into the labor market, seems to create some tension, assuming that this young work generation will differ significantly from precedent generations. For instance, it is assumed that generation Z will behave more narcissistic, mentally instable, achievement-oriented, and socially interconnected than their predecessors ( Schroth, 2019 ). Generational differences in the workplace hit the popular press as well as empirical research, but with divergent conclusions. Consulting popular press gives the impression that generational differences in the work environment are axiomatic and have to result in new leadership guidance. On the contrary, empirical evidence for the stated differences seems to be weak and/or divergent ( Rudolph et al., 2018 ). Against this background we decided to test for generational differences in the 14 work values assessed by the NWVS. We found significant differences in only 6 out of these 14 work values:

• Clarity : having a strong need for structure, rules, and guidelines that provide stability, consistency, and orientation

• Money : being motivated by monetary rewards

• Career : considering career development opportunities as very important

• Development : always striving to be at the best and to therefore develop knowledge, skills, and competencies

• Stimulation : wanting variety, challenges, and much going on

• Relating : placing great emphasis on good social relationships

Thus, work values associated with sustainable organizational development (i.e., Readiness for Change , CSR , and Inclusion ), basic needs (except for Clarity ; i.e., Security , Participation , and Flexibility ) as well as highly intrinsic aspects (i.e., Autonomy and Meaning ) were pretty similar across different generations. The main generational differences became apparent in individual motivators only and here between the two older compared to the two younger cohorts. Baby boomers and Generation X valued Clarity more than did Generation Y and Z, whereas Generation Y and Z reported stronger values towards Money , Career , and Stimulation compared to their predecessors. Additionally, results showed a significant difference between Generation Z and Generation X in regard to Development and Relating , with Generation Z putting more emphasis on these two work values.

It seems not surprising that the youngest work generation holds the strongest emphasis on Development , considering that they are in the early beginning of their careers. Similarly, the high emphasis of Generation Z towards Relating might represent an age-related preference for intense social exchange, that might decrease as soon as people feel settled in their family and social lives. In general, our results can be considered as supportive for the theory of life-stages or life-cycles rather than the assumption of significantly distinct generational work identities ( cf. Joshi et al., 2010 ).

Interestingly, overall, all generations put a relatively low emphasis on money as the key motivator to work. Although this might appear surprising at a first glance, it is in line with the idea of Jahoda’s latent benefits of work. According to Jahoda (1981) , important drivers why people seek to work are symbolic attributes, such as time structure, social experiences, and personal identity and growth. Stiglbauer and Batinic (2012) could demonstrate that the amount to which employers provide access to these latent benefits significantly contributes to employee’s commitment to work. Transferring this rationale to the context of employer branding, a good salary might be important but not sufficient to motivate people to work for a certain organization. The distinction between a “good” and “not so good” employer or place to work may be based on other factors, such as the amount of appreciation in terms of involvement and flexibility an organization shows to its employees. In its theoretical implication, this notion is in line with Herzberg’s seminal two-factor theory of work motivation, which claims that money, defined as hygiene factor, has the potential to prevent job-dissatisfaction and causes dissatisfaction if not adequately provided. On the contrary, monetary rewards do not significantly contribute to satisfaction and work motivation (for an overview about Herzberg’s theory, e.g., Alshmemri et al., 2017 ). Or, put into other words, money just matters, if not satisfied.

Limitations, future directions, and implications

As the NWVS is a newly developed scale, of course more research is needed to further prove its validity. Another limitation might be represented by the fact that data collection took place in times of COVID-19 pandemic, which affected the labor market at a great pace. It can be hypothesized that this radical change also influenced participant’s work values that might have been rated differently before the labor market shock. Consequently, we consider our results representative for the current work situation, which is a post pandemic situation. Future research will be needed to explore situational differences vs. stability in employees’ work values based on the NWVS. We further want to mention that the NWVS solely considers the perspective of an employee or future job candidate by neglecting the perspective of the organization. It might represent a promising future project to work on a complementary tool, which addresses those attributes that an organization considers as important and essential for creating the organizational brand and attract the ideal job candidates.

However, we would like to highlight that the NWVS is unique in its conception and should provide high practical value in the context of employer branding. Major advantages are the short assessment time and the broad coverage of work values that all have been shown to significantly contribute to the attractiveness of an employer’s brand. The NWVS can therefore be used by companies who consider to reevaluate their strategies to attract talents and/or improve the retention of already hired personnel, for example by implementing benefits and conveniences suited to the desires of their members. Furthermore, it can be used as convenient research instrument in the context of employer branding and employer attractiveness. In addition to this, the NWVS is also suitable for questions relating to person-organization fit. Tanwar and Kumar (2019) state that value-based employer brand dimensions (e.g., positive work culture, corporate social responsibility, or salary) help for the creation of a person-organization-fit. Person-organization fit has been increasingly considered as “extra-role behavior” in personnel selection processes. It is concluded that the congruence in certain values that employees experience with their employers (and vice versa) regulates the selection process as well as positive outcomes, such as climate for well-being, low levels of turnover and absenteeism, and cooperation ( Morley, 2007 ; Schneider, 2001 ). Congruence in values between an employee and its employer was further shown to significantly contribute to job satisfaction, organizational commitment, and intention to remain in a certain job ( Westerman and Cyr, 2004 ). The NWVS can therefore help to find the ideal organization that fits best with individual preferences and can be considered as a low-threshold career counseling tool.

Having applied the NWVS to different generations, we agree with several other empirical examinations that failed to find considerable generational differences in regard to work attributes, preferences, and values (reviews, e.g., Cucina et al., 2018 ; Lyons and Kuron, 2014 ; Parry and Urwin, 2011 ; Rudolph et al., 2018 ). At the same time, we would like to point out that the lack of substantial generational differences in work values does not prove the absence of those differences. Originally emerged from the field of sociology, the theory of distinguishable generations, that hold respective shared collective memories and identities, was not conceptualized to be tested at quantitative level. As a consequence, quantitative research regarding generational differences is hypothesized to be flawed by imprecise definitions (and operationalizations of those definitions) of what exactly is expected from a certain generation to be unique ( Rudolph et al., 2018 ). Results of studies on generational differences are further confounded by effects that might rather be due to age (maturation effects) or a certain period or timeframe, when a certain measurement was provided (period effects). Those confounds are referred to as age-period-cohort problem ( Browning et al., 2012 ; Parry and Urwin, 2011 ), and, of course, also are true for our research.

Overall, our research supports the notion that Babyboomers , Generation X , Generation Y , and Generation Z do not differ as much in their work values as postulated by popular media. Particularly, as long as work values refer to sustainable organizational development, basic needs, and highly intrinsic aspects, the generations seem to be highly similar. And the fact that younger as compared to older generations are more strongly motivated by Money , Career , and Development , or also by Stimulation and Relating , is highly plausible from a lifecycle perspective. For organizations, we therefore highly recommend to adopt a lifecycle perspective to attract and retain employees.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

BS and BB contributed to conception and design of the study and organized the database. BS performed the statistical analyses. BS and MP wrote the first draft and sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

The Johannes Kepler University Linz offered funding for open access publication fees.

Acknowledgments

We would like to thank the people involved in the item generation process for the NWVS, in particular Nina Grossi; also our colleagues Andreas Winklbauer and Sabrina Schopf for translating the questionnaire items, and Andrew van Schaack for proofreading the manuscript.

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1028072/full#supplementary-material

1. ^ The SDT is a macro theory of human motivation that has successfully been applied to the context of work motivation. It postulates three basic psychological needs: the need for autonomy, competence and relatedness that, when satisfied, promote motivation, wellbeing, and effective performance ( Deci et al., 2017 ). Based on SDT, employers should support their employees to gain competencies and feel confident, provide freedom in the work processes to allow to develop and live their own working behaviors, and provide respect and belonging ( Deci et al., 2017 ). Van den Broeck et al. (2016) recommend to apply SDT to organizational thinking, as competence, autonomy and relatedness are considered to improve the work environment if addressed adequately.

2. ^ Please note that there is no uniform definition of the cohorts, and, depending on the authors, the classification of the cohorts differs by a few years.

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Keywords: work values, work orientations, generations, scale development, employer branding

Citation: Stiglbauer B, Penz M and Batinic B (2022) Work values across generations: Development of the New Work Values Scale (NWVS) and examination of generational differences. Front. Psychol . 13:1028072. doi: 10.3389/fpsyg.2022.1028072

Received: 25 August 2022; Accepted: 17 October 2022; Published: 07 November 2022.

Reviewed by:

Copyright © 2022 Stiglbauer, Penz and Batinic. 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: Barbara Stiglbauer, [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.

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Values at Work: The Impact of Personal Values in Organisations

2018, Applied Psychology

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Personal Values and Innovative Behavior of Employees

Innovations are based on the good ideas of individuals; therefore, it is very important to better understand the role that individuals and their personal characteristics play in innovative initiatives. The aim of the current study was to test the relationships between employees’ personal values and their innovative behavior. It was hypothesized that these relationships are mediated by an employee’s job autonomy. We integrated Schwartz’s basic human values theory with the notion that job autonomy is an important job characteristic that can be redesigned to better fit employees’ preferences. The study results (obtained from 263 employees in different branches) showed that openness to change and self-enhancement values are positively related to job autonomy, whereas conservation and self-transcendence values are negatively related to job autonomy, which confirms that personal values are important in explaining autonomy in the workplace. In addition, employees’ self-enhancement values are positively related to their innovative behavior, while conservation and self-transcendence values are negatively related to innovative behavior. Mediation analysis with a bias-corrected bootstrapping method showed that job autonomy is a significant mediator of the relationships between employees’ personal values (except for openness to change) and their innovative behavior. Our research extends the theory of basic human values, showing that values serve as a personal basis for innovative behavior. Our results also contribute to the innovation research by demonstrating the importance of personal values and job autonomy for innovative behavior in organizations.

Introduction

Innovation is widely recognized as important for the effectiveness and success of organizations ( Yuan and Woodman, 2010 ; Anderson et al., 2014 ; Razmus and Laguna, 2018 ). Due to the growing demands and expectations of customers and the global expansion of markets, innovation has become important for companies ( Anderson et al., 2018 ). The importance of innovation has also been noted by scientists, and research concerning innovation and creativity has garnered much attention from scholars in the last 20–30 years ( de Jong and den Hartog, 2010 ). Although there is a significant amount of empirical evidence regarding the antecedents of innovative behavior in organizations, there is still a need for more research on predictors ( Hammond et al., 2011 ). It is particularly important to better recognize the psychological mechanisms that are conducive to employee innovation, extending the knowledge gained from management research investigating organizational variables. In their recent review, Anderson et al. (2018) called for more research to broaden our understanding of individual innovation in organizations. Addressing this gap in the literature, we explain employees’ innovative behavior in our study.

Employees are the individuals who create and implement innovative solutions in organizations; therefore, their behaviors are critical to organizational innovation. The literature provides evidence of some individual innovation antecedents (for a review, see West, 2002 ; Anderson et al., 2004 , 2014 , 2018 ; Hammond et al., 2011 ); however, only recently has research started to investigate the role of personal values in explaining innovation. Because personal values are the guiding principles in people’s lives, affecting their goals and actions ( Schwartz, 1992 ), it is important to study their roles in employees’ innovation ( Anderson et al., 2014 ). It is particularly promising because values are postulated as being important drivers of actions in organizational settings ( Meglino and Ravlin, 1998 ; Sagiv et al., 2011a ). However, empirical studies concerning these relationships are scarce.

Responding to this literature gap, the present study applies Schwartz’s theory of basic human values ( Schwartz, 1992 ) to explain which factors foster innovative behavior in employees. We also postulate the potential mechanism, testing job autonomy as a mediator in the relationships between personal values and innovative behavior. In addition, we propose a new approach to job autonomy as an individual perception of a workplace setting that can be fostered by an employee’s personal values. In the subsequent sections, detailed explanations concerning all relationships that are considered in this study will be presented.

The study contributes to the literature by providing new insight into Schwartz’s theory of basic human values ( Schwartz, 1992 ), job characteristics theory ( Hackman and Oldham, 1976 ), and the innovation literature. Namely, it extends these theories by testing whether personal values motivate people to shape their work conditions and stimulate their innovative behavior in the workplace. Moreover, whereas most of the previous research has focused on the organizational level of innovation (see meta-analyses: Damanpour, 1991 ; Rosenbusch et al., 2011 ), our research proposes a conceptual model of a mechanism stimulating employees’ workplace innovation, combining both individual and contextual factors. Based on this approach, we answer the recent call in the innovation literature to reveal the mechanisms through which innovation can be driven ( Hammond et al., 2011 ; Anderson et al., 2014 , 2018 ).

Relationships Between Employees’ Personal Values and Innovative Behavior

Innovation, defined as the generation or adoption of useful and novel ideas that are effectively introduced in organizations ( Amabile, 1988 ; Verhees and Meulenberg, 2004 ; Rosenbusch et al., 2011 ), is important for their business success ( Rosenbusch et al., 2011 ). Innovation in organizations includes the introduction not only of big ideas that significantly change existing practices but also of small, incremental improvements in coping with daily challenges at work ( Amabile, 1988 ; Camisón-Zornoza et al., 2004 ; Weinberger et al., 2018 ). The small-scale innovations manifesting themselves in everyday innovative behavior are based on creative ideas ( Weinberger et al., 2018 ). However, innovative behavior includes not only generating ideas (which is specific for creativity; Amabile, 1988 ) but also implementing them in organizations ( Scott and Bruce, 1994 ). As creativity is considered a first step toward innovation ( West and Farr, 1992 ; Amabile, 1996 ; Anderson et al., 2014 ), in the subsequent sections, we utilize both the creativity and innovation literature to build our arguments and hypotheses concerning the relationship between employees’ personal values and innovative behavior.

The theory of basic human values proposed by Schwartz (1992) is currently considered to be the most comprehensive and empirically grounded approach to human values ( Sagiv et al., 2011a ; Cieciuch, 2013 ). Schwartz argued that values are “desirable transsituational goals, varying in importance, that serve as guiding principles in the life of a person or other social entity” ( Schwartz, 1994 , p. 21). Values have motivational power by providing direction and emotional intensity to action and by being acquired through socialization, in the context of dominant group values, as well as through individual learning ( Schwartz, 1994 ). The central assumption of the theory is that basic values form a universal, circular continuum and are organized in accordance with the motivation that they express. Relationships between motivations can be compatible, conflictual, or irrelevant to one another ( Schwartz, 1992 ). Due to its structure, the value continuum can be partitioned in different manners ( Sagiv and Roccas, 2017 ). Ten initially described basic values can be structured into the following two bipolar dimensions: (1) openness to change (self-direction and stimulation) versus conservation (tradition, conformity, and security) and (2) self-transcendence (universalism and benevolence) versus self-enhancement (power and achievement); hedonism values share aspects of both dimensions ( Schwartz, 1992 ). A distinction between the four higher-order values representing the endpoints of these two dimensions (i.e., openness to change, conservation, self-enhancement, and self-transcendence) is frequently used in research (e.g., Vecchione et al., 2015 ; Cieciuch et al., 2016 ) and will be applied in our study.

Personal values are closely related to motivation and thus help explain behavior ( Cieciuch, 2017 ). Behavior, which expresses people’s individual values, enables them to attain their goals and personal aspirations ( Sagiv and Schwartz, 2000 ; Bardi and Schwartz, 2003 ). People are motivated to behave in accordance with their values because they look for a sense of consistency between their beliefs and actions ( Rokeach, 1973 ). Employees are therefore willing to rely on their personal values in making decisions, choosing actions, and justifying their behavior ( Arieli and Tenne-Gazit, 2017 ). The inability to implement and realize individual values in the workplace has been found to be positively related to job burnout ( Retowski and Podsiadły, 2016 ) and negatively related to job satisfaction ( Amos and Weathington, 2008 ).

Personal values, being guiding principles in life, can also affect people’s creativity and innovative behavior ( Anderson et al., 2014 ). Indeed, some studies have shown such relationships ( Rice, 2006 ; Dollinger et al., 2007 ; Kasof et al., 2007 ; Lipponen et al., 2008 ; Sousa and Coelho, 2011 ). Nevertheless, this evidence is relatively scarce and is partially derived from student samples (e.g., Dollinger et al., 2007 ; Kasof et al., 2007 ). Therefore, there is a need to systematically examine how exactly personal values are related to innovative behavior in the workplace, a point that has been recently emphasized by scholars ( Anderson et al., 2014 , 2018 ). Bardi and Schwartz (2003) , p. 5, stated that “the natural way to pursue important values is to behave in ways that express them or promote their attainment.” Therefore, we expect some values to foster innovative behavior in employees and others to be negatively related to it. As very little empirical research investigating such relationships has been conducted, our hypotheses are based mostly on theoretical assumptions derived from Schwartz’s values theory ( Schwartz, 1992 ) and on research findings concerning creativity.

The higher-order value of openness to change comprises self-direction and stimulation ( Schwartz, 1992 ). According to the theory of basic human values, the motivational goals of openness to change are a willingness to choose, create, and explore and a preference for novelty ( Schwartz, 1992 ) and change ( Ros et al., 1999 ). Self-direction has been argued to be the value that is the most important for creativity for at least two reasons ( Dollinger et al., 2007 ). First, creativity was one of the specific values used by Schwartz (1992) to capture self-direction. Second, because the motivational goal of self-direction involves independence in thought and action, self-direction can be reflected through exploration and free choice in following individual interests, which are perceived to be crucial for creative individuals ( Helson, 1990 ; Barron, 1997 ). Because self-directed people prefer to be independent both in thought and in action, this value seems to be conducive not only to the generation of creative ideas but also to innovation implementation. The motivational goal of stimulation in Schwartz’s theory ( Schwartz, 1992 ) involves seeking novelty, excitement and challenges in life. Therefore, the value of stimulation also seems to promote innovative behavior as a method of attaining these goals. These characteristics allow us to suppose that openness to change values will be the most favorable to innovative behavior among all other higher-order values. Indeed, researchers have reasoned that due to their motivational meanings, openness to change values are associated with innovation and creativity ( Arieli and Tenne-Gazit, 2017 ), and previous empirical research has confirmed the relationships between these constructs. Kasof et al. (2007) found that both self-direction and stimulation are positively related to individual creative performance. Another study showed that openness to change values positively predict creativity ( Dollinger et al., 2007 ). Employees who ranked low on openness to change were found to be less creative ( Sousa and Coelho, 2011 ), and self-direction was positively related to employee creative behavior ( Rice, 2006 ). Based on Schwartz’s (1992) theory, we can expect that when employees strongly value novelty, experimentation, and exploration (typical for people with openness to change values), they will be willing to behave innovatively. Because of these theoretical assumptions and previous research findings, we developed the following hypothesis.

Hypothesis 1a . Employees’ openness to change values are positively related to their innovative behavior.

In contrast, conservation values, which include conformity, security, and tradition ( Schwartz, 1992 ), seem to have a negative effect on employees’ innovativeness. Such values predispose an individual to accept customary behavior and established procedures and ideas, which are undoubtedly not conducive to innovativeness ( Schwartz and Bardi, 2001 ; Schwartz, 2006 ; Sousa et al., 2012 ). Because the motivational goal of conformity is to restrain actions, inclinations, and impulses to avoid upsetting or violating social norms and expectations ( Schwartz, 1992 ), employees who attribute high importance to this value may avoid undertaking innovative initiatives because it may produce changes that are not easily welcomed by others in their organizations. Employees’ security values also do not seem to be favorable for innovative behavior because they focus on stability, safety, and harmony, whereas implementing innovations in companies often requires breaking the status quo and disrupting established organizational conventions, norms, and procedures. Tradition values emphasize the acceptance of imposed, traditional customs and ideas ( Schwartz, 1992 ). Innovative activities are not congruent with such an approach, and employees who want their innovative ideas to be implemented in organizations should definitely take the initiative on their own and strive for idea realization, which sometimes requires substantial effort. These theoretical expectations are somewhat supported by previous research findings. Dollinger et al. (2007) confirmed the negative relationship between conservation values and creative accomplishments. A study conducted by Lipponen et al. (2008) revealed that employees who emphasized conservation versus openness to change values suggested fewer new initiatives for change in the workplace. In addition, employees who ranked high on conservation values tended to be less creative than those who ranked high on openness to change values ( Sousa and Coelho, 2011 ), and employees who prioritized conformity were less creative than those who instead preferred self-direction ( Rice, 2006 ). Moreover, Kasof et al. (2007) found that all three conservation components – tradition, conformity, and security – were negatively related to creative performance. Based on these premises, we expect that an employee who is not willing to introduce novelty and rejects alternative, unfamiliar methods and new perspectives (which is typical for those who hold conservation values) will not be willing to behave innovatively (including idea generation, promotion and implementation) because it may potentially disturb the status quo. Thus, we developed the following hypothesis.

Hypothesis 1b. Employees’ conservation values are negatively related to their innovative behavior.

Self-enhancement values are reflected in power and achievement ( Schwartz, 1992 ), both of which focus on social esteem. Power reflects the goals of prestige, social status attainment, and control or dominance over people and resources. Implementing innovative ideas in the workplace can be a potential method of attaining such goals because employees who behave innovatively can be appreciated by managers who strive for innovative performance at their firms ( Janssen et al., 2004 ). An employee can also perceive innovative behavior as a means to obtain social prestige in an organization and to have a leading, distinguished position among others. Furthermore, the central goal of the value of achievement is personal success, which is accomplished by demonstrating competence, in accordance with social standards ( Schwartz, 1992 ). Innovative activities can help to achieve such a goal because an innovative employee may attain a distinguished position among co-workers and can be perceived as being successful. An employee’s innovative behavior may also be appreciated by supervisors, leading to benefits such as financial bonuses or promotions, which may indicate prestige and status. Nevertheless, previous research findings concerning relationships between self-enhancement values and creativity are not consistent. On the one hand, Dollinger et al. (2007) found that power values had a negative effect on creativity. On the other hand, Sousa and Coelho (2011) found that bank employees who attributed high importance to self-enhancement values were more creative in their work. In addition, Taştan and Davoudi (2017) demonstrated that both power and achievement values had a positive effect on organizational innovativeness among employees in managerial positions. These results seem to correspond with the finding that power motivation is important for creative personality ( Helson, 1996 ), and the notion that strong achievement orientation is demonstrated by creative people ( Mumford, 2000 ; Sousa and Coelho, 2011 ). Attaining goals related to power and achievement values may be possible when people promote and implement their creative ideas. These activities can help employees gain prestige, increase their organizational status, and be perceived as successful by co-workers and supervisors. In conclusion, we postulate that employees who attribute high importance to self-enhancement values are more willing to behave innovatively.

Hypothesis 1c. Employees’ self-enhancement values are positively related to their innovative behavior.

Self-transcendence values consist of universalism and benevolence ( Schwartz, 1992 ). They reflect an individual’s basic need to establish social relations with other people ( Arieli and Tenne-Gazit, 2017 ). Being the most abstract among values, they seem to be the most unrelated to the work context ( Sousa et al., 2012 ). However, there is some empirical evidence showing that they can be related to creativity. Gump (2007) found that universalism positively predicts creativity among college students. Similarly, Kasof et al. (2007) showed that universalism is positively correlated with undergraduate students’ creative performance. In the study conducted by Dollinger et al. (2007) , higher self-transcendence values predicted both higher creative accomplishments and more creative products. Nevertheless, these studies were conducted using student samples. Conversely, Sousa and Coelho (2011) found that frontline bank employees who attributed high importance to self-transcendence were less creative than those who had stronger self-enhancement values. Although there is some empirical evidence concerning the relationship between self-transcendence and creativity, we do not consider it to be sufficient to postulate a specific hypothesis about how these values are related to innovative behavior in the workplace as an activity that includes idea generation, promotion, and implementation. The lack of sufficient evidence is due to some inconsistencies in previous research and – above all – the lack of clear theoretical premises on the potential relationship direction between these variables.

Relationships Between Employees’ Personal Values and Autonomy

Personal values are considered to be the core of personality, affecting attitudes, evaluations, and decisions ( Feather, 1988 ) and acting as a guiding force to peoples’ perceptions and actions ( Schwartz, 1994 ). Therefore, values can also be related to employees’ job autonomy. We propose that a specific set of values can predispose people to proactively strive for autonomy in their work, while other values may not motivate such a pursuit.

Autonomy is known to be one of the most frequently studied phenomena in work and organizational settings ( Morgeson and Humphrey, 2006 ). It is a motivational tool ( Sarros et al., 2002 ; Biron and Bamberger, 2010 ) leading to positive work outcomes, such as innovation and creativity ( Hammond et al., 2011 ; Liu et al., 2011 ; De Spiegelaere et al., 2014 ), job satisfaction, internal work motivation (see Humphrey et al., 2007 ) and work engagement ( Halbesleben, 2010 ). In most of these studies, autonomy is conceptualized, following Hackman and Oldham’s (1976) job characteristics theory, which classifies autonomy as one of the core job characteristics and defines it as the degree of freedom and independence provided by a job. Such freedom can be reflected in making decisions, scheduling work, and determining work methods and procedures applied in an organization. Another meaningful theoretical approach is self-determination theory ( Ryan and Deci, 2000 ), which considers autonomy as one of the three basic psychological needs and suggests that the satisfaction of these needs is necessary for people to flourish ( Deci and Ryan, 2000 ). In this context, autonomy is known to be supported by supervisors and their human resource practices ( Park and Jang, 2015 ), whereas in job characteristics theory ( Hackman and Oldham, 1976 , 1980 ), autonomy is acknowledged as an objective task characteristic that can also be provided by the job itself. To integrate these approaches and to extend them using insights from new theories explaining employees’ proactive functioning ( Wrzesniewski and Dutton, 2001 ; Tims and Bakker, 2010 ), we propose another perspective to capture employee job autonomy. We suggest that (1) job autonomy is, to some extent, dependent on the work environment and supervisor actions, such as human resource practices, as postulated by the job characteristics theory ( Hackman and Oldham, 1976 , 1980 ); however, (2) to some extent, job autonomy can also be shaped by the employee on his/her own. This argument aligns with the conception of proactive actions as “the physical and cognitive changes individuals make in the task or relational boundaries of their work” ( Wrzesniewski and Dutton, 2001 , p. 179). Employees make such self-initiated changes in their job features to customize them to fit their strengths, passions, and motives ( Berg et al., 2008 ). Traditional job design theories, such as job characteristics theory ( Hackman and Oldham, 1976 ), consider managers as job crafters because they design tasks for their subordinates and, therefore, can change their motivations and satisfaction ( Wrzesniewski and Dutton, 2001 ). However, employees are able to proactively redesign their jobs on their own, and such self-initiated changes made in an employee’s own job demands and job resources are postulated to help them attain or optimize their work goals ( Tims et al., 2012 ). Indeed, research has shown that employees who participated in job redesign initiatives experienced increases in job autonomy after 2 months ( Tims et al., 2013 ). Therefore, there is support for the theoretical postulates that job autonomy can be influenced not only by managers through top-down processes but also by employees on their own. In this manner, we define job autonomy by integrating existing theoretical conceptions.

As noted by Morgeson and Humphrey (2006) , autonomy has a central place in motivational work approaches. In addition to being the most widely studied job characteristic, it is also the most influential ( Humphrey and Morgeson, 2008 ). Moreover, job autonomy is the job characteristic related to innovative behavior (e.g., Liu et al., 2011 ; De Spiegelaere et al., 2014 ; Orth and Volmer, 2017 ), and it also seems to be related to personal values. Thus, we concentrate on job autonomy in our study.

We assume that an individual can strive to have more autonomy at work when it is congruent with his/her personal values. As personal values have been proven to develop in the early stages of life and then be relatively stable across time ( Vecchione et al., 2015 , 2016 ; Cieciuch et al., 2016 ), and as job autonomy is more likely to change in relation to the organizational context, the job itself, and the relationship between the supervisor and the subordinate ( Hackman and Oldham, 1976 , 1980 ), we treat values as predictors of job autonomy. Based on Schwartz’s (1992) theory, we expect that employees can be more or less disposed toward seeking autonomy in their work based on the basic personal values they prefer. A person can be highly motivated to have an opportunity to make decisions and feel independent at work because it is of central significance to him/her, while another person might focus on other attributes of the job and not strive for autonomy because he/she does not consider it to be important for his/her work functioning. As noted by Sagiv and Roccas (2017) , p. 4, values “represent desirable goals and reflect what people consider important and worthy.” For instance, when an employee attributes high importance to openness to change values, which focuses on autonomy in thought and action, novelty, and challenge, we can expect that he/she will pursue the highest possible job autonomy. However, when an employee emphasizes conservation values, the core of which is to maintain the status quo and to follow norms and rules, he/she will be not as motivated to strive for autonomy at work. Certainly, the fact that a job is autonomous is also, to some extent, determined by other factors, such as the nature of the job itself (e.g., artistic professions will be naturally more autonomous than receptionist or cashier jobs), or by managers, who may or may not allow their subordinates to make decisions, schedule their work, or choose work methods on their own. Nevertheless, drawing on the basic human values theory ( Schwartz, 1992 ), we expect job autonomy to be predicted by employees’ personal values. Below, we formulate hypotheses related to each of the four higher-order values.

Schwartz’s (1992) theory characterizes openness to change values, which includes self-direction and stimulation, as being focused on “independent action, thought and feeling, and readiness for new experience” ( Schwartz, 2003a , p. 269). The central goal of self-direction is the person’s independence, both in thinking and in acting. Schwartz states that self-direction is based on the organismic needs for, on the one hand, control and mastery and, on the other hand, requirements of autonomy and independence ( Schwartz, 1992 ). Stimulation is described as being focused on novelty, challenge, and excitement. This value type is derived from a need for stimulation and variety to maintain an optimal level of activation ( Schwartz, 1992 ). These theoretical assumptions concerning self-direction and stimulation, which constitute the openness to change values, suppose that these values are particularly conducive to job autonomy in employees. Sagiv and Schwartz (2004) argued that among career counseling clients, self-direction is relevant to initiating actions, self-reliance, and independence of thought in making career decisions. Indeed, their findings confirmed that the priority clients gave to self-direction was positively correlated with their independent behavior, as assessed by career counselors. There is also some evidence concerning the role of personal values in professional choice. A stronger emphasis on openness to change values predicts artistic and investigative careers ( Sagiv, 2002 ; Knafo and Sagiv, 2004 ) and entrepreneurial career intentions ( Gorgievski et al., 2017 ). Based on these theoretical and empirical premises, we expect that the importance that employees attribute to openness to change values is positively related to their work autonomy.

Hypothesis 2a. Employees’ openness to change values are positively related to their job autonomy.

Conservation values, which include the values of conformity, security, and tradition, are in conflict with openness to change. Conformity values are focused on self-restraint, including self-restraint of actions, impulses, and inclinations, which are reflected in everyday interactions with close others ( Schwartz, 1992 ). Valuing security motivates the maintenance of harmony, stability, and safety of the self and relationships with others and society. The tradition value emphasizes the respect and acceptance of imposed traditional ideas and customs. Together, the conservation values encourage status quo maintenance, resistance to change and self-restriction to avoid violating social norms ( Schwartz, 2003a ). These characteristics do not seem to be conducive to pursuing autonomy in the workplace. An employee who attributes high importance to conservation values may accept the existing situation and not strive to enhance his/her job autonomy because it can be harmful for organizational rules and norms. He/she may be afraid that attempts to increase work autonomy could be negatively perceived by superiors or other co-workers. There is some empirical evidence that can shed some light on the potential relationship direction between an employee’s conservation values and his/her work autonomy. In a study conducted by Sagiv and Schwartz (2004) , clients’ emphasis on conformity values was found to be negatively related to their independent behavior, which they expressed in the career counseling process. Moreover, an emphasis on conservation values predisposes individuals to engage in rather conventional professions, such as accountants, administrative managers, or receptionists, and to hold vocational interests that demand following well-defined instructions and rules, systematic operations, and obeying norms ( Sagiv, 2002 ; Knafo and Sagiv, 2004 ). Because peoples’ professional choices affect the types of behavior in which they are willing to engage in the workplace ( Holland, 1997 ; Arieli and Tenne-Gazit, 2017 ), we expect that people who attribute great importance to conservation values do not strive to enhance their job autonomy because it is not congruent with their values. Therefore, we developed the following hypothesis.

Hypothesis 2b. Employees’ conservation values are negatively related to their job autonomy.

The higher-order value of self-enhancement includes power and achievement ( Schwartz, 1992 ). Power is focused on attaining prestige, social status, dominant position, and control over people and resources. The value of achievement is concentrated on personal success, which can be attained through competence demonstration. The theoretical assumption is that employees who attribute high importance to self-enhancement values will strive to have more autonomy in their workplaces. Feeling autonomous and independent at work seems to be crucial to attaining dominance and control over other co-workers and to developing self-interest goals. An employee who is self-confident and autonomous can express his/her competence in the workplace. Thus, the motivational goals of self-enhancement can be attained. A highly autonomous job is more challenging and creates feelings of personal responsibility and control of outcomes at work ( Hackman and Oldham, 1980 ; Sousa et al., 2012 ). Mumford (2000) argued that power and achievement are strong motives for people who tend to be independent. In addition, career counseling clients’ achievement values were found to be positively related to their independent behavior (rated by counselors) ( Sagiv and Schwartz, 2004 ), and higher self-enhancement values predicted entrepreneurial career intentions in students from different countries ( Gorgievski et al., 2017 ). Based on these premises, we expect that employees’ self-enhancement values are positively related to their autonomy at work.

Hypothesis 2c. Employees’ self-enhancement values are positively related to their job autonomy.

The higher-order value of self-transcendence encompasses universalism and benevolence. Universalism is focused on the welfare of all people, as well as nature ( Schwartz, 1992 ). The motivational goal of benevolence involves concern for people who are relatively close, and this concern is expressed in everyday interactions. As previously stated, self-transcendence is the most abstract higher-order value, and it has been argued that it is not as strongly related to work context as other values ( Sousa et al., 2012 ). Nevertheless, there is some empirical evidence concerning the effects of the self-transcendence values on work-related issues. These values were found to be positively related to altruistic and pro-social behaviors at work, in contrast to the self-enhancement values ( Sosik et al., 2009 ; Schwartz, 2010 ). Moreover, in a study that used social dilemma games, Sagiv et al. (2011b) found that the participants who attributed high importance to self-transcendence were more willing to cooperate with others than those who emphasized self-enhancement. However, there is no empirical evidence on the relationship between self-transcendence and work autonomy. Based on theoretical assumptions, we can expect that striving for autonomy is not highly important to employees who emphasize self-transcendence. Instead, these employees are likely focused on cooperating with co-workers, showing their concern for others and being tolerant of all people. Nevertheless, theoretical and empirical evidence does not seem to be sufficient to postulate a direct relationship between employees’ self-transcendence and autonomy.

Job Autonomy and Innovative Behavior

Job autonomy is known to be an important contextual antecedent of creativity and innovation ( Amabile et al., 1996 ; Hammond et al., 2011 ; Anderson et al., 2014 ). In the meta-analysis conducted by Hammond et al. (2011) , job characteristics, including job autonomy, were found to be the strongest predictors of creativity and innovation among all predictors evaluated in their study. Having freedom in performing their work, employees are able to find and develop working methods that fit them optimally ( De Spiegelaere et al., 2015 ). Such “space” is necessary for creativity and innovative behavior because these actions are focused on experimenting and developing the best approaches to solve problems ( De Spiegelaere et al., 2015 ). Accordingly, Dierdorff and Morgeson (2013) , p. 694, argued that “by having freedom in the work role (autonomy), individuals are able to take the initiative and perform in a creative manner because they are less constrained in their role performance.”

A number of studies have confirmed that autonomy is positively related to creativity and innovation. Job autonomy was found to be positively related to employees’ innovative behavior at work ( Axtell et al., 2000 ; Ramamoorthy et al., 2005 ; De Spiegelaere et al., 2014 , 2015 , 2016 ) and to job creativity ( Liu et al., 2011 ). In line with job characteristics theory ( Hackman and Oldham, 1976 , 1980 ) and the self-determination theory ( Deci et al., 1989 ; Ryan and Deci, 2000 ), which emphasize the motivational role of job autonomy, and based on the previous research findings, we postulate that job autonomy is positively related to employees’ innovative behavior.

Hypothesis 3. Employees’ job autonomy is positively related to their innovative behavior.

Job Autonomy as a Mediator Between Personal Values and Innovative Behavior

Although personal values have been examined as predictors of creativity and innovation in several studies ( Dollinger et al., 2007 ; Kasof et al., 2007 ; Sousa and Coelho, 2011 ), it is still uncertain exactly how these relationships occur. For example, in a study conducted by Choi (2004) , there was no confirmation of the mechanism proposing that innovative organizational culture is related to innovation-use behavior through innovative values. Therefore, it is necessary to seek other mechanisms explaining individual innovation. Hence, responding to this need, we not only postulate direct relationships between employees’ personal values and their innovative behavior but also propose that job autonomy can mediate these relationships ( Figure 1 ).

Conceptual model of the relationships tested in the study.

It should be mentioned that some previous studies have treated job autonomy as a moderator, rather than a mediator, of relationships between personal values and work outcomes (e.g., Sousa et al., 2012 ; Sousa and Coelho, 2013 ). However, we propose to go beyond this pattern and verify whether job autonomy can play a mediating role. We base our expectation on an understanding of job autonomy as not only “given” by managers or derived from the job itself, as postulated by the traditional job design framework ( Hackman and Oldham, 1976 , 1980 ), but also strengthened by employees on their own, which is consistent with the proactivity conceptions ( Wrzesniewski and Dutton, 2001 ; Berg et al., 2008 ; Tims and Bakker, 2010 ). Morgeson and Humphrey (2008) admit that managers are often responsible for designing or redesigning their subordinates’ work and frequently must even customize the work design to their employees’ individual competencies. Nevertheless, they stress that workers also play the role of proactive ‘crafters’ of their work, dynamically redesigning work to be more suitable to their specific capabilities, interests, or to the situation ( Morgeson and Humphrey, 2008 ). We agree with this argument and expect that employees’ pursuit of job autonomy is predicted by their personal values, which are cognitive representations of their basic motivations and, therefore, affect their choices, decisions, and behaviors ( Arieli and Tenne-Gazit, 2017 ; Sagiv and Roccas, 2017 ). Thus, we propose testing whether job autonomy mediates the relationship between personal values and employees’ innovative behavior.

Hypothesis 4. Employees’ job autonomy mediates the relationships between openness to change ( H4a ), conservation ( H4b ), self-enhancement ( H4c ), self-transcendence (H4d ) values, and innovative behavior.

Materials and Methods

Private sector employees who worked in micro-, small-, and medium-sized enterprises operating in Poland that employ 1–250 employees were invited to participate in the study. Employees working for larger companies or corporations and in the public sector were not asked to participate, as their work may be regulated by stricter company rules ( Frant, 1993 ). The data were gathered through direct contact with the participants using a paper-and-pencil questionnaire. Participation in the study was voluntary, and the participants did not receive any reward. Confidentiality and anonymity were ensured.

Participants

A total of 263 employees (including 138 women) participated in this study. They ranged in age from 19 to 74 years ( M  = 33.88, SD  = 10.62). Concerning work contracts, 155 (55.6%) of the participants were employed under full-time permanent contracts, 49 (18.6%) had temporary contracts, and 59 (25.8%) had another type of work contract. Concerning education, 44.1% of the respondents had a master’s degree, 10.3% had a bachelor’s degree, 34.6% had finished high school, and only 8.7% had graduated from vocational school; six participants (2.3%) did not provide information about their education. The participants’ overall work tenure ranged from 2 months to 46 years ( M  = 11.43 years, SD  = 10.22). Their tenure in their present company ranged from 1 month to 32 years ( M  = 5.63 years, SD  = 6.51). The companies at which they worked employed, on average, approximately 56 people ( M  = 54.94, SD  = 61.73, Me  = 20.00). The participants represented seven types of occupations classified according to the International Standard Classification of Occupations ( ISCO-08, 2012 ): professionals (36.9%), craft and related trade workers (19.8%), service workers and shop sales workers (18.3%), technicians and associate professionals (13.7%), clerks (4.9%), plant and machine operators and assemblers (3.0%), and elementary occupations (1.9%).

Personal Values

To measure the personal values of the employees, the 21-item Portrait Values Questionnaire (PVQ-21) was used ( Schwartz, 2003a ). The measure is not cognitively challenging and is appropriate even for people with little or no formal schooling ( Cieciuch, 2013 ; Roccas et al., 2017 ). The measure includes 21 statements that provide a verbal portrait of different people (i.e., their goals, aspirations, or wishes), implicitly indicating the significance of different value types ( Schwartz, 2003a ). Sample items include the following: Thinking up new ideas and being creative is important to him. He likes to do things in his own original way ; It is important to him to show his abilities. He wants people to admire what he does . Using a 6-point scale ranging from 1 =  very much like me to 6 =  not like me at all , for each item, the participants answered the question, “ How much like you is this person? ” The participants’ responses were recoded so that a higher score reflected a greater importance of the value. Particular items reflect basic types of values, which can be structured into four higher-order values, namely openness to change, conservation, self-enhancement, and self-transcendence. Because hedonism is a component of both self-enhancement and openness to change values ( Schwartz, 2003a ), we decided to exclude it from all further analyses, following previous research (e.g., Huysentruyt et al., 2015 ). Schwartz (2003a) claims that people can differ in their tendencies to respond to value measures when using the response scale (e.g., some people are likely to use only one part of the response scale). Therefore, in most statistical analyses, it is critical to control for such individual differences ( Schwartz, 2003b ). Following this recommendation, we centered raw scores by computing each person’s mean score for all 21 items (i.e., the MRAT), and then, we calculated the corrected scores by subtracting the MRAT from the individual mean score for each value. In the current study, the internal consistency of the scale was evaluated using Cronbach’s alpha, with the following results: 0.56 for openness to change, 0.67 for conservation, 0.68 for self-enhancement, and 0.72 for self-transcendence. Such relatively low reliability results are typical for this measure because of the structure of the questionnaire (i.e., different components of the values are included in each higher-order value) and because every higher-order value was composed of a relatively small number of items ( Schwartz, 2003a ). Therefore, the reliability results should not be treated as a measurement weakness nor should they be considered problematic for the research results. To verify the factorial structure of the measure, confirmatory factor analysis (CFA) using AMOS software ( Arbuckle, 2005 ; Brown, 2006 ) was performed. When assessing the model fit, CFI values higher than 0.90 indicate an acceptable model fit, and for the RMSEA and SRMR indices, values below 0.05 indicate a good model fit and values below 0.08 (and up to 0.10) indicate an acceptable fit; the lower the AIC index is, the better the model fits the data ( Brown, 2006 ). According to previous recommendations ( Cieciuch and Davidov, 2012 ), a one-factor model was tested separately for each of four higher-order values. Concerning openness to change, the factorial model showed good fit to the data ( χ 2 (2)  = 0.089, p =  0.956, CFI = 1.00, AIC = 16.089, RMSEA = 0.000, SRMR = 0.005). In the case of conservation, the model also showed good fit to the data ( χ 2 (9)  = 23.666, p =  0.005, CFI = 0.922, AIC = 47.666, RMSEA = 0.077, SRMR = 0.047). When testing the self-enhancement model, the analysis revealed that it had acceptable fit ( χ 2 (8)  = 6.343, p =  0.042, CFI = 0.975, AIC = 22.343, RMSEA = 0.088, SRMR = 0.032). Finally, when we analyzed the self-transcendence model, it also showed acceptable fit to the data ( χ 2 (5)  = 16.039, p =  0.007, CFI = 0.954, AIC = 36.039, RMSEA = 0.089, SRMR = 0.038).

Job Autonomy

The autonomy experienced by employees at work was measured with four items of the autonomy scale from the Work Design Questionnaire ( Morgeson and Humphrey, 2006 ). Each item of the scale is a statement (e.g., My job allows me to make a lot of decisions on my own ; The job gives me a chance to use my personal initiative or judgment in carrying out the work ) to which the participants should respond using a five-point answer scale that ranges from 1 =  strongly disagree to 5 =  strongly agree. In the present study, Cronbach’s alpha for the autonomy scale was 0.90, which implies very good scale reliability. We conducted CFA, and the one-factor model showed good fit to the data ( χ 2 (1)  = 0.249, p =  0.617, CFI = 1.00, AIC = 36.039, RMSEA = 0.000, SRMR = 0.003).

Innovative Behavior

The participants’ innovative behavior was measured using the Innovative Behavior Questionnaire ( Scott and Bruce, 1994 ). The questionnaire includes six items (e.g., I generate creative ideas ; I develop adequate plans and schedules for the implementation of new ideas ). For each statement, the participants answered how frequently they behaved as described in the statement, using a five-point scale ranging from 1 =  never to 5 =  very often . In the present study, the scale reliability was 0.85, which indicates good internal consistency. CFA, which was conducted following Purc and Laguna’s (2017) recommendations, confirmed the one-factor structure of the measure. The present study confirmed that such a model fits well with the data ( χ 2 (6)  = 7.089, p =  0.313, CFI = 0.998, AIC = 37.089, RMSEA = 0.026, SRMR = 0.015).

Data Analysis Strategy

The first step was to examine whether the data suffered from the common method variance problem. Therefore, Harman’s single factor test was employed ( Podsakoff et al., 2012 ). This technique is currently considered to be the most effective and simplest method of testing common method variance ( Fuller et al., 2016 ). It can be applied by conducting exploratory factor analysis ( Razmus and Mielniczuk, 2018 ). If the one-factor solution reveals that the percent of its explained variance exceeds 50–60%, then the results suffer from the common method variance ( Fuller et al., 2016 ).

In the next step, descriptive statistics and correlations between study variables were analyzed. Previous studies have suggested that creativity and innovative behavior can be affected by sex and age ( Slagter, 2009 ; Alsos et al., 2013 ; Smith et al., 2016 ; Hollanders and Es-Sadki, 2017 ). Therefore, we conducted a hierarchical multivariate regression analysis to test whether there were statistically significant effects of sex and age on innovative behavior and, consequently, whether it was necessary to control for these variables in further analyses.

The fundamental part of the data analysis was testing the postulated hypotheses. To test the hypotheses, particularly to verify whether job autonomy mediates the relationships between personal values and innovative behavior, Model 4 in the PROCESS macro ( Hayes, 2013 ) for SPSS was applied. The use of this macro allows the estimation of an indirect effect by using a bootstrapping technique. Bias-corrected and accelerated bootstrapping with 5,000 repetitions (5,000 samples randomly generated from the whole sample) was applied in the present analyses. In terms of interpreting the results, if the confidence interval does not include zero, it indicates a statistically significant mediation (indirect) effect. The hypotheses concerning direct relationships were also verified based on the PROCESS macro results.

Common Method Variance Test

To examine whether the data gathered in the study suffer from the common method variance, Harman’s single factor test was applied ( Podsakoff et al., 2003 ; Fuller et al., 2016 ). All items of all measures used in the study were loaded into an exploratory factor analysis. An unrotated solution was used. The results showed that a single factor that accounted for most of the covariance among measures did not appear. A three-factor solution was obtained, and the first factor explained 43.93% of the variance, which did not exceed 50% of the variance explained ( Fuller et al., 2016 ). Therefore, it was not necessary to control for common method variance in further analyses.

Descriptive Statistics and Correlations Between the Study Variables

Descriptive statistics and Pearson’s correlations are reported in Table 1 . In terms of personal values, the correlations between both bipolar dimensions are statistically significant and negative, which reflect their opposite positions on the motivational value continuum and aligns with Schwartz’s values theory ( Schwartz, 1992 ). Openness to change values were negatively correlated with conservation ( r  = −0.69, p  < 0.001), and self-enhancement values were negatively correlated with self-transcendence values ( r  = −0.54, p  < 0.001).

Means, standard deviations, and correlations between study variables ( N  = 263).

Note: Pearson’s r correlations are reported. Openness to change, conservation, self-enhancement, and self-transcendence were centered.

Control Variables

A hierarchical multivariate regression analysis was applied to test whether there was a statistically significant effect of sex and age on innovative behavior. Therefore, these two variables were entered into the model as predictors explaining innovative behavior. Sex was coded as a dichotomous variable (0 = male and 1 = female). The regression analysis results showed that neither sex nor age was significant predictors of innovative behavior ( β  = 0.04, p  = 0.531; β  = −0.02, p  = 0.785, respectively). Thus, we did not control for these variables in further analyses.

Hypotheses Testing

Mediation analyses were performed using the PROCESS macro (Model 4, Hayes, 2013 ). All direct and indirect effects were estimated based on these bootstrapped samples. For each of the analyses, direct effects (a, b, and c, as shown in Figure 1 ), an indirect effect (c′) with the bootstrapped standard error ( SEB ), and 95% confidence intervals (CIs) are reported in Table 2 .

Results of mediation analyses.

Note: a  = personal values – autonomy direct effect; b  = autonomy – innovative behavior direct effect; c  = personal values – innovative behavior direct effect; c′  = indirect effect of autonomy. SEB =  bootstrapped standard error; 95% CI  = 95% confidence interval. For a , b , c and c′ effects, unstandardized B coefficients are reported.

First, direct effects were examined to verify hypotheses H1a–H1c. The analysis showed that two of the four higher-order values were related to innovative behavior. Openness to change values were not found to be a significant predictor of employee innovative behavior ( B  = 0.08, SEB  = 0.06, p  = 0.187). Therefore, hypothesis H1a is rejected. Conservation values showed a significant negative effect on innovative behavior ( B  = −0.20, SEB  = 0.07, p  = 0.004), which supports hypothesis H1b . Self-enhancement values were also revealed to be directly related to innovative behavior. As expected, the higher the importance that employees attributed to self-enhancement values, the higher their innovative behavior ( B  = 0.17, SEB  = 0.06, p  = 0.005). Thus, hypothesis H1c is supported. When investigating the relationship between self-transcendence values and innovative behavior, no statistically significant effect was detected ( B  = 0.002, SEB  = 0.07, p  = 0.973). We also examined whether employees’ personal values have a direct relationship with their job autonomy, which was postulated in hypotheses H2a–H2c . The results showed that all four higher-order values were statistically significant predictors of employees’ job autonomy. Openness to change values were found to be a positive predictor of autonomy ( B  = 0.18, SEB  = 0.09, p  = 0.046), which allows hypothesis H2a to be accepted. Conservation values were negatively related to perceived employees’ job autonomy ( B  = −0.22, SEB  = 0.10, p  = 0.024), confirming hypothesis H2b . Self-enhancement values were also shown to be a significant predictor of job autonomy, and this effect was positive ( B  = 0.30, SEB  = 0.09, p  < 0.001). This result indicates that hypothesis H2c is supported. Self-transcendence values were found to negatively predict employees’ job autonomy ( B  = −0.33, SEB  = 0.10, p  = 0.001). Therefore, we can conclude that there is a significant negative relationship between self-transcendence values and job autonomy in employees.

Hypothesis H3 aimed to test the potential positive relationship between employees’ job autonomy and their innovative behavior. The examination of a direct effect of autonomy on innovative behavior confirms this hypothesis; job autonomy was a significant predictor of innovative behavior, and this relationship was positive in each of the four equations, including different personal values (for openness to change, B  = 0.37, SEB  = 0.04, p  < 0.001; for conservation, B  = 0.36, SEB  = 0.04, p  < 0.001; for self-enhancement, B  = 0.35, SEB  = 0.04, p  < 0.001; and for self-transcendence, B  = 0.38, SEB  = 0.04, p  < 0.001).

Next, indirect bootstrapped effects were analyzed to verify hypotheses H4a–H4d . Hypothesis H4a postulated that the relationship between openness to change values and innovative behavior is mediated by job autonomy. The mediation analysis showed that the indirect effect was nonsignificant ( B  = 0.07, SEB  = 0.04, 95% CI [−0.01, 0.16]) because the 95% CI included zero. Therefore, hypothesis H4a is not accepted. The indirect effect of job autonomy on the relationship between conservation values and innovative behavior was found to be significant ( B  = −0.08, SEB  = 0.04, 95% CI [−0.17, −0.01]), thereby confirming hypothesis H4b . Hypothesis H4c is supported as well – job autonomy was found to be a significant mediator of the relationship between self-enhancement values and innovative behavior ( B  = 0.10, SEB  = 0.03, 95% CI [0.04, 0.18]). Finally, there was also a significant indirect effect of job autonomy in the relationship between self-transcendence values and innovative behavior ( B  = −0.13, SEB  = 0.04, 95% CI [−0.21, −0.05]). Thus, this result indicates that hypothesis H4d is also confirmed. In summary, three of the four specific mediation hypotheses are supported. We can conclude that job autonomy is a significant mediator of the relationships between personal values (i.e., conservation, self-enhancement, and self-transcendence, but not openness to change) and employees’ innovative behavior.

The present study aimed to investigate the relationships between personal values, job autonomy, and innovative behavior of employees. We tested whether employees’ personal values predict their innovative behavior, on the one hand, and their job autonomy, on the other hand. We also expected that job autonomy mediates the relationships between values and innovative behavior. The study results confirmed most of our expectations.

Regarding the relationship between personal values and employees’ innovative behavior, our results revealed the effects of two of the four higher-order values, namely conservation and self-enhancement values. As expected, employees who attributed high importance to conservation values, which involve maintaining the status quo and being resistant to change, are less willing to behave innovatively at work. This result supports the postulates derived from Schwartz’s (1992) theory, which states that accepting established procedures, norms, and customary manners of behavior, which are typical for conservation values, is not conducive to innovative behavior ( Schwartz and Bardi, 2001 ; Schwartz, 2006 ; Sousa et al., 2012 ). This result is also consistent with previous research demonstrating negative relationships between conservation values and creativity ( Rice, 2006 ; Dollinger et al., 2007 ; Kasof et al., 2007 ; Lipponen et al., 2008 ; Sousa and Coelho, 2011 ).

Our results also showed that self-enhancement values positively predict employees’ innovative behavior. We postulated that being innovative can help to attain personal success and achieve a dominant position among other co-workers, which are the central goals of self-enhancement values ( Schwartz, 1992 ). Employees who strongly preferred this set of higher-order values were found to be more innovative, which is consistent with previous research findings obtained by Sousa and Coelho (2011) and Taştan and Davoudi (2017) . Nevertheless, our results are contradictory to those found by Dollinger et al. (2007) , who found that power is negatively related to creativity. However, in their study, creativity was measured by applying methods, such as drawing creative products, developing creative stories or photo essays, which focused on the artistic aspect of creativity of university students. Therefore, the research context of this previous study differs substantially from that in the present study, in which the sample consisted of employees, and aims to investigate not creativity but innovative behavior, which is strongly grounded in the work context.

Our results also showed that there is no significant relationship between employees’ self-transcendence values and innovative behavior. This result aligns with the notion suggested by Sousa et al. (2012) , who argued that self-transcendence values do not seem to be more strongly related to the work context than other higher-order values. However, Arieli and Tenne-Gazit (2017) recently proposed that universalism can be related to creativity and innovation, and other research findings showed that prosocial motivation may encourage idea development in employees ( Grant and Berry, 2011 ). Therefore, more research concerning this issue is needed. Future research should particularly test such relationships among employees and explain not only idea generation (creativity) but also idea implementation (innovative behavior). It may also be valuable for future research to take into account prosocial motivation and include the context of social relationships in organizations.

Job autonomy was found to be predicted by all four higher-order values. As we expected, employees who attribute high importance to openness to change values (self-direction and stimulation) experience more autonomy in their work. This result aligns with Schwartz’s theory, which postulates that openness to change values are focused on independent action and thought and willingness to new experiences ( Schwartz, 2003a ). Our results confirmed that such motivation in employees is accompanied by striving for more autonomy in their workplace. Similarly, when employees value highly self-enhancement (power and achievement), they also experience more autonomy in their work. This result corresponds with the theory of basic human values because people who attribute high importance to self-enhancement values aim to attain success, prestige, and a dominant position over other people and demonstrate competence ( Schwartz, 1992 ). These goals seem to be impossible to attain without having a substantial level of autonomy at work. This result is also consistent with Mumford’s arguments that power and achievement are strong motives of independent people ( Mumford, 2000 ).

Employees’ conservation values were found to be negatively related to job autonomy, which supports our expectations [derived from Schwartz’s value theory ( Schwartz, 1992 )]. This result also aligns with the results obtained by Sagiv and Schwartz (2004) , who demonstrated that career counseling clients who attributed high importance to conformity values behaved less independently during the counseling process. In sum, people who attribute high importance to conservation values (conformity, security and tradition), which focus on maintaining the status quo, self-restriction, and resistance to change, are not strongly predisposed to strive for job autonomy because it is potentially disturbing to established social organizational norms.

Our results also revealed that employees’ self-transcendence values are negatively related to their autonomy at work. It seems that people for whom these values are of great importance are not as focused on themselves but instead care about other people and the environment ( Schwartz, 1992 ). They are more concentrated on pro-social and altruistic behaviors at work ( Sosik et al., 2009 ; Schwartz, 2010 ) and on cooperating with others ( Sagiv et al., 2011b ) than on increasing their own job autonomy, which can be harmful for the autonomy of their co-workers or managers.

In summary, the results of the present study confirmed our expectations that employees’ personal values are important predictors of their job autonomy. The results seem to support the argument that jobs may be proactively redesigned by employees to be more convergent with their preferences and characteristics, such as their personal values ( Wrzesniewski and Dutton, 2001 ; Berg et al., 2010 ). Nonetheless, it should be noted that, according to the traditional approach to job design ( Hackman and Oldham, 1976 , 1980 ), employee job autonomy is usually treated as a rather objective job characteristic, which depends on the nature of the job itself and on supervisors ( Park and Jang, 2015 ). Thus, job autonomy is often considered to be a contextual moderator in explaining organizational phenomena (e.g., Molleman and van den Beukel, 2007 ; Sousa et al., 2012 ). However, we conducted an additional analysis of differences between various occupations 1 , and no significant differences in job autonomy were revealed ( F (6, 251)  = 1.29, p  = 0.261). Therefore, the level of job autonomy does not depend on the occupation type. This result supports our approach and suggests that the fact that different people have different levels of job autonomy may be a result of their own efforts; some people are motivated to strive for job autonomy (because it is congruent with their personal values), whereas others are not (when their values do not foster being autonomous). It is not only the nature of a job (assuming that some jobs are more autonomous than others) but also the personal characteristics of an employee that can shape the level of job autonomy that he/she experiences at work. This notion is in line with job crafting theory, which emphasizes that employees are proactive crafters of their work environment – their role is not reduced to working under the conditions imposed by their managers, as they can also actively shape their jobs to make them better fit their expectations and preferences ( Wrzesniewski and Dutton, 2001 ; Berg et al., 2010 ). Future studies may examine behaviors that help to craft a job in terms of job autonomy to fit employees’ personal value preferences.

In the present study, we did not find a significant relationship between employees’ openness to change values and their innovative behavior. Schwartz’s (1992) theory allows us to postulate that these values are positively related to creativity and innovation. Motivational goals of openness to change (i.e., the willingness to create, choose, explore, preference for novelty, and change) seem to encourage people to behave in innovative ways. Indeed, previous research findings have shown that there are positive relationships between openness to change values and creativity ( Rice, 2006 ; Dollinger et al., 2007 ; Kasof et al., 2007 ; Sousa and Coelho, 2011 ). However, in the present study, the relationship between openness to change and innovative behavior was not statistically significant. We consider several potential reasons for this result. First, some previous studies that found a positive association between openness to change values and creativity were conducted with student samples ( Dollinger et al., 2007 ; Kasof et al., 2007 ). Second, because of a lack of research examining the role of individuals’ personal values on their innovative behavior, our expectations were primarily based on theoretical contributions and previous research on creativity. However, although creativity and innovative behavior are similar constructs, they are not equivalent ( Anderson et al., 2014 ; Purc et al., 2015 ). Innovative behavior includes not only idea generation but also seeking support for the idea and its implementation ( Scott and Bruce, 1994 ; Amabile, 1997 ; West, 2002 ; Anderson et al., 2004 ; Hammond et al., 2011 ), which demands cooperation with others within an organization. This again raises the issue of social relationships between managers and employees as well as among employees, which may be considered in future studies.

The present study aimed to explain the mechanism through which the personal values of employees relate to their innovative behavior. Our results revealed that job autonomy was a significant mediator of the relationships between three among four higher-order values (conservation, self-enhancement, and self-transcendence) and innovative behavior. The relationship between openness to change and innovative behavior was not mediated by job autonomy. Future research is needed to find other mechanisms through which such a relationship may occur. The mediation analysis results generally support our postulations that personal values not only motivate the pursuit of job autonomy but also are indirectly associated with innovative behavior. In addition, our study supports other findings indicating that job autonomy predicts innovative behavior ( Axtell et al., 2000 ; Ramamoorthy et al., 2005 ; De Spiegelaere et al., 2014 , 2015 , 2016 ). We can conclude that employees’ personal values serve as a predisposition for functioning in the workplace and, together with other variables, such as job autonomy, relate to innovative behavior.

Limitations

When testing the mediation mechanism, we should remember that the present study is cross-sectional, and thus, no causal conclusions can be drawn, which constitute a limitation of this study. Nevertheless, as personal values develop in childhood ( Vecchione et al., 2015 , 2016 ; Cieciuch et al., 2016 ), job autonomy is relatively changeable because it is dependent on the organizational context ( Hackman and Oldham, 1976 , 1980 ) and because innovative behavior based on creative ideas is performed during daily work duties ( Weinberger et al., 2018 ), which justifies the direction of variables included in our model. However, further research concerning the relationships between employees’ personal values, job autonomy, and innovative behavior employing a longitudinal or experimental design is needed to discover the interplay between these variables over time.

In the current study, we concentrated on the role of job autonomy as a central motivational work characteristic ( Morgeson and Humphrey, 2006 ). However, job autonomy is only one of the job features described by Hackman and Oldham’s (1976) job characteristics theory. Therefore, future studies should investigate the role of other job characteristics in relation to personal values and in stimulating innovative behavior in organizations.

Another limitation that should be addressed is that we used self-reports to measure study variables. Self-report measures seem to be the most appropriate solution to assess personal values because values are subjective motivational goals ( Roccas et al., 2017 ). Similarly, the measurement of job autonomy seems to be necessarily subjective because the most important aspect is how an employee perceives autonomy in his/her work, not how others observe it. Objective measures of autonomy are difficult to obtain and may not refer to an employee’s actual feeling of being independent at work. Thus, self-report measures seem to be the best solution to capture perceived job autonomy. Innovative behavior was subjectively rated by employees as well, which may not reflect their actual behavior, and responses can be biased due to social desirability ( Zacher et al., 2016 ). However, it was found that there is a significant positive correlation between the self-ratings of innovative behavior and the objective measure of invention disclosures ( Scott and Bruce, 1994 ). In addition, Janssen (2000 , 2001) found that employees’ self-ratings of innovative behavior were correlated with their supervisors’ ratings. Some researchers have also argued that employees are a good source of information about their own creativity and innovative performance ( Organ and Konovsky, 1989 ; Janssen, 2000 , 2004 ; Shalley et al., 2009 ) because it is a rather discretionary behavior, and the ratings of other people (e.g., managers or co-workers) may miss subtle, less visible innovative activities, capturing only those that are designed to make an impression. Future research should consider such problems, and researchers may use other measures.

Our study was performed in a single country, namely Poland. Because cultural differences at the societal level ( Hofstede, 1980 ) have been considered important with respect to innovation ( Rosenbusch et al., 2011 ), these differences may also influence the relationships between values and innovative behavior. Therefore, future cross-cultural research and/or research in other cultural contexts is encouraged.

Practical Implications

The results of the study have some practical implications, which can be useful for managers or human resource specialists. First, it is very important to better understand the predictors of innovative behavior in organizations because innovation is one of the sources of organizational success and competitiveness ( Woodman et al., 1993 ). Personal values are relatively stable characteristics ( Schwartz, 1992 ), and as such, it is not easy to adapt them to specific situations. Therefore, knowing which of employees’ values are positively related to their innovative behavior, human resource departments can use this knowledge in the selection and recruitment process as well as in job design initiatives. Employing and retaining employees with high levels of self-enhancement values may increase the innovativeness of an organization. Moreover, entrepreneurs and managers may support employees’ innovative behavior by providing them with more autonomy at work and, in this way, building a more innovation-friendly job environment. It is also possible to develop innovativeness through training programmes stimulating creativity and teamwork that increase competencies to shape an environment that promotes innovation and cooperate in introducing changes ( Białoń, 2010 ).

Conclusions

Despite some limitations, our study offers valuable empirical evidence that allows for theory development. The results provide insight into the relationships between employees’ personal values, job autonomy, and innovative behavior, which have not been studied to date. Thus, they constitute a new perspective in innovation research, extending insights from Schwartz’s (1992) theory of basic human values to a new context. Namely, our results show that personal values can stimulate innovative behavior in the workplace. In addition, the present study investigated not only the direct relationships between personal values and innovative behavior but also the mediation mechanism. Thus, we attempted to respond to the call to reveal the mechanisms through which innovation can be driven, which was recently emphasized in the innovation literature ( Hammond et al., 2011 ; Anderson et al., 2014 , 2018). Moreover, we also addressed suggestions that personal values, as well as contextual factors, can explain behavior ( Sousa et al., 2012 ; Arieli and Tenne-Gazit, 2017 ), considering job autonomy as an indicator of work context. Therefore, applying a personal values perspective to examine antecedents of job autonomy brings new insights to both basic human values theory and job design theory.

Ethics Statement

All procedures performed in this study were in accordance with the ethical standards. Informed consent was obtained from all individual participants included in the study. Participation in the study was voluntary and the participants did not receive any reward. Respondents were asked to fill in a set of questionnaires. They were able to withdraw from the study at each moment. The confidentiality and anonymity were ensured. The study received the approval from the Ethical Committee of The John Paul II Catholic University of Lublin, Institute of Psychology.

Author Contributions

EP and ML were involved in formulating the research question, designing the study, writing the article, and drafting and approving the final manuscript. EP was responsible for collecting and analyzing the data.

Conflict of Interest Statement

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.

1 Occupations were differentiated according to the International Standard Classification of Occupations ( ISCO-08, 2012 ).

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Work values.

Assessing Work Values

Over the years, there have been many inquiries into the nature of work values in counseling and industrial psychology. The concept of work values harkens back to the early studies of job satisfaction, in which it was found that job characteristics such as pay, safety, or working hours could be valued differentially and summed into a single index of satisfaction. It followed naturally to inquire into differential valuing of various attributes of an occupation or a career.

Two lines of conceptualization and research on work values are currently prominent: Rene Dawis and Lloyd Lofquist’s Theory of Work Adjustment (TWA) and Donald Super’s multinational Work Importance Study (WIS). Dawis and Lofquist assembled a measure of 21 “needs” to which diverse attributes of jobs or work are responsive. They are all positive in tone, such as achievement, variety, and creativity. The developers’ extensive research with workers at many levels of the employment strata found that values are robust predictors of job satisfaction and that different jobs and occupations offer satisfaction to different combinations of needs.

Factor analysis of the 21 needs yielded six combinations, which they labeled as values. They are as follows:

• Achievement: feeling of accomplishment, using one’s abilities
• Comfort: comfort on the job, absence of stress
• Status: recognition, dominance over others
• Altruism: helping others, doing good
• Safety: structure in the job, predictability
• Autonomy: independence, being in command

Satisfaction, derived from the correspondence of workers’ values and job reinforcers, combined with “satisfactoriness” derived from the correspondence of workers’ skills and abilities and job performance requirements, result in workers’ tenure, being retained, promoted, or terminated.

The parallelism of the terms need and value in the two lines of research led Super to attempt to differentiate them. He viewed the two concepts as two sides of the same coin—needs representing something lacking and seeking satisfaction, and values representing the satisfactions that are sought. He believed that both lead to the activities in which needs or values are satisfied, which is to say, interests.

In a multinational study, the WIS team of researchers formulated 18 values adaptable to any work role. Having been derived from some of the same sources, 12 are identical or very similar to those of the TWA. Of interest is their inclusion of “risk,” which is not in the same positive frame of reference as the remainder of their list and is not of the TWA set. Factor analysis reduced the 18 values to five factors, which were identified as follows:

• Utilitarian: achievement, prestige, ability utilization
• Self-actualization: personal development, ability utilization
• Individualistic: autonomy, lifestyle
• Social: social interaction, social relations
• Adventurous: risk, authority

The WIS found a remarkable degree of consistency for these factors in the value set across the 11 countries on five continents included in their study. Of more importance was the finding that the most prevalent values across all nationalities were personal development, ability utilization, and achievement, or in sum, self-actualization. Super and Branimir Sverko are careful to point out that all of their subjects had attained some degree of education, which in many of the countries they studied would be indicative of valuing self-actualization. On the other hand, willingness to take risks, as well as valuing prestige and authority, were of little importance.

Assessing Work Values In Career Development

The two streams of research on work values have fostered two career development applications of their two assessments. The values survey used in the Super and Sverko study was published for use in career counseling, but has unfortunately gone out of print. A precursor, Super’s Work Values Inventory (SWVI-R) has been revised by dropping three scales thought to be redundant with interest constructs and lengthening the remainder. It is available for use in career applications. Donald Zytowski cautions that occupations are not as homogenous for value orientation as they are for interests, so a work value profile should not be applied exclusively to select an occupational objective. At middle and secondary school levels, the inventory is probably best used to illustrate the utility of developing a concept of one’s values and to help individuals identify attributes of work and occupations that might be of greatest importance to them. With college students and adults, value assessment might be profitably applied to prioritizing or selecting among several “interesting” possibilities.

The WIS values assessment has been adapted for counseling use as the Work Importance Locator (WIL). It uses a card-sorting procedure with 20 of the original needs to rank-order the importance of six values. One is hand scored and includes a list of occupations that are responsive to each value orientation. The other version is administered online and scored automatically. Users of both the WVI and the WIL can access the master list of occupations that are responsive to their value orientations in an O*NET Occupations Master List. This online document lists 15 to 20 O*NET occupations at five educational levels for each value orientation. The instructions for application of the list cautions that it should not to be used for hiring or employment decisions but is most appropriate for career exploration and counseling purposes.

• Allport-Vernon-Lindzey Study of Values
• Work Values Inventory

References:

• Dawis, R. V. and Lofquist, L. H. 1984. A Psychological Theory of Work Adjustment: An Individual-Differences Model and Its Application. Minneapolis: University of Minnesota Press.
• Super, D. E. and Sverko, B., eds. 1995. Life Roles, Values, and Careers: International Findings of the Work Importance Study. San Francisco, CA: Jossey-Bass. Zytowski, D. G. 1994. “A Super Contribution to Vocational Theory: Work Values.” Career Development Quarterly 42:25-31.

Work domain modeling of human-automation interaction for in-vehicle automation

• Open access
• Published: 05 September 2024

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• You Zhang 1 &
• Gavan Lintern   ORCID: orcid.org/0000-0002-4489-4857 2  

Automated driving systems are deployed on public roads with little empirical support for the dominant justifications of enhanced safety and enhanced productivity. Furthermore, development of automated driving systems has been piecemeal rather than systematic while research on driver-automation interaction has relied on individual analysis of accidents and on observational studies of driving behavior in a simulator or on the road. In this paper, we apply Work Domain Analysis to develop a more systematic and comprehensive model of automated driving. We use a strategy of layering the driving automation onto the resulting Abstraction-Decomposition Space for manual driving to mimic the existing design strategy of introducing automation to take over driving functions previously the responsibility of the human driver. Our analysis shows that automation does not unequivocally supports dominant driving values. Furthermore, our analysis revealed subtle interdependencies between human and technological functions. We conclude that an Abstraction Decomposition Space offers a systematic view of driver-automation interaction that can suggest new insights for automation design.

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1 Introduction

The introduction of autonomous vehicles into the road transportation network is most frequently justified in terms of enhanced safety, enhanced productivity from multitasking, and enhanced efficiency from reduced travel times (Gkartzonikas and Gkritza 2019 ). However, the development of automated functions appears to have been piecemeal. Rather than being guided by the need to support those three values, it appears to have been driven largely by a design philosophy of replacing selected human driving functions with automated functions wherever technologically feasible. Nowhere in the corpus of literature we reviewed for our analysis as reported in this paper could we find an explicit reference to a values-based rationale for the development of automated vehicles. In this paper, we examine the functional structure of autonomous driving systems to assess whether current designs support realization of the three values identified by Gkartzonikas and Gkritza ( 2019 ).

1.1 Work domain analysis

We use Work Domain Analysis to map the functional structure of autonomous driving and the immediate driving environment onto an Abstraction–Decomposition Space, where the term functional refers to an activity-independent capability to accomplish a specific outcome (Lintern 2013 ; Naikar 2013 ). This functional structure has both intentional and physical properties where the term intentional refers to the purposeful properties and the term physical refers to physical objects. Work Domain Analysis identifies the purposes to be achieved with the available physical resources as consistent with dominant values. It maps technical functions afforded by physical resources through domain functions to system values that constrain the manner in which system purpose is realized (Naikar 2013 ).

An Abstraction–Decomposition Space, as the analytic product of Work Domain Analysis, depicts that mapping. Work Domain Analysis is one stage of Cognitive Work Analysis (Vicente 1999 ). Work Domain Analysis models the functional structure of the workspace in which cognitive work is undertaken. A poorly designed workspace will interfere with the proper execution of cognitive work. Work Domain Analysis can be used to explore how well a workspace is designed.

Figure  1 shows a minimalist but illustrative Abstraction–Decomposition Space for the driving domain. The vertical dimension is defined by a functional abstraction whereby means-ends relationships between levels of abstraction show how resources or constraints at one level support the resources or constraints available at the next level up. A valid Abstraction-Decomposition Space is internally coherent in the sense that all Physical Resources are connected to System Purpose via means-ends links through the intervening levels. It should be possible to read the sequence from bottom to top as in the following example: a map (Physical Resource), shows route information (Technical Function) that supports route planning (Driving Function), which enhances operational efficiency (Driving Value), which contributes to personal mobility (System Purpose). It should also be possible to reverse this reading from System Purpose at the top level to Physical Resource at the bottom level.

A preliminary abstraction-decomposition space for manual driving (for illustrative purposes)

The Abstraction-Decomposition Space maps interdependencies explicitly in a manner that is unique among analytic techniques for human systems. For example, referencing Fig.  1 , although route information primarily supports route planning, it also supports maneuvering by encouraging the driver to transition to the appropriate lane in anticipation of an exit from a multi-lane carriageway. Figure  1 shows a means-ends link from route information (Technical Function) to vehicle control (Driving Function) to reflect that.

Furthermore, an Abstraction-Decomposition Space offers a comprehensive systems view of the functional structure of a work domain (in this case, the driving domain). Domain values that are not supported adequately by suitable Physical Resources (via appropriate Technical Functions and Driving Functions) cannot be satisfied. Empirical testing can be brought to bear on specific issues (e.g., does automatic cruise control enhance safety) but it is never possible to test all possible situational variations and nuances. An Abstraction-Decomposition Space supports a comprehensive assessment of whether the system is designed in a way that, in principle at least, could support the desired human values.

1.2 Work domain modelling of automated systems

In this paper, we report an analysis of in-vehicle automation as described by Society of Automotive Engineers (SAE) Levels (Table  1 ). Following a strategy pioneered by Li and Burns ( 2017 ), we develop a base Abstraction-Decomposition Space for an SAE Level 0 Manual Driving vehicle followed by Abstraction-Decomposition Spaces for an SAE Level 2 Partial Automation vehicle and an SAE Level 4 High Automation vehicle. Our analytic sequence largely tracks the dominant engineering design philosophy of replacing selected human driving functions with automated functions.

Our analysis centers on how the functions of automated driving interact with a human driver within urban traffic. While rural travel could benefit enormously from automated driver-assist functions, current automated-driving research and development is largely ignoring the challenges posed by the types of driving conditions (e.g., unmade roads and limited signage) frequently found in rural areas (Peiris et al. 2020 ). Subsequently, we excluded consideration of these conditions from our analytic framework.

1.3 Scenario mapping

In this paper, we mapped accident scenarios onto relevant Abstraction-Decomposition Spaces. Naikar ( 2013 ) has argued that scenario mapping offers a strategy for validating a work domain model. By mapping scenarios onto the model, it is possible to establish whether the model is consistent with documented activities or issues in use of the modelled system. Scenario mapping can thereby identify the functions or systems responsible for any problematic behaviours. Following development of the Abstraction-Decomposition Spaces for SAE Level 2 Partial Automation and SAE Level 4 Full Automation vehicles, we mapped accidents documented in National Transportation Safety Board reports for both levels of automation onto the relevant Abstraction-Decomposition Spaces to validate our models and to clarify issues that could guide redesign.

2.1 Analysis scope and procedure

The analysis was undertaken with reference to a single on-road vehicle driven in an urban, real-world traffic setting. The frame of reference for the analysis included nonprofessional driving such as commuting, shopping, and personal business, but excluded professional activities such as passenger and goods transportation.

We first developed an Abstraction–Decomposition Space for a system in which the driver performs all driving tasks. We then developed Abstraction–Decomposition Spaces for SAE Level 2 Partial Automation and SAE Level 4 High Automation vehicles.

For SAE Level 2 Partial Automation, prominent examples are market-available vehicles like Tesla model 3 (Tesla 2023 ) or Cadillac CT6 (Cadillac 2020 ) which implement Adaptive Cruise Control with Auto-Lane Following and Auto-Lane Change within limited driving constraints (e.g., speed range, road types, lane curvatures, environmental conditions). With a clear road ahead, Adaptive Cruise Control maintains a speed set by the driver. If the vehicle closes on another vehicle from the rear, it slows to maintain a set distance behind the slower vehicle. Auto-Lane Following maintains the vehicle between two lane markings or, if only one lane is marked, it maintains a set distance from that lane. With Auto-Lane Following active, Auto-Lane Change moves the vehicle into the adjacent lane when the driver activates the turn signal. The driver retains responsibility for ensuring that the lane is clear before activating Auto-Lane Change.

SAE Level 4 vehicles automate all aspects of driving under specific roadway and environmental conditions (e.g., road type, geographical range, speed). The system may request intervention, but the driver-occupant need not supervise or respond. If an intervention request remains unanswered, the system will enter a minimal risk condition (e.g., move to the side of the road and park in a safe area). SAE Level 4 vehicles are still under test and are not available for on-road use in urban, real-world traffic settings Footnote 1 .

The analysis excluded consideration of vehicle parts such as engines, tires, and transmission, that do not directly shape on-the-road maneuvering, tactics and strategies for vehicle control.

2.2 Analytic strategy

Primary source documents for this analysis were engineering, sales, and driver licensing documents (Appendix – source documents for Work Domain Analysis). As both authors of this paper are experienced urban drivers, we used our own knowledge to fill out gaps in the documented information.

Development of the Abstraction–Decomposition Space for a non-automated vehicle was guided by our understanding that driving is predominantly a control task involving both action constraints (laws of motion related to force, inertia, resistance) as described by Jagacinski and Flach ( 2003 ) and information requirements (observability, feedback) as described by Flach and Voorhorst ( 2016 ) and Mole Lappi, Giles et al. ( 2019 ). This task is overlaid with elements relating to tactics and strategy (Michon 1985 ; SAE International 2021 ).

Our development was also guided by contemporary views of situation awareness as it applies to extraction and processing of information for driving (Banks et al. 2018 ; Shinar 2017 ). Endsley ( 1995 ) has defined situation awareness as the perception of the elements in the environment within a volume of time and space , the comprehension of their meaning , and the projection of their status into the near future . Based on this definition, to maintain situation awareness and take appropriate action, a driver would have to detect things (e.g. roadway geometry, other vehicles), their relationship to own vehicle and the dynamics of all vehicles (own and other), comprehend the significance of all relationships to own vehicle in light of own goals, and comprehend the future implications of those relationships. Merat et al. ( 2019 ) suggest that the situation awareness model can be used to characterize the monitoring involved in driving tasks as described by SAE ( 2021 ).

Vehicle resources, instruction manuals, and point-of-sale briefings provide technical functionalities in support of the driving functions associated with manual control, tactical control, and strategic management. Externalities such as roadway infrastructure, weather, other traffic, and physical objects modify some of the technical functionalities. In a non-automated vehicle, the driver contributes important technical functionality via sensors, actuators, and more generally, via their own physical and cognitive systems. Within automated vehicles, automated sub-systems substitute for resources provided by the driver, depending on the level of automation. An automated vehicle requires an automation management system not found in a manual vehicle.

2.3 Content of the abstraction–decomposition space

Labels for levels of abstraction are as shown in Fig.  1 . Type of content for each of the abstraction levels was referenced against descriptions provided by Naikar ( 2013 , p 182). Based on our frame of reference as described above, we specified System Purpose as mobility in an urban environment (specific to use of an automobile).

A review of more than 40 published surveys on autonomous vehicles (Gkartzonikas and Gkritza 2019 ) identified safety, productivity, and efficiency as the three dominant benefits of driving automation. We used these as the driving values for our analysis. We drew the Driving Functions of operational (manual) control, tactical (maneuvering) control, and strategic (planning) management from Michon ( 1985 ; also see Mole et al. 2019 and SAE International 2021 ) after assessing that these functions were decomposable and that, as a set, they were comprehensive of the driving domain. In addition, these functions parse the driving domain in conceptually distinctive ways; they are associated with different time scales (Michon 1985 ) in that operational activity is responsible for moment-to-moment control of the vehicle, tactical activity is responsible for decisions that lead to adjustments in operational activity in response to situational opportunities and hazards, and strategic activity is responsible for trip planning and scheduling (SAE International 2021 ).

Physical Resources and Technical Functions were identified largely from the document analysis, although the relevant documents rarely distinguished resources from technical functions. Because an essential requirement for a valid Abstraction-Decomposition Space is an unbroken chain of means-ends relationships from Physical Resources through Technical Functions to System Purpose, we had to infer content at both of the lower levels based on our knowledge of how technical systems work. In addition, documents rarely contain any explicit discussion of anything that could be interpreted as a means-ends relationship. We inferred these at the two lower levels based on our knowledge of how technical systems work and at the three higher levels based on our knowledge of how socio-technical systems work.

Vehicle automation functionality was drawn from owner’s manuals provided by manufacturers (see Appendix). We developed an Abstraction–Decomposition Space for an SAE Level 2 vehicle by substituting automation elements for some driver sensors and actuators, supplemented with essential automation management resources. We developed an Abstraction–Decomposition Space for an SAE Level 4 vehicle by complementing SAE Level 2 automation elements with further automation elements that satisfied the need for fully autonomous travel. For the SAE Level 4 Abstraction–Decomposition Space, we deleted all means-ends links from the driver and most means-ends links from the controls and displays used for manual driving.

2.4 Representation strategy

An Abstraction-Decomposition Space for an operational system can become cluttered and crowded. Following Lintern ( 2013 ), we employed two representational strategies to reduce clutter and crowding to the extent possible. As illustrated in Fig.  1 , we made more efficient use of space by nesting decompositions within aggregated functions as an alternative to the standard form of depicting decompositions in columns. In addition, where all decompositions of an aggregate function have means-ends relations with a function at another level, the link is shown from the aggregate function rather than individually from the decompositions (e.g., in Fig.  1 , see links from physical resources of externalities and driver to the technical function of vehicle operation).

2.5 Scenario mapping

We mapped issues identified by the reports US National Transportation Safety Board (NTSB, 2017, 2019c, 2020a, 2020b) onto the Abstraction-Decomposition Spaces for SAE Level 2 and NTSB (2019a, 2019b) onto the Abstraction-Decomposition Spaces for SAE Level 4 Vehicles.

Here we report the results of work domain analyses for manual, partial automation, and high automation driving systems as those terms are defined by SAE International ( 2021 ) and described in Table  1 and the Method section of this paper. We also report the results of the scenario mapping exercises for partial automation and high automation driving systems.

3.1 SAE level 0 manual driving

Figure  2 depicts the Abstraction–Decomposition Space for manual driving. The essential technical functions are vehicle operation, detection of in-vehicle constraints, and detection of constraints on vehicle passage. In addition, navigation and planning information is important and the driver will often find a need to signal their intent to other road users. These technical functions are decomposed to a more fine-grained level of analysis. For example, the technical function of detection of in-vehicle constraints is decomposed into detection of vehicle state, vehicle dynamics, power state, and driver state of alertness and competence.

Abstraction-decomposition space for an SAE level 0 manual driving system

Technical functions are enabled by the physical resources of documents and briefings, vehicle, driver, externalities, and navigation and planning resources. Vehicle resources support vehicle operation. Externalities constrain vehicle passage, while documents and briefings guide vehicle operation. These physical resources are also decomposed to a more fine-grained level of analysis. In some cases, not all subsystems of a physical resource enable all subsystems of a technical function, in which case, means-ends links connect the appropriate subsystems. However, to reduce clutter in the figure, means-ends links are connected to a whole physical or technical resource wherever possible.

In this analysis, where the ultimate goal is to replace the driver with automated systems, it is imperative to identify what the driver does in manual operation. Here we depict the driver as an information processor (including decision and judgment) with actuators and sensors. As shown by means-ends links, those capabilities provide critical support for all technical functions. There can be no vehicle operation, and no external communication without driver involvement. In addition, the driver must detect the constraints imposed by vehicle systems and externalities and must process the information provided by navigation and planning resources.

Following Michon ( 1985 ), driving functions are classified as operational (manual) control, tactical (maneuvering) control, and strategic (planning) management (also see Mole et al. 2019 ). These functions are decomposed into constituent sub-functions. For example, operational (manual) control is decomposed into speed management, lane following, and operational maneuvering. Many of the means-ends links from technical functions connect to more than one driving function.

Notably, and as consistent with the arguments of Mole et al. ( 2019 ), Fig.  2 shows that effective operational (manual) control demands attunement of vehicle operation to in-vehicle constraints and constraints on vehicle passage. At this level, drivers are deciding between courses of action and developing situation awareness (Endsley 1995 ; Merat et al. 2019 ). As depicted in Fig.  2 , drivers must be aware of obstructions, other traffic, and own state. They must also be attuned to the control dynamics of the vehicle, which will change with variations in vehicle speed and road surfaces (Mole et al. 2019 ).

Following Gkartzonikas and Gkritza ( 2019 ), Fig.  2 shows the values as safety, productivity (multitasking), and operational efficiency. None of the driving functions of a manual vehicle support productivity (multitasking). In this analysis, we take productive multi-tasking to include high-attentional demand activities such as cell phone conversations, but not low-attentional demand activities related to listening passively to music or news. Safety is supported by operational (manual) control and tactical (maneuvering) control while operational efficiency is supported by strategic (planning) management. These two values reflect the general desires we infer for most urban drivers to arrive at and return from destinations in a timely matter while avoiding accidents. The question marks used to annotate the means-ends links reflect the belief implied in the Gkartzonikas and Gkritza ( 2019 ) analysis that there is a need for better support for these values.

3.2 SAE level 2 partial automation

Figure  3 shows the Abstraction–Decomposition Space for an SAE Level 2 Partial Automation vehicle while its driver-assist automation is active. The activation function from manual operation to driver-assist automation is also shown. With automatic systems inactive, the appropriate Abstraction–Decomposition Space otherwise conforms to that of an SAE Level 0 vehicle (Fig.  2 ).

Abstraction-decomposition space for an SAE level 2 partial automation (adaptive cruise control, auto lane following, auto lane change) driving system

The substantive addition to Physical Resources in comparison to manual driving is an automation module (with sensors, actuators, and a processor) that assumes some driver roles. Automation management is undertaken via the mode selector resource. The Level 2 automation takes over operational control, although the driver must retain a supervisory role to ensure they are ready for tactical or strategic demands. The alerting system reminds any driver who does not maintain active supervision to reengage with the driving task. A mode annunciator shows when the driver-assist resources are active.

Those nodes in the Abstraction–Decomposition Space that are managed by automation are shown in dark fill with those managed by driver or automation shown in dark-light fill. In-vehicle control, resources like the brake pedal, accelerator pedal, and steering wheel are no longer used continuously or to their full extent. Instead, drivers may use these controls to adjust or deactivate the automation as needed. Notably, the design of automation controls differs between manufacturers. For instance, Tesla 2023 users can deactivate the automation by moving the driver stalk once, while Cadillac ( 2020 ) users can achieve the same effect by pressing a button located on the steering wheel. Also, contemporary interactive design allows users to adjust the automation settings through a touchscreen (Tesla 2023 ).

The automation registers constraints on vehicle passage to manage operational control while the driver detects those constraints for tactical control. Subsequently, the driver no longer has to engage with externalities on a moment-to-moment basis, although a new means-ends link from documents and briefings reflects instructions that specify the driver is to maintain continuous engagement with those externalities. Notably, with traditional pedals and steering wheel no longer in continuous use under SAE Level 2 automation, the active feedback loop of vehicle dynamics that enables the driver to respond quickly to an ever-changing environment during manual driving is disrupted (Mole et al. 2019 ). Consequently, the driver may not be able to perceive changes in vehicle dynamic, such as those generated by changes in road surface.

Lane Following automation relies crucially on detection of lane and roadway constraints as revealed by well-articulated lane markings. Those markings can be concealed in adverse conditions such as heavy rain or snow cover. Most troubling, they will generally be concealed to the driver in conditions that also conceal them to the automation sensors (Endsley 2017 ).

Historically, new automobile features have been introduced incrementally without posing any substantial challenge for workability. Drivers have typically become familiar with how things work by observing other drivers or by a brief introduction. Vehicle automation, in contrast, poses a more substantive challenge (Casner and Hutchins 2019 ). Workability of many automation features is not self-evident from inspection or manipulation of the interface or by observation of other drivers using them (Banks et al. 2018 ; Endsley 2017 ). Instruction manuals and sales outlet briefings become more important in the development of operational knowledge and are subsequently shown in Fig.  3 with a dark fill.

Support for driving values changes only marginally from a fully manual vehicle. SAE Level 2 Partial Automation does not provide any support for productivity (multitasking) or any additional support for operational efficiency. Adaptive Cruise Control, Auto Lane Following, and Auto Lane Change can control speed, prevent lane departure, and reduce blind-spot risk, and may thereby mitigate speeding and reckless driving which Stewart ( 2022 ) has identified as major causes of traffic incidents. Also, as observed by Endsley ( 2017 ), SAE Level 2 automation can allow the driver to develop better situation awareness relating to events external to the vehicle. However, the use of SAE Level 2 automation is associated with an elevated risk of drivers engaging in non-driving related tasks, which could potentially compromise safety by impairing driver awareness of critical roadway information and traffic (Dunn et al. 2019 ). As indicated by the question marks used to annotate the means-ends links, it remains uncertain whether these systems enhance or degrade safety.

3.3 SAE level 4 high automation

Figure  4 shows the Abstraction–Decomposition Space for an SAE Level 4 Full Automation vehicle while it is in full-automation mode. An SAE Level 4 High Automation system performs all aspects of the driving task with the exception that the driver remains responsible for selecting the destination and any waypoints. The driver is not required to supervise a High Automation system during the trip and may even sleep while the trip is in progress (SAE International 2021 ). The driver is able to intervene or take full control, and the system may even request intervention in the face of an unexpected situation. However, the driver is not required to respond to an intervention request, in which case the system will adapt in a manner that will ensure continued safety.

The abstraction-decomposition space for an SAE level 4 high automation driving system

In developing the Abstraction-Decomposition Space shown in Fig.  4 , we imagined a scenario in which the driver enters a destination into the system and then activates it so that the vehicle proceeds without further driver intervention. During the trip, the driver may decide to stop at an unplanned location, thereby establishing a new waypoint. The driver may or may not attend to events external to the vehicle, but if they do so, they may engage in adaptive replanning if a need is detected.

Within specific conditions, SAE Level 4 Full Automation supports all Technical Functions required for driving (SAE International 2021 ). In comparison to Fig.  3 , the only nodal addition to the Automation in Fig.  4 is the Navigation System, although the automation has far more functional capability than a Partial Automation system. Except for the external lights and horn, all non-automated vehicle resources are redundant. Externalities are now registered by the automation with no detection responsibility left to the driver. Now redundant Physical Resources are shown as faded in Fig.  4 and their means-ends links to Technical Functions have been deleted. Notably, the Technical Function of driver state detection (shown as faded) is no longer needed.

Driver sensors and actuators as components of the driver cognitive system play no operational or tactical roles in a High Automation system. The Driver remains responsible for selection of destination and waypoints but plays no active role during the driving event, absent any need to adjust waypoints or destination. For support of in-travel waypoint or destination adjustment, the driver cognitive system retains an active means-ends link to destination and waypoint options, with that means-ends chain continuing to destination and waypoint selection.

High Automation is designed to enhance productivity by relieving the driver of all operational and tactical control responsibilities under specific conditions. In that the driver now does not have to attend to any driving duties during the trip (and might thereby be better designated as the occupant), there is no need for multitasking. The driver-occupant can devote their full attention to any non-driving task or interest. Provided that High Automation operates under the intended conditions, the system seemingly provides adequate support for the other two values of safety and efficiency.

3.4 Scenario mapping; partial automation

We mapped several accidents with Partial Automation vehicles onto the Abstraction-Decomposition Space for SAE Level 2 Partial Automation (Fig.  5 ). All involved Tesla vehicles with Autopilot engaged (National Transportation Safety Board 2017 , 2019c , 2020a , b ). With such a small number of accidents, is not possible to make any strong generalizations, but these accidents do raise some troubling issues that mesh with implications of our Work Domain Analysis.

Accident scenarios mapped onto the abstraction-decomposition space for SAE level 2 partial automation

National Transportation Safety Board ( 2017 , 2020b ) report accidents in which Tesla vehicles struck semitrailer trucks, one turning and the other crossing in front of the Tesla path of travel. On impact, each of the Tesla vehicles was traveling at the cruise speed set by their driver and neither the Tesla automation nor either of the drivers executed any evasive manoeuvre in advance of the accidents. National Transportation Safety Board ( 2019c , 2020a ) report accidents in which Tesla vehicles had slowed from set cruise speed to follow another vehicle but had then increased speed, even with a collision imminent, when that vehicle had changed lanes so that it was no longer leading the Tesla. Apparently, the lane following logic failed to register an object ahead once the vehicle it had registered moved out of the lane.

In all four of these accidents, the automation sensors had failed to detect the collision potential of objects ahead and the driver had not maintained their attention on the driving environment as required by documents and briefings. Detection of objects ahead had possibly been compromised in two of these accidents (National Transportation Safety Board 2019c , 2020a ) by the lane-following logic when an automobile ahead had moved out of the lane occupied by the Tesla vehicle. Furthermore, the inability of human drivers to regain control when automation fails highlights the conflicts between human operators and driving automation (Vanderhaegen 2021 ). Specifically, the automated vehicle failed to react to the object ahead, while the human driver remained unaware of the impending hazard.In reference to Fig.  5 , Tesla designed Autopilot to relieve the driver of some operational control responsibilities and to thereby enhance driver comfort but did not design it to support multitasking. The system requires an attentive driver who remains responsible for safety (Tesla 2023 ). Possibly, some drivers do not appreciate the need, which might represent an inadequacy with documents and briefings. Alternatively, it might be a consequence of the limited interface between automation and driver, which for Tesla vehicles currently consists of an in-dash mode symbol and an aural signal, limited actuator functionality, and an alarm that will sound if the driver does not maintain active contact with the steering wheel. Some drivers apparently choose to ignore the constraint that they engage with the driving task even under Autopilot control and seek to defeat the alarm function by moving the steering wheel with sufficient frequency while they timeshare with a nondriving activity.

In the reported accidents, the functionality of the automation sensors was limited to the extent that they did not fully compensate for driver inattention. These sensors proved to be unreliable for detection of some complex or unusual shapes. There were two types of sensors (camera and radar), but the detection algorithm required agreement between the two. Independence would possibly result in more reliable and faster detection of critical objects, although at the likely cost of a higher false alarm rate. Although documents pointed to sensor limitations (National Transportation Safety Board 2017 , 2019c ), it is not clear that buyers of new Level 2 vehicles become fully aware of those limitations.

Given the issues raised by these accidents, the status of the means-ends supports for the value of safety remains questionable in Fig.  5 .

3.5 Scenario mapping; high automation

We mapped two accidents related to High Automation test vehicles onto the Abstraction-Decomposition Space for SAE Level 4 High Automation (Fig.  6 ). Again, it is not possible to make strong generalizations from this small number of accidents, but the implications deserve consideration.

Accident scenarios mapped onto the abstraction-decomposition space for SAE level 4 high automation

In one accident (National Transportation Safety Board 2019a ), the autonomous vehicle struck a pedestrian who was crossing the driving lane. In the other (National Transportation Safety Board 2019b ), the autonomous vehicle came to a safe stop behind a stationary truck, but the truck then reversed slowly into the autonomous vehicle. In that these vehicles were being used to test high automation in normal traffic, both had on-board attendants with the responsibility to intervene if the automation was unable to handle a situation safely. Although in both cases, the on-board attendants failed to avoid the accidents, our analysis focuses on the failure of the autonomous system to maintain safety.

In reference to Fig.  6 , High Automation should relieve the driver-occupant of all operational and tactical control responsibilities. As with Partial Automation systems, automation sensors proved to be unreliable for detection of complex or unusual shapes. An additional problem, not found with Partial Automation, became apparent in one accident. The operational control functionality of the system did not cope with the unexpected maneuver of another vehicle. This raises a general concern about how well High Automation systems will maintain safety within traffic that contains a mix of manual, partial automation, and high automation vehicles. Given this concern, the status of the means-ends supports for the value of safety are returned to questionable in Fig.  6 .

4 Discussion

Although safety is possibly the most important of the values identified by Gkartzonikas and Gkritza ( 2019 ), there is no clear statistical evidence bearing on the safety of automated versus manual vehicles (Kalra and Paddock 2016 ) even as serious accidents involving automated vehicles are accumulating (Blumenthal and Fraade-Blanar 2020 ). Such evidence would have to take account of the fact that automated vehicles are currently operated only under almost ideal conditions (McCarthy 2022 ) and as yet have not accumulated the driven distances required to provide statistically sound evidence of autonomous vehicle safety relative to manual vehicle safety (Kalra and Paddock 2016 ).

Conceivably, the sensor systems of automated vehicles could enhance safety by eliminating accidents that result when human drivers fail to perceive seemingly obvious critical events. For example, rear-end accidents are generally viewed as resulting from driver inattention (National Transportation Safety Board 2015 ). Another common pattern of collision occurs when an automobile turns across the path of the oncoming motorcycle (Caird and Hancock 1994 ; Pammer et al. 2018 ; Wulf et al. 1989 ). Post-accident, the automobile driver often states that they did not see the seemingly visible motorcycle until too late to avoid collision. Although we could find no recorded cases of such accident patterns with Level 2 or Level 4 vehicles, there were cases in which automation sensor packages failed to detect other types of critical objects as collision became imminent (see National Transportation Safety Board 2017 , 2019a , c , 2020a , b ). At this stage, it is not possible to establish that sensor systems are a safer alternative to human perceptual systems, but it does seem that the critical objects missed by sensor systems differ from those missed by human perceptual systems.

It is tempting to see the solution to the safety problem in a function allocation method that plays to the strengths of automation versus human. That type of substitution-based function allocation method has, however, already revealed its limitations in design of a variety of other types of systems in which automation has been used to replace human functionality (Dekker and Woods 2002 ). As revealed by the failure to detect oncoming motorcycles (Pammer et al. 2018 ) and the failure to respond to a reversing truck (National Transportation Safety Board 2019b ), this is not just a sensory registration problem. Rather, it is a failure of situation awareness as described by Endsley ( 1995 ); a failure to become aware of the object that is impinging on the human or the technological sensor, a failure to appreciate the current state of that object, and a failure to anticipate its near-future state. Subsequently, instead of optimizing sensor capabilities by distributing tasks between human and technological agents according to some differential performance standard, we need to develop systems that enhance situation awareness.

4.1.1 Safety: level 2 vehicle

Figure  3 shows that lane following under automated control with Partial Automation is supported by detection of lanes and roadway. There are, however, many situations in which automated lane following becomes unusable, sometimes with little warning (e.g., where road construction temporarily obliterates or diverts lanes). In addition, Endsley ( 2017 ) has noted that Partial Automation does not perform satisfactorily where lanes split or merge (also see National Transportation Safety Board 2020a ). Sometimes lane following becomes unusable when drivers most need assistance (e.g., lanes concealed by snow or heavy rain). This presents as a problem of clumsy automation (assistance from automation is unavailable when it is most needed) of a type widely acknowledged within aviation (Sarter et al. 1997 ; Wiener 1989 ).

An additional concern is that to assume full control after assisted driving, the driver must be attuned to the vehicle dynamics. In manual systems, detection of vehicle dynamics is supported concurrently by driver actuators and driver sensors in dynamic engagement with the vehicle dynamics. Under automated control with Partial Automation, only driver actuators are employed and then only to deactivate Lane Following or Adaptive Cruise Control. There is no continuous detection of vehicle dynamics as there is in manual driving. Deskilling as caused by a long-term absence from active control is a recognized issue in aviation (Casner et al. 2014 ). However, even a short-term absence can create control problems if vehicle dynamics change (National Transportation Safety Board 1994 ). In a road vehicle, the same snow cover that would conceal lane markings might also ice the roadway surface, thereby challenging the driver of a partially automated vehicle with a sudden change in vehicle dynamics just as an important source of information for automated control became inaccessible.

Figure  3 reveals other contingent complications in the way vehicle resources are used under automated control with a Partial Automation system, where many resources crucial for manual driving play a diminished or ambiguous role. For example, operational control is largely automated whereas tactical control is not. Technical Functions such as detection of obstructions and events, lanes and roadway, and traffic can be supported exclusively by automation until a requirement for tactical control emerges. At that point, driver sensors plus a number of in-vehicle resources become important. Of concern is that the driver can ignore such resources on a moment-to-moment basis but must be aware of them when there is a need for tactical maneuvering. In executing a lane change for example, the driver who does not anticipate the need, may find it challenging to detect traffic proximity at a moment when that detection becomes critical.

The contingent pattern of work sharing in a Partial Automation system also sometimes involves a more crucial emphasis on resources that play a minor role in manual driving. Documents and briefings offer an example. Those provided for SAE level 2 Systems have generally been modeled on the documents and briefings that have been provided over decades for manual vehicles. Casner and Hutchins ( 2019 ) have noted their inadequacies, while Endsley ( 2017 ) has remarked on the casual familiarization offered her on delivery of a new SAE level 2 automobile. Whether or not driving manuals in general conform to sound usability principles is questionable but Casner and Hutchins ( 2019 ) have argued that automated vehicles need something better. They suggest that standards developed over many years in aviation offer an important guide for redesigning automobile manuals for an automation age (Casner and Hutchins 2019 ) while Vanderhaegen ( 2021 ) inverts this problem by suggesting that autonomous driving systems could be designed to develop sensitivity to habits of human drivers.

Interface issues create other types of challenges. Endsley ( 2017 ) observed that she, as a driver of a Tesla Autopilot , was not always clear about which automated mode was active or how an active mode would behave. The in-dash activation information generally provided in Level 2 automobiles (see Fig.  3 , the means-ends link between driver physical resources and the technical function of detection of automation state) is apparently not fully adequate. In one driving incident, Endsley ( 2017 ) had thought the Adaptive Cruise Control had disengaged when she had taken over steering control. She was subsequently surprised when she entered a curve at an uncomfortably high speed and had to brake to disengage the Adaptive Cruise Control. Although disengagement of Cruise Control can generally be accomplished with relatively light pressure, a startled braking reaction at uncomfortably high speed in a tight curve situation could conceivably exacerbate an already precarious situation.

Furthermore, the strategies implemented by Tesla for control of Autosteer can generate uncertainty. Lane Following disengages if a driver moves the steering wheel. That action could be inadvertent or could be seamless with other activity as in the case, for example, where a driver takes over momentarily to avoid a small object on a highway surface. A chime sounds and the Autosteer icon changes from blue to gray when Autosteer disengages (Tesla 2023 ), but the change is not one that a driver will always notice.

The Cadillac Super Cruise system, in contrast, does not disengage Lane Following if the driver takes over momentarily. It remains unclear whether or not the Cadillac Super Cruise strategy is preferable, but the fact that two leading manufacturers have different strategies raises the potential for negative transfer where a driver may establish a habit with one of these vehicles but then, on transition to the other, find that the established habit continues to interfere. The potential for negative transfer, widely appreciated within the manual control literature (Lintern and Gopher 1978 ), constitutes one dimension of behavioral adaptation as discussed by Smiley and Rudin-Brown ( 2020 ), by Blanco et al. ( 2015 ), and by Skottke et al. ( 2014 ). In fact, there are likely to be many negative transfer traps confronting drivers who switch between different types of automated vehicles or switch from automated to manual vehicles.

More generally, the human interfaces for Level 2 automated driving do not appear to have been guided by an appeal to well-known human-interface design principles or subjected to rigorous testing to ensure that they properly serve the needs for operational and tactical control. Most critically, there is an obvious need to develop better alarm and monitoring systems for guiding the attention of the human driver (Vanderhaegen 2021 ).

4.1.2 Safety: level 4 vehicle

One documented accident with a high automation vehicle resulted from a lack of capability for the automation to respond tactically to an unanticipated maneuver by the driver of another vehicle (National Transportation Safety Board 2019b ). In any traffic mix foreseeable in the near term, high automation vehicles will need to respond adaptively to a diverse array of spontaneous and unusual maneuvers by drivers of manual and partially automated vehicles.

4.2 Productivity and multi-tasking

The Gkartzonikas and Gkritza ( 2019 ) review identified enhanced productivity, often with reference to multi-tasking, as the second most frequently cited benefit of automated vehicles. It is questionable however, whether multi-tasking, in the sense that we attend to two or more demanding activities at the same time, is a real phenomenon (Hadlington 2017 ). Rather, what is generally taken to be multi-tasking is most likely a switching whereby activities are interleaved by alternating attentional focus between them. This results in frequent suspension and resumption of activities, with momentary focus on one at the expense of the other. There are cognitive costs associated with resuming a suspended task, which can be cumulative (Chen et al. 2024 ). If any one of these tasks is safety-critical, so-called multitasking could increase the risk of accident (Hadlington 2017 ). Some think they are immune to the danger, but research suggests that those who think they are good at multitasking are less able to do it than those who choose to avoid it (Hadlington 2017 ).

Our analyses reveal that productivity is enhanced in a high automation system without the need for multi-tasking (see Fig.  4 ). Our analyses also reveal that multi-tasking is not supported in a partial automation system, although some drivers appear to believe it is (see Fig.  5 ). There is anecdotal evidence from accident reports that safety is compromised when drivers with more discretion in attentional focus under Partial Automation divert their attention from critical elements of the driving task (National Transportation Safety Board 2017 , 2019c , 2020a , b ). Furthermore, some drivers do not appear to appreciate the risks associated with diverting attention from driving under partial automation or the risks associated with actively defeating the alerting system (National Transportation Safety Board 2017 , 2019c , 2020a , b ). Blanco et al. ( 2015 ) designate this as a Primary Task Reversal effect that emerges when the appearance of the system suggests that the automation can cope fully with the driving task so that the driver can now give primary task status to non-driving tasks. Blanco et al. ( 2015 ) do not offer any solution to this problem but most generally it seems that the current messaging regarding capabilities of partially automated vehicles must be revised.

4.3 Efficiency and navigation

The Gkartzonikas and Gkritza ( 2019 ) review identified enhanced efficiency from reduced travel times as a primary benefit of automated vehicles. The reason that automated vehicles might reduce travel times was not specified, and it is not clear that they do, but for this discussion, we assume that better navigation to unfamiliar destinations would be a major benefit. Our own driving experience suggests that current driving navigation systems are not error-free, but for this paper, we envisioned a near future in which current issues related to inefficient routing and updating of temporary and permanent changes to traffic flows and traffic infrastructure are largely resolved.

We should recognize, however, that automated planning can limit development of skills and knowledge for dynamic replanning (Cook et al. 1996 ). Even high automation vehicles do not react adaptively or creatively to unanticipated, occasional events like traffic jams. In such cases, the human driver needs to engage in dynamic replanning, which requires an updated appreciation of potential options and conflicts. Cook et al. ( 1996 ) have argued that automated planning can limit the development of the essential situation awareness for replanning. Conceivably, the occupant-driver who relies exclusively on automated route planning may never develop a competent appreciation of the layout and routing options even within a frequently traversed neighborhood. It remains uncertain whether general degradation in this currently common competence is a matter of concern.

4.4 Model validation

Model validation refers to the process of confirming that the model satisfies the goals set for it. A model, can be assessed in terms of construct validity (does the model faithfully represent the essential modeling formalisms) and content validity (does the model capture the essential properties of the system being modelled).

For a representational model such as the Abstraction-Decomposition Space, construct validity refers to good form and internal consistency or, as noted in our Introduction, internal coherence. Node entries and the labels attached to them need to be appropriate to the definition of the level of description. As noted in the method section, we extracted content for our Work Domain Analysis from domain reviews, domain standards, and other source documents (see appendix), and then assigned the content against the descriptions for the five levels as provided by Naikar ( 2013 ). In addition, the chains of means-ends links must be continuous from the lowest to the highest level of abstraction. All nodes, except for those at the highest level, must be linked to (must support) something in the adjacent level above. All nodes, except for those in the lowest level, must be linked to (must be supported by) something in the adjacent level below.

In our analysis, we leveraged departures from this formal requirement to highlight system anomalies. In Fig.  3 , the Domain Value of productivity (multi-tasking) is shown as unsupported because there are no functions within a partial automation system that have been designed to support it. Furthermore, we used coding of means-ends links to identify support we judged to be weak or questionable. For example, in Fig.  5 and in Fig.  6 we show the value of safety as having questionable support from the technical functions of operational and tactical control.

Content validity refers to whether purpose, values, domain functions, technical functions and physical resources and their means-ends links (as entered into the Abstraction-Decomposition Space) accurately and comprehensively represent the target system. Naikar ( 2013 ) proposed model review with experienced operators and scenario mapping as suitable content validation methods. As experienced drivers, we validated the model content by reference to our own driving experience. We then further validated model content by mapping descriptive scenarios from accident reports onto the models and then assessing whether the model can be used to reason about the responses of agents (i.e., human and automation) in those scenarios. This scenario mapping strategy recognizes that an Abstraction-Decomposition Space is event independent and accordingly, that it should be possible to use such a model to reason about the behavior of the agents in any situation, including novel or unanticipated ones (Burns et al. 2001 ; Naikar 2013 ). A scenario-mapping exercise of this type can verify the content of the model or can otherwise reveal problems with the model, such as missing or inaccurate information.

5 Conclusion

Prior to our analysis, we could not identify any systems-based rationale for the development of automated vehicles within the research literature or within manufacturers’ documents. Somewhat troubling is that documents such as SAE International ( 2021 ) imply that the overall aim is to develop an autonomous vehicle. Automation is thereby posed as an over-arching design requirement and also as the ultimate design solution. This approach violates a fundamental principle of Systems Engineering; a requirement should not be specified in terms of a design solution. Also troubling is that the documents we consulted in building our Abstraction-Decomposition Spaces treated values such as safety and productivity as independent entities without discussion of potential conflicts or compromises.

Work Domain Analysis, leading to development of an Abstraction-Decomposition Space, offers a different way of viewing the problem. Given the constraint that personal mobility in an urban environment will continue to rely heavily on personal automobiles, the upper three levels of the Abstraction-Decomposition Space can be viewed, in Systems Engineering terms, as the problem space, with the lower two levels representing the solution space. From this perspective, the design challenge becomes one of establishing how driving functions can be enabled to promote satisfaction of driving values while ensuring that efforts directed at satisfying one value do not compromise satisfaction of other values.

In undertaking our analysis, we accepted standard claims relating to benefits of automation with regard to improved safety and enhanced productivity via multi-tasking. Our analyses did not, however, offer unequivocal support for those claims. Tentatively, we suggest that automated systems reduce the risk of some types of accidents but increase the risk of other types. Furthermore, automated systems are typically unusable in some of the more challenging driving conditions that compromise safety. In addition, we suggest that the general beliefs around multi-tasking as it relates to productivity are misguided. Rather than being a timesharing activity where drivers are engaged in two cognitive activities simultaneously, multi-tasking involves task switching where the switching adds a cognitive cost. Such task switching compromises safety in partially automated vehicles and is unnecessary in fully automated vehicles.

Most generally, the common automated vehicle developmental strategy of direct substitution not only fails to resolve many of the safety and performance challenges inherent in manual driving, but it also leaves the human driver less prepared to deal with them than would be the case under full manual control. One major contribution of Work Domain Analysis leading to development of an Abstraction-Decomposition Space is that it can help identify these issues in advance and can stimulate the development of appropriate design solutions.

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Zhang, Y., Lintern, G. Work domain modeling of human-automation interaction for in-vehicle automation. Cogn Tech Work (2024). https://doi.org/10.1007/s10111-024-00780-8

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