literature review in research bullying

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Published: 14 December 2021

Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

Håkan Källmén 1 &
Mats Hallgren ORCID: orcid.org/0000-0002-0599-2403 2

Child and Adolescent Psychiatry and Mental Health volume 15 , Article number: 74 ( 2021 ) Cite this article

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To examine recent trends in bullying and mental health problems among adolescents and the association between them.

A questionnaire measuring mental health problems, bullying at school, socio-economic status, and the school environment was distributed to all secondary school students aged 15 (school-year 9) and 18 (school-year 11) in Stockholm during 2014, 2018, and 2020 (n = 32,722). Associations between bullying and mental health problems were assessed using logistic regression analyses adjusting for relevant demographic, socio-economic, and school-related factors.

The prevalence of bullying remained stable and was highest among girls in year 9; range = 4.9% to 16.9%. Mental health problems increased; range = + 1.2% (year 9 boys) to + 4.6% (year 11 girls) and were consistently higher among girls (17.2% in year 11, 2020). In adjusted models, having been bullied was detrimentally associated with mental health (OR = 2.57 [2.24–2.96]). Reports of mental health problems were four times higher among boys who had been bullied compared to those not bullied. The corresponding figure for girls was 2.4 times higher.

Conclusions

Exposure to bullying at school was associated with higher odds of mental health problems. Boys appear to be more vulnerable to the deleterious effects of bullying than girls.

Introduction

Bullying involves repeated hurtful actions between peers where an imbalance of power exists [ 1 ]. Arseneault et al. [ 2 ] conducted a review of the mental health consequences of bullying for children and adolescents and found that bullying is associated with severe symptoms of mental health problems, including self-harm and suicidality. Bullying was shown to have detrimental effects that persist into late adolescence and contribute independently to mental health problems. Updated reviews have presented evidence indicating that bullying is causative of mental illness in many adolescents [ 3 , 4 ].

There are indications that mental health problems are increasing among adolescents in some Nordic countries. Hagquist et al. [ 5 ] examined trends in mental health among Scandinavian adolescents (n = 116, 531) aged 11–15 years between 1993 and 2014. Mental health problems were operationalized as difficulty concentrating, sleep disorders, headache, stomach pain, feeling tense, sad and/or dizzy. The study revealed increasing rates of adolescent mental health problems in all four counties (Finland, Sweden, Norway, and Denmark), with Sweden experiencing the sharpest increase among older adolescents, particularly girls. Worsening adolescent mental health has also been reported in the United Kingdom. A study of 28,100 school-aged adolescents in England found that two out of five young people scored above thresholds for emotional problems, conduct problems or hyperactivity [ 6 ]. Female gender, deprivation, high needs status (educational/social), ethnic background, and older age were all associated with higher odds of experiencing mental health difficulties.

Bullying is shown to increase the risk of poor mental health and may partly explain these detrimental changes. Le et al. [ 7 ] reported an inverse association between bullying and mental health among 11–16-year-olds in Vietnam. They also found that poor mental health can make some children and adolescents more vulnerable to bullying at school. Bayer et al. [ 8 ] examined links between bullying at school and mental health among 8–9-year-old children in Australia. Those who experienced bullying more than once a week had poorer mental health than children who experienced bullying less frequently. Friendships moderated this association, such that children with more friends experienced fewer mental health problems (protective effect). Hysing et al. [ 9 ] investigated the association between experiences of bullying (as a victim or perpetrator) and mental health, sleep disorders, and school performance among 16–19 year olds from Norway (n = 10,200). Participants were categorized as victims, bullies, or bully-victims (that is, victims who also bullied others). All three categories were associated with worse mental health, school performance, and sleeping difficulties. Those who had been bullied also reported more emotional problems, while those who bullied others reported more conduct disorders [ 9 ].

As most adolescents spend a considerable amount of time at school, the school environment has been a major focus of mental health research [ 10 , 11 ]. In a recent review, Saminathen et al. [ 12 ] concluded that school is a potential protective factor against mental health problems, as it provides a socially supportive context and prepares students for higher education and employment. However, it may also be the primary setting for protracted bullying and stress [ 13 ]. Another factor associated with adolescent mental health is parental socio-economic status (SES) [ 14 ]. A systematic review indicated that lower parental SES is associated with poorer adolescent mental health [ 15 ]. However, no previous studies have examined whether SES modifies or attenuates the association between bullying and mental health. Similarly, it remains unclear whether school related factors, such as school grades and the school environment, influence the relationship between bullying and mental health. This information could help to identify those adolescents most at risk of harm from bullying.

To address these issues, we investigated the prevalence of bullying at school and mental health problems among Swedish adolescents aged 15–18 years between 2014 and 2020 using a population-based school survey. We also examined associations between bullying at school and mental health problems adjusting for relevant demographic, socioeconomic, and school-related factors. We hypothesized that: (1) bullying and adolescent mental health problems have increased over time; (2) There is an association between bullying victimization and mental health, so that mental health problems are more prevalent among those who have been victims of bullying; and (3) that school-related factors would attenuate the association between bullying and mental health.

Participants

The Stockholm school survey is completed every other year by students in lower secondary school (year 9—compulsory) and upper secondary school (year 11). The survey is mandatory for public schools, but voluntary for private schools. The purpose of the survey is to help inform decision making by local authorities that will ultimately improve students’ wellbeing. The questions relate to life circumstances, including SES, schoolwork, bullying, drug use, health, and crime. Non-completers are those who were absent from school when the survey was completed (< 5%). Response rates vary from year to year but are typically around 75%. For the current study data were available for 2014, 2018 and 2020. In 2014; 5235 boys and 5761 girls responded, in 2018; 5017 boys and 5211 girls responded, and in 2020; 5633 boys and 5865 girls responded (total n = 32,722). Data for the exposure variable, bullied at school, were missing for 4159 students, leaving 28,563 participants in the crude model. The fully adjusted model (described below) included 15,985 participants. The mean age in grade 9 was 15.3 years (SD = 0.51) and in grade 11, 17.3 years (SD = 0.61). As the data are completely anonymous, the study was exempt from ethical approval according to an earlier decision from the Ethical Review Board in Stockholm (2010-241 31-5). Details of the survey are available via a website [ 16 ], and are described in a previous paper [ 17 ].

Students completed the questionnaire during a school lesson, placed it in a sealed envelope and handed it to their teacher. Student were permitted the entire lesson (about 40 min) to complete the questionnaire and were informed that participation was voluntary (and that they were free to cancel their participation at any time without consequences). Students were also informed that the Origo Group was responsible for collection of the data on behalf of the City of Stockholm.

Study outcome

Mental health problems were assessed by using a modified version of the Psychosomatic Problem Scale [ 18 ] shown to be appropriate for children and adolescents and invariant across gender and years. The scale was later modified [ 19 ]. In the modified version, items about difficulty concentrating and feeling giddy were deleted and an item about ‘life being great to live’ was added. Seven different symptoms or problems, such as headaches, depression, feeling fear, stomach problems, difficulty sleeping, believing it’s great to live (coded negatively as seldom or rarely) and poor appetite were used. Students who responded (on a 5-point scale) that any of these problems typically occurs ‘at least once a week’ were considered as having indicators of a mental health problem. Cronbach alpha was 0.69 across the whole sample. Adding these problem areas, a total index was created from 0 to 7 mental health symptoms. Those who scored between 0 and 4 points on the total symptoms index were considered to have a low indication of mental health problems (coded as 0); those who scored between 5 and 7 symptoms were considered as likely having mental health problems (coded as 1).

Primary exposure

Experiences of bullying were measured by the following two questions: Have you felt bullied or harassed during the past school year? Have you been involved in bullying or harassing other students during this school year? Alternatives for the first question were: yes or no with several options describing how the bullying had taken place (if yes). Alternatives indicating emotional bullying were feelings of being mocked, ridiculed, socially excluded, or teased. Alternatives indicating physical bullying were being beaten, kicked, forced to do something against their will, robbed, or locked away somewhere. The response alternatives for the second question gave an estimation of how often the respondent had participated in bullying others (from once to several times a week). Combining the answers to these two questions, five different categories of bullying were identified: (1) never been bullied and never bully others; (2) victims of emotional (verbal) bullying who have never bullied others; (3) victims of physical bullying who have never bullied others; (4) victims of bullying who have also bullied others; and (5) perpetrators of bullying, but not victims. As the number of positive cases in the last three categories was low (range = 3–15 cases) bully categories 2–4 were combined into one primary exposure variable: ‘bullied at school’.

Assessment year was operationalized as the year when data was collected: 2014, 2018, and 2020. Age was operationalized as school grade 9 (15–16 years) or 11 (17–18 years). Gender was self-reported (boy or girl). The school situation To assess experiences of the school situation, students responded to 18 statements about well-being in school, participation in important school matters, perceptions of their teachers, and teaching quality. Responses were given on a four-point Likert scale ranging from ‘do not agree at all’ to ‘fully agree’. To reduce the 18-items down to their essential factors, we performed a principal axis factor analysis. Results showed that the 18 statements formed five factors which, according to the Kaiser criterion (eigen values > 1) explained 56% of the covariance in the student’s experience of the school situation. The five factors identified were: (1) Participation in school; (2) Interesting and meaningful work; (3) Feeling well at school; (4) Structured school lessons; and (5) Praise for achievements. For each factor, an index was created that was dichotomised (poor versus good circumstance) using the median-split and dummy coded with ‘good circumstance’ as reference. A description of the items included in each factor is available as Additional file 1 . Socio-economic status (SES) was assessed with three questions about the education level of the student’s mother and father (dichotomized as university degree versus not), and the amount of spending money the student typically received for entertainment each month (> SEK 1000 [approximately $120] versus less). Higher parental education and more spending money were used as reference categories. School grades in Swedish, English, and mathematics were measured separately on a 7-point scale and dichotomized as high (grades A, B, and C) versus low (grades D, E, and F). High school grades were used as the reference category.

Statistical analyses

The prevalence of mental health problems and bullying at school are presented using descriptive statistics, stratified by survey year (2014, 2018, 2020), gender, and school year (9 versus 11). As noted, we reduced the 18-item questionnaire assessing school function down to five essential factors by conducting a principal axis factor analysis (see Additional file 1 ). We then calculated the association between bullying at school (defined above) and mental health problems using multivariable logistic regression. Results are presented as odds ratios (OR) with 95% confidence intervals (Cis). To assess the contribution of SES and school-related factors to this association, three models are presented: Crude, Model 1 adjusted for demographic factors: age, gender, and assessment year; Model 2 adjusted for Model 1 plus SES (parental education and student spending money), and Model 3 adjusted for Model 2 plus school-related factors (school grades and the five factors identified in the principal factor analysis). These covariates were entered into the regression models in three blocks, where the final model represents the fully adjusted analyses. In all models, the category ‘not bullied at school’ was used as the reference. Pseudo R-square was calculated to estimate what proportion of the variance in mental health problems was explained by each model. Unlike the R-square statistic derived from linear regression, the Pseudo R-square statistic derived from logistic regression gives an indicator of the explained variance, as opposed to an exact estimate, and is considered informative in identifying the relative contribution of each model to the outcome [ 20 ]. All analyses were performed using SPSS v. 26.0.

Prevalence of bullying at school and mental health problems

Estimates of the prevalence of bullying at school and mental health problems across the 12 strata of data (3 years × 2 school grades × 2 genders) are shown in Table 1 . The prevalence of bullying at school increased minimally (< 1%) between 2014 and 2020, except among girls in grade 11 (2.5% increase). Mental health problems increased between 2014 and 2020 (range = 1.2% [boys in year 11] to 4.6% [girls in year 11]); were three to four times more prevalent among girls (range = 11.6% to 17.2%) compared to boys (range = 2.6% to 4.9%); and were more prevalent among older adolescents compared to younger adolescents (range = 1% to 3.1% higher). Pooling all data, reports of mental health problems were four times more prevalent among boys who had been victims of bullying compared to those who reported no experiences with bullying. The corresponding figure for girls was two and a half times as prevalent.

Associations between bullying at school and mental health problems

Table 2 shows the association between bullying at school and mental health problems after adjustment for relevant covariates. Demographic factors, including female gender (OR = 3.87; CI 3.48–4.29), older age (OR = 1.38, CI 1.26–1.50), and more recent assessment year (OR = 1.18, CI 1.13–1.25) were associated with higher odds of mental health problems. In Model 2, none of the included SES variables (parental education and student spending money) were associated with mental health problems. In Model 3 (fully adjusted), the following school-related factors were associated with higher odds of mental health problems: lower grades in Swedish (OR = 1.42, CI 1.22–1.67); uninteresting or meaningless schoolwork (OR = 2.44, CI 2.13–2.78); feeling unwell at school (OR = 1.64, CI 1.34–1.85); unstructured school lessons (OR = 1.31, CI = 1.16–1.47); and no praise for achievements (OR = 1.19, CI 1.06–1.34). After adjustment for all covariates, being bullied at school remained associated with higher odds of mental health problems (OR = 2.57; CI 2.24–2.96). Demographic and school-related factors explained 12% and 6% of the variance in mental health problems, respectively (Pseudo R-Square). The inclusion of socioeconomic factors did not alter the variance explained.

Our findings indicate that mental health problems increased among Swedish adolescents between 2014 and 2020, while the prevalence of bullying at school remained stable (< 1% increase), except among girls in year 11, where the prevalence increased by 2.5%. As previously reported [ 5 , 6 ], mental health problems were more common among girls and older adolescents. These findings align with previous studies showing that adolescents who are bullied at school are more likely to experience mental health problems compared to those who are not bullied [ 3 , 4 , 9 ]. This detrimental relationship was observed after adjustment for school-related factors shown to be associated with adolescent mental health [ 10 ].

A novel finding was that boys who had been bullied at school reported a four-times higher prevalence of mental health problems compared to non-bullied boys. The corresponding figure for girls was 2.5 times higher for those who were bullied compared to non-bullied girls, which could indicate that boys are more vulnerable to the deleterious effects of bullying than girls. Alternatively, it may indicate that boys are (on average) bullied more frequently or more intensely than girls, leading to worse mental health. Social support could also play a role; adolescent girls often have stronger social networks than boys and could be more inclined to voice concerns about bullying to significant others, who in turn may offer supports which are protective [ 21 ]. Related studies partly confirm this speculative explanation. An Estonian study involving 2048 children and adolescents aged 10–16 years found that, compared to girls, boys who had been bullied were more likely to report severe distress, measured by poor mental health and feelings of hopelessness [ 22 ].

Other studies suggest that heritable traits, such as the tendency to internalize problems and having low self-esteem are associated with being a bully-victim [ 23 ]. Genetics are understood to explain a large proportion of bullying-related behaviors among adolescents. A study from the Netherlands involving 8215 primary school children found that genetics explained approximately 65% of the risk of being a bully-victim [ 24 ]. This proportion was similar for boys and girls. Higher than average body mass index (BMI) is another recognized risk factor [ 25 ]. A recent Australian trial involving 13 schools and 1087 students (mean age = 13 years) targeted adolescents with high-risk personality traits (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) to reduce bullying at school; both as victims and perpetrators [ 26 ]. There was no significant intervention effect for bullying victimization or perpetration in the total sample. In a secondary analysis, compared to the control schools, intervention school students showed greater reductions in victimization, suicidal ideation, and emotional symptoms. These findings potentially support targeting high-risk personality traits in bullying prevention [ 26 ].

The relative stability of bullying at school between 2014 and 2020 suggests that other factors may better explain the increase in mental health problems seen here. Many factors could be contributing to these changes, including the increasingly competitive labour market, higher demands for education, and the rapid expansion of social media [ 19 , 27 , 28 ]. A recent Swedish study involving 29,199 students aged between 11 and 16 years found that the effects of school stress on psychosomatic symptoms have become stronger over time (1993–2017) and have increased more among girls than among boys [ 10 ]. Research is needed examining possible gender differences in perceived school stress and how these differences moderate associations between bullying and mental health.

Strengths and limitations

Strengths of the current study include the large participant sample from diverse schools; public and private, theoretical and practical orientations. The survey included items measuring diverse aspects of the school environment; factors previously linked to adolescent mental health but rarely included as covariates in studies of bullying and mental health. Some limitations are also acknowledged. These data are cross-sectional which means that the direction of the associations cannot be determined. Moreover, all the variables measured were self-reported. Previous studies indicate that students tend to under-report bullying and mental health problems [ 29 ]; thus, our results may underestimate the prevalence of these behaviors.

In conclusion, consistent with our stated hypotheses, we observed an increase in self-reported mental health problems among Swedish adolescents, and a detrimental association between bullying at school and mental health problems. Although bullying at school does not appear to be the primary explanation for these changes, bullying was detrimentally associated with mental health after adjustment for relevant demographic, socio-economic, and school-related factors, confirming our third hypothesis. The finding that boys are potentially more vulnerable than girls to the deleterious effects of bullying should be replicated in future studies, and the mechanisms investigated. Future studies should examine the longitudinal association between bullying and mental health, including which factors mediate/moderate this relationship. Epigenetic studies are also required to better understand the complex interaction between environmental and biological risk factors for adolescent mental health [ 24 ].

Availability of data and materials

Data requests will be considered on a case-by-case basis; please email the corresponding author.

Code availability

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Acknowledgements

Authors are grateful to the Department for Social Affairs, Stockholm, for permission to use data from the Stockholm School Survey.

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Håkan Källmén

Epidemiology of Psychiatric Conditions, Substance Use and Social Environment (EPiCSS), Department of Global Public Health, Karolinska Institutet, Level 6, Solnavägen 1e, Solna, Sweden

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HK conceived the study and analyzed the data (with input from MH). HK and MH interpreted the data and jointly wrote the manuscript. All authors read and approved the final manuscript.

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Additional file 1..

Principal factor analysis description.

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Källmén, H., Hallgren, M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional study. Child Adolesc Psychiatry Ment Health 15 , 74 (2021). https://doi.org/10.1186/s13034-021-00425-y

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Anti-bullying interventions in schools: a systematic literature review

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1 Departamento de Enfermagem Materno Infantil e Saúde Pública, Escola de Enfermagem de Ribeirão Preto, USP. Av. Bandeirantes 3900, Monte Alegre. 14040-902 Ribeirão Preto SP Brasil. [email protected].
2 Departamento de Enfermagem Psiquiátrica e Ciências Humanas, Escola de Enfermagem de Ribeirão Preto, USP. Ribeirão Preto SP Brasil.
3 Departamento de Psicologia, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, USP. Ribeirão Preto SP Brasil.
PMID: 28724015
DOI: 10.1590/1413-81232017227.16242015

This paper presents a systematic literature review addressing rigorously planned and assessed interventions intended to reduce school bullying. The search for papers was performed in four databases (Lilacs, Psycinfo, Scielo and Web of Science) and guided by the question: What are the interventions used to reduce bullying in schools? Only case-control studies specifically focusing on school bullying without a time frame were included. The methodological quality of investigations was assessed using the SIGN checklist. A total of 18 papers composed the corpus of analysis and all were considered to have high methodological quality. The interventions conducted in the revised studies were divided into four categories: multi-component or whole-school, social skills training, curricular, and computerized. The review synthesizes knowledge that can be used to contemplate practices and intervention programs in the education and health fields with a multidisciplinary nature.

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OPINION article

Strategies for preventing school bullying—a life education perspective.

$\r\nJason Cong Lin$

1 Department of International Education, The Education University of Hong Kong, Hong Kong, Tai Po, Hong Kong SAR, China
2 Center of Teacher Education, Minghsin University of Science and Technology, Hsinchu, Taiwan

1 Introduction

The implementation of education affects a country's development and success, and education is a catalyst for personal development and a powerful tool for global change. Its influence extends beyond the development of skills required for economic success. It can contribute to nation-building and reconciliation. This affirms education's essential role in life ( Wang and Shih, 2022 ; Muweesi et al., 2024 ).

Challenges such as school bullying often occur in educational activities. Bullying is a complex social relationship problem that typically involves repeated, deliberate intent to harm or intimidate others by exploiting power imbalances. Bullying is generally ongoing and measured in terms of frequency (e.g., daily) and duration (e.g., during the previous school term). Although extensive research has been conducted on the topic, bullying remains a global concern that can have long-term negative personal, social, emotional, academic, and economic consequences ( Deborah et al., 2023 ; Green et al., 2023 ). Bullying is one of the most prevalent problems in schools. The term bullying encompasses physical and verbal aggression, prejudice, and discrimination. Parents, family members, and teachers have become increasingly concerned about this problem ( Barros, 2024 ).

Most antibullying curricula in schools are based on a social–ecological perspective. Whole-school approaches to developing antibullying policy from this perspective have the potential to empower school communities to address aggression by involving parents, teachers, administrators, and community members. This opinion piece explored how life education can enhance a social–ecological approach to antibullying programs ( Donoghue et al., 2023 ).

2 School bullying

Bullying is a pervasive problem that impacts children and youth in schools involved as perpetrators, victims, and bystanders. Bullying is a form of violence and often has psychological effects. The targets of bullying are typically individuals perceived to be weak or unpopular, and the aim of the action is to intimidate and disempower such individuals ( Nickerson, 2019 ; Huang, 2023 ).

School bullying involves direct or indirect continual use of language, words, pictures, symbols, body movements, or other means by individuals or groups within or between schools, inside or outside the school setting. Bullying individuals or groups engage in behaviors that deliberately belittle, exclude, bully, harass, or tease others, which creates a hostile or unfriendly learning environment that is difficult to cope with and causes mental, physical, or property damage, thereby disrupting normal learning activities ( Wu and Lin, 2005 ; Nickerson, 2019 ).

School bullying is a form of bullying that occurs within schools that is characterized by intentionally harmful and recurring behavior that targets the same children. Bullying has existed in human society for a considerable amount of time. Among the various forms of bullying, violent bullying of children has garnered particular attention. Bullying can also involve oppressive behavior resulting from unequal power dynamics among children. Bullying can be considered to have occurred any time a student experiences prolonged and repeated bullying or harassment by one or more students inside or outside the school that can cause physical and mental pain ( Wu and Lin, 2005 ).

3 Life education

In fact, the meaning of life is love. That is, to be loved and to love. From another perspective, the meaning of life is to recognize the presence of love and expand one's life with love. Without love, life has no meaning. Love is the core of life. Life is the starting point and the primary focus of all educational activities. Creating conditions that support individual life development is a core focus of education. Education should foster healthy development of life force, and it encompasses various elements that have potential to boost vitality ( Yao, 2023 ; Shih, 2024 ).

Life education guides students in transitioning from a focus on limited, short-term desires to a focus on unlimited, long-term intangible value. It helps them understand life's infinite possibilities, harness their life force, and achieve their life goals. Life education involves the meaning, ideals, and practice of being human and focuses on deepening one's outlook on life, internalizing values, and integrating mobility. Therefore, it helps students establish a complete outlook on life and personal values ( Shih, 2022a ; Yao, 2023 ).

The goal of life education is to help students discover meaning in life and realize their potential throughout their life journey. It teaches students to understand themselves, cherish life, respect others, care for all living things, live authentically, realize their potential, and contribute to the public good. In life education, students explore the entire process of life; they learn to face and solve problems with a positive and optimistic attitude, which can improve their quality of life ( Sun, 2000 ; Shih, 2020a ).

4 Discussion: strategies for preventing school bullying from a life education perspective

4.1 implementation of life education to reduce students' loneliness to prevent school bullying.

By exploring the meaning of life, life education teaches individuals to face and solve problems with a positive and optimistic attitude, thus improving their quality of life ( Sun, 2000 ). Feelings of loneliness arise when people lose their sense of meaning in life and fail to understand their life's value. Implementing life education to reduce students' loneliness and prevent school bullying involves fostering a supportive and inclusive school environment ( Sun, 2000 ). Life education can encompass various aspects, including emotional intelligence, social skills, empathy, and community building. Here are some strategies that can be implemented.

4.1.1 Social-emotional learning programs

Integrating Social-emotional learning (SEL) into the curriculum to teach students skills like empathy, self-awareness, and emotional regulation. These skills can help students better understand their own emotions and those of others, reducing feelings of isolation and increasing positive social interactions ( Jones and Bouffard, 2012 ; Kim et al., 2024 ).

4.1.2 Peer support systems

To establish peer mentoring or buddy systems where older or more socially adept students support younger or more isolated peers. This can help foster connections and reduce feelings of loneliness among students ( Seery et al., 2021 ; Hayman et al., 2022 ).

4.1.3 Inclusive activities and clubs

Feelings of acceptance within school communities can promote positive psychological outcomes. Despite occurring outside of the classroom, youth who engage in extracurricular activities typically report greater school belonging. Teachers should encourage students participate in group activities, clubs, and extracurricular programs that emphasize teamwork, cooperation, and inclusion. These settings provide opportunities for students to form friendships and feel a sense of belonging ( O'Donnell et al., 2023 ).

4.1.4 Bullying prevention programs

Implementing comprehensive bullying prevention programs that educate students about the impact of bullying, promote positive behavior, and provide clear guidelines for addressing bullying incidents. Creating a safe environment where students feel supported can reduce the prevalence of bullying ( Gaffney et al., 2021 ).

4.1.5 Mindfulness and wellbeing practices

The use of mindfulness in schools has greatly expanded over the past 10 years. Research has demonstrated positive psychological effects of mindfulness for students as well as teachers. Teachers should incorporate mindfulness practices and discussions on mental health into the school routine. This can help students manage stress and emotions, leading to a more positive school experience ( Garro et al., 2023 ).

4.1.6 Self-awareness

Self-awareness is often seen as a critical component in leadership and career success. Self-awareness is the ability to recognize and understand one's own thoughts, emotions, and behaviors. It involves a conscious knowledge of one's character, feelings, motives, and desires. Here are some key points about self-awareness: (1) emotional self-awareness: understanding one's own emotions and their impact; (2) accurate self-assessment: recognizing one's strengths and limitations; (3) self-confidence: a sense of self-worth and capabilities ( Axelrod, 2012 ; Carden et al., 2022 ).

4.1.7 Parental and community involvement

Engage parents and the community in life education efforts. Workshops and seminars can educate parents on supporting their children's emotional and social development at home, creating a more cohesive support system ( Shih, 2022a ).

4.1.8 Counseling and support services

Ensure access to counseling services for students who may need additional support. School counselors can provide a safe space for students to discuss their feelings and work through challenges.

4.1.9 Curriculum integration

Integrate themes of empathy, respect, and community into various subjects. For example, literature and history classes can explore stories and events that highlight these values ( Shih, 2022b ).

4.1.10 Teacher training

Provide teachers with training on identifying signs of loneliness and bullying and strategies for fostering a supportive classroom environment.

By focusing on these areas, schools can create a nurturing environment that promotes positive relationships, reduces loneliness, and ultimately prevent school bullying.

4.2 Implementation of life education to reduce students' stress, depression, and anxiety levels and prevent school bullying

School bullying is a critical problem of global concern and potentially leads to serious health consequences for students. Research indicates that bullying is a significant risk factor for adolescent mental and physical health in the short and long term ( Wang and Chen, 2024 ). The prevalence of bullying among students in schools has increased rapidly. Bullying can lead to various problems such as stress, depression, anxiety, and impaired academic performance ( Yosep et al., 2024 ). Research indicates that significant correlations exist between school bullying experiences and psychological problems ( Zhao et al., 2024 ).

Life education is an integrative, experiential, and continual form of whole-person education that places students at the center of teaching design, with various teaching activities used to achieve goals related to knowledge, emotion, and intention. The main axis of the Life Education Curriculum is the connotation of life education, and units within the curriculum, such as “Knowing Yourself,” “Knowing Others,” “Emotional Education,” and “Care and Cooperation,” are designed in a step-by-step manner. The teaching design and arrangement of the curriculum enables students to relieve stress, feelings of depression, and anxiety while enhancing their emotional development ( Sun, 2000 ; Shih, 2022a ).

In conclusion, implementing life education in schools can be an effective way to address and reduce students' stress, depression, anxiety, and school bullying. Here are some strategies that can be employed.

4.2.1 Curriculum integration

4.2.1.1 emotional intelligence.

Young people go through extreme ups and downs at different stages of their lives, especially during puberty. Without proper support and guidance, some children and adolescents can find it hard to understand why they have unhelpful responses to their emotional reactions. Teachers should incorporate lessons that teach students about emotional awareness, regulation, and empathy. This can help them understand their own emotions and those of others, leading to more positive interactions ( Quan and Yao, 2014 ; Gonzales, 2022 ).

4.2.1.2 Mindfulness and stress management

Teach techniques such as mindfulness, meditation, and relaxation exercises to help students manage stress and anxiety ( Garro et al., 2023 ).

4.2.1.3 Resilience and coping skills

Develop students' resilience by teaching coping strategies for dealing with setbacks, failures, and challenges.

4.2.1.4 Emotional engagement

Emotional engagement is important for behavioral and psychological outcomes has also been explored in the school connection literature. Emotional engagement refers to the extent to which individuals are involved, invested, and emotionally connected to an activity, task, or environment. In educational settings, emotional engagement is crucial for fostering a deep connection to learning and enhancing overall academic experience. For students, emotional engagement can be fostered through interactive learning. Hands-on activities and interactive lessons help students feel more involved and interested. In addition, supportive and nurturing relationships with teachers and peers enhance emotional investment ( Markowitz, 2017 ; Luo et al., 2019 ).

4.2.2 Supportive environment

4.2.2.1 emotional safety.

Emotional safety in schools refers to how safe a student feels in expressing their emotions in school. Students should feel secure and confident as they express themselves and take on challenges that encourage them to try something new. Emotional safety is considered a defining component of a positive learning environment and is related to psychological wellbeing, and positive academic and social outcomes. In schools, emotional safety is developed through supportive relationships. Teachers should create a school environment where students feel safe to express their feelings and concerns, and develop students' sense of emotional safety. This can include counseling services, peer support groups, and designated areas where students can take a break if they feel overwhelmed ( Shean and Mander, 2020 ; National Center on Safe Supportive Learning Environments, 2024 ).

4.2.2.2 Anti-bullying policies

Implementing clear policies and procedures for addressing bullying, including prevention programs, reporting mechanisms, and consequences for bullying behavior.

4.2.3 Teacher training and support

4.2.3.1 professional development.

Schoolteachers are often the first to respond when a student presents with a mental health issue in the classroom. Therefore, provide teachers with training on recognizing signs of stress, anxiety, and depression in students. Equip them with skills to provide appropriate support or referrals to professional help ( Gunawardena et al., 2024 ).

4.2.3.2 Modeling positive behavior

Encourage teachers to model positive behaviors, such as active listening, empathy, and respectful communication, to create a supportive classroom atmosphere.

4.2.4 Regular assessment and feedback

4.2.4.1 monitoring mental health.

Regularly assess students' mental health and wellbeing through surveys, interviews, or observation. Use this data to inform and adjust programs as needed ( Park et al., 2020 ).

4.2.4.2 Student feedback

Encourage students to provide feedback on the life education programs, ensuring they are meeting the students' needs and preferences.

4.2.5 Promotion of positive peer relationships

4.2.5.1 social skills training.

Social skills training is one of the oldest and widely studied approaches to psychiatric rehabilitation. Teach social skills for students, such as effective communication, conflict resolution, and teamwork, to help students build positive relationships with their peers ( Mueser and Bellack, 2007 ).

4.2.5.2 Peer mentorship programs

Establish peer mentorship or buddy systems to support students who may be struggling with social integration or emotional challenges ( Seery et al., 2021 ; Hayman et al., 2022 ).

4.2.6 Incorporating cultural sensitivity

4.2.6.1 cultural awareness.

Include components of cultural awareness and sensitivity in the curriculum to foster understanding and respect among students from diverse backgrounds ( Shih, 2020b ).

4.2.6.2 Tailored programs

Adapt life education programs to reflect the cultural and social context of the students, making them more relevant and effective.

By implementing these strategies, schools can create a more supportive and inclusive environment that promotes students' overall wellbeing and reduces the incidence of stress, depression, anxiety, and bullying.

5 Reflections and conclusions

5.1 reflections.

Bullying occurs when students repeatedly subject a peer to negative activities. It is a problem involving unsocial and rule-breaking behavior. Bullies are often impulsive and have a strong need to dominate others; they lack empathy and are generally physically stronger than their victims. Conversely, victims tend to be vigilant, sensitive, and quiet students with low self-confidence. They often lack social competencies that could help them divert bullying, such as the ability to use humor. When bullied, they typically respond emotionally, shedding tears or becoming irritated, which often encourages the bully. Childhood bullying has increasingly been reported to be one of the most common and widespread forms of school violence ( Taj et al., 2024 ).

School bullying can occur in the neighborhoods surrounding schools in addition to in the schools themselves. Traditionally, governance of school bullying has focused on visible and physical harm to victims and has not accounted for the psychological problems caused by bullying behavior, which can pose a danger to the lives of those being bullied. Consequently, school bullying remains a major problem that affects students' academic achievement and general wellbeing worldwide. It is a complex phenomenon that varies across cultural and regional contexts, and understanding the nuances of these contexts is crucial to the development of effective interventions ( Hasibuan and Rizana, 2023 ; Jin, 2023 ).

5.2 Conclusions

School bullying is particularly prevalent among teenagers. Bullying behavior can have detrimental effects on victims and perpetrators, potentially leading to lower academic achievement, problems with socialization, and disruption of physical and mental health. Intervention is required to address the problem of bullying ( Yuhbaba et al., 2023 ).

This opinion piece proposes that life education can help address the problem of school bullying and suggests the following prevention strategies: (1) implementing life education to reduce students' loneliness and prevent school bullying and (2) implementing life education to reduce students' stress, depression, and anxiety levels.

In conclusion, exposure to bullying at school is strongly associated with loneliness. Implementing life education can reduce bullying at school and enable development of effective interventions to mitigate the persistent loneliness associated with bullying among adolescents. Because bullying and loneliness frequently occur in schools, they are ideal settings for bullying-focused interventions. Interventions implemented in school settings can be used to target the entire adolescent population, and evidence supports the effectiveness of life education interventions in preventing school bullying ( Madsen et al., 2024 ). Greater emphasis has been placed on the importance of life education in the literature. The current study may assist with the development of life education curricula in schools and with reducing students' emotional distress, anxiety, and bullying behaviors and therefore serves as a valuable contribution to the literature on the prevention and treatment of school bullying.

Author contributions

JL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Y-HS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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.

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Keywords: anxiety, depression, life education, loneliness, school bullying, stress

Citation: Lin JC and Shih Y-H (2024) Strategies for preventing school bullying—A life education perspective. Front. Psychol. 15:1429215. doi: 10.3389/fpsyg.2024.1429215

Received: 07 May 2024; Accepted: 13 August 2024; Published: 18 September 2024.

Reviewed by:

Copyright © 2024 Lin and Shih. 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: Yi-Huang Shih, shih262@gmail.com

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.

A Review of Behavior-Based Interventions that Address Bullying, Aggressive, and Inappropriate Student Behavior during Recess

LITERATURE REVIEW
Published: 31 March 2020
Volume 43 , pages 377–391, ( 2020 )

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Laura Kern 1 ,
Brandi Simonsen 2 &
Sarah Wilkinson 2

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The purpose of this literature review is to examine the research base of interventions focused on reducing bullying, aggressive, or inappropriate behavior in recess settings through behavioral-based interventions. This review extends the literature by synthesizing findings from experimental, quasi-experimental, and single-case research on the characteristics and components of effective interventions. Many of the interventions focused on social skills training of the students, with a few addressing the adult behavior of active supervision. Findings suggest that more research is needed in school recess settings to determine the effective components of interventions for students, especially for social skills, and to address the adult behavior of active supervision.

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Kern, L., Simonsen, B. & Wilkinson, S. A Review of Behavior-Based Interventions that Address Bullying, Aggressive, and Inappropriate Student Behavior during Recess. Educ. Treat. Child. 43 , 377–391 (2020). https://doi.org/10.1007/s43494-020-00018-y

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Bullying: What We Know Based On 40 Years of Research

APA journal examines science aimed at understanding causes, prevention

WASHINGTON — A special issue of American Psychologist ® provides a comprehensive review of over 40 years of research on bullying among school age youth, documenting the current understanding of the complexity of the issue and suggesting directions for future research.

“The lore of bullies has long permeated literature and popular culture. Yet bullying as a distinct form of interpersonal aggression was not systematically studied until the 1970s. Attention to the topic has since grown exponentially,” said Shelley Hymel, PhD, professor of human development, learning and culture at the University of British Columbia, a scholarly lead on the special issue along with Susan M. Swearer, PhD, professor of school psychology at the University of Nebraska-Lincoln. “Inspired by the 2011 U.S. White House Conference on Bullying Prevention, this collection of articles documents current understanding of school bullying.”

The special issue consists of an introductory overview (PDF, 90KB) by Hymel and Swearer, co-directors of the Bullying Research Network, and five articles on various research areas of bullying including the long-term effects of bullying into adulthood, reasons children bully others, the effects of anti-bullying laws and ways of translating research into anti-bullying practice.

Articles in the issue:

Long-Term Adult Outcomes of Peer Victimization in Childhood and Adolescence: Pathways to Adjustment and Maladjustment (PDF, 122KB) by Patricia McDougall, PhD, University of Saskatchewan, and Tracy Vaillancourt, PhD, University of Ottawa.

The experience of being bullied is painful and difficult. Its negative impact — on academic functioning, physical and mental health, social relationships and self-perceptions — can endure across the school years. But not every victimized child develops into a maladjusted adult. In this article, the authors provide an overview of the negative outcomes experienced by victims through childhood and adolescence and sometimes into adulthood. They then analyze findings from prospective studies to identify factors that lead to different outcomes in different people, including in their biology, timing, support systems and self-perception.

Patricia McDougall can be contacted by email or by phone at (306) 966-6203.

A Relational Framework for Understanding Bullying: Developmental Antecedents and Outcomes (PDF, 151KB) by Philip Rodkin, PhD, and Dorothy Espelage, PhD, University of Illinois, Urbana-Champaign, and Laura Hanish, PhD, Arizona State University.

How do you distinguish bullying from aggression in general? In this review, the authors describe bullying from a relationship perspective. In order for bullying to be distinguished from other forms of aggression, a relationship must exist between the bully and the victim, there must be an imbalance of power between the two and it must take place over a period of time. “Bullying is perpetrated within a relationship, albeit a coercive, unequal, asymmetric relationship characterized by aggression,” wrote the authors. Within that perspective, the image of bullies as socially incompetent youth who rely on physical coercion to resolve conflicts is nothing more than a stereotype. While this type of “bully-victim” does exist and is primarily male, the authors describe another type of bully who is more socially integrated and has surprisingly high levels of popularity among his or her peers. As for the gender of victims, bullying is just as likely to occur between boys and girls as it is to occur in same-gender groups.

Dorothy Espelage can be contacted by email or by phone at (217) 333-9139.

Translating Research to Practice in Bullying Prevention (PDF, 157KB) by Catherine Bradshaw, PhD, University of Virginia.

This paper reviews the research and related science to develop a set of recommendations for effective bullying prevention programs. From mixed findings on existing programs, the author identifies core elements of promising prevention approaches (e.g., close playground supervision, family involvement, and consistent classroom management strategies) and recommends a three-tiered public health approach that can attend to students at all risk levels. However, the author notes, prevention efforts must be sustained and integrated to effect change.

Catherine Bradshaw can be contacted by email or by phone at (434) 924-8121.

Law and Policy on the Concept of Bullying at School (PDF, 126KB) by Dewey Cornell, PhD, University of Virginia, and Susan Limber, PhD, Clemson University.

Since the shooting at Columbine High School in 1999, all states but one have passed anti-bullying laws, and multiple court decisions have made schools more accountable for peer victimization. Unfortunately, current legal and policy approaches, which are strongly rooted in laws regarding harassment and discrimination, do not provide adequate protection for all bullied students. In this article, the authors provide a review of the legal framework underpinning many anti-bullying laws and make recommendations on best practices for legislation and school policies to effectively address the problem of bullying.

Dewey Cornell can be contacted by email or by phone at (434) 924-0793.

Understanding the Psychology of Bullying: Moving Toward a Social-Ecological Diathesis-Stress Model by Susan Swearer, PhD, University of Nebraska-Lincoln, and Shelley Hymel, PhD, University of British Columbia.

Children’s involvement in bullying varies across roles and over time. A student may be victimized by classmates but bully a sibling at home. Bullying is a complex form of interpersonal aggression that can be both a one-on-one process and a group phenomenon. It negatively affects not only the victim, but the bully and witnesses as well. In this paper, the authors suggest an integrated model for examining bullying and victimization that recognizes the complex and dynamic nature of bullying across multiple settings over time.

Susan Swearer can be contacted by email or by phone at (402) 472-1741. Shelley Hymel can be contacted by email or by phone at (604) 822-6022.

Copies of articles are also available from APA Public Affairs , (202) 336-5700.

The American Psychological Association, in Washington, D.C., is the largest scientific and professional organization representing psychology in the United States. APA's membership includes more than 122,500 researchers, educators, clinicians, consultants and students. Through its divisions in 54 subfields of psychology and affiliations with 60 state, territorial and Canadian provincial associations, APA works to advance the creation, communication and application of psychological knowledge to benefit society and improve people's lives.

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Literature Review of School Bullying 1 Literature Review of Bullying at Schools

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Outline BULLYING IN SCHOOLS 1 Specific Purpose: To inform my classmates about the prevalence and dangers of bullying in schools Thesis Statement: Despite the numerous efforts put in place by the government, bullying has remained prevalent, leading to adverse impacts of depression, suicide, and dropout from schools. This outline offers a blueprint for analyzing the problem through the lens of its definition, precipitating factors, severity, impacts, and viable solutions.

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Although the bully-victim conflict is an age-old scenario, researchers only began studying it in school settings 45 years ago. The most agreed upon definition of bullying includes three criteria: 1) intentionality (desire or goal of inflicting harm, intimidation, and/or humiliation), 2) some repetitiveness, and most importantly, 3) a power imbalance between the socially or physically more prominent bully and the more vulnerable victim. The power differential can manifest among a variety of factors, such as physical dominance, self- confidence, peer group status, etc. Conversely, conflict between equals is not considered bullying, but rather, general aggression. Another, more recent concept that has emerged in the field of bullying research is the category of “bully-victims,” a smaller subset of youth who both perpetrate and experience bullying. The forms bullying can take include: direct aggression (e.g., name calling, hitting, belittling someone in front of others) or indirect, relational aggression (e.g., spreading rumors, exclusion from the group, hurting another’s reputation). Often occurring in school contexts, which has expanded in recent years to include cyberbullying in the virtual worlds of digital and social media, bullying takes place throughout the school years, from elementary to high school and has likewise been studied across the grades. And since bullying is a familiar, if not intimate, school experience for most people, it is sometimes easy or tempting to accept it as a rite of passage or a typical childhood experience, rather than a problem that needs to be addressed. As Olweus (2013) explains, “being bullied by peers represents a serious violation of the fundamental rights of the child or youth exposed” (p. 770). It is with this understanding of bullying – as a violation of basic human rights – that this two-part brief explores the phenomenon (history, prevalence, risk factors, and consequences) in Part I and reviews research- based interventions in Part II.

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How to Conduct Effective Literature Reviews – A Step-by-Step Guide Based on Academic Research

A literature review is a critical component of academic research, serving as the foundation upon which new knowledge is built. It’s more than just a summary of existing research; it’s a systematic and critical analysis of relevant literature that identifies key themes, gaps, and controversies in a specific field.

Conducting a literature review can be a daunting task, especially for those new to academic research. This comprehensive guide will provide a step-by-step approach to conducting effective literature reviews, drawing on insights from academic research.

Introduction

A literature review is an essential part of any research project, dissertation, or thesis. It involves systematically identifying, evaluating, and synthesizing existing research on a specific topic. An effective literature review not only demonstrates your understanding of the field but also positions your research within the broader context of existing knowledge.

Step 1: Define Your Research Question

The first step in conducting a literature review is to define your research question or topic. This will help you focus your search and ensure that the literature you review is relevant to your research. Your research question should be clear, concise, and specific.

Broad Topic: The impact of social media on mental health.
Focused Research Question: What is the relationship between social media use and depression among adolescents?

Step 2: Develop a Search Strategy

Once you have defined your research question, you need to develop a search strategy. This involves identifying relevant databases and search terms.

General: Google Scholar, Web of Science, Scopus
Discipline-specific: PubMed (medicine), PsycINFO (psychology), ERIC (education)
Use keywords related to your research question.
Use Boolean operators (AND, OR, NOT) to combine search terms.
Use truncation (*) to search for variations of a word.
Search terms for the research question above: social media, depression, adolescents, teenagers, mental health.
Combined search using Boolean operators: (social media OR Facebook OR Instagram) AND (depression OR anxiety) AND (adolescents OR teenagers)

Step 3: Select and Evaluate Sources

Once you have conducted your search, you will need to select and evaluate the sources that are most relevant to your research question. It is important to consider the quality and credibility of the sources you select.

Relevance: Does the source address your research question?
Authority: Is the source written by an expert in the field?
Currency: Is the source up-to-date?
Objectivity: Is the source free from bias?
Methodology: Is the research design sound?

Types of sources:

Peer-reviewed journal articles: These are the most credible sources, as they have been reviewed by experts in the field.
Books: Books can provide a comprehensive overview of a topic.
Conference papers: Conference papers can provide insights into current research.
Government reports: Government reports can provide valuable data.
Websites: Websites can be a useful source of information, but it is important to evaluate their credibility.

Step 4: Read and Analyze the Literature

Once you have selected your sources, you need to read and analyze them carefully. Take notes on key findings, methodologies, and theoretical frameworks.

Skim the article first: Read the abstract, introduction, and conclusion to get a general overview of the article.
Read the article in detail: Pay attention to the research question, methodology, results, and discussion.
Take notes: Summarize key points, identify strengths and weaknesses, and note any connections to other sources.
Use a literature review matrix: A matrix can help you organize and compare key information from different sources.

Step 5: Synthesize and Organize the Literature

Once you have read and analyzed the literature, you need to synthesize and organize the information. This involves identifying key themes, patterns, and gaps in the literature.

Identify key themes: What are the main topics or issues that emerge from the literature?
Identify patterns: Are there any trends or similarities across different sources?
Identify gaps: What areas have not been adequately addressed in the literature?
Organize the information: Create an outline or concept map to visualize the relationships between different sources and ideas.

Step 6: Write the Literature Review

The final step is to write the literature review. This involves integrating the information you have gathered into a coherent and well-organized narrative.

Introduction: Introduce the topic and provide an overview of the literature review.
Body: Present the key themes, patterns, and gaps in the literature.
Conclusion: Summarize the main findings and discuss their implications for your research.

Tips for writing a literature review:

Use clear and concise language.
Cite your sources properly.
Avoid plagiarism.
Proofread your work carefully.

Example of a Literature Review Paragraph

Topic: The impact of social media on mental health among adolescents

“Recent research has explored the complex relationship between social media use and mental health among adolescents. Studies have shown a correlation between increased social media use and symptoms of depression and anxiety (Smith et al., 2019; Johnson & Brown, 2020). However, the direction of this relationship remains unclear. Some studies suggest that social media use may lead to mental health problems, while others suggest that individuals with pre-existing mental health problems may be more likely to use social media excessively (Lee & Kim, 2021). Further research is needed to understand the causal mechanisms underlying this relationship and to identify potential interventions to mitigate the negative impact of social media on adolescent mental health. “

Advanced Tips for Conducting Effective Literature Reviews

Use a variety of search strategies: In addition to keyword searches, consider using citation tracking and subject heading searches.
Consult with a librarian: Librarians can provide valuable assistance in developing search strategies and locating relevant sources.
Use reference management software: Software such as EndNote or Zotero can help you organize and manage your references.
Keep track of your search process: Document the databases you searched, the search terms you used, and the number of results you obtained.
Revise and refine your literature review: As you read more literature, you may need to revise and refine your literature review.

Conducting a literature review is a crucial step in the research process. It requires careful planning, systematic searching, critical evaluation, and effective synthesis of information. By following the steps outlined in this guide, you can conduct a literature review that is comprehensive, informative, and relevant to your research question.

Remember, a literature review is not just a summary of existing research; it’s an opportunity to demonstrate your understanding of the field, identify gaps in knowledge, and position your research within the broader context of existing scholarship.

Machine learning in business and finance: a literature review and research opportunities

Hanyao Gao 1 ,
Gang Kou 2 ,
Haiming Liang 1 ,
Hengjie Zhang 3 ,
Xiangrui Chao 1 ,
Cong-Cong Li 5 &
Yucheng Dong 1 , 4

Financial Innovation volume 10 , Article number: 86 ( 2024 ) Cite this article

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This study provides a comprehensive review of machine learning (ML) applications in the fields of business and finance. First, it introduces the most commonly used ML techniques and explores their diverse applications in marketing, stock analysis, demand forecasting, and energy marketing. In particular, this review critically analyzes over 100 articles and reveals a strong inclination toward deep learning techniques, such as deep neural, convolutional neural, and recurrent neural networks, which have garnered immense popularity in financial contexts owing to their remarkable performance. This review shows that ML techniques, particularly deep learning, demonstrate substantial potential for enhancing business decision-making processes and achieving more accurate and efficient predictions of financial outcomes. In particular, ML techniques exhibit promising research prospects in cryptocurrencies, financial crime detection, and marketing, underscoring the extensive opportunities in these areas. However, some limitations regarding ML applications in the business and finance domains remain, including issues related to linguistic information processes, interpretability, data quality, generalization, and the oversights related to social networks and causal relationships. Thus, addressing these challenges is a promising avenue for future research.

Introduction

The rapid development of information and database technologies, coupled with notable progress in data analysis methods and computer hardware, has led to an exponential increase in the application of ML techniques in various areas, including business and finance (Ghoddusi et al. 2019 ; Gogas and Papadimitriou 2021 ; Chen et al. 2022 ; Hoang and Wiegratz 2022 ; Nazareth and Ramana 2023 ; Ozbayoglu et al. 2020 ; Xiao and Ke 2021 ). The progress in ML techniques in business and finance applications, such as marketing, e-commerce, and energy, has been highly successful, yielding promising results (Athey and Imbens 2019 ). Compared to traditional econometric models, ML techniques can more effectively handle large amounts of structured and unstructured data, enabling rapid decision-making and forecasting. These benefits stem from ML techniques’ ability to avoid making specific assumptions about the functional form, parameter distribution, or variable interactions and instead focus on making accurate predictions about the dependent variables based on other variables.

Exploring scientific databases, such as the Thomson Reuters Web of Science, reveals a significant exponential increase in the utilization of ML in business and finance. Figure 1 illustrates the outcomes of an inquiry into fundamental ML applications in emerging business and financial domains over the past few decades. Numerous studies in this field have applied ML techniques to resolve business and financial problems. Table 1 lists some of their applications. Boughanmi and Ansari ( 2021 ) developed a multimodal ML framework that integrates different types of non-parametric data to accommodate diverse effects. Additionally, they combined multimedia data in creative product settings and applied their model to predict the success of musical albums and playlists. Zhu et al. ( 2021 ) asserted that accurate demand forecasting is critical for supply chain efficiency, especially for the pharmaceutical supply chain, owing to its unique characteristics. However, a lack of sufficient data has prevented forecasters from pursuing advanced models. Accordingly, they proposed a demand forecasting framework that “borrows” time-series data from many other products and trains the data with advanced ML models. Yan and Ouyang ( 2018 ) proposed a time-series prediction model that combines wavelet analysis with a long short-term memory neural network to capture the complex features of financial time series and showed that this neural network had a better prediction effect. Zhang et al. ( 2020a , b ) employed a Bayesian learning model with a rich dataset to analyze the decision-making behavior of taxi drivers in a large Asian city to understand the key factors that drive the supply side of urban mobility markets.

Trend of articles on applied ML techniques in business and finance (2007–2021)

Several review papers have explored the potential of ML to enhance various domains, including agriculture (Raj et al. 2015 ; Coble et al. 2018 ; Kamilaris and Prenafeta-Boldu 2018 ; Storm et al. 2020 ), economic analysis (Einav and Levin 2014 ; Bajari et al. 2015 ; Grimmer 2015 ; Nguyen et al. 2020 ; Nosratabadi et al. 2020 ), and financial crisis prediction (Lin et al. 2012 ; Canhoto 2021 ; Dastile et al. 2020 ; Nanduri et al. 2020 ). Kou et al. ( 2019 ) conducted a survey encompassing research and methodologies related to the assessment and measurement of financial systemic risk that incorporated various ML techniques, including big data analysis, network analysis, and sentiment analysis. Meng and Khushi ( 2019 ) reviewed articles that focused on stock/forex prediction or trading, where reinforcement learning served as the primary ML method. Similarly, Nti et al. ( 2020 ) reviewed approximately 122 pertinent studies published in academic journals over an 11-year span, concentrating on the application of ML to stock market prediction.

Despite these valuable contributions, it is worth noting that the existing review papers primarily concentrate on specific issues within the realm of business and finance, such as the financial system and stock market. Consequently, although a substantial body of research exists in this area, a comprehensive and systematic review of the extensive applications of ML in various aspects of business and finance is lacking. In addition, existing review articles do not provide a comprehensive review of common ML techniques utilized in business and finance. To bridge the aforementioned gaps in the literature, we aim to provide an all-encompassing and methodological review of the extensive spectrum of ML applications in the business and finance domains. To begin with, we identify the most commonly utilized ML techniques in the business and finance domains. Then we introduce the fundamental ML concepts and frequently employed techniques and algorithms. Next, we systematically examine the extensive applications of ML in various sub-domains within business and finance, including marketing, stock markets, e-commerce, cryptocurrency, finance, accounting, credit risk management, and energy. We critically analyze the existing research that explores the implementation of ML techniques in business and finance to offer valuable insights to researchers, practitioners, and decision-makers, thereby facilitating better-informed decision-making and driving future research directions in this field.

The remainder of this paper is organized as follows. Section “ Keywords, distribution of articles, and common technologies in the application of ML techniques in business and finance ” outlines the literature retrieval process and presents the statistical findings from the literature analysis, including an analysis of common application trends and ML techniques. Section “ Machine learning: a brief introduction ” introduces fundamental concepts and terminology related to ML. Sections “ Supervised learning ” and “ Unsupervised learning ” explore in-depth common supervised and unsupervised learning techniques, respectively. Section “ Applications of machine learning techniques in business and finance ” discusses the most recent applications of ML in business and finance. Section “ Critical discussions and future research directions ” discusses some limitations of ML in this domain and analyzes future research opportunities. Finally, “ Conclusions ” section concludes.

Keywords, distribution of articles, and common technologies in the application of ML techniques in business and finance

The primary focus of this review is to explore the advancements in ML in business- and finance-related fields involving ML applications in various market-related issues, including prices, investments, and customer behaviors. This review employs the following strategies to identify existing literature. Initially, we identify relevant journals known for publishing papers that utilize ML techniques to address business and finance problems, such as the UTD-24. Table 2 lists the keywords used in the literature search. During the search process, we input various combinations of ML keywords and business/finance keywords, such as “support vector machine” and “marketing.” By cross-referencing the selected journals and keywords and thoroughly examining the citations of highly cited papers, we aimed to achieve a comprehensive and unbiased representation of the current literature.

After identifying journals and keywords, we searched for articles in the Thomson Reuters Web of Science and Elsevier Scopus databases using the same set of keywords. Once the collection phase was complete, the filtering process was initiated. Initially, duplicate articles were excluded to ensure that only unique articles remained for further analysis. Subsequently, we carefully reviewed the full text of each article to eliminate irrelevant or inappropriate items and thus ensure that the final selection comprised relevant and meaningful literature.

Figure 2 illustrates the process of article selection for the review. In the identification phase, we retrieved 154 articles from the search and identified an additional 37 articles through reference checking. During the second phase, duplicates and inappropriate articles were filtered out, resulting in a total of 68 articles eligible for inclusion in this study. Based on the review of these articles, we categorized them into seven different applications: stock market, marketing, e-commerce, energy marketing, cryptocurrency, accounting, and credit risk management, as depicted in Fig. 3 and Tables 3 , 4 , 5 , 6 , 7 , 8 and 9 . Statistical analyses have revealed that ML research in the business and finance domain is predominantly concentrated in the areas of stock market and marketing. The research on e-commerce, cryptocurrency, and energy market applications is nearly equivalent in quantity. Conversely, articles focusing on accounting and credit risk management applications are relatively limited. Figure 4 provides a summary of the ML techniques employed in the reviewed articles. Deep learning, support vector machine, and decision tree methods emerged as the most prominent research technologies. In contrast, the application of unsupervised learning techniques, such as k-means and reinforcement learning, were less common.

Flow diagram for article identification and filtering

Number of papers employing ML techniques

Prominent methods applied in the business and finance domains

Machine learning: a brief introduction

This section introduces the basic concepts of ML, including its goals and terminology. Thereafter, we present the model selection method and how to improve the performance.

Goals and terminology

The key objective in various scientific disciplines is to model the relationships between multiple explanatory variables and a set of dependent variables. When a theoretical mathematical model is established, researchers can use it to predict or control desired variables. However, in real-world scenarios, the underlying model is often too complex to be formulated as a closed-form input–output relationship. This complexity has led researchers in the field of ML to focus on developing algorithms (Wu et al. 2008 ; Chao et al. 2018 ). The primary goal of these algorithms is to predict certain variables based on other variables or to classify units using limited information; for example, they can be used to classify handwritten digits based on pixel values. ML techniques can automatically construct computational models that capture the intricate relationships present in available data by maximizing the problem-dependent performance criterion or minimizing the error term, which allows them to establish a robust representation of the underlying relationships.

In the context of ML, the sample used to estimate the parameters is usually referred to as a “training sample,” and the procedure for estimating the parameters is known as “training.” Let N be the sample size, k be the number of features, and q be the number of all possible outcomes. ML can be classified into two main types: supervised and unsupervised. In supervised learning problems, we know both the feature ${\mathbf{X}}_{i} = (x_{i1} ,...,x_{ik} ),\; \, i = 1,2,...,N$ and the outcome $Y_{i} = (y_{i1} ,y_{i2} ,...,y_{iq} )$ , where $y_{ij}$ represents the outcome of $y_{i}$ in the dimension $j$ . For example, in a recommendation system, the quality of product can be scored from 1 to 5, indicating that “q” equals 5. In unsupervised learning problems, we only observe the features ${\mathbf{X}}_{i}$ (input data) and aim to group them into clusters based on their similarities or patterns.

Cross-validation, overfitting, and regularization

Cross-validation is frequently used for model selection in ML that is applied to each model; the technique is applied to each model and the one with the lowest expected out-of-sample prediction error is selected.

The ML literature shows significantly higher concern about overfitting than the standard statistics or econometrics literature. In the ML community, the degrees of freedom are not explicitly considered, and many ML methods involve a large number of parameters, which can potentially lead to negative degrees of freedom.

Limiting overfitting is commonly achieved through regularization in ML, which controls the complexity of a model. As stated by Vapnik ( 2013 ), the regularization theory was one of the first signs of intelligent inference. The complexity of the model describes its ability to approximate various functions. As the complexity increases, the risk of overfitting also increases, whereas less complex and more regularized models may lead to underfitting. Regularization is often implemented by selecting a parsimonious number of variables and using specific functional forms without explicitly controlling for overfitting. Instead of directly optimizing an objective function, a regularization term is added to the objective function, which penalizes the complexity of the model. This approach encourages the model to generalize better and avoids overfitting by promoting simpler and more interpretable solutions.

Here, we provide an example to illustrate how regularization works. The following linear regression model was used:

where N is the sample size, k is the numbers of features, and q is the number of all possible outcomes. The variable $y_{{ij}} (i = 1,2,...,N,\quad j = 1,2,...,q)$ represents the outcome of $y_{i}$ in the j th dimension. Additionally, $b_{pj} (p = 1,2,...,k,j = 1,2,...,q)$ represents the coefficient of feature p in the j th dimension. By using vector notations, ${{\varvec{\upsigma}}} = (\sigma_{1} ,...,\sigma_{q} )^{{ \top }}$ , ${\mathbf{b}} = (b_{{11}} ,b_{{21}} ,...,b_{{k1}} ,b_{{12}} ,b_{{22}} ,...,b_{{k2}} ,...,b_{{1q}} ,b_{{2q}} ,...,b_{{kq}} )^{{ \top }}$ and $Y_{i} = (y_{i1} ,y_{i2} ,...,y_{iq} )$ , we can rewrite Eq. ( 1 ) as follows:

where ${\mathbf{b}}$ is the solution of

$\lambda$ is a penalty parameter that can be selected through out-of-sample cross-validation to optimize the model’s out-of-sample predictive performance.

Supervised learning

This section introduces common supervised learning technologies. Compared to traditional statistics, supervised learning methods exhibit certain desired properties when optimizing predictions in large datasets, such as transaction and financial time series data. In business and finance, supervised learning models have proven to be among the most effective tools for detecting credit card fraud (Lebichot et al. 2021 ). In the following subsections, we briefly describe the commonly used supervised ML methods for business and finance.

Shrinkage methods

The traditional least-squares method often yields complex models with an excessive number of explanatory variables. In particular, when the number of features, k , is large compared to the sample size N , the least-squares estimator, ${\hat{\mathbf{b}}}$ , does not have good predictive properties, even if the conditional mean of the outcome is linear. To address this problem, regularization is typically used to adjust the estimation parameters dynamically and reduce the complexity of the model. The shrinkage method is the most common regularization method and can reduce the values of the parameters to be estimated. Shrinkage methods, such as ridge regression (Hoerl and Kennard 1970 ) and least absolute shrinkage and selection operator (LASSO) (Tibshirani 1996 ), are linear regression models that add a penalty term to the size of the coefficients. This penalty term pushes the coefficients towards zero, effectively shrinking their values. Shrinkage methods can be effectively used to predict continuous outcomes or classification tasks, particularly when dealing with datasets containing numerous explanatory variables.

Compared to the traditional approach that estimates the regression function using least squares,

shrinkage methods add a penalty term that shrinks ${\mathbf{b}}$ toward zero, aiming to minimize the following objective function:

where $\left\| {\mathbf{b}} \right\|_{q} = \sum\nolimits_{i = 1}^{N} {\left| {b_{i} } \right|^{q} }$ . In $q = 1$ , this formulation leads to a LASSO. However, when $q = 2$ is used, this formulation degenerates ridge regression.

Tree-based method

Regression trees (Breiman et al. 1984 ) and random forests (Breiman 2001 ) are effective methods for estimating regression functions with minimal tuning, especially when out-of-sample predictive abilities are required. Considering a sample $(x_{i1} ,...,x_{ik} ,Y_{i} )$ for $i = 1,2,...,N$ , the idea of a regression tree is to split the sample into subsamples where the regression functions are being estimated. The splits process is sequential and based on feature value $x_{ij}$ exceeding threshold $c$ . Let $R_{1} (j,c)$ and $R_{2} (j,c)$ be two sets based on the feature $j$ and threshold $c$ , where $R_{1} (j,c) = \left\{ {{\mathbf{X}}_{i} |x_{ij} \le c} \right\}$ and $R_{2} (j,c) = \left\{ {{\mathbf{X}}_{i} |x_{ij} > c} \right\}$ . Naturally, the dataset $R$ is divided into two parts, $R_{1}$ and $R_{2}$ , based on the chosen feature and threshold.

Let $c_{1} = \frac{1}{{|R_{1} |}}\sum\nolimits_{{{\mathbf{X}}_{i} \in R_{1} }} {x_{ij} }$ and $c_{2} = \frac{1}{{|R_{2} |}}\sum\nolimits_{{{\mathbf{X}}_{i} \in R_{2} }} {x_{ij} }$ , where $| \bullet |$ refer to the cardinality of the set. Then we can construct the following optimization model to calculate the errors of the $R_{1}$ and $R_{2}$ datasets:

For all $x_{ij}$ and threshold $c \in ( - \infty , + \infty )$ , the method finds the optimal feature $j^{*}$ and threshold $c^{*}$ that minimizes errors and splits the sample into subsets based on these criteria. By selecting the best feature and threshold, the method obtains the optimal classification of $R_{1}^{*}$ and $R_{2}^{*}$ . This process is repeated recursively, leading to further splits that minimize the squared error and improve the overall model performance. However, researchers should be cautious about overfitting, wherein the model fits the training data too closely and fails to generalize well to new data. To address this issue, a penalty term can be added to the objective function to encourage simpler and more regularized models. The coefficients of the model are then selected through cross-validation, optimizing the penalty parameter to achieve the best trade-off between model complexity and predictive performance on new, unseen data. This helps prevent overfitting and ensures that the model's performance is robust and reliable.

Random forest builds on the tree algorithm to better estimate the regression function. This approach smooths the regression function by averaging across multiple trees, thus exhibiting two distinct differences. First, instead of using the original sample, each tree is constructed based on a bootstrap sample or a subsample of the data, a technique known as “bagging.” Second, at each stage of building a tree, the splits are not optimized over all possible features (covariates) but rather over a random subset of the features. Consequently, feature selection varies in each split, which enhances the diversity of the individual trees.

Deep learning and neural networks

Deep learning and neural networks have been proven to be highly effective in complex settings. However, it is worth noting that the practical implementation of deep learning often demands a considerable amount of tuning compared to other methods, such as decision trees or random forests.

Deep neural networks

As with any other supervised learning methods, deep neural networks (DNNs) can be viewed as a straightforward mapping $y=f(x;\theta )$ from the input feature vector $x$ to the output vector or scalar $y$ , which is governed by the unknown parameters $\theta$ . This mapping typically consists of layers that form chain-like structures. Figure 5 illustrates the structure of the DNN. For a DNN with multiple layers, the structure can be represented as

Structure of DNN

In a fully connected DNN, the $i$ th layer has a structure given by $h^{(i)} = f^{(i)} (x) = g^{(i)} ({\mathbf{W}}^{(i)} h^{(i - 1)} + {\mathbf{b}}^{(i)} )$ , where ${\mathbf{W}}$ is the matrix of unknown parameters and ${\mathbf{b}}^{\left( i \right)}$ is the vector of basis factors. A typical choice for $g^{\left( i \right)}$ , called the “activation function,” can be a rectified linear unit, tanh transformation function, or sigmoid function. The 0th layer $h^{(0)} = x$ , which represents the input vector. The row dimension of $b$ or the column dimension of the ${\mathbf{W}}$ species is the number of neurons in each layer. The weight matrix ${\mathbf{W}}$ is learned by minimizing a loss function, which can be the mean squared error for regression tasks or the cross-entropy for classification tasks. In particular, when the DNN has one layer, $y$ is scalar. The activation function is set to linear or logistic, and we obtain a linear or logistic regression.

Convolutional neural networks

Although neural networks have many different architectures, the two most classical and relevant are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). A classical CNN structure, which contains three main components—convolutional, pooling, and fully connected layers—is shown in Fig. 6 . In contrast to the previously mentioned fully connected structure, in the convolutional layer, each neuron connects with only a small fraction of the neurons from the former layer; however, they share the same parameters. Therefore, sparse connections and parameter sharing significantly reduces the number of estimated parameters.

Structure of CNN

Different layers play different roles in the training process and are introduced in more detail as follows:

Convolutional layer : This layer comprises a collection of trained filters that are used to extract features from the input data. Assuming that $X$ is the input and there are $k$ filters, the output of the convolutional layer can be formulated as follows:

where $\omega_{j}$ and $b_{j}$ denote the weights and bias, respectively; $f$ represents the activation function; and $*$ denotes the convolutional operator.

Pooling layer : This layer reduces the features and parameters of the network. The most popular pooling methods are the maximum and average pooling.

CNN are designed to handle one-dimensional time-series data or images. Intuitively, each convolutional layer can be considered a set of filters that move across images or shift along time sequences. For example, some filters may learn to detect textures, whereas others may identify specific shapes. Each filter generates a feature map and the subsequent convolutional layer integrates these features to create a more complex structure, resulting in a map of learned features. Suppose that $S$ is an $p \times p$ window size. Then the average pooling process can be formulated as

where $x_{ij}$ is the activation value at location $(i,j)$ , and N is the total number of $S$ .

Recurrent neural networks

Recurrent neural networks (RNNs) are well suited for processing sequential data, dynamic relations, and long-term dependencies. RNNs, particularly those employing long short-term memory (LSTM) cells, have become popular and have shown significant potential in natural language processing (Schmidhuber 2015 ). A key feature of this architecture is its ability to maintain past information over time using a cell-state vector. In each time step, new variables are combined with past information in the cell vector, enabling the RNN to learn how to encode information and determine which encoded information should be retained or forgotten. Similar to CNNs, RNN benefit from parameter sharing, which allows them to detect specific patterns in sequential data.

Figure 7 illustrates the structure of the LSTM network, which contains a memory unit ${C}_{t}$ , a hidden state ${h}_{t}$ , and three types of gates. Index $t$ refers to the time step. At each step $t$ , the LTSM combines input ${x}_{t}$ with the previous hidden state ${h}_{t-1}$ , calculates the activations of all gates, and updates the memory units and hidden states accordingly.

Structure of LSTM

The computations of LSTM networks are described as follows:

where $W$ denotes the weight of the inputs, and $\omega_{f}$ and $\omega_{i}$ represent the weights of the outputs and biases, respectively. The subscript $f,i,{\text{ and }}O$ refer to the forget, input, and output gate vectors, respectively. $b$ indicates biases and $\circ$ is an element-wise multiplication.

Wavelet neural networks

Wavelet neural networks (Zhang and Benveniste 1992 ) use the wavelet function as the activation function, thus combining the advantages of both the wavelet transform and neural networks. The structure of wavelet neural networks is based on backpropagation neural networks, and the transfer function of the hidden layer neuron is the mother wavelet function. For input features ${\mathbf{x}} = (x_{1} ,...,x_{n} )$ , the output of the hidden layer can be expressed as follows:

where $h(j)$ is the output value for neuron $j$ , $h_{j}$ is the mother wavelet function, $\omega_{ij}$ is the weight between the input and hidden layers, $b_{j}$ is the shift factor, and $a_{j}$ is the stretch factor for $h_{j}$ .

Support vector machine and kernels

Support vector machines (SVM) are flexible classification methods (Cortes and Vapnik 1995 ). Let us consider a binary classification problem, where we have an $N$ observation ${\mathbf{X}}_{i}$ , each with $k$ features, and a binary label $y_{i} \in \{ - 1,1\}$ . Subsequently, a hyperplane $x \in {\mathbf{\mathbb{R}}}$ s. t. $w^{{ \top }} {\mathbf{X}}_{i} + b = 0$ is defined, which can be considered a binary classifier ${\text{sgn}} (w^{{ \top }} {\mathbf{X}}_{i} + b)$ . The goal of SVM is to find a hyperplane such that the observations can be separated into two classes: + 1 and − 1. From the hyperplane space, SVM selects the option that maximizes the distance from the closest sample. In an SVM, there is typically a small set of samples with the same maximal distance, which are referred to as “support vectors.”

The above-mentioned process can be written as the following optimization model:

To solve the above optimization model, we rewrite it in terms of Lagrangian multipliers as follows:

where $\alpha_{i}$ is the Lagrangian multiplier of the original restriction and $Y_{i} (\omega^{{ \top }} {\mathbf{X}}_{i} + b) \ge 1$ . The model above is equivalent to

We can obtain the Lagrangian multiplier ${{\varvec{\upalpha}}} = (\alpha_{1} ,...,\alpha_{N} )$ from Model ( 15 ), and then $\widehat{b}$ can be solved from $\sum\nolimits_{i = 1}^{N} {\hat{\alpha }_{i} (Y_{i} (\omega^{{ \top }} {\mathbf{X}}_{i} + b) - 1)} = 0$ . Furthermore, we can obtain the classifier:

Traditional SVM assumes linearly separable training samples. However, SVM can also deal with non-linear cases by mapping the original covariates to a new feature space using the function $\phi ({\mathbf{X}}_{i} )$ and then finding the optimal hyperplane in this transformed feature space; that is, $f(x_{i} ) = \omega^{{ \top }} \phi (x_{i} ) + b$ . Thus, the optimization problem in the transformed feature space can be formulated as

where $K({\mathbf{X}}_{i} ,{\mathbf{X}}_{j} ) = \phi ({\mathbf{X}}_{i} )^{{ \top }} \phi ({\mathbf{X}}_{j} )$ . The kernel function $K( \bullet )$ can be linear, polynomial, or sigmoid. Once the kernel function is determined, we can solve for the value of the Lagrangian multiplier $\alpha$ . Then $\widehat{b}$ can be solved from $\sum\nolimits_{i = 1}^{N} {\hat{\alpha }_{i} (Y_{i} (\omega^{{ \top }} {\mathbf{X}}_{i} + b) - 1)} = 0$ , which allows us to derive the classifier:

Bayesian classifier

A Bayesian network is a graphical model that represents the probabilistic relationships among a set of features (Friedman et al. 1997 ). The Bayesian network structure $S$ is a directed acyclic graph. Formally, a Bayesian network is a pair $B = \left\langle {G,\Theta } \right\rangle$ , where $G$ is a directed acyclic graph whose nodes represent the random variable $\left( {X_{1} ,...,X_{n} } \right)$ , whose edges represent the dependencies between variables, and $\Theta$ is the set of parameters that quantify the graph.

Assuming that there are $q$ labels; that is, ${\mathbf{Y}} = \{ c_{1} ,...,c_{q} \}$ , $\lambda_{ij}$ is the loss caused by misclassifying the sample with the true label $c_{j}$ as $c_{i}$ , and ${\mathbb{X}}$ represents the sample space. Then, based on the posterior probability $P(c_{i} |{\mathbf{x}})$ , we can calculate the expected loss of classifying sample ${\mathbf{x}}$ into the label $c_{i}$ as follows:

Therefore, the aim of the Bayesian classifier is to find a criterion $h:{\mathbb{X}} \to {\mathbf{Y}}$ that minimizes the total risk

Obviously, for each sample ${\mathbf{x}}$ , when $h$ can minimize the conditional risk $R(h({\mathbf{x}})|{\mathbf{x}})$ , the total risk $R(h)$ will also be minimized. This leads to the concept of Bayes decision rules: to minimize the total risk, we need to classify each sample into the label that minimizes the conditional risk $R(h({\mathbf{x}})|{\mathbf{x}})$ , namely

We then used $h^{*}$ as the Bayes-optimal classifier and $R(h^{*} )$ as the Bayes risk.

K-nearest neighbor

The K-nearest neighbor (KNN) algorithm is a lazy-learning algorithm because it defers to the induction process until classification is required (Wettschereck et al. 1997 ). The lazy-learning algorithm requires less computation time during the training process compared to eager-learning algorithms such as decision trees, neural networks, and Bayes networks. However, it may require additional time during the classification phase.

The kNN algorithm is based on the assumption that instances close to each other in a feature space are likely to have similar properties. If instances with the same classification label are found nearby, an unlabeled instance can be assigned the same class label as its nearest neighbors. kNN locates the k-nearest instances to the unlabeled instance and determines its label by observing the most frequent class label among these neighbors.

The choice of k significantly affects the performance of the kNN algorithm. Let us discuss the performance of kNN during $k = 1$ . Given sample ${\mathbf{x}}$ and its nearest sample ${\mathbf{z}}$ , the probability of error can be expressed as follows:

Suppose the samples are independent and identically distributed. For any ${\mathbf{x}}$ and any positive number $\delta$ , there always exists at least one sample ${\mathbf{z}}$ within a distance of $\delta$ from ${\mathbf{x}}$ . Let $c^{*} ({\mathbf{x}})\mathop {\arg \min }\limits_{{c \in {\mathbf{Y}}}} P(c|{\mathbf{x}})$ be the outcome the Bayes optimal classifier. Then we have:

According to (23), despite the simplicity of kNN, the generalization error is no more than twice that of the Bayes-optimal classifier.

Unsupervised learning

In unsupervised learning, researchers can only access observations without any labeled information, and their primary interest lies in partitioning a sample into subsamples or clusters. Unsupervised learning methods are particularly useful in descriptive tasks because they aim to find relationships in a data structure without measuring the outcomes. Several approaches commonly used in business and finance research fall under the umbrella of unsupervised learning, including k-means clustering and reinforcement learning. Accordingly, unsupervised learning can be used in qualitative business and finance. For example, it can be particularly beneficial during stakeholder analysis, when stakeholders must be mapped and classified by considering certain predefined attributes. It can also be useful for customer management. A company can employ an unsupervised ML method to cluster guests, which influences its marketing strategy for specific groups and leads to a competitive advantage. This section introduces unsupervised learning technologies that are widely used in business and finance.

K-means clustering

The K-means algorithm aims to find K points in the sample space and classify the samples that are closest to these points. Using an iterative method, the values of each cluster center are updated step-by-step to achieve the best clustering results. When partitioning the feature space into K clusters, the k-means algorithm selects centroids and assigns observations to clusters based on their proximity to them. $b_{1} ,...,b_{k}$ . The algorithm proceeds as follows. First, we begin with the K centroids $b_{1} ,...,b_{k}$ , which are initially scattered throughout the feature space. Next, in accordance with the chosen centroids, each observation is assigned to clusters that minimize the distance between the observation and the centroid of the cluster:

Next, we update the centroid by computing the average of $X_{i}$ across each cluster:

where $I( \bullet )$ is the indicative function. When choosing the number of clusters, K, we must exercise caution because no cross-validation method is available to compare the values.

Reinforcement learning

Reinforcement learning (RL) draws inspiration from the trial-and-error procedure conducted by Thorndike in his 1898 study of cat behavior. Originating from animal learning, RL aims to mimic human behavior by making decisions that maximize profits through interactions with the environment. Mnih et al. ( 2015 ) proposed deep RL by employing a deep Q-network to create an agent that outperformed a professional player in a game and further advanced the field of RL.

In deep RL, the learning algorithm plays an essential role in improving efficiency. These algorithms can be categorized into three types: value-based, policy-based, and model-based RL, as illustrated in Fig. 8 .

Learning algorithm-based reinforcement learning

RL consists of four components—agent, state, action and reward—with the agent as its core. When an action leads to a profitable state, it receives a reward, otherwise, it is discouraged. In RL, an agent is defined as any decision-maker, while everything else is considered the environment. The interactions between the environments and the agents are described by state $s$ , action $a$ , and reward $r$ . At time step $t$ , the environment is in state $s_{t}$ , and the agent takes action $a_{t}$ . Consequently, the environment transitions to state $s_{t + 1}$ and rewards agent $r_{t + 1}$ .

The agent’s decision is formalized by a policy $\pi$ , which maps state $s$ to action $a$ . This is deterministic when the probability of choosing action $a$ in state $s$ equals one (i.e., $\pi (a|s) = p(a|s) = 1$ ). In contrast, it is stochastic when $p(a|s) < 1$ is used. Policy $\pi$ can be defined as the probability distribution of all actions selected from a certain $s$ , as follows:

where $\Delta_{\pi }$ represents all possible actions of $\pi$ .

In each step, the agent receives an immediate reward $r_{t + 1}$ until it reaches the final state $s_{T}$ . However, the immediate reward does not ensure a long-term profit. To address this, a generalized return value is used at time step $t$ , defined as $R_{t}$ :

where $0 \le \gamma \le 1$ . The agents become more farsighted when $\gamma$ approaches 1, and more shortsighted when it approaches 0.

The next step is to define a score function $V$ to estimate the goodness of the state:

Then, we determine the goodness of a state-action pair $(s,a)$ :

Finally, we access the goodness between two policies:

Finally, we can expand $V_{\pi } (s)$ and $Q_{\pi } (s,a)$ through $R_{t}$ to represent the relationship between $s$ and $s_{t + 1}$ as

where $W_{{s \to s^{\prime}|a}} = E[r_{t + 1} |s_{t} = s,a_{t} = a,s_{t + 1} = s^{\prime}]$ . By solving ( 31 ) and ( 32 ), we obtain $V$ and $S$ , respectively.

Restricted Boltzmann machines

As Fig. 9 shows, a restricted Boltzmann machine (RBM) can be considered an undirected neural network with two layers, called the “hidden” and “visible” layers. Hidden layers are used to detect the features, whereas visible layers are used to train the input data. Given the $n$ visible layers $v$ and $m$ hidden layers $h$ , the energy function is given by

where $\alpha_{ij}$ is the weight between the unit $i$ $j$ , and $a_{i}$ and $b_{j}$ are the biases for $v$ and $h$ , respectively.

Structure of RBM

Applications of machine learning techniques in business and finance

This section considers the application fields in the following categories: marketing, stock market, e-commerce, cryptocurrency, finance, accounting, credit risk management, and energy economy. This study reviews the application status of ML in these fields.

ML is an innovative technology that can potentially improve forecasting models and assist in management decision-making. ML applications can be highly beneficial in the marketing domain because they rely heavily on building accurate predictive models from databases. Compared to the traditional statistical approach for forecasting consumer behavior, researchers have recently applied ML technology, which offers several distinctive advantages for data mining with large, noisy databases (Sirignano and Cont 2019 ). An early example of ML in marketing can be found in the work of Zahavi and Levin ( 1997 ), who used neural networks (NNs) to model consumer responses to direct marketing. Compared with the statistical approach, simple forms of NNs are free from the assumptions of normality or complete data, making them particularly robust in handling noisy data. Recently, as shown in Table 3 , ML techniques have been predominantly used to study customer behaviors and demands. These applications enable marketers to gain valuable insights and make data-driven decisions to optimize marketing strategies.

Consumer behavior refers to the actions taken by consumers to request, use, and dispose of consumer goods, as well as the decision-making process that precedes and determines these actions. In the context of direct marketing, Cui et al. ( 2006 ) proposed Bayesian networks that learn by evolutionary programming to model consumer responses to direct marketing using a large direct marketing dataset. In the supply chain domain, Melancon et al. ( 2021 ) used gradient-boosted decision trees to predict service-level failures in advance and provide timely alerts to planners for proactive actions. Regarding unsupervised learning in consumer behavior analysis, Dingli et al. ( 2017 ) implemented a CNN and an RBM to predict customer churn. However, they found that their performance was comparable to that of supervised learning when introducing added complexity in specific operations and settings. Overall, ML techniques have demonstrated their potential for understanding and predicting consumer behavior, thereby enabling businesses to make informed decisions and optimize their marketing strategies (Machado and Karray 2022 ; Mao and Chao 2021 ).

Predicting consumer demand plays a critical role in helping enterprises efficiently arrange production and generate profits. Timoshenko and Hauser ( 2019 ) used a CNN to facilitate qualitative analysis by selecting the content for an efficient review. Zhang et al. ( 2020a , b ) used a Bayesian learning model with a rich dataset to analyze the decision-making behavior of taxi drivers in a large Asian city to understand the key factors that drive the supply side of urban mobility markets. Ferreira et al. ( 2016 ) employed ML techniques to estimate historical lost sales and predict future demand for new products. For the application of consumer demand-level prediction, most of the research we reviewed used supervised learning technologies because learning consumer consumption preferences requires historical data of consumers, and only clustering consumers is insufficient to predict their consumption levels.

Stock market

ML applications in the stock market have gained immense popularity, with the majority focusing on financial time series for stock price predictions. Table 4 summarizes the reviewed articles that employed ML methods in stock market studies, including references, research objectives, data sources, applied techniques, and journals. Investing in the stock market can be highly profitable but also entails risk. Therefore, investors always try to determine and estimate stock values before taking any action. Researchers have mostly used ML techniques to predict stock prices (Bennett et al. 2022 ; Moon and Kim 2019 ). However, predicting stock values can be challenging due to the influence of uncontrollable economic and political factors that make it difficult to identify future market trends. Additionally, financial time-series data are often noisy and non-stationary, rendering traditional forecasting methods less reliable for stock value predictions. Researchers have explored ML in sentiment analysis to identify future trends in the stock market (Baba and Sevil 2021 ). Furthermore, other studies have focused on objectives such as algorithmic trading, portfolio management, and S&P 500 index trend prediction using ML techniques (Cuomo et al. 2022 ; Go and Hong 2019 ).

Various ML techniques have been successfully applied for stock price predictions. Fischer and Krauss ( 2018 ) applied LSTM networks to predict the out-of-sample directional movements of the constituent stocks of the S&P 500 from 1992 to 2015, demonstrating that LSTM networks outperform memory-free classification methods. Wu et al. ( 2021 ) applied LASSO, random forest, gradient boosting, and a DNN to cross-sectional return predictions in hedge fund selection and found that ML techniques significantly outperformed four styles of hedge fund research indices in almost all situations. Bao et al. ( 2017 ) fed high-level denoising features into the LSTM to forecast the next day’s closing price. Sabeena and Venkata ( 2019 ) proposed a modified adversarial-network-based framework that integrated a gated recurrent unit and a CNN to acquire data from online financial sites and processed the obtained information using an adversarial network to generate predictions. Song et al. ( 2019 ) used deep learning methods to predict future stock prices. Sohangir et al. ( 2018 ) applied several NN models to stock market opinions posted on StockTwits to determine whether deep learning models could be adapted to improve the performance of sentiment analysis on StockTwits. Bianchi et al. ( 2021 ) showed that extreme trees and NNs provide strong statistical evidence in favor of bond return predictability. Vo et al. ( 2019 ) proposed a deep responsible investment portfolio model containing an LSTM network to predict stock returns. All of these stock price applications use supervised learning techniques and financial time-series data to supervise learning. In contrast, it is challenging to apply unsupervised learning methods, particularly clustering, in this domain (Chullamonthon and Tangamchit 2023 ). However, RL still has certain applications in the stock markets. Lei ( 2020 ) combined deep learning and RL models to develop a time-driven, feature-aware joint deep RL model for financial time-series forecasting in algorithmic trading, thus demonstrating the potential of RL in this domain.

Additionally, the evidence suggests that hybrid LSTM methods can outperform other single-supervised ML methods in certain scenarios. Thus, in applying ML to the stock market, researchers have explored the combination of LSTM with different methods to develop hybrid models for improved performance. For instance, Tamura et al. ( 2018 ) used LSTM to predict stock prices and reported that the accuracy test results outperformed those of other models, indicating the effectiveness of the hybrid LSTM approach in stock price prediction.

Researchers have explored various hybrid approaches that combine wavelet transforms and LSTM with other techniques to predict stock prices and financial time series. Bao et al. ( 2017 ) established a new method for predicting stock prices that integrated wavelet transforms, stacked autoencoders, and LSTM. In the first stage, they eliminate noise to decompose the stock price time series. In the next stage, predictive features for the stock price are created. Finally, LSTM is applied to predict the next day’s closing price based on the features of the previous stage. The authors claimed that their model outperformed state-of-the-art models in terms of predictive accuracy and profitability. To address the non-linearity and non-stationary characteristics of financial time series, Yan and Ouyang ( 2018 ) integrated wavelet analysis with LSTM to forecast the daily closing price of the Shanghai Composite Index. Their proposed model outperformed multiple layer perceptron, SVM, and KNN with respect to finding patterns in financial time-series data. Fang et al. ( 2019 ) developed a methodology to predict exchange trade–fund option prices by integrating LSTM with support vector regression (SVR). They used two LSTM-SVR models to model the final transaction price. In the second generation of LSTM-SVR, the hidden state vectors of the LSTM and the seven factors affecting the option price were considered as SVR inputs. Their proposed model outperformed other methods, including LSTM and RF, in predicting option prices.

Online shopping, which allows users to purchase products from companies via the Internet, falls under the umbrella of e-commerce. In today’s rapidly evolving online shopping landscape, companies employ effective methods to recognize their buyers’ purchasing patterns, thereby enhancing their overall client experience. Customer reviews play a crucial role in this process as they are not only utilized by companies to improve their products and services but also by customers to assess the quality of a product and make informed purchase decisions (Da et al. 2022 ). Consequently, the decision-making process is significantly improved through analysis of reviews that provide valuable insights to customers.

Traditionally, enterprises’ e-commerce strategic planning involves assessing the performance of organizational e-commerce adoption behavior at the strategic level. In this context, the decision-making process exhibits typical behavioral characteristics. With regard to organizations’ adoption of technology, it is important to note that the entity adopting the technology is no longer an individual but the organization as a whole. However, technology adoption decisions are still made by people within an organization, and these decisions are influenced by individual cognitive factors (Zha et al. 2021 ). Individuals involved in the decision-making process have their own perspectives, beliefs, and cognitive biases, which can significantly impact an organization’s technology adoption choices and strategies (Li et al. 2019 ; Xu et al. 2021 ). Therefore, the behavioral perspective of technology acceptance provides a new perspective for e-commerce strategic planning research. With the development of ML, research on technology acceptance has been hindered by the limitations of traditional strategic e-commerce planning. Different general models of information technology acceptance behaviors are commonly explored.

Table 5 provides a summary of the aforementioned studies. Cui et al. ( 2021 ) constructed an e-commerce product marketing model based on an SVM to improve the marketing effects of e-commerce products. Pang and Zhang ( 2021 ) built an SVM model to more effectively solve the decision support problem of e-commerce strategic planning. To increase buyers’ trust in the quality of the products and encourage online purchases, Saravanan and Charanya ( 2018 ) designed an algorithm that categorizes products based on several criteria, including reviews and ratings from other users. They proposed a hybrid feature-extraction method using an SVM to classify and separate products based on their features, best product ratings, and positive reviews. Wang et al. ( 2018a , b , c ) employed LSTM to improve the effectiveness and efficiency of mapping customer requirements to design parameters. The results of their model revealed the superior performance of the RNN over the KNN. Xu et al. ( 2019 ) designed an advanced credit risk evaluation system for e-commerce platforms to minimize the transaction risks associated with buyers and sellers. To this end, they employed a hybrid ML model combined with a decision tree ANN (DT-ANN) and found that it had high accuracy and outperformed other hybrid ML models, such as logistic regression and dynamic Bayesian network. Cai et al. ( 2018 ) used deep RL to develop an algorithm to address the allocation of impression problems on e-commerce websites such as www.taobao.com , www.ebay.com , and www.amazon.com . In this algorithm, buyers are allocated to sellers based on their impressions and strategies to maximize the income of the platform. To do so, they applied a gated recurrent unit, and their findings demonstrated that it outperformed a deep deterministic policy gradient. Wu and Yan ( 2018 ) claimed that the main assumption of current production recommender models for e-commerce websites is that all historical user data are recorded. In practice, however, many platforms fail to capture such data. Consequently, they devised a list-wise DNN to model the temporal online behavior of users and offered recommendations for anonymous users.

In the accounting field, ML techniques are employed to detect fraud and estimate accounting indicators. Most companies’ financial statements reflect accounts or disclosure amounts that require estimations. Accounting estimates are pervasive in financial statements and often significantly impact a company’s financial position and operational results. The evolution of financial reporting frameworks has led to the increased use of fair value measurements, which necessitates estimation. Most financial statement items are based on subjective managerial estimates and ML has the potential to provide an independent estimate generator (Kou et al. 2021 ).

Chen and Shi ( 2020 ) utilized bagging and boosting ensemble strategies to develop two models: bagged-proportion support vector machines (pSVM) and boosted-pSVMs. Using datasets from LibSVM, they tested their models and demonstrated that ensemble learning strategies significantly enhanced model performance in bankruptcy prediction. Lin et al. ( 2019 ) emphasized the importance of finding the best match between feature selection and classification techniques to improve the prediction performance of bankruptcy prediction models. Their results revealed that using a genetic algorithm as the wrapper-based feature selection method, combined with naïve Bayes and support vector machine classifiers, resulted in remarkable predictive performance. Faris et al. ( 2019 ) investigated a combination of resampling (oversampling) techniques and multiple election method features to improve the accuracy of bankruptcy prediction methods. According to their findings, employing the oversampling technique and the AdaBoost ensemble method using a reduced error pruning (REP) tree provided reliable and promising results for bankruptcy prediction.

The earlier studies by Perols ( 2011 ) and Perols et al. ( 2017 ) were among the first to predict accounting fraud. Two recent studies by Bao et al. ( 2020 ) and Bertomeu et al. (2020) used various accounting variables to improve the detection of ongoing irregularities. Bao et al. ( 2020 ) employed ensemble learning to develop a fraud-prediction model that demonstrated superior performance compared to the logistic regression and support vector machine models with a financial kernel. Huang et al. ( 2014 ) used Bayesian networks to extract textual opinions, and their findings showed that they outperformed dictionary-based approaches, both general and financial. Ding et al. ( 2020 ) used insurance companies’ data on loss reserve estimates and realizations and documented that the loss estimates generated by ML were superior to the actual managerial estimates reported in financial statements in four out of the five insurance lines examined.

Many companies commission accounting firms to handle accounting and bookkeeping and provide them access to transaction data, documentation, and other relevant information. Mapping daily financial transactions into accounts is one of the most common accounting tasks. Therefore, Jorgensen and Igel ( 2021 ) devised ML systems based on random forest to automate the mapping process of financial transfers to the appropriate accounts. Their approach achieved an impressive accuracy of 80.50%, outperforming baseline methods that either excluded transaction text or relied on lexical bag-of-words text representations. The success of ML systems indicates the potential of ML to streamline accounting processes and increase the efficiency of financial transaction’ mapping. Table 6 summarizes the ML techniques described in “ Accounting ” section.

Credit risk management

The scoring process is an essential part of the credit risk management system used in financial institutions to predict the risk of loan applications because credit scores imply a certain probability of default. Hence, credit scoring modes have been widely developed and investigated for credit approval assessment of new applicants. This process uses a statistical model that considers both the application and performance data of a credit or loan applicant to estimate the likelihood of default, which is the most significant factor used by lenders to prioritize applicants in decision-making. Given the substantial volume of decisions involved in the consumer lending business, it is necessary to rely on models and algorithms rather than on human discretion (Bao et al. 2019 ; Husmann et al. 2022 ; Liu et al. 2019 ). Furthermore, such algorithmic decisions are based on “hard” information, such as consumer credit file characteristics collected by credit bureau agencies.

Supervised and unsupervised ML methods are widely used for credit risk management. Supervised ML techniques are used in credit scoring models to determine the relationships between customer features and credit default risk and subsequently predict classifications. Unsupervised techniques, mainly clustering algorithms, are used as data mining techniques to group samples into clusters (Wang et al. 2019 ). Hence, unsupervised learning techniques often complement supervised techniques in credit risk management.

Despite the high accuracy of ML, it is not possible to explain its predictions. However, financial institutions must maintain transparency in their decision-making processes. Fortunately, researchers have shown that ML can deduce rules to mitigate a lack of transparency without compromising accuracy (Baesens et al. 2003 ). Table 7 summarizes the recent applications of ML methods in credit risk management. Liu et al. ( 2022 ) use KNN, SVM, and random forest to predict the default probability of online loan borrowers and compare their prediction performance with that of a logistic model. Khandani et al. ( 2010 ) applied regression trees to construct non-linear, non-parametric forecasting models for consumer credit risk.

Cryptocurrency

A cryptocurrency is a digital or virtual currency used to securely exchange and transfer assets. Cryptography is used to securely transfer assets, control and regulate the addition of cryptocurrencies, and secure their transactions (Garcia et al. 2014 ); hence, the term “cryptocurrency.” In contrast to standard currencies, which depend on the central banking system, cryptocurrencies are founded on the principle of decentralized control (Zhao 2021 ). Owing to its uncontrolled and untraceable nature, the cryptocurrency market has evolved exponentially over a short period. The growing interest in cryptocurrencies in the fields of economics and finance has drawn the attention of researchers in this domain. However, the applications of cryptocurrencies and associated technologies are not limited to financing. There is a significant body of computer science literature that focuses on the supporting technologies of cryptocurrencies, which can lead to innovative and efficient approaches for handling Bitcoin and other cryptocurrencies, as well as addressing their price volatility and other related technologies (Khedr et al. 2021 ).

Generating an accurate prediction model for such complex problems is challenging. As a result, cryptocurrency price prediction is still in its nascent stages and further research efforts are required to explore this area. In recent years, ML has become one of the most popular approaches for cryptocurrency price prediction owing to its ability to identify general trends and fluctuations. Table 8 presents a survey of cryptocurrency price prediction research using ML methods. Derbentsev et al. ( 2019 ) presented a short-term forecasting model to predict the cryptocurrency prices of Ripples, Bitcoin, and Ethereum using an ML approach. Greaves and Au ( 2015 ) applied blockchain data to Bitcoin price predictions and employed various ML techniques, including SVM, ANN, and linear and logistic regression. Among the ML classifiers used, the NN classifier with two hidden layers achieved the highest price accuracy of 55%, followed by logistic regression and SVM. Additionally, the research mentioned an analysis using several tree-based models and KNN.

The most recent LSTM networks appear to be more suitable and convenient for handling sequential data, such as time series. Lahmiri and Bekiros ( 2019 ) were the first to use LSTM to predict the digital currency prices of the three currencies that were used the most at the time they conducted their study: Bitcoin, Ripple, and digital cash. In their study, long memory was used to assess the market efficiency of cryptocurrencies, and the inherent non-linear dynamics encompassing chaoticity and fractality were examined to gauge the predictability of digital currencies. Chowdhury et al. ( 2020 ) applied LSTM to the indices and constituents of cryptocurrencies to predict prices. Lahmiri and Bekiros ( 2019 ) implemented LSTM to forecast the prices of the three most widely traded cryptocurrencies. Furthermore, Altan et al. ( 2019 ) built a novel hybrid forecasting model based on LSTM to predict digital currency time series.

The existing applications of ML techniques in energy economics can be classified into two major categories: energy price and energy demand prediction. Energy prices typically demonstrate complex features, such as non-linearity, lag dependence, and non-stationarity, which present challenges for the application of simple traditional models (Chen et al. 2018 ). Owing to their high flexibility, ML techniques can provide superior prediction performance. In energy demand predictions, lagged values of consumption and socioeconomic and technological variables, such as GDP per capita, population, and technology trends, are typically utilized. Table 9 presents a summary of these studies. A critical distinction between “price” and “consumption” prediction is that the latter is not subject to market efficiency dynamics. The prediction of consumption has little effect on the actual consumption of the agents. However, price prediction tends to offset itself by creating opportunities for traders to use this information.

Predicting prices in energy markets is a complicated process because prices are subject to physical constraints on electricity generation and transmission and market power potential (Young et al. 2014 ). Predicting prices using ML techniques is one of the oldest applications in energy economics. In the early 2000s, a wave of studies attempted to forecast electricity prices using conventional ANN techniques. Ding ( 2018 ) combined ensemble empirical mode decomposition and an artificial NN to forecast international crude oil prices. Zhang et al. ( 2020a , b ) employed the LSTM method to forecast day-ahead electricity prices in a deregulated electricity market. They also investigated the intricate dependence structure within the price-forecasting model. Peng et al. ( 2018 ) applied LSTM with a differential evolution algorithm to predict electricity prices. Lago et al. ( 2018 ) first proposed a DNN to improve the predictive accuracy in a local market and then proposed a second model that simultaneously predicts prices from two markets to further improve the forecasting accuracy. Huang and Wang ( 2018 ) proposed a model that combines wavelet NNs with random time-effective functions to improve the prediction accuracy of crude oil price fluctuations.

Understanding the future energy demand and consumption is essential for short- and long-term planning. A wide range of users, including government agencies, local development authorities, financial institutions, and trading institutions, are interested in obtaining realistic forecasts of future consumption portfolios (Lei et al. 2020 ). For demand prediction, Chen et al. ( 2018 ) used ridge regression to combine extreme gradient boosting forest and feedforward deep networks to predict the annual household electricity consumption. Wang et al. ( 2018a , b , c ) first built a model using a self-adaptive multi-verse optimizer to optimize the SVM and then employed it to predict China’s primary energy consumption.

Critical discussions and future research directions

ML techniques have proven valuable in establishing computational models that capture complex relationships with the available data. Consequently, ML has become a useful tool in business and finance. This section critically discusses the existing research and outlines future directions.

Critical discussions

Although ML techniques are widely employed in business and finance, several issues need to be addressed.

Linguistic information is abundant in business and finance, encompassing online commodity comments and investors’ emotional responses in the stock market. Nonetheless, the existing research has predominantly concentrated on processing numerical data. When juxtaposed with numerical information, linguistic data harbor intricate characteristics, notably personalized individual semantics (Li et al. 2022a , b ; Zhang et al. 2021a , b ; Hoang and Wiegratz 2022 ).

The integration of ML into business and finance can lead to interpretability issues. In ML, an interpretable model refers to one in which a human observer can readily comprehend how the model transforms an observation into a prediction (Freitas 2014 ). Typically, decision-makers are hesitant to accept recommendations generated by ML techniques unless they can grasp the reasoning behind them. Unfortunately, the existing research in business and finance, particularly those employing DNNs, has seldom emphasized the interpretability of their models.

Social networks are prevalent in the marketing domain within businesses (Zha et al. 2020 ). For instance, social networks exist among consumers, whose purchasing behavior is influenced by the opinions of trusted peers or friends. However, the existing research that applies ML to marketing has predominantly concentrated on personal customer attributes, such as personality, purchasing power, and preferences (Dong et al. 2021 ). Regrettably, the potential impact of social networks and their influence on customer behavior have been largely overlooked in these studies.

ML techniques typically focus on exploring the statistical relationships between dependent and independent variables and emphasize feature correlations. However, in the context of business and finance applications, causal relationships exist between variables. For instance, consider a study suggesting that girls who have breakfast tend to have lower weights than those who do not’, based on which one might conclude that having breakfast aids in weight loss. However, in reality, these two events may only exhibit a correlation rather than causation (Yao et al. 2021 ). Causality plays a significant role in ML techniques’ performance. However, many current business and finance applications have failed to account for this crucial factor. Ignoring causality may lead to misleading conclusions and hinder accurate modeling of real-world scenarios. Therefore, incorporating causality into ML methodologies within the business and finance domains is essential for enhancing the reliability and validity of predictive models and decision-making processes.

In the emerging cryptocurrency field, although traditional statistical methods are simple to implement and interpret, they require many unrealistic statistical assumptions, making ML the best technology in this field. Although many ML techniques exist, challenges remain in accurately predicting cryptocurrency prices. However, most ML techniques require further investigation.

In recent years, rapid growth in digital payments has led to significant shifts in fraud and financial crimes (Canhoto 2021 ; Prusti et al. 2022 ; Wang et al. 2023 ). While some studies have shown the effective use of ML in detecting financial crimes, there remains a limitation in the research dedicated to this area. As highlighted by Pourhabibi et al. ( 2020 ), the complex nature of financial crime detection applications poses challenges in terms of deploying and achieving the desired detection performance levels. These challenges are manifested in two primary aspects. First, ML solutions encounter substantial pressure to deliver real-time responses owing to the constraints of processing data in real time. Second, in addition to inherent data noise, criminals often attempt to introduce deceptive data to obfuscate illicit activities (Pitropakis et al. 2019 ). Regrettably, few studies have investigated the robustness and performance of the underlying algorithmic solutions when confronted with data quality issues.

In the finance domain, an important limitation of the current literature on energy and ML is that most works highlight the computer science perspective to optimize computational parameters (e.g., the accuracy rate), while finance intuition may be ignored.

Future research directions

Thus, we propose that future research on this topic follow the directions below:

As analyzed above, there is abundant linguistic information exists in business and finance. Consequently, leveraging natural language processing technology to handle and analyze linguistic data in these domains represents a highly promising research direction.

The amalgamation of theoretical models using ML techniques is an important research topic. The incorporation of interpretable models can effectively reveal the black-box nature of ML-driven analyses, thereby elucidating the underlying reasoning behind the results. Consequently, the introduction of interpretable models into business and finance while applying ML can yield substantial benefits.

The interactions and behaviors are often intertwined within social networks, making it crucial to incorporate social network dynamics when modeling their influence on consumer behavior. Introducing the social network aspect into ML models has tremendous potential for enhancing marketing strategies and outcomes (Trandafili and Biba 2013 ).

Causality has garnered increasing attention in the field of ML in recent years. Accordingly, we believe it is an intriguing avenue to explore when applying ML to address problems in business and finance.

Further studies need to include all relevant factors affecting market mood and track them over a longer period to understand the anomalous behavior of cryptocurrencies and their prices. We recommend that researchers analyze the use of LSTM models in future research, such as CNN LSTM and encoder–decoder LSTM, and compare the results to obtain future insights and improve price prediction results. In addition, researchers can apply sentiment analysis to collect social signals, which can be further enhanced by improving the quality of content and using more content sources. Another area of opportunity is the use of more specialized models with different types of approaches, such as LSTM networks.

Graph NNs and emerging adaptive solutions provide important opportunities for shaping the future of fraud and financial crime detection owing to their parallel structures. Because of the complexity of digital transaction processing and the ever-changing nature of fraud, robustness should be treated as the primary design goal when applying ML to detect financial crimes. Finally, focusing on real-time responses and data noise issues is necessary to improve the performance of current ML solutions for financial crime detection.

Currently, the application of unsupervised learning methods in different areas, such as marketing and risk management, is limited. Some problems related to marketing and customer management could be analyzed using clustering techniques, such as K-means, to segment clients by different demographic or behavioral characteristics and by their likelihood of default or switching companies. In energy risk management, extreme events can be identified as outliers using principal component analysis or ranking algorithms.

Conclusions

Having already made notable contributions to business and finance, ML techniques for addressing issues in these domains are significantly increasing. This review discusses advancements in ML in business and finance by examining seven research directions of ML techniques: cryptocurrency, marketing, e-commerce, energy marketing, stock market, accounting, and credit risk management. Deep learning models, such as DNN, CNN, RNN, random forests, and SVM are highlighted in almost every domain of business and finance. Finally, we analyze some limitations of existing studies and suggest several avenues for future research. This review is helpful for researchers in understanding the progress of ML applications in business and finance, thereby promoting further developments in these fields.

Availability of data and materials

Not applicable.

Abbreviations

Machine learning

Long short-term memory

Support vector machine

Restricted Boltzmann machine

Least absolute shrinkage and selection operator

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Acknowledgements

We would like to acknowledge financial support from the grant (No. 72271171) from the National Natural Science Foundation of China, the grant (No. sksy12021-02) from Sichuan University, and National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (71725001).

This work was supported by the grant (No. 72271171) from the National Natural Science Foundation of China, the grant (No. sksy12021-02) from Sichuan University, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (71725001), and the Open Project of Xiangjiang Laboratory (No. 22XJ03028).

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Xiangjiang Laboratory, Changsha, 410205, China

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HG, GK and YD contributed to the completion of the idea and writing of this paper. HG, GK and YD contributed to the discussion of the content of the organization and HL and HZ contributed to the improvement of the text of the manuscript. HG and HL contributed to Methodology. XC, and CL contributed to the literature collection of this paper. All authors read and approved the final manuscript.

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Gao, H., Kou, G., Liang, H. et al. Machine learning in business and finance: a literature review and research opportunities. Financ Innov 10 , 86 (2024). https://doi.org/10.1186/s40854-024-00629-z

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Received : 10 June 2022

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DOI : https://doi.org/10.1186/s40854-024-00629-z

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Effects of coffee on gut microbiota and bowel functions in health and diseases: a literature review, 1. introduction, 2. effects of coffee on the gut microbiota, 2.1. effect of coffee on microbiota composition.

Samples Studied	Treatments	Increased Phyla/Class/Genera	Decreased Phyla/Class/Genera	References
Human fecal samples	Mannooligosaccharides from coffee	↑ Actinobacteria	↓ Firmicutes; ↓ Lactobacillus	Umemura et al., 2004 [ ]
Human fecal samples	Coffee fibres	↑ Bacteroides; ↑ Prevotella grp		Gniechwitz et al., 2007 [ ]
Human fecal samples	Three cups of coffee daily for 3 days	↑ Actinobacteria; ↑ Bifidobacterium spp.		Jaquet et al., 2009 [ ]
Human fecal samples	Coffee extract	↑ Actinobacteria; ↑ Firmicutes; ↑ Bifidobacterium spp.	↓ Proteobacteria; ↓ E. coli	Benitez et al., 2019 [ ]
Human fecal samples	Coffee extract and chlorogenic acids	↑ Actinobacteria; ↑ Firmicutes; ↑ Bifidobacterium spp.		Tomas-Barberan et al., 2014 [ ]
Human fecal samples	Chlorogenic acid (C-QA)	↑ Actinobacteria; ↑ Firmicutes; ↑ Bifidobacterium spp.	↓ Bacteroidetes	de Cosío-Barr´on et al., 2020 [ ]
Human fecal samples	Nescafe coffee extracts	↑ Actinobacteria; ↑ Firmicutes; ↑ Bifidobacterium spp.		Mills et al., 2015 [ ]
Human fecal samples	Spent coffee	↑ Bacteroidetes; ↑ Firmicutes; ↑ Barnesiella; ↑ Butyricicoccus; ↑ Veillonella	↓ Actinobacteria; ↓ Faecalibacterium; ↓ Ruminococcus; ↓ Blautia	Perez-Burillo et al., 2020 [ ]
Mice fecal samples	Coffee and amoxicillin	↑ Proteobacteria; ↑ Burkholderiaceae	↓ Burkholderia cepacia	Diamond et al., 2021 [ ]
Rat fecal samples	Caffeinated and decaffeinated coffee		↓ Enterobacteria; ↓ gamma-Proteobacteria	Hegde et al., 2022 [ ]
Rat fecal samples (paradoxical sleep deprivation)	Caffeinated and decaffeinated coffee	↑ Akkermansia; ↑ Klebsiella	↓ S24-7; ↓ Lachnospiraceae; ↓ Oscillospira; ↓ Parabacteroides	Gu et al., 2022 [ ]
Tsumura Suzuki obese diabetes mice feces	Coffee, caffeine and chlorogenic acid	↑ Firmicutes	↓ Bacteroidetes	Nishitsuji et al., 2018 [ ]
Mice fecal samples	Coffee and galacto-oligosaccharide	↑ Bifidobacterium spp.	↓ E. coli; ↓ Clostridium spp.	Nakayama & Oishi, 2013 [ ]

2.2. Effect of Coffee on Microbiota Diversity

2.3. effect of coffee on microbiota growth, 3. effect of coffee on gastrointestinal infections and immunity, 4. effects of coffee on gastrointestinal motility and secretion, 5. effect of coffee on the gut-microbiota–brain axis, 6. effect of coffee on absorption and nutrition, 7. coffee and medication interaction, 8. effect of coffee on oral microbiome, 9. summary and conclusions, author contributions, conflicts of interest.

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Design	Treatment	Significance	Microbiota Change	Reference
In vivo rats	Coffee	Reduction in liver triglycerides	Clostridium Cluster XI ↓	Cowan et al. 2014 [ ]
In vivo rats	Coffee	Reduction in obesity, metabolic syndrome, and inflammation; increase in gut barrier function	Firmicutes (F)-to-Bacteroidetes (B) ratio ↓	Cowan et al. 2014 [ ]
In vivo rats	Coffee	Potential for gut dysbiosis, antibiotic resistance, opportunistic infection; may be involved with insulin resistance	Enterobacteriaceae ↑	Cowan et al. 2014 [ ]
In vivo mice	Caffeine	Protection of gut lining, barrier function, and production of SCFAs	Dubosiella, Bifidobacterium and Desulfovibrio ↑	Chen et al. 2023 [ ]
In vivo mice	Caffeine	Reduction in nutrient breakdown and immune modulation, but potentially, a restoration from dysbiosis	Bacteroides, Lactobacillus and Lactococcus ↓	Chen et al. 2023 [ ]
In vivo mice	Coffee	Improvement in endotoxemia and systemic inflammation	Prevotella ↓	Nishitsuji, Watanabe, & Xiao 2018 [ ]
In vivo mice	Coffee	Increase in gastrointestinal polypeptide; stimulation of insulin secretion and protection against metabolic syndrome	Coprococcus ↑	Nishitsuji, Watanabe, & Xiao 2018 [ ]
In vivo humans In vivo mice	Coffee	Support in acetate production, but a marker of high-fat diet	Blautia ↓	Martinez et al. 2013 [ ]; Nishitsuji, Watanabe, & Xiao 2018 [ ]

Design	Treatment	Nutrient Effects	Mechanism	References
In vivo human	Coffee	Circulating Vitamin D ↑	Inhibition of Vitamin D receptors	Al-Othman et al. 2012 [ ]
In vivo human	Caffeine	Calcium levels ↓	Imbalance of Calcium/inhibition of Vitamin D receptors	Barger-Lux & Heaney 1995 [ ]
In vivo human	Coffee	Zinc levels ↓	Binding of phenolic compounds	Pécoud et al. 1975 [ ]
In vivo human	Coffee	Iron absorption ↓	Phenolic binding of nonheme iron in the lumen	Morck 1983 [ ]
In vivo human	Coffee	B vitamin levels ↓	Complex formation with polyphenols	Ulvik 2008 [ ]
In vivo human	Coffee	Glucose ↓	Inhibition of hepatic glucose-6-phosphate translocase	Ohnaka 2012 [ ]

Design	Treatment	Medication Interaction	Mechanism	References
In vivo mice	Coffee	Aspirin absorption ↑	Proteobacteria, Helicobacteraceae, and Bacteroidaceae ↓; Lactobacillaceae ↑	Kim et al. 2022 [ ]
In vivo human	Coffee	Paracetamol Absorption ↑	Competitive binding to adenosine receptors	Iqbal 1995 [ ]
In vivo human	Coffee	Clozapine, Lithium, Theophylline, Warfarin Absorption ↓	Activation of CYP enzymes by metabolites *	Belayneh et al. 2020 [ ]
In vitro mice	Coffee	Amoxicillin	Slowed growth of Burkholderiaceae	Diamond et al. 2021 [ ]
In vivo mice	Cholinergic acid	Geniposide absorption ↑	Protect gut barrier function	Peng et al. 2018 [ ]

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Saygili, S.; Hegde, S.; Shi, X.-Z. Effects of Coffee on Gut Microbiota and Bowel Functions in Health and Diseases: A Literature Review. Nutrients 2024 , 16 , 3155. https://doi.org/10.3390/nu16183155

Saygili S, Hegde S, Shi X-Z. Effects of Coffee on Gut Microbiota and Bowel Functions in Health and Diseases: A Literature Review. Nutrients . 2024; 16(18):3155. https://doi.org/10.3390/nu16183155

Saygili, Sena, Shrilakshmi Hegde, and Xuan-Zheng Shi. 2024. "Effects of Coffee on Gut Microbiota and Bowel Functions in Health and Diseases: A Literature Review" Nutrients 16, no. 18: 3155. https://doi.org/10.3390/nu16183155

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Int J Environ Res Public Health

Prevalence, Antecedents, and Consequences of Workplace Bullying among Nurses—A Summary of Reviews

Hongli sam goh.

1 IPE Management School Paris, 21 Rue Erard, 75012 Paris, France; moc.liamg@shgrun

Siti Hosier

2 Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore; gs.ude.sun@hzrun

Despite over 25 years of extensive research about the workplace bullying phenomenon in various disciplines, there have been mixed conclusions about its prevalence, antecedents, and consequences among nurses reported by multiple systematic reviews. This summary review used the Cochrane’s Overview of Reviews method to examine the prevalence, antecedents, coping behaviors, and consequences of workplace bullying among nurses to understand the interplay of these variables in healthcare workplace contexts. A total of 12 systematic reviews published between 2013 and 2020 were included based on the eligibility criteria. There were differences in workplace bullying prevalence across different reviews, ranging from 1 to 90.4%, but a more recent review estimated the pooled prevalence at 26.3%. This review identified at least five main types of antecedents for workplace bullying: demographics, personality, organizational culture, work characteristics, and leadership and hierarchy. Workplace bullying affected nurses, organizational outcomes, and patient safety. This review proposes an integrative model to explain workplace bullying among nurses and highlights the need for more studies to evaluate interventions to address this phenomenon.

1. Introduction

Nursing has long been recognized as a challenging career, which is beset with workplace adversities, such as stress and bullying, of which the latter warrants a cause for concern. Nurse bullying is not new and has been the subject of research studies for over 25 years. This phenomenon was suggested to affect nurses in the United States (US) more than 100 years ago based on a New York Times article in 1909, “The hospital tyrants” [ 1 ]. Unfortunately, despite years of research in this area, nurses continue to experience bullying today as many leaders, institutions, and even the nurses themselves either deny its existence or accept it as the norm, creating a culture of silence that impedes solutions to the problem [ 1 ].

Within the broader literature, Nielsen and Einarsen defined workplace bullying as extensive exposure to repeated negative behaviors at the workplace, leaving individuals to feel defenseless against such behaviors [ 2 ]. Within the nursing literature, workplace bullying is an umbrella term for most types of workplace aggression and violence, ranging from emotional neglect to threats of violence and physical assault [ 3 ]. Terms that fall under this umbrella include incivility, harassment, and workplace violence. The subject has been extensively studied internationally, across disciplines, particularly within healthcare settings [ 3 ]. Workplace bullying occurs when individuals perceive that they are the target of negative actions from one or more persons over time.

According to Trépanier et al. [ 4 ], up to 40% of nurses faced bullying behaviors at work, while Houck and Colbert [ 3 ] reported prevalence rates ranging from 26 to 77%. These figures suggest that the healthcare industry seems to be acutely affected by this phenomenon. In contrast, a systematic review, which examined non-healthcare studies, reports an estimated global prevalence of only 15%, suggesting that workplace bullying in general workplace settings might be less prevalent than in a healthcare context [ 5 ]. The high bullying prevalence rate reported among nurses warrants an urgent need for nurse leaders to address this issue.

The high prevalence rate of workplace bullying among nurses is alarming given the consequences and impact on nurses and organizations. Exposure to bullying is associated with symptoms of depression, anxiety, and psychological distress in nurses [ 6 , 7 ], as well as somatic physical health problems, including insomnia and headache [ 8 ]. Workplace bullying can also undermine nurses’ professional well-being, decreasing engagement and quality of work motivation, and increasing absenteeism, turnover, and burnout symptoms [ 4 , 9 , 10 ].

There have been multiple systematic reviews that evaluated workplace bullying in nursing. Most of them reported mixed or inconclusive findings of the prevalence, antecedents, and consequences to address workplace bullying due to the heterogeneity in study designs, measurement instruments, and contextual variations across the included studies. For example, some reviews examined workplace bullying prevalence only, while others focused on its triggering factors. Still, other reviews only focus on specific consequences of workplace bullying [ 1 , 2 ]. The different reviews make it difficult for nursing leaders to comprehend the scope and extent of workplace bullying, much less know how to manage or address it. Castronovo et al. [ 1 ] lamented the persistent existence of these problems despite years of research in this area. In light of the varied conclusions, we decided to conduct a summary review with the aim of summarizing the findings from existing systematic reviews, which examined the prevalence, antecedents, and consequences of workplace bullying among nurses to understand the interplay of these variables within healthcare. At the end of the review, these findings will be used to develop a theoretical framework for analyzing workplace bullying in nursing.

2. Materials and Methods

The summary review of systematic reviews was conducted using Cochrane’s Overview of Reviews method to synthesize reviews examining workplace bullying and its prevalence, trend, antecedents, consequences, and interventions. There have been extensive publications of studies in nursing and healthcare literature. This method was adopted because it provides an explicit and structured approach to extract and analyze results across the topic of interest [ 11 ]. As there have been multiple reviews that focus on different aspects of workplace bullying, this method allows us to compare strengths of evidence derived from varied review designs to draw meaningful conclusions. Finally, the Cochrane Overview of Reviews method allows us to summarize the findings from different reviews about workplace bullying for clinicians and decision-makers rather than leaving them to assimilate the results of multiple systematic reviews themselves [ 12 ]. The Cochrane’s Overview of Reviews method comprises five steps: (i) defining the review and questions; (ii) outlining the search strategy to retrieve systematic reviews (with or without meta-analyses); (iii) establishing clear eligibility criteria for article selection; (iv) extraction of data from each review, including its characteristics, risk of bias and outcomes; and (v) collation and summary of results in accordance to the specific objectives or questions of the review [ 11 ].

2.1. Defining the Review Questions

Three questions for the summary review were developed based on the authors’ preliminary literature review:

What are the prevalence and trends in workplace bullying among nurses?
What are the antecedents for workplace bullying among nurses?
What are the consequences of workplace bullying for nurses?

2.2. Search Strategy

A comprehensive literature search was conducted between April 2021 and December 2021 to search for relevant systematic reviews using the following key search terms and related text words: ‘workplace bullying,’ ‘nurs*,’ and ‘review.’ The search for literature was limited to those published within the past ten years, as this paper aimed to provide a comprehensive review of all recently published reviews on nurse bullying. A total of seven electronic databases were searched: PubMed, ScienceDirect, Medline, Scopus, CINAHL, Web of Science, and PsycINFO. The search was conducted using different combinations of exact keywords on titles and abstracts. Thereafter, the retrieved articles were screened for relevance to the review questions.

2.3. Article Selection

The selection of the studies was conducted independently by two authors based on the eligibility criteria. Disagreement during the selection was resolved by discussion with a third-party arbiter. The inclusion criteria were: (i) derived from a systematic review; (ii) involved nursing professionals; (iii) addressed the review questions; and (iv) published in English. In addition, we excluded studies that were merely literature reviews or any other review that did not demonstrate a systematic process, did not focus on nurses, or were published in other languages with no English translation. The decision-making process and the search results at each step of the course are depicted in the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) diagram ( Figure 1 ).

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PRISMA diagram.

2.4. Data Extraction and Quality Assessment

Data were extracted by one author (SG) and verified by another (ZH) for relevancy and accuracy. The two authors then independently extracted the data, including the review objectives, design, search strategy, number of included studies and sample size, geographical location, main findings, and quality appraisal using the ROBIS tool. The ROBIS tool was developed by clinicians at Bristol Medical School with the aim of providing an effective yet robust method to assess the risk of bias for systematic review, and has been recommended by Cochrane for the review method [ 13 ]. The tool can also be used to compare the overall risk of bias across various reviews to derive meaningful comparison and contrast of the various findings [ 13 ]. When there was a disagreement on the quality of an article, this would be resolved through discussion with a third author (SH) until consensus was achieved. An annotated bibliography was developed to tabulate the characteristics and findings of the studies. The reference management software Mendeley and Microsoft Excel were used to sort the records.

A total of 12 reviews about workplace bullying were included in this summary review ( Table 1 ). The types of reviews included were: quantitative systematic reviews ( n = 2), mixed-methods systematic reviews ( n = 1), integrative reviews ( n = 5), narrative reviews or systematic reviews with qualitative synthesis ( n = 3), and scoping reviews ( n = 1). The samples ranged from 61 to 151,347 participants, and the number of databases searched among the reviews ranged from 3 to 8. The reviews were published between 2013 and 2020, and included studies that were published from the earliest date to 2019. With the exception of one review, which focused solely on studies from Australia [ 14 ], most reviews included studies from different countries. Most of the studies were conducted in North America, Europe, and Australia compared to other regions. Rutherford et al. attributed this observation to the inclusion criteria of mostly English-language papers by most reviews and that most journals and databases use English for communication [ 15 ]. The summary review also assessed the methodological quality of all 12 reviews, as shown in Table 2 . Based on the overall quality assessment of the included reviews, only one review was at low risk of bias for the overall study [ 16 ].

Summary table for included systematic reviews.

S/N	Article	Objectives	Review Typology	Search Strategy	Number of Included Studies/Total Participants	Geographical Location	Findings
1.	Hutchinson & Jackson (2013) [ ]	To examine the relationship between the various forms of hostile clinician behaviors and patient care.	A mixed-methods systematic review	8 databases (CINAHL, Health Collection (Informit), Medline (Ovid), Ovid, ProQuest Health and Medicine, PsycINFO, PubMed and Cochrane library), including hand searching of reference lists. Search period: Between 1990 and 2011. Inclusion: Exclusion:	30 studies = 102,909	USA (16), Australia (7), Canada (3), United Kingdom (1), New Zealand (1), Iceland (1), Finland (1)	Q3: Consequences on patient safety included:
2.	Spector et al. (2014) [ ]	To provide a quantitative review that estimates exposure rates by type of violence, setting, source, and world region.	A quantitative review	5 databases (Embase, MEDLINE, PsycINFO, PubMed and Web of Science). Search period: From earliest date to October 2012. Inclusion: Exclusion:	136 studies = 151,347	Worldwide	Q1: Prevalence & trends: Violence types are divided into: Prevalence rates: Geographical locations:
3.	Trépanier et al. (2016) [ ]	To provide an overview of the current state of knowledge on work environment antecedents of workplace bullying.	Systematic review with narrative synthesis	3 databases (PsycINFO, ProQuest and CINAHL). Search period: From earliest date to 2014. Inclusion: Exclusion:	12 studies = 4177	North America (7), Australia (3), Turkey (2)	Q2: Identified four main categories of work-related antecedents of workplace bullying: (a) job characteristics, (b) quality of interpersonal relationships, (c) leadership styles, and (d) organizational culture.
4.	Houck & Colbert (2017) [ ]	To explore and synthesize the published articles that address the impact of workplace nurse bullying on patient safety.	Integrative review	5 databases (PubMed, CINAHL, PsycINFO, Cochrane library and MEDLINE). Search period: Between 1995 and March 2016. Inclusion:	11 studies = 16,137	USA (7), Australia (2), Canada (1), and United Kingdom (1)	Q3: The effect of bullying on nurses’ work was not sufficient to reveal all risks to patient safety.
5.	Pfeifer & Vessey (2017) [ ]	To synthesize and evaluate the existing literature on workplace bullying and lateral violence in the Magnet setting.	Integrative review	5 databases (CINAHL, MEDLINE, PsycINFO, Cochrane library and Web of Science). Search period: Between January 2008 and February 2017. Inclusion: Exclusion:	11 studies = 7657	USA	Q2: Magnet nurses reported lower WB scores than nurses working in non-Magnet organizations (based on four studies).
6.	Bambi et al. (2018) [ ]	To detect specifically the prevalence of workplace incivility (WI), lateral violence (LV), and bullying among nurses.	Narrative review	3 databases (MEDLINE, CINAHL and Embase). Search period: No time limitation. Inclusion: Exclusion:	Workplace incivility:16 studies = 12,246 Lateral violence:25 studies = 25,375	Workplace incivility: Canada (8), USA (5), China (1), Egypt (1), Pakistan (1) Lateral violence: USA (15), Europe (5), Asia (2)—Turkey & South Korea, South Africa (1), New Zealand (1), Jamaica (1)	Q1: Prevalence: Q3: Consequences: < 0.001). < 0.01).
7.	Hartin et al. (2018) [ ]	To discuss the current state of knowledge about bullying in the nursing profession in Australia.	Integrative review	3 databases (MEDLINE, CINAHL and Scopus). Search period: Between January 1991 and December 2016. Inclusion: Exclusion:	23 studies = 16,168	Australia	Q1: 61% of respondents reported WB within the last 12 months. Nurse-to-nurse aggression was the most distressing type of bullying, and statistics were likely to be under-reported. Q3:
8.	Crawford et al. (2019) [ ]	To examine the evidence regarding nurse-to-nurse incivility, bullying, and workplace violence for the 4 nursing populations (student nurses, new graduate nurses, experienced nurses, and academic faculty).	Integrative review	6 databases (CINAHL, Cochrane library, Embase, ERIC, PsycINFO, and PubMed). Include Google Search. Search period: Between 2010 and 2016. Inclusion: Exclusion:	21 studies = Not reported	USA and Canada	Q1: No number reported. Highlighted that WB prevalence rates among nurses have not changed in more than 20 years. Q2: Antecedents were divided into 3 layers of WB triggers: organizational, work environment, and personal. Suggested that young nurses were at a higher risk. Q3: Identified 84 negative academic, organizational, work unit, and personal outcomes. Others:
9.	Hawkins et al. (2019) [ ]	To synthesize evidence on negative workplace behavior experienced by new graduate nurses in acute care setting and discuss implications for the nursing profession.	Integrative review	5 databases (CINAHL, MEDLINE, ProQuest, JBI and Scopus). Search period: Between 2007 and 2017. Inclusion: Exclusion:	16 studies (14 published articles & 2 dissertations) = 3043	Canada (6), USA (3), Australia (2), Taiwan (2), Ireland (1), South Korea (1), Singapore (1)	Q1: ). Q2: Three antecedents were identified: Q3: Individual impact and patient care identified: Others:
10.	Lever et al. (2019) [ ]	To review both mental and physical health consequences of bullying for healthcare employees.	Systematic review (quantitative studies)	5 databases (Embase MEDLINE, PsycINFO, PubMed and Web of Science). Search period: Between 2005 and January 2017. Inclusion: Exclusion:	45 studies = varied from 61 to 9949.	15 studies in North America (Canada-10; USA-5) 15 studies in Europe (UK-4; Denmark-4; Norway-3; Portugal-2; Germany-1; Bosnia-1) 6 studies in Australia (6) 7 studies in Asia (Turkey-4; Japan-2; China-1) 2 studies (Mixed profiles)	Q1: Prevalence: Q3: Consequences divided into 2 types:
11.	Johnson & Benham-Hutchins (2020) [ ]	To examine the influence of nurse bullying on nursing practice errors and patient outcomes.	A systematic review (involving qualitative synthesis)	4 databases (CINAHL, MEDLINE, Cochrane Library, and PsycINFO). Search period: Between January 2012 and November 2017. Inclusion: Exclusion:	14 studies = Not reported	Not reported	Q1: ED setting: 60% of respondents reported WB events. OR setting: 59% witnessed WB events, but only 6% self-reported such events in perioperative environment in the USA. Two types of bullying trends were identified: (a) work-related bullying originating from workplace environment, and (b) person-related bullying originating from informal personal relationships. Q3: Consequences included:
12.	Karatuna et al. (2020) [ ]	To examine WB research among nurses with the focus on sources, antecedents, outcomes, and coping responses from a cross-cultural perspective during the years 2001–2019.	A cross-cultural scoping review	4 databases (CINAHL, PubMed, PsycINFO and Web of Science). Search period: Between 2001 and 2019. Inclusion: Exclusion:	166 studies = Not reported	29 countries worldwide, although research was mostly conducted in the Anglo cluster	Q2: Antecedents varied across cultures and classified as: (a) individual (demographics and personality traits); (b) organizational (leadership, work characteristics, and organizational culture). Other results included: Q3: Consequences:

Legend: WB (Workplace bullying); USA (United States of America); WI (workplace incivility), LV (lateral violence); ASSIA (Applied Social Sciences Index and Abstracts); BSP (Business Source Premier); CINAHL (Cumulated Index to Nursing and Allied Health Literature); Embase (Excerpta Medica database); JBI (Joanna Briggs Institute); MEDLINE (Medical Literature Analysis and Retrieval System Online); IBSS (International Bibliography of the Social Sciences); Q1 (Question 1—What are the prevalence in workplace bullying in nursing studies?) Q2 (Question 2—What are the antecedents for workplace bullying in nursing?); Q3 (Question 3—What are the consequences of workplace bullying in nursing?).

Quality appraisal of included systematic reviews.

S/N	Article	Quality of Study Using ROBIS Tool					Strengths	Limitations
S/N	Article	D1	D2	D3	D4	O	Strengths	Limitations
1.	Hutchinson & Jackson (2013) [ ] Mixed-methods systematic review
2.	Spector et al. (2014) [ ] Quantitative review
3.	Trépanier et al. (2016) [ ] Systematic review with narrative synthesis							= 12), which were insufficient to justify the model development.
4.	Houck & Colbert (2017) [ ] Integrative review							= 11).
5.	Pfeifer & Vessey (2017) [ ] Integrative review
6.	Bambi et al. (2018) [ ] Narrative review
7.	Hartin et al. (2018) [ ] Integrative review
8.	Crawford et al. (2019) [ ] Integrative review							within the article, but there might be a potential risk of bias on lack of clarity over data collection and quality appraisal process.
9.	Hawkins et al. (2019) [ ] Integrative review							= 16).
10.	Lever et al. (2019) [ ] Systematic review (quantitative studies)
11.	Johnson & Benham-Hutchins (2020) [ ] Systematic review (involving qualitative synthesis							= 14).
12.	Karatuna et al. (2020) [ ] Scoping review

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3.1. Question 1—What Are the Prevalence and Trends in Workplace Bullying among Nurses?

Seven reviews addressed the prevalence of workplace bullying within the nursing and healthcare literature ( Table 3 ). Two reviews conducted a pooled estimation of workplace bullying prevalence and reported a mean prevalence of 26.3 and 66.9% among nurses [ 8 , 18 ]. Spector et al. seemed to be the most comprehensive review between these two reviews, having conducted a quantitative review of 136 healthcare studies on the global nursing violence literature to examine the extent (prevalence), sources, and subtypes of bullying and violence across countries and prevalence. They reported workplace bullying prevalence ranges from 57.6% in hospital settings to 67.7% in psychiatric settings. The mean percentage of perceived bullying also varied across different geographical regions: Middle East (86.5%), Anglo (39.5%), Asia (29.8%), and Europe (8.8%). The highest rate of non-physical violence from peers and colleagues occurred among nurses working in Asia (50.2%), followed by the Middle East (44.9%), Anglo countries (US, Canada, UK, and Australia) (37.4%), and Europe (27.6%). Asian, Anglo, and Middle Eastern nurses suffered similar rates of physical violence at 7.3, 6.6, and 6.0%, respectively. Similarly, a more recent quantitative systematic review involving 45 studies reported a lower percentage of workplace bullying among nurses. They have classified workplace bullying in general terms, demonstrating that the trend in workplace prevalence among nurses has remained vastly varied across different regions [ 8 ].

Summary table of prevalence rate for workplace bullying among nurses.

No.	Evidence/Reference	Prevalence Rate
1.	Spector et al. (2014) [ ]	Prevalence rate: 25–66.9%. Specific: Physical violence (36.4%), non-physical (66.9%), bullying and others (39.7%), sexual harassment (25%), injured (32.7%).
2.	Houck and Colbert (2017) [ ]	Prevalence of bullying among nurses was observed to be between 26% and 77%.
3.	Bambi et al. (2018) [ ]	% of bullying prevalence: 2.4 to 81%. % of workplace incivility: 67.5 to 90.4%. % of lateral violence (peer violence): 1 to 87.4%.
4	Hartin et al. (2018) [ ]	61% of respondents in Australia reported workplace bullying events within the last 12 months.
5.	Hawkins et al. (2019) [ ]	Prevalence ranged widely from 0.3 to 73.1% (variations attributed to the workplace context and instrument measuring workplace bullying events [e.g., daily basis, over the past 1 month, or over the past 12 months]). Studies measuring workplace bullying within past 6–12 months reported a more consistent prevalence ranging from 25.6 to 73.1%.
6.	Lever et al. (2019) [ ]	Bullying prevalence ranged from 3.9 to 86.5%, with a pooled mean estimate of 26.3%. The pooled mean prevalence of bullying by region: Asia (47.1%), Australia (36.1%), Europe (18.4%), and North America (24.5%).
7.	Johnson and Benham-Hutchins (2020) [ ]	% of bullying prevalence in emergency department setting: 60%. % of bullying prevalence in Operating Room setting: 59% witnessed workplace bullying events, but only 6% self-reported such events in the USA.

There were vast differences in workplace bullying prevalence across all seven reviews, with one review reporting the greatest prevalence range from 1 to 90.4% [ 8 ]. Other reviews also reported a similar prevalence range [ 8 , 22 ]. The vast discrepancies in the reported bullying rates across different nursing studies might suggest regional and country differences in the workplace bullying incidence rates and sources of violence, making it difficult for researchers to grasp its extent and impact. One possible explanation for such discrepancies could be that some countries or cultures may trivialize or pay little attention to the problem, leading to under-reporting issues (Spector et al., 2014). Another reason could be the different ways bullying is defined and measured, inconsistent research methods, and an absence of longitudinal studies [ 24 ]. The current lack of local data on the extent of the phenomenon could impede nursing leaders from developing and implementing tailored interventions to address these issues in their specific settings.

Workplace bullying seems more prevalent in hospitals’ high-stress work environments, such as emergency departments, operating theaters, intensive care units, and surgical and psychiatric settings [ 20 , 22 , 23 ]. However, this trend might not be generalizable across different countries, as Bambi et al. highlighted obstetrics wards as the most affected units in public hospitals in Cape Town, South Africa. Additionally, it appears that nurses in Asian and Middle Eastern countries have a higher prevalence of workplace bullying, and physical and non-physical violence than their counterparts from other regions [ 8 , 18 ].

3.2. Question 2—What Are the Antecedents for Workplace Bullying among Nurses?

Five reviews identified five antecedents for workplace bullying within the nursing and healthcare literature ( Table 4 ). Among the five reviews, the most comprehensive was Karatuna et al.’s scoping review, which included 166 studies on workplace bullying among nurses. The review was also the most recent, with included studies published between 2001 and 2019. Hence, we used their review to guide the categorization of antecedents into five main types: demographics, personality, organizational culture, work characteristics, and leadership. These five antecedents can also be grouped under two main layers of antecedents—individual-level or organizational-level [ 21 ].

Summary table of antecedents for workplace bullying.

No.	Types of Antecedents	Subtypes	Association	Evidence
1.	Demographics (Individual-level)	Age	Negatively associated with workplace bullying.	Karatuna et al. (2020) [ ] Crawford et al. (2019) [ ]
		Length of experience/service	Negatively associated with workplace bullying.	Karatuna et al. (2020) [ ]
		Gender	No association.	Karatuna et al. (2020) [ ]
		Marital status	No association.	Karatuna et al. (2020) [ ]
		Education level	No association.	Karatuna et al. (2020) [ ]
		Minority race or ethnicity	Association reported in Anglo, Southern Asia.	Karatuna et al. (2020) [ ]
		Disability	Association reported in Anglo.	Karatuna et al. (2020) [ ]
		Having children	Association reported in Latin America and Eastern Europe.	Karatuna et al. (2020) [ ]
2.	Personality (Individual-level)	Locus of control/assertiveness	Lower locus of control (assertiveness) is negatively associated with workplace bullying.	Karatuna et al. (2020) [ ]
		Psychological capital	Less psychological capital is negatively associated with workplace bullying.	Karatuna et al. (2020) [ ]
		Vulnerable traits or personality/poor compliance to social norms	Negatively associated with workplace bullying.	Karatuna et al. (2020) [ ]
3.	Organizational culture (Organizational-level)	Organizational culture promotes staff empowerment, distributive justice, and zero tolerance for bullying/Magnet organizational culture	Perceived healthy work environment is negatively associated with workplace bullying.	Karatuna et al. (2020) [ ] Pfeifer and Vessey (2017) [ ]
3.	Organizational culture (Organizational-level)	Quality of interpersonal relationships	Association varies according to regions. Vertical bullying was most prevalent in higher power distance cultures, whereas horizontal bullying was either more or equally prevalent in lower power distance cultures.	Crawford et al. (2019) [ ] Hawkins et al. (2019) [ ]
4.	Work characteristics (Organizational-level)	Work overload	Higher workload is positively associated with workplace bullying.	Karatuna et al. (2020) [ ] Trépanier et al. (2016) [ ]
		Staff shortages	More severe staff shortages are positively associated with workplace bullying.	Trépanier et al. (2016) [ ]
		Stressful working conditions	High-stress work environment is positively associated with workplace bullying.	Trépanier et al. (2016) [ ]
5.	Leadership and hierarchy (Organizational-level)	Leadership styles	Autocratic, unsupportive, and disengaged leadership tends to perpetuate high-power distance clusters and increased bullying behaviors.	Trépanier et al. (2016) [ ] Crawford et al. (2019) [ ] Hawkins et al. (2019) [ ] Karatuna et al. (2020) [ ]

3.2.1. Individual-Level Antecedents

Individual antecedents include demographics and personality traits of individuals who contributed to the occurrence of workplace bullying. The results showed some similarities in the demographical antecedents of bullying across clusters that differ in their cultural practices. In terms of demographics, they found that most studies reported no associations between gender, education level, marital status, and workplace bullying. Conversely, age and length of experience/service were found to be negatively associated with workplace bullying. Other demographical antecedents were found to vary across different geographical clusters and subject to the different socio-cultural and politico-economic influences. For example, nurses considered “vulnerable” to workplace bullying in Anglo countries belong to a certain race, ethnicity, or disability, while those in Latin America and Eastern Europe have children. For personality characteristics, nurses with less locus of control, psychological capital, or poor compliance to social norms were associated with a greater risk of workplace bullying than others [ 16 ].

3.2.2. Organizational-Level Antecedents

Organizational-level antecedents included leadership, work characteristics, and organizational culture. For example, an organizational culture that is performance-oriented is more likely to tolerate workplace bullying, while cultures that emphasize people-orientation tolerate such behaviors if the group views the victim as inconsistent with social norms or misaligned with the organizational structure and hierarchy [ 16 ]. These findings highlighted group inclusivity within the organization, which is highly dependent and varies according to the larger socio-cultural context.

As for work characteristics, Karatuna et al. (2020) reported that negative work environments and characteristics include work overload, staffing shortages, and stressful working conditions. These variables were found to be reported across all clusters. Trépanier et al. [ 4 ] conducted a systematic literature review specifically examining work-related antecedents of workplace bullying in nursing and retrieved 12 relevant studies. They reported similar results to Karatuna et al. based on their four categories of work-related antecedents: (1) job characteristics, (2) quality of interpersonal relationships, (3) leadership styles, and (4) organizational culture. They found that nurses’ better job characteristics, higher quality of interpersonal working relationships, people-centric leadership styles, and positive organizational culture (promoting staff empowerment, distributive justice, and zero tolerance for bullying) were associated with less workplace bullying. Pfeifer and Vessey [ 19 ] conducted an integrative review focusing on examining bullying issues among nurses in Magnet ® organizations, which are designated hospitals that meet the quality benchmark for providing quality of care and nursing excellence. They found 11 articles (eight quantitative and three qualitative studies). Their review demonstrated emerging evidence on how a positive work environment could contribute to reduced reports of verbal abuse, incivilities, and hostile encounters from colleagues. Despite the positive and significant findings, Pfeifer and Vessey cautioned that workplace bullying can still affect nurses in the Magnet ® environment and highlighted the complex interplay of individual and organizational factors in influencing the occurrences of workplace bullying [ 19 ].

Leadership and hierarchy seem to mediate in organizational culture and work characteristics. For example, Karatuna et al. reported that autocratic, unsupportive, and disengaged leadership perpetuates high-power distance clusters and increased bullying behaviors [ 16 ]. On the other hand, Trépanier et al. [ 4 ] found three studies examining how authentic (positive) leadership significantly reduced workplace bullying and burnout reports. All four reviews stated positive leadership mediated the workplace environmental factors by promoting a climate of trust, positive collegial relationships, and mitigating stressful work environments and workplace bullying events [ 4 , 16 , 21 , 22 ].

3.3. Question 3—What Are the Consequences of Workplace Bullying for Nurses?

The workplace culture and pervasive nature of bullying have a significant negative impact on nurses, organizations, and patient outcomes. Nine reviews reported the consequences of workplace bullying among nurses [ 3 , 8 , 14 , 16 , 17 , 20 , 21 , 22 , 23 ]. The summary review generated five types of consequences: psychosocial well-being, physical well-being, work performance, organizational impact, and patient outcomes ( Table 5 ).

Summary table of consequences of workplace bullying.

No.	Types of Consequences	Subtypes	Evidence
1.	Psychosocial well-being	Psychological stress	Hartin et al. (2018) [ ]; Bambi et al. (2018) [ ]; Hawkins et al. (2019) [ ]; Crawford et al. (2019) [ ]; Johnson and Benham-Hutchins (2020) [ ]
		Depression	Hartin et al. (2018) [ ]; Bambi et al. (2018) [ ]; Hawkins et al. (2019) [ ]
		Burnout	Hartin et al. (2018) [ ]; Hawkins et al. (2019) [ ]
		Professional confidence	Hartin et al. (2018) [ ]
		Sense of self-worth	Hartin et al. (2018) [ ]
		Work motivation	Hartin et al. (2018) [ ]; Johnson and Benham-Hutchins (2020) [ ]
2.	Physical well-being	Sleep-related issues	Karatuna et al. (2020) [ ]; Lever et al. (2019) [ ]
		Headaches	Karatuna et al. (2020) [ ]; Lever et al. (2019) [ ]
		Gastrointestinal problems, and to a lesser extent,	Karatuna et al. (2020) [ ]; Lever et al. (2019) [ ]
		Back and joint pain	Lever et al. (2019) [ ]
		Cardiac-related symptoms, tachycardia, or blood pressure changes	Karatuna et al. (2020) [ ]; Lever et al. (2019) [ ]
		Sick leave/absenteeism	Bambi et al. (2018) [ ]; Lever et al. (2019) [ ]; Hawkins et al. (2019) [ ]; Johnson and Benham-Hutchins (2020) [ ]
3.	Work performance	Avoidance behavior, delay in effective communication, or impaired peer relations	Hutchinson and Jackson (2013) [ ]; Houck and Colbert (2017) [ ]; Crawford et al. (2019) [ ]; Johnson and Benham-Hutchins (2020) [ ]
		Poor concentration at work, preventing them from delivering safe and effective nursing care	Hutchinson and Jackson (2013) [ ]; Houck and Colbert (2017) [ ]; Bambi et al. (2018) [ ]; Hawkins et al. (2019) [ ]; Johnson and Benham-Hutchins (2020) [ ]
		Fail to raise safety concerns and seek assistance/delayed care	Hutchinson and Jackson (2013) [ ]; Houck and Colbert (2017) [ ]; Hawkins et al. (2019) [ ]
		Become hostile and perpetrators of similar bullying behaviors	Hutchinson and Jackson (2013) [ ]
4.	Organizational impact	Job dissatisfaction	Hartin et al. (2018) [ ]; Hawkins et al. (2019) [ ]; Crawford et al. (2019) [ ]; Johnson and Benham-Hutchins (2020) [ ]
		Increased intention to quit	Johnson and Benham-Hutchins (2020) [ ]
		Increased staff turnover/attrition rate	Bambi et al. (2018) [ ]; Johnson and Benham-Hutchins (2020) [ ]; Hawkins et al. (2019) [ ]
		Higher organizational costs due to recruitment and retention difficulties	Johnson and Benham-Hutchins (2020) [ ]
5.	Patient outcomes	Patient falls	Houck and Colbert (2017) [ ]
		Errors in treatments or medications	Houck and Colbert (2017) [ ]
		Adverse event or patient mortality	Houck and Colbert (2017) [ ]
		Patient satisfaction or patient complaints	Houck and Colbert (2017) [ ] Hutchinson and Jackson (2013) [ ]

3.3.1. Psychosocial Well-Being

From the literature, workplace bullying affects nurses’ psychosocial well-being. Hartin et al. [ 25 ] conducted an integrative review of 23 Australian nursing studies. They reported that nurses who experienced workplace bullying faced greater risks of poor psychosocial outcomes such as psychological distress, depression, and burnout. It also undermines the nurses’ professional confidence and decreases their self-worth, motivation, and work ethic. In another systematic review, Johnson and Benham-Hutchins [ 23 ] reported similar psychosocial consequences of bullying, including increased stress, somatic symptoms, frustration, absenteeism, and lack of concentration. These findings were retrieved from 14 relevant nursing studies conducted in multiple healthcare settings, suggesting the significance of the issues in nursing. Of the nursing population, Hawkins et al. [ 22 ] suggested that workplace bullying might affect new graduate nurses, particularly as this group mainly holds subordinate positions and experiences much uncertainty during their adaption to the workplace. They conducted an integrative review of studies that examined this phenomenon among new graduate nurses and found 16 studies from Canada, the US, Australia, Korea, Singapore, and Ireland. They reported similar consequences on the new nurses, specifically, job satisfaction, burnout, intention to leave, and turnover.

3.3.2. Physical Well-Being

Based on two reviews, workplace bullying is also reported to affect nurses’ physical well-being. The review by Johnson and Benham-Hutchins [ 23 ] found one study that surveyed 248 nurses in the Midwest US using an electronic questionnaire and found that work-related bullying showed a highly significant positive relationship with psychological/behavioral responses. However, they did not specify the types of physical outcomes being affected. In another review, Karatuna et al. [ 16 ] reported headache, tachycardia, fatigue, sleep disorders, and pseudo-neurological and gastrointestinal complaints as common physiological health outcomes of workplace bullying in their review of 166 studies in different countries. Lever et al. conducted a systematic review specifically looking at the health consequences in the healthcare workplace [ 8 ]. They retrieved 45 studies published between 2005 and 2017, with 40 studies examining mental health outcomes and 15 on physical health. They reported that nurses who encountered workplace bullying face a greater risk of developing sleep-related issues, headaches, gastrointestinal problems, and to a lesser extent, back and joint pain and blood pressure changes. As a result, these staff are more likely to report sick leave than those not affected by workplace bullying [ 8 ].

3.3.3. Work Performance

The review outlines two types of organizational-related consequences from the review. The first is about the nurses’ work performance. Workplace bullying reduces nursing performance by affecting nurses’ state of mind and impairs their ability to seek help at work, engage in effective and timely communication, and make clinical judgments. As a result, nurses cannot deliver patient care in a safe and effective manner. Hutchinson and Jackson [ 17 ] conducted a mixed-methods systematic review to determine how workplace bullying can affect patient care. They found 30 appropriate studies and conducted a content analysis to generate four themes: (1) physician–nurse relations and patient care, (2) nurse–nurse bullying, intimidation, and patient care, (3) reduced nurse performance related to exposure to hostile clinician behaviors, and (4) nurses and physicians directly implicating patients. The first two themes highlighted that physicians and nursing colleagues were the two main sources of bullying behaviors. In comparison, the last two themes revealed how bullying behaviors reduce nurses’ work performance. They reported that nurses affected by workplace bullying were reported to (1) avoid or delay effective communication, (2) experience poor concentration at work, preventing them from delivering safe and effective nursing care, (3) fail to raise safety concerns and seek assistance, and (4) become hostile and perpetrators of similar bullying behaviors.

3.3.4. Organizational Impact

The second organizational-related consequence is the organizational impact. Hartin et al. reported that workplace bullying decreases nurses’ job satisfaction and productivity, such as increased absenteeism and committing errors during work [ 25 ]. Johnson and Benham-Hutchins [ 23 ] reported that workplace bullying created a negative and hostile work environment, where teamwork and communication are being impeded. Both reviews reported that this indirectly leads to decreased job satisfaction, increased intention to quit, and staff turnover/attrition rate, leading to a higher organizational cost due to recruitment and retention difficulties. Crawford et al. analyzed 21 studies involving nursing students, new graduates, and experienced and academic faculty [ 21 ]. They reported that new graduate nurses face a higher risk of workplace bullying and difficulty coping with their new role. This situation is especially significant if the workplace environment is perceived as hostile, toxic, and unforgiving. If not managed properly, these events could negatively impact new nurses’ transition experiences and result in impaired peer relations and even higher staff attrition.

3.3.5. Patient Outcomes

In terms of patient outcomes, workplace bullying indirectly influences patient outcomes by negatively affecting nurses’ work performance. Houck and Colbert conducted an integrative review to examine the association between workplace bullying and patient safety outcomes [ 3 ]. They retrieved 11 studies conducted between 1995 and March 2016 in Anglo countries (US, Canada, UK, and Australia). They reported seven patient safety consequences of workplace bullying: (1) patient falls, (2) errors in treatments or medications, (3) patient satisfaction or patient complaints, (4) adverse event or patient mortality, (5) altered thinking or concentration, (6) silence or inhibited communication, and (7) delayed care. Among these themes, the first four were reported as patient-related consequences of workplace bullying. The last three revolved around the negative impact on nursing performance related to patient safety. These findings concur with the review by Hutchinson and Jackson [ 17 ] about patient-related consequences. They also reported similar outcomes such as medication errors, surgical errors, and failure to report clinical issues of concern resulting in adverse events. Additionally, Hutchinson and Jackson highlighted how open displays of workplace bullying could erode patients’ confidence in nurses’ capability and instances of how bullied nurses may, in turn, display hostile behaviors or non-emphatic care, resulting in poor patient satisfaction [ 23 ].

4. Discussion

Workplace bullying is a complex and dynamic social phenomenon that generates various definitions and concepts, making it hard to unify or standardize. Instead, our summary review compared nursing and non-healthcare literature to provide an overview of the various concepts and terms about workplace bullying, as shown in Table 6 [ 2 , 4 , 22 , 26 , 27 , 28 , 29 , 30 , 31 ].

Summary of concepts, terms, measurement tools, and theories for workplace bullying in nursing and non-healthcare literature.

Concepts/Terms	Examples
Sources	Management, leaders, peers, non-nursing colleagues, patients, and family members
Direction	Horizontal, lateral, and vertical
Manifestations	Incivility, disruptive behaviors, threats, mistreatment, hostility, bullying, abuse, aggression, violence, mobbing, sexual harassment
Forms	Covert behaviors (e.g., sabotage, withholding support) and overt forms (verbal and physical)
Measurement instruments
Theories

* more commonly used in nursing literature.

4.1. Prevalence and Trends of Workplace Bullying among Nurses

The prevalence rate of workplace bullying varies widely. Nevertheless, there is empirical evidence to show the widespread prevalence of workplace bullying in nursing across different countries and healthcare contexts when the data is considered collectively from the included systematic reviews. The review by Lever et al. [ 8 ] showed that the pooled workplace bullying prevalence among nurses is estimated at 26.3%, which was similar to the pooled prevalence rate of 22% as reported by a Korean-language systematic review that examined 23 nursing studies [ 32 ]. However, it was higher than the prevalence rate of 11 to 18%, as reported by a non-nursing systematic review and meta-analysis that extracted 86 studies from various industry fields [ 5 ]. The higher-than-average prevalence rate observed in the healthcare sector could be attributed to several factors, including the highly stressful environment faced by healthcare professionals around the world, availability of reporting systems, and greater staff willingness to recognize and report workplace bullying events [ 8 , 18 ].

A remarkable proportion of nurses in hospital settings have experienced workplace violence, with bullying being the most common. The international variation in workplace bullying prevalence could be due to differences in sample size, type of measurement used, organizational/service setting, and reporting culture [ 2 , 8 , 18 ]. We attributed the extreme prevalence rate, either too high or too low, to the following reasons: (1) poorly defined or inconsistent terms; (2) different measurement tools used to measure workplace bullying events; (3) under-reporting due to a lack of reporting system or fear of repercussions; (4) over-sensitive reporting. Therefore, researchers need to consider the study designs, socio-cultural, and organizational contexts when interpreting the prevalence rates. Additionally, it is good for researchers to consider measuring other indirect measures of workplace bullying, such as job satisfaction, intention to leave, etc.

4.2. Antecedents of Workplace Bullying among Nurses

Workplace bullying can stem from various triggering factors (antecedents) and develop through multiple sources. We identified at least five main types of antecedents. These five can be grouped under two main levels: individual and organizational antecedents ( Table 3 ). Although Johnson (2011) and Samnani and Singh (2012) have suggested the role of societal-level antecedents, such as the societal culture of individualism versus collectivism [ 29 , 33 ], we concurred with the findings by Karatuna et al. that both individual and organizational antecedents exert an overlapping but greater immediate effect on workplace bullying than societal cultures or norms [ 16 ]. This proposition can also be explained by two dominant workplace bullying doctrines: the work environment hypothesis and the individual-dispositions hypothesis [ 31 ]. It is important to note that these antecedents were not mutually exclusive, but reflect the dynamic and mutual interactions between situational and individual factors within the workplace [ 31 ]. The findings from this summary review were also consistent with other rigorous reviews in other fields [ 2 , 16 , 30 , 34 ].

4.3. Consequences of Workplace Bullying among Nurses

This summary review also shows that workplace bullying has many detrimental consequences, not only in terms of the health and well-being of nurses, but also patient safety. For example, Lever et al. reported 45 studies highlighting the mental and physical problems that have afflicted nurses who encountered workplace bullying [ 8 ]. These issues could lead to more staff taking sick leave and providing less-than-effective care at work. In addition, Hutchinson and Jackson found 30 studies demonstrating how workplace bullying reduces nurses’ work performance and productivity and prevents effective teamwork and communication [ 17 ]. This inevitably creates a negative and hostile work environment, leading to organizational consequences, such as reduced job satisfaction, increased intention to quit, and staff turnover/attrition rate, which inevitably leads to higher organizational costs due to recruitment and retention difficulties [ 14 , 23 ].

4.4. Strengths and Limitations of This Umbrella Review

This is the first summary review to synthesize an extensive body of systematic reviews about workplace bullying to the best of our knowledge. We conducted a comprehensive search strategy and critical appraisal of the published reviews under the Cochrane Overview of Reviews method. Ultimately, we generated a conceptual framework to help clinicians and researchers understand the extent of research underlying this topic ( Figure 2 ). However, this review is not without its limitations. First, we excluded several reviews that did not focus primarily on nurses, were published outside the last ten years, did not specify any systematic review methodology, or were published in non-English language [ 1 , 35 , 36 , 37 ]. We acknowledge that this could potentially result in the omission of several systematic reviews and their findings. Second, as we only included peer-reviewed journal publications, there is a possibility of publication bias, with studies reporting only positive results more likely to be published. These positive effects may be compounded in our included reviews [ 12 ]. Finally, we did not conduct a re-analysis of possible meta-analysis within the included reviews due to heterogeneity in measurement outcomes and study designs. This aspect may have limited the extent to which we could draw convincing conclusions about the review findings and any associations of variables within the conceptual framework.

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Conceptual Framework for Workplace Bullying among Nurses.

4.5. Implications for Further Research

Bullying is a social phenomenon that has been extensively studied within nursing and non-nursing literature. This review found that current studies over-utilized cross-sectional survey designs and generated varied and conflicting results in the literature, making it difficult to determine whether the key correlates of bullying are predictors, consequences, or both. For example, there were times when the occurrence of bullying caused a poor work environment or times when it became vice versa [ 4 , 16 ]. Based on the review, the associations between bullying and correlates are likely characterized by reciprocal relationships. This finding aligns with bullying as a dynamic social phenomenon [ 2 ]. Therefore, there is a need for more advanced study designs where one can also identify and determine directionality between variables based on individual contexts.

Next, there is a need to design robust and effective interventions to address workplace bullying. Although this summary review did not extract systematic reviews focusing on workplace bullying interventions, we observed only a few reviews that addressed this issue. Additionally, these reviews only retrieved a few studies that reported bullying intervention’s effectiveness, highlighting a lack of studies in this area [ 38 , 39 ]. To achieve this, clinicians could consider using advanced and sound methodological designs and a well-developed theoretical framework [ 2 ]. Experimental research designs or survey studies following the same individuals over several time points (e.g., diary studies or longitudinal studies with multiple measurement points) are also needed to provide better indications of causality and intervention effectiveness [ 38 , 39 ].

5. Conclusions

This summary review evaluated the prevalence, antecedents, and consequences of workplace bullying among nurses based on an extensive body of systematic reviews published between 2013 and 2021. Workplace bullying was reported to affect at least one-quarter of the nursing population, higher than in other professions. The huge variation in prevalence rates from 1 to 90% reported across different reviews could be attributed to socio-cultural differences, workplace differences, heterogeneity in study designs, and operationalization of terms and measurement tools. The review findings on the antecedents and consequences demonstrated the complex and overlapping dynamics in the relationships among different variables for workplace bullying. We synthesized the findings from the included reviews and proposed an integrative model to explain this phenomenon and serve as the basis for future research.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization: H.S.G. and S.H.; methodology: H.Z.; formal analysis: H.S.G. and H.Z.; writing—original draft preparation: S.H.; writing—review and editing: H.S.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Bullying at school and mental health problems among adolescents: a repeated cross-sectional study

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Associations between bullying at school and mental health problems

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OPINION article

1 Introduction

2 School bullying

3 Life education

4 Discussion: strategies for preventing school bullying from a life education perspective

4.1.1 Social-emotional learning programs

4.1.2 Peer support systems

4.1.3 Inclusive activities and clubs

4.1.4 Bullying prevention programs

4.1.5 Mindfulness and wellbeing practices

4.1.6 Self-awareness

4.1.7 Parental and community involvement

4.1.8 Counseling and support services

4.1.9 Curriculum integration

4.1.10 Teacher training

4.2 Implementation of life education to reduce students' stress, depression, and anxiety levels and prevent school bullying

4.2.1 Curriculum integration

4.2.1.2 Mindfulness and stress management

4.2.1.3 Resilience and coping skills

4.2.1.4 Emotional engagement

4.2.2 Supportive environment

4.2.2.2 Anti-bullying policies

4.2.3 Teacher training and support

4.2.3.2 Modeling positive behavior

4.2.4 Regular assessment and feedback

4.2.4.2 Student feedback

4.2.5 Promotion of positive peer relationships

4.2.5.2 Peer mentorship programs

4.2.6 Incorporating cultural sensitivity

4.2.6.2 Tailored programs

5 Reflections and conclusions

5.2 Conclusions

Author contributions

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A Review of Behavior-Based Interventions that Address Bullying, Aggressive, and Inappropriate Student Behavior during Recess

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Bullying: What We Know Based On 40 Years of Research

Literature Review of School Bullying 1 Literature Review of Bullying at Schools