Correlations of workload with personality traits,perceived negative and positive behaviors,and organizational factors: A meta-analysis

Abstract

Background

Excessive workload, a key workplace stressor, occurs when job demands exceed employees’ physical and mental capacities because of limited resources and time.

Objective

Through a meta-analysis, we examined the correlations of workload with personality traits, perceived negative and positive behaviors, and organizational factors.

Methods

We conducted a meta-analysis of 106 Taiwanese studies on workload published from 1999 to 2022.

Results

Workload was positively correlated with personality traits and organizational factors; individuals with certain traits or organizational environments experienced elevated workload. Furthermore, workload was positively correlated with perceived negative behaviors, such as job stress and burnout, but negatively correlated with perceived positive behaviors, such as job satisfaction and well-being. These results highlight the complex and multidimensional effects of workload on employees and organizations.

Conclusions

This study offers a comprehensive econometric analysis of workload in the Taiwanese context and underscores the importance of effective workload management in enhancing employee well-being and organizational efficiency. Practical recommendations include optimizing task allocation on the basis of individual personality traits, implementing supportive organizational policies, and fostering a positive work culture. Future studies should explore additional moderating variables and compare domestic and international data to gain deeper insights into workload dynamics.

Keywords

workload personality behavior organizations stress meta-research

Introduction

Advancements in automation and computer technology have markedly increased job complexity and diversity. Consequently, organizations have imposed stringent expertise requirements, prioritizing productivity and efficiency. Since the 1970s, researchers have paid increasing attention to the considerable effects of workload on employees and organizations.

Excessive workload is a major contributor to job stress and dissatisfaction globally. According to 2021 data from the Organisation for Economic Co-operation and Development,¹ the average annual working hours of employees vary substantially across countries. For example, employees in Japan work an average of 1607 h annually, whereas those in South Korea work 1915 h. Conversely, employees in China and Singapore work 2174 and 2237 h per year, respectively. Taiwan, where employees work an average of 2000 h, ranks above Japan and South Korea. These figures indicate a trend of long working hours in major Asian economies and highlight the intensive work culture prevalent in the region.

Overtime is also a major contributor to increased workload. A 2022 survey by Taiwan's Ministry of Labor revealed that 42% of employees work overtime, with employees working an additional 15 h per month on average. These findings align with data from the International Labour Organization, which indicate that >35% of workers in Asia regularly exceed recommended working hours, contributing to job stress and health risks. In a survey conducted among Taiwanese employees, workload was a major reason for job dissatisfaction (reported by 64.1% of all respondents), ranking third after low pay and limited career advancement opportunities. These results underscore the urgent need to address workload-related challenges, particularly those compromising employee well-being and organizational sustainability.

Excessive workload has major implications at both individual and organizational levels. Overwork is associated with job stress, emotional exhaustion, and turnover intentions at the individual level² and with reduced productivity and employee engagement at the organizational level. Job stress–related losses lead to a nearly 10% reduction in the global GDP annually, underscoring the economic ramifications of unaddressed workload.

Workload becomes a key workplace stressor when a job's demands exceed employees’ physical and psychological resources because they are given insufficient time and support to meet those demands. Although the negative effects of an excessive workload have been broadly recognized, the mechanisms underlying its influence—including personality traits, organizational factors, and perceived behaviors—remain underexplored. The majority of studies that have examined these factors have done so in isolation, leaving a gap in the understanding of how they interact and contribute to workload-related stress. Although multiple studies have highlighted the relationship between workload and various job-related outcomes, meta-analytic research focusing on this relationship in Taiwan remains scarce.

In this study, we utilized the meta-analytic procedures of Rosenthal³ to bridge the aforementioned gap by synthesizing the findings of 106 Taiwanese studies published between 1999 and 2022. Through a comprehensive meta-analysis, we explored not only the correlations between workload and various individual and organizational factors but also the underlying psychological and behavioral dynamics. This meta-analysis provides a comprehensive understanding of the complex relationships of excessive workload with personality traits, perceived negative and positive behaviors, and organizational factors. The findings can assist with the optimization of workload management practices in Taiwan. Our findings contribute to the literature by enhancing the understanding of workload dynamics in a specific cultural and organizational context.

Literature review

Although some of the theoretical references predate 2015, they were retained because they reflect the conceptual frameworks commonly used in the empirical studies included in this meta-analysis. To maintain consistency in coding, interpretation, and effect size comparison, the present study adopted these foundational models as the basis for synthesis.

Workload

Beehr et al.⁴ defined workload as a stressor that arises when an individual must complete an overwhelming number of tasks within a limited time frame, which leads to physical and mental fatigue. These situations may involve excessive work or excessively long working hours. Moray⁵ indicated that workload occurs when an individual must complete one or multiple tasks with limited resources. When job demands exceed available resources, performance may deteriorate. Empirical studies have reported that workload is a dynamic concept influenced by job demands, job complexity, and employee capabilities. Workload arises when job demands surpass an employee's capacity; excessive work or excessively long working hours with limited time and resources can result in physical and mental strain. Requiring an individual to complete additional work within a short time frame without adequate resources can lead to performance irregularities.

Moray⁶ integrated his own research with studies conducted from 1979 to 1988 and proposed four dimensions of workload: theoretical, physiological, behavioral, and subjective. These dimensions reflect the gradual evolution of workload from a singular resource-based perspective to a multidimensional concept. Studies have divided workload into three broad categories: the amount of work and number of things to do, time and the specific aspect of time considered, and the subjective psychological experiences of the worker. Spector and Jex⁷ indicated two dimensions of workload: tempo and amount. Cropley et al.⁸ divided workload into chronic internal and external categories; chronic internal workload involves excessive tasks and an inability to take breaks, whereas chronic external workload involves the use of mobile technology for work-related purposes during leisure. On the basis of these definitions, we defined workload as the state of physical and mental overload experienced by employees when they are unable to effectively complete assigned tasks at the required quality and quantity levels because of limited resources and time. Taiwanese researchers have explored workload across various themes, subjects, and variables, yielding diverse findings. An analysis of the Taiwanese articles included in the present study revealed four key factors associated with workload: personality traits, perceived negative behaviors, perceived positive behaviors, and organizational factors.

Empirical research has demonstrated the impact of faculty workload, occupational stress, and well-being across different educational and professional settings. In a study conducted at the University of Maryland School of Pharmacy, Lebovitz et al.⁹ analyzed different workload models among 28 peer institutions. In addition to significant variability in designs and implementation methods, they reported themes consistent with national findings that faculty workload models can reinforce inequities and reduce productivity, satisfaction, and retention. Additionally, in a study of 2302 Chinese university faculty members, Zhao et al.¹⁰ applied job demands–resources theory and reported that excessive teaching hours negatively affected well-being, whereas job resources such as decision participation and professional training had positive effects. They further reported that work stress and work–life balance mediate these relationships. In a study on the coping behaviors of 400 university students, Maskova¹¹ discovered that personal excellence, rather than academic achievement, had a protective effect against burnout. In a quasi-experimental study of 33 Korean university students, An and Kim¹² reported that a 10-week lifestyle redesign intervention had a positive effect on occupational participation and stress management, indicating the benefits of structured well-being programs. Taken together, these findings indicate that institutions must implement policies that mitigate workload stress and enhance well-being through resource allocation and structured support.

In a study on 891 Iranian teachers in secondary education, Ghasemi and Herman¹³ identified associations between counterproductive work behaviors and factors such as age, gender, class size, and working hours. They reported that younger male teachers in public schools exhibited higher levels of counterproductive work behaviors, with psychological demands increasing the degree of engagement in such behaviors. They also indicated that social support and decision latitude were protective factors against such behaviors. Additionally, in a study on burnout that involved 331 Portuguese faculty members during the COVID-19 pandemic, Castro et al.¹⁴ discovered that resilience, stress, depression, and sleep pattern changes significantly predicted burnout levels. In a review of longitudinal studies on Norwegian physicians, Oftung and Tyssen¹⁵ indicated that although the overall degree of occupational stress declined with experience, general practitioners experienced a fourfold increase in effort–reward imbalance between 2010 and 2019. Multiple studies have indicated that policy changes influence workload dynamics. For instance, in a study examining Dianping app data from 2020 to 2022, Zhao et al.¹⁰ explored China's double reduction policy and reported a decrease in language tutoring enrollment and an increase in sports training participation that were driven by shifts in training institutions and parental attitudes. The aforementioned findings underscore the need for evidence-based strategies to reduce occupational stress, promote well-being, and ensure sustainable work environments.

Correlation between workload and personality traits

In psychology, personality is often used to explain or predict behaviors. Scholars have defined personality as consistent and stable patterns of behaviors and thoughts that individuals exhibit when responding to stimuli in complex environments. Personality is also defined as a dynamic organization within an individual's psychological system, with unique patterns that shape adaptation to external environments and influence thoughts and behaviors. Additionally, personality is considered a collection of persistent traits and tendencies that can be used to differentiate individuals. The Big Five Model of Personality, commonly referred to as the OCEAN (openness, conscientiousness, extraversion, agreeableness, and neuroticism) model, is widely used as a fundamental framework for understanding and studying personality.

Taiwanese studies have revealed that employees with high levels of initiative and diligence perceive workload as a challenge and experience increased job satisfaction.¹⁶ These individuals are also more likely than others to engage in altruistic behaviors,¹⁷ turning obstacles into creative strengths and generating substantive positive outcomes.¹⁸ Furthermore, proactive individuals are more likely than others to offer constructive suggestions to break the status quo and improve organizational operations in the face of increasing workload.¹⁹ They also help mitigate workload-induced work–family conflicts²⁰ and regulate organizational citizenship behaviors.²¹ Evidence suggests that extraversion moderates the correlation between workload and job satisfaction.²² Neuroticism, which is significantly and positively correlated with workload, can make individuals feel overburdened.^23,24 A study on work-related stress among long-term home-care workers revealed positive correlations between workload and the following personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism.²⁵ Emotional intelligence moderates the correlation between job stress and turnover intentions.²⁶ Among civil servants, emotional intelligence moderates the correlation between workload and emotional exhaustion.²⁷ Among operating room nurses, emotional intelligence—particularly awareness of others’ emotions—is positively correlated with different dimensions of workload, which negatively affects work performance and frustration levels.²⁸ Adversary quotient and job stressors (e.g., workload, role ambiguity, and role conflicts) are negatively correlated with job satisfaction.²⁹ Maladaptive perfectionism and person–environment fit can regulate the correlation between perceived stress and work–family conflicts.³⁰ However, the Confucian principles of self-watchfulness, leniency, and sincerity, as outlined in The Doctrine of the Mean (Zhongyong), exert no moderating or mediating effect on the correlation between workload and job burnout.³¹

Numerous studies on workload have confirmed the importance of personality. Personality can serve as a mediating, influencing, or moderating factor, shaping individuals’ cognitive appraisal of stressors and thus their perceived stress levels. It also affects emotional and mental states. For example, when facing stress, task challenges, or novel experiences, neurotic individuals tend to experience greater worry and anxiety, whereas open individuals are more likely to respond with curiosity and positivity. High workload may amplify these personality traits, intensifying their effects on individuals’ perceptions and performance. Therefore, accounting for interindividual differences in personality is crucial for a comprehensive understanding of its effects on individuals. Accordingly, we proposed the following hypothesis:

Hypothesis 1: Workload is positively correlated with personality traits.

Correlation between workload and perceived negative behaviors

Affective Events Theory posits that work events directly or indirectly influence emotional responses, which in turn shape work attitudes and behaviors. For example, pleasant work events foster positive attitudes and behaviors, enhancing motivation and passion. Conversely, unpleasant work events may lead to dissatisfaction, resulting in negative attitudes and behaviors. Perceived work overload exerts detrimental effects on individuals; for example, it can exacerbate role conflicts, cause emotional exhaustion, elevate personal frustration, and increase physical injury risks. Chronic workload and persistent despondency contribute to burnout; when individuals perceive themselves as unable to meet job demands or manage workload, they may experience physical and mental imbalance and thus feel threatened, frustrated, or powerless. Positive and negative emotions shape not only employees’ individual work attitudes and behaviors but also those of their coworkers, creating ripple effects throughout the workplace.

Perceived negative behaviors can lead to avoidance and fear. Negative emotions such as anxiety, nervousness, anger, depression, sadness, pain, repression, and fatigue arise when individuals encounter difficulties, stress, or setbacks in personal or professional interactions. These emotions directly affect work behaviors, attitudes, physical health, and mental health. If left unaddressed, perceived negative behaviors can cause long-term physical and mental harm, affecting performance and achievements in both personal and professional domains.

Taiwanese studies have indicated that workload is significantly and positively correlated with job stress,^32–35 which results in exhaustion, burnout, and fatigue. Long working hours and high workload lead to fatigue.³⁶ Work overload is significantly and positively correlated with job burnout,^37–39 which can lead to a reduced sense of accomplishment and increased risk of career burnout.⁴⁰ Workload influences emotional exhaustion,⁴¹ with work tempo and volume exerting significant and positive effects on emotional exhaustion.²⁷ A study conducted during the COVID-19 pandemic observed a strong negative correlation between workload and turnover intentions.⁴² Remote work was found to regulate the positive correlation between workload and emotional exhaustion.⁴³ Increasing focus on work–life balance has intensified discussions on the correlation between workload and work–family or family–work conflicts. Workload has been identified to be a significant and positive predictor of work–family conflicts.^44–46 Furthermore, workload moderates the correlation between work policy and work–family conflicts; for example, when an organization imposes a light workload and offers paid maternity leave to its female employees, the employees perceive less work–family conflicts.⁴⁷ A higher level of perceived workload is associated with a higher level of work–family or family–work conflicts. Work–family conflicts have a complete mediating effect on the correlation between workload and job satisfaction, whereas family–work conflicts have a partial mediating effect.⁴⁸ Workload exerts positive effects on work–family and family–work conflicts.⁴⁹

The findings of the aforementioned studies suggest that perceived negative behaviors play pivotal roles in mediating, influencing, and regulating the effects of workload. Workload directly and indirectly affects employees’ physiological, psychological, and behavioral responses, resulting in nervousness, insecurity, anxiety, burnout, dissatisfaction, and even physical or mental health problems. These effects extend beyond individuals to their family members and organizations. Accordingly, we proposed the following hypothesis:

Hypothesis 2: Workload is positively correlated with perceived negative behaviors.

Correlation between workload and perceived positive behaviors

The term “positive” denotes affirmation and proactivity. Positive psychology has emerged as a major psychological branch that emphasizes individuals’ positive traits, emotions, and experiences and their effects on well-being, health, and productivity. Unlike traditional psychology, which often focuses on negative emotions such as sadness, fear, anger, and pain, positive psychology highlights the beneficial aspects of human experience, aiming to enhance happiness, well-being, and life satisfaction. Researchers have asserted that traditional psychology perpetuates elements of antihappiness philosophies by prioritizing the study of negative emotions while limiting the focus on positive emotions such as love, trust, and laughter. Most studies have primarily analyzed the effects of negative mental states on physical and mental health. However, positive emotions not only enhance well-being but also reduce disease risks. Positive emotions represent affirmative and pleasant responses to stimuli that individuals perceive as beneficial to themselves. These emotions extend beyond momentary happiness, exerting lasting effects. Positive emotions play various roles, such as promoting physical health, enhancing perseverance, fostering creativity, facilitating agile thinking, and cultivating resilience. These benefits help individuals cope with stress, improving work efficiency and productivity. The study of positive behaviors, particularly in relation to emotions, experiences, and traits, is an evolving field.

Taiwanese studies have reported a significant and negative correlation between workload and job satisfaction.^46,50,51 For example, among kindergarten teachers, a lower level of perceived workload is associated with a higher level of job satisfaction.⁵² However, other studies have indicated that workload is positively correlated with job performance^2,17,53,54 and well-being.⁵⁵ For example, high workload was determined to enhance job performance and well-being among workers in the Hsinchu Science Park.⁵⁵ Furthermore, high workload was found to be significantly correlated with workplace well-being among kindergarten teachers⁵⁶ and employees in the online game industry⁵⁷ but negatively correlated with workplace well-being among personnel of the Border Affairs Corps stationed at Taoyuan International Airport.⁵⁸

Regarding retention intentions and other perceived positive behaviors, a higher level of perceived workload is associated with a lower level of retention intention.^59–62 Evidence suggests that the concept of “independent self-construal” mitigates the effect of workload on work–family conflicts. Workload is positively correlated with a sense of purpose⁶³ and significantly and positively correlated with work engagement.⁵¹ A study conducted among pharmacists reported that workload moderated the effect of health-promoting lifestyles on work–life balance.⁶³ Workload was also noted to moderate the correlation between learning motivation and learning engagement among adults. A higher level of perceived workload was determined to be associated with a higher level of internal work quality among Taiwan's infantry personnel.⁶⁴

The findings of the aforementioned studies suggest that workload is predominantly negatively correlated with perceived positive behaviors and is mainly perceived as an adverse factor exerting unfavorable physical and mental effects. Studies have explored the positive effect of workload on perceived positive behaviors by investigating whether a reasonable workload can enhance employees’ motivation, work efficiency, work achievement, and fulfillment, thereby benefiting organizations. Dunk⁶⁵ demonstrated that under specific conditions, workload can function as a motivator, ultimately enhancing employee performance. Evidence suggests that particular levels of workload can promote creativity. Nevertheless, the correlation between workload and perceived positive behaviors remains largely negative. Accordingly, we proposed the following hypothesis:

Hypothesis 3: Workload is negatively correlated with perceived positive behaviors.

Correlation between workload and organizational factors

Organizations are an indispensable part of modern society. They comprise individuals working together to achieve common goals. Five major factors define an organization: social structure, which refers to the patterns and rules governing staff relationships; personnel, which refers to the individuals within the organization (members); objectives, which guide members in their tasks; technology, which includes techniques and resources introduced to enhance performance; and environment, which may directly or indirectly affect operations. The development and operations of an organization are influenced by several factors, particularly organizational culture. Organizational culture involves shared values, beliefs, customs, behavioral patterns, and attitudes that influence employees’ behaviors and attitudes, ultimately affecting organizational performance and efficiency. Organizational traits and processes influence job stress. Workplace structures and environments influence employees’ emotions and work attitudes, leading to job stress. Additionally, organizational climate contributes to various stress responses. Empirical studies have demonstrated that social support at work can prevent or alleviate the adverse effects of high job stress. This support includes emotional support and substantive assistance from colleagues, superiors, family, and friends, which enable employees to effectively manage job stress.

Leader–member exchange relationships are significantly and positively correlated with workload⁶⁶; thus, organizations with stronger leader–member exchange relationships may impose higher levels of workload.⁶⁷ Workload moderates the leader–member exchange relationship that involves contributions and compensation, leading to turnover intentions.⁶⁸ Perceived supervisor support reduces workload and enhances perceived justice within an organization.⁶² In jobs characterized by knowledge and social characteristics, higher levels of perceived workload reinforce the positive correlations of job characteristics with tasks and contextual performance.² The effect of workload on emotional exhaustion is stronger among civil servants responsible for tasks with higher job demands, such as skill diversity, job integrity, job importance, and job feedback.²⁷ In environments where employees’ rights and benefits are comprehensively protected, the effect of increased workload on stress is reduced.³² For example, firefighters with more rationalized work schedules experience lower levels of workload.⁶⁹ Among civil engineers, work overload reduces organizational commitment.⁷⁰ By contrast, among junior high school teachers, a higher level of perceived workload is associated with a higher level of organizational commitment.⁷¹ Workload exerts a significant and negative effect on relational psychological contracts; however, this effect can be mitigated by enhancing the intervention and positive support offered by such contracts.⁷² Chang et al.⁵³ asserted that when workload is perceived as a challenge, employees exhibit increased willingness to engage in altruistic behaviors; this finding highlights a positive correlation between workload and organizational citizenship behaviors. However, positive organizational citizenship behaviors can increase workload, and employees with stronger proactive personalities experience higher levels of workload²¹; this finding indicates a causal relationship between workload and organizational citizenship behaviors.

The findings of the aforementioned studies suggest that organizational factors play crucial roles in mediating, influencing, and regulating the effects of workload. The organizational environment significantly affects workload. Factors such as economic downturns, political instability, and pandemics pose challenges that affect operations and increase work pressure. To address these challenges, organizations must strengthen human resource management and prioritize employees’ physical and mental well-being to foster a healthy organizational culture and maintain sustainable competitiveness. Workload influences employee performance to varying degrees, ultimately affecting organizational outcomes. Employees’ perceived workload may be shaped by various organizational factors. Accordingly, we proposed the following hypothesis:

Hypothesis 4: Workload is positively correlated with organizational factors.

Methods

Research theme and scope

Considering the research objective, we confirmed the definition of “workload”—our research theme—and explored articles related to this theme. Articles highly relevant to the research theme were included in the analysis.

Data source

Relevant articles were collected from various online sources and digital academic databases, including the electronic resources of the National Tsing Hua University Library, Airiti Library, and the National Digital Library of Theses and Dissertations in Taiwan. To ensure comprehensiveness, we reviewed article bibliographies, abstracts, and citation indices, identifying as many relevant articles as possible and preventing the omission of vital studies. Thus, a total of 106 articles were collected: 39 were collected from books, journals, and periodicals published between 1999 and 2022, and 67 were theses and dissertations written between 2008 and 2022.

Article classification and selection criteria

The included articles were summarized and classified on the basis of the research theme, methods, and variables. Sample tables were prepared to clearly present the characteristics of the included studies and relevant data reported in corresponding articles. Because we sought to perform a meta-analysis, sufficient quantitative data—for example, sample sizes, mean ± standard deviation values, and correlation coefficients—were required to calculate effect sizes (ESs). Studies with data collection errors or significant statistical inaccuracies were excluded to avoid the inclusion of inappropriate literature and minimize the risk of bias.

Data collection

The included articles were tabulated and encoded on the basis of their original content. The following raw data were collected: code, author(s), theme(s), subject(s), literature type (periodical, book, thesis, or dissertation), and publication year. Moreover, the following data on research methods were collected: research design, sampling method, and study outcomes. The following descriptive data were collected: independent variables, dependent variables, sample sizes, and correlation coefficients.

Research framework

Data from the included articles were entered into a Microsoft Excel spreadsheet for sorting and encoding. The characteristics of each study were presented in a descriptive table, facilitating the collection of data on personality traits, perceived negative behaviors, perceived positive behaviors, and organizational factors. Then, the research framework was established accordingly.

ES calculation

To establish a threshold for ES comparisons and mitigate potential blind spots or biases arising from the researchers’ intuitive thoughts, we converted descriptive data (χ², t, F, and p values) into equivalent ESs. To determine the ES (r value) for each study and the mean ES, we performed a meta-analysis on the basis of degrees of freedom.³ The literature corresponding to each selection code was tallied, and ESs were calculated using this approach. Comprehensive Meta-Analysis software was used to interpret the results and provide insights for further research.

To mitigate potential publication bias and ensure that the analysis was not constrained by a limited availability of data, we generated a funnel plot displaying the distribution of the included studies; it helped identify potential gaps or biases in the analysis. A symmetric funnel plot indicates minimal biases, whereas an asymmetric funnel plot (skewness) indicates the need for further data review.

Considering the potential risk of bias toward published academic journals, we accounted for the possibility of ES overestimation. Thus, the fail-safe number was calculated to determine how many nonsignificant articles were required to overturn the significant results. If the fail-safe number exceeded the tolerance level (5N + 10, where N represents the total number of articles included in the meta-analysis), the nonsignificant articles were assumed to have no effect on the overall significance.

Hypothesis validation

We conducted a meta-analysis to determine the correlations between personality traits, perceived negative behaviors, perceived positive behaviors, and organizational factors and verify the validity of the proposed hypotheses. On the basis of the meta-analysis results, we provided recommendations for effective workload management and proposed future research directions.

Results

Correlation of workload with personality traits

A total of 43 studies were included. The homogeneity test was significant (Q = 517.302, p < .001; I² = 91.88%), indicating considerable heterogeneity; thus, a random-effects model was used. The pooled effect size (ES) was 0.110 (95% CI: 0.043–0.175), showing a small but significant positive correlation (Z = 3.239, p < .001). No publication bias was detected (fail-safe N = 1549 > 5N + 10). Hypothesis 1 was supported (see Table 1 and Figure 1).

Correlation of workload with perceived negative behaviors

From 92 studies, a significant heterogeneity was observed (Q = 6950.851, p < .001; I² = 98.69%), supporting the use of a random-effects model. The ES was 0.406 (95% CI: 0.354–0.455), indicating a moderate positive correlation (Z = 13.859, p < .001). Funnel plot analysis and fail-safe N (167,334 > 5N + 10) suggested no publication bias. Hypothesis 2 was supported (see Table 1 and Figure 2).

Correlation of workload with perceived positive behaviors

We analyzed 93 studies, with substantial heterogeneity (Q = 4149.253, p < .001; I² = 97.78%). A small negative correlation was found (ES = −0.080; 95% CI: −0.154 to −0.006; Z = −2.109, p < .05). No publication bias was detected (fail-safe N = 3844). Hypothesis 3 was supported (see Table 1 and Figure 3).

Correlation of workload with organizational factors

Among 40 studies, significant heterogeneity was found (Q = 2160.092, p < .001; I² = 98.20%). The ES was 0.156 (95% CI: 0.035–0.273), reflecting a modest positive correlation (Z = 2.515, p < .05). With a fail-safe N of 3,515, no publication bias was evident. Hypothesis 4 was supported.

Among the included studies, 56% reported participants’ industry type, while only 38% provided detailed age range information and 25% reported education level. Preliminary coding showed that studies from high-demand sectors (e.g., healthcare, education, and public safety) tended to report stronger correlations between workload and negative behavioral outcomes. While significant heterogeneity was observed across all meta-analytic models, subgroup analyses based on potential moderators such as industry type, age group, education level, and occupation type were not conducted due to insufficient reporting in a large proportion of the included studies. This limitation restricted our ability to conduct detailed moderator analyses across all dimensions. Future research should systematically code and report these demographic and contextual variables to enable meta-regression or subgroup analysis (see Table 1 and Figure 4).

Discussion

This meta-analysis investigated how excessive workload correlates with personality traits, perceived negative and positive behaviors, and organizational factors, using 106 Taiwanese studies published between 1999 and 2022. The results affirm that excessive workload has notable psychological and organizational consequences, though its impact is shaped by various contextual and individual-level factors.

Excessive workload and personality traits

The observed positive correlation between workload and personality traits indicates that individual differences influence how workload is perceived and managed. Employees with high levels of conscientiousness and proactiveness tend to frame heavy workload as a challenge, which promotes adaptation and persistence.^16,17 In contrast, those high in neuroticism experience increased stress when facing similar demands.^25,26

These findings support the mediating role of personality in stress appraisal and coping. Organizations should align task assignments with individual personality profiles and offer targeted support for emotionally vulnerable employees. Interventions such as resilience training and stress management programs could further reduce the psychological burden associated with workload.⁷³

Excessive workload and perceived negative behaviors

A strong positive correlation emerged between excessive workload and negative behaviors, such as job stress, burnout, emotional exhaustion, and work–family conflict.^28,33,42 Prolonged overwork contributes to fatigue, dissatisfaction, and heightened turnover intentions,^34,35 with negative effects often extending beyond the workplace into employees’ family and personal lives.^20,30

To mitigate these consequences, organizations should adopt workload management strategies, including flexible scheduling, job redesign, and monitoring systems.^39,47 Furthermore, building a supportive organizational climate through supervisor involvement and mental health resources can buffer the adverse effects of heavy workload.^46,56

Excessive workload and perceived positive behaviors

Excessive workload was moderately and negatively associated with positive behaviors such as job satisfaction and well-being.^31,73 Although moderate workload may enhance engagement, overwhelming demands tend to erode motivation, creativity, and psychological resilience over time.^63,74

This impact varies by profession. In high-intensity sectors like healthcare and technology, short-term productivity gains from increased workload may mask long-term burnout risks if recovery opportunities are insufficient.⁷⁵ To maintain sustainable performance, organizations should promote autonomy, recognize contributions, and nurture a culture that prioritizes work–life balance.⁷⁶

Excessive workload and organizational factors

Workload was positively correlated with organizational factors, indicating that culture, leadership, and structural conditions influence how employees experience and respond to workload.^26,68 Strong leader–member exchange and organizational citizenship behaviors were associated with increased workload, as engaged employees often take on additional responsibilities.⁶⁷ However, this dynamic may contribute to role overload and eventual burnout.

Organizational commitment can act as a protective factor. Employees who perceive fair workload distribution and strong institutional support report greater satisfaction and reduced turnover intentions.^49,60 These findings highlight the importance of transparent leadership, equitable task allocation, and proactive support systems in maintaining both individual well-being and organizational effectiveness.⁷¹

Conclusions and recommendations

Workload and personality traits

The effects of workload on individuals vary by personality traits. In general, highly responsible, pressure-resistant, adaptive, and self-demanding employees can endure high levels of workload while maintaining positive work attitudes and strong performance. Enhancing self-cognition, management capabilities, empathy, and self-efficacy in these employees can mitigate the negative effect of workload, thereby improving individual and team performance and enhancing individual work efficiency and quality of life.

Organizations should recognize and leverage employees’ personality traits to optimize human resource management. Understanding how employees cope with workload can help organizations assign tasks that align with employees’ strengths and preferences. Moreover, insights into employees’ coping mechanisms can facilitate workload management through appropriate task distribution and work environment adjustments, thereby fostering perceived positive behaviors such as cohesion, job identification, and job satisfaction. Consequently, employees exhibit improved job performance and leverage their strengths across tasks, contributing to organizational growth.

Workload and perceived negative behaviors

Increased workload exacerbates perceived negative behaviors such as stress, fatigue, burnout, emotional labor, work–family conflicts, job dissatisfaction, and workplace misconduct, ultimately intensifying turnover intentions. Prolonged work overload results in physical and mental fatigue and elicits negative emotions such as anxiety and depression. Overtime reduces personal and family time, straining familial relationships. To mitigate these adverse effects, employees can adopt personal coping mechanisms such as scheduling work, managing time, developing stress management skills, cultivating positive attitudes, improving physical resilience, and establishing support systems.

Excessive workload causes physical and mental exhaustion, reducing job performance, work quality, productivity, and competitiveness. It further leads to absenteeism, physical and mental health problems, and increased employee turnover, negatively affecting both organizational attractiveness and retention rates. To minimize employee stress and burden, organizations should monitor and regulate workload by optimizing work procedures and management practices, designing effective work structures, implementing efficient time management strategies, and allocating appropriate assignments, thereby providing employees with greater autonomy. These measures increase organizational commitment and investment, ultimately promoting both organizational and personal growth within an environment of sustained development.

Workload and perceived positive behaviors

Increased workload may gradually reduce employees’ job engagement, proactivity, passion, creativity, and innovation, thereby negatively affecting perceived positive behaviors such as job performance, job satisfaction, physical and mental health, quality of life, workplace well-being, and self-efficacy. Strengthening personal coping mechanisms can help employees reframe workload from a threat to a challenge that fosters positive experiences. Finding meaning in their work and recognizing their own value are key to employees maintaining positive work attitudes.

Organizations must assess and balance workload to ensure that it remains reasonable and manageable. This can be achieved by avoiding excessive assignments and prolonged working hours that undermine employees’ perceived positive behaviors and overall well-being. Establishing a positive work environment and culture fosters adaptability to evolving job demands and improves quality of life both at work and at home, resulting in sustained positive outcomes.

Workload and organizational factors

Employees can actively engage in organizational groups, interdepartmental collaborations, projects, employee services, and social interactions. These initiatives provide opportunities to effectively communicate needs, feedback, and expectations and strengthen connections within the organization. Participation in these initiatives can help employees share their ideas and obtain support and assistance, which in turn enhances their work efficiency and job satisfaction, ultimately improving work environment and culture.

Organizations can cultivate a supportive work environment by providing ample resources, clearly defining roles and objectives, assigning tasks on the basis of aptitudes, fostering a healthy management culture, streamlining workflows, promoting organizational commitment, enhancing policy alignment, respecting employees’ rights, implementing recognition and reward mechanisms, offering competitive compensation and benefits, providing professional psychological support, and encouraging physical and mental well-being initiatives. These measures enhance employees’ interest in their work and their sense of purpose, fostering high-quality subordinate relationships and strong organizational support. Consequently, employees perceive workload as a challenge rather than a burden; this approach encourages employees to excel in their work, promoting a healthy, safe, and positive work environment that counterbalances the negative effects of workload.

Future research directions

This meta-analysis explored the correlations of workload with personality traits, perceived negative behaviors, perceived positive behaviors, and organizational factors. Future studies should conduct additional meta-analyses on individual variables within each dimension as more data become available. Several variables and dimensions warrant further investigation—for example, the personality traits in the Big Five Model of Personality, proactiveness, job burnout, work–family conflicts, emotional exhaustion, organizational citizenship behaviors, job satisfaction, well-being, leadership behaviors, organizational commitment, and social support. We excluded demographic variables from the analysis. Future studies can analyze the effects of workload on employees across industries while accounting for demographic factors such as age, education level, organization type, organizational scale, and occupation type. Such analyses can reveal industries at risks of overburdening workers and inform targeted countermeasures.

Expanding the research scope and increasing sample diversity are key considerations for future studies on workload. The articles included in the present study were limited to theses, dissertations, and publications focusing on Taiwan. Future studies should include data from research projects funded by the National Science and Technology Council to provide more comprehensive insights. Our exclusive focus on Taiwan precluded the inclusion of international literature for comparison; this omission was due to resource constraints and national and cultural differences in workload perceptions. However, globalization and advancements in information technology have increased interest in transnational cultural studies. Future studies should thus incorporate international literature to increase sample diversity and analyze the effect of cultural differences on workload perception. Expanding sampling criteria to include diverse data sources, languages, and countries could yield valuable insights into regional and cultural variations in workload cognition, leading to more comprehensive academic and practical inferences.

Our study relied primarily on quantitative data collected through questionnaire surveys. However, these surveys have inherent limitations, such as respondent honesty, recall bias, and selection bias, which may skew the data and reduce reliability and validity. Thus, our sample may not accurately represent the target population, which limits the generalizability of our findings. Future studies should incorporate additional data collection methods, particularly qualitative approaches such as interviews, case studies, and focus groups, to supplement questionnaire surveys. Long-term observations and in-depth discussions of individual workload experiences can provide further insights into workload manifestations and associated factors. Incorporating qualitative research can enhance the breadth and depth of workload-related studies. Furthermore, a textual analysis of qualitative research findings, along with quantitative analyses, can facilitate comprehensive discussions and guide the development of effective workload measurement tools.

Limitations

Although this study provides valuable insights into the correlations between excessive workload, personality traits, perceived behaviors, and organizational factors, several limitations must be acknowledged. These limitations may influence the generalizability and validity of the findings and should be considered in interpreting the results.

First, this meta-analysis exclusively focused on 106 Taiwanese studies published between 1999 and 2022. Although this enabled a comprehensive overview of excessive workload within the unique sociocultural and economic context of Taiwan, it limits the generalizability of the findings to other regions. Workload perceptions and their effects may significantly differ across cultural and organizational settings, and therefore, comparative studies with international data are required to establish broader applicability.

Second, studies were selected from specific academic databases, including the National Digital Library of Theses and Dissertations in Taiwan and Airiti Library. This may have introduced selection bias because these sources primarily include peer-reviewed journal articles, dissertations, and theses; consequently, this study did not consider relevant industry reports, government publications, and unpublished studies. Future studies should expand their data sources to ensure a more comprehensive representation of workload-related research.

Third, although this study synthesized findings from 106 studies, we acknowledge limitations in the methodological quality of the included works. While most studies employed standardized measurement instruments for workload and related variables (e.g., validated job stress or job satisfaction scales), few explicitly reported psychometric properties such as reliability coefficients. In addition, the risk of bias was not systematically assessed in the primary studies, and some did not report sample representativeness or response rates.

To enhance methodological transparency, we conducted a brief appraisal of study quality using adapted criteria from AMSTAR 2 and PRISMA guidelines, tailored for non-clinical meta-analyses. Key items assessed included (1) clarity of research objectives, (2) use of validated measurement tools, (3) appropriateness of statistical analysis, and (4) completeness of data reporting. Approximately 78% of the included studies met at least three of these four quality indicators. Nonetheless, variations in study design, measurement consistency, and reporting detail may have introduced heterogeneity and affected effect size estimations. Future meta-analyses would benefit from applying a full quality appraisal checklist to further minimize methodological bias.

Fourth, we utilized the meta-analytic procedures of Rosenthal³ to synthesize findings. This approach involved converting different statistical measures (e.g., χ², t, F, and p) into effect sizes. Although this approach enables comparison of diverse studies, variations in research methodologies, sample populations, and measurement instruments may have introduced heterogeneity into the results. In the current study, a random-effects model was applied to account for these differences. However, the potential influence of unmeasured moderating variables, such as industry type, job role, and demographic factors, should be further investigated.

Fifth, although this meta-analysis identified significant correlations between workload and various psychological and organizational factors, the cross-sectional nature of the included studies prevented causal inferences from being drawn. The relationships observed in this study may have been influenced by additional variables, such as job autonomy, leadership style, or external economic conditions, which were not explicitly analyzed. Therefore, further longitudinal and experimental studies are required to better understand the causal mechanisms and moderating factors underlying workload dynamics.

Finally, although this study revealed key findings, its practical applications require further validation in real-world settings. Organizations seeking to implement workload management strategies should consider industry-specific variations and employee differences with respect to coping mechanisms. Future studies should explore intervention-based approaches to workload reduction and evaluate their effectiveness in improving employee well-being and organizational efficiency.

This study lays a foundation for future research seeking to expand on workload-related meta-analyses by incorporating a broader range of studies, cross-cultural comparisons, and methodological advancements to enhance the robustness of the findings.

Figure 1.

Funnel plot for the correlation of workload with personality.

Figure 2.

Funnel plot for the correlation of workload with perceived negative behaviors.

Figure 3.

Funnel plot for the correlation of workload with perceived positive behaviors.

Figure 4.

Funnel plot for the correlation of workload with organizational factors.

Table 1.

Key factors associated with workload in Taiwanese employees.

Variables	Sample size	Effect size	95% confidence interval		Z value	Homogeneity test		Fail-safe number
Variables	Sample size	Effect size	Lower limit	Upper limit	Z value	Q value	I ²	Fail-safe number
Personality traits	43	0.110	0.043	0.175	3.239**	517.302***	91.881	1549
Perceived negative behaviors	92	0.406	0.354	0.455	13.859***	6950.851***	98.691	167,334
Perceived positive behaviors	93	−0.080	−0.154	−0.006	−2.109*	4149.253***	97.783	3844
Organizational factors	40	0.156	0.035	0.273	2.515*	2160.092***	98.195	3515

*p < .05, **p < .001, and ***p < .0001.

Footnotes

ORCID iD

Yin-Che Chen

Ethical considerations

As this study is a meta-analysis of previously published data, it does not involve direct interaction with human subjects or access to identifiable personal information. Therefore, ethical approval was not required.

Informed consent

As this research involved the synthesis of data from previously published studies and did not include the collection of new data from human participants, informed consent was not required.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

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