research paper of employee turnover

Assessing the Effects of Workplace Contextual Factors on Turnover Intention: Evidence from U.S. Federal Employees

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• Published: 17 June 2024

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• Yongbeom Hur   ORCID: orcid.org/0000-0001-9480-2917 1  

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This study delves into the crucial topic of turnover intention among U.S. Federal Employees, shedding light on the workplace factors that play a pivotal role in mitigating this issue. Through the utilization of Ordinary Least Squares (OLS) regression analyses, the study meticulously examines various workplace contextual factors and their impact on turnover intention. The standout finding of the study underscores the paramount importance of opportunities for growth and development (Factor 4) in reducing turnover intention. This emphasizes the significance of investing in employees’ professional advancement and creating avenues for them to expand their skills and progress in their careers. Moreover, the study brings to the forefront additional factors that contribute to decreased turnover intention, such as longer tenure, higher salary, gender (with females exhibiting lower turnover intention), and age. These insights not only provide a nuanced understanding of the dynamics at play but also offer actionable strategies for organizations to address turnover concerns effectively. By identifying these key factors, the study equips HR professionals and decision-makers with valuable guidance for implementing tailored HRM practices aimed at curbing turnover and fostering a more stable and satisfied workforce within the U.S. Federal sector. Ultimately, these findings have the potential to drive tangible improvements in employee retention and organizational performance.

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Introduction

Employee turnover imposes significant costs on organizations. Upon an employee’s resignation, the organization must navigate a protracted process involving recruiting, hiring, and training a replacement—an undertaking accompanied by substantial tangible expenses (Moynihan & Landuyt, 2008 ; Tziner & Birati, 1996 ). In addition to the quantifiable costs linked to this replacement cycle, voluntary turnover brings about intangible costs including loss of knowledge, decreased customer satisfaction, disruptions in organizational performance, and diminished morale among remaining employees (Dess & Shaw, 2001 ; Felps et al., 2009 ; Hausknecht et al., 2009 ). Considering these multifaceted costs, it is understandable that scholars and practitioners have given particular attention to comprehending the reasons behind employee departures since the early 20th century (Hom et al., 2017 ).

Since the pioneering work of March and Simon in 1958, researchers have strived to predict voluntary turnover using various models. A recent meta-analysis of studies on voluntary turnover studies (Holtom et al., 2008 ) show that more than 50 broad antecedents have been recognized as scientifically valuable predictors of voluntary turnover. While factors such as personality, job satisfaction, job characteristics, and job stress have traditionally been extensively studied in turnover research, the organizational context, including aspects such as organizational culture and employees’ working environment, has only recently garnered recognition from researchers (Holtom et al., 2008 ; Hom et al., 2017 ). The term “context” can be defined as “stimuli and phenomena that surround and exist in the environment external to the individual” (Mowday & Sutton, 1993 ).

Given the pivotal role of context in studies of organizational behavior, it is noteworthy that researchers have not adequately delved into the impact of contextual factors on voluntary turnover. Unlike existing turnover studies, which mainly focus on specific contextual factors like organizational justice (Hemdi & Nasurdin, 2007 ), trust (Jabeen & Isakovic, 2018 ), and organizational culture (Campbell & Im, 2016 ), this study undertakes a more comprehensive examination and endeavors to address a significant gap in the literature by examining the combined influence of various workplace characteristics on turnover intention. Through a comprehensive review of workplace contextual factors and their effects on turnover intention, this study aims to reduce undesired voluntary turnover by gaining a nuanced understanding of the broader organizational context.

Theoretical Backgrounds

Turnover is defined as the extent of individual movement across the membership boundary of a social system (Price, 1977 , P. 3). When an employee decides to cross the membership boundary of an organization, it is termed voluntary turnover, whereas involuntary turnover occurs when an employer makes this decision, such as through firing or layoffs. This study specifically centers on voluntary turnover, with turnover intention serving as a proxy for this phenomenon. Turnover intention refers to an individual’s thoughts about leaving their current organization. Although turnover intention may not always lead to actual turnover, research suggests a substantial relationship between the two (Park & Shaw, 2013 ), with turnover intention frequently regarded as an alternative measure for actual turnover (Price, 2001 ). Indeed, turnover intention has emerged as a widely acknowledged proxy for actual voluntary turnover in studies conducted in both the public and private sectors (Bertelli, 2007 ; Caillier, 2011 ; Cho & Lewis, 2012 ; Cohen et al., 2016 ; Moynihan & Landuyt, 2008 ; Pitts et al., 2011 ).

Determinants of Voluntary Turnover

The cost of replacing a departing employee is estimated to be approximately twice their annual salary (Allen et al., 2010 ). This significant expense has motivated researchers’ efforts over the past century to understand the reasons behind employee departures (Hom et al., 2017 ). Resulting in over 1,500 academic studies, these investigations have identified 50 broad antecedents of voluntary turnover (Holtom et al., 2008 ). Determinants of voluntary turnover are typically categorized into external environmental, work-related organizational, and individual characteristic factors (Mobley et al., 1979 ). External environmental factors encompass perceived alternatives (such as job availability) and the unemployment rate (e.g., Anderson & Milkovich, 1980 ; Carsten & Spector, 1987 ; Fields, 1976 ; Lee et al., 2017 ). Research indicates a positive correlation between employees’ turnover and turnover intention with job availability and a negative correlation with the unemployment rate (Peters et al., 1981 ). Work-related organizational factors include various aspects like job satisfaction (Medina, 2012 ), development and growth opportunities (Weng et al., 2010 ), promotion (Yücel, 2012 ), pay (Irvine & Evans, 1995 ), perceived autonomy (Gillet et al., 2013 ), goal clarity (Davis & Stazyk, 2015 ), and job stress (Arshadi & Hayavi, 2013 ). In general, turnover or turnover intention tends to decrease when employees experience high job satisfaction, ample development and growth opportunities, increased promotion prospects, better compensation, greater autonomy, clearly defined goals, or minimal job stress (Griffeth et al., 2000 ). Individual characteristic factors encompass demographic variables such as gender (Sabharwal, 2015 ), education level (Lambert et al., 2001 ), tenure (Um & Harrison, 1998 ), race (Jones & Harter, 2004 ), age (Emiroğlu et al., 2015 ), and marital status (Kim et al., 2012 ). Typically, turnover or turnover intention tends to decrease when employees are female, less educated, long-tenured, white, older, or married (Griffeth et al., 2000 ).

Remarkably, the exploration of job turnover in public sector settings did not receive significant attention until the new millennium (Lee & Jimenez, 2011 ). Determinants of turnover for public employees have been identified across various areas, including job characteristics (Kim, 2012 ), human resource management practices (Tinti et al., 2017 ), person-organizational fit (Ballinger et al., 2016 ), and public service motivation (Bright, 2007 ).

Contextual Factor Studies

The organizational context has only recently garnered attention from turnover researchers, despite its significant impact on the occurrence and interpretation of organizational behavior (Rubenstein et al., 2018 , p. 38). Traditionally, turnover researchers have focused on examining the effects of selected contextual factors on voluntary turnover, considering both the organizational context level and the person-context interface (Holtom et al., 2008 ; Rubenstein et al., 2018 ). Factors at the organizational context level that have captured turnover researchers’ attention include organizational support (Amarneh et al., 2021 ), engagement aggregated (Harter et al., 2003 ), organizational citizenship behavior (Mallick et al., 2014 ), organizational size (Guan et al., 2014 ), and diversity level (Choi, 2013 ). Generally, researchers have found that turnover or turnover intention decreases when employees experience high organizational support, exhibit high engagement, engage in organizational citizenship behaviors, work in larger organizations, and experience lower levels of diversity.

Person-context interface factors encompass elements such as offered rewards (Miao et al., 2013 ), organizational justice (George & Wallio, 2017 ), trust (Ellickson & Logsdon, 2001 ), and organizational culture (Egan, 2008 ). Researchers generally found that employees’ turnover or turnover intention decreases when employees are satisfied with rewards, experience justice and trust in their organization, or when a positive organizational culture (e.g., high-performing or learning culture) prevails.

Workplace and Demographic Factors

The study employed the Merit Principles Survey 2016 Data, providing a comprehensive perspective on various workplace aspects. Utilizing exploratory factor analysis, we grouped 20 workplace variables into four distinct factors. Further elaboration on the methodology of the factor analysis will be provided in the subsequent methods section. In this section, our focus shifts to proposing hypotheses centered around each of the identified factors and demographic variables, which will serve as focal points in our regression analyses.

Workplace Factors

Happy and innovative working climate (factor 1).

Studies suggest that when employees feel valued, their inclination to leave decreases (Mancuso et al., 2010 ). For example, expatriate educators are more inclined to stay in their current positions when they experience a positive work environment, characterized by recognition from both peers and management (Odland & Ruzicka, 2009 ). An innovative work environment encourages employees to explore fresh ideas, nurturing a heightened sense of psychological empowerment and job satisfaction (Hsu & Chen, 2017 ). As a result, an innovative work environment has been linked to a reduction in turnover intention (Yeun, 2014 ).

H1: Employees working in happy and innovative working climate tend to have a low level of turnover intention.

Feeling Valued and Trusted (Factor 2)

Employees who perceive low support are less likely to feel valued, and this has been linked to higher turnover, particularly in the retail sector (Shanock & Eisenberger, 2006 ). The sense of not feeling valued is identified as a significant factor contributing to voluntary turnover among U.S. child welfare employees (Nittoli, 2003 ). Conversely, when employees feel trusted, they experience higher levels of autonomy, fostering a desire to remain in their current organizations (Dirks & Skarlicki, 2004 ).

H2: When employees feel valued and trusted, they tend to have a low level of turnover intention.

Coworker Support and the Spirit of Camaraderie (Factor 3)

The relationship with coworkers and their support play a crucial role in determining organizational departure (Feeley, 2000 ). Employees who maintain positive relationships with coworkers and receive support from them exhibit lower turnover intentions (Young, 2015 ). Furthermore, a strong sense of camaraderie also diminishes turnover intentions (Lopes Morrison, 2005 ).

H3: When the spirit of camaraderie exists in an organization and employees receive support from coworker, they tend to have a low level of turnover intention.

Opportunities for Growth and Development (Factor 4)

Employees often depart organizations in pursuit of better growth and development opportunities (Quarles, 1994 ). For instance, the availability of career advancement prospects within current organizations fosters organizational commitment, potentially reducing turnover intentions among employees of public accounting firms (Nouri & Parker, 2013 ).

H4: When employees are satisfied with opportunities for growth and development, they tend to have a low level of turnover intention.

Demographic Factors

In this study, various demographic factors were included in regression analyses, revealing that turnover intention generally decreases due to certain demographic factors. Employees with longer tenure (Griffeth et al., 2000 ), a managerial status (Farris, 1969 ), higher salary (Sturman & Trevor, 2001 ), an older age (van Hooft et al., 2021 ), union membership or a teleworker status (Peters et al., 2004 ) typically exhibit lower levels of turnover intention. Conversely, turnover intention tends to increase among female employees (Karatepe et al., 2008 ), racial minorities (Xue, 2015 ), or those with higher levels of education (Choi, 2006 ).

H5-1: Employees with longer tenure show a lower level of turnover intention than employees with shorter tenure.

H5-2: Employees with higher managerial status show a lower level of turnover intention than employees with lower managerial status or non-managerial status.

H5-3: Employees with higher salary show a lower level of turnover intention than employees with lower salary.

H5-4: Employees with older age show a lower level of turnover intention than employees with younger age.

H5-5: Employees with a labor union membership show a lower level of turnover intention than employees without a labor union membership.

H5-6: Teleworkers show a lower level of turnover intention than non-teleworker.

H5-7: Female employees show a higher level of turnover intention than male counterparts.

H5-8: Employees with racial minority status show a higher level of turnover intention than employees with racial majority status.

H5-9: Employees with more education show a higher level of turnover intention than employees with less education.

This study employed ordinary least squares (OLS) regression analyses to investigate the influence of various workplace characteristics on turnover intention. Before conducting the OLS regression, exploratory factor analysis was utilized to uncover underlying factors among the 20 workplace variables. The data for this study were obtained from the Merit System Protection Board (MSPB)’s 2016 Merit Principles Survey (MPS) dataset ‘path 2.’ The sample consisted of 14,473 full-time civilian federal employees from 24 federal agencies, with a response rate of 38.7% for the dataset ‘path 2’ (Merit System Protection Board, 2016 ).

Major Variables

Dependent variable

In this study, turnover intention was used as the dependent variable. While turnover intention may not perfectly align with actual turnover, a strong correlation between the two exists (Belkhir, 2009 ), and turnover intention has remained a key focus in turnover research (Cohen et al., 2016 ). Consequently, turnover intention frequently acts as a proxy measure for actual turnover in public administration literature (Moynihan & Landuyt, 2008 ), as well as in general turnover studies (Griffeth et al., 2000 ). In the 2016 MPS dataset, survey participants were asked to indicate their agreement level regarding the possibility of transitioning to a different occupation or line of work, with responses rated on a scale from 1 (strongly disagree) to 5 (strongly agree).

Independent variables

Various aspects of the workplace for federal employees served as independent variables in this study. Survey participants were asked to express their level of agreement on diverse workplace variables, rated on a scale from 1 (strongly disagree) to 5 (strongly agree). To identify underlying dimensions among the 20 workplace variables, an exploratory factor analysis was conducted. In determining the number of factors, both eigenvalues and an eigenvalue scree plot were considered, as suggested by methodologists (Ferguson & Cox, 1993 ; Hayton et al., 2004 ). While one factor exhibited an eigenvalue greater than one (see Appendix Table 4 ), the eigenvalue scree plot indicated that the slope of the graph did not change significantly after the fourth factor (see Appendix Fig. 1 ). Following the scree test guideline (DeCoster, 1998 ; Yong & Pearce, 2013 ), it was recommended to retain all factors until the slope displayed minimal change. Consequently, this study identified four factors. Further details regarding the factor analysis can be found in Appendix . After the factor analysis, appropriate names were assigned to the extracted factors, acknowledging that the given names may not fully encapsulate the meaning of all component variables in each factor. The four attained factors, along with their component variables, are outlined below. The meanings of the component variables in each factor are detailed in Appendix Table 5 .

Factor 1 (happy and innovative working climate): w9, w10, w11, w12, w14, w16, w17

Factor 2 (feeling valued and trusted): w2, w4, w5, w6, w7, w8

Factor 3 (coworker support and the spirit of camaraderie): w1, w3, w8, w13, w15

Factor 4 (opportunities for growth and development): w19, w20

The internal consistency or reliability of these four factors was evaluated by computing Cronbach’s alpha values. The Cronbach’s alpha values for all four factors fell within the range of 0.85 to 0.95 (refer to Appendix Table 5 ). According to the standards for internal consistency (Cronbach, 1951 ; Nunnally, 1978 ), a set of variables is considered to have satisfactory internal consistency or reliability when the Cronbach’s alpha value exceeds 0.7.

Demographic variables

Nine demographic variables were utilized in this study, with numbers assigned to each variable as follows.

Years with current agency (d2)

1: 3 years or less, 2: 4 years or more,

Supervisory status (d4)

1: non-supervisor, 2: team leader, 3: supervisor, 4: manager, 5: executive

Union membership (d8)

0: non-union membership, 1: dues-paying union membership

Salary level (d10)

1: $74,999 or less, 2: $75,000-$99,999, 3: $100,000-$149,999, 4: $150,000 or more

Racial minority (d11)

0: non-minority, 1: minority

Gender (d12)

0: male, 1: female

Age group (d15)

1: 39 and under, 2: 40 and over

Education level (d16)

1: less than AA degree, 2: AA or BA degree, 3: graduate degree

Teleworker status (d18)

0: non-teleworker, 1: teleworker

Descriptive Statistics and Correlations

Mean values and standard deviations for both workplace and demographic variables are presented in Tables 1  and 2 . Table 1  demonstrates that all correlations between workplace variables were significant and positive ( p  <.01), and likewise, all correlations between workplace variables and turnover intention were significant and negative ( p  <.01). In essence, turnover intention tends to decrease when employees express agreement or strong agreement with various workplace variables.

On average, federal employees did not demonstrate a high level of turnover intention (mean = 2.33 out of 5). Among the 20 workplace variables, the top five variables with which employees reported a high level of agreement were as follows: “I understand how I contribute to my agency’s mission” (w18, mean = 4.17), “My judgment is trusted and relied on at work” (w6, mean = 3.93), “I feel needed and depended on at work” (w4, mean = 3.88), “I like the quality of relationships I have with my coworkers” (w13, mean = 3.82), and “I feel comfortable being myself at work” (w17, mean = 3.78). It is worth noting that three of these variables (w18, w6, and w4) belong to Factor 2 (Feeling valued and trusted), while none are from Factor 4 (Opportunities for growth and development).

Conversely, the bottom five variables with which employees reported a low level of agreement were: “I am able to share my true thoughts and feelings at work” (w11, mean = 3.38), “I feel encouraged to try new things in my work” (w10, mean = 3.43), “I feel fully appreciated at work” (w9, mean = 3.44), “There is a culture of openness and support for new or different perspectives in my work unit” (w3, mean = 3.45), and “I feel cared about personally at work” (w12, mean = 3.46). Remarkably, four of these variables (w11, w10, w9, and w12) belong to Factor 1 (Happy and innovative working climate).

In Table 2 , it is noted that, on average, survey participants remained in their current agencies for more than 4 years and held positions as team leaders or higher-level managers (mean = 2.25). Moreover, 15% of survey participants were union members, 33% were racial minorities, 42% were female, and 56% had the option to telework. Additionally, the average salary range fell between $75,000 and $150,000, the average age exceeded 40 years, and the typical education level was an associate’s (AA) or bachelor’s (BA) degree.

Workplace Variables, Demographics, and Turnover Intention

OLS regression analyses were conducted to determine which aspects of workplace characteristics contribute to decreased turnover intention and how demographic variables influence turnover intention. Variance inflation factor (VIF) was assessed during regression runs to detect potential multicollinearity issues arising from high correlations among workplace variables (see Table 1 ). Severe multicollinearity in regression analysis can compromise the statistical significance of each independent variable and render the results unreliable (Mansfield & Helms, 1982 ). However, the average VIF was found to be 2.79. According to suggested guidelines (Mansfield & Helms, 1982 ; Miles, 2005 ), multicollinearity is not a concern in regression analysis if the average VIF value is below 10.

The regression analyses began with only demographic variables. As depicted in Model 1 (Table  3 ), turnover intention decreased as salary increased (coefficient: − 0.170, p  <.001), as employees aged (coefficient: − 0.094, p  <.05), or when employees had the option to telework (coefficient: − 0.057, p  <.05). Conversely, turnover intention increased if employees were union members (coefficient: 0.102, p  <.01) or belonged to racial minorities (coefficient: 0.343, p  <.001).

In Model 2 (Table  3 ), the four workplace variable factors (consisting of 20 variables) were incorporated with the demographic variables from Model 1. Notably, more demographic variables demonstrated significant effects on turnover intention in this model. In addition to the demographic variables that showed significant effects in Model 1, turnover intention decreased with longer tenure at current agencies (coefficient: − 0.096, p  <.05), or among female employees (coefficient: − 0.059, p  <.05). Conversely, turnover intention increased when employees held higher managerial status (coefficient: 0.051, p  <.001). However, telework status and education level did not exhibit a significant effect on turnover intention.

In summary, out of the nine hypotheses regarding the effects of demographic variables, four were supported. Longer tenure (H5-1), higher salary (H5-3), and older age (H5-4) were associated with lower turnover intention, while racial minority status (H5-8) was linked to higher turnover intention. The effects of higher managerial status (H5-2), union membership (H5-5), and being female (H5-7) contradicted the predictions in the hypotheses. Unexpectedly, turnover intention increased when employees held higher managerial status or were union members, but decreased when employees were female. These unexpected findings will be discussed in the last section.

Regarding the effects of workplace variables, Model 2 revealed that three out of seven variables in Factor 1, three out of six variables in Factor 2, one out of five variables in Factor 3, and two out of two variables in Factor 4 had significant and negative effects on turnover intention. In other words, turnover intention significantly decreased when employees agreed or strongly agreed with those workplace variables. Overall, H1 and H2 were partially supported, H3 was not supported, and H4 was supported. Specifically, turnover intention significantly decreased when employees agreed or strongly agreed with Factor 4 (Opportunities for growth and development), whereas turnover intention was not affected by most variables in Factor 3 (Coworker support and the spirit of camaraderie). Among Factor 1 variables, turnover intention significantly decreased when employees agreed or strongly agreed with “I feel fully appreciated at work” (w9), “I feel comfortable talking to my supervisor about the things that matter to me at work” (w14), and “I feel comfortable being myself at work” (w17). For Factor 2 variables, turnover intention significantly decreased when employees agreed or strongly agreed with “My judgment is trusted and relied on at work” (w6), “I feel valued at work” (w7), and “I understand how I contribute to my agency’s mission” (w18).

Managerial Implications and Contributions to the Literature

This study identifies specific workplace factors that significantly influence turnover intention. Factor 4, which pertains to opportunities for growth and development, emerged as the most influential, indicating that investing in employee development can significantly reduce turnover intention. Conversely, Factor 3, related to coworker support and camaraderie, showed the least impact. This suggests that while social support is important, it may not be as influential in retaining employees as opportunities for professional growth. HR managers can use these findings to tailor their strategies and initiatives to address turnover. Emphasizing and enhancing opportunities for growth and development, such as training programs, career advancement paths, and skill-building opportunities, can be particularly effective in reducing turnover intention. Additionally, fostering a positive and innovative working climate (Factor 1) and ensuring employees feel valued and trusted (Factor 2) are also crucial for mitigating turnover intention.

This study also highlights the influence of demographic variables on turnover intention, including tenure, salary, age, race, managerial position, union membership, and gender. Understanding these demographic trends can help managers identify at-risk groups and implement targeted retention strategies. For example, recognizing that employees promoted to higher managerial positions may experience increased turnover intention due to heightened stress can guide the design of leadership development and support programs. In summary, this study underscores the importance of considering diverse workplace factors and demographic variables in understanding and addressing turnover intention. By leveraging these insights, organizations can develop targeted HRM practices that foster employee engagement, satisfaction, and retention.

The findings of this study make a significant contribution to the existing literature on turnover intention and workplace factors. The study fills a gap in the turnover literature by examining a wide array of organizational contextual factors that have not received significant attention previously. Unlike prior studies that often focused on specific factors such as organizational justice or culture, this research considers all contextual factors simultaneously. This holistic approach provides a comprehensive understanding of how various organizational elements interact to influence employee turnover intentions. By identifying which contextual factors have a more pronounced effect on turnover intention, the study offers valuable insights for organizations seeking to implement targeted retention strategies. For example, it highlights the significant impact of opportunities for growth and development (Factor 4) in reducing turnover intention. Understanding the relative importance of different factors can help organizations allocate resources more effectively to address turnover. While acknowledging the need for caution in interpreting findings, the study’s comprehensive approach to examining workplace factors and turnover intention enhances the generalizability of its results. By considering a diverse range of demographic variables and organizational contexts, this study provides insights that can be applicable across various industries and settings.

Discussion and Conclusion

This study aimed to identify workplace factors that could potentially alleviate turnover intention. Initial correlation analyses suggested a general inverse relationship between turnover intention and various workplace factors. However, further examination through Ordinary Least Squares (OLS) regression analyses revealed that turnover intention was primarily influenced by Factor 4 (opportunities for growth and development), while Factor 3 (coworker support and camaraderie) had the least impact. Additional findings from the OLS regression analyses indicated that Factor 1 (a positive and innovative working climate) and Factor 2 (feeling valued and trusted) exerted a partial influence on turnover intention. These results emphasize the nuanced interplay of workplace elements in shaping employee attitudes toward turnover.

This study also delved into the impact of major demographic variables on turnover intention. Consistent with existing literature, employees with longer tenure, higher salaries, or older age exhibited a lower level of turnover intention, while those from racial minority backgrounds displayed a higher level of turnover intention. However, certain findings contradicted initial hypotheses. Contrary to the prediction in hypothesis H5-2, turnover intention increased when employees were promoted to higher managerial positions. Despite the general tendency for managers to stay in their current organizations due to higher investment (Farris, 1969 ), the study suggests that the heightened stress associated with elevated managerial roles, as indicated by Cavanaugh et al. ( 2000 ), may outweigh the perceived value of their investment, potentially leading to resignation. Additionally, turnover intention showed an unexpected increase among union members, contrary to the prediction in hypothesis H5-5. This finding supports an argument that union members typically experience lower job satisfaction than non-members (Hammer & Avgar, 2017 ). Despite the protective measures offered by unions (Freeman & Medoff, 1984 ), this study suggests that the reported lower job satisfaction among union members might contribute to their decision to resign. Contrary to the prediction in hypothesis H5-7, female employees exhibited a lower level of turnover intention than their male counterparts. This unexpected finding may be attributed to the observed higher job satisfaction among female employees, despite typically facing working conditions and rewards inferior to those of their male counterparts, as noted in some studies (e.g., Clark, 1997 ). The implication is that the presence of high job satisfaction among female employees serves as a mitigating factor against turnover intention, influencing their decision to remain in their current roles.

Despite the valuable contributions made by this study, it is essential to approach the interpretation of its findings with caution. Several factors warrant consideration. Firstly, while the focus on a limited number of workplace contextual factors and their impact on turnover intention is helpful, it is important to note that there is no universally agreed-upon set of workplace factors. The definition of workplace contextual factors may vary based on the survey questions posed. Conducting studies across diverse settings is crucial to establish more commonly agreed-upon workplace factors, enhancing the generalizability of findings. Secondly, the applicability of the findings in this study to public employees at different levels of government, such as state and local governments, or in various countries, remains uncertain. Given that the MPS data included only federal employees in the U.S., further research in different settings is necessary before extrapolating the findings to other contexts. Expanding the scope of investigation to include a broader range of participants will contribute to a more comprehensive understanding of the relationships between workplace contextual factors and turnover intention. Overall, this study advances our understanding of turnover intention by shedding light on the nuanced interplay of workplace elements and their impact on employee attitudes. Its findings offer actionable insights for HR practitioners and provide a foundation for future research in this field.

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Appendix. Factor Analysis of Work Environments

Scree plot of eigenvalues after factor analysis

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Factors impacting employee turnover intentions among professionals in Sri Lankan startups

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft

Affiliations SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka, Ceyentra Technologies, Panadura, Sri Lanka

Roles Conceptualization, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Information Management, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

• Lakshmi Kanchana, 
• Ruwan Jayathilaka

• Published: February 10, 2023
• https://doi.org/10.1371/journal.pone.0281729
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Employee turnover is one of the topical issues worldwide. The impact of factors affecting employee turnover varies occasionally and new factors are considered. Many countries have examined various factors that affect employee turnover. The main objective of this research is to consider psychographics and socio-demographic factors in one study and analyse the impact on employee turnover. A Probit regression model through the stepwise technique was used to analyse the collected data. Using ventures in Sri Lanka as a case study, this study demonstrates that employee turnover occurs in different stages and independent factors impact differently in each stage. The study population was professionals who have been a key part of Sri Lankan startups, which involved 230 respondents. Data analysis was performed through a forward stepwise technique through STATA. The results verified that job satisfaction and co-worker support negatively impact employee turnover, whereas leader member exchange positively impacts employee turnover. This study also proved a significant positive relationship between male employees in their thirties and high employee turnover. This study’s findings help to identify the areas management should focus on to minimise employee turnover to retain experienced and skilled employees.

Citation: Kanchana L, Jayathilaka R (2023) Factors impacting employee turnover intentions among professionals in Sri Lankan startups. PLoS ONE 18(2): e0281729. https://doi.org/10.1371/journal.pone.0281729

Editor: Muhammad Fareed, Universiti Utara Malaysia, MALAYSIA

Received: November 7, 2022; Accepted: January 31, 2023; Published: February 10, 2023

Copyright: © 2023 Kanchana, Jayathilaka. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files ( S2 Appendix . Data File).

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Having the right combination of human resources/employees can assist firms to be effective in driving change, boosting business performance, as well as to achieving and sustaining a competitive edge. Companies need to give high priority to employee development and predict employee behaviour [ 1 ]. Organisations spend more time and take much effort to identify the good fit employees for the company. Companies invest in many ways for employees, as they are one of the organisation’s valuable assets [ 2 ]. Organisations conduct workshops for employees, buy online tutorials, evaluate employee performance, and provide feedback to them, which are some common types of investments in human resources. These processes sharpen employees’ skills and capabilities, directly affecting the organisation’s success. However, some organisations are weak in strategy adoption while not focusing constantly on these processes or employee voice. As such, these employees suddenly quit the company resulting in increased employee turnover. The issue of employee turnover is considered as one of the global obstacles for organisations worldwide, which directly and adversely affects strategic plans and opportunities of gaining competitive advantages [ 3 ]. As such, this issue can have massive effects on a company’s performance, especially for new businesses and startups. Therefore, it is essential to identify the factors that affect employee retention, which is also a topical issue worldwide. This type of approach enables businesses to achieve its strategic goals while retaining satisfied and skilful employees.

Many variables influence employee turnover intentions [ 4 – 6 ]. Previous studies imply that job satisfaction, work-life balance, trust, and management support are the critical factors that impact employee retention [ 7 – 9 ]. Further, promoting employee well-being leads to decrease employee turnover [ 10 ]. Providing psychological and social support through counselling promotes the quality of work-life [ 11 ]. With time, newly considered factors such as leader member exchange, workplace culture, happiness, joy in the workplace, career management, innovative work behaviour and employee delight are equally important and have been identified. As such, it is important to focus on these factors and build relationships between employees and the organisation.

Firm performance reflects the ability of an organisation to use its human resources and other material resources to achieve its goals and objectives. Firm performance belongs to the economic category, and it should consider the use of business means efficiently during the production and consumption process [ 12 ]. Employee retention is defined as encouraging employees to remain in the organisation for a long period or the organisation’s ability to minimised employee turnover [ 13 ]. Turnover intention is the intention of the employee to change the job or organisation voluntarily [ 14 ].

Sri Lankan business firms were chosen as a case study to examine this resarch problem. In Sri Lanka, over 1 million (Mn) businesses operate. By 2018, 10,510 new businesses had been registered in Sri Lanka. Among these companies, startup companies play a key role in the Sri Lankan economy. Startups come up with radical innovations and changes, and these disrupt the existing market with new products and services. Furthermore, Sri Lanka has a middle rank of ease of doing business. With these favourable conditions and educational and family backgrounds, many people like to apply their new idea and fill the market gap. The new generation in Sri Lanka are interested/are keen on innovations at work and being a part of unique products or services. Currently, most startups are technology-driven and do not have geographical limitations.

Startups are expanding day by day. These businesses are in different stages as ideation, traction, break-even, profit, scaling and stable. According to the “Sri Lanka Startup Report 2019” issued by PricewaterhouseCoopers (PWC), “55% of startups responded are in the growing revenue or expansion stage, 29% of respondents reported an annual revenue of more than Sri Lankan Rupees (LKR) 10 Mn, 40% are still in the less than LKR 1 Mn revenue category and 61% of respondents reported being profitable”. In this setting, employee turnover can be a setback for most startups yet to reach business stability.

Most startups are relatively new. According to (PWC) [ 15 ], 36% of the businesses have operated for less than a year, 44% have been in operation for 1–3 years and only 8% have operated for more than five years. These are still growing and in the early stages of executing their strategies. In this situation, most companies are willing to expand their staff strength. PricewaterhouseCoopers [ 15 ] evidenced that 82% of companies were willing to do so in the next year.

Studies conducted in Asian countries on this subject are assumably similar to the situation of Sri Lanka [ 4 , 5 , 16 ]. This study aims to create a model with critical and newly identified independent factors (job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support) influencing employee turnover in Sri Lankan startups.

Based on their knowledge and the existing literature, authors have considered widely used factors to investigate the employee turnover issue. Therefore, job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange and co-worker support were selected based on previous literature findings [ 4 – 6 , 8 , 17 – 19 ]. As in the previous papers and along with the current study’s results, authors identified both positive and negative impacts on employee turnover among Sri Lankan startups.

This study aims to analyse the impact of job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange, and co-worker support on employee turnover in startups in Sri Lanka. The present study’s scientific value can be elaborated by comparing it with previous studies. This study’s contribution can be explained in five ways. Firstly, the most critical and newly considered factors were identified together with the support of past literature. Secondly, the present study was classified into different levels of employee turnover. As such, by considering the various levels, the micro-level changes and probabilities of the impact on employee turnover can be better identified. Further, this study helps to reduce the methodological gap. Thirdly, the Sri Lankan context has been selected as the case study. This is because, to the best of the authors’ knowledge, there was no previous research done by local researchers that includes all the widely measured variables investigating the combined effect on employee turnover. Fourthly, the analysis results can be used to identify the strengths and weaknesses of startups in Sri Lanka. Finally, this study identifies the challenges faced by startups and identifies how policy modifications can strengthen the startup ecosystem.

The upcoming sections of this paper are structured as follows. Section 2 discusses the literature review, and section 3 explains data and methodology, Section 4 contains results and discussion highlighting how the research objectives are achieved. Section 5 marks the conclusion, with implications, research limitations and future research directions.

Literature review

As employee turnover is one of the most critical indicators for an organisation, many studies have been conducted on this topic with dissimilar demographical and geographical samples. The existing literature adds theoretical or methodological improvements to this topic. Accoridngly, this study included most variables that significantly impact employee turnover, summarising the independent variables that affect employee retention.

This study is based on the initially defined 47 journal articles through advanced filtration. Reputed journal databases, such as Emerald insight, Science Direct, Taylor & Francis, SAGE journals, ResearchGate, Sabinet, IEEE Xplore and Google Scholar were referred. Fig 1 below describes the literature search flow. Thirteen articles were excluded due to overlapping, insufficient information and irrelevant to the topic. The selected articles have been sorted according to the independent variables.

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Source: Based on authors’ observations.

https://doi.org/10.1371/journal.pone.0281729.g001

S4 Appendix contains the literature summary of the above presented literature search flow diagram. The following sections present the details of each category.

Job satisfaction

Job satisfaction refers to the employee’s positive emotions, feeling and attitudes on the job and workplace. Positive emotional experiences directly affect higher job satisfaction [ 7 ]. Kim, Knutson [ 7 ] found that satisfaction significantly affects employee turnover regardless of the generation of the employee. Gen Y employees do not easily build loyalty toward the organisation unlike older employees. Turnover intentions seem significantly higher in new generations compared to older generations. New generations are impatient with their organisation and older generations are more patient with it. However, even the new generation of employees tends to stay in their organisation if their level of satisfaction is acceptable. They found that newer the generation of employee, satisfaction level and loyalty is lower than the older generation. This shows that employee turnover is higher in newer generations. Feedback obtained from most employees in generations Y and Z in startups supports this finding.

Da Camara, Dulewicz [ 20 ] found that organisational emotional intelligence has a larger effect on employee satisfaction. Further, this study has discovered that organisational emotional intelligence helped improve job satisfaction and commitment, which reduced turnover intentions significantly. However, organisational commitment and satisfaction describe only 19% of the total intention to leave. Moreover, the descriptive statistics found a high level of job satisfaction and the intention to leave was at the mid or average level of the scale. Camara further stated that job satisfaction clearly implies the feeling about their job. But some research findings can be contradictory. Some employees are fully satisfied with the job and still want to leave the organisation for various reasons. However, this research focused only on charity workers. As such, it is important to gather many indicators that affect employee turnover and thereafter, one can analyse the real situation and generalise the findings.

Satisfaction also depends on the number of employees at the same level. When it gets higher, job satisfaction increases and reduces the intention to leave [ 8 ]. This study found that female employees are more satisfied with their jobs, while older employees are more likely to leave the organisation. However, this study focused only on online-level employees and supervisors.

Oosthuizen, Coetze and Munro studied the relationship between job satisfaction and turnover intention in the IT industry. Oosthuizen, Coetzee [ 6 ] revealed that job satisfaction significantly predicted employee turnover. The study also found that the work-home life balance has a major effect on job satisfaction. Predicting turnover intention based on overall work-life balance is a tough task. The findings further proved that white employees show less job satisfaction compared to black employees. However, they didn’t observe any significant interaction between overall work-life balance and job satisfaction in predicting employee turnover intention. With these results, this indicator must be examined further.

Considering the Asian context, Pakistan IT professionals’ turnover intentions were studied in a similar research [ 21 ]. Recruitment & section, team & management support, performance & career management, salary & compensation, employee commitment, job security, recognition, organisational demographics, and personal demographics have an effect on job satisfaction. However, this study suggested adding more factors, such as work-life balance and employee engagement, which may significantly impact employee retention. This means that human resource management has a significant influence on job satisfaction.

The study by Zeffane and Bani Melhem [ 22 ] investigated the turnover intention of public and private sector employees in the United Arab Emirates. Here, the researchers revealed that government employees are more satisfied with their job and are most unlikely to leave than private sector employees. The turnover intentions of private sector employees are not significantly affected by job satisfaction, whereas the public sector is almost affected by it. Kaur and Randhawa [ 16 ] examined the turnover intention of Indian private school teachers. It revealed that job satisfaction has a direct link with the civil status of the teachers, explaining that married teachers tend to have less job satisfaction. However, for unmarried teachers, there is more intention to leave organisations. Supervisor’s influence had indirect impacts on turnover intentions. However, this research limited the sample to private school female teachers. Here, the study highlighted the importance of having more influencing variables on employee retention and recommended considering these for a comprehensive analysis. Only then the model can be near to the real situation.

Thomas A. Wright [ 2 ] discovered that the employee’s well-being moderates the relationship between satisfaction and turnover intention. Satisfaction had a strong negative relationship with turnover intention, while well-being remained low. The study by Nae and Choi [ 23 ] evidenced the direct relationship between job satisfaction and employee turnover. However, this also pointed out that employee well-being moderates the indirect relationship between job satisfaction and turnover. However, this moderator was significant only for a few specified occasions, such as employees having a highly secure attachment, and low counter-dependent and over-dependent attachment styles.

As per the literature, job satisfaction is an important factor in determining the impact on employee turnover. Accordingly, hypothesis one has been developed.

Work-life balance

Work-life balance can be identified as the satisfactory co-existing of an employee’s work-life and personal life. On one hand. this led to a positive influence on both employees and the organisation. On the other hand, negative work-life-balance has harmful effects on employees. Most employees had abuse alcohol due to this issue in the hospitality industry, which indirectly influences the organisation’s productivity. Additionally, most women have suffered from depression due to poor work-life balance in the hospitality industry. Besides, burnout, exhaustion, and stress are common among employees with poor work-life balance. Therefore, the employee’s commitment heavily depends on work-life balance, an essential requirement for employee retention [ 24 ]. This study states that it can be developed by adding more independent variables such as commitment and job satisfaction.

The highly negative work-life interference has amplified the turnover intentions of IT employees in Pakistan. They also found that the organisation that invested heavily in creating proper work-life balance recorded the lowest turnover among other organisations in the IT industry in Pakistan. Oosthuizen, Coetzee [ 6 ] revealed that the overall work-life balance had no clear influence on the satisfaction of an employee’s current job. Gender was a primary separation point of work-life balance variation among employees. Female employees looked more satisfied with their work-life balance than male employees [ 6 ]. In this light, work-life balance is one part of quality work life other than career opportunities and job characteristics. Organisational embeddedness has a positive and strong relationship with work-life balance. Positive work-life balance has a negative relationship with turnover intention [ 25 ]. However, the sample of this research was based on two healthcare firms. Since the whole world is tech-driven, it is realistic to focus on the IT industry too for generalisability of findings.

According to this study, superiors’ influence on work-life balance highly impacts job satisfaction. Supportiveness and the supervisor’s flexibility on subordinates’ help achieve the desired work-life balance for employees. As noted before, the employee turnover intention is heavily dependent on work-life balance. As such, a study on work-life balance can predict the turnover intention of an employee accurately compared to other factors. Work-life balance can be measured and categorised into three. Interference of work on personal life, work and family conflict and facilitation of work and family are those categories that the researcher suggested. The sample for the study of Kaur and Randhawa [ 16 ] was Indian private school teachers. The researcher suggested that formulating teacher-friendly policies to enhance work-life balance will reduce teachers’ turnover intentions. The researcher also suggested that the imbalance workload on employees supports increasing employee turnover intentions. However, most of the employees in this study were females.

Organisations that focused on employees’ proper work-life balance have recorded better efficiency, innovation, and talent retention [ 26 ]. Employee engagement and life satisfaction have been significantly mediated by the work-life balance of restaurant employees in Nevada, USA [ 27 ]. However, there are not sufficient recent researchers in Sri Lanka on work-life balance and employee retention. Therefore, taking up this study as an opportunity to research is essential. According to the above literature, hypothesis two has been constructed; work-life balance has a negative impact on employee turnover.

Employee happiness is a psychological feeling they have with the workplace. This is an essential factor in maintaining a successful and profitable organisation.Wright and Cropanzano [ 17 ] described happiness as phycological well-being. Personal well-being is one better way to explain employee retention. By moderating this factor, firms can achieve better employee turnover.

The workplace must be a source of happiness for employees. Unhappy employees in a workplace tend to increase employee turnover, absenteeism, low productivity, and time wasted deadlines. Creating happiness within the workplace is not a simple process. It is a comprehensive and continuous process. Happy employees generally have a fair idea of the organisation’s vision, mission and values. Employees in each department should have a clear idea about their goals [ 28 ]. However, happy employees are not always productive. But they can guide and explore things without organisations forcing them. Those employees required proper career management and support to be productive.

The workplace’s physical environment plays a major role in employee happiness and cheerfulness and friendliness of the physical environment are fundamentals. Employee’s attitude also has a more significant effect on happiness. Gratitude, appreciation, servant leadership from the organisation, hope and interpersonal connection are the main factors that affect the employee’s positive attitude. Humour, fun and games also play a major role in keeping employees happy. Other than those factors, wellness activities, celebrations and compensation are the minor factors affecting employee happiness. Based on the above cited literature, hypothesis three can be developed; employee happiness has a negative impact on employee turnover.

Management support

Management support is a must in the move from a good to a great company. Management stands by employees and supports them mentally and physically. Van den Heuvel, Freese [ 29 ] conducted research from the data of 699 employees at three divisions within the Dutch subsidiary of a multinational organisation. Management increased employee autonomy by supporting them to work from anywhere at any hour. This positively affected employee engagement and was negatively related to employee retention. Trust in management is a critical factor in employee turnover.

A cross-sectional survey has been conducted for front-line healthcare staff in China by Li, Mohamed [ 30 ] to measure the impact of organisational support on employee turnover intention. This study’s results could verify that organisational support negatively affected employee turnover intention. Saoula and Johari [ 31 ] studied this area and determined a negative relationship between organisational support and employee turnover intention. As both of the above explained research have been conducted in non-Western countries, the findings help to complete the theoretical framework for the current study in the Sri Lankan context.

Wong and Wong [ 5 ] researched the world’s most populous county, China, to identify the relationship between perceived organisational support and employee turnover. The findings suggested that trust, job security and distributive justice negatively impact employee turnover. However, China is an Asian country, and these similarities may apply to specific research findings in the Sri Lankan context.

Employee perception of management support for employee health is a factor in employee retention. Xiu, Dauner [ 32 ] studied this area with employees’ data from a public university, which was the first empirical examination of organisational support for employee health and retention. This kind of approach leads to building trust with employees. Moreover, these findings are essential to human resource managers who are willing to promote employee well-being at the workplace. Hypothesis four has been developed based on above discussed literature.

Career management

Initiatives must carry out different strategies for old and young employees because their priorities are different. Digest [ 18 ] discloses that young employees are impressed by flexible working opportunities, career advancement, positive working relationships and inclusive management forms. Young employees are more likely to be talented, leading to an organisation’s success and they can also become key workers in the company.

Saoula and Johari [ 31 ] researched the effect of personality traits (big five) on employee turnover intention. The researchers state that the relationship between the big five personality traits and turnover intention will support early prediction of employee turnover intentions. Identifying employee’s personalities and helping them to find the most suitable job role is a long-term process, though it will be highly advantageous for both employees and the organisation.

Rawashdeh and Tamimi [ 33 ] focused on the latest management developments of leading organisations worldwide. They state that there is a strong relationship between the availability of training and supervisor support for training and organisational commitment. Further, they proved that there is a strong negative association between organisational commitment and employee retention. These research findings verify the social exchange theory [ 34 ]. However, the research suggested that the above study can improve by adding new factors like motivation and co-worker support for training. Hypothesis five has been developed by concluding the above explained literature.

Innovative work behaviour

Innovative behaviour is a leading factor in gaining a competitive advantage. Shih, Posthuma [ 35 ] investigated the negative impacts of innovative work behaviour on employee turnover and conflict with co-workers. According to the studies, there is a positive relationship between innovative work behaviour and employee turnover. Further, it found that perceived distributive fairness can negatively moderate this relationship. However, the writer has suggested extending the research to different geographical locations and industries.

The organisational learning culture is a key factor for innovative work behaviour. Saoula, Fareed [ 36 ] conducted research in Malayasia, a developing country in Asia to examine the relationship between organisational learning culture and employee turnover intention. The organisational learning culture improves learning capability, supports sustainable development, and affects organisation’s positive changes. As organisational learning culture and employee turnover intention have a negative relationship, the result helps to identify the impact of innovative work behaviour. According to the existing literature, limited studies have been conducted on this topic.

Agarwal, Datta [ 4 ] conducted research with managerial employees in India to examine the relationship between innovative work behaviour and employee turnover. This study asserted that the variables have an inverse relationship. As innovative work behaviour examinations in an Asian county country like India, it is important to consider this variable in this model. With the presence of the above mentioned literature, hypothesis six has been formulated.

Leader member exchange

As per many leadership methods, leader member exchange depends on the leadership style. Tobias M. Huning [ 37 ] conducted research to identify the effect of servant leadership on employee turnover. Servant leadership supports employee empowerment, development, interpersonal acceptance, and courage. This study found that servant leadership negatively impacts employee turnover. However, this leadership style does not directly affect employee retention. Gyensare, Kumedzro [ 38 ] studied the impact of transformational leadership on employee turnover. This type of leadership supports work engagement of the employee, and it negatively relates to employee retention. Considering both aspects, the study found that increasing work engagement is vital to curtail employee retention.

Leader support is an indirect factor in employee retention. According to the studies, employee engagement and work-life balance act as mediation for perceived supervisor support and employee turnover relationship [ 16 ]. The supervisor supports the career success of employees and it affects both directly and indirectly the career success of the employee and retention one year later [ 9 ]. Therefore, this study shows that co-worker support has a significantly positive impact on employee turnover. However, these results maintained the diversity of the sample. As this has been examined in India, a South Asian country, the same results can apply to the Sri Lankan context. Based on the above-mentioned literature, hypothesis seven has been developed; leader member exchange has a negative impact on employee turnover.

Co-worker support

Co-worker support will be in both formal and informal ways and in two different forms, emotional support, and instrumental support. The support of co-workers enhances the confidence level of the employee. Further, it helps to accept challenges in the work environment. Kmieciak [ 19 ] has worked on research to identify the effect of co-worker support on employee retention in the IT industry. However, a significantly negative impact was not evident on co-worker support. As this is a recently published research paper, the results are more valuable to the current research. The researcher has investigated more about the impact of subordinates’ support. Here, the analysis has been done only with 118 employees from a Polish software company. Considering the above limitations enables researchers to further study this topic with a larger sample size for generalisability of findings.

Abugre and Acquaah [ 39 ] researched in Ghana to identify the relationship between co-worker relationships and employee retention. The findings of this research imply that co-worker support is negatively associated with employee turnover. It further stated that cynicism of the employee is positively associated with employee turnover. The speciality of this research is identifying the importance of encouraging co-worker support rather than employee cynicism. These newly published research results can be used along with all other variables that affect employee turnover. According to the above literature, hypothesis eight has been constructed.

These studies have a common limitation in gathering more independent variables and analysing the impact. Therefore, a need exists to measure the effect of job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support together on employee turnover.

In Sri Lanka, no research has so far considered all eight factors affecting employee turnover in one study. With the above-mentioned literature findings, this study assists the government in identifying the impact of every factor on employee turnover in startups in Sri Lanka.

Data and methodology

This study was reviewed and approved by Sri Lanka Institute of Information Technology Business School and the Sri Lanka Institute of Information Technology ethical review board. Data were collected through a questionnaire using both online and manual channels. Each individual in this study gave verbal consent prior to the formal interview. The data was collected from August to September 2022 ( S1 Appendix ). The authors directly distributed the questionnaire. Moreover, authors could contact management in startups and distribute the questionnaire in their organisation. The questionnaire is composed of ten (10) sections. The first part of the questionnaire was designed to collect the demographic characteristics of the correspondents. The second to ninth sections focused on independent variables, job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support. Finally, the tenth section was designed to identify employee turnover indicator. A minimum of four questions was added under each indicator. The researchers facilitated anonymously answering all the questions in the questionnaire. The participants should be a part of startup and he/she should consider the behaviour and culture of that startup when answering the questions. All nine indicators were covered by Likert scale questions from 1 to 5 rating scale, depicting (1) strongly disagree to (5) strongly agree to collect respondents’ attitudes and opinions. Each respondent took about 10–15 minutes to complete answering the questionnaire and took approximately 5–7 minutes to fill out the questionnaire. Furthermore, the average values were calculated to measure the value given by respondents for each indicator. The data file used for the study is presented in S2 Appendix .

PricewaterhouseCoopers [ 15 ] statistics determined the study’s population and it explained the total number of elements to be focused on in this study. The researchers applied a random sampling method, mainly employees who are a part of or have been a part of the startup. This sampling technique was appropriate because it was free of bias. The sample size was selected by referencing the Krejcie and Morgan sampling table and Calculator.net [ 40 ] with a confidence level of 95% and 7% of margin of error. The calculation results indicated a minimum of 171 professionals. A stepwise ordered probit analysis method was used as the selected variables are widely used indicators for employee turnover; therefore, a micro-level analysis was required to study how these variables impact. A pilot survey was conducted to identify whether the purpose of the questions was clear to the respondents.

The data used for the estimation include 83 low employee turnover, 79 moderate employee turnover and 68 high employee turnovers of employees in Sri Lankan startups. The initial estimation results are presented in Table 1 .

https://doi.org/10.1371/journal.pone.0281729.t001

The mean values of all independent variables are greater than 2.5. Respondents were further grouped as per demographic and geographic characteristics. The respondents’ gender identity ratio is nearly 1: 2. When considering the age groups, most are in 20–30 years. Many employees in startup companies are in their twenties and are graduates. The respondents represent all the districts in Sri Lanka, most of which are from Kalutara, Colombo, Galle and Matara districts.

Research framework and hypothesis

The conceptual framework was developed with the literature review and existing knowledge, as illustrated in Fig 2 . This model was developed with the combination of eight hypotheses. These independent variables have been identified as critical factors that impact employee turnover.

Source: Authors’ compilation.

https://doi.org/10.1371/journal.pone.0281729.g002

The following hypotheses have been developed in line with the research framework.

• Hypothesis 1 : Job satisfaction has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 2 : Work-life balance has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 3 : Happiness has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 4 : Management support has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 5 : Career management has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 6 : Innovative work behaviour has an impact on employee turnover in startups in Sri Lanka.
• Hypothesis 7 : Leader member exchange has an impact on employee turnover in startups in Sri Lanka.
• Hypothesis 8 : Co-worker support has a negative impact on employee turnover in startups in Sri Lanka.

Methodology

This study focuses on the demographical variables that affect employee turnover. For this, the present study’s authors considered employee feedback concerning Sri Lankan startups. The ordered probit regression determines the significant variables [ 41 ]. The probit model is an estimation technique for equations with dummy dependent variables that avoids the unboundedness problem of the linear probability model by using a variant of the cumulative normal distribution [ 42 ]. Further, this study examines the likelihood of three types of employee turnover. Accordingly, employee turnover is divided into three categories, considering the equality of data for each category based on employee turnover.

• Group 1 (y = 1): low = mean value of the employee turnover less than 1.50
• Group 2 (y = 2): moderate = mean value of the employee turnover greater than 1.5 and less than or equal to 2.25
• Group 3 (y = 3): high = mean value of the employee turnover greater than 2.25 and less than or equal to 5

The following equation represents the general form of the ordered probit model.

The y i value represents i th value of the dependent variable, employee turnover and x i represents the i th common independent variable. The β value is a vector parameter and ℇ i considered as the normally distributed random error term with a zero mean. The following ordered probit model has been developed by detailing the general equation.

Table 2 indicates the variables explained in previous literature and definitions of the previously mentioned equation that affects employee retention. The forward stepwise regression model has been used to analyse the data set.

https://doi.org/10.1371/journal.pone.0281729.t002

Results and discussions

It is mandatory to test the internal consistency reliability before data analysis. The most common measure of reliability is Cronbach’s alpha (α) value, which determines whether the internal instruments are constant [ 43 ]. The reliability results for each indicator are presented in Table 3 . As all the Cronbach alpha values are greater than 0.6 scale reliability coefficients, all variables in this study are acceptable.

https://doi.org/10.1371/journal.pone.0281729.t003

In the first step, the initial ordered probit model was executed, and this model explained 73% of the variation in employee retention by the variation in independent variables. S3 Appendix contains the table of the initial ordered probit regression model. The ordered probit model forwarded with the forward stepwise technique to identify the exact number of variables that impact employee turnover. A forward stepwise technique was adopted for the variable selection in each specification. Here, the new variables for selection were considered with a p-value < 0.20 and the previously selected variable for removal with a p-value ≥ 0.25. Three different model diagnostic criteria were considered in assessing the reliability of the results. The forward stepwise methodology suggested that the significance of the existing variables could be increased by adding more variables to the model. Marginal effects were separately calculated for low, moderate, and high employee turnover. Table 4 presents the final estimation results of the ordered probit model and illustrates the substantive effects of the independent variables. Here, 71.74% of the variation in employee turnover is explained by the variation in job satisfaction, LMX and co-worker support, considering the sample size and independent variables.

https://doi.org/10.1371/journal.pone.0281729.t004

Looking at the signs of the marginal effects in Table 4 , overall, high employee turnover is negatively associated with job satisfaction, co-worker support, and innovative work behaviour, whereas high employee turnover is positively associated with leader member exchange.

To control for the potential effect on different levels of employee turnover, the age factor was also included in the model, the coefficient of which implies that high employee turnover is 0.20 points and 0.60 points for the 20–30 years age range and 31–40 years age range, respectively. Employee turnover in 31–40 years age range employees is higher than that of other age ranges.

The marginal effects of the psychographic variables reveal that a 1% increase in job satisfaction increases the probability of low employee turnover by 0.47 percentage points. Similarly, 1% increase in job satisfaction decreases the probability for high employee turnover by 0.43 percentage points. With this observation, it can be stated that improving job satisfaction will highly affect to reduce high employee turnover. These results verify the existing statements indicating that job satisfaction has the highest significant and negative estimate value.

The estimated marginal effect of low employee turnover is 0.47 percentage points higher for employees in Sri Lankan Startups with a 1% increase in leader member exchange. High employee turnover is associated with leader member exchange increasing probability by 0.43. However, this study reflects similar findings to those of Tymon, Stumpf [ 9 ]. The reason behind the positive relation is employees learn fast and get qualified with the support of their leaders and then quit the company within the next few years.

Both leader member exchange and co-worker support are significant at the 99% level of employee turnover in the Sri Lankan context. When considering the independent variables for employee turnover in startups in Sri Lanka, co-worker support is a critical factor in determining the level of employee turnover. The 1% increase in co-worker support will also increase the probability of low employee turnover by 0.40 percentage points. But concurrently, change in co-worker support will negatively impact high employee turnover. The results ensure that encouraging co-worker support is crucial rather than employee cynicism.

Innovative work behaviour is one of the most critical factors in employee turnover. With a 1% increase in innovative work behaviour, the estimated marginal effect of high employee turnover is 0.27 percentage points lower for employees in Sri Lankan startups. The results of Shih, Posthuma [ 35 ] indicate a positive relationship exists between innovative work behaviour and employee turnover. However, this study concludes by emphasising the importance of retaining the innovative employees to remain competitive in the industry. For this, startups need to improve and enhance employees’ innovative behaviour and, concurrently, to prevent such employee retention.

Entrepreneurs are the founders of startups. Employees’ entrepreneurial dreams positively affect employee intention to startups. Employees in the startups also will have an ideation to start their own business. According to the study by Li, Li [ 44 ] the mediating role of employees’ entrepreneurial self-efficacy and the moderating role of job embeddedness in the influence of entrepreneurial dreams on employees’ turnover intention to startup.

The main objective of this research is to analyse the impact of critical and newly identified factors on employee turnover in one study. This issue occurs when employees leave the company by giving short notice or quitting unexpectedly. The analysis found that gender and age impact employee turnover in startups in Sri Lanka. In startups, many employees are in the 20 to 30 years age range. Employees between 31 and 40 years show a higher tendency to leave the startups. In Sri Lanka, only 8% of startups have been in operation for more than five years [ 15 ], indicating that the businesses are not stabilised and are still in its early stages. To prevent employee turnover, startups must improve employee job satisfaction. As per the findings, increasing job satisfaction has a significant impact on reducing employee turnover. For most employees in startups, it is their first job. During this time, employees gain work experience and become experts in the field. The leaders allocate much time to train their human resources and the company should gain strategic benefits from this investment. The results of the study prove that leader member exchange has a positive impact on employee turnover, as verified by Tymon, Stumpf [ 9 ] too about this relationship. To overcome this situation, as managers, it is vital to discuss with employees about their career paths, employee interests and company’s business plans while improving their technical skills and experience. This way, the mutual interest of both the employee and the company can be identified and handled. It also builds trust between the company and the employees. Regular support environment and ease of doing business is 66% highly important factor for the success of Sri Lankan startups [ 15 ]. This environment can be easily created with the level of co-worker support to the employee. Employee turnover can be more costly than a startup can imagine, with disruptions to business operations when their employees’ suddenly quit jobs. Therefore, it is must to attain above discussed facts. These results and discussions can be taken as insights to better understand and curtail employee turnover. This study will assist Sri Lankan startups where their skilled employees, who are also experts plausibly remain, enabling the businesses to expand to new markets. Usually, issues relevant to profit-making and business performance, such as a drop in sales and manufacturing are identified by startups. However, employee turnover is generally not identified as an organisational issue.

Theoretical implications

The current study empirically investigated the impact of job satisfaction, innovative work behaviour, co-worker support and leader member exchange on employee turnover. According to the authors’ knowledge, no prior studies were conducted considering the combined impact of all the independent variables on employee turnover. Therefore, this study strengthens the literature by demonstrating how job satisfaction, innovative work behaviour, co-worker support and leader member exchange impact employee turnover in Sri Lankan startups.

The findings reveal that job satisfaction has a negative impact on employee turnover. This finding is consistent with the previous study, job satisfaction significantly predicted employee turnover [ 6 ]. This study consolidates past findings that male employees have higher turnover intention than female employees. Female employees have comparatively higher-level job satisfaction [ 8 ]. This study implies that employees age 31 to 40 years have high employee turnover intention. The research findings are similar to Lu, Lu [ 8 ]; the older employees have high intentions to leave the company.

Practical implications

The study’s findings illustrate the importance of job satisfaction, innovative work behaviour, co-worker support and leader member exchange in affecting employee turnover in startups. This study provides managerial insights on lowering employee turnover in Sri Lankan startups. First, startups need to be aware that experienced employees in startups can be easily taken by well-established companies because, later, they have hand on experience and skills. Therefore, it is important to implement strategies for a solid career development plan, career growth, personal status, and employee recognition. As job satisfaction can predict employee turnover, it is a must to measure those indicators and maintain a favourable level at all times.

Innovative work behaviour is increasingly becoming a significant factor in employee retention. As good startups are a blend of creativity and competitive advantage, it is a must to focus on the IWB of the employee. LMX is a turning point for expanding the business. More importantly, healthy LMX can boost employees’ work engagement. This healthy level can maintain by conducting regular meetings, training programs and informal mentorship with employees’ immediate supervisors [ 8 ]. Further, management can allow employees at all levels to present their fresh ideas and incorporate them to influence organisation’s decision making process. These processes can lower employee hierarchy and build strong relationships while recognising them in the company.

It is important to retain trained and skilled employees who started their career paths in the organisation. Such employees can drive the organisation to success. While measuring employees’ job satisfaction, managers nee to conduct standard ways on performance and improvements of the organisation. It is better if companies can create their key performance indicators because it will help protect the organisation’s core values while expanding the company. Furthermore, having a flexible approach to work in an organisation culture will increase the trust between employees and the organisation. Giving the freedom to take risks and not allowing them to feel alone during work will give value to employees. Finally, all the above actions will strongly impact reducing employee intention to leave the organisation.

Research limitations and future research directions

Further research can improve the study as follows. First, this research includes feedback from 230 employees. More than one-third of these employees are from the IT industry. Since Sri Lankan startups are technology-driven, this ratio is more reliable. However, this research can be generalised by obtaining employees’ feedback from other industries. Secondly, in this questionnaire, the minimum number of questions for independent factors is four. This is to minimise the possibility of demotivating the employee by giving a lengthy and complex questionnaire. Therefore, in future, researchers can design questionnaires incorporating more questions to cover a wider range of independent factors, including open-ended ones. Thirdly, in this sample, many employees were in their twenties, and most hadn’t worked for more than two companies (i.e. employers). As such, it is reasonable to assume that participants’ response is somewhat limited to obtain the broader picture of the research problem. Future researchers can focus on different age groups and analyse the same factors concerning employee retention. Finally, new research can be executed by adopting a case study approach (including case studies representing various types of industries etc), such as employees in multinational companies.

Supporting information

S1 appendix. questioner..

https://doi.org/10.1371/journal.pone.0281729.s001

S2 Appendix. Data file.

https://doi.org/10.1371/journal.pone.0281729.s002

S3 Appendix. Initial ordered probit regression results.

https://doi.org/10.1371/journal.pone.0281729.s003

S4 Appendix. Literature summary.

https://doi.org/10.1371/journal.pone.0281729.s004

Acknowledgments

The authors would like to thank Ms. Gayendri Karunarathne for proof-reading and editing this manuscript.

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Development and Learning in Organizations

ISSN : 1477-7282

Article publication date: 3 February 2022

Issue publication date: 16 May 2022

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Negative consequences of high employee turnover provide much cause for concern in many organizations. Adopting transformational leadership behaviors better positions managers to address the issue and reduce turnover intentions at both individual and collective levels.

Originality/value

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

• Transformational leadership
• Turnover intentions
• Collective turnover
• Turnover contagion

(2022), "Factors that impact on employee turnover intentions: How transformational leadership can help", Development and Learning in Organizations , Vol. 36 No. 4, pp. 41-43. https://doi.org/10.1108/DLO-01-2022-0016

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Employee Turnover: Causes, Importance and Retention Strategies

• Walid Abdullah Al-Suraihi  

Walid Abdullah Al-Suraihi

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• Siti Aida Samikon  

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This research aims to understand the causes of employee turnover and retention strategies in an organization. Key research findings indicate that employees have several reasons to leave their workplaces, such as job stress, job satisfaction, job security, work environment, motivation, wages, and rewards. Furthermore, employee turnover has a huge impact on an organization due to the costs associated with employee turnover and can negatively impact the productivity, sustainability, competitiveness, and profitability of an organization. However, the organization must understand the needs of its employees, which will help organizations, adopt certain strategies to improve employee performance and reduce turnover. Thus, implementing strategies will increase job satisfaction, motivation and the productivity of individuals and organizations, which can reduce employment problems, absenteeism, and employee turnover.

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“Cause, Effect and Remedies of Employee Turnover”: A Critical Literature Review

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Manu Melwin Joy

Given the widespread research on the area of voluntary employee turnover in the past decade as well as new managerial approaches to employee retention, labour market dynamism, and evolution in research methodology and technology, it is imperative for researchers to evaluate the current state of the field. It is generally shown that in the traditional attitude turnover model, the process of employees' volunteer turnover is the reversed transformation process of employees' retention psychology and behaviours, mainly consisting of four sectors (Lee and Mitchell, 1999): first is the quit process resulting from job dissatisfaction; then, employees' search for substitutable jobs before turnover; is evaluation on such substitutable jobs; and result is occurrence of turnover behaviour. Finally, an integrative model of the relationship is put forward which argues that performance character may lead to withdraw tendency even turnover behaviour through four different routes with the introduction of the Job-Coupling variable. The main objective of this study is to illustrate the evolution of turnover research from its modest beginnings to the multifaceted research stream it has become.

Considering the extensive research on the topic of voluntary employee turnover in the past 10 years as well as new managerial outlook to employee retention, labor market dynamism, and evolution in research methodology and technology, it is critical that researchers evaluate the current state of the field. In the past decades, turnover research has experienced substantial theoretical expansion. Specifically, the last decade was characterized by seven major trends: (1) new individual difference forecast of turnover; (2) an extended focus on stress-and change-related attitudes (3) empirical research on the unfolding model; (4) more focus on contextual variables with an emphasis on interpersonal relationships (5) an enhanced focus on factors related to staying (6) a dynamic modelling of turnover processes with the consideration of time and (7) increasing our understanding of previously identified relationships. This study focuses on exploring the intricacies of the new turnover trends and analyzes the impact of these trends on the future of turnover research. Turnover Research from 1995 until the Present In the past 10 years, turnover research has gone through considerable theoretical expansion. The last decade was characterized by seven major trends: (1) new individual difference forecast of turnover; (2) an extended focus on stress-and change-related attitudes (3) empirical research on the unfolding model; (4) more focus on contextual variables with an emphasis on interpersonal relationships (5) an enhanced focus on factors related to staying (6) a dynamic modelling of turnover processes with the consideration of time and (7) increasing our understanding of previously identified relationships. Even though there are more theoretical constructs to explain turnover, there is less theoretical consensus among the researchers and still a relatively small amount of overall variance in turnover explained. The result we believe is that the field of study is richer, but perhaps farther from a unified view of the turnover process than ever before. Trend 1: Individual Differences Studies that investigated individual difference predictors of turnover have looked at both direct effects and moderators. According to Barrick and Zimmerman (2005), personality may be operating directly on whether one leaves his or her job. He found out that self-confidence and decisiveness combined with bio-data measured during the recruitment process were negatively associated with turnover. Articles by Pelled and Xin (1999) and Thoresen, Kaplan and Barsky (2003) suggest that negative affectivity is likely to result in higher intentions to leave and actual turnover. Study of Allen, Moffit and Weeks (2005) points to the moderating influences of individual differences. They demonstrated that low self monitors and employees with low risk aversion were more likely to translate their intentions to leave into actual turnover. Maertz and Campion (2004) combined content and process models of turnover by proving that their previously developed eight turnover motive forces (affective, calculative, contractual, behavioural, alternative, normative, moral, and constituent forces)are systematically related to four turnover decision types (impulsive, comparison, pre-planned and conditional quitters) such that different groups of quitters are stimulated by different forces. They claim to have identified the eight proximal causes of turnover cognitions and suggest that these causes

Tanaaz Khan

IJAERS Journal

Productivity is the most important factor for any industry or the organization. There are number of factors on which the productivity of the organization depends upon. And employee turnover is considered to be one of the challenging issues in the today competitive business nowadays. The employee turnover has received considerable attention by the organizations this days as it is very closely related to the growth and development of any organizations Employees turnover is an important issue for not only the automobile industry but also the service and IT industry.It has proven to be one of the most costly and seemingly inflexible human resource challenges confronting by several organizations globally All important factors such as Productivity, overall efficiency, quality etc gets affected by an issue of employee turnover. Therefore impact of turnover has received considerable attentionby senior management, professionals and industrial psychologists. This research paper focus on the different factors that are responsible for the labor turnover in an automobile industry. It also focus on the different major to be consider in order to reduce these problem from all the different sector of the industry.

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Factors impacting employee turnover intentions among professionals in Sri Lankan startups

Lakshmi Kanchana

1 SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

2 Ceyentra Technologies, Panadura, Sri Lanka

Ruwan Jayathilaka

3 Department of Information Management, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Associated Data

All relevant data are within the paper and its Supporting Information files ( S2 Appendix . Data File).

Employee turnover is one of the topical issues worldwide. The impact of factors affecting employee turnover varies occasionally and new factors are considered. Many countries have examined various factors that affect employee turnover. The main objective of this research is to consider psychographics and socio-demographic factors in one study and analyse the impact on employee turnover. A Probit regression model through the stepwise technique was used to analyse the collected data. Using ventures in Sri Lanka as a case study, this study demonstrates that employee turnover occurs in different stages and independent factors impact differently in each stage. The study population was professionals who have been a key part of Sri Lankan startups, which involved 230 respondents. Data analysis was performed through a forward stepwise technique through STATA. The results verified that job satisfaction and co-worker support negatively impact employee turnover, whereas leader member exchange positively impacts employee turnover. This study also proved a significant positive relationship between male employees in their thirties and high employee turnover. This study’s findings help to identify the areas management should focus on to minimise employee turnover to retain experienced and skilled employees.

Introduction

Having the right combination of human resources/employees can assist firms to be effective in driving change, boosting business performance, as well as to achieving and sustaining a competitive edge. Companies need to give high priority to employee development and predict employee behaviour [ 1 ]. Organisations spend more time and take much effort to identify the good fit employees for the company. Companies invest in many ways for employees, as they are one of the organisation’s valuable assets [ 2 ]. Organisations conduct workshops for employees, buy online tutorials, evaluate employee performance, and provide feedback to them, which are some common types of investments in human resources. These processes sharpen employees’ skills and capabilities, directly affecting the organisation’s success. However, some organisations are weak in strategy adoption while not focusing constantly on these processes or employee voice. As such, these employees suddenly quit the company resulting in increased employee turnover. The issue of employee turnover is considered as one of the global obstacles for organisations worldwide, which directly and adversely affects strategic plans and opportunities of gaining competitive advantages [ 3 ]. As such, this issue can have massive effects on a company’s performance, especially for new businesses and startups. Therefore, it is essential to identify the factors that affect employee retention, which is also a topical issue worldwide. This type of approach enables businesses to achieve its strategic goals while retaining satisfied and skilful employees.

Many variables influence employee turnover intentions [ 4 – 6 ]. Previous studies imply that job satisfaction, work-life balance, trust, and management support are the critical factors that impact employee retention [ 7 – 9 ]. Further, promoting employee well-being leads to decrease employee turnover [ 10 ]. Providing psychological and social support through counselling promotes the quality of work-life [ 11 ]. With time, newly considered factors such as leader member exchange, workplace culture, happiness, joy in the workplace, career management, innovative work behaviour and employee delight are equally important and have been identified. As such, it is important to focus on these factors and build relationships between employees and the organisation.

Firm performance reflects the ability of an organisation to use its human resources and other material resources to achieve its goals and objectives. Firm performance belongs to the economic category, and it should consider the use of business means efficiently during the production and consumption process [ 12 ]. Employee retention is defined as encouraging employees to remain in the organisation for a long period or the organisation’s ability to minimised employee turnover [ 13 ]. Turnover intention is the intention of the employee to change the job or organisation voluntarily [ 14 ].

Sri Lankan business firms were chosen as a case study to examine this resarch problem. In Sri Lanka, over 1 million (Mn) businesses operate. By 2018, 10,510 new businesses had been registered in Sri Lanka. Among these companies, startup companies play a key role in the Sri Lankan economy. Startups come up with radical innovations and changes, and these disrupt the existing market with new products and services. Furthermore, Sri Lanka has a middle rank of ease of doing business. With these favourable conditions and educational and family backgrounds, many people like to apply their new idea and fill the market gap. The new generation in Sri Lanka are interested/are keen on innovations at work and being a part of unique products or services. Currently, most startups are technology-driven and do not have geographical limitations.

Startups are expanding day by day. These businesses are in different stages as ideation, traction, break-even, profit, scaling and stable. According to the “Sri Lanka Startup Report 2019” issued by PricewaterhouseCoopers (PWC), “55% of startups responded are in the growing revenue or expansion stage, 29% of respondents reported an annual revenue of more than Sri Lankan Rupees (LKR) 10 Mn, 40% are still in the less than LKR 1 Mn revenue category and 61% of respondents reported being profitable”. In this setting, employee turnover can be a setback for most startups yet to reach business stability.

Most startups are relatively new. According to (PWC) [ 15 ], 36% of the businesses have operated for less than a year, 44% have been in operation for 1–3 years and only 8% have operated for more than five years. These are still growing and in the early stages of executing their strategies. In this situation, most companies are willing to expand their staff strength. PricewaterhouseCoopers [ 15 ] evidenced that 82% of companies were willing to do so in the next year.

Studies conducted in Asian countries on this subject are assumably similar to the situation of Sri Lanka [ 4 , 5 , 16 ]. This study aims to create a model with critical and newly identified independent factors (job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support) influencing employee turnover in Sri Lankan startups.

Based on their knowledge and the existing literature, authors have considered widely used factors to investigate the employee turnover issue. Therefore, job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange and co-worker support were selected based on previous literature findings [ 4 – 6 , 8 , 17 – 19 ]. As in the previous papers and along with the current study’s results, authors identified both positive and negative impacts on employee turnover among Sri Lankan startups.

This study aims to analyse the impact of job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange, and co-worker support on employee turnover in startups in Sri Lanka. The present study’s scientific value can be elaborated by comparing it with previous studies. This study’s contribution can be explained in five ways. Firstly, the most critical and newly considered factors were identified together with the support of past literature. Secondly, the present study was classified into different levels of employee turnover. As such, by considering the various levels, the micro-level changes and probabilities of the impact on employee turnover can be better identified. Further, this study helps to reduce the methodological gap. Thirdly, the Sri Lankan context has been selected as the case study. This is because, to the best of the authors’ knowledge, there was no previous research done by local researchers that includes all the widely measured variables investigating the combined effect on employee turnover. Fourthly, the analysis results can be used to identify the strengths and weaknesses of startups in Sri Lanka. Finally, this study identifies the challenges faced by startups and identifies how policy modifications can strengthen the startup ecosystem.

The upcoming sections of this paper are structured as follows. Section 2 discusses the literature review, and section 3 explains data and methodology, Section 4 contains results and discussion highlighting how the research objectives are achieved. Section 5 marks the conclusion, with implications, research limitations and future research directions.

Literature review

As employee turnover is one of the most critical indicators for an organisation, many studies have been conducted on this topic with dissimilar demographical and geographical samples. The existing literature adds theoretical or methodological improvements to this topic. Accoridngly, this study included most variables that significantly impact employee turnover, summarising the independent variables that affect employee retention.

This study is based on the initially defined 47 journal articles through advanced filtration. Reputed journal databases, such as Emerald insight, Science Direct, Taylor & Francis, SAGE journals, ResearchGate, Sabinet, IEEE Xplore and Google Scholar were referred. Fig 1 below describes the literature search flow. Thirteen articles were excluded due to overlapping, insufficient information and irrelevant to the topic. The selected articles have been sorted according to the independent variables.

Source: Based on authors’ observations.

S4 Appendix contains the literature summary of the above presented literature search flow diagram. The following sections present the details of each category.

Job satisfaction

Job satisfaction refers to the employee’s positive emotions, feeling and attitudes on the job and workplace. Positive emotional experiences directly affect higher job satisfaction [ 7 ]. Kim, Knutson [ 7 ] found that satisfaction significantly affects employee turnover regardless of the generation of the employee. Gen Y employees do not easily build loyalty toward the organisation unlike older employees. Turnover intentions seem significantly higher in new generations compared to older generations. New generations are impatient with their organisation and older generations are more patient with it. However, even the new generation of employees tends to stay in their organisation if their level of satisfaction is acceptable. They found that newer the generation of employee, satisfaction level and loyalty is lower than the older generation. This shows that employee turnover is higher in newer generations. Feedback obtained from most employees in generations Y and Z in startups supports this finding.

Da Camara, Dulewicz [ 20 ] found that organisational emotional intelligence has a larger effect on employee satisfaction. Further, this study has discovered that organisational emotional intelligence helped improve job satisfaction and commitment, which reduced turnover intentions significantly. However, organisational commitment and satisfaction describe only 19% of the total intention to leave. Moreover, the descriptive statistics found a high level of job satisfaction and the intention to leave was at the mid or average level of the scale. Camara further stated that job satisfaction clearly implies the feeling about their job. But some research findings can be contradictory. Some employees are fully satisfied with the job and still want to leave the organisation for various reasons. However, this research focused only on charity workers. As such, it is important to gather many indicators that affect employee turnover and thereafter, one can analyse the real situation and generalise the findings.

Satisfaction also depends on the number of employees at the same level. When it gets higher, job satisfaction increases and reduces the intention to leave [ 8 ]. This study found that female employees are more satisfied with their jobs, while older employees are more likely to leave the organisation. However, this study focused only on online-level employees and supervisors.

Oosthuizen, Coetze and Munro studied the relationship between job satisfaction and turnover intention in the IT industry. Oosthuizen, Coetzee [ 6 ] revealed that job satisfaction significantly predicted employee turnover. The study also found that the work-home life balance has a major effect on job satisfaction. Predicting turnover intention based on overall work-life balance is a tough task. The findings further proved that white employees show less job satisfaction compared to black employees. However, they didn’t observe any significant interaction between overall work-life balance and job satisfaction in predicting employee turnover intention. With these results, this indicator must be examined further.

Considering the Asian context, Pakistan IT professionals’ turnover intentions were studied in a similar research [ 21 ]. Recruitment & section, team & management support, performance & career management, salary & compensation, employee commitment, job security, recognition, organisational demographics, and personal demographics have an effect on job satisfaction. However, this study suggested adding more factors, such as work-life balance and employee engagement, which may significantly impact employee retention. This means that human resource management has a significant influence on job satisfaction.

The study by Zeffane and Bani Melhem [ 22 ] investigated the turnover intention of public and private sector employees in the United Arab Emirates. Here, the researchers revealed that government employees are more satisfied with their job and are most unlikely to leave than private sector employees. The turnover intentions of private sector employees are not significantly affected by job satisfaction, whereas the public sector is almost affected by it. Kaur and Randhawa [ 16 ] examined the turnover intention of Indian private school teachers. It revealed that job satisfaction has a direct link with the civil status of the teachers, explaining that married teachers tend to have less job satisfaction. However, for unmarried teachers, there is more intention to leave organisations. Supervisor’s influence had indirect impacts on turnover intentions. However, this research limited the sample to private school female teachers. Here, the study highlighted the importance of having more influencing variables on employee retention and recommended considering these for a comprehensive analysis. Only then the model can be near to the real situation.

Thomas A. Wright [ 2 ] discovered that the employee’s well-being moderates the relationship between satisfaction and turnover intention. Satisfaction had a strong negative relationship with turnover intention, while well-being remained low. The study by Nae and Choi [ 23 ] evidenced the direct relationship between job satisfaction and employee turnover. However, this also pointed out that employee well-being moderates the indirect relationship between job satisfaction and turnover. However, this moderator was significant only for a few specified occasions, such as employees having a highly secure attachment, and low counter-dependent and over-dependent attachment styles.

As per the literature, job satisfaction is an important factor in determining the impact on employee turnover. Accordingly, hypothesis one has been developed.

Work-life balance

Work-life balance can be identified as the satisfactory co-existing of an employee’s work-life and personal life. On one hand. this led to a positive influence on both employees and the organisation. On the other hand, negative work-life-balance has harmful effects on employees. Most employees had abuse alcohol due to this issue in the hospitality industry, which indirectly influences the organisation’s productivity. Additionally, most women have suffered from depression due to poor work-life balance in the hospitality industry. Besides, burnout, exhaustion, and stress are common among employees with poor work-life balance. Therefore, the employee’s commitment heavily depends on work-life balance, an essential requirement for employee retention [ 24 ]. This study states that it can be developed by adding more independent variables such as commitment and job satisfaction.

The highly negative work-life interference has amplified the turnover intentions of IT employees in Pakistan. They also found that the organisation that invested heavily in creating proper work-life balance recorded the lowest turnover among other organisations in the IT industry in Pakistan. Oosthuizen, Coetzee [ 6 ] revealed that the overall work-life balance had no clear influence on the satisfaction of an employee’s current job. Gender was a primary separation point of work-life balance variation among employees. Female employees looked more satisfied with their work-life balance than male employees [ 6 ]. In this light, work-life balance is one part of quality work life other than career opportunities and job characteristics. Organisational embeddedness has a positive and strong relationship with work-life balance. Positive work-life balance has a negative relationship with turnover intention [ 25 ]. However, the sample of this research was based on two healthcare firms. Since the whole world is tech-driven, it is realistic to focus on the IT industry too for generalisability of findings.

According to this study, superiors’ influence on work-life balance highly impacts job satisfaction. Supportiveness and the supervisor’s flexibility on subordinates’ help achieve the desired work-life balance for employees. As noted before, the employee turnover intention is heavily dependent on work-life balance. As such, a study on work-life balance can predict the turnover intention of an employee accurately compared to other factors. Work-life balance can be measured and categorised into three. Interference of work on personal life, work and family conflict and facilitation of work and family are those categories that the researcher suggested. The sample for the study of Kaur and Randhawa [ 16 ] was Indian private school teachers. The researcher suggested that formulating teacher-friendly policies to enhance work-life balance will reduce teachers’ turnover intentions. The researcher also suggested that the imbalance workload on employees supports increasing employee turnover intentions. However, most of the employees in this study were females.

Organisations that focused on employees’ proper work-life balance have recorded better efficiency, innovation, and talent retention [ 26 ]. Employee engagement and life satisfaction have been significantly mediated by the work-life balance of restaurant employees in Nevada, USA [ 27 ]. However, there are not sufficient recent researchers in Sri Lanka on work-life balance and employee retention. Therefore, taking up this study as an opportunity to research is essential. According to the above literature, hypothesis two has been constructed; work-life balance has a negative impact on employee turnover.

Employee happiness is a psychological feeling they have with the workplace. This is an essential factor in maintaining a successful and profitable organisation.Wright and Cropanzano [ 17 ] described happiness as phycological well-being. Personal well-being is one better way to explain employee retention. By moderating this factor, firms can achieve better employee turnover.

The workplace must be a source of happiness for employees. Unhappy employees in a workplace tend to increase employee turnover, absenteeism, low productivity, and time wasted deadlines. Creating happiness within the workplace is not a simple process. It is a comprehensive and continuous process. Happy employees generally have a fair idea of the organisation’s vision, mission and values. Employees in each department should have a clear idea about their goals [ 28 ]. However, happy employees are not always productive. But they can guide and explore things without organisations forcing them. Those employees required proper career management and support to be productive.

The workplace’s physical environment plays a major role in employee happiness and cheerfulness and friendliness of the physical environment are fundamentals. Employee’s attitude also has a more significant effect on happiness. Gratitude, appreciation, servant leadership from the organisation, hope and interpersonal connection are the main factors that affect the employee’s positive attitude. Humour, fun and games also play a major role in keeping employees happy. Other than those factors, wellness activities, celebrations and compensation are the minor factors affecting employee happiness. Based on the above cited literature, hypothesis three can be developed; employee happiness has a negative impact on employee turnover.

Management support

Management support is a must in the move from a good to a great company. Management stands by employees and supports them mentally and physically. Van den Heuvel, Freese [ 29 ] conducted research from the data of 699 employees at three divisions within the Dutch subsidiary of a multinational organisation. Management increased employee autonomy by supporting them to work from anywhere at any hour. This positively affected employee engagement and was negatively related to employee retention. Trust in management is a critical factor in employee turnover.

A cross-sectional survey has been conducted for front-line healthcare staff in China by Li, Mohamed [ 30 ] to measure the impact of organisational support on employee turnover intention. This study’s results could verify that organisational support negatively affected employee turnover intention. Saoula and Johari [ 31 ] studied this area and determined a negative relationship between organisational support and employee turnover intention. As both of the above explained research have been conducted in non-Western countries, the findings help to complete the theoretical framework for the current study in the Sri Lankan context.

Wong and Wong [ 5 ] researched the world’s most populous county, China, to identify the relationship between perceived organisational support and employee turnover. The findings suggested that trust, job security and distributive justice negatively impact employee turnover. However, China is an Asian country, and these similarities may apply to specific research findings in the Sri Lankan context.

Employee perception of management support for employee health is a factor in employee retention. Xiu, Dauner [ 32 ] studied this area with employees’ data from a public university, which was the first empirical examination of organisational support for employee health and retention. This kind of approach leads to building trust with employees. Moreover, these findings are essential to human resource managers who are willing to promote employee well-being at the workplace. Hypothesis four has been developed based on above discussed literature.

Career management

Initiatives must carry out different strategies for old and young employees because their priorities are different. Digest [ 18 ] discloses that young employees are impressed by flexible working opportunities, career advancement, positive working relationships and inclusive management forms. Young employees are more likely to be talented, leading to an organisation’s success and they can also become key workers in the company.

Saoula and Johari [ 31 ] researched the effect of personality traits (big five) on employee turnover intention. The researchers state that the relationship between the big five personality traits and turnover intention will support early prediction of employee turnover intentions. Identifying employee’s personalities and helping them to find the most suitable job role is a long-term process, though it will be highly advantageous for both employees and the organisation.

Rawashdeh and Tamimi [ 33 ] focused on the latest management developments of leading organisations worldwide. They state that there is a strong relationship between the availability of training and supervisor support for training and organisational commitment. Further, they proved that there is a strong negative association between organisational commitment and employee retention. These research findings verify the social exchange theory [ 34 ]. However, the research suggested that the above study can improve by adding new factors like motivation and co-worker support for training. Hypothesis five has been developed by concluding the above explained literature.

Innovative work behaviour

Innovative behaviour is a leading factor in gaining a competitive advantage. Shih, Posthuma [ 35 ] investigated the negative impacts of innovative work behaviour on employee turnover and conflict with co-workers. According to the studies, there is a positive relationship between innovative work behaviour and employee turnover. Further, it found that perceived distributive fairness can negatively moderate this relationship. However, the writer has suggested extending the research to different geographical locations and industries.

The organisational learning culture is a key factor for innovative work behaviour. Saoula, Fareed [ 36 ] conducted research in Malayasia, a developing country in Asia to examine the relationship between organisational learning culture and employee turnover intention. The organisational learning culture improves learning capability, supports sustainable development, and affects organisation’s positive changes. As organisational learning culture and employee turnover intention have a negative relationship, the result helps to identify the impact of innovative work behaviour. According to the existing literature, limited studies have been conducted on this topic.

Agarwal, Datta [ 4 ] conducted research with managerial employees in India to examine the relationship between innovative work behaviour and employee turnover. This study asserted that the variables have an inverse relationship. As innovative work behaviour examinations in an Asian county country like India, it is important to consider this variable in this model. With the presence of the above mentioned literature, hypothesis six has been formulated.

Leader member exchange

As per many leadership methods, leader member exchange depends on the leadership style. Tobias M. Huning [ 37 ] conducted research to identify the effect of servant leadership on employee turnover. Servant leadership supports employee empowerment, development, interpersonal acceptance, and courage. This study found that servant leadership negatively impacts employee turnover. However, this leadership style does not directly affect employee retention. Gyensare, Kumedzro [ 38 ] studied the impact of transformational leadership on employee turnover. This type of leadership supports work engagement of the employee, and it negatively relates to employee retention. Considering both aspects, the study found that increasing work engagement is vital to curtail employee retention.

Leader support is an indirect factor in employee retention. According to the studies, employee engagement and work-life balance act as mediation for perceived supervisor support and employee turnover relationship [ 16 ]. The supervisor supports the career success of employees and it affects both directly and indirectly the career success of the employee and retention one year later [ 9 ]. Therefore, this study shows that co-worker support has a significantly positive impact on employee turnover. However, these results maintained the diversity of the sample. As this has been examined in India, a South Asian country, the same results can apply to the Sri Lankan context. Based on the above-mentioned literature, hypothesis seven has been developed; leader member exchange has a negative impact on employee turnover.

Co-worker support

Co-worker support will be in both formal and informal ways and in two different forms, emotional support, and instrumental support. The support of co-workers enhances the confidence level of the employee. Further, it helps to accept challenges in the work environment. Kmieciak [ 19 ] has worked on research to identify the effect of co-worker support on employee retention in the IT industry. However, a significantly negative impact was not evident on co-worker support. As this is a recently published research paper, the results are more valuable to the current research. The researcher has investigated more about the impact of subordinates’ support. Here, the analysis has been done only with 118 employees from a Polish software company. Considering the above limitations enables researchers to further study this topic with a larger sample size for generalisability of findings.

Abugre and Acquaah [ 39 ] researched in Ghana to identify the relationship between co-worker relationships and employee retention. The findings of this research imply that co-worker support is negatively associated with employee turnover. It further stated that cynicism of the employee is positively associated with employee turnover. The speciality of this research is identifying the importance of encouraging co-worker support rather than employee cynicism. These newly published research results can be used along with all other variables that affect employee turnover. According to the above literature, hypothesis eight has been constructed.

These studies have a common limitation in gathering more independent variables and analysing the impact. Therefore, a need exists to measure the effect of job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support together on employee turnover.

In Sri Lanka, no research has so far considered all eight factors affecting employee turnover in one study. With the above-mentioned literature findings, this study assists the government in identifying the impact of every factor on employee turnover in startups in Sri Lanka.

Data and methodology

This study was reviewed and approved by Sri Lanka Institute of Information Technology Business School and the Sri Lanka Institute of Information Technology ethical review board. Data were collected through a questionnaire using both online and manual channels. Each individual in this study gave verbal consent prior to the formal interview. The data was collected from August to September 2022 ( S1 Appendix ). The authors directly distributed the questionnaire. Moreover, authors could contact management in startups and distribute the questionnaire in their organisation. The questionnaire is composed of ten (10) sections. The first part of the questionnaire was designed to collect the demographic characteristics of the correspondents. The second to ninth sections focused on independent variables, job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support. Finally, the tenth section was designed to identify employee turnover indicator. A minimum of four questions was added under each indicator. The researchers facilitated anonymously answering all the questions in the questionnaire. The participants should be a part of startup and he/she should consider the behaviour and culture of that startup when answering the questions. All nine indicators were covered by Likert scale questions from 1 to 5 rating scale, depicting (1) strongly disagree to (5) strongly agree to collect respondents’ attitudes and opinions. Each respondent took about 10–15 minutes to complete answering the questionnaire and took approximately 5–7 minutes to fill out the questionnaire. Furthermore, the average values were calculated to measure the value given by respondents for each indicator. The data file used for the study is presented in S2 Appendix .

PricewaterhouseCoopers [ 15 ] statistics determined the study’s population and it explained the total number of elements to be focused on in this study. The researchers applied a random sampling method, mainly employees who are a part of or have been a part of the startup. This sampling technique was appropriate because it was free of bias. The sample size was selected by referencing the Krejcie and Morgan sampling table and Calculator.net [ 40 ] with a confidence level of 95% and 7% of margin of error. The calculation results indicated a minimum of 171 professionals. A stepwise ordered probit analysis method was used as the selected variables are widely used indicators for employee turnover; therefore, a micro-level analysis was required to study how these variables impact. A pilot survey was conducted to identify whether the purpose of the questions was clear to the respondents.

The data used for the estimation include 83 low employee turnover, 79 moderate employee turnover and 68 high employee turnovers of employees in Sri Lankan startups. The initial estimation results are presented in Table 1 .

Source: Authors’ compilation based on survey data.

The mean values of all independent variables are greater than 2.5. Respondents were further grouped as per demographic and geographic characteristics. The respondents’ gender identity ratio is nearly 1: 2. When considering the age groups, most are in 20–30 years. Many employees in startup companies are in their twenties and are graduates. The respondents represent all the districts in Sri Lanka, most of which are from Kalutara, Colombo, Galle and Matara districts.

Research framework and hypothesis

The conceptual framework was developed with the literature review and existing knowledge, as illustrated in Fig 2 . This model was developed with the combination of eight hypotheses. These independent variables have been identified as critical factors that impact employee turnover.

Source: Authors’ compilation.

The following hypotheses have been developed in line with the research framework.

• Hypothesis 1 : Job satisfaction has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 2 : Work-life balance has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 3 : Happiness has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 4 : Management support has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 5 : Career management has a negative impact on employee turnover in startups in Sri Lanka.
• Hypothesis 6 : Innovative work behaviour has an impact on employee turnover in startups in Sri Lanka.
• Hypothesis 7 : Leader member exchange has an impact on employee turnover in startups in Sri Lanka.
• Hypothesis 8 : Co-worker support has a negative impact on employee turnover in startups in Sri Lanka.

Methodology

This study focuses on the demographical variables that affect employee turnover. For this, the present study’s authors considered employee feedback concerning Sri Lankan startups. The ordered probit regression determines the significant variables [ 41 ]. The probit model is an estimation technique for equations with dummy dependent variables that avoids the unboundedness problem of the linear probability model by using a variant of the cumulative normal distribution [ 42 ]. Further, this study examines the likelihood of three types of employee turnover. Accordingly, employee turnover is divided into three categories, considering the equality of data for each category based on employee turnover.

• Group 1 (y = 1): low = mean value of the employee turnover less than 1.50
• Group 2 (y = 2): moderate = mean value of the employee turnover greater than 1.5 and less than or equal to 2.25
• Group 3 (y = 3): high = mean value of the employee turnover greater than 2.25 and less than or equal to 5

The following equation represents the general form of the ordered probit model.

The y i value represents i th value of the dependent variable, employee turnover and x i represents the i th common independent variable. The β value is a vector parameter and ℇ i considered as the normally distributed random error term with a zero mean. The following ordered probit model has been developed by detailing the general equation.

Table 2 indicates the variables explained in previous literature and definitions of the previously mentioned equation that affects employee retention. The forward stepwise regression model has been used to analyse the data set.

Results and discussions

It is mandatory to test the internal consistency reliability before data analysis. The most common measure of reliability is Cronbach’s alpha (α) value, which determines whether the internal instruments are constant [ 43 ]. The reliability results for each indicator are presented in Table 3 . As all the Cronbach alpha values are greater than 0.6 scale reliability coefficients, all variables in this study are acceptable.

Analytical sample (N = 230)

Source: Authors’ calculation based on survey data

In the first step, the initial ordered probit model was executed, and this model explained 73% of the variation in employee retention by the variation in independent variables. S3 Appendix contains the table of the initial ordered probit regression model. The ordered probit model forwarded with the forward stepwise technique to identify the exact number of variables that impact employee turnover. A forward stepwise technique was adopted for the variable selection in each specification. Here, the new variables for selection were considered with a p-value < 0.20 and the previously selected variable for removal with a p-value ≥ 0.25. Three different model diagnostic criteria were considered in assessing the reliability of the results. The forward stepwise methodology suggested that the significance of the existing variables could be increased by adding more variables to the model. Marginal effects were separately calculated for low, moderate, and high employee turnover. Table 4 presents the final estimation results of the ordered probit model and illustrates the substantive effects of the independent variables. Here, 71.74% of the variation in employee turnover is explained by the variation in job satisfaction, LMX and co-worker support, considering the sample size and independent variables.

*** significant at the 1% level

** significant at the 5% level and * significant at the 10% level.

Source: Authors’ calculation based on surveying data.

Looking at the signs of the marginal effects in Table 4 , overall, high employee turnover is negatively associated with job satisfaction, co-worker support, and innovative work behaviour, whereas high employee turnover is positively associated with leader member exchange.

To control for the potential effect on different levels of employee turnover, the age factor was also included in the model, the coefficient of which implies that high employee turnover is 0.20 points and 0.60 points for the 20–30 years age range and 31–40 years age range, respectively. Employee turnover in 31–40 years age range employees is higher than that of other age ranges.

The marginal effects of the psychographic variables reveal that a 1% increase in job satisfaction increases the probability of low employee turnover by 0.47 percentage points. Similarly, 1% increase in job satisfaction decreases the probability for high employee turnover by 0.43 percentage points. With this observation, it can be stated that improving job satisfaction will highly affect to reduce high employee turnover. These results verify the existing statements indicating that job satisfaction has the highest significant and negative estimate value.

The estimated marginal effect of low employee turnover is 0.47 percentage points higher for employees in Sri Lankan Startups with a 1% increase in leader member exchange. High employee turnover is associated with leader member exchange increasing probability by 0.43. However, this study reflects similar findings to those of Tymon, Stumpf [ 9 ]. The reason behind the positive relation is employees learn fast and get qualified with the support of their leaders and then quit the company within the next few years.

Both leader member exchange and co-worker support are significant at the 99% level of employee turnover in the Sri Lankan context. When considering the independent variables for employee turnover in startups in Sri Lanka, co-worker support is a critical factor in determining the level of employee turnover. The 1% increase in co-worker support will also increase the probability of low employee turnover by 0.40 percentage points. But concurrently, change in co-worker support will negatively impact high employee turnover. The results ensure that encouraging co-worker support is crucial rather than employee cynicism.

Innovative work behaviour is one of the most critical factors in employee turnover. With a 1% increase in innovative work behaviour, the estimated marginal effect of high employee turnover is 0.27 percentage points lower for employees in Sri Lankan startups. The results of Shih, Posthuma [ 35 ] indicate a positive relationship exists between innovative work behaviour and employee turnover. However, this study concludes by emphasising the importance of retaining the innovative employees to remain competitive in the industry. For this, startups need to improve and enhance employees’ innovative behaviour and, concurrently, to prevent such employee retention.

Entrepreneurs are the founders of startups. Employees’ entrepreneurial dreams positively affect employee intention to startups. Employees in the startups also will have an ideation to start their own business. According to the study by Li, Li [ 44 ] the mediating role of employees’ entrepreneurial self-efficacy and the moderating role of job embeddedness in the influence of entrepreneurial dreams on employees’ turnover intention to startup.

The main objective of this research is to analyse the impact of critical and newly identified factors on employee turnover in one study. This issue occurs when employees leave the company by giving short notice or quitting unexpectedly. The analysis found that gender and age impact employee turnover in startups in Sri Lanka. In startups, many employees are in the 20 to 30 years age range. Employees between 31 and 40 years show a higher tendency to leave the startups. In Sri Lanka, only 8% of startups have been in operation for more than five years [ 15 ], indicating that the businesses are not stabilised and are still in its early stages. To prevent employee turnover, startups must improve employee job satisfaction. As per the findings, increasing job satisfaction has a significant impact on reducing employee turnover. For most employees in startups, it is their first job. During this time, employees gain work experience and become experts in the field. The leaders allocate much time to train their human resources and the company should gain strategic benefits from this investment. The results of the study prove that leader member exchange has a positive impact on employee turnover, as verified by Tymon, Stumpf [ 9 ] too about this relationship. To overcome this situation, as managers, it is vital to discuss with employees about their career paths, employee interests and company’s business plans while improving their technical skills and experience. This way, the mutual interest of both the employee and the company can be identified and handled. It also builds trust between the company and the employees. Regular support environment and ease of doing business is 66% highly important factor for the success of Sri Lankan startups [ 15 ]. This environment can be easily created with the level of co-worker support to the employee. Employee turnover can be more costly than a startup can imagine, with disruptions to business operations when their employees’ suddenly quit jobs. Therefore, it is must to attain above discussed facts. These results and discussions can be taken as insights to better understand and curtail employee turnover. This study will assist Sri Lankan startups where their skilled employees, who are also experts plausibly remain, enabling the businesses to expand to new markets. Usually, issues relevant to profit-making and business performance, such as a drop in sales and manufacturing are identified by startups. However, employee turnover is generally not identified as an organisational issue.

Theoretical implications

The current study empirically investigated the impact of job satisfaction, innovative work behaviour, co-worker support and leader member exchange on employee turnover. According to the authors’ knowledge, no prior studies were conducted considering the combined impact of all the independent variables on employee turnover. Therefore, this study strengthens the literature by demonstrating how job satisfaction, innovative work behaviour, co-worker support and leader member exchange impact employee turnover in Sri Lankan startups.

The findings reveal that job satisfaction has a negative impact on employee turnover. This finding is consistent with the previous study, job satisfaction significantly predicted employee turnover [ 6 ]. This study consolidates past findings that male employees have higher turnover intention than female employees. Female employees have comparatively higher-level job satisfaction [ 8 ]. This study implies that employees age 31 to 40 years have high employee turnover intention. The research findings are similar to Lu, Lu [ 8 ]; the older employees have high intentions to leave the company.

Practical implications

The study’s findings illustrate the importance of job satisfaction, innovative work behaviour, co-worker support and leader member exchange in affecting employee turnover in startups. This study provides managerial insights on lowering employee turnover in Sri Lankan startups. First, startups need to be aware that experienced employees in startups can be easily taken by well-established companies because, later, they have hand on experience and skills. Therefore, it is important to implement strategies for a solid career development plan, career growth, personal status, and employee recognition. As job satisfaction can predict employee turnover, it is a must to measure those indicators and maintain a favourable level at all times.

Innovative work behaviour is increasingly becoming a significant factor in employee retention. As good startups are a blend of creativity and competitive advantage, it is a must to focus on the IWB of the employee. LMX is a turning point for expanding the business. More importantly, healthy LMX can boost employees’ work engagement. This healthy level can maintain by conducting regular meetings, training programs and informal mentorship with employees’ immediate supervisors [ 8 ]. Further, management can allow employees at all levels to present their fresh ideas and incorporate them to influence organisation’s decision making process. These processes can lower employee hierarchy and build strong relationships while recognising them in the company.

It is important to retain trained and skilled employees who started their career paths in the organisation. Such employees can drive the organisation to success. While measuring employees’ job satisfaction, managers nee to conduct standard ways on performance and improvements of the organisation. It is better if companies can create their key performance indicators because it will help protect the organisation’s core values while expanding the company. Furthermore, having a flexible approach to work in an organisation culture will increase the trust between employees and the organisation. Giving the freedom to take risks and not allowing them to feel alone during work will give value to employees. Finally, all the above actions will strongly impact reducing employee intention to leave the organisation.

Research limitations and future research directions

Further research can improve the study as follows. First, this research includes feedback from 230 employees. More than one-third of these employees are from the IT industry. Since Sri Lankan startups are technology-driven, this ratio is more reliable. However, this research can be generalised by obtaining employees’ feedback from other industries. Secondly, in this questionnaire, the minimum number of questions for independent factors is four. This is to minimise the possibility of demotivating the employee by giving a lengthy and complex questionnaire. Therefore, in future, researchers can design questionnaires incorporating more questions to cover a wider range of independent factors, including open-ended ones. Thirdly, in this sample, many employees were in their twenties, and most hadn’t worked for more than two companies (i.e. employers). As such, it is reasonable to assume that participants’ response is somewhat limited to obtain the broader picture of the research problem. Future researchers can focus on different age groups and analyse the same factors concerning employee retention. Finally, new research can be executed by adopting a case study approach (including case studies representing various types of industries etc), such as employees in multinational companies.

Supporting information

S1 appendix, s2 appendix, s3 appendix, s4 appendix, acknowledgments.

The authors would like to thank Ms. Gayendri Karunarathne for proof-reading and editing this manuscript.

Funding Statement

The authors received no specific funding for this work.

Data Availability

• PLoS One. 2023; 18(2): e0281729.

Decision Letter 0

21 Dec 2022

PONE-D-22-30684How are employee turnover intentions created in Sri Lankan Startups?PLOS ONE

Dear Dr. Ruwan Jayathilaka,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================Reviewer#1Abstract: Rewrite the abstract after manuscript correction and provide picture of whole study.

In the first paragraph of introduction used only this (2016) citation. This citation not justify the paragraph.

Introduction paragraph is not justifying the problem and bag-round of study. Revised the introduction and use the recent citations to justify and logically make connection with them.

 In the introduction (second paragraph) , the contribution of study is confused with variable relationships; why are these relationships a contribution of study? Need strong justification.

 Overall, I suggest a major rewrite of the introduction. It should provide an overview of and focus on one issue with recent citations.

Revised all literature variables and link with variables with new citation

In the literature, justify these hypotheses with literary support.

In literature, justify the conceptual model and theoretical gap.

Where is the total population? How did you choose the sample size? And how did you choose which method, unit of analysis, and research technique to use? Provide justification. Why is this method appropriate for this data set?

General: identifying flaws in the study's design (revised methodology section) and justifying technique

Write the theoretical contribution related to a model. Reviewer#2

Mention the scope of the study, the population, simple size, data collected from…..

Mention the analysis technique/ tool used in the study

Introduction:

The introduction is not clear and very less literature is used. Follow these instruction: The introduction should briefly place the study in a broad context and highlight why it is important. It should define the purpose of the work and its significance.

The current state of the research field should be reviewed carefully and key publications cited. Briefly mention the main aim of the work and highlight the main conclusions. Keep the introduction comprehensible to scientists working outside the topic of the paper.

What is the main research focus?? Firm performance? Employee retention? Employee turnover intention?

Focus more on the main issue of the study

Make the theoretical and practical gaps more clear ?

Why Sri Lanka?

Why stratup s in Sri Lanka? Why employee working in startups in Sri Lanka

What was the key motivation behind focusing on factors affecting employee turnover intention in stratups in Srilanka?

Please, properly justify why the selected variables are included in the model. How did you derive the 08 variables ??

As ,many studies conducted in the world and in Sri Lanka about this topic, what us the main contribution of your study?

The paper should incorporate a more solid argumentation that allows to justify the reason that allows to select the explanatory variables that are considered in the empirical analysis.

Literature and hypotheses development"

Improve the argumentation of hypothesis. Whether, the hypotheses are formulated separately or after the literature review of each section, it should be properly argued.

Each hypothesis should be formulated at the end of a literature section of the each variable presenting the different findings that have been made throughout the literature. With these arguments a reasoning should be developed in a certain direction and the conclusion of that reasoning should be the formulated hypothesis. In the current version of this manuscript the authors are including different aspects of previous literature, but it does not exist any convincing storyline in any direction.

Highlight controversial and diverging hypotheses when necessary.

Researcher should include a summary table / review on studies conducted on Employee Turnover Intention in Sri Lanka to support the literature and arguments.

Below papers has some interesting implications and understanding of concepts and relations that you could discuss in your introduction and literature review and how it relates to your work:

-Li, M., Li, J., Chen, X. “Employees’ Entrepreneurial Dreams and Turnover Intention to Start-Up: The Moderating Role of Job Embeddedness”, 2022, International Journal of Environmental Research and Public Health 19(15),9360

- Saoula, O.,Johari, H, “The mediating effect of organizational citizenship behavior on the relationship between perceived organizational support and turnover intention: A proposed framework” International Review of Management and Marketing, 2016, 6(7), pp. 345–354

- Saoula, O., Johari, H., Bhatti, M.A “The mediating effect of organizational citizenship behaviour on the relationship between personality traits (Big Five) and turnover intention: A proposed framework”, International Business Management, 2016, 10(20), pp. 4755–4766.

Zito, M., Emanuel, F., Molino, M., Cortese, C. G., Ghislieri, C., & Colombo, L. (2018). Turnover intentions in a call center: The role of emotional dissonance, job resources, and job satisfaction. PloS one, 13(2), e0192126.

- Saoula, O., Johari, H., Fareed, M, “A conceptualization of the role of organisational learning culture and organisational citizenship behaviour in reducing turnover intention”, Journal of Business and Retail Management Research, 2018, 12(4), pp. 126–133

- Saoula, O., Fareed, M., Ismail, S.A., Husin, N.S., Hamid, R.A, “A conceptualization of the effect of organisational justice on turnover intention: The mediating role of organisational citizenship behaviour”, International Journal of Financial Research, 2019, 10(5), pp. 327–337.

Poku, C. A., Alem, J. N., Poku, R. O., Osei, S. A., Amoah, E. O., & Ofei, A. M. A. (2022). Quality of work-life and turnover intentions among the Ghanaian nursing workforce: A multicentre study. PloS one, 17(9), e0272597.

- Saoula, O., Fareed, M., Hamid, R.A., Al-Rejal, H.M.E.A., Ismail, S.A, “The moderating role of job embeddedness on the effect of organisational justice and organisational learning culture on turnover intention: A conceptual review”, Humanities and Social Sciences Reviews, 2019, 7(2), pp. 563–571

-Li, Q., Mohamed, R., Mahomed, A., & Khan, H. (2022). The Effect of Perceived Organizational Support and Employee Care on Turnover Intention and Work Engagement: A Mediated Moderation Model Using Age in the Post Pandemic Period. Sustainability, 14(15), 9125.

- Amin, M., Othman, S.Z., Saoula, O, “The Effect of Organizational Justice and Job Embeddedness on Turnover Intention in Textile Sector of Pakistan: The Mediating Role of Work Engagement” Central Asia and the Caucasus, 2021, 22(5), pp. 930–950

Methodology:

How experiment was conducted?

How participants were recruited?

What are the instructions of experiment?

How much was time given to each participant?

What are the theoretical implications of the study ?

Practical implications needs further discussion.

Add/ involve more recent citations/studies where necessary

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Additional Editor Comments:

The paper is generally well written and structured. However, I believe that paper has some shortcomings in terms of

Abstract: Rewrite the abstract after manuscript correction and provide picture of whole study.

In the introduction (second paragraph) , the contribution of study is confused with variable relationships; why are these relationships a contribution of study? Need strong justification.

�Overall, I suggest a major rewrite of the introduction. It should provide an overview of and focus on one issue with recent citations.

�Write the theoretical contribution related to a model.

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Reviewer #2: Revised topic after correction

 The paper is generally well written and structured. However, I believe that paper has some shortcomings in terms of

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Author response to Decision Letter 0

16 Jan 2023

Point by point response to editor and reviewers

Dear editor and the reviewers,

We would like to express our profound appreciation to the editor and the reviewers for the valuable comments and suggestions made on our manuscript which were very helpful in revising and improving it.

Please note that the line numbers referred in this document is aligned with the revised manuscript which has track changes.

Reviewer 1 Comment 1: Abstract: Mention the scope of the study, the population, simple size, data collected from…

Authors’ Response: Thank you very much for the valuable comment. The suggestions have been incorporated in the revised manuscript from lines 30 to 32.

“…The study population was professionals who have been a key part of Sri Lankan startups, which involved 230 respondents. …”

Reviewer 1 Comment 2: Abstract: Mention the analysis technique/ tool used in the study

Authors’ Response: Thank you very much. Your comment is well noted. The analysis technique was added in the abstract of the revised manuscript from lines 32 to 33.

“…Data analysis was performed through a forward stepwise technique through STATA …”

Reviewer 1 Comment 3: Introduction: The introduction is not clear and very less literature is used. Follow these instructions: The introduction should briefly place the study in a broad context and highlight why it is important. It should define the purpose of the work and its significance.

Authors’ Response: Thank you very much for the detailed comment. This helps to strength the introduction with recent literatures, better argument, and justifications. The following literature were added in the introduction section with citation nos. 10, 11, 1, and 3 of the revised manuscript.

Reviewer 1 Comment 4: Introduction: The current state of the research field should be reviewed carefully, and key publications cited. Briefly mention the main aim of the work and highlight the main conclusions. Keep the introduction comprehensible to scientists working outside the topic of the paper.

Authors’ Response: Well noted your comment. A major update has done in the introduction section of the study.

Reviewer 1 Comment 5: Introduction: What is the main research focus?? Firm performance? Employee retention? Employee turnover intention?

Authors’ Response: Thank you very much for your comment. This study focuses/ aims to analyse the impact of job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader-member-exchange, and co-worker support on employee turnover in startups in Sri Lanka. New paragraph has been added in the revised manuscript to explain more about firm performance, employee retention, employee turnover intention from lines 74 to 80.

“Firm performance reflects the ability of an organisation to use its human resources and other material resources to achieve its goals and objectives. Firm performance belongs to the economic category, and it should consider the use of business means efficiently during the production and consumption process [12]. Employee retention is defined as encouraging employees to remain in the organisation for a long period or the organisation’s ability to minimised employee turnover [13]. Turnover intention is the intention of the employee to change the job or organisation voluntarily [14].”

Reviewer 1 Comment 6: Introduction: Focus more on the main issue of the study

Authors’ Response: Thank you for your valuable comment. More priority was given to discuss the main issue of the study. In the first paragraph of the introduction section have updated to highlight the main issue with latest literature. New content has been added from lines 55 to 58.

“…The issue of employee turnover is considered as one of the global obstacles for organisations worldwide, which directly and adversely affects strategic plans and opportunities of gaining competitive advantages [3].…”

Reviewer 1 Comment 7: Introduction: Make the theoretical and practical gaps more clear

Authors’ Response: Thank you very much for the comment. The revised manuscript has been updated by pointing out the existing research gaps. New content has been added from lines 128 to 131.

“…to the best of the authors’ knowledge, there was no previous research done by local researchers that includes all the widely measured variables investigating the combined effect on employee turnover…”

Reviewer 1 Comment 8: Introduction: Why Sri Lanka?

Authors’ Response: Thank you for the comment. Sri Lanka has selected as the case study because to the best of the authors’ knowledge, no any previous research has been done by local researchers considering all the widely affected eight variables together. It leads to improve the introduction part of the paper. Suggestions have been incorporated in the revised manuscript from lines 127 to 130.

“…Sri Lankan context has been selected as the case study. This is because, to the best of the authors’ knowledge, there was no previous research done by local researchers that includes all the widely measured variables investigating the combined effect on employee turnover…”

Reviewer 1 Comment 9: Introduction: Why stratup s in Sri Lanka? Why employee working in startups in Sri Lanka

Authors’ Response: Thank you very much for the comment and this is well noted. Suggestions have been incorporated in the revised manuscript from lines 85 to 89.

“…Sri Lanka has a middle rank of ease of doing business. With these favourable conditions and educational and family backgrounds, many people like to operate/apply their new idea and fill the market gap. The new generation in Sri Lanka are interested/are keen on innovations at work and being a part of unique products or services…”

Reviewer 1 Comment 10: Introduction: What was the key motivation behind focusing on factors affecting employee turnover intention in stratups in Sri Lanka?

Authors’ Response: Thank you very much for the valuable comment. The key motivation of focusing on factors affecting employee turnover intention was to gather widely affected factors together and measure the impact of each indicator at the micro level. The idea was added in revised manuscript from line 110 to 111.

“Based on their knowledge and the existing literature, authors have considered widely used factors to investigate the employee turnover issue …”

Reviewer 1 Comment 11: Introduction: Please, properly justify why the selected variables are included in the model. How did you derive the 08 variables?

Authors’ Response: Thank you for the comment. According to the past literature authors have selected widely used indicators for employee turnover and among these eight variables have been selected. The justification has included in the revised manuscript from line 110 to 117.

“Based on their knowledge and the existing literature, authors have considered widely used factors to investigate the employee turnover issue. Therefore, job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange and co-worker support were selected based on previous literature findings [4-6, 8, 17-19]. As in the previous papers and along with the current study’s results, authors identified both positive and negative impacts on employee turnover among Sri Lankan startups..”

Reviewer 1 Comment 12: Introduction: As, many studies conducted in the world and in Sri Lanka about this topic, what us the main contribution of your study?

Authors’ Response: Well noted your comment. Thank you! The contribution of the study has highlighted in the revised manuscript from lines 120 to 133.

“…The present study’s scientific value can be elaborated by comparing it with previous studies. This study’s contribution can be explained in five ways. Firstly, the most critical and newly considered factors were identified together with the support of past literature. Secondly, the present study was divided/classified into different levels of employee turnover. As such, by y considering the various levels, the micro-level changes, and probabilities of the impact on employee turnover can be better identified. Further, this study helps to reduce the methodological gap. Thirdly, the Sri Lankan context has been selected as the case study. This is because, to the best of the authors’ knowledge, there was no previous research done by local researchers that includes all the widely measured variables investigating the combined effect on employee turnover. Fourthly, the analysis results can be used to identify the strengths and weaknesses of startups in Sri Lanka. Finally, this study identifies the challenges faced by startups and identifies how policy modifications can strengthen the startup ecosystem.”

Reviewer 1 Comment 13: Introduction: The paper should incorporate a more solid argumentation that allows to justify the reason that allows to select the explanatory variables that are considered in the empirical analysis.

Authors’ Response: Well noted your comment. Thank you! In the revised manuscript a paragraph was added to present the justification to select the variables in the empirical analysis from lines 110 to 117.

“Based on their knowledge and the existing literature, authors have considered widely used factors to investigate the employee turnover issue. Therefore, job satisfaction, happiness, work-life balance, career management, management support, innovative work behaviour, leader member exchange and co-worker support were selected based on previous literature findings [4-6, 8, 17-19]. As in the previous papers and along with the current study’s results, authors identified both positive and negative impacts on employee turnover among Sri Lankan startup.”

Reviewer 1 Comment 14: Literature and hypotheses development: Improve the argumentation of hypothesis. Whether the hypotheses are formulated separately or after the literature review of each section, it should be properly argued.

Authors’ Response: Thank you very much for your comment. The paper has been updated with the improved argument in literature review. The hypotheses have been formulated at the end of each sub section of literature review. New contents have incorporated as per the below line numbers.

(Line numbers 238 and 240)

“As per the literature, job satisfaction is an important factor in determining the impact on employee turnover. Accordingly, hypothesis one has been developed.”

(Line numbers from 283 to 284)

“…According to the above literature, hypothesis two has been constructed; work-life balance has a negative impact on employee turnover.”

(Line numbers from 306 to 308)

“…Based on the above cited literature, hypothesis three can be developed; employee happiness has a negative impact on employee turnover.”

(Line numbers from 336 to 337)

“…Hypothesis four has been developed based on above discussed literature.”

(Line numbers from 356 to 357)

“…Hypothesis five has been developed by concluding the above explained literature.”

(Line numbers 378 and 379)

“…With the presence of the above-mentioned literature, hypothesis six has been formulated.”

(Line numbers from 397 to 399)

“…Based on the above-mentioned literature, hypothesis seven has been developed; leader member exchange has a negative impact on employee turnover.”

(Line numbers from 417 to 419)

“…These newly published research results can be used along with all other variables that affect employee turnover. According to the above literature, hypothesis eight has been constructed.”

Reviewer 1 Comment 15: Literature and hypotheses development: Each hypothesis should be formulated at the end of a literature section of each variable presenting the different findings that have been made throughout the literature. With these arguments a reasoning should be developed in a certain direction and the conclusion of that reasoning should be the formulated hypothesis. In the current version of this manuscript the authors are including different aspects of previous literature, but it does not exist any convincing storyline in any direction.

Authors’ Response: Thank you very much for this detailed comment. The revised version has strengthened the formulation of hypothesis. Hypothesises formulations has been incorporated at the end of each sub section in the literature review and the storyline has been built. New contents have been incorporated as per the below line numbers.

Reviewer 1 Comment 16: Literature and hypotheses development: Highlight controversial and diverging hypotheses when necessary.

Authors’ Response: Thank you for your valuable comment. This leads to build a discussion in literature review independent variables sub section. New contents have been included as per the below line numbers.

(Line numbers from 187 to 191)

“…Moreover, the descriptive statistics found a high level of job satisfaction and the intention to leave was at the mid or average level of the scale. Camara further stated that job satisfaction clearly implies the feeling about their job. But some research findings can be contradictory. Some employees are fully satisfied with the job and still want to leave the organisation for various reasons…”

(Line numbers from 205 to 207)

“…However, they didn’t observe any significant interaction between overall work-life balance and job satisfaction in predicting employee turnover intention. With these results, this indicator must be examined further.”

(Line numbers from 414 to 415)

“…It further stated that cynicism of the employee is positively associated with employee turnover…”

(Line numbers from 405 to 406)

“…However, a significantly negative impact was not evident on co-worker support.…”

Reviewer 1 Comment 17: Literature and hypotheses development: Researcher should include a summary table / review on studies conducted on Employee Turnover Intention in Sri Lanka to support the literature and arguments

Authors’ Response: Thank you very much for your comment, Literature summary table was added as an appendix, and it was cited in the revised manuscript from line numbers 168 to 169.

“Appendix S4 contains the literature summary of the above presented literature search flow diagram. The following sections present the details of each category.”

Reviewer 1 Comment 18: Literature and hypotheses development: Below papers has some interesting implications and understanding of concepts and relations that you could discuss in your introduction and literature review and how it relates to your work:

- Zito, M., Emanuel, F., Molino, M., Cortese, C. G., Ghislieri, C., & Colombo, L. (2018). Turnover intentions in a call center: The role of emotional dissonance, job resources, and job satisfaction. PloS one, 13(2), e0192126.

- Poku, C. A., Alem, J. N., Poku, R. O., Osei, S. A., Amoah, E. O., & Ofei, A. M. A. (2022). Quality of work-life and turnover intentions among the Ghanaian nursing workforce: A multicentre study. PloS one, 17(9), e0272597.

Authors’ Response: Thank you very much for the detailed comment and sharing the latest literature related to this paper. New literature has been incorporated in the introduction, literature review and results and discussion sections the paper as per the below line numbers.

(Line numbers from 45 to 46)

“…Companies need to give high priority to employee development and predict employee behaviour [1]…”

(Line numbers from 55 to 58)

“…The issue of employee turnover is considered as one of the global obstacles for organisations worldwide, which directly and adversely affects strategic plans and opportunities of gaining competitive advantages [3]…”

(Line numbers from 66 to 68)

“…Further, promoting employee well-being leads to decrease employee turnover [10]. Providing psychological and social support through counselling promotes the quality of work-life [11]...”

(Line numbers from 318 to 325)

“A cross-sectional survey has been conducted for front-line healthcare staff in China by Li, Mohamed [30] to measure the impact of organisational support on employee turnover intention. This study’s results could verify that organisational support negatively affected employee turnover intention. Saoula and Johari [31] studied this area and determined a negative relationship between organisational support and employee turnover intention. As both of the above explained research have been conducted in non-Western countries, the findings help to complete the theoretical framework for the current study in the Sri Lankan context.”

(Line numbers from 344 to 349)

“Saoula and Johari [31] researched the effect of personality traits (big five) on employee turnover intention. The researchers state that the relationship between the big five personality traits and turnover intention will support early prediction of employee turnover intentions. Identifying employee’s personalities and helping them to find the most suitable job role is a long-term process, though it will be highly advantageous for both employees and the organisation.”

(Line numbers from 365 to 373)

“The organisational learning culture is a key factor for innovative work behaviour. Saoula, Fareed [36] conducted research in Malaysia, a developing country in Asia to examine the relationship between organisational learning culture and employee turnover intention. The organisational learning culture improves learning capability, supports sustainable development, and affects organisation's positive changes. As organisational learning culture and employee turnover intention have a negative relationship, the result helps to identify the impact of innovative work behaviour. According to the existing/available literature, limited studies have been conducted on this topic.”

(Line numbers from 608 to 612)

“Entrepreneurs are the founders of startups. Employees’ entrepreneurial dreams positively affect employee intention to startups. Employees in the startups also will have an ideation to start their own business. According to the study by Li, Li [44] the mediating role of employees’ entrepreneurial self-efficacy and the moderating role of job embeddedness in the influence of entrepreneurial dreams on employees’ turnover intention to startup.”

Reviewer 1 Comment 19: Methodology: How experiment was conducted?

Authors’ Response: Thank you for the comment. The flow of methodology could improve with the help of the next three comments, including this. The experiment was conducted using both online and manual channels.

Accordingly, the revised manuscript is updated as follows in lines 432 and 435.

“…The authors directly distributed the questionnaire. Moreover, authors could contact management in startups and distribute the questionnaire in their organisation.…”

Reviewer 1 Comment 20: Methodology: How participants were recruited?

Authors’ Response: Duly noted with thanks! The participants were selected by random sampling method. Authors could contact the management of respective organisations to reach the respondents.

The methodology part has been written in descriptive manner in the revised document. From lines 432 to 435 and lines 457 to 458 were newly added.

“…The authors directly distributed the questionnaire. Moreover, authors could contact management in startups and distribute the questionnaire in their organisation….”

“…The researchers applied a random sampling method, mainly employees who are a part of or have been a part of the startup …”

Reviewer 1 Comment 21: Methodology: What are the instructions of experiment?

Authors’ Response: Thank you very much for the comment and well noted.

Instructions for the experiments were.

• Participants should be a part of the startup

• He/she should consider the behaviour and culture of that startup when answering the questions

• Respondent should answer all the questions

The instructions given in the questionnaire has included in the revised manuscript from lines 444 to 449.

“…A minimum of four questions was added under each indicator. The researchers facilitated anonymously answering all the questions in the questionnaire. The participants should be a part of startup and he/she should consider the behaviour and culture of that startup when answering the questions. All nine indicators were covered by Likert scale questions from 1 to 5 rating scale, depicting (1) strongly disagree to (5) strongly agree to collect respondents’ attitudes and opinions…”

Reviewer 1 Comment 22: Methodology: How much was time given to each participant?

Authors’ Response: Thank you very much for the comments. This helps to build the story line in methodology part. 15 minutes time were given to answer the questionnaire. New content has added from lines 449 to 452

“…Each respondent took about 10-15 minutes to complete answering the questionnaire and took approximately 5-7 minutes to fill out the questionnaire…”

Reviewer 1 Comment 23: What are the theoretical implications of the study?

Authors’ Response: Well noted your comment. Thank you very much! In revised manuscript has added new sub section to discuss theoretical implications from lines 648 to 654.

“The current study empirically investigated the impact of job satisfaction, innovative work behaviour, co-worker support and leader member exchange on employee turnover. According to the authors’ knowledge, no prior studies were conducted considering the combined impact of all the independent variables on employee turnover. Therefore, this study strengthens the literature by demonstrating how job satisfaction, innovative work behaviour, co-worker support and leader member exchange impact employee turnover in Sri Lankan startups.

The findings reveal that job satisfaction has a negative impact on employee turnover. This finding is consistent with the previous study, job satisfaction significantly predicted employee turnover [6]. This study consolidates past findings that male employees have higher turnover intention than female employees. Female employees have comparatively higher-level job satisfaction [8]. This study implies that employees age 31 to 40 years have high employee turnover intention. The research findings are similar to Lu, Lu [8]; the older employees have high intentions to leave the company.”

Reviewer 1 Comment 24: Practical implications need further discussion.

Authors’ Response: Thank you very much for your valuable comment. Practical implications section was improved with further discussion. New contents have been added from lines 665 to 671, 674 to 680, 686 to 690.

“…This study provides managerial insights on lowering employee turnover in Sri Lankan startups. First, startups need to be aware that experienced employees in startups can be easily taken by well-established companies because, later, they have hand on experience and skills. Therefore, it is important to implement strategies for a solid career development plan, career growth, personal status, and employee recognition. As job satisfaction can predict employee turnover, it is a must to measure those indicators and maintain a favourable level at all times.”

“…More importantly, healthy LMX can boost employees’ work engagement. This healthy level can maintain by conducting regular meetings, training programs and informal mentorship with employees’ immediate supervisors [8]. Further, management can allow employees at all levels to present their fresh ideas and incorporate them to influence organisation’s decision-making process. These processes can lower employee hierarchy and build strong relationships while recognising them in the company.”

“…Furthermore, having a flexible approach to work in an organisation culture will increase the trust between employees and the organisation. Giving the freedom to take risks and not allowing them to feel alone during work will give value to employees. Finally, all the above actions will strongly impact reducing employee intention to leave the organisation.”

Reviewer 1 Comment 25: Add/ involve more recent citations/studies where necessary

Authors’ Response: Thank you very much for the comment and this is well noted. New citations were added in revised manuscript in introduction, literature review and results and discussions sections as per the below line numbers.

(Line numbers from 612 to 616)

Reviewer 2 Comment 1: Abstract: Rewrite the abstract after manuscript correction and provide picture of whole study.

Authors’ Response: Thank you very much for your comment. Abstract has been rewritten after doing the manuscript corrections.

Reviewer 2 Comment 2: In the first paragraph of introduction used only this (2016) citation. This citation does not justify the paragraph.

Authors’ Response: Thank you very much for the comment. The 1st paragraph of introduction section has upgraded with recent literatures with the citation nos. 1, and 3.

New contents have included from lines 45 to 46, and lines 55 to 58.

Reviewer 2 Comment 3: Introduction paragraph is not justifying the problem and bag-round of study. Revised the introduction and use the recent citations to justify and logically make connection with them.

Authors’ Response: Thank you so much for the comment. The introduction section was upgraded with recent literatures with the citation nos. 10, 11, 1, and 3 and justifications.

Reviewer 2 Comment 4: In the introduction (second paragraph), the contribution of study is confused with variable relationships; why are these relationships a contribution of study? Need strong justification.

Authors’ Response: Thank you very much for your valuable comment. The content has been updated with recent citations in line 64.

“Many variables influence employee turnover intentions [4-6]…”

Reviewer 2 Comment 5: Overall, I suggest a major rewrite of the introduction. It should provide an overview of and focus on one issue with recent citations.

Authors’ Response: Thank you very much, the comment well noted. New literatures have added in revised manuscript and highlighted the main issues and the research gaps. Every paragraph of the introduction has updated according to the reviewers’ comments.

Reviewer 2 Comment 6: Revised all literature variables and link with variables with new citation.

Authors’ Response: Thank you very much for the comment and well noted. After adding new literatures, revised all literature variables and linked with variables with new citation.

Reviewer 2 Comment 7: In the literature, justify these hypotheses with literary support.

Authors’ Response: Thank you very much for the comment. A storyline was developed on hypothesis formulation. New contents have been incorporated as per the below line numbers.

Reviewer 2 Comment 8: In literature, justify the conceptual model and theoretical gap.

Authors’ Response: Well noted your comment. Thank you! Conceptual model and theoretical gap have justified in the revised manuscript from lines 420 to 424.

“These studies have a common limitation in gathering more independent variables and analysing the impact. Therefore, a need exists to measure the effect of job satisfaction, work-life balance, happiness, management support, career management, innovative work behaviour, leader member exchange, and co-worker support together on employee turnover.”

Reviewer 2 Comment 9: Where is the total population? How did you choose the sample size? And how did you choose which method, unit of analysis, and research technique to use? Provide justification. Why is this method appropriate for this data set?

Authors’ Response: Thank you very much for this valuable comment.

Total population was 1300 and sample size identified by referring calculator.net online sample size calculator. A stepwise ordered probit analysis method was used as the selected variables are widely used indicators for employee turnover therefore authors required to do a micro level analysis for these variables.

The details of sampling have added in revised manuscript from lines 459 to 465.

“…The sample size was selected by referencing the Krejcie and Morgan sampling table and Calculator.net [40] with a confidence level of 95% and 7% of margin of error. The calculation results indicated a minimum of 171 professionals. A stepwise ordered probit analysis method was used as the selected variables are widely used indicators for employee turnover; therefore, a micro-level analysis was required to study how these variables impact. A pilot survey was conducted to identify whether the purpose of the questions was clear to the respondents.”

Reviewer 2 Comment 10: General: Identifying flaws in the study's design (revised methodology section) and justifying technique.

Authors’ Response: Well noted your comment. Thank you! Methodology section has been updated in revised manuscript from line 520 to 522.

“…The probit model is an estimation technique for equations with dummy dependent variables that avoids the unboundedness problem of the linear probability model by using a variant of the cumulative normal distribution [42]…”

Reviewer 2 Comment 11: Discussion: Write the theoretical contribution related to a model.

Authors’ Response: Thank you very much for your valuable comment.

The revised manuscript has been updated with a new sub section to discuss theoretical implications from lines 648 to 661.

Reviewer 2 Comment 12: Revised topic after correction

Authors’ Response: Well noted your comment. The topic has been updated in lines 1 to 2 and 19 to 20, and the new topic is,

“Factors impacting employee turnover intentions among professionals in Sri Lankan startups”

Submitted filename: Response to the Reviewers.docx

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• Published: 12 June 2024

Hybrid working from home improves retention without damaging performance

• Nicholas Bloom   ORCID: orcid.org/0000-0002-1600-7819 1   na1 ,
• Ruobing Han   ORCID: orcid.org/0000-0001-9126-5503 2   na1 &
• James Liang 3 , 4  

Nature ( 2024 ) Cite this article

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• Research management

Working from home has become standard for employees with a university degree. The most common scheme, which has been adopted by around 100 million employees in Europe and North America, is a hybrid schedule, in which individuals spend a mix of days at home and at work each week 1 , 2 . However, the effects of hybrid working on employees and firms have been debated, and some executives argue that it damages productivity, innovation and career development 3 , 4 , 5 . Here we ran a six-month randomized control trial investigating the effects of hybrid working from home on 1,612 employees in a Chinese technology company in 2021–2022. We found that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes. Null equivalence tests showed that hybrid working did not affect performance grades over the next two years of reviews. We found no evidence for a difference in promotions over the next two years overall, or for any major employee subgroup. Finally, null equivalence tests showed that hybrid working had no effect on the lines of code written by computer-engineer employees. We also found that the 395 managers in the experiment revised their surveyed views about the effect of hybrid working on productivity, from a perceived negative effect (−2.6% on average) before the experiment to a perceived positive one (+1.0%) after the experiment. These results indicate that a hybrid schedule with two days a week working from home does not damage performance.

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Working from home (WFH) surged after the COVID-19 pandemic, with university-graduate employees typically WFH for one to two days a week during 2023 (refs. 2 , 6 ). Previous causal research on WFH has focused on employees who are fully remote, usually working on independent tasks in call-centre, data-entry and helpdesk roles. This literature has found that the effects of fully remote working on productivity are often negative, which has resulted in calls to curtail WFH 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 . However, there are two challenges when it comes to interpreting this literature. First, more than 70% of employees WFH globally are on a hybrid schedule. This group comprises more than 100 million individuals, with the most common working pattern being three days a week in the office and two days a week at home 2 , 8 , 9 . Second, most employees who are regularly WFH are university graduates in creative team jobs that are important in science, law, finance, information technology (IT) and other industries, rather than performing repetitive data-entry or call processing tasks 10 , 11 .

This paper addresses the gap in previous studies in two key ways. First, it uses a randomized control trial to examine the causal effect of a hybrid schedule in which employees are allowed to WFH two days per week. Second, it focuses on university-graduate employees in software engineering, marketing, accounting and finance, whose activities are mainly creative team tasks.

Our study describes a randomized control trial from August 2021 to January 2022, which involved 1,612 graduate employees in the Airfare and IT divisions of a large Chinese travel technology multinational called Trip.com. Employees were randomized by even or odd birthdays into the option to WFH on Wednesday and Friday and come into the office on the other three days, or to come into the office on all five days.

We found that in the hybrid WFH (‘treatment’) group, attrition rates dropped by one-third (mean control  = 7.20, mean treat  = 4.80, t (1610) = 2.02, P  = 0.043) and work satisfaction scores improved (mean control  = 7.84, mean treat  = 8.19, t (1343) = 4.17, P  < 0.001). Employees reported that WFH saved on commuting time and costs and afforded them the flexibility to attend to occasional personal tasks during the day (and catch up in the evenings or weekends). These effects on reduced attrition were significant for non-managerial employees (mean control  = 8.59, mean treat  = 5.33, t (1215) = 2.23, P  = 0.026), female employees (mean control  = 9.19, mean treat  = 4.18, t (568) = 2.40, P  = 0.017) and those with long (above-median) commutes (mean control  = 6.00, mean treat  = 2.89, t (609) = 1.87, P  = 0.062).

At the same time, we found no evidence of a significant effect on employees’ performance reviews, on the basis of null equivalence tests, and no evidence of a difference in promotion rates over periods of up to two years (‘Null results’ section of the Methods ). We did find significant differences in pre-experiment beliefs about the effects of WFH on productivity between non-managers and managers. Before the experiment, managers tended to have more negative views, reporting that hybrid WFH would be likely to affect productivity by −2.6%, whereas non-managers had more positive views (+0.7%) ( t (1313) = −4.56, P  < 0.001). After the experiment, the views of managers increased to +1.0%, converging towards non-managers’ views (mean non-manager  = 1.62, mean manager  = 1.05, t (1343) = −0.945, P  = 0.345). This highlights how the experience of hybrid working leads to a more positive assessment of its effect on productivity—consistent with the overall experience in Asia, the Americas and Europe throughout the pandemic, where perceptions of WFH improved considerably 13 .

The experiment

The experiment took place at Trip.com, the third-largest global travel agent by sales in 2019. Trip.com was established in 1999, was quoted on NASDAQ in 2003 and was worth about US$20 billion at the time of the experiment. It is headquartered in Shanghai, with offices across China and internationally, and has roughly 35,000 employees.

In the summer of 2021, Trip.com decided to evaluate the effects of hybrid WFH on the 1,612 engineering, marketing and finance employees in the Airfare and IT divisions, spanning 395 managers and 1,217 non-managers. All experimental participants were surveyed at baseline, with questions on expectations, background and their interest in volunteering for early participation in the experiment. The firm randomized employees with an odd-number birthday (born on the first, third, fifth and so on day of the month) into the treatment group.

Figure 1 shows two pictures of employees working in the office to highlight three points. First, in the second half of 2021, COVID incidence rates in Shanghai were so low that employees were neither masked nor socially distanced at the office. Although the COVID pandemic had led to lockdowns in early 2020 and during 2022, during the second half of 2021, Shanghai employees were free to come to work, and typically were unmasked in the office. Second, employees worked in modern open-plan offices in desk groupings of four or six colleagues from the same team, reflecting the importance of collaboration. Third, the office is a large modern building, similar to many large Asian, European and North American offices.

Pictures of Trip.com employees in the office during the experiment. The people in the experimental sample are typically in their mid-30s, and 65% are male. All of them have a university undergraduate degree and 32% have a postgraduate degree, usually in computer science, accounting or finance, at the master’s or PhD level. They have 6.4 years tenure on average and 48% of employees have children (Extended Data Table 1 ).

Effects on employee retention

One key motivation for Trip.com in running the experiment was to evaluate how hybrid WFH affected employee attrition and job satisfaction. The net effect was to reduce attrition over the experiment by 2.4%, which against the control-group base of 7.2% was a one-third (33%) reduction in attrition (mean control  = 7.20, mean treat  = 4.80, t (1610) = 2.02, P  = 0.043). Consistent with this reduction in quit rates, employees in the treatment group also registered more positive responses to job-satisfaction surveys (mean control  = 7.84, mean treat  = 8.19, t (1343) = 4.17, P  < 0.001). Employees were anonymously surveyed on 21 January 2022, and employees in the treatment group showed significantly higher scores on a scale from 0 (lowest) to 10 (highest) in ‘work–life balance’, ‘work satisfaction’, ‘life satisfaction’ and ‘recommendation to friends’, and significantly lower scores in ‘intention to quit’ (Extended Data Table 2 ).

One possible explanation for the lower quit rates in the treatment group is that quit rates in the control group increased because the individuals in this group were annoyed about being randomized out of the experiment. However, quit rates in the same Airfare and IT divisions were 9.8% in the six months before the experiment—higher than the rate for the control group during the experimental period. Quit rates over the experimental period in the two other Trip.com divisions for which we have data (Business Trips and Marketing) were 10.5% and 9.8%—again higher than that for the control group during the experimental period. This suggests that, if anything, the control-group quit rates were reduced rather than increased by the experiment, possibly because some of them guessed (correctly) that the policy would be rolled out to all employees once the experiment ended.

Figure 2 shows the change in attrition rates by three splits of the data. First, we examined the effect on attrition for the 1,217 non-managers and 395 managers separately. We saw a significant drop in attrition of 3.3 percentage points for the non-managers, which against a control-group base of 8.6% is a 40% reduction (mean control  = 8.59, mean treat  = 5.33, t (1215) = 2.23, P  = 0.026). By contrast, there was an insignificant increase in attrition for managers (mean control  = 2.96, mean treat  = 3.13, t (393) = −0.098, P  = 0.922). We also found that non-managers were more enthusiastic before the experiment, with a volunteering rate of 35% (versus 22% for managers), matching the media sentiment that although non-managerial employees are enthusiastic about WFH, many managers are not ( t (1610) = 4.86, P  < 0.001).

Data on 1,612 employees’ attrition until 23 January 2022. Top left, all employees. Only 1,259 employees filled out the baseline survey question on commuting length, so the commute-length (two ways) sample is for 1,259 employees. Sample sizes are 820 and 792 for control and treatment; 1,217 and 395 for non-managers and managers; 570 and 1,042 for women and men; and 648 and 611 for short and long commuters, respectively. Two-tailed t -tests for the attrition difference within each group between the control and treatment groups are (difference = 2.40, s.e. = 1.18, confidence interval (CI) = [0.0748, 4.72], P  = 0.043) for all employees; (difference = 3.26, s.e. = 1.46, CI = [0.392, 6.12], P  = 0.026) for non-managers; (difference = −0.169, s.e. = 1.73, CI = [−3.57, 3.23], P  = 0.922) for managers; (difference = 5.01, s.e. = 2.08, CI = [0.915, 9.10], P  = 0.017) for women; (difference = 0.997, s.e. = 1.43, CI = [−1.82, 3.81], P  = 0.487) for men; (difference = 2.61, s.e. = 1.93, CI = [−1.19, 6.41], P  = 0.178) for employees with median (90 min, two-way) or shorter commutes; and (difference = 3.11, s.e. = 1.66, CI = [−0.156, 6.37], P  = 0.062) for above-median (90 min, two-way) commuters.

Second, we examined the effect on attrition by total commute length, splitting the sample into people with shorter and longer total commutes on the basis of the median commute duration (two-way commutes of 1.5 h or less versus those exceeding 1.5 h, with 648 and 611 employees, respectively). We found that there was a larger reduction in quit rates (52%) for those with a long commute (mean control  = 6.00, mean treat  = 2.89, t (609) = 1.87, P  = 0.062). The reduction in quit rates was similarly large for employees with a long commute if we instead defined a long commute as a two-way commute time exceeding 2 h (mean control  = 7.33, mean treat  = 1.89, t (307) = 2.31, P  = 0.021). Employees who volunteered to take part in the experiment had longer one-way commute durations (Extended Data Table 3 ; mean non-volunteer  = 0.80, mean volunteer  = 0.89, t (1257) = −3.68, P  < 0.001). This is not surprising given that the most frequently cited benefit of WFH is no commute 1 .

Third, we examined the effect on attrition by gender, examining the 570 female and 1,042 male employees separately. We found that there was a 54% reduction in quit rates for female employees (mean control  = 9.2, mean treat  = 4.2, t (568) = 2.40, P  = 0.017). For male employees, there was an insignificant 16% reduction in quit rates (mean control  = 6.15, mean treat  = 5.15, t (1040) = 0.70, P  = 0.487). This greater reduction in quit rates among female individuals echoes the findings of previous studies 6 , 14 , 15 , 16 , which suggest that women place greater value on remote work than men do. Notably, although the treatment effect of WFH was significantly larger for female employees, volunteers were less likely to be female (mean non-volunteer  = 0.37, mean volunteer  = 0.32, t (1610) = −2.02, P  = 0.043); this might suggest that women have greater concerns about negative career signalling by volunteering to WFH.

Employee performance and promotions

Another key question for Trip.com was the effect of hybrid WFH on employee performance. To assess that, we examined four measures of performance: six-monthly performance reviews and promotion outcomes for up to two years after the start of the experiment, detailed performance evaluations, and the lines of code written by the computer engineers. We also collected self-assessed productivity effects of hybrid working from experimental participants before and after the experiment to evaluate employee perceptions.

Performance reviews are important within Trip.com as they determine employees’ pay and career progression, so are carefully conducted. The review process for each employee is built on formal assessments provided by their managers, co-workers, direct reports and, if appropriate, customers. They are reviewed by employees, collated by managers and by the human resources team, and then discussed between the manager and the employee. This lengthy process takes several weeks, providing a well-grounded measure of employee performance. Although these reviews are not perfect, given their tight link to pay and career development, both managers and employees put a large amount of effort into making these informative measures of performance.

Figure 3 reports the distribution of performance grades for treatment and control employees for the four half-year periods: July to December 2021, January to June 2022, July to December 2022 and January to June 2023. These four performance reviews span a two-year period from the start of the experimental period. Across all review periods, we found no difference in reviews between the treatment and control groups (Extended Data Table 4 and ‘Null results’ section of the Methods ).

Results from performance reviews of 1,507 employees in July–December 2021, 1,355 employees in January–June 2022, 1,301 employees in July–December 2022 and 1,254 employees in January–June 2023. Samples are lower over time owing to employee attrition from the original experimental sample. Two-tailed t -tests for the performance difference within each period between the control and treatment groups, after assigning each letter grade a numeric value from 1 (D) to 5 (A), are (difference = 0.056, s.e. = 0.043, CI = [−0.029, 0.14], P  = 0.198) for July–December 2021; (difference = 0.034, s.e. = 0.044, CI = [−0.0529, 0.122], P  = 0.440) for January–June 2022; (difference = −0.019, s.e. = 0.046, CI = [−0.11, 0.072], P  = 0.677) for July to December 2022; and (difference = 0.046, s.e. = 0.051, CI = [−0.054, 0.146], P  = 0.369) for January–June 2023. The null equivalence tests are included in the ‘Null results’ section of the Methods .

Figure 4 reports the distribution of promotion outcomes for the treatment and control employees for the same periods. We see no evidence of a difference in promotion rates across treatment and control employees. This is an important result given the evidence that fully remote working can damage employee development and promotions 14 , 17 , 18 .

Promotion outcomes for 1,522 employees in July–December 2021, 1,378 employees in January–June 2022, 1,314 employees in July–December 2022 and 1,283 employees in January–June 2023. Samples are lower over time owing to employee attrition from the original experimental sample. Two-tailed t -tests for the promotion difference within each period between the control and treatment groups are (difference = −0.86, s.e. = 1.34, CI = [−3.51, 1.74], P  = 0.509) for July–December 2021 promotions; (difference = 0.12, s.e. = 0.85, CI = [−1.54, 1.78], P  = 0.892) for January–June 2022 promotions; (difference = −0.51, s.e. = 1.12, CI = [−2.72, 1.70], P  = 0.651) for July–December 2022 promotions; and (difference = −0.99, s.e. = 1.02, CI = [−2.99, 1.00], P  = 0.328) for January–June 2023 promotions. The null equivalence tests are included in the ‘Null results’ section of the Methods .

We also analysed the effects of treatment on performance grades and promotions for a variety of subgroups, including managers, employees with a manager in the treatment group, longer-tenured employees, longer-commuting employees, women, employees with children, computer engineers and those living further away, as well as looking at whether internet speed had any effect. We found no evidence of a difference in response to treatment across these groups (Extended Data Table 5 ).

The experiment also analysed two other measures of employee performance. First, the performance reviews at Trip.com have subcomponents for individual activities such as ‘innovation’, ‘leadership’, ‘development’ and ‘execution’ (nine categories in all) when these are important for an individual employee’s role. We collected these data and analysed these scores for the four six-month performance review periods. We found no evidence of a difference across these nine major categories over the four performance review periods (Extended Data Table 6 ). This indicates that for categories that involve softer skills or more team-focused activities—such as development and innovation—there is no evidence for a material effect of being randomized into the hybrid WFH treatment. Second, for the 653 computer engineers, we obtained data on the lines of code uploaded by each engineer each day. For this ‘lines of code submitted’ measure, we found no difference between employees in the control and treatment groups (Extended Data Fig. 1 and ‘Null results’ section of the Methods ).

Self-assessed productivity

All experiment participants were polled before the experiment in a baseline survey on 29 and 30 July 2021, which included a two-part question on their beliefs about the effects of hybrid WFH on productivity. Employees were asked ‘What is your expectation for the impact of hybrid WFH on your productivity?’, with three options of ‘positive’, ‘about the same’ or ‘negative’. Individuals who chose the answer ‘positive’ were then offered a set of options asking how positive they felt, ranging from [5% to 15%] up to [35% or more], and similarly so for negative choices. For aggregate impacts we took the mid-points of each bin, and 42.5% for >35% and –42.5% for <−35%. Employees were resurveyed with the same question after the end of the experiment on 21 January 2022.

The left panel of Fig. 5 shows that employees’ pre-experimental beliefs about WFH and productivity were extremely varied. The baseline mean was –0.1%, but with widespread variation (standard deviation of 11%). This spread should be unsurprising to anyone who has been following the active debate about the effects of remote work on productivity. At the end-line survey conducted on 21 January 2022, the mean of these beliefs had significantly increased to 1.5%, revealing that the experience of hybrid working led to a small improvement in average employee beliefs about the productivity impact of hybrid working (mean baseline  = −0.06%, mean endline  = 1.48%, t (2658) = −3.84, P  < 0.001). This could be because hybrid WFH saves employees commuting time and is less physically tiring, and, with intermittent breaks between group time and quiet individual time, can improve performance 19 , 20 , 21 , 22 .

Sample from 1,315 employees (314 managers, 1,001 non-managers) at the baseline and 1,345 employees (324 managers, 1,021 non-managers) at the end line. Two-tailed t -tests for the difference in productivity expectations between baseline and end line, after assigning a numeric value corresponding to the midpoint of the bucket, are (baseline mean = −0.058, end-line mean = 1.48, difference = −1.54, s.e. = 0.40, CI = [−2.33, −0.753], P  < 0.001). Two-tailed t -tests for the baseline difference between the productivity expectations of managers and non-managers are (difference = −3.28, s.e. = 0.72, CI = [−4.69, −1.86], P  < 0.001), and the t -tests for the end-line difference are (difference = −0.571, s.e. = 0.604, CI = [−1.76, 0.615], P  = 0.345).

The right panel of Fig. 5 shows that in the baseline survey, managers were negative about the perceived effect of hybrid work on their productivity, with a mean effect of −2.6%. Non-managers, by contrast, were significantly more positive, at +0.7% in the baseline survey (mean non-manager  = 0.7%, mean manager  = −2.6%, t (1313) = −4.56, P  < 0.001). At the end of the experiment, the views of managers improved to 1.0%, with no evidence of a difference from the non-managers’ mean value of 1.6% (mean non-manager  = 1.62%, mean manager  = 1.05%, t (1343) = −0.95, P  = 0.345). Hence, the experiment led managers to positively update their views about how hybrid WFH affects productivity, and to more closely align with non-managers.

Of note, we saw that employees in the treatment and control groups had similar increases in self-assessed productivity (difference 0.58%, s.d. = 0.59%). Employees from four other divisions in Trip.com were also polled about the productivity impact of hybrid WFH after the end of the experiment in March 2022, with a mean estimate of +2.8% on a sample of 3,461 responses—similar to the 1.5% end line for the experimental sample. This suggests that even close exposure to hybrid WFH is sufficient for employees to change their views, consistent with previous evidence of a positive society-wide shift in perceptions about WFH productivity after the 2020 pandemic 8 .

Once the experiment ended, the Trip.com executive committee examined the data and voted to extend the hybrid WFH policy to all employees in all divisions of the company with immediate effect. Their logic was that each quit cost the company approximately US$20,000 in recruitment and training, so a one-third reduction in attrition for the firm would generate millions of dollars in savings. This was publicly announced on 14 February 2022, with wide coverage in the Chinese media. Since then, other Chinese tech firms have adopted similar hybrid policies 23 .

This highlights how, contrary to the previous causal research focused on fully remote work, which found mostly negative effects on productivity 5 , 6 , 7 , hybrid remote work can leave performance unchanged. This suggests that hybrid working can be profitably adopted by organizations, given its effect on reducing attrition, which is estimated to cost about 50% of an individual’s annual salary for graduate employees 24 . Hybrid working also offers large gains for society by providing a valuable amenity (perk) to employees, reducing commuting and easing child-care 6 , 25 , 26 .

The experiment was conducted in a Chinese technology firm based in Shanghai. Although it might not be possible to replicate these results perfectly in other situations, Trip.com is a large multinational firm with global suppliers, customers and investors. Its offices are modern buildings that look similar to those in many American, Asian and European cities. Trip employees worked 8.6 h per day on average, close to the 8 h per day that is usual for US graduate employees 27 . The business had a large drop in revenue in 2020 (see Extended Data Fig. 4 ), followed by roughly flat revenues through the 2021 experiment period into 2022, so this was not a period of exceptionally fast or slow growth. As such, we believe that these results— that is, the finding that allowing employees to WFH two days per week reduces quit rates and has a limited effect on performance—would probably extend to other organizations. Also, this experiment analysed the effects of working three days per week in the office and two days per week from home. So, our findings might not replicate to all other hybrid work arrangements, but we believe that they could extend to other hybrid settings with a similar number of days in the office, such as two or four days a week. We are not sure whether the results would extend to more remote settings such as one day a week (or less) in the office, owing to potential challenges around training, innovating and culture in fully remote settings.

Finally, we should point out two implications of the experimental design. First, full enrolment into hybrid schemes is important because of concerns that volunteering might be seen as a negative signal about career ambitions. The low volunteer rate among female employees, despite their high implied value (from the large reductions in quit rates observed), is particularly notable in this regard. Second, there is value in experimentation. Before the experiment, managers were net-negative in their views on the productivity impact of hybrid working, but after the experiment, their views became net-positive. This highlights the benefits of experimentation for firms to evaluate new working practices and technologies.

Location and set-up

Our experiment took place at Trip.com in Shanghai, China. In July 2021, Trip.com decided to evaluate hybrid WFH after seeing its popularity amongst US tech firms. The first step took place on 27 July 2021, when the firm surveyed 1,612 eligible engineers, marketing and finance employees in the Airfare and IT divisions about the option of hybrid WFH. They excluded interns and rookies who were in probation periods because on-site learning and mentoring are particularly important for those individuals. Trip.com chose these two divisions as representative of the firm, with a mix of employee types to assess any potentially heterogeneous impacts. About half of the employees in these divisions are technical employees, writing software code for the website, and front-end or back-end operating systems. The remainder work in business development, with tasks such as talking to airlines, travel agents or vendors to develop new services and products; in market planning and executing advertising and marketing campaigns; and in business services, dealing with a range of financial, regulatory and strategy issues. Across these groups, 395 individuals were managers and 1,217 non-managers, providing a large enough sample of both groups to evaluate their response to hybrid WFH.

Randomization

The employees were sent an email outlining how the six-month experiment offered them the option (but not the obligation) to WFH on Wednesday and Friday. After the initial email and two follow-up reminders, a group of 518 employees volunteered. The firm randomized employees with odd birthdays—those born on the first, third, fifth and so on of the month—into eligibility for the hybrid WFH scheme starting on the week of 9 August. Those with even birthdays—born on the second, fourth, sixth and so on of the month—were not eligible, so formed the control group.

The top management at the firm was surprised at the low volunteer rate for the optional hybrid WFH scheme. They suspected that many employees were hesitating because of concerns that volunteering would be seen as a negative signal of ambition and productivity. This is not unreasonable. For example, a previous study 28 found in the US firm they evaluated that WFH employees were negatively selected on productivity. So, on 6 September, all of the remaining 1,094 non-volunteer employees were told that they were also included in the program. The odd-birthday employees were again randomized into the hybrid WFH treatment and began the experiment on the week of 13 September. In this paper we analyse the two groups together, but examining the volunteer and non-volunteer groups individually yields similar findings of reduced quit rates and no impact on performance.

Employee characteristics and balancing tests

Figure 1 shows some pictures of employees working in the office (left side). Employees all worked in modern open-plan offices in desk groupings of four or six colleagues from the same team. By contrast, when WFH, they usually worked alone in their apartments, typically in the living room or kitchen (see Extended Data Fig. 2 ).

The individuals in the experimental sample are typically in their mid-30s. About two-thirds are male, all of them have a university undergraduate degree and almost one-third have a graduate degree (typically a master’s degree). In addition, nearly half of the employees have children (details in Extended Data Table 1 ).

In Extended Data Table 7 we confirm that this sample is also balanced across the treatment and control groups, by conducting a two-sample t -test. The exceptions are from random variation given that the sampling was by even or odd day-of-month birthday—the control sample is 0.5 years older ( P  = 0.06), and this is presumably linked to why those in this group have 0.06% more children ( P  = 0.02) and 0.4 years more tenure ( P  = 0.09).

In Extended Data Table 3 , we examine the decision to volunteer for the WFH experiment. We see that volunteers were significantly less likely to be managers (mean non-volunteer  = 0.28, mean volunteer  = 0.17, t (1610) = −4.85, P  < 0.001) and had longer commute times (hours) (mean non-volunteer  = 0.80, mean volunteer  = 0.89, t (1257) = 3.68, P  < 0.001). Notably, we don’t find evidence of a relationship between volunteering and previous performance scores (mean non-volunteer  = 3.81, mean volunteer  = 3.81, t (1580) = −0.02, P  = 0.985), highlighting, at least in this case, the lack of evidence for any negative (or positive) selection effects around WFH.

Extended Data Fig. 3 plots the take-up rates of WFH on Wednesday and Friday by volunteer and non-volunteer groups. We see a few notable facts. First, take-up overall was about 55% for volunteers and 40% for non-volunteers, indicating that both groups tended to WFH only one day, typically Friday, each week. At Trip.com, large meetings and product launches often happen mid-week, so Fridays are seen as a better day to WFH. Second, the take-up rate even for non-volunteers was 40%, indicating that Trip.com’s suspicion that many employees did not volunteer out of fear of negative signalling was well-founded, and highlighting that amenities like WFH, holiday, maternity or paternity leave might need to be mandatory to ensure reasonable take-up rates. Third, take-up surged on Fridays before major holidays. Many employees returned to their home towns, using their WFH day to travel home on the quieter Thursday evening or Friday morning. Finally, take-up rates jumped for both treatment-group and control-group employees in late January 2022 after a case of COVID in the Shanghai headquarters. Trip.com allowed all employees at that point to WFH, so the experiment effectively ended early on Friday 21 January. The measure of an employee’s daily WFH take-up excludes leave, sick leave or occasions when they cannot come to the office owing to extreme bad weather (typhoon) or to the COVID outbreak in the company.

Null results

To interpret the main null results, we conduct null equivalence tests using the two one-sided tests (TOST) procedure in R (refs. 29 , 30 ). This test required us to specify the smallest effect size of interest (SESOI). For the results pertaining to performance review measures, we use 0.5 as the SESOI. This corresponds to half of a consecutive letter grade increase or decrease, because we had assigned numeric values to performance letter grades in increments of 1, with the lowest letter grade D being 1, and the highest letter grade A being 5. We performed equivalence tests for a two-sample Welch’s t -test using equivalence bounds of ±0.5. The TOST procedure yielded significant results using the default alpha of 0.05 for the tests against both the upper and the lower equivalence bounds for the performance measures for July–December 2021 ( t (1504) = −10.20, P  < 0.001)), January–June 2022 ( t (1353) = −10.57, P  < 0.001)), July–December 2022 ( t (1299) = 10.34, P  < 0.001)) and January–June 2023 ( t (1248) = −8.80, P  < 0.001)). The equivalence test is therefore significant, which means we can reject the hypothesis that the true effect of the treatment on performance is larger than 0.5 or smaller than −0.5. So, we interpret the performance effects of the treatment to be actually null on the basis of the SESOI we used, as opposed to no evidence of a difference in performance.

We conducted null equivalence results for the effect of the treatment on promotions using 2 as the SESOI, corresponding to ±2 percentage points (pp) difference in promotion rates. Although we can reject the null hypothesis that the true effect of treatment on promotion is larger than 2 pp or smaller than −2 pp in January–June 2022 ( t (1376) = −2.22, P  = 0.013) and July–December 2022 ( t (1306) = 1.33, P  = 0.092), we fail to reject the null equivalence hypothesis in July–December 2021 ( t (1513) = 0.83, P  = 0.203) and January–June 2023 ( t (1250) = 0.98, P  = 0.163). Thus, we interpret the results on promotion as no evidence of a difference between promotion rates across treatment and control employees.

We also conducted the equivalence test for lines of code using 29 lines of code per day as the SESOI, which corresponds to 10% of the mean number of lines of code for the control group. We arrive at this SESOI on the basis of rounding down the productivity effects of previous findings 8 , 10 . We can reject the equivalence null hypothesis for lines of code ( t (92362) = −2.74, P  = 0.003)) so we interpret the effect of the treatment as a null effect.

Volunteer versus non-volunteer groups

In the main paper we pool the volunteer and non-volunteer groups. In Extended Data Table 5 we examine the impacts on performance and promotions and we see no evidence of a difference in performance and promotion treatment effects for volunteer versus non-volunteer groups (column 9).

Performance subcategories

The company has a rigorous performance-reviewing process every six months that determines employees’ pay and promotion, so is carefully conducted. The review process for each employee is built on formal reviews provided by their managers, project leaders and sometimes co-workers (peer review). Managers are more like an employee’s direct managers for organizational purposes, but for a particular project, the project leader could be another higher-level employee. In such a case, the manager of the employee would ask that project leader for an opinion on the employee’s contribution to the project. An individual’s overall score is a weighted sum of scores from various subcategories that managers have broad flexibility over defining, because tasks differ across employees, and managers would give a score for each task. For example, an employee running a team themselves will have subcategories around developing their direct reports (leadership and communication), whereas an employee running a server network will have subcategories around efficiency and execution. The performance subcategory data come from the text of the performance review. We first used the most popular Chinese word segmentation package in Python, named Jieba, to identify the most frequent Chinese words from task titles across four performance reviews. We also removed meaningless words and incorporated common expressions such as key performance indicators (‘KPI’), objectives and key results (‘OKR’), ‘rate’ and ‘%’. This process resulted in a total of 236 unique words and expressions. We then manually categorized those most frequent keywords into nine major subcategories (see below) by meanings and relevance. Finally, on the basis of the presence of keywords in the task title, tasks were grouped into the following subcategories:

Communication tasks are those that involve communication, collaboration, cooperation, coordination, participation, suggestion, assistance, organization, sharing and relationships.

Development tasks are those that involve coding or codes, data or datasets, systems, techniques and skills.

Efficiency tasks are those that involve cost reduction, ratios, return on investment (ROI), rate, %, improvement, growth, lifting, adding, optimizing, profit, receiving, gross merchandise value (GMV), OKR, KPI, work and goal.

Execution tasks are those that involve execution, conducting, maintenance, delivery, output, quality, contribution and workload.

Innovation tasks are those that involve development, R&D and innovation.

Leadership tasks are those that involve leadership, managing or management, approval, internal, strategy, coordination and planning.

Learning tasks are those that involve learning, growing, maturing, talent, ability, value competitiveness and personal improvement.

Project tasks are those that involve project, supply, product, business line, cooperation and clients.

Risk tasks are those that involve risk, compliance, supervision, recording and monitoring, safety, rules and privacy.

Data sources

Data were provided by a combination of Trip.com sources, including human resources records, performance reviews and two surveys. All data were anonymized and coded using a scrambled individual ID code, so no personally identifiable information was shared with the Stanford team. The data were drawn directly from the Trip.com administrative data systems on a monthly basis. Gender is collected by Trip.com from employees when they join the company.

The full sample has 1,612 experiment participants, but we have 1,507, 1,355, 1,301 and 1,254 employees, respectively, in the subsamples for the four performance reviews from July–December 2021, January–June 2022, July–December 2022 and January–June 2023. These smaller samples are due to attrition. In addition, for the first performance review in July–December 2021, 105 employees did not have sufficient pre-experiment tenure to support a performance review (they had joined the firm less than three months before the experimental draw). The review text data covers 1,507,1,339,1,290 and 1,246 people, as some employees do have an overall score and review text but do not have additional and task-specific scores. The reason is that these employees do not have the full range of all tasks, so their managers did not write the full review script. For the two surveys, Trip.com used Starbucks vouchers to incentivize response and collected responses from 1,315 employees (314 managers, 1,001 non-managers) at the baseline on the left, and that of 1,345 employees (324 managers, 1,021 non-managers) at the end line.

All tests used two-sided Student t -tests unless otherwise stated. Analysis was run on Stata v17 and v18, R version 4.2.2. Unless stated otherwise, no additional covariates are included in the tests. The null hypothesis for all of the tests excluding null equivalence tests is a coefficient of zero (for example, zero difference between treatment and control).

Inclusion and ethics statement

The design and execution of the experiment was run by Trip.com. No participants were forced to WFH owing to the experiment (the entire firm was, however, forced to WFH during the pandemic lockdown). The treatment sample had the option but not the obligation to WFH on Wednesday or Friday. The experiment was designed, initiated and run by Trip.com. N.B. and R.H. were invited to analyse the data from the experiment, with consent for data collection coming from Trip.com internally. The experiment was exempt under institutional review board (IRB) approval guidelines because it was designed and initiated by Trip.com, before N.B. and R.H. were invited to analyse the data. Only anonymous data were shared with the Stanford team. Trip.com based the experimental design and execution on their previous experience with WFH randomized control trials 17 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data necessary to reproduce the primary results of this study can be found at https://doi.org/10.7910/DVN/6X4ZZL . These data have been anonymized and split into individual files to ensure that no individual is identifiable. All figures and tables can be replicated using this data.

Code availability

The code necessary to reproduce the primary results of this study can be found at https://doi.org/10.7910/DVN/6X4ZZL .

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Acknowledgements

We thank the Smith Richardson Foundation for funding; J. Cao, T. Zhang, S. Ye, F. Chen, X. Zhang, Y. He, J. Li, B. Ye and M. Akan for data, advice and logistical support; D. Yilin for research assistance; S. Ayan, S. Buckman, S. Gurung, M. Jackson and P. Lambert for draft feedback; and J. Sun for project leadership.

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These authors contributed equally: Nicholas Bloom, Ruobing Han

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Department of Economics, Stanford University, Stanford, CA, USA

Nicholas Bloom

Shenzhen Finance lnstitute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China

Ruobing Han

National School of Development, Peking University, Beijing, China

James Liang

Trip.com, Shanghai, China

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Contributions

N.B. oversaw the analysis, presented the results and wrote the main drafts of the paper. He was the principal investigator on the research grant supporting the research. R.H. supervised data collection and analysed the data, presented the results and helped to draft the paper. J.L. initiated and designed the study, discussed the results and analysis and facilitated the Trip.com engagement. N.B. and R.H. are co-first authors.

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Correspondence to Nicholas Bloom , Ruobing Han or James Liang .

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Competing interests.

No funding was received from Trip.com. J.L. is the co-founder, former CEO and current chairman of Trip.com, with equity holdings in Trip.com. No other co-author has any financial relationship with Trip.com. Neither the results nor the paper was pre-screened by anyone. The experiment was registered with the American Economic Association on 16 August 2021 after the experiment had begun but before N.B. and R.H. had received any data. Only anonymous data were shared with the Stanford team.

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Extended data figures and tables

Extended data fig. 1 wfh had no effect on lines of code written..

The data coves the experimental period starting on 9 August 2021 for the first wave and 13 September for the second wave, running to 23 January 2022, for both waves. Lines of code submitted per day is available for 653 employees whose primary role was writing code, spanning a total of 95,494 days. Lines are those uploaded to trip.com on a daily basis. Data plotted on a log-2 scale for readability. Reported P value is calculated using a two-sided t -test on the number of code lines and the difference is for control minus treatment. When using log 2 (code lines) the difference has a P value of 0.750 (noting the sample is 27,605 days because of dropping 0 values). When using log 2 (1 + code lines) the difference has a P value of 0.0103, with treatment having the higher average values. The null equivalence tests are included in the ‘Null results’ section of the Methods .

Extended Data Fig. 2 Home (October 2021).

Employees set up basic working environments in their living rooms, studies, or kitchens, and bring back company laptops if necessary.

Extended Data Fig. 3 Take-up rate for WFH treatment and control by volunteer status.

Data for 1,612 employees from 9 August 2021 (volunteers) and 13 September (non-volunteers) to 23 January 2022. Public holidays, personal holidays and excused absence (for example, sick leave) are excluded. Take-up rate is percentage of Wednesday and Friday each week they WFH.

Extended Data Fig. 4 Trip.com revenues.

Trip.com revenues from 2000 to 2023.

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• Corpus ID: 270611909

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New insights into physician burnout and turnover intent: a validated measure of physician fortitude

• Laurence Weinzimmer 1 &
• Stephen Hippler 2  

BMC Health Services Research volume  24 , Article number:  748 ( 2024 ) Cite this article

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Metrics details

Given the increasing prevalence of the physician burnout, this study provides new insights into the antecedents driving burnout and turnover intent. By introducing the concept of physician fortitude, we develop a valid and statistically-reliable measure that increases our understanding of these issues.

A two-sample design was employed. Using a sample of 909 physicians, Advanced Practice Providers (APPs) and healthcare leaders, exploratory factor analysis was employed to create a 12-item fortitude scale. In the second study, using a sample of 212 of practicing physicians, APPs and healthcare leaders, bivariate and tetrachoric correlations, and ordinary least square regression modeling were able to establish reliability and validity.

The fortitude scale shows sufficient reliability. Moreover, we found significant support for convergent and criterion-related validity. Fortitude was significantly related to all three subdimensions of burnout, including emotional exhaustion ( r  = -.62, p  < .01), depersonalization ( r  = -.70, p  < .01) and personal accomplishment ( r  = .65, p  < .01), and turnover intent ( r  = -.55, p  < .01). Moreover, the fortitude measure explained more variance in all three subdimensions of burnout and turnover intent compared to common measures, including grit, hardiness, mental toughness and resilience ( p  < .01).

Conclusions

Results from this study empirically demonstrate that fortitude is significantly related to burnout, and turnover intent. This new fortitude measure adds a new perspective to assist in the development of more effective interventions. Opportunities for future research are discussed.

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For nearly thirty years, researchers have sought to understand the antecedents and consequences of physician burnout, yet the problem continues. Notably, Shanafelt et al., found that 62.8% of physicians had at least one manifestation of burnout in 2021, compared with 45.5% in 2011 [ 1 ]. Consequently, patients have a higher likelihood of being treated by a burned-out physician today than one who is not [ 1 , 2 ]. This is associated with decreased quality of care, medical errors, poor patient satisfaction, and limited patient access [ 3 , 4 ]. Moreover, beyond the human cost of burnout on individuals, the financial cost of burnout related turnover on the healthcare system is estimated to be $2.6 to $6.3 billion (USD) a year, or $7,600 (USD) per employed physician [ 5 ]. Clearly more research needs to be conducted to understand the antecedents of physician burnout and turnover so that more effective strategies can be developed to address this growing problem.

Overview of physician burnout

Existing research on physician burnout has primarily examined two sets of drivers, intrinsic personality traits and extrinsic work environments. Intrinsic personality traits such as high neuroticism, low agreeableness and low conscientiousness as measured by the Big 5 Inventory have been associated with an increased risk of burnout [ 6 ]. Likewise, extrinsic work environmental factors such as excessive workload, poor work-life balance, low autonomy and systemic barriers all contribute to burnout and intent to leave. [ 7 , 8 ].

Much recent research focused on the importance of the work environment has led to interventions to improve physician wellbeing [ 9 , 10 , 11 , 12 ]. However, in a recent meta-analysis of 38 randomized trials using different interventions focused on improving physician burnout, results suggested these efforts did not result in meaningful impacts on clinical burnout [ 13 ]. Furthermore, the authors suggest that a more nuanced understanding of the causes of burnout is needed to develop more effective interventions. Similarly, Cataputo et al., looked at interventions focused on mitigating work-related stress in healthcare using cognitive behavioral therapy, relaxation therapy; and interventions focused on the organization. They concluded that individual-level interventions were beneficial over the short term, but organizational-level intervention failed to show any benefit in reducing burnout [ 14 ]. Although these studies have provided important insights into physician burnout, there are still significant opportunities to extend these findings.

Consequently, a critical question to consider is while it is estimated that up to 50% of physicians are suffering from burnout at any one time, why do 50% of physicians in presumably similar circumstances not burnout? One possible explanation may be that individuals can perceive their work environment very differently. In a study of UK house officers, differences in self-reported trainee stress levels were shown to be related to individual differences within the doctors themselves and not organizational factors present or the administrative structure of hospitals [ 15 ]. Furthermore, in a separate study, researchers determined how doctors perceived their workplace climate and workload can be partially predicted by trait measures of personality taken five years earlier [ 16 ]. That is not to say that factors such as work environment and systemic factors are unimportant but suggests that there is a need to build upon this past empirical research to increase our collective understanding of how an individual’s perspective and attitude may contribute to their unique responses to environmental stressors in healthcare.

Extant literature has yielded a significant amount of research assessing the relationship between intra-personal attributes with burnout and/or turnover across multiple professions. Specifically, there is a growing body of research that has demonstrated how grit [ 17 , 18 , 19 ], self-efficacy [ 20 ], hardiness [ 21 , 22 , 23 ], resilience [ 24 , 25 ], mental toughness [ 26 , 27 ] and hope [ 28 , 29 ] individually mitigate the relationship between environmental stressors and burnout-related phenomena in healthcare and many other professions. While these unique individual attributes have proven to be beneficial, we believe there is a significant opportunity to integrate these constructs to develop a more holistic understanding of how individuals use all of these attributes to respond to their unique work-related stressors.

Integration of concepts

There have been previous attempts to integrate the abovementioned attributes. The limited literature on fortitude shows promise as a unifying construct [ 30 , 31 , 32 ]. However, this body of research has been studied using different definitions, antecedents and outcome measures. For example, Pretorius et al. defined fortitude as an attitude to manage stress and stay well. He postulated that this strength derives from an appraisal of the self, the family and support from others. Their 20 item Fortitude Questionnaire (FORQ) was tested and validated in undergraduate psychology students [ 31 ]. Henttonen et al. attempted to measure fortitude by developing a scale based on the Finnish cultural attribute of sisu , defined as determination and resoluteness in the face of adversity. They combined multiple intrapersonal attributes with personality traits to develop a 18-item scale in a general working population [ 30 ]. Similarly, VanTongeren et al. developed a validated measure for spiritual fortitude. Their measure includes items for spiritual enterprise, spiritual endurance and redemptive purpose and was validated in a volunteer population [ 32 ].

While there has been significant value from the fortitude research, its applicability and generalizability to physician burnout and turnover is limited. To advance the literature and extend our understanding of physician burnout, there is a need to arrive at a more precise definition relevant to healthcare. Subsequently, we suggest fortitude to be an interpersonal attitudinal attribute that enables one to succeed under repeated pressure and stress. Therefore, the purpose of this study is to gain new insights into burnout and turnover by investigating fortitude in healthcare and developing a statistically valid and reliable scale.

To assess the potential impact of physician fortitude on burnout and turnover intent, we drew on previous research that has focused on explaining how individuals overcome adversity. We employed a two-sample design to establish content validity, internal consistency, empirical reliability, unidimensionality, convergent validity and ultimately criterion-related validity. Surveys were approved by the University of Illinois College of Medicine Institutional Review Board. Informed consent was attained by all survey participants prior to completion of the survey.

Item generation

To ensure we met the psychometric assumptions proposed by our latent construct we define as fortitude, we used a deductive approach [ 33 ]. Grounded in extant literatures that have shown empirical promise regarding how individuals experience and ultimately overcome adversity, seven specific areas of research were identified. Specifically, we drew on grit, hardiness, mental toughness, resilience, hope, optimism and self-efficacy to generate a potential list of items for our integrative fortitude construct. Based on commonalities across these dimensions, 64 potential items were identified. We then engaged 73 physicians and non-physician healthcare leaders in 13 focus groups to provide feedback on the suitability of the potential items in a healthcare context. Based on relevancy, these focus groups reduced the potential list of survey items to 45.

Next, we ensured sufficient interrater reliability to evaluate internal consistency of potential items for the fortitude scale [ 34 ]. A panel of five academic researchers actively engaged in burnout and turnover research were queried and asked to match potential survey items with the fortitude construct. Crocker and Algina define a minimum value greater than 0.70 to be acceptable for consistency estimates of interrater reliability [ 35 ]. Consequently, if an item had an interrater reliability value that did not meet the 0.70 threshold, it was deleted as a possible item for the fortitude scale. This resulted in an interrater reliability 0.88. Results from the interrater reliability assessment yielded 34 potential items.

Data reduction, reliability and unidimensionality

To further reduce potential items, in our first study (study 1) we collected data from a sample of 909 practicing physicians, APPs and healthcare leaders from a large U.S. healthcare system. Respondents were asked to rate the degree to which each statement accurately described their own individual attributes on the 34 items using a seven-point Likert Scale, where 1 = strongly disagree and 7 = strongly agree. A seven-point Likert Scale was used, as this is most consistent with the literatures we used to develop our potential list of items.

Results from study 1 provided initial evidence of reliability, construct validity and unidimensionality. Exploratory factor analysis (EFA) was used to assess potential items. Given the data were normally distributed, a maximum likelihood extraction method and Varimax rotation were used [ 36 ]. We were able to identify potential items that had individual factor loadings greater than 0.70. This yielded a 12-item scale measuring fortitude in healthcare (HCF-12). Example items include “I am excited about working on achieving my goals,” “I am determined to succeed in achieving my goals,” “I am passionate about the work I do.”

The EFA provided initial support for the unidimensionality of a single factor for the HCF-12 scale, as well as initial construct validity. Specifically, scree plots indicated unidimensionality for our HCF-12 measure. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.88, well above the suggested threshold of 0.5 [ 37 ], and Bartlett’s test of sphericity was significant ( p  < 0.05). Initial reliability was encouraging, yielding a Cronbach alpha of 0.89.

Convergent and criterion-related validity

After finding encouraging evidence of reliability and unidimensionality, we collected data from a second sample (study 2) to measure convergent validity and criterion-related validity. The survey in study 2 was sent to practicing physicians and healthcare leaders ( n  = 212). Similar to Study 1, we found encouraging reliability with a Cronbach’s alpha = 0.93. Measures for internal consistency and factor loadings of the 12 items in the HCF-12 for Study 2 can be seen in Table  1 .

To test for convergent validity, Study 2 included the HCF-12 scale and scales grit [ 17 ], hardiness [ 21 ], resilience [ 24 ], mental toughness [ 27 ]. To establish criterion-related validity, the survey also included scales for all three subdimensions of burnout (emotional exhaustion, depersonalization, and personal achievement [ 38 ], and turnover intent [ 39 ], as previous literature has shown that chronic burnout leads to turnover [ 40 , 41 , 42 ]. Additionally, based on focus-group insights, control variables were added including age, gender, race, hours worked and call burden. Note that reliabilities for all scales used in Study 2 are included in Table  2 .

Given the HCF-12 draws on attitudinal measures specifically related to stressors, including grit, hardiness, resilience and mental toughness, we would expect strong relationships among these variables. To assess convergent validity, we used correlational analyses. We found significant correlations between fortitude and hardiness ( r  = 0.28, p < 0.01) and resilience ( r  = 0.71, p  < 0.01). However, there were not significant relationships between our fortitude measure with grit or mental toughness . To assess criterion-related validity, we would expect that the HCF-12 measure to be negatively related to burnout and turnover intent. Fortitude was significantly related all three subdimensions of burnout, specifically emotional exhaustion ( r  = -0.62, p  < 0.01), depersonalization ( r  = -0.70, p  < 0.01) and personal accomplishment ( r  = 0.65, p  < 0.01), and turnover intent ( r  = -0.55, p  < 0.01). Given that fortitude was significantly related to all of these constructs in the predicted directions, this provides strong initial evidence for criterion-related validity.

Findings and results

Based on encouraging results regarding reliability and validities of the HCF-12, this creates a platform that allows us to compare fortitude with the scales currently recognized as antecedents in the extant burnout and turnover-intent literatures. Subsequently, we used ordinary least-squared (OLS) regression modeling to test significance levels and explained variance to compared the HCF-12 to grit, hardiness, mental toughness and resilience based on data from the 212 respondents in study 2,

Sample characteristics

Of the 212 respondents who completed the survey, 76% were female, 21% were male and 3% preferred not to answer. For race, 91% of respondents were White, 5% were Asian, 2% were Hispanic and 2% were Black. The average age of respondents was 47.4 years old; they worked an average of 51.3 h per week and worked for an average of 8.8 years in the current hospital system. Finally, in terms of their role, respondents were asked if they were a physician leader (26%), a non-physician leader (49%) or a practicing clinician (29%).

Descriptive statistics

Means, standard deviations, reliability scores, and bivariate correlations can be seen in Table  2 . Note that reliabilities for all scales met the minimum threshold of 0.70 (with the exceptions of hardiness) and the HCF-12 had the highest reliability score of 0.93. Also note that the fortitude measure was significantly related to all three subdimensions of burnout and turnover intent, providing encouraging support for the consideration of the new scale.

Ordinary least squares regression results

In order to compare the HCF-12 with existing measures, regression analyses were performed. Specifically, in Tables 3 – 6 , fortitude, grit, hardiness, mental toughness and resilience were all regressed on turnover intent and the three subdimensions of burnout, namely emotional exhaustion, depersonalization and personal accomplishment.

In Table  3 , all five scales were negatively related with turnover intent. Specifically, fortitude (β = -0.55), grit (β = -0.23) hardiness (β = -0.31), mental toughness (β = -0.27) and resilience (β = -0.28) were all statistically significant ( p  < 0.01). Note that there was a statistically significant difference in explained variance between fortitude and all of the other measures ( p  < 0.01). The adjusted R 2 for fortitude was 0.30 compared to grit (0.04), hardiness (0.09), mental toughness (0.06) and resilience (0.07).

In Table  4 , all five scales were negatively related with the burnout subdimension of emotional exhaustion. Specifically, fortitude (β = -0.62), grit (β = -0.35) hardiness (β = -0.56), mental toughness (β = -0.45) and resilience (β = -0.38) were all statistically significant ( p  < 0.01). Note that there was a statistically significant difference in explained variance between fortitude and all of the other measures ( p  < 0.01). The adjusted R 2 for fortitude was 0.39 compared to grit (0.12), hardiness (0.32), mental toughness (0.29) and resilience (0.15).

In Table  5 , all five scales were negatively related with the burnout subdimension of depersonalization. Specifically, fortitude (β = -0.69), grit (β = -0.36) hardiness (β = -0.50), mental toughness (β = -0.47) and resilience (β = -0.40) were all statistically significant ( p  < 0.01). Note that there was a statistically significant difference in explained variance between fortitude and all of the other measures ( p  < 0.01). The adjusted R 2 for fortitude was 0.48 compared to grit (0.13), hardiness (0.25), mental toughness (0.22) and resilience (0.16).

In Table  6 , all five scales were positively related with the burnout subdimension of personal accomplishment. Specifically, fortitude (β = 0.65), grit (β = 0.41) hardiness (β = 0.49), mental toughness (β = 0.49) and resilience (β = 0.59) were all statistically significant ( p  < 0.01). Note that there was a statistically significant difference in explained variance between fortitude and all of the other measures ( p  < 0.01). The adjusted R 2 for fortitude was 0.42 compared to grit (0.16), hardiness (0.23), mental toughness (0.23) and resilience (0.34).

In summary, fortitude explained significantly more variance in turnover intent, emotional exhaustion, depersonalization and personal accomplishment than any of the existing measures.

The primary focus of this research was to investigate the potential use of a new fortitude scale to increase our understanding of antecedents to physician burnout and turnover intent. Results from this study provide encouraging evidence that the integration of grit, hardiness, mental toughness and resilience, in a latent the construct defined as fortitude, provides new insights into understanding how the combination of these intrapersonal attributes contribute to physician burnout and turnover intent. Our proposed HCF-12 scale exhibited encouraging findings to support the psychometric properties of a physician fortitude scale. Specifically, based on a two-study design, we were able to establish content validity, unidimensionality, empirical reliability, convergent validity and criterion-related validity. Moreover, we were able to show that the HCF-12 resulted in significantly higher levels of explained variances for all three subdimensions of burnout and turnover intent when compared to all existing scales found in the extant literature. Consequently, findings from our study provide additional insights into understanding physician burnout and turnover intent. Specifically, our findings make several contributions.

First, our findings extend previous fortitude research that showed the combination of different psychological antecedents of well-being provide additional insights beyond single constructs alone. Specifically, Pretorius et al. derived their measure from the constructs of hardiness, sense of cohesion and potency and found that fortitude, defined as the strength to manage stress and to stay well, was significantly related to students’ well-being and distress [ 31 ]. Similarly, Henttonen et al. combined attributes of mental toughness, grit, hardiness, resilience, hope and self-efficacy with personality traits and studied the effects of beneficial and harmful sisu on the happiness and wellbeing of survey participants [ 30 ]. Likewise, VanTongeren et. al. developed a unique scale for spiritual fortitude which predicted variance in meaning in life, spiritual well-being, religious coping and adversity-related anxiety [ 32 ]. Our study and the Healthcare Fortitude Scale (HF-12) extends these studies conceptually and relies on well-established constructs and validated instruments. Furthermore, we validate this scale so that it is applicable to healthcare workers.

Second, our findings support and extend research examining the impact of resilience on burnout [ 24 , 25 ]. In a study by Roslen et. al. the authors contend the concept of resilience has unfortunately been used interchangeably with mental toughness, hardiness, and grit, adding to confusion in the literature [ 43 ]. Stoffel and Cain likewise state that it is often difficult to tell whether the constructs of resilience, grit, hardiness and mental toughness are distinct from each other as some authors use these terms interchangeably [ 44 ]. Consequently, by combining these concepts using the HCF-12, our findings support and extend this research by empirically demonstrating that an elevated concept of fortitude is a better and more precise construct than an expanded definition of resilience. Furthermore, our fortitude measure explains more variance than any of the individual concepts, including resilience alone, proving that the combination of constructs is better than any single one alone.

Third, the findings from this study may help to change the conversation regarding physician burnout and turnover intent. Our work suggests that the interpersonal attribute of fortitude leads to decreased burnout and turnover intent. Moreover, fortitude can be viewed as attitudinal and therefore malleable when compared to personality traits. Subsequently, it may help explain the interaction between each individual and their work environment. As such, interventions that are mindful of empowering individuals to develop fortitude in addition to changes in the work environment may lead to faster progress in mitigating this important issue in healthcare.

Limitations and future research

We recognize potential limitations of the current study. First, we used a cross-sectional sample, collected at one point in time. Second, we recognize the possibility of common-methods bias, even though cross-sectional sampling is considered an acceptable method of collecting perceptual data [ 45 ].

Future research should strive to obtain longitudinal data and sources of secondary data to improve criterion-related validity. Likewise, future research on fortitude may want to consider the impact of fortitude on organizational context measures, such as perceived supervisor support (PSS) and organizational culture. It may not be the environment or the person alone, but the interaction between the adequacy of the multiple skills and attributes of the individual with ever evolving demands of the environment that contributes to wellbeing. Given the encouraging measures of validity and reliability of the HCF-12, future research can also test the moderating and mediating roles of fortitude on the relationship between work stress and burnout, work stress and turnover intent and burnout and turnover intent. Finally, this research was performed on a U.S.-based sample. Future research can assess fortitude among physicians in other countries to increase generalizability.

This study represents the first attempt to define and measure fortitude using a U.S.-based sample in a healthcare environment. Moreover, this study develops a valid, reliable and generalizable scale that extends our understanding of antecedents that lead to physician burnout and turnover intent. As a malleable attribute, fortitude also provides new opportunities for targeted intervention strategies to improve physician wellbeing.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Laurence Weinzimmer

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Weinzimmer, L., Hippler, S. New insights into physician burnout and turnover intent: a validated measure of physician fortitude. BMC Health Serv Res 24 , 748 (2024). https://doi.org/10.1186/s12913-024-11186-7

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Build a Corporate Culture That Works

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

• EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

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Almost half of Singapore employees feel underpaid

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If you feel underpaid, you are not alone – 48 per cent of Singaporeans surveyed by ADP Research Institute said their salaries did not commensurate with what they brought to the table.

In the People At Work 2024: A Global Workforce View report, the percentage of Singapore respondents who felt underpaid was the highest in Asia-Pacific.

Singapore workers in the Arts and Culture industry felt the most underpaid at 67 per cent, followed by those in the Professional Services industry (55 per cent) and the Architecture, Engineering and Building industry (50 per cent).

ADP vice-president of HR APAC Yvonne Teo said: “Given the high importance employees place on salary in a job, an alarmingly high percentage of workers feel unsatisfied with their salary. Unhappy employees can result in a less engaged workforce and high turnover, negatively impacting business performances.

“Employers need to manage their employees’ changing, sometimes lofty, expectations."

Survey respondents said they would be happy with other forms of compensation – one-off bonus, extra time off and grocery or shopping vouchers – if they could not get a pay increase.

OCBC gives employees $1,000 to help cope with rising costs

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