# Predictors of Burnout among Entrepreneurs: A Longitudinal Fixed-Effects Analysis

Mathieu Le Moal, Olivier Torrès

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Mathieu Le Moal, Olivier Torrès. Predictors of Burnout among Entrepreneurs: A Longitudinal Fixed-Effects Analysis. International Journal of Entrepreneurship and Small Business, In press. hal-05507725

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# Predictors of Burnout among Entrepreneurs: A Longitudinal Fixed-Effects Analysis

Mathieu Le Moal¹,² and Olivier Torrès¹

¹Université de Montpellier - MOMA, LabEx Entreprendre, MRM, Montpellier, France

²Université Paul-Valery, Laboratoire EPSYLON, Montpellier, France

## Author Note

Mathieu Le Moal is an occupational psychologist and doctoral student in management science at Montpellier University (MOMA-MRM) and Paul Valery University (EPSYLON). His research focuses on the health of entrepreneurs, with a particular emphasis on work recovery and the articulation of life domains.

Olivier Torrès is Professor at the University of Montpellier (MOMA-MRM) and holder of the Chaire Santé des Entrepreneurs (Labex Entreprendre). In 2009, he founded Amarok, the first observatory on the health of non-salaried workers (shopkeepers, craftsmen, farmers, company directors, etc.). He edited a book on the health of managers, published by De Boeck. AMAROK is committed to the idea that a mature society must protect those who make it work.

**ORCID:** Mathieu Le Moal: 0000-0003-3592-4126; Olivier Torrès: 0000-0002-7104-0486

The present study was supported by the French Federation of Building (FFB), which contributed to the development and distribution of the survey. It was also supported by LabEx Entreprendre, University of Montpellier (ANR-10-Labex-11-01, Agence Nationale de la Recherche).

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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

Correspondence concerning this article should be Mathieu Le Moal.

Email: mathieu.lemoal.pro@gmail.com

**Data Availability Statement:** The raw data supporting the conclusions of this article are available here: https://osf.io/u6bgj/
# Predictors of Burnout among Entrepreneurs: A Longitudinal Fixed-Effects Analysis

**Abstract:** This longitudinal study explored links between workplace loneliness, health, sleep, working hours, and burnout among 349 small business owners in construction/public works, using four waves of telephone data (2015–2016). A fixed-effects panel regression assessed within-person changes over time. Workplace loneliness was positively associated with burnout ($\beta$ = 0.28, $p < .001$), while mental health ($\beta$ = -0.27, $p < .001$) and sleep duration ($\beta$ = -0.17, $p = .018$) were negatively associated. Physical health and working hours showed no significant effects. Additional analyses revealed stronger associations between loneliness, mental health, and burnout in SMEs compared to microenterprises, where sleep had a modest protective effect. These results underscore the need to address loneliness, support mental health, and promote sleep to reduce burnout in small business owners. Interventions should be adapted to the distinct needs of microenterprises and SMEs.

**Keywords:** Burnout, Workplace loneliness, Mental health, Sleep duration, Small business owners, Fixed-effects analysis, Longitudinal study, Entrepreneurs

## 1 Introduction

Burnout has been studied intensively for decades since being introduced by Freudenberger (1974) and Maslach (1976). Burnout is defined as an occupational syndrome of chronic work-related stress, marked by persistent exhaustion and a cynical or negative attitude toward one's job (Demerouti et al., 2021). It is often accompanied by a sense of diminished professional efficacy or accomplishment (Mäkikangas et al., 2021). These three dimensions—exhaustion, cynicism (also termed depersonalization), and reduced efficacy—were first identified by Maslach and Jackson (1981) in their study of human services workers. Early researchers initially viewed burnout as a response to overwhelming emotional demands in caregiving and service roles (Demerouti et al., 2021). However, by the 1990s it became evident that burnout is not exclusive to helping professions; it can develop in virtually any occupation under chronic stress, leading to a more general conception of burnout beyond the human services context (Demerouti et al., 2021). Entrepreneurs facing prolonged work-related stress may experience reduced cognitive control, which makes it harder to regulate emotions and recover effectively from daily job demands. These cognitive and emotional impairments are particularly relevant in the entrepreneurial context, where workload is high and boundaries between work and recovery are often blurred.

Despite extensive research, there is ongoing debate about the precise definition and measurement of burnout (Demerouti et al., 2021). Nevertheless, scholars largely agree that emotional exhaustion is the core component of burnout, with nearly all formal definitions of the syndrome including exhaustion as a central element (Demerouti et al., 2021). In comparison, cynicism (or depersonalization) and feelings of reduced efficacy
are also important but are often seen as consequences or concomitants of prolonged
exhaustion.

While burnout has been extensively studied in traditional employee populations, to our knowledge, no longitudinal study has investigated its predictors among entrepreneurs using within-person designs. Most existing research in this area relies on cross-sectional data, which limit causal inference and fail to capture intra-individual dynamics over time (Lechat and Torrès, 2016; Palmer et al., 2021). A recent systematic review confirmed this methodological gap, highlighting that the overwhelming reliance on cross-sectional designs (92.3% of studies examined) makes it impossible to establish causality or track changes in health over time in the entrepreneurial context (Gardiner et al., 2024). Some exceptions include two-wave studies that establish temporal ordering (e.g., Wincent et al., 2008), but these do not account for unobserved individual heterogeneity or intra-individual variability. In contrast, our study adopts a fixed-effects longitudinal approach that isolates within-person changes and strengthens causal interpretation. Additionally, although recent studies acknowledge the rising prevalence of burnout in entrepreneurship (e.g., Torrès et al., 2022; Torrès and Kinowski-Moysan, 2019), few have examined how psychosocial (e.g., workplace loneliness; Fernet et al., 2016) and behavioural factors (e.g., sleep duration; Guiliani and Torrès, 2018) jointly influence burnout in small business settings. Furthermore, few research has compared these effects across microenterprises and SMEs.

The present study addresses these gaps by adopting a four-wave longitudinal panel design and fixed-effects regression to model within-person changes in burnout. This methodological approach enables the examination of dynamic associations between psychosocial and behavioural predictors and burnout over time, while controlling for unobserved time-invariant individual characteristics. Our main research question is: To what extent do workplace loneliness, perceived mental and physical health, sleep duration, and working hours predict changes in burnout over time among entrepreneurs? We also investigate whether these associations differ according to enterprise size.

## 2 Theoretical Framework and Hypotheses

To explain how burnout develops, the Job Demands-Resources (JD-R) model is a prominent theoretical framework (Demerouti et al., 2001). Despite its widespread application in occupational health, JD-R theory itself has yet to be properly introduced and integratively tested in the entrepreneurship literature, which often focuses on isolated components rather than a holistic model (Obschonka et al., 2023). The JD-R model, originally formulated by Demerouti et al. (2001), posits that burnout arises from an imbalance between job demands and job resources. Specifically, excessive or poorly managed job demands (such as heavy workload, long hours, or role conflicts) drive a health-impairment process that produces exhaustion, while a lack of sufficient job resources (such as support, autonomy, and feedback) triggers a motivational decline leading to withdrawal and cynicism (Demerouti et al., 2021). Empirical evidence supports this model, showing that chronic high demands combined with low resources are significant predictors of burnout across various occupations (Demerouti et al., 2021). The JD-R perspective has been influential in burnout research as it integrates the key dimensions of burnout with its hypothesized causes, emphasizing both the critical role of exhaustion and the contextual factors that precipitate the burnout process.
## 2.1 Burnout and Workplace Loneliness

Research suggests a clear connection between workplace loneliness and burnout, with loneliness acting as a significant predictor of burnout symptoms (Lam and Lau, 2012; Ozcelik and Barsade, 2018). Workplace loneliness can be defined as “a psychological state that results from deficiencies in a person’s social relationships at work, either qualitatively or quantitatively” (Wright et al., 2006, p. 59, adapt from Perlman and Peplau, 1981 p. 31). It reflects a perceived discrepancy between the actual and desired quality of interpersonal relationships at work, which can generate feelings of isolation even in the presence of coworkers. A meta-analysis highlighted that loneliness at work correlates positively with burnout, job stress, and turnover intentions, while being negatively linked to job satisfaction and organizational commitment (Bryan et al., 2023). This relationship is particularly evident in roles with high emotional demands, such as healthcare professionals, where loneliness contributes to emotional exhaustion and reduced job performance (Karcz et al., 2022). Multiple studies have highlighted the significance of loneliness in entrepreneurship (Akande, 1994; Gumpert and Boyd, 1984). Fernet et al. (2016) extend this understanding to small business owners, demonstrating that workplace loneliness mediates the relationship between job stressors and burnout, especially when entrepreneurial orientation is low. Entrepreneurs often experience loneliness because of high levels of uncertainty, resource constraints, responsibility, and time pressure (Cardon and Arwine, 2024). Loneliness can be perceived as either harmful or irrelevant, depending on an individual’s coping mechanisms (Cardon and Arwine, 2024). Furthermore, loneliness and relationship strain can intensify feelings of failure and shame among entrepreneurs, potentially leading to depression and burnout (Cubbon et al., 2021). Research has shown that prolonged exposure to workplace stressors, such as role ambiguity and work pressure, exacerbates emotional exhaustion, which is a core component of burnout (Sardeshmukh et al., 2021).

## 2.2 Burnout and Mental Health

Perceived mental health refers to an individual's subjective assessment of their emotional and psychological well-being, including aspects such as mood, anxiety, and resilience (WHO, 2004). Specifically, an entrepreneur's capacity for mentalizing—defined as the ability to understand one's own and others' behavior by interpreting reasons, causes, and motives—is considered a pillar of mental health and can partially help avoid entrepreneurial burnout (Manzano-García et al., 2021). This cognitive skill enables emotional regulation and the creation of positive interpersonal relationships. Much of the research linking burnout and mental health has historically focused on healthcare professionals, such as nurses and physicians, where emotional demands and chronic stress are prominent (Maslach and Leiter, 2016; Salvagioni et al., 2017; Schaufeli et al., 2009). Although entrepreneurs operate in a different context, they too face chronic stressors—such as uncertainty, responsibility, and workload—which may impair mental health in similar ways. Burnout profoundly affects mental health professionals, diminishing both their well-being and the quality of care they provide (Barling, 2001; Lasalvia and Tansella, 2011). Mental health workers, such as nurses and physicians, are particularly prone to burnout due to the emotionally intense nature of their work, which often involves caring for seriously ill patients in the long term. This environment can foster feelings of powerlessness and frustration (Beschoner et al., 2019; Lasalvia and Tansella, 2011). The emotional toll of this work frequently leads to occupational stress,
which, if sustained, escalates into burnout, further aggravating mental health issues, such as depression and anxiety (Beschoner et al., 2019; Lasalvia and Tansella, 2011). Burnout among mental health providers remains a critical concern, as high prevalence rates highlight its impact on both psychological well-being and the quality of professional care (Dreison et al., 2018; Rabin et al., 2011). Research also suggests that burnout mediates the relationship between spirituality and mental health, where higher burnout levels are associated with increased depression and anxiety, while daily spiritual experiences may offer some protection against these mental health challenges (Ho et al., 2016). The organizational context is another key factor in mitigating or exacerbating burnout. High workloads, insufficient rewards, and limited decision-making power are strong predictors of burnout, including emotional exhaustion and reduced professional efficacy (Lasalvia and Tansella, 2011). Longitudinal studies indicate that individuals with persistently high or moderate mental health symptoms are at an increased risk of burnout and work-family conflict, underscoring the importance of early identification and preventative strategies (Nilsen et al., 2016). In emergency healthcare settings, the link between post-traumatic stress disorder, burnout, and mood disorders further illustrates the complex interplay between mental health and burnout, with notable comorbidity between post-traumatic stress disorder and depressive symptoms among healthcare workers (Carmassi et al., 2020). This intricate connection suggests that burnout not only affects mental health, but is also shaped by it, creating a feedback loop that calls for comprehensive intervention strategies. Such interventions should combine individual and organizational approaches tailored to the unique needs of mental health professionals to effectively reduce stress and burnout, thus enhancing their mental health and the quality of care provided (Beschoner et al., 2019; Dreison et al., 2018). Addressing burnout within mental health settings is essential for improving provider well-being and patient outcomes, emphasizing the need for ongoing research and intervention (Dreison et al., 2018; Rabin et al., 2011).

## 2.3 Burnout and Physical Health

Although the relationship between burnout and physical health has been extensively studied among employees (Melamed et al., 2006; Salvagioni et al., 2017), this association has received much less attention in entrepreneurship research, with only a few exceptions (e.g., Hatak et al., 2015). Perceived physical health refers to a person's overall self-assessment of their physical condition, including levels of vitality, presence of symptoms, and functional capacity (Idler and Benyamini, 1997). In the French context, the GEM 2024/2025 report reveals that perceived health has improved within the general population, emerging entrepreneurs, and established entrepreneurs, with the latter two categories reporting the highest levels of good or excellent health. This finding suggests that entrepreneurship may be associated with better self-rated health (Messeghem et al., 2025). While some studies emphasize the negative impact of burnout on physical health, there is evidence supporting the reverse effect. For instance, Biksegn et al. (2016) found that a history of physical illness predicts burnout among healthcare providers, suggesting that poor physical health may contribute to burnout rather than being solely a consequence. Similarly, research on entrepreneurs highlights that those in better physical condition are less susceptible to burnout (Hatak et al., 2015), further reinforcing the idea that maintaining good physical health may serve as a protective factor against burnout. The physiological mechanisms underlying this relationship remain insufficiently explored, but impaired physical health can exacerbate burnout through increased physiological
stress, reduced resilience, and diminished capacity to cope with occupational demands. The negative impact of burnout on physical health has been well documented, with studies linking it to increased cardiovascular risk (Melamed et al., 2006) and long-term physical impairments (Lheureux et al., 2016). However, both articles acknowledged that the relationship may be bidirectional, suggesting that poor physical health could also contribute to burnout rather than solely as a consequence. Moreover, the intricate link between physical activity, cardiovascular health, and brain function suggests that burnout and physical health continuously influence one another, potentially creating a vicious cycle (Pihlaja et al., 2022). In the entrepreneurial context, empirical studies exploring these mechanisms remain scarce, pointing to an important avenue for future research and intervention development (Palmer et al., 2021).

## 2.4 Burnout and Sleep Hours

The association between sleep duration and burnout in entrepreneurs highlights the critical role of sleep in mitigating emotional exhaustion and its related symptoms. Research has shown that inadequate sleep can exacerbate stress levels, impair cognitive functioning, and hinder decision-making, thereby contributing directly to burnout (Grossi et al., 2015; Levasseur et al., 2019). Poor sleep quality and sleep disturbances are closely associated with burnout, particularly in terms of emotional exhaustion, suggesting that sleep issues contribute significantly to burnout independent of job demands (Armon et al., 2008). Additionally, irregular sleep patterns have been shown to create a cycle of burnout risk, where disrupted sleep and burnout symptoms reinforce each other over time (Von Kentzinsky et al., 2020). Emerging evidence indicates that insufficient sleep increases cortisol levels, leading to chronic stress that exacerbates burnout (Van Laethem et al., 2015). Additionally, chronic work stress has been linked to attentional impairments and cognitive difficulties, which can further increase exhaustion and burnout risk (Van Der Linden et al., 2005). The COVID-19 pandemic has further highlighted this dynamic, as increased financial stress has aggravated both sleep disturbances and burnout among entrepreneurs (Torrès et al., 2021). These findings underscore the importance of interventions aimed at improving sleep quality as preventive measures against burnout. Studies suggest that establishing consistent sleep routines can reduce emotional exhaustion and increase resilience to workplace stressors, especially for entrepreneurs facing unpredictable demands (Barber et al., 2013). By enhancing cognitive function, a well-regulated sleep schedule may help entrepreneurs manage the cognitive load associated with high job demands, thus reducing their risk of burnout (Gish et al., 2019). Addressing sleep issues can enhance entrepreneurs’ mental resilience, reduce burnout risk, and support sustained business success (Guiliani and Torrès, 2018).

## 2.5 Burnout and Work Hours

In most countries, entrepreneurs have a significantly higher workload than other workers do (Blanchflower, 2004). In France, according to the DARES (2021) – French Ministry of Labor, 30.6% of self-employed workers and up to 62.4% of farm operators experience a high level of work strain, compared to only 3.9% of employees. This difference is largely explained by the longer working hours of self-employed individuals. Based on Eurostat data for French workers in 2019 (2020, 2021, and 2022 were disrupted by the COVID-19 pandemic), self-employed workers, on average, worked more hours per week than salaried employees (Eurostat, 2024). The gap increases from 36.4 hours to
52 hours, representing a 43% increase. Additionally, weekend work, which remains a minority among salaried workers (27.3%), is the norm for business owners (63%). However, the most striking difference concerns ‘long working hours’. According to Eurostat, 7.6% of salaried employees fall into this category regarding long working hours (defined as working 48 hours or more per week), compared to 36.2% of solo self-employed workers and 61.9% of entrepreneurs with employees under their responsibility (Eurostat, 2024). Consequently, entrepreneurs are particularly vulnerable to burnout compared to salaried employees (Lin et al., 2020), as they experience significant stress because of their financial and managerial responsibilities (Dahl et al., 2010), work longer hours (Bodier et al., 2010), sleep less (Guiliani and Torrès, 2018; Gunia, 2018), and dedicate less time to leisure activities (Van Der Zwan et al., 2018). Regarding the relationship with workload, measured by weekly working hours, research has consistently identified a positive correlation with burnout risk both among employees in general (Maslach and Leiter, 1997) and specifically among entrepreneurs (Voltmer et al., 2011; Wei et al., 2015).

## 2.6 *Entrepreneurial Context and the Role of Firm Size*

While the predictors examined in this study—workplace loneliness, mental and physical health, sleep duration, and working hours—have been widely linked to burnout in employee populations, it is important to acknowledge that our hypotheses are primarily grounded in this literature. Research on entrepreneurial populations remains comparatively scarce, and much of our theoretical reasoning relies on evidence derived from workplace contexts such as healthcare, education, or corporate employment (e.g., Maslach & Leiter, 2016; Salvagioni et al., 2017).

Nevertheless, these predictors capture fundamental psychosocial and behavioural mechanisms—social connectedness, mental and physical well-being, and recovery through sleep—that are likely to be relevant to entrepreneurs as well. What remains uncertain is whether their influence is of similar magnitude, or whether the entrepreneurial context, characterized by high levels of autonomy, uncertainty, resource scarcity, and blurred boundaries between work and non-work domains (Stephan, 2018; Palmer et al., 2021), modifies these associations. For example, workplace loneliness may be especially detrimental for business owners who lack structured social networks (Fernet et al., 2016), while the absence of formal boundaries in microenterprises may increase work-life interference and make sleep a particularly vital resource (Guiliani & Torrès, 2018). Firm size may further act as a contingency factor shaping the development of burnout. Microentrepreneurs often operate with limited human and financial resources, take on a broader range of responsibilities, and experience higher levels of isolation compared to small business owners with employees (Palmer et al., 2021). These structural and organisational differences may alter the strength or direction of associations between psychosocial predictors and burnout. However, few studies have explicitly tested whether such relationships vary by enterprise size.

To address this gap, the present study adopts a longitudinal panel design to examine within-person changes in burnout across four waves of data among French entrepreneurs. This design allows us to isolate temporal variations in burnout and test whether their predictors differ between micro- and small enterprises—an aspect rarely addressed in entrepreneurship research to date.
Building on the theoretical framework and prior research, this study investigates how key psychosocial and behavioural factors—namely workplace loneliness, perceived mental and physical health, sleep duration, and weekly working hours—relate to changes in burnout levels among small business owners over time. By leveraging a within-person panel design, we aim to identify which of these predictors are most strongly associated with burnout dynamics in entrepreneurial settings. Drawing on the Job Demands-Resources (JD-R) model, we formulated the following hypotheses:

H1: Higher levels of workplace loneliness will be positively associated with burnout.

H2: Better perceived mental health will be negatively associated with burnout.

H3: Better perceived physical health will be negatively associated with burnout.

H4: Longer sleep duration will be negatively associated with burnout.

H5: A greater number of weekly working hours will be positively associated with burnout.

# 3 Methods

## 3.1 Data and Sample

We selected a longitudinal panel design to examine within-person changes in burnout and its psychosocial predictors. This approach supports a dynamic view of psychological functioning over time, in line with recent methodological developments in occupational health research (Allison, 2009; Hamaker and Muthén, 2020). Data for this study were collected via telephone interviews conducted between 2015 and 2016. The sampling frame was predominantly sector-based, focusing on members of the French Federation of Building (FFB), representing professionals in the construction and public works sectors. The sample size consisted of 349 small business owners in Wave 1, 295 in Wave 2, 287 in Wave 3, and 245 in Wave 4, resulting in a total of 1176 observations. Sociodemographic variables, such as age, sex, and life partner, were included in the questionnaire. Additionally, we collected data on the size of the enterprises, distinguishing between microenterprises (fewer than 10 employees) and small and medium-sized enterprises (SME; more than 10 employees). This distinction was included to explore the potential differences in burnout predictors between the small business owners of microenterprises and SME.

## 3.2 Outcome Variable

Burnout refers to a state of physical and emotional exhaustion that impairs normal functioning in the workplace, arising when job demands exceed an individual's capacity to meet them. The risk of burnout was measured using the Burnout Measure Short Version (BMS) developed by A. Pines (Malach-Pines, 2005) and translated into French by Lourel et al. (2007), which emphasizes emotional exhaustion, the most predictive component of burnout (Leiter, 1993). To streamline the assessment, we used three items with strong psychometric properties (Soenen et al., 2019). These items followed the prompt: 'When you think about your work, at present, how often do you...?' The items were: 'feel tired', 'feel hopeless', and 'feel depressed'. Responses were recorded on a 7-point Likert scale ranging from 1 (never) to 7 (always), with the intermediate response options as follows: never = 1, rarely = 3, sometimes = 4, often = 5, very often = 6, and always = 7. Burnout scores were calculated by averaging the responses across the three
items, resulting in a scale ranging from one to seven. Higher scores indicated greater burnout symptoms. In this study, McDonalds' Ω for the scale was 0.82 across all waves.

### 3.3 Predictor Variables

The workplace loneliness experienced in the workplace was assessed via the following question: 'In the past month, in your role as an entrepreneur, did you feel 1 = *Very* surrounded; 2 = *Somewhat surrounded*; 3 = *Neither surrounded nor lonely*; 4 = *Somewhat lonely*; 5 = *Very lonely*'. See Fernet et al. (2016), inspired by Stephens et al. (2011) and Stickley et al. (2013).

Perceived health was assessed through three dimensions (physical and mental health), each measured by a single item on a 5-point scale ranging from 1 = *poor*, 2 = *fair*, 3 = *good*, 4 = *very good*, and 5 = *excellent*. These self-assessment dimensions are similar to those collected in large national and international surveys, such as the World Value Survey, European Value Survey (Mansyur et al., 2008), National Health and Nutrition Examination Survey in the United States, and SHARE in Europe. These surveys include the SF-36 survey instrument (Ware and Gandek, 1998), and are recommended for health surveys (Robine et al., 2008). Physical health was measured with one item adapted from Heyman and Jeffers (1963): 'During the last month, would you say that your physical health was'. Mental health, adapted from Friedsam and Martin (1963), was assessed using the following item: 'During the last month, would you say that your mental health was'.

Sleep was measured as the average number of hours of sleep per night during the previous month, as reported by the respondents. This measure was used in a previous study by Guiliani and Torrès (2018).

Work hours were measured using a single item on a 5-point scale ranging from 1 = 40 hours or less, 2 = between 40 and 50 hours, 3 = between 50 and 60 hours, 4 = between 60 and 70 hours, and 5 = more than 70 hours. This measure was previously used by Torrès et al. (2022).

While perceived health and sleep were each assessed using single-item measures, this approach is justified for several reasons. First, single-item self-assessments of physical and mental health have been shown to possess strong predictive validity in population-based studies, including associations with mortality, functional decline, and healthcare use (Idler and Benyamini, 1997; DeSalvo et al., 2006). Second, these measures are widely used in national and international health surveys such as the World Value Survey and SHARE (Mansyur et al., 2008) and remain recommended in brief health assessments. Third, given the practical constraints of repeated telephone-based data collection with entrepreneurs, concise measures helped limit respondent burden and maintain data quality across waves (Rolstad et al., 2011). Forth, in the context of fixed-effects models, single-item measures are sufficiently sensitive to detect intra-individual changes over time, which aligns with the objective of our longitudinal design. Finally, similar single-item measures of self-rated health have been successfully used in research on entrepreneurs and health (e.g., Le Moal et al., 2024; Lechat and Torrès, 2017). However, while robust for capturing overall health trends and within-person changes over time, it is acknowledged that these measures, especially for physical health, might be less sensitive to subtle physiological shifts specifically influencing burnout in highly active self-employed populations, a nuance further explored in our discussion.
## 3.4 Analytic Strategy

Linear fixed-effects panel regression modelling was used to estimate the longitudinal association between workplace loneliness, perceived health, sleep (as predictor variables), and burnout (as the outcome variable) across the four waves of data collection. This approach focuses on within-individual variations over time, effectively controlling for all time-invariant characteristics among individuals (e.g., age, sex, or occupation) (Allison, 2009; Brüderl and Ludwig, 2014; Wooldridge, 2010). To provide standardized coefficients, all predictor variables were demeaned and standardized, allowing for a clearer interpretation of the magnitude of the associations. By controlling for stable individual characteristics, fixed-effects models isolate within-person changes and eliminate potential confounders from between-participant differences. Fixed-effects modelling is widely regarded as a robust approach for analysing longitudinal data, as it leverages within-person variations to approximate causal inferences in observational settings (Bliese et al., 2020; Halaby, 2004; Osgood, 2010). In this study, fixed-effects regression allowed us to examine the immediate impact of predictors such as workplace loneliness, mental and physical health, and sleep duration on burnout, supporting a focus on contemporaneous associations. Although fixed effects modelling originated in economics and sociology, it is increasingly being adopted in psychology to examine within-person variations (Hamaker and Muthén, 2020; McNeish and Kelley, 2019; Sommet and Lipps, 2024). Despite the observed attrition over the study period, the model's focus on within-participant changes mitigates potential biases introduced by participant loss, as it controls for all stable characteristics. To ensure that the results were not driven by collinearity among the predictor variables, we computed the Variance Inflation Factors (VIF) for each independent variable. All VIF values were below 5, suggesting no problematic level of multicollinearity (Hair et al., 2010).

The model is structured as follows.

$$ \text{burnout}_{it} = B_1 \times \text{loneliness}_{it} + B_2 \times \text{health}_{it} + B_3 \times \text{sleep}_{it} + B_4 \times $$

$$ \text{work hours}_{it} = a_i + \lambda_t + e_{it} $$

...where i = 1, 2, ..., N participants, t = 1, 2, 3, 4 waves, αi are participant fixed effects (N - 1 dummies), λt are time fixed effects (t - 1 dummies), eit is the within-participant residual

# 4. Results

## 4.1 Main Analysis

Table 1 presents the results of the fixed-effects regression model, which examined the longitudinal associations between burnout and key predictors including workplace loneliness, mental and physical health, sleep duration, and weekly working hours. Workplace loneliness was significantly positively associated with burnout, β = 0.28, 95% CI [0.17, 0.38], SE = 0.06, p < .001, meaning that an increase in workplace loneliness over time was linked to a corresponding rise in burnout scores. Conversely, mental health was significantly negatively associated with burnout β = -0.27, 95% CI [-0.41, -0.14], SE = 0.07, p < .001, indicating that improvements in mental health were related to reductions
in burnout. Sleep duration was also negatively related to burnout $\beta$ = -0.17, 95% CI [-0.31, -0.03], *SE* = 0.07, *p* = .018, suggesting that more hours of sleep per night was associated with lower levels of burnout. Physical health and weekly working hours were not significantly associated with burnout in this model. We also tested the interactions between workplace loneliness and sleep, as well as mental and physical health, but found no significant effects.

While these results support our general hypotheses, they do not account for structural heterogeneity across entrepreneurial contexts. We therefore examined whether the associations between burnout and its predictors differ by firm size, comparing microenterprises and SMEs.

## 4.2 Firm Size as a Moderating Factor: Microenterprises vs. SMEs

In addition to the main analysis, we explored the potential differences between microenterprises and SME to determine whether company size moderated the associations between the predictors and burnout. Separate fixed-effect models were used for each group. The results revealed distinct patterns in the effects of the key predictors of workplace loneliness and mental health across micro-and SME (see Table 2). For microentrepreneurs, workplace loneliness showed a significant positive association with burnout (p = 0.006), indicating that higher levels of workplace loneliness are linked to increased burnout. Mental health also had a marginally significant negative association with burnout (p = 0.064), suggesting that better mental health may reduce burnout levels among microentrepreneurs, although this effect did not reach significance. Sleep hours were marginally significant (p = 0.041), indicating a potential protective effect, where more sleep could be associated with lower burnout. Other predictors such as physical health and weekly working hours were not significantly associated with burnout in this group. In SME, both workplace loneliness (p = 0.0001) and mental health (p = 0.0002) were strongly and significantly associated with burnout. Higher workplace loneliness was associated with increased burnout, whereas better mental health was linked to reduced burnout levels. In contrast to microenterprises, sleep duration did not show a significant effect on burnout in this group (p = 0.095), although it followed a similar protective trend. The remaining predictors—physical health and weekly working hours—were not significantly associated with burnout. While we hypothesized that company size might moderate the relationship between burnout and its predictors, our analyses did not reveal substantial differences in the overall direction of these effects. However, the significance levels and effect sizes for workplace loneliness and mental health were more pronounced among SME, while the effect of sleep appeared to be more relevant for microentrepreneurs. These results underscore the critical roles of both workplace loneliness and mental health as predictors of burnout in both groups, with sleep having a possible protective effect, specifically for microentrepreneurs.

## 5 Discussion

This study aimed to test five hypotheses concerning the predictors of burnout among small business owners in the construction and public works sectors, using a longitudinal fixed-effects design. Specifically, we hypothesized that: (H1) higher workplace loneliness would be positively associated with burnout; (H2) better perceived mental health would be negatively associated with burnout; (H3) better perceived physical health
would be negatively associated with burnout; (H4) longer sleep duration would be negatively associated with burnout; and (H5) a greater number of weekly working hours would be positively associated with burnout. The results provided strong support for H1 and H2, partial support for H4, while H3 and H5 were not supported by the data.

Workplace loneliness was strongly and positively associated with burnout. This finding aligns with prior research indicating that loneliness in organizational settings can exacerbate emotional exhaustion and undermine health (Ozcelik and Barsade, 2018). Beyond the absolute experience of loneliness, Wemken et al. (2021) argue that burnout is also shaped by how individuals perceive their demands and control in comparison to others. Entrepreneurs, who often lack clear points of social comparison, may be especially susceptible to negative self-evaluation, which could further intensify the effects of loneliness on burnout. Mental health was negatively associated with burnout. This finding supports prior evidence suggesting that psychological well-being can serve as a protective factor against burnout progression (Halbesleben and Buckley, 2004). Contrary to expectations, physical health did not emerge as a statistically significant predictor of burnout in our model. While both mental and physical health are undeniably important, emotional and cognitive strains may take precedence over physical ailments in driving burnout among small business owners. Regarding sleep, our results indicated that longer sleep duration was linked to lower burnout levels, corroborating studies that have highlighted the importance of adequate sleep on burnout (Guiliani and Torrès, 2018). Contrary to our fourth hypothesis, weekly working hours are not significantly associated with burnout. This suggests that the impact of work hours alone may be overshadowed by other elements, such as workplace loneliness, mental health, and sleep, which directly influence whether small business owners experience chronic stress or remain resilient.

Exploring potential differences between microenterprises and SMEs revealed that while the direction of the effects for workplace loneliness and mental health was broadly consistent across both groups, there were notable variations in magnitude and statistical significance. Among microentrepreneurs, workplace loneliness showed a significantly positive relationship with burnout, whereas mental health and sleep duration demonstrated marginally significant protective effects. In contrast, in SMEs, both workplace loneliness and mental health emerged as strong predictors of burnout, while the protective effect of sleep trended in a similar direction, although not statistically significant. These findings support the idea that burnout among entrepreneurs is shaped not only by individual psychological and behavioural factors, but also by contextual characteristics related to business structure. The stronger association of sleep with burnout among microentrepreneurs may reflect their higher exposure to resource scarcity, blurred work-life boundaries, and reduced opportunities for recovery (Guiliani and Torrès, 2018; Hülsheger et al., 2015). These entrepreneurs often lack formal organisational structures and must manage a wide range of responsibilities alone, increasing their vulnerability to chronic strain (Thurik et al., 2024). In contrast, small business owners in SMEs may benefit from more distributed workloads or social resources, which could attenuate the role of sleep in buffering exhaustion (Stephan, 2018). The more pronounced influence of mental health and loneliness in SMEs may reflect more complex interpersonal and managerial demands, including team leadership and stakeholder management (Fernet et al., 2016). Overall, these results underline the importance of contingency-based approaches in burnout research, where the strength of psychosocial risk and protective factors varies depending on enterprise size and organisational context (Palmer et al., 2021; De Mol et al., 2018). Physical health and
weekly working hours were not significantly associated with burnout in either group. This non-significant association between physical health and burnout, despite the general predictive validity of single-item measures, can be explained by several factors. Firstly, in the early phases of strain, psychological and emotional markers (such as fatigue or detachment) may be more salient than somatic symptoms (Schaufeli and Enzmann, 1998). Secondly, entrepreneurs may underreport physical complaints or downplay their health issues in favour of business responsibilities, leading to a possible underestimation of the true relationship (Stephan, 2018). Finally, the single-item measure of physical health itself, while adequate for broader assessments, may be less sensitive to the subtle, specific physiological changes that directly interact with burnout in this highly active population over time. This non-significant association between physical health and burnout may reflect the fact that, in the early phases of strain, psychological and emotional markers (such as fatigue or detachment) are more salient than somatic symptoms (Schaufeli and Enzmann, 1998). Moreover, entrepreneurs may underreport physical complaints or downplay their health issues in favour of business responsibilities, leading to a possible underestimation of the true relationship (Stephan, 2018). Finally, single-item measures of physical health may be less sensitive to subtle changes in health over time, especially in highly active self-employed populations. These findings extend earlier research underscoring the value of social support, mental health, and adequate sleep to mitigate burnout (Hülsheger et al., 2015; Schaufeli et al., 2009) and echo prior evidence that loneliness and limited social interaction increase emotional exhaustion (Schaufeli et al., 2009). Moreover, the protective role of mental health aligns with studies indicating that psychological health can buffer the negative effects of burnout (Stephan, 2018). The pronounced importance of sleep among microentrepreneurs points to a unique dynamic in smaller enterprises, where leaders often face greater demands and resource constraints, making sufficient rest particularly crucial—an insight that builds upon findings in broader workforce populations (Guiliani and Torrès, 2018; Hülsheger et al., 2015). A similar dynamic has been observed in other leadership roles. Marsh et al. (2022), in a longitudinal study of over 3,600 school principals, showed that job demands significantly predicted burnout and job satisfaction over time, and that burnout itself could influence subsequent perceptions of demands and resilience. These reciprocal effects, tested within a JD-R framework, underline the need to consider how burnout among leaders can become self-reinforcing over time. In addition to sleep, a study focusing specifically on entrepreneurs indicated that certain recovery experiences, particularly psychological detachment, relaxation, and control, significantly mitigate burnout among small business owners (Le Moal et al., 2025).

This study contributes to the growing literature on entrepreneurial mental health by adopting a longitudinal, within-person design to examine how psychological and behavioural factors relate to burnout in small business owners. While research has increasingly recognised the unique psychological vulnerabilities of entrepreneurs, such as isolation, emotional demands, and blurred work-life boundaries (Stephan, 2018; Palmer et al., 2021), most existing studies remain cross-sectional and overlook intra-individual dynamics. Our study responds to this gap by offering a rare longitudinal perspective on entrepreneurial burnout, capturing how psychological and behavioural indicators fluctuate within individuals over time. This intra-individual focus enables a more dynamic understanding of entrepreneurial strain and recovery processes, moving beyond static risk factor models (Bliese et al., 2020). Moreover, by showing that predictors such as sleep and loneliness have distinct effects depending on firm size, our study responds to
calls for greater contextualisation in entrepreneurial health research. As the field of
entrepreneurial mental health continues to evolve, longitudinal and context-sensitive
studies such as ours are essential to refine theoretical models and inform more targeted
interventions (Benzari et al., 2024; Palmer et al., 2021; Stephan, 2018).

6 Practical implications

The results of this study have several practical implications. First, interventions aimed at reducing burnout among small business owners should prioritize reducing workplace loneliness, perhaps through initiatives that foster-peer networking, mentoring, and social support structures (McKee-Ryan et al., 2005). These efforts could help mitigate the effects of loneliness, especially in smaller enterprises where leaders may feel isolated in their responsibilities. Second, it is essential to promote good mental health. Strategies could include workshops on stress management, psychological health services, and mindfulness practices to enhance well-being (Fazia et al., 2023; Hülsheger et al., 2015). Finally, given the protective role of sleep, particularly for microentrepreneurs, sleep health education should be integrated into workplace wellness programs (Hirshkowitz et al., 2015). Emphasizing the importance of sleep hygiene could help leaders manage their stress and reduce burnout. Given that work context plays a crucial role in shaping the health outcomes of entrepreneurs (Stephan and Roesler, 2010), practical interventions should focus not only on individual factors but also on modifying structural conditions such as workload distribution, autonomy, and access to social support. Beyond individual interventions, these findings also have practical implications for other stakeholders. Business support organisations and professional associations could develop structured peer mentoring initiatives or collective support groups to combat entrepreneurial isolation and promote mental health. Public policymakers might consider integrating burnout prevention into SME support programmes—for instance, through subsidised access to psychological services, awareness campaigns on sleep and stress management, or modules within entrepreneurship training schemes. These initiatives may be especially relevant for microentrepreneurs, who often face more pronounced resource constraints and less access to institutional support.

One of the strengths of this study was the use of a longitudinal fixed-effects model, which allowed for the examination of within-individual changes over time, controlling for time-invariant confounders (Allison, 2009). This methodology enhances the causal robustness of our findings by focusing on variations within participants. It revealed that changes in workplace loneliness and mental health over time were systematically associated with changes in burnout within the same individuals. For instance, when entrepreneurs reported a deterioration in their mental health or an increase in perceived loneliness, their burnout levels also rose during the same period. These intra-individual associations would have remained undetected in a cross-sectional design, which conflates between-person and within-person effects. Importantly, we also found that the protective role of sleep was more pronounced among microentrepreneurs, suggesting that fluctuations in recovery behaviours can buffer burnout more effectively in highly resource-constrained environments. This provides novel evidence that the temporal dynamics of burnout are not only driven by stable traits or average exposure to stressors, but also by short-term variations in psychosocial and behavioural states.
# 7 Limitations

This study has several limitations that should be acknowledged. First, although the fixed-effects approach mitigates concerns related to time-invariant confounding variables, participant attrition across the four waves may have introduced selection bias, particularly if dropout was non-random and associated with burnout severity. Second, the reliance on self-reported measures raises the possibility of common method bias (Podsakoff et al., 2003), especially for sensitive constructs such as mental health and sleep duration, which are prone to social desirability or recall effects.

Third, the data were collected in 2015–2016, before the COVID-19 pandemic. Given the substantial evidence that the pandemic has exacerbated psychosocial stress and increased burnout risk among workers, including entrepreneurs (Torrès et al., 2022), the generalisability of these findings to the post-pandemic context may be limited. Longitudinal replications using post-COVID data are needed to examine whether the observed associations have strengthened or shifted under new stress paradigms and economic conditions.

Lastly, the sample was drawn exclusively from the construction and public works sector, which may limit external validity. Entrepreneurs in other sectors—such as services or agriculture—might face different types of demands and recovery opportunities. Sector-specific stressors (e.g., physical workload, regulatory pressure, project cycles) may also influence the salience of predictors such as sleep or loneliness.

# 8 Future directions

Future research could address these limitations by incorporating objective measures, such as wearable technology, to track sleep and physical activity and provide more accurate data (De Zambotti et al., 2019; Shin et al., 2019). Further studies should explore the role of workplace culture, leadership style, and specific stressors unique to the construction sector to deepen the understanding of burnout in this context. Additionally, examining how small business owners navigate resource constraints and stressors compared with those in larger enterprises could inform more targeted interventions. Another promising avenue would be to investigate how recovery strategies could mitigate the impact of burnout among entrepreneurs. The effort-recovery theory (Meijman and Mulder, 1998) highlights the importance of psychological detachment from work and engagement in nonwork activities to restore depleted resources. Oerlemans and Bakker (2014) demonstrated that employees at risk of burnout benefit from low-effort, social, and physical activities outside of work, improving their recovery levels. Given the blurred boundaries between work and personal life for entrepreneurs, future studies should examine whether structured recovery interventions—such as designated recovery periods or digital tools—could provide similar protective effects in this population.

# 9 Conclusion

This study highlights the significant roles of workplace loneliness, mental health, and sleep in predicting burnout among small business owners in the construction and public work sectors. These findings underscore the need for targeted interventions that address
loneliness, promote mental health, and encourage adequate sleep, especially among microentrepreneurs who might be more susceptible to these stressors. Beyond these contextual factors, individual characteristics, such as job fit and passion orientation, also play a crucial role in shaping burnout risk. Research suggests that entrepreneurs who perceive a strong alignment between their work and personal values are less likely to experience burnout, particularly when their passion for their work is harmonious rather than obsessive (De Mol et al., 2018). While physical health was not a significant predictor, future research could explore the complex interplay between these elements within the broader of workplace stress and recovery. Addressing these issues holistically could lead to more effective strategies for preventing burnout and promoting sustainable leadership in small enterprises.

# 10 Conflicts of interest declaration

All authors declare that they have no conflicts of interest context

# 11 Ethics Statement

This study is based on data collected between 2015 and 2016, before institutional review board (IRB) approval became a standardized requirement for research involving workplace stress and burnout among entrepreneurs. Participants were recruited via telephone interviews, during which they provided oral informed consent before responding to the survey. They were informed about the study's objectives, the voluntary nature of their participation, and their right to withdraw at any time without consequences. To ensure confidentiality, all collected data were fully anonymized prior to analysis, with no personally identifiable information recorded or stored. The study was conducted in accordance with ethical research practices in place at the time of data collection, adhering to principles of voluntary participation, confidentiality, and data protection.

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<caption>**Table 1**</caption>

*Fixed-Effects within-Person Regression Results, Burnout, and Health Predictors*

<table><thead><tr><th rowspan="2">Variables</th><th colspan="3">Burnout as dependent variable</th></tr><tr><th>β</th><th>Low 95% CI</th><th>Upper 95% CI</th></tr></thead><tbody><tr><td>Workplace loneliness</td><td>0.28***</td><td>0.17</td><td>0.39</td></tr><tr><td>Mental health</td><td>-0.27***</td><td>-0.41</td><td>-0.14</td></tr><tr><td>Physical health</td><td>0.10</td><td>-0.05</td><td>0.17</td></tr><tr><td>Sleep hours</td><td>-0.17*</td><td>-0.31</td><td>-0.03</td></tr><tr><td>Work hours (Weekly)</td><td>0.04</td><td>-0.11</td><td>0.19</td></tr></tbody></table>

Notes: All variables are standardized; ***p < 0.001, **p < 0.01, and *p < 0.05.

<caption>**Table 2**</caption>
Fixed-Effects within-Person Regression Results, Burnout, and Health Predictors Stratified by Micro and Small Enterprises

<table><thead><tr><th rowspan="2">Variables</th><th colspan="4">Burnout as dependent variable</th></tr><tr><th>β</th><th>Low CI</th><th>95%</th><th>Upper 95% CI</th></tr></thead><tbody><tr><td colspan="5"><strong>Microenterprises (n = 348)</strong></td></tr><tr><td>Workplace loneliness</td><td>0.37**</td><td>0.17</td><td>0.39</td><td></td></tr><tr><td>Mental health</td><td>-0.32°</td><td>-0.41</td><td>-0.14</td><td></td></tr><tr><td>Physical health</td><td>0.02</td><td>-0.05</td><td>0.17</td><td></td></tr><tr><td>Sleep hours</td><td>-0.28°</td><td>-0.31</td><td>-0.03</td><td></td></tr><tr><td>Work hours (Weekly)</td><td>0.01</td><td>-0.11</td><td>0.19</td><td></td></tr><tr><td colspan="5"><strong>SME (n = 805)</strong></td></tr><tr><td>Workplace loneliness</td><td>0.24***</td><td>0.17</td><td>0.39</td><td></td></tr><tr><td>Mental health</td><td>-0.26***</td><td>-0.41</td><td>-0.14</td><td></td></tr><tr><td>Physical health</td><td>0.08</td><td>-0.05</td><td>0.17</td><td></td></tr><tr><td>Sleep hours</td><td>-0.14°</td><td>-0.31</td><td>-0.03</td><td></td></tr><tr><td>Work hours (Weekly)</td><td>0.07</td><td>-0.11</td><td>0.19</td><td></td></tr></tbody></table>

Note. All variables were standardized; ***p < 0.001; **p < 0.01; *p < 0.05; °p < 0.10.

**Keywords:** Burnout, Workplace loneliness, Mental health, Sleep duration, Small business owners, Fixed-effects analysis, Longitudinal study, Entrepreneurs