Are you in or are you out? A longitudinal person-centered study of health and entrance and exit into self-employment

Bergman, Louise E.; Bujacz, Aleksandra; Leineweber, Constanze; Toivanen, Susanna; Bernhard-Oettel, Claudia Article Are you in or are you out? A longitudinal personcentered study of health and entrance and exit into selfemployment BRQ Business Research Quarterly Provided in Cooperation with: Asociación Científica de Economía y Dirección de Empresas (ACEDE), Madrid Suggested Citation: Bergman, Louise E.; Bujacz, Aleksandra; Leineweber, Constanze; Toivanen, Susanna; Bernhard-Oettel, Claudia (2025) : Are you in or are you out? A longitudinal personcentered study of health and entrance and exit into self-employment, BRQ Business Research Quarterly, ISSN 2340-9444, Sage Publishing, London, Vol. 28, Iss. 3, pp. 678-694, https://doi.org/10.1177/23409444241277831 This Version is available at: https://hdl.handle.net/10419/327091 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Despite being a smaller segment of the workforce (Ekonomifakta, 2023), self-employed workers make significant contributions to our society in terms of economic productivity, job opportunities, and innovation (Van Praag & Versloot, 2007). However, many workers who enter self-employment leave again, and this may have detrimental effects for the individual (Nikolova et al., 2021). Thus, a crucial area of research in the field of self-employment revolves around understanding the factors that influence individuals entering and exiting self-employment. An important aspect of keeping a business running is the health of the self-employed worker, as research indicates that this is related to the success and survival of their businesses (Koch et al., 2021). It is well documented that—on average—self-employed workers experience better health compared to employed workers (Stephan, 2018). However, self-employed workers comprise a highly diverse group when it comes to who they are, how they work, and their health (Bernhard-Oettel et al., 2024). Furthermore, health is a complex construct, and depending on how health is assessed, results may vary (Stephan et al., 2023). Therefore, studying self-employed workers as one, homogeneous group and relying on averages from single aspects of health is insufficient to provide researchers, policymakers, and organizations with a comprehensive Are you in or are you out? A longitudinal person-centered study of health and entrance and exit into self-employment Louise E. Bergman1, Aleksandra Bujacz2, Constanze Leineweber1, Susanna Toivanen3 and Claudia Bernhard-Oettel1 Abstract This study addresses the scarcity of research on health developments in the heterogeneous group of self-employed workers. It aims at understanding typical health progressions in this group and associations with demographic factors, work characteristics, and self-employment decisions. We investigate health profiles based on mental health problems, self-rated health, and work satisfaction, as well as transitions between them in relation to work effort, reward, overcommitment, demographic characteristics, and entrance and exit into self-employment. Using latent transition analysis, we analyzed data from the Swedish Longitudinal Occupational Survey of Health (SLOSH), including data from 593 participants. We identified four distinct, stable health profiles, revealing associations with work effort, reward, overcommitment, and self-employment decisions. No meaningful relations existed for demographic characteristics. Overall, the findings offer a comprehensive perspective on the health dynamics of self-employed individuals, their associations with work characteristics and decisions to enter and exit self-employment. JEL CLASSIFICATION: J62; J81; L26 Keywords Self-employment, mental health, person-centered analysis, effort reward model, entrepreneurship 1 Section for Work and Organizational Psychology, Department of Psychology, Stockholm University, Stockholm, Sweden 2 Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden 3 The School of health, care and social welfare, Mälardalen University, Västerås, Sweden Corresponding author: Louise E. Bergman, Section for work and organizational psychology, Department of Psychology, Stockholm University, Stockholm 106 91, Sweden. Email: [email protected] 1277831BRQ0010.1177/23409444241277831Business Research QuarterlyBergman et al. research-article2024 Special Issue: Entrepreneurship and Well-Being: Research Progress and Challenges Ahead Bergman et al. 679 understanding of health development in this specific group of workers in relation to their demographic and work characteristics, and entrance and exit into self-employment. Although there is existing research of entrance and exit of self-employment, few studies take a longitudinal approach with scientific rigor, following individuals in and out of self-employment, and even fewer examine the effects on various aspects of health (Gish, Lerner, et al., 2022). In this study, we aim to shed light on the development of health in relation to self-employment, demographic and work characteristics, and decisions to enter or exist self-employment. In doing so, we make several important contributions to the literature. First, since health may differ in the heterogeneous group of workers that enter, exit, or remain in self-employment over a period of 4 years, we will identify subgroups based on health profiles and study how workers change between these profiles over time. Furthermore, we aim to increase understanding for the link between health developments and decisions to enter, stay in, or exit self-employment, and therefore, we investigate how entrance into and exit out of self-employment affect health. In addition to this, we will also investigate how these developments associate with demographic and work characteristics. We study work characteristics through the lens of the effort reward imbalance (ERI) model (Siegrist et al., 1986), which explain the relationship between work and health by the efforts a worker put into work and the rewards received. If there is a balance, this is beneficial for worker’s health, whereas imbalances in work efforts and rewards constitute threats to health. This adds to both the theoretical understanding of the diversity in self-employment concerning health developments and decisions to start or remain in this employment form. The analysis of individual and work characteristics also provides insights of practical significance because it may help to identify vulnerable groups for which interventions may be needed to improve possibilities to set up and run businesses successfully. Self-employment Self-employed workers engage in an activity for their own account and are able to recruit their own employees (European Union Foundation, 2009). Over time, workers may change between employment forms—self-employment and organizational employment—or work in both, referred to as being a combiner (Bergman et al., 2021) or hybrid entrepreneur (Folta et al., 2010). Thus, it is important not to study only workers that are stably self-employed but also those who enter and exit self-employment. While self-employed workers are often referred to as entrepreneurs, it is a term that more accurately describes individuals who become self-employed due to having an idea or innovation but excludes those who enter self-employment out of necessity. Thus, for this study, we will use the term self-employed workers. Self-employment grants freedom to organize work according to own preferences, affecting various aspects of work, including task selection, time scheduling, and skill development (Obschonka & Silbereisen, 2015). However, alongside the freedom that comes with self-employment, challenges such as potential income restrictions, blurred boundaries between work and personal life, lack of peer support, and responsibility for employees may arise (Stephan, 2018). One of the few studies focusing on various aspects of health in self-employed workers, indicate that self-employed workers experience both happiness and negative feelings, and more health problems than employed workers (Bencsik & Chuluun, 2021). Thus, the idea that self-employment has an entirely positive relationship with health does not seem to hold true, warranting a closer look into mental health of self-employed workers. Self-employment and illbeing in terms of mental health problems Mental health of self-employed workers has thus far most prevalently been studied through the absence of mental health problems such as stress, emotional exhaustion, depressive symptoms, and sleep disturbances (Stephan, 2018), which may be defined as illbeing. Stress is a nonspecific perceived response to demands (Åkerstedt et al., 2015). Extant research shows that workers feel stressed when they experience work situations with an imbalance between perceived efforts put into work and rewards received (Siegrist et al., 1986). If this exposure is repeated, without allowing the worker recovery between stressful periods, stress can become a chronic experience. Consequently, the individual may suffer from health problems such as emotional exhaustion (Åkerstedt et al., 2016). Emotional exhaustion is a state characterized by of profound fatigue and depletion, and a sense of being burned out (Melamed et al., 1992; Shirom, 1989; Shirom & Melamed, 2006). Emotional exhaustion is a serious and common problem in the work force. Studies suggest that self-employed workers may be particularly vulnerable to it, due to their unique work circumstances (McDowell et al., 2019). Furthermore, emotional exhaustion may drive intention to exit self-employment (Sardeshmukh et al., 2021). Depressive symptoms are another common mental health problem in the working force. These encompass a broad range of emotional, cognitive, and physical symptoms, including feelings of sadness, low energy levels, lack of interest, and excessive worrying (Magnusson Hanson et al., 2014). It is important to note that emotional exhaustion and depressive symptoms can co-occur. Selfemployed workers may be sensitive to depressive symptoms because they often lack the social support that organisationally employed workers benefit from and may have a tendency to internalize emotions due to stigma and fear of failure (Cubbon et al., 2021). 680 Business Research Quarterly 28(3) Stress, emotional exhaustion, and depressive symptoms are all connected to sleep disturbances, a common problem among workers, who often attribute poor sleep to factors at work (Linton et al., 2015). Stress arousal, worrying, and emotional depletion may all make sleep difficult. Sleep disturbances include difficulties falling asleep, restless sleep, and premature awakening (Nordin et al., 2013). Poor sleep has a negative, reciprocal relationship with mental health problems (Freeman et al., 2020). Sleep is one of the major means of recovering from stressors (Åkerstedt et al., 2016). In sum, illbeing in terms of these mental health problems may have negative effects, both for the individual worker in terms of suffering, but also for society, since workers’ sick leave and income loss amounts to substantial economic costs and losses. A more comprehensive view of health of workers engaging in self-employment While health of self-employed workers has been studied with a focus on illbeing in terms of mental health problems (Stephan, 2018), a more comprehensive view on health also encompasses aspects of well-being (World Health Organization, 1958, 1984), and ratings of overall health, often called self-rated health (SRH; Jylhä, 2009). Well-being may be defined as experiences of pleasure and happiness, and a striving for optimal psychological functioning and self-realization. Wiklund et al. (2019) highlight the central role of well-being in comprehending the health of self-employed individuals and show that selfemployment is intricately linked to personal growth, performance, and fulfillment. In addition, experiences of well-being are beneficial for health and longevity, for example, when it comes to the cardiovascular, immune, and endocrine systems (Diener et al., 2018). Individuals experiencing well-being tend to live a healthier life and have better social relationships, have higher work performance, and contribute more to society in general (Diener et al., 2018). With regard to work, well-being is often studied through work satisfaction. Work satisfaction is just one aspect of well-being, but it plays a crucial role in an individual’s overall well-being and quality of life (Cranny et al., 1992; Locke, 1969). A satisfying work experience is characterized by positive affect (Warr & Inceoglu, 2012). Generally, work satisfaction represents a relatively stable evaluation of one’s work, combining both cognitive appraisal and affective reactions derived from that appraisal. In addition to feeling satisfied with one’s work, feeling healthy or satisfied with one’s health has also been conceptualized as an important domain of well-being by Diener et al. (1999). In other words, well-being relates to a person’s overall contentment, satisfaction, and a positive feeling about life in general or specific domains (Danna & Griffin, 1999). In general, the relationship between well-being and health problems, social life, and work performance seem reciprocal. For example, well-being helps prevent health problems, but absence of health problems also generates well-being (Diener et al., 2018). However, an individual’s comprehensive health experience might not equal a simple equation in which positive and negative aspects of health problems or well-being can merely be added and subtracted. While the presence of both physical and mental illbeing has a negative relationship with the experience of well-being, individuals make overall subjective judgments concerning their health based on their own situation and its development over time. Thus, one cannot assume that individuals experiencing illbeing necessarily experience impaired well-being and health (Bujacz et al., 2020; Wikman et al., 2005). Hence, in addition to assessing illbeing and well-being, a more comprehensive understanding of an individual’s health experience can be derived by also assessing the individuals SRH. In earlier research, health of self-employed workers has been operationalised as anything from pure mortality rates (Toivanen et al., 2016) to happiness (Bujacz et al., 2017), or self ratings of health (Bernhard-Oettel et al., 2024). Although these studies cover important health problems, aspects of well-being, or overall individual judgments concerning health, they do not take a comprehensive approach. This yields a lack of understanding of how illbeing, well-being, and SRH together contribute to an individual’s health. Thus, in our study, we will study health of self-employed workers through illbeing in terms of mental health problems, wellbeing in terms of work satisfaction, and more overall health in terms of SRH of self-employed workers. Health of self-employed workers has often been studied in comparison to employed workers (Bergman et al., 2021; Gonçalves & Martins, 2018). In general, these studies find that, on average, self-employed workers are somewhat healthier and experience higher levels of well-being than their organisationally employed counterparts. However, it is also established by research that self-employed workers experience higher levels of work stress than employees (Stephan, 2018). Yet, many of these comparative studies have used cross-sectional designs, so that the development of health over time, both in those who are consistently selfemployed, and those who switch between self-employment and organizational employment, is poorly understood. Furthermore, the paradox of high stress and high wellbeing highlights the complexity of health as a concept, and there is a need of a more comprehensive, long-term view of health in research of self-employed workers, taking their heterogeneity into regard. Understanding the relationship between health and work Work constitutes a significant aspect of individuals’ lives and can have profound effects on their health. Illbeing, particularly stress, often has roots in the workplace. The relationship between health and work can be understood through work-stress models such as the Job demands-resources model (JD-R; Bakker et al., 2004) or Effort-reward Bergman et al. 681 imbalance model (ERI; Siegrist et al., 1986). The JD-R model has dominated studies of self-employment, although it has been noted that JD-R may be too narrow to fully capture the experiences of work outside of the traditional organizational framework (Bernhard-Oettel et al., 2019; Bujacz et al., 2018; Stephan, 2018). Thus, ERI might be a more fruitful model when trying to understand the relationship between self-employed work and health. ERI shifts the focus from resources and demands provided by organizations to the efforts individuals invest in their work, the rewards they receive in return, their tendencies to overcommit, and its impact on health (Siegrist, 1996; Siegrist et al., 1986; Siegrist et al., 2004). ERI postulates that employees invest effort in their work with the expectation of receiving rewards in return (Siegrist, 1996; Siegrist et al., 2004). Effort refers to the strain perceived by employees due to various job demands and responsibilities, such as interruptions, overtime, and obligations, while reward encompasses the opportunities provided by the job, including salary, esteem, job security, and career advancement. In ERI, a third component interacts with these two factors: overcommitment. Overcommitment refers to the tendency to respond to imbalance between effort and reward by excessively engaging in work and desiring control. Taking this together, the model rests upon the assumptions that an imbalance between high effort and low reward increases the risk of impaired health and that overcommitted employees are at greater risk of health deterioration (Siegrist, 1996; Siegrist et al., 2004). With these assumptions, the ERI model predicts workers’ health as a function of work circumstances and personal dispositions. As we have argued, self-employed workers are not a homogeneous group, as previously assumed in many studies, and this heterogeneity extends to their work circumstances. The businesses of self-employed individuals vary greatly, ranging from solo entrepreneurs to owners of companies with hundreds of employees. They may face challenges or thrive, and their work hours can vary significantly. Consequently, the levels of effort and reward experienced by these workers vary greatly, and personal characteristics, such as overcommitment, further influence how these circumstances relate to their health (Siegrist & Li, 2016). In summary, ERI underscores the multifaceted nature of self-employment and its impact on health, highlighting the importance of a nuanced examination of entrepreneurial health profiles. A person-centered approach to longitudinal developments of comprehensive health In recent years, there has been a growing interest among researchers in examining the long-term health of selfemployed workers. However, a key limitation of these studies is that they often treat workers engaging in self-employment as a homogeneous group, assuming they are the same (Arshi et al., 2021; Hessels et al., 2020; Sawang et al., 2020), or predefined subpopulations based on their demographics (Caliendo et al., 2023; Litsardopoulos et al., 2021), or career pattern (Koch et al., 2021). This means that the diversity in terms of health has not been studied in much detail longitudinally. One approach to examining health diversity among self-employed workers is through person-centered methods, such as latent profile (LPA) and latent transition analysis (LTA). These methodologies have recently gained attention in research within the fields of work and organizational sciences. They enable the creation of parsimonious groupings of respondents that are both conceptually meaningful and methodologically valuable. While these methods are exploratory in nature, they should be guided by theory (Spurk et al., 2020). Based on a couple of noteworthy studies focusing on well-being of self-employed workers, we argue that a person-centered approach may be fruitful to identify existing subgroups, each characterized by their distinct health profiles in the heterogeneous population of workers who enter, remain in or exit self-employment. Gish, Guedes, et al. (2022) studied self-employed and employed workers in two different samples. They based their profiles on wellbeing and personality, finding four profiles of varying wellbeing. In another study, Bujacz et al. (2020), studied only self-employed workers, and identified six distinct profiles based on well-being. These ranged from flourishing to unhappy. Both studies highlight the heterogeneity of wellbeing in self-employed workers. These studies show that profile analysis is a beneficial approach to assessing health in self-employed workers to understand differences in their health. However, they did not assess illbeing, nor development of well-being or health over time. Thus, insights of these aspects of health in self-employed workers are still lacking. While it is difficult to suggest the exact number and shape of subgroup profiles beforehand, based on earlier research, we expect at least one profile with good health on all or most aspects of health, one profile with mostly poor health, and one or more with different combinations of good and poor health. Furthermore, we expect that the profiles will be relatively stable over time (the same profiles occurring over time and a majority of workers staying in the same health profile over time) while change may occur for smaller groups. Our first research question therefore is: RQ1. Which health profiles can be distinguished among workers who engage in self-employment, how prevalent are those profiles, and how do the workers transition between these profiles? Health profiles over time in relation to work effort, reward, and overcommitment ERI can provide a structured approach to understand the complex relationship between self-employed work and health and can thus be used to validate profiles of health. ERI’s unique perspective of extrinsic (effort and reward) 682 Business Research Quarterly 28(3) and intrinsic (overcommitment) factors may provide an understanding of the interaction of work and health in ways that other work-stress models have yet failed to explain. Overcommitment might be particularly relevant when studying self-employed workers. They are at a higher risk of working excessively (Balducci et al., 2021) and often experience difficulties in detaching from work (Taris et al., 2008). Thus, ERI may be uniquely fit to study work stress in self-employed workers. Indeed, many studies have confirmed the relationship between ERI and mental health problems in the general workforce (Håkansson et al., 2020; Harvey et al., 2017; Hinsch et al., 2019; Leineweber et al., 2020). Further research also indicates a relationship between ERI and work/life satisfaction (Braunheim et al., 2023; Ge et al., 2021; Kinman, 2016). Earlier research using ERI to understand work stress of self-employed workers is scarce. One exception is the study by Wolfe and Patel (2019), who used ERI to explain the positive relationship between stress and autonomy with imbalance in effort and reward, and especially overcommitment, leading to excessive work. In this study, we will use effort, reward, and overcommitment as separate factors to portray work-related factors, since the self-employed are known to be agentic and pro-active and invest a lot of effort into their business operations. To what extent they may compensate ERI through overcommitment is not well-known, but relevant if we seek to understand health developments yet better. However, the work-related factors should vary with health according to theory and thus validate the profiles. Thus, our second research question is: RQ2. How is membership in health profiles at each time point related to effort, reward, and overcommitment? Relationships of health and demographic characteristics of the worker Many longitudinal studies include demographic characteristics as controls, and in these, results are contradictive. For example, Nguyen and Sawang (2016) found no meaningful relationship between health (in terms of mental health and work and life satisfaction), and gender, age, and educational level, but Dawson (2017) found that highly educated self-employed workers reported lower levels of work satisfaction than their less-educated counterparts. Finally, with a meta-analytic approach Solomon et al. (2022) found that higher education in self-employed workers leads to trade-offs, resulting in increased job stress and decreased job satisfaction. With regard to gender, when analyzing the data for women and men separately, Litsardopoulos et al. (2021), found that women might face greater challenges when initially transitioning to self-employment, but over time, their health benefited from being self-employed more than was the case for men. Stephan, Li and Qu (2020) further add to this by showing that men experience health improvements when becoming self-employed, but women do not. These examples illustrate that the relationship between health of self-employed workers and demographic characteristics is complex and seems to vary depending on how health is assessed, which demographic characteristics are focused upon, and whether studies take longer time frames into account. The varying results of previous studies highlight the need for longitudinal studies of these factors together and to study not only the prevalence of certain demographics in different profiles of health, but also if, and how, they predict changes between these profiles. This allows validation of the profiles as meaningful descriptions of qualitatively different groups, that cannot just be reduced to demographic differences. RQ3a. Are demographic characteristics related to health profile membership? RQ3b. Does demographic characteristics predict transitions between health profiles? Health, entrance, and exit into self-employment There is a growing body of research focused on the topic of entering into and exiting out of self-employment. However, the majority of these studies are cross-sectional in nature, primarily examining individuals’ intentions to enter or exit self-employment, without studying actual transitions. The cross-sectional research examining health, find for example that good health is related to intention to enter selfemployment (Sweida & Sherman, 2020), whereas poor health has been linked to the intention to exit self-employment (Lindblom et al., 2020; Sardeshmukh et al., 2021). Few longitudinal studies research the relationship of health and entrance and exit. With regard to entrance, in a study utilizing a quasi-experimental design, B. Nikolaev et al. (2020) found that health in terms of positive and negative affect, is related to entering self-employment. Their findings indicate that individuals who are organisationally employed and experience higher levels of negative affect are more inclined to transition into self-employment. Nikolova (2019) conducted a study tracking workers’ health before and after entering self-employment. The findings indicate that mental health improved for individuals after entering self-employment, while improvements in physical health were only observed among those who entered self-employment due to opportunity (in comparison to those who became self-employed out of necessity), even after controlling for changes in income, psychosocial factors, personality, and local unemployment conditions. Stephan, Li and Qu (2020) also studied mental and physical health, and found that those with poorer mental health self-selected into self-employment, and experienced a short-term improvement in mental health. In a similar Bergman et al. 683 study, focusing on well-being, Georgellis and Yusuf (2016) found that work satisfaction increased immediately after transitioning into self-employment but declined in subsequent years, potentially due to unmet expectations and the fading novelty of the new venture. In terms of longitudinal studies of exiting self-employment, Nikolova et al. (2021) found that transitioning from self-employment to organizational employment was associated with modest improvements in health. Examining self-employment stability, Koch et al. (2021) discovered a positive relationship between stability in self-employment and better health. Furthermore, Bernhard-Oettel et al. (2019) investigated emotional exhaustion and psychosocial factors, revealing that individuals who transitioned from permanent organizational work to self-employment experienced reduced exhaustion and more favorable psychosocial factors. However, exiting self-employment and returning to permanent organizational work showed little to no change in these factors. These studies empathize the importance of considering the specific circumstances surrounding the transition and the need to study health more comprehensively. RQ4. Does entrance or exit into self-employment predict transitions between health profiles beyond the previous profile membership? Method Participants Since 2006, Statistics Sweden (SCB), on behalf of the Stress Research Institute, collects data for the Swedish Longitudinal Occupational Survey of Health (SLOSH), a national representative cohort study (Magnusson Hanson et al., 2018). SLOSH is a follow-up of the participants of the Swedish Work Environment Surveys (SWES) and comprises today all SWES participants 2003–2019 (n = 51,412). As SLOSH is based on the SWES, it can be regarded as approximately representative of the Swedish working labor market. All labor market sectors and occupations are represented, and the number of men and women is approximately equal. SLOSH was conducted every second year (since 2022 every year) by means of a pen-and-paper questionnaire in two versions; one for respondents who work at least 30% (which in Sweden generally is 12 h per week) and one for those who have left the working force, either permanently or temporarily. This study is based on to the fifth to seventh data collection (hence forth called waves 1, 2, and 3) of SLOSH conducted in 2014, 2016, and 2018 (for information of attrition, see Magnusson Hanson et al., 2018). We selected those who were self-employed at least during one of the three waves (N = 2,327). This selection criteria were based on the fact that our study aimed at investigating self-employment entrance and exit and associations to health profiles, as well as profile change. As some of the respondents had not responded to all waves, we selected those who had answered at least one item in each of the six health constructs in two of the three waves, one of them having to be wave 2 (2,016; N = 593 participants). For information of missing on item level, see supplementary materials. This selection criteria were employed to achieve a valid health profile classification, which the entire study rests upon. The final sample size of 593 participants that was used in this study is well above the required sample size of 500 participants needed for the employed analytic techniques (Nylund-Gibson & Choi, 2018). Assessment tools We assessed health in terms of mental health problems, well-being, and general health, all responses given on Likert-type-scales. We assessed mental health problems in terms of stress, emotional exhaustion, depressive symptoms, and sleep disturbances. We assessed stress with three items, asking how the participants felt during the three preceding months (Åkerstedt et al., 2015). We used the revised 6-item subscale for emotional exhaustion and fatigue from the Shirom Melamed Burnout Questionnaire (SMBQ) to assess emotional exhaustion. The scale is deemed sufficient for describing emotional exhaustion in the general population (Shirom, 1989; Shirom & Melamed, 2006). We assessed depressive symptoms with the symptom checklistcore depression (SCL-CD6) including six items. SCL-CD6 uses a small number of depression core characteristics necessary for diagnosis so that the scores sum up to a meaningful severity assessment (Magnusson Hanson et al., 2014). Finally, we assessed sleep disturbances using a subscale of sleep disturbances from Karolinska Sleep Questionnaire (KSQ). KSQ was developed to describe subjective sleep and sleepiness in a general population (Åkerstedt et al., 2016; Kecklund & Åkerstedt, 1992). SCL-CD6, SMBQ, and KSQ have been tested and deemed fit to assess these constructs in self-employed workers (Bergman et al., 2021). We assessed well-being, in terms of work satisfaction, with the item Roughly, how satisfied are you with your work?, and SRH general health with the item How would you rate your general state of health? Demographic characteristics included information of gender (female = 0, male = 1), living conditions (married/ cohabited = 0, living alone = 1; no children under 18 living at home = 0, children under 18 living at home = 1), education (up to university = 0, university = 1), age (55 or younger = 0, 56 or older = 1). The cut-off of age was based on the median age, as when dichotomisation is needed, this should be done as close to the median as possible, to avoid type I error (Chen et al., 2007). We coded entrance and exit to self-employment for each of the two transitions (from wave 1 to 2, and from 2 to 3), so that two variables were 684 Business Research Quarterly 28(3) Figure 1. Final four-profile solution identified in the self-employed workers. Profile indicators are factor scores with mean of 0 and a standard deviation of 1 with 95% confidence intervals. Low scores on stress and exhaustion indicate good health, and high scores bad health. Low scores on all other constructs indicate bad health and low satisfaction, and high scores good health and high satisfaction. Estimates from the dispersional similarity model. created: Entering self-employment (all other = 0, entering = 1), and exiting employment (all other = 0, exiting = 1). To assess effort, reward, and overcommitment we used the scales developed by Siegrist et al. (1986). We revised the scale, excluding items that were not applicable for self-employed workers, keeping three items from each subscale. All tables and figures noted with S can be found in Supplementary materials. Study items and factor loadings are presented in Tables S3 and S5, results from confirmatory factor analysis (CFA) in Tables S2 and S4, correlations Table S6, and reliability assessments in Tables S7 and S8. Analysis strategy We analyzed the data using LPA and LTA. These are person-centered analyses, aiming to identify subgroups of participants who share a similar profile of scores on the variables of interests (Morin et al., 2020). Thus, we were able to identify groups with distinct health profiles. First, we saved factor scores from measurement models of the health indicators and covariates to use in the analyses. These measurement models (tested with CFA) provided validity evidence of the scales assessing one, defined construct each. Second, to establish which profiles of health are present among self-employed workers, we ran LPAs for each wave; thus, following the analysis strategy described by Morin et al. (2020). Third, we tested profile similarity, which is equivalent to conducting analyses of longitudinal measurement invariance. This step also provided validity evidence for the profiles’ longitudinal similarity. Fourth, we followed with LTA, testing transitions between profiles over time. Fifth, utilizing a stepwise approach, so that the new variables would not affect the profile membership, we added demographic characteristics, entrance into and exiting out of self-employment as predictors of transitions above and beyond previous profile membership, and effort, reward, and overcommitment as covariates. Health in covariation with effort, reward, and overcommitment provided validity evidence for the hypothesized covariance in accordance with the ERI model. We conducted all these analyses in Mplus 8.6 (Muthén & Muthen, 2017). In addition, we used chi-square tests to test whether certain demographic characteristics were more prevalent in any of the health profiles. We conducted these analyses in R (R Core Team, 2019). More details about the process are presented in the supplementary materials. Results RQ1. Which health profiles can be distinguished among workers who engage in self-employment, how prevalent are those profiles, and how do the workers transition between these profiles? The analysis of measurement models revealed that the models fitted well in all three waves (Table S2–S5). Thus, the six indicators of health were used to form profiles at each wave. We chose the four-profile solution in all three waves, because it was supported both theoretically and empirically, as the found profiles were alike at each wave (Table S9). We named the profiles (1) Moderate profile (average on all health assessments), (2) Mentally healthy profile (few problems with mental health, sleep disturbances and stress, but below average general health and work satisfaction), (3) Relaxed and satisfied profile (very low experiences of exhaustion, and high general health and work satisfaction), and (4) Exhausted and dissatisfied (high exhaustion and low general health and work satisfaction; Figure 1 and Table 1). Bergman et al. 685 The longitudinal design of the study allowed us to estimate the prevalence of profiles at each wave (Tables S10 and S11). The prevalence of all four profiles stayed similar over time. The model that specified transitions between waves 1 and 2, and between 2 and 3 as stationary had the best fit (Table S12), indicating that changes were stable over time. This model was used for the following analyses. The moderate profile was the largest at all time points, followed by the Relaxed and satisfied and the Exhausted and dissatisfied profiles, whereas the Mentally healthy profile was smallest. Transitions between profiles are represented by the percentage of self-employed workers who changed profile between waves (Figure 2, corresponding to Table S15). Most participants stayed in the same profile over time (61.6%–76.3%). Participants who changed profiles mostly moved from the Moderate to the Relaxed and satisfied profile (23.2%–28.7%), and from the Exhausted and dissatisfied to the Moderate profile (18.7%–26.5%). Few participants switched from the Relaxed and satisfied to the Exhausted and dissatisfied profile (1.9%–7.6%). RQ2. How is membership in health profiles at each time point related to effort, reward, and overcommitment? The analysis of measurement models revealed that the models for effort, reward, and overcommitment fitted well in all three waves (Tables S4 and S5). The similarity model that restrained relationships to be the same over time had better fit than the free model (Table S17). Participants of the Moderate and Relaxed and satisfied profiles were distinguished by above average effort and reward, but below average overcommitment. For the participants of the Moderate profile, these estimates were around the overall mean, but for the ones in the Relaxed and satisfied profile, the pattern was more distinct. Participants of the Mentally healthy and the Exhausted and dissatisfied profile experienced low effort, low reward and high overcommitment. In the Mentally healthy profile, these experiences were around the sample mean; however, for the participants of the Exhausted and dissatisfied profile they more distinct (Figure 3, corresponding to Table S18). RQ3a. Are demographic characteristics related to health profile membership? With a couple of exceptions, demographic characteristics were similar in all profiles over the three waves (for overall sample characteristics, see Table S16). There was a statistically significant difference in age between members of the Moderate (m = 55 years, 95% confidence interval [CI] = 54, 57) and Mentally health profiles (m = 51 years, 95% CI = 48, 53) during wave 1. Furthermore, statistically significant differences existed between the frequencies of educational level during wave 2 (χ2 = 8.3, df = 3, p = .04). Post hoc tests indicated that these statistical differences existed between the Moderate (54% went to university) and Exhausted and dissatisfied profiles (41% went to university; χ2 = 4.4, df = 1, p = .04), and the Relaxed and satisfied (59% went to university) and Exhausted and dissatisfied profiles (χ2 = 7.1, df = 1, p = > .01). In comparison, 55% of participants in the overall sample went to university. RQ3b. Does demographic characteristics predict transitions between health profiles? For the LTA, both the free and the similarity model that restrained relationships to be the same over time indicated that some demographic characteristics had a small, but statistically significant relationship with profile membership. The similarity model, which indicated better fit than the free model, demonstrated that self-employed workers who had higher education were slightly more likely to transfer to the Exhausted and dissatisfied profile than the others (Moderate profile: odds ratio [OR] = 1.25, 95% CI = 1.03–1.52, p = .02, Mentally healthy profile: OR = 1.28, 95% CI = 1.08–1.53, p = .01, Relaxed and satisfied profile: OR = 1.16, 95% CI = 1.01–1.35, p = .04). However, in the LTA, the null model had better fit than both the free and the similarity model, indicating that the demographic characteristics were Table 1. Means and standard deviations from the dispersional similarity model of health indicators in each profile on their original item response scales, which are presented under the variable names. Profiles: 1. Moderate, 2. Mentally healthy, 3. Relaxed and satisfied, and 4. Exhausted and dissatisfied. Stress Depressive symptoms Emotional exhaustion Sleep disturbances Work satisfaction SRH 1–4 1–5 1–7 1–6 1–8 1–5 1 1.70 (0.52) 1.95 (0.90) 2.43 (1.22) 2.86 (1.04) 6.46 (1.24) 4.03 (0.60) 2 1.17 (0.29) 1.23 (0.25) 2.49 (1.27) 1.94 (0.64) 6.22 (2.00) 3.91 (0.82) 3 1.64 (0.60) 1.97 (0.79) 1.84 (0.76) 2.85 (1.02) 6.92 (1.20) 4.59 (0.62) 4 1.96 (0.82) 2.07 (0.88) 3.52 (1.53) 3.07 (1.08) 5.91 (1.52) 3.41 (0.78) 692 Business Research Quarterly 28(3) Bujacz, A., Eib, C., & Toivanen, S. (2020). Not all are equal: A latent profile analysis of well-being among the selfemployed. Journal of Happiness Studies, 21(5), 1661–1680. Caliendo, M., Graeber, D., Kritikos, A. S., & Seebauer, J. (2023). 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