Values and subjective well-being of European entrepreneurs: a configurational analysis across technological levels
- Open Access
- 13-03-2025
- Original Paper
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Abstract
1 Introduction
The study of entrepreneurs’ subjective well-being (SWB) has gained significant traction in recent years, with mounting evidence underscoring its importance for both personal fulfillment and business performance (Carree and Verheul 2012; Dijkhuizen et al. 2018; Drnovšek et al. 2024; Patel and Wolfe 2020; Stephan 2018). However, investigating SWB among entrepreneurs presents unique challenges due to the dual nature of entrepreneurship (Dijkhuizen et al. 2016). While entrepreneurship offers autonomy, flexibility, and opportunities for skill utilization (Benz and Frey 2008; Birley and Westhead 1994; Hundley 2001), it also exposes individuals to significant risks, uncertainty, and stress (Cardon and Patel 2015; Lewin-Epstein and Yuchtman-Yaar 1991; Parasuraman and Simmers 2001). This duality can both enhance and compromise entrepreneurs’ well-being. Human values (HV) also play a crucial role in shaping entrepreneurial intentions and outcomes (Gorgievski et al. 2011, 2018; Hueso et al. 2020; Looi 2019; Tomczyk & Winslow, 2013), suggesting a potential link between HV and entrepreneurial well-being – a connection that, to the best of our knowledge, remains unexplored in prior entrepreneurship research. Furthermore, sector-specific characteristics, such as technological intensity, may moderate the relationship between HV and entrepreneurs’ SWB, but this dimension has yet to be studied. This research seeks to identify which configurations of HV contribute to high levels of SWB among European entrepreneurs operating in sectors with differing levels of technological intensity.
Previous studies consistently show that entrepreneurs report higher levels of satisfaction than employees (Benz and Frey 2003; Blanchflower and Oswald 1992, 1998; Brandley & Roberts, 2004; Parasuraman and Simmers 2001). However, recent research has yielded more nuanced and sometimes contradictory findings, reflecting the complex nature of well-being in entrepreneurship. This complexity arises from factors such as country-specific and cultural characteristics, entrepreneurial motivations, and the diverse dimensions of well-being (financial versus non-financial satisfaction) (Amorós et al. 2021; Bencsik and Chuluun 2021; Binder and Coad 2013; Crum and Chen 2015; Larsson and Thulin 2019; Millán et al. 2013; Nikolaev et al. 2020, 2023; Stephan et al. 2023a; van der Zwan et al. 2018). Despite growing scholarly interest, the mechanisms underpinning the relationship between entrepreneurship and SWB remain poorly understood, underscoring the need for more comprehensive research (Patel and Wolfe 2020; Stephan et al. 2023b; Wiklund et al. 2019).
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The alignment between individuals’ values and actions is central to life satisfaction, as misalignment often leads to internal conflict (Bardi and Schwartz 2003). Despite the significance of this connection, research specifically investigating entrepreneurs’ HV is limited (Fayolle et al. 2014). Studies have examined how values shape entrepreneurial intentions, behaviors, and success, but the findings have been inconsistent (Hueso et al. 2021). Although a number of studies have explored the relationship between values and well-being across different cultural contexts, they have primarily focused on the general population (Sortheix and Schwartz 2017), overlooking entrepreneurs as a distinct group. These studies suggest that specific values – particularly openness to change and benevolence – are positively associated with SWB, whereas values such as power show negative associations. However, the effects of other values remain inconclusive. Recent longitudinal studies reaffirm earlier findings on openness to change but present mixed evidence regarding the role of conservation values in well-being (Grosz et al. 2021; Fetvadjiev and He 2019).
This study addresses a critical gap in entrepreneurship literature by examining the relationship between HV and SWB. Using the Theory of Basic Human Values (Schwartz 1992, 2012), we analyze which HV configurations contribute to high SWB among entrepreneurs. Our analysis is based on a sample of 527 respondents from 27 European countries, utilizing data from the European Social Survey (ESS). The multifaceted nature of SWB poses challenges for traditional statistical methods, which often fail to capture its complexity adequately (Binder and Coad 2011). To address this, we use fuzzy Qualitative Comparative Analysis (fsQCA), a method that has gained popularity in innovation and entrepreneurship research, especially since 2012, for its ability to reveal complex, multifactorial processes (Kraus et al. 2018). While fsQCA has been applied in (1) leadership and strategic management, (2) corporate social responsibility and culture, and (3) innovation and entrepreneurship within management and business sciences (Kumar et al. 2022), it remains underutilized in studying the HV–SWB relationship among entrepreneurs (e.g., Jacobs et al. 2016; Pereira et al. 2023). This configurational approach identifies distinct combinations of values associated with high SWB, offering deeper insights into this complex phenomenon than conventional methods.
Entrepreneurship research emphasizes the importance of contextual factors (Aldrich and Martinez 2001; Ben-Hafaïedh et al., 2024; Welter 2011), particularly how sectoral and technological contexts shape entrepreneurial innovation (Autio et al. 2014). De Massi et al. (2018) highlight the pivotal role of ‘sector-specific entrepreneurial capabilities’ – a set of actions, processes, and routines uniquely characteristic of a given sector that enable opportunity identification and resource optimization. However, entrepreneurial actions are shaped by values, raising the question of whether values are similarly shaped by sectoral contexts. While previous research has demonstrated that sectoral context significantly affects employee well-being (Qu and Robichau 2024), the relationship between HV and SWB among entrepreneurs across different sectors remains underexplored. Our research advances Schwartz’s Theory of Basic Human Values by resolving contradictions in previous findings on the relationship between HV and SWB. We accomplish this by taking a sector-specific perspective (i.e., focusing on the technological context) and employing a configurational approach. This allows us to provide valuable insights into the specific ways in which HV and SWB are linked among entrepreneurs. The findings provide an integrative perspective with practical implications for designing targeted support mechanisms tailored to entrepreneurs in different sectors.
2 Theoretical background
2.1 Entrepreneurial value preferences
Entrepreneurship research has traditionally focused on individuals’ attitudes, skills, and personality traits to understand the entrepreneurial mindset (Daspit et al., 2023; Salmony and Kanbach 2022). However, more recent studies have shifted attention to values, which also shape human subjectivity. While values are not as fixed as personality traits, they remain relatively stable and influence people’s thinking, decision-making, and actions (Feather 1988, 1995; Rokeach 1973; Sagiv and Schwartz 2000; Schwartz 2012). Given their impact on behavior, it is not surprising that research on values in entrepreneurship has gained increasing interest.
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The Theory of Basic Human Values explores the structure of HV preferences (Schwartz 1992, 2012), emphasizing both their universal meanings and cultural variations (Schwartz and Bilsky 1987, 1990). It identifies ten values organized along two dimensions: personal vs. social focus and growth vs. self-protection (Fig. 1). While the theory is widely recognized for its systematic framework and measurement tool (PVQ1), critics have raised concerns about its cross-cultural validity, completeness, and ability to capture complex value relationships (Belic et al. 2022; de Wet et al. 2022).
Studies indicate that entrepreneurs, compared to non-entrepreneurs, tend to prioritize personal-focus values such as self-direction, stimulation, power, and achievement (Csite et al. 2012; Holt 1997; Noseleit 2010). These values align with key entrepreneurial traits including independence, opportunity recognition, leadership ambition, and risk-taking. However, other research challenges this view, offering a more nuanced perspective on the values driving entrepreneurial intention and success. Looi (2019) found that openness to change values enhances entrepreneurial intentions, while Espíritu-Olmos and Sastre-Castillo (2015) observed only a moderate effect of self-enhancement values. In contrast, Karimi and Makreet (2020) found no significant relationship between these values and entrepreneurial outcomes. Holland and Shepherd (2013) initially hypothesized that values supporting autonomy (achievement and power) play a crucial role in entrepreneurial persistence. However, their findings ultimately supported the view that contextual factors (e.g., family support and economic conditions) exert a greater influence than values. Hueso et al. (2020) added further nuance, suggesting that individualistic and collectivistic values are not necessarily opposed but can, in certain contexts, reinforce each other in shaping entrepreneurial intentions.
Values, much like well-being, significantly influence business success. Kotey and Meredith (1997) found that leaders who prioritize values that encourage innovation and risk-taking achieve higher growth and profitability, whereas those with more conservative values tend to underperform. However, they also found that most leaders hold a mixed value profile. Similarly, Berson et al. (2007) observed that self-directed leaders cultivate innovative organizational cultures that drive revenue growth, although conservative values may enhance efficiency but at the cost of employee satisfaction. Furthermore, Tomczyk and Winslow (2013) and Huysentruyt et al. (2015) highlight that self-transcendence values foster both innovation and financial success when combined with participative leadership. Beyond objective measures of success, values also shape subjective success. Gaile et al. (2022) found that leaders’ perceptions of career success were positively influenced by values such as power and self-direction. Gorgievski et al. (2011) argue that success can take many forms – financial gain, personal satisfaction, or social impact – and that people’s perception of success varies significantly depending on their value system.
Another explanation is that different contexts shape value preferences, steering the study of value theory in entrepreneurship toward cultural and social influences. For example, Morales et al. (2019) found that the roles of openness to change and self-enhancement in entrepreneurship depend on a culture’s individualistic or collectivistic nature, and the country’s economic stability. Similarly, Grünhut et al. (2022) observed that Eastern European entrepreneurs often exhibit low self-direction but strong orientations towards power, security, and conformity values, which may limit their risk-taking. Beyond cultural influence, motivations and industry-specific factors also influence entrepreneurial value preferences. Studying new technology ventures in Italy, Bolzani and Foo (2018) found that achievement, power, and self-direction play key roles in international growth, while self-transcendence and security are crucial for fostering partnerships and employee engagement. Likewise, Liñán and Kurczewska (2017) noted that opportunity-driven Spanish entrepreneurs tend to value openness to change, whereas necessity-driven entrepreneurs align more with conservation and self-transcendence values.
These studies suggest that there is no universal entrepreneurial value preference. According to the person-entrepreneurship fit theory (Markman and Baron 2003), individuals thrive when their values align with cultural, social, market-related, or industry-specific demands. While values supporting openness and innovation promote success, conservative and security-oriented values can also be advantageous in certain contexts.
2.2 SWB and firm performance
Well-being is a multidimensional concept (Diener et al. 1999; Kashdan et al. 2008) that encompasses both hedonic aspects (pleasure, life satisfaction, happiness) and eudaimonic aspects (psychological functioning, life purpose) (Ryff and Keyes 1995). The OECD defines SWB as ‘good mental states’ that reflect positive and negative life evaluations and emotional reactions to experiences (OECD 2013).
Research on the relationship between self-employment and well-being has yielded mixed results. Several studies suggest that self-employment enhances well-being (Binder and Coad 2013; Blanchflower and Oswald 1992; Hessels et al. 2017; Nikolaev et al. 2020; van der Zwan et al. 2018), with Hundley (2001) attributing this to the independence associated with entrepreneurship. However, Bencsik and Chuluu (2021) found that self-employed individuals in the U.S. reported lower life satisfaction than paid employees. Chrum and Chen (2015) showed that the well-being benefits of self-employment vary by gender and a country’s level of development. Adding further complexity, Larsson and Thulin (2019) suggested that only opportunity-driven entrepreneurship enhances well-being, whereas Amorós et al. (2021), using the same Global Entrepreneurship Monitor dataset, found no significant well-being difference between opportunity- and necessity-driven entrepreneurs.
These contrasting findings suggest that various contextual factors mediate the relationship between entrepreneurship and SWB (Stephan et al. 2023b). Key mediators include business network usage (Newman et al. 2018), supervisory responsibilities (Warr 2018), personal wellness beliefs (Patel and Wolfe 2020), problem-focused coping strategies (Nikolaev et al. 2023), work-life balance, and experiences of flow at work (Drnovšek et al. 2024). Beyond individual satisfaction, entrepreneurs’ well-being is increasingly recognized for its potential impact on firm performance. While research in this area is still evolving, a key question remains: does entrepreneurial success lead to happiness, or does happiness drive success (Patel and Wolfe 2020)? Many studies suggest that happier entrepreneurs manage better-performing businesses (Stephan 2018), while others indicate that improved business performance enhances overall satisfaction (Carree and Verheul 2012). Drnovšek et al. (2024) argue that higher SWB among entrepreneurs strengthens their personal resources – such as mental energy, optimism, and resilience –, which, in turn, fosters business growth. These findings suggest a bi-directional relationship between well-being and success (Dijkhuizen et al. 2018). Despite the mixed results, research consistently underscores the link between well-being and business performance, highlighting the importance of studying SWB in entrepreneurship.
2.3 The link between HV and SWB
Studies examining the relationship between HV and SWB have yielded complex findings (e.g., Oishi et al. 1999; Sagiv et al. 2004; Sagiv and Schwartz 2000; Sortheix and Schwartz 2017).
Growth-oriented values – such as self-direction, stimulation, benevolence, and universalism – enhance well-being by fostering intrinsic motivation and personal development. Conversely, self-protective values – including tradition, conformity, security, and power – are often associated with lower well-being (Bilsky and Schwartz 1994; Sagiv and Schwartz 2000; Schwartz and Sortheix 2018). Sortheix and Schwartz (2017) found that only personal-focused growth values (self-direction, stimulation) enhanced SWB, whereas social-focused and self-protection values (tradition, conformity, security) diminished it. These findings align with a substantial body of research (Bobowik et al. 2011; Cohen and Shamai 2009; Ryan and Deci 2000; Sagiv and Schwartz 2000). Adding further complexity, Sortheix and Lönnqvist (2014) highlighted that the social and cultural context influences whether self-enhancement values (personal-focus) or self-transcendence values (social-focus) contribute to SWB.
Despite these patterns, Messner (2023) found that both social-focused values (conformity, tradition, benevolence) and personal-focused values (self-direction, hedonism) can enhance SWB, while universalism and power were negatively associated with it. This challenges earlier findings suggesting universalism positively affects well-being, whereas conformity and tradition have adverse effects. Similarly, longitudinal panel data further complicate the narrative. Analyzing 12 waves of German panel data, Grosz et al. (2021) found no predominant causal effect of openness-to-change values or SWB over the other, suggesting a bidirectional relationship. Furthermore, Fetvadjiev and He (2019) observed inconsistent contributions of both openness-to-change and conservation values to SWB, highlighting the variability of these relationships over time and across different contexts. These contradictions underscore the need for a more critical and integrative approach to understanding the HV–SWB relationship.
Drawing from the literature on entrepreneurial value preferences, entrepreneurial well-being, and the HV–SWB relationship, several key research gaps emerge (Fig. 2):
1.
HV–Entrepreneurship (A1): It remains unclear which HV influences entrepreneurial intention and firm success. Success requires the alignment of internal individual value preferences and success goals with external environmental demands. While cultural and societal influences have been widely studied, the role of industry-specific factors (e.g., technological intensity) remains underexplored.
2.
SWB–Entrepreneurship (A2): Research presents mixed results regarding the direction of this relationship. Identifying mediating factors could help clarify these inconsistencies. Although value preferences play a crucial role in entrepreneurial success, their impact on entrepreneurs’ SWB has not been a primary focus of research.
3.
HV–SWB (A3): The HV–SWB relationship has been primarily studied in the general population, where the effects of specific values (e.g., self-transcendence, conservatism) remain inconsistent. However, the entrepreneurial context has not been a significant research focus, despite its potential to offer new insights.
4.
Given the contradictions and gaps in these three areas, a promising research avenue lies in exploring the relationship between HV and the SWB of entrepreneurs while considering industry-specific characteristics (A4).
Fig. 2
Research directions and gaps based on the literature review.
Source Own edition
3 Data and method
3.1 Sample
Our study is based on data from the ninth round of the European Social Survey (ESS9, 2018/19), which includes responses from 49,519 individuals across 29 countries2. For this study, we analyzed data from 27 countries, comprising both European Union (EU) member states and four non-EU countries (Iceland, Norway, Switzerland, and the United Kingdom)3. We chose ESS9 over the more recent ESS10 (2020) because the latter was conducted during the COVID-19 pandemic and covered fewer countries.
The ESS9 variable emplrel4 indicates respondents’ employment status, distinguishing between employees and the self-employed. Since we focus on entrepreneurs, we included only self-employed individuals – solo and non-solo – resulting in a sample of 4,785 respondents. Entrepreneurship encompasses a broad spectrum of activities, each with distinct economic implications (Nightingale and Coad 2014). Baumol (1990) classifies entrepreneurship as productive, unproductive, or destructive. Productive entrepreneurship, as described by Baumol (1993), refers to “any entrepreneurial activity that contributes directly or indirectly to the net output of the economy or to the capacity to produce additional output” (p. 30). Our study adopts a comprehensive definition of entrepreneurship, encompassing high-growth, innovative ventures, and smaller-scale ‘main street’ businesses. However, we acknowledge that academic discourse often takes a narrower view, prioritizing high-tech, high-growth, and highly innovative businesses. Audretsch (2021) criticizes this perspective, arguing that it overlooks significant research on ‘main street’ entrepreneurship, such as self-employment, business ownership, and family businesses. He warns that this narrow focus can lead to biased policies favoring high-tech ventures while neglecting other vital forms of entrepreneurship. Since entrepreneurship is frequently measured through self-employment or business ownership (Farè et al. 2023), we focus on self-employed individuals to capture diverse entrepreneurial activities beyond the high technology, high-growth narrative. The ESS categorizes employment status into two groups: self-employed and employees. In this classification, self-employment includes both solo and non-solo entrepreneurs, encompassing all individuals who are not employees. This broad categorization aligns with our inclusive definition of entrepreneurship.
Next, we categorized the entrepreneurs in our sample into three groups based on the technological intensity of their business sectors. This classification follows the official Eurostat classification system5, which assigns technological intensity levels to sectors (identified by NACE Rev. 2 codes). We used the ESS variable nacer26 to classify each entrepreneur’s venture, which provides information on the firm’s primary economic activity. The categorization was as follows:
-
High-technology entrepreneurs (HTE): Self-employed individuals whose firms operate in high-technology manufacturing industries (NACE 21, 26) or high-tech knowledge-intensive services (KIS) (NACE 59–63, 72). Due to the limited number of high-tech manufacturing firms in our sample, we included high-tech KIS sectors in this category.
-
Medium-technology entrepreneurs (MTE): Self-employed individuals whose firms operate in medium-high technology manufacturing industries (NACE 20, 27–30) or medium-low technology manufacturing industries (NACE 19, 22–25).
-
Low-technology entrepreneurs (LTE): Self-employed individuals whose firms operate in low-technology manufacturing industries (NACE 10–18, 31–32).
After excluding 69 cases with missing data, our final sample consisted of 527 self-employed individuals. This sample is not representative in a conventional sense. It includes 184 HTE, 143 MTE, and 200 LTE (Table 1). Of the total sample, 400 respondents (75.9%) are male, and 127 (24.1%) are female.
Table 1
The sample size and distribution across countries and by technological intensity of the sectors
Country | Code | Total | Sectors by technological intensity | ||
|---|---|---|---|---|---|
HTE | MTE | LTE | |||
EU countries | |||||
Austria | AT | 24 | 5 | 6 | 13 |
Belgium | BE | 23 | 13 | 4 | 6 |
Bulgaria | BG | 5 | 2 | 0 | 3 |
Croatia | HR | 11 | 2 | 4 | 5 |
Cyprus | CY | 16 | 2 | 5 | 9 |
Czechia | CZ | 22 | 12 | 5 | 5 |
Denmark | DK | 17 | 9 | 3 | 5 |
Estonia | EE | 23 | 3 | 11 | 9 |
Finland | FI | 20 | 7 | 11 | 2 |
France | FR | 17 | 7 | 4 | 6 |
Germany | DE | 22 | 7 | 7 | 8 |
Hungary | HU | 14 | 1 | 2 | 11 |
Ireland | IE | 21 | 10 | 3 | 8 |
Italy | IT | 31 | 5 | 9 | 17 |
Latvia | LV | 2 | 1 | 0 | 1 |
Lithuania | LT | 6 | 1 | 2 | 3 |
Netherlands | NL | 36 | 22 | 7 | 7 |
Poland | PL | 11 | 0 | 3 | 8 |
Portugal | PT | 27 | 7 | 5 | 15 |
Slovakia | SK | 13 | 2 | 7 | 4 |
Slovenia | SI | 26 | 10 | 6 | 10 |
Spain | ES | 24 | 6 | 7 | 11 |
Sweden | SE | 32 | 11 | 12 | 9 |
Non-EU countries | |||||
Iceland | IS | 12 | 3 | 3 | 6 |
Norway | NO | 14 | 8 | 1 | 5 |
Switzerland | CH | 19 | 8 | 7 | 4 |
United Kingdom | GB | 39 | 20 | 9 | 10 |
Total | 527 | 184 (34.9%) | 143 (27.1%) | 200 (38.0%) | |
3.2 Variables
3.2.1 Casual conditions
As the first step in our model specification, we identified a set of causal conditions that could contribute to the outcome. Schwartz’s ten HV are categorized into five main groups: (1) openness to change, (2) self-enhancement, (3) hedonism, (4) self-transcendence, and (5) conservation. For methodological reasons, we used these five broader categories as causal conditions in our study. This decision helps mitigate the problem of limited diversity7.
In the ESS questionnaire, participants rated how much they identified with different human values on a scale from 1 to 6, where 1 meant strong identification (“very much like me”), and 6 meant no identification (“not like me at all”). For consistency, we reversed this scale in our study, so that 1 = “not like me at all” and 6 = “very much like me”. The five main human value categories were then calculated by averaging the related human value variables. Table T1 in Online Source 1 provides detailed information on the structure and content of these five categories based on the ESS variable definitions.
3.2.2 Outcome variable
SWB is the outcome variable, calculated as the average of responses to three ESS questions:
-
Happy = Taking all things together, how happy would you say you are? [0 = Extremely unhappy, 10 = Extremely happy]
-
Stflife = All things considered, how satisfied are you with your life as a whole nowadays? [0 = Extremely dissatisfied, 10 = Extremely satisfied]
-
Hincfel = Which of the following descriptions comes closest to how you feel about your household’s income nowadays? [1 = Living comfortably on present income, 2 = Coping on present income, 3 = Difficult on present income, 4 = Very difficult on present income]
Since these three indicators were initially on different scales, we normalized them to a 0 to 1 scale using z-score transformation (Table 2).
Table 2
Descriptive statistics of input and outcome variables
Variables | N | Minimum | Maximum | Mean | Std. dev. |
|---|---|---|---|---|---|
Causal conditions | |||||
1. Openness to change | 527 | 1.50 | 6.00 | 4.35 | 0.88 |
2. Self-enhancement | 527 | 1.00 | 6.00 | 3.63 | 1.01 |
3. Hedonism | 527 | 1.00 | 6.00 | 4.04 | 1.11 |
4. Conservation | 527 | 1.17 | 6.00 | 4.22 | 0.85 |
5. Self-transcendence | 527 | 2.83 | 6.00 | 4.90 | 0.65 |
Subjective well-being | 527 | 0.13 | 1.00 | 0.76 | 0.17 |
4 Method
Qualitative Comparative Analysis (QCA) systematically explores complex causal relationships by examining how multiple factors collectively contribute to an outcome (causes-of-effect), in contrast to traditional statistical methods, which isolate the impact of individual variable impacts (effect-of-causes) (Oana et al. 2021). Rather than identifying a single causal model that best fits the data, QCA uncovers multiple causal pathways leading to the same outcome (Ragin 1987). It emphasizes that outcomes result from the interaction of multiple factors (multiple conjunctural causation), challenges the assumption of constant causal relationships (asymmetry), and acknowledge that different combinations of factors can produce the same result (equifinality) (Ragin 2008).
Using a set-theoretic approach, QCA identifies necessary and sufficient conditions for specific outcomes. If condition x is sufficient for outcome y, it means that whenever x occurs, y will also occur, indicating that x is a subset of y. However, y can still occur in the absence of x. Other set relations are needed to ensure that x is always true when y is true. Conversely, for x to be a necessary condition for y, y must never occur without x, meaning x is a superset of y (Oana et al. 2021). Fuzzy-set QCA (fsQCA) extends this framework by allowing for partial membership, with values ranging from 0 (full non-membership) to 1 (full membership), overcoming the limitations of crisp-set QCA. By combining qualitative and quantitative research, QCA is useful especially for small and medium-sized samples (Ragin 2008; Rihoux and Ragin 2009; Schneider and Wagemann 2012), but an increasing number of researchers are also applying it to large datasets (Kraus et al. 2018).
As a configurational approach, fsQCA is a valuable analytical tool that supports complexity theory by helping to understand the nonlinear and multifactor dynamics of complex phenomena, particularly in business research (Kumar et al. 2022). Entrepreneurs’ value preferences are context-dependent and often conflicting; making it unrealistic to assume a single dominant value drives them. This complexity necessitates a configurational approach. QCA can be applied inductively or deductively: a deductive approach tests existing theories with precise predictions, typically in well-established research domains, while an inductive approach is exploratory, developing new theories based on empirical results (Di Paola et al. 2025). Our study adopts an inductive approach to support theory development by addressing contradictory findings and advancing Schwartz’s theory with sector-specific HV-SWB patterns.
4.1 Calibration
In QCA, all variables are treated as sets. During calibration, data is converted into set membership scores based on predefined thresholds. To ensure meaningful calibrations, thresholds should be grounded in solid theoretical knowledge of the research area. The most common calibration method, direct calibration, uses three qualitative breakpoints to define each case’s level of membership in a fuzzy set. In fuzzy QCA, a score of 1 represents full membership, 0 represents full non-membership, and 0.5 is a crossover point, indicating maximum ambiguity, where a case is both a member and a non-member of the set (Ragin 2008).
The literature on human values (HV) does not provide clear cutoff points for distinguishing ‘high’ and ‘low’ values. For instance, there is no consensus on what qualifies someone as ‘highly’ hedonistic or ‘highly’ traditional. As discussed earlier, HVs in the ESS are measured on a Likert scale from 1 to 6. However, determining whether a score of 4 or 5 constitutes a ‘high’ level of hedonism remains unclear. Rubinson et al. (2019) caution against mechanically assigning 0.0 to the lowest value, 0.5 to the middle, and 1.0 to the highest when calibrating Likert-type scales. Instead, the meaning of each scale point should be carefully considered. Following these guidelines, we set the crossover point for HV at 4.5 (membership = 50%). Scores of 5.5 or higher indicate full membership, while scores of 3.5 or lower indicate full non-membership. Thus, entrepreneurs scoring above 5.5 belong to the ‘high’ HV set, while those scoring below 3.5 belong to the ‘low’ HV set.
For SWB as the outcome variable, we used external sources, such as the Eurostat Statistical Book 2015 (EC Eurostat, 2015)8, to establish meaningful thresholds. Following QCA’s core calibration principles (Ragin 2008), we defined the high SWB set as scores ≥ 0.90, the crossover point at 0.70, and the low SWB set as scores ≤ 0.50. Entrepreneurs scoring above 0.90 belong to the high SWB set, while those below 0.50 fall into the low SWB set.
By carefully setting thresholds for both input and outcome variables, we avoided the common mistake in QCA studies of relying on mechanical or distribution-based calibration methods (Rubinson et al. 2019). The final calibration was performed using fsQCA software (version 3.0) and the calibrate function. The specific calibration thresholds are presented in Table 3.
Table 3
Uncalibrated and calibrated data
Conditions / Output | Min. value | Full non-membership | Crossover point | Full membership | Max. value | |||||
|---|---|---|---|---|---|---|---|---|---|---|
Input Conditions | Uncalib. | Calib. | Uncalib. | Calib. | Uncalib. | Calib. | Uncalib. | Calib. | Uncalib. | Calib. |
Openness to change | 1.50 | 0.00 | 3.50 | 0.05 | 4.50 | 0.50 | 5.50 | 0.95 | 6.00 | 0.99 |
Self-enhancement | 1.00 | 0.00 | 3.50 | 0.05 | 4.50 | 0.50 | 5.50 | 0.95 | 6.00 | 0.99 |
Hedonism | 1.00 | 0.00 | 3.50 | 0.05 | 4.50 | 0.50 | 5.50 | 0.95 | 6.00 | 0.99 |
Conservation | 1.17 | 0.00 | 3.50 | 0.05 | 4.50 | 0.50 | 5.50 | 0.95 | 6.00 | 0.99 |
Self-transcendence | 2.83 | 0.01 | 3.50 | 0.05 | 4.50 | 0.50 | 5.50 | 0.95 | 6.00 | 0.99 |
Outcome variable | ||||||||||
SWB | 0.13 | 0.00 | 0.50 | 0.05 | 0.70 | 0.50 | 0.90 | 0.95 | 1.00 | 0.99 |
5 Results
Using the high-, medium-, and low-technology firm classification, we developed three models to examine the combinations of human values that lead to high SWB among European entrepreneurs, analyzing each category separately. In all three models, SWB is the outcome set, while the five main human value categories act as causal sets.
Model 1:
$$ \begin{aligned} {\text{High}} - {\text{level SWB}}^{{high - tech}} = & {\text{ Open to Change}}^{{high - tech}} + {\text{ Self}} - {\text{enhancement}}^{{high - tech}} \\ & \quad + {\text{ Hedonism}}^{{high - tech}} + {\text{ Conservation}}^{{high - tech}} \\ & \quad + {\text{ Self}} - {\text{transcendence}}^{{high - tech}} \\ \end{aligned} $$
Model 2:
$$ \begin{aligned} {\text{High}} - {\text{level SWB}}^{{medium - tech}} = & {\text{ Open to Change}}^{{medium - tech}} + {\text{ Self}} - {\text{enhancement}}^{{medium - tech}} \\ & \quad + {\text{ Hedonism}}^{{medium - tech}} + {\text{ Conservation}}^{{medium - tech}} \\ & \quad + {\text{ Self}} - {\text{transcendence}}^{{medium - tech}} \\ \end{aligned} $$
Model 3:
$$ \begin{aligned} {\text{High}} - {\text{level SWB}}^{{low - tech}} = & {\text{ Open to Change}}^{{low - tech}} + {\text{ Self}} - {\text{enhancement}}^{{low - tech}} \\ & \quad + {\text{ Hedonism}}^{{low - tech}} + {\text{ Conservation}}^{{low - tech}} \\ & \quad + {\text{ Self}} - {\text{transcendence}}^{{low - tech}} \\ \end{aligned} $$
5.1 Necessary conditions for high-level SWB
In QCA, an Analysis of Necessary Conditions is recommended before conducting a sufficiency analysis to identify conditions that must be present for the outcome to occur. This step helps prevent the elimination of necessary conditions during the minimization process (Schneider and Wagemann 2012; El Sherif et al. 2024; Oana et al. 2021). To assess necessity, we use the consistency for necessity (Consnec) measure (Formula 1), which quantifies the proportion of the outcome set (y) contained within the condition set (x). The score ranges from 0 to 1, indicating how closely the set relation aligns with a perfect superset relationship. A consistency score above 0.90 is considered ‘always’ necessary, above 0.80 ‘almost always’ necessary, and between 0.65 and 0.80 ‘usually’ necessary (Ide and Mello 2022; Ragin 2008; Schneider and Wagemann 2012).
$$ Cons_{{nec}} = \frac{{\sum {{\text{min}}\left( {x_{i},~y_{i} } \right)} }}{{~\sum {y_{i} } }} $$
(1)
$$ Cov_{{nec}} = \frac{{\sum {{\text{min}}\left( {x_{i},~y_{i} } \right)} }}{{~\sum {x_{i} } }} $$
(2)
xi = causal condition of the ith self-employed individual.
yi = outcome variable the ith self-employed individual.
To assess the empirical significance of causal factors, we calculate the coverage for necessity (Covnec) measure, which ranges from 0 to 1 (Formula 2). Coverage indicates the proportion of the causal condition (x) that overlaps with the outcome variable (y). A coverage value greater than 0.5 suggests empirical relevance for an outcome (Oana et al. 2021).
The results of the necessity analysis in Table 4 indicate that no condition qualifies as ‘always necessary’. However, self-transcendence emerges as a key factor for entrepreneurs across all technological sectors. Specifically, in high- and low-technology sectors, self-transcendence is ‘usually’ necessary (consistency ≥ 0.65) for achieving high SWB. While in medium-technology sectors, self-transcendence is ‘almost always’ necessary (consistency ≥ 0.80). Thus, while self-transcendence is not classified as strictly necessary, we anticipate its significance in the sufficiency analysis that follows.
Table 4
Human value model based on five main categories
Since our five main human value categories were derived from ten individual HV, we also conducted a necessity analysis for these ten conditions to gain a more detailed perspective (Table 5). The results indicate that no single value is always necessary. However, key differences emerge across technological sectors: (1) For high-tech entrepreneurs, self-direction (from openness to change) is ‘almost always’ necessary (consistency = 0.85) for achieving high SWB. (2) For medium- and low-tech entrepreneurs, benevolence (from self-transcendence) is ‘almost always’ necessary (consistency = 0.83 and 0.81, respectively). These findings suggest that high-tech firm owners prioritize personal-focused goals, while medium- and low-tech firm owners emphasize social-focused values in achieving high SWB.
Table 5
Human value model based on ten basic human values
5.2 Sufficient configurations for high-level SWB
The first step in fsQCA involves creating a truth table from the calibrated data. This table maps the distribution of cases across all possible combinations of causal conditions (configurations) and assesses the consistency of each configuration with the outcome (Ragin 2008). With five causal conditions, there are 32 possible configurations (2⁵), and the 527 entrepreneurs are distributed across these configurations with varying degrees of membership in this five-dimensional space. At this stage, researchers must determine which configurations are relevant by setting a frequency threshold, ensuring that only configurations with a sufficient number of cases are retained. This is done by considering cases with greater than 0.5 membership in a given configuration (as shown in the truth table). When case numbers are small, a threshold of 1 or 2 cases is recommended (Ragin 2017). In our study, we set a threshold of 1, meaning that configurations with at least one entrepreneur are retained, while others are discarded. Next, we identified configurations that are consistent subsets of the outcome. Ragin (2008) recommends setting a consistency threshold close to 1. Recent QCA reviews suggest using at least a 0.8 cutoff (Rubinson et al. 2019). Therefore, we applied a 0.8 consistency cutoff, meaning that a configuration is considered a sufficient condition for the outcome if it is consistent in 80% or more of cases. The truth tables with the valid configurations are presented in Online Source 2.
As part of the sufficiency analysis, we calculated two key measures: Consistency for sufficiency (Conssuf) is the proportion of cases where both x and y occur relative to all cases where x occurs (Formula 3). This score, ranging from 0 to 1, indicates how consistently a specific combination of conditions leads to the outcome. Coverage for sufficiency (Covsuf) is the proportion of the outcome (y) explained by a given configuration (Formula 4). This score reflects how much of the outcome can be attributed to a specific causal combination (Oana et al. 2021).
$$ Cons_{{suf}} = \frac{{\sum {{\text{min}}\left( {x_{i},~y_{i} } \right)} }}{{\sum {x_{i} } }} $$
(3)
$$ Cov_{{suf}} = \frac{{\sum {{\text{min}}\left( {x_{i},~y_{i} } \right)} }}{{\sum {y_{i} } }} $$
(4)
xi = causal condition of the ith self-employed individual.
yi = outcome variable the ith self-employed individual.
Using the constructed truth tables, we conducted fsQCA’s Standard Analyses, which apply the Quine-McCluskey algorithm to logically simplify the truth table into solutions for the desired outcome (Ragin 2017). This minimization process produces complex, intermediate, and parsimonious solutions. In this study, we use standard notation from the literature to represent the results: a black circle (●) represents the presence of a condition, and a crossed-out circle \( \left( \otimes \right) \) refers to the absence of a condition. Following Ragin’s (2008) recommendations, we present intermediate solutions, which tend to be the most interpretable. When comparing solutions, we distinguish between core and peripheral conditions: core conditions (derived from parsimonious solutions, represented by large circles) and peripheral conditions (based on intermediate solutions, represented by small circles). Blank cells indicate a ‘do not care’ situation, meaning the condition can be present or absent without affecting the outcome (Fiss 2011).
We identified four configurations for high-, eight for medium-, and seven for low-technology sectors that lead to high SWB. All configurations meet the empirical criteria of overall consistency ≥ 0.75 and overall coverage ≥ 0.25 (Woodside 2013). In high-technology manufacturing and knowledge-intensive service sectors, entrepreneurs achieve high SWB through four distinct configurations of human values (Table 6). In three configurations (C1, C2, C3), hedonism (personal-focused) is dominant. Other personal-focused values, such as self-enhancement and openness to change, may also be present or hold a ‘do not care’ status (as in C2 and C3). Social-focused values like self-transcendence and conservatism are either absent or in a ‘do not care’ status. However, the fourth configuration (C4) is solely driven by conservatism (social-focused), making it the key factor for high SWB in this case.
Table 6
Configurations of human values for high-level SWB among self-employed having a business in the high-tech manufacturing or knowledge-intensive service sectors
Conditions | Configuration | |||
|---|---|---|---|---|
Hedonism surplus | Conservation surplus | |||
C1 | C2 | C3 | C4 | |
Open to change | \( \bigotimes \) | ● | \( \bigotimes \) | |
Self-enhancement | ⚫ | ⚫ | \( \otimes \) | |
Hedonism | ⚫ | ⚫ | ⚫ | \( \otimes \) |
Conservation | \( \bigotimes \) | \( \bigotimes \) | ⚫ | |
Self-transcend | \( \bigotimes \) | \( \bigotimes \) | ||
Raw coverage | 0.20 | 0.16 | 0.10 | 0.11 |
Unique coverage | 0.08 | 0.04 | 0.01 | 0.04 |
Consistency | 0.92 | 0.92 | 0.90 | 0.91 |
Solution coverage | 0.31 | |||
Solution consistency | 0.91 | |||
Entrepreneurs in medium-technology manufacturing sectors exhibit eight configurations of HV that lead to high SWB (Table 7). These configurations reflect mixed-value profiles that do not fit strictly into personal- or social-focus categories. Five configurations (C1–C5) are growth-oriented, featuring values like openness to change, self-transcendence, and hedonism, while self-protection values are either absent or in a ‘do not care’ status. Two configurations (C6, C7) are mixed, combining personal-focus values (openness to change, self-enhancement) with social-focus values (conservatism). One configuration (C8) is driven solely by conservatism, with hedonism in a ‘do not care’ status and all other values absent.
Entrepreneurs in low-technology manufacturing sectors achieve high SWB through seven configurations (Table 8). Six configurations (C1–C6) include self-transcendence (social-focus), suggesting that low-technology business owners generally prioritize growth-oriented values, with all configurations featuring at least one growth-related value. Among them, four configurations (C1–C4) include both personal- and social-focus values, while two configurations (C5, C6) focus exclusively on social values. One configuration (C7) is growth-oriented but centered solely on personal values.
Table 7
Configurations of human values for high-level SWB among self-employed having a business in the medium-tech manufacturing sector
Configurations | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Conditions | Growth-oriented | Mixed-valued | Conservation surplus | |||||||||
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | |||||
Open to change | \( \bigotimes \) | ⚫ | ⚫ | ⚫ | \( \bigotimes \) | ⚫ | \( \bigotimes \) | |||||
Self-enhancement | \( \bigotimes \) | \( \bigotimes \) | \( \bigotimes \) | ● | \( \bigotimes \) | |||||||
Hedonism | ⚫ | ⚫ | \( \bigotimes \) | \( \bigotimes \) | ⚫ | \( \bigotimes \) | ||||||
Conservation | \( \bigotimes \) | \( \bigotimes \) | ⚫ | ⚫ | ⚫ | |||||||
Self-transcendence | ⚫ | ⚫ | ⚫ | ⚫ | \( \bigotimes \) | \( \bigotimes \) | ||||||
Raw coverage | 0.23 | 0.37 | 0.33 | 0.23 | 0.30 | 0.31 | 0.07 | 0.18 | ||||
Unique coverage | 0.01 | 0.03 | 0.01 | 0.01 | 0.06 | 0.02 | 0.005 | 0.02 | ||||
Consistency | 0.84 | 0.80 | 0.80 | 0.87 | 0.81 | 0.80 | 0.90 | 0.81 | ||||
Solution coverage | 0.70 | |||||||||||
Solution consistency | 0.77 | |||||||||||
Table 8
Configurations of human values for high-level SWB among self-employed having a business in the low-tech manufacturing sector
Conditions | Configuration | ||||||||
|---|---|---|---|---|---|---|---|---|---|
Growth-oriented (personal & social focus) | Growth-oriented (social focus) | Growth oriented (personal focus) | |||||||
C1 | C2 | C3 | C4 | C5 | C6 | C7 | |||
Open to change | ⚫ | ⚫ | ⚫ | ||||||
Self-enhance | ● | \( \otimes \) | \( \otimes \) | ||||||
Hedonism | ⚫ | \( \otimes \) | ⚫ | \( \otimes \) | |||||
Conservation | ● | \( \bigotimes \) | \( \bigotimes \) | \( \bigotimes \) | |||||
Self-transcend | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | ⚫ | \( \otimes \) | ||
Raw coverage | 0.46 | 0.28 | 0.14 | 0.31 | 0.41 | 0.46 | 0.32 | ||
Unique coverage | 0.04 | 0.01 | 0.01 | 0.002 | 0.00 | 0.01 | 0.03 | ||
Consistency | 0.78 | 0.79 | 0.83 | 0.82 | 0.82 | 0.83 | 0.83 | ||
Solution coverage | 0.71 | ||||||||
Solution consistency | 0.76 | ||||||||
Despite being a qualitative method, many QCA studies overlook detailed case examination within each identified configuration (Rubinson et al. 2019). As shown earlier, the proportion of self-employed individuals is similar across all technology groups (34.9% in high-, 27% in medium-, and 38% in low technology). However, fewer high-technology entrepreneurs achieve SWB than those in medium- and low-technology sectors. Only 7.6% (14 individuals) of high-tech entrepreneurs achieve high SWB through one of the four identified value configurations. In contrast, 49% (70 individuals) in medium-technology and 52.5% (105 individuals) in low-tech technology reach high SWB. This is because the medium- and low-technology sectors have twice as many configurations leading to high SWB (8 and 7 configurations, respectively) compared to the high-technology sector.
Of the 14 high-technology entrepreneurs with high SWB, 11 (78.6%) are men and 3 (21.4%) are women. The majority work in computer programming and consultancy (NACE 62) and information service activities (NACE 63), while others work in motion picture and music production (NACE 59) and scientific research and development (NACE 72). In the medium-technology sector, 56 (80%) of the 70 entrepreneurs with high SWB are men, and 14 (20%) are women. They are spread across 10 industries, with many working in: manufacture of basic metals (NACE 24), fabricated metal products (NACE 25), electrical equipment (NACE 27), machinery and equipment (NACE 28), repair installation of machinery (NACE 33). Among the 105 low-tech entrepreneurs with high SWB, 63 (60%) are men, and 42 (40%) are women. They work across 10 industries, primarily in food production (NACE 10), wearing apparel (NACE 14), wood products (NACE 16), furniture manufacturing (NACE 31), and other manufacturing (NACE 32).
Additionally, Online Source 3 lists of cases and configurations that led to high SWB across the three sectors. Using identification codes, we can determine the country of residence of each self-employed individual. However, no clear spatial patterns emerge – each configuration includes individuals from various regions, indicating that no configuration is specific to Western or Eastern Europe.
5.3 Robustness check
The discussion on robustness tests in QCA is still evolving, with research suggesting that robustness should be evaluated from multiple perspectives (Wagemann et al. 2016; Oana and Schneider 2024). In our study, we applied three robustness checks based on Schneider and Wagemann (2012): adjusting calibration thresholds, changing consistency thresholds, and adding or dropping cases. While adjusting calibration thresholds is a common robustness test, we opted not to conduct it because our anchors are conceptually based – the Likert scale for causal conditions and external benchmarks for SWB. Since QCA is inherently sensitive to calibration changes, different cross-case patterns are expected when modifying case definitions (Rohlfing and Schneider 2014). Thus, we consider arbitrary recalibrations to be of limited value, serving only as a mathematical exercise (Rutten 2022). Instead, we tested robustness by adjusting consistency thresholds by ± 0.01, as recommended by Schneider and Wagemann (2012). Table T8 (Online Source 4) shows that these minor adjustments did not significantly affect the parameters of fit, and the solutions remained logical supersets. Finally, while dropping cases is another recommended robustness check (Schneider and Wagemann 2012; Rutten 2022), we did not perform this test due to the lack of clear guidelines on which cases to exclude, how many should be dropped, or how many reruns are necessary for valid conclusions.
6 Discussion
This study examines which configurations of HV lead to high levels of SWB among European entrepreneurs across sectors with varying technology intensity. The results have both theoretical and practical implications.
According to the person–entrepreneurship fit concept (Markman and Baron 2003), entrepreneurs are more likely to succeed when their values align with the external demands of entrepreneurship. De Massis et al. (2018) argue that these entrepreneurial demands are linked to sector-specific capabilities, which shape entrepreneurial behavior and actions unique to each sector. However, external entrepreneurial demands are only one side of the coin – on the other side, entrepreneurs’ values also play a significant role in shaping their actions (Gorgievski et al. 2011). Our findings support De Massis et al.’s (2018) argument, demonstrating that entrepreneurs’ HV configurations vary significantly across technological contexts. Specifically, entrepreneurs in high-, medium-, and low-technology sectors display distinct HV configurations that contribute to high SWB. This suggests that the values shaping entrepreneurial decision-making are not neutral but are instead influenced by sector-specific technological demands. These findings highlight the need for a more nuanced approach to entrepreneurship research that incorporates technological context.
As discussed in Sect. 2, current research on HV and SWB, primarily focused on the general population, has yielded mixed results regarding which values contribute most to high SWB. Schwartz and Bardi (2001) found that benevolence, universalism (self-transcendence), and self-direction were consistently the most important for well-being, while power, tradition, and stimulation were the least important. Our findings suggest that for entrepreneurs, self-transcendence (growth-oriented, social-focus) is ‘usually’ necessary for high SWB across all sectors. However, the values that matter most vary by sector: in low- and medium-technology sectors, benevolence is ‘almost always’ essential, whereas in high-technology sectors, self-direction (growth-oriented, personal-focus) plays a crucial role. On the one hand, our results confirm previous research indicating that benevolence, self-direction, and universalism are key to high SWB. While on the other hand, the relative importance of these values shifts depending on the sector’s technological intensity, adding a new dimension to existing findings.
In addition, Sortheix and Schwartz (2017) found that personal-focus values (e.g., openness to change) enhance SWB, while self-protection-oriented, social-focus values (e.g., conservation) have a negative impact. However, recent studies report mixed findings regarding conservation (Fetvadjiev et al., 2019; Hueso et al. 2020). Our research resolves this contradiction by considering sector-specific contexts. In the high-tech sector, hedonism (personal-focus, growth-oriented) often emerges as the dominant value, either alone or alongside other self-focused values such as openness to change or self-enhancement. High-technology entrepreneurs, particularly in the ITC sector, tend to have highly self-oriented value structures. In the medium-technology sector, both growth- and self-protection-oriented value configurations are observed, along with mixed-value profiles, aligning with Kotey and Meredith’s (1997) findings. In the low-technology sector, growth-oriented values prevail, with some entrepreneurs prioritizing personal-focus values, others emphasizing social-focus values, and some integrating both. Self-transcendence (social-focus, growth-oriented) is key in nearly all low-technology configurations. Across all sectors, we identified configurations with a strong emphasis on conservation that led to high SWB. These findings significantly advance Schwartz’s Theory by demonstrating that aligning entrepreneurs’ values with their specific entrepreneurial contexts is crucial for attaining high SWB. Schwartz’s model explores the relationships between values, but a key criticism is that its two-dimensional framework may not fully capture the complexity of how values interact. By applying a configurational approach using QCA, our findings reveal that while some configurations align with the two-dimensional structure, many entrepreneurs exhibit mixed value profiles that do not fit neatly within these categories. This supports the critique that value relationships are more complex than Schwartz’s model suggests.
The practical significance of this research lies in emphasizing that entrepreneurs must clearly understand their values and align their leadership style with these values and their industry’s unique demands. By recognizing how personal values interact with sector-specific challenges, entrepreneurs can make more informed decisions about leading their ventures effectively. Our findings suggest that with appropriate support, such as entrepreneurial coaching or mentoring, entrepreneurs can better align their values with business demands, i.e., develop a better person–entrepreneurship fit. This alignment between personal values and business demands enhances individual satisfaction. However, satisfied entrepreneurs tend to lead higher-performing and more successful firms, highlighting the substantial impact of value-driven alignment on both personal fulfillment and business outcomes.
6.1 Limitations
This study focused on the evaluative well-being of entrepreneurs, particularly their overall life satisfaction and cognitive judgments about life, such as fulfillment and perceived quality of life. However, other dimensions of well-being, such as affective and eudaimonic, may involve different value configurations for high SWB.
Our sample includes both solo and non-solo entrepreneurs, and examining these groups separately could reveal distinct value patterns. We categorized ventures based on their sector’s technological intensity, however future analyses within these groups could offer deeper insights into the relationship between HV and SWB, accounting for sector-specific nuances. Additionally, factors such as age, gender, and location may further influence SWB configurations.
While Schwartz’s model aims to be universal, some value categories may not generalize across cultures. Although our study focuses on culturally similar European countries, future research could explore its global applicability. Furthermore, critics argue that Schwartz’s model may not capture all relevant values. Investigating alternative value models (e.g., Rokeach’s value system) could provide new directions for research.
Declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
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