Personality traits and beliefs on self-employment: An empirical study of the Middle East and North Africa region

Abstract

This cross-cultural entrepreneurial study examines the effects of personality traits and beliefs, specifically the internal locus of control (ILC) and religiosity, on self-employment in the Middle East and North Africa (MENA) region. In addition to these two cultural dimensions, our model controls for contextual factors. We estimate a two-level logistic regression model using data from the sixth and seventh waves of the World Values Survey and combining individual-level and contextual (country-year) predictors to explore the potential heterogeneous effects of ILC and religiosity. The findings reveal that the impact of ILC on the likelihood of self-employment across countries is positive and relatively homogeneous. However, the effect of religiosity on self-employment can be positive or negative. It is heterogeneous and contingent on country-year predictors. The country's economic context explains these cross-country variations in the influence of religiosity. Finally, differences in self-employment across countries are mainly driven by individual-level factors, while country-year variables account for only a small proportion of these variations. Given the MENA region's diverse cultural, social, and economic landscape, this study sheds light on an important yet underexplored research area in entrepreneurship: the homogeneous or heterogeneous impacts of ILC and religiosity on self-employment. It offers valuable theoretical and practical insights and policy recommendations for this unique entrepreneurial context.

Keywords

entrepreneurship self-employment culture internal locus of control religiosity Middle East and North Africa

Introduction

Culture, as a shared system of values, beliefs, and practices, shapes economic behaviors and entrepreneurship. The Middle East and North Africa (MENA) region, with its rich heritage and strong religious traditions, offers a unique setting for exploring the role of culture in labor market outcome decisions. Two key dimensions are particularly relevant: Internal locus of control (ILC) and religiosity (Asante & Affum-Osei, 2019). ILC reflects individuals’ belief in personal accountability for outcomes, fostering risk-taking and entrepreneurial behavior (Rotter, 1966). In contrast, religiosity, as the depository of values (Dana, 2009), is deeply embedded in the MENA region and often attributes outcomes to divine will or fate (Pargament, 2001).

This study addresses the lack of regionally contextualized empirical analyses on personality and cultural traits, particularly in the MENA region, despite the region's unique socio-religious dynamics and rising trends in self-employment. It addresses three key research gaps. First, while prior research highlights the role of ILC and religiosity in entrepreneurship (Caliendo et al., 2014; Simoes et al., 2016), their impact on self-employment across MENA countries remains underexplored. Our study postulates that these impacts could be heterogeneous across MENA countries. Second, it integrates country-level factors, such as GDP per capita, unemployment rate, corporate tax rate, bank deposits, and the Human Development Index (HDI), to offer a comprehensive view of self-employment drivers. Third, it examines how religiosity moderates the relationship between ILC and self-employment, shedding light on internal and external cultural influences. By analyzing 14 MENA countries, this study contributes to the literature and practice by focusing on an important yet underexplored research area in entrepreneurship: the homogeneous or heterogeneous impacts of ILC and religiosity on self-employment in a region shaped by unique but diverse sociocultural, religious, and economic factors.

This study applies Attribution Theory (Heider, 1958; Weiner, 1985) to examine how internal (ILC) and external (religiosity) attributions shape self-employment in the MENA region. It also builds on Informal Institutional Theory (North, 1990) to assess the impact of religiosity, addressing its underexplored role in entrepreneurship (Audretsch et al., 2013; Hoogendoorn et al., 2016). As a key societal factor, religiosity influences decision-making and entrepreneurial activity (Audretsch et al., 2013; Rietveld & Hoogendoorn, 2022).

We use data from the sixth and seventh waves of the World Values Survey (WVS). We employ multilevel logit models (with random intercepts and random slopes) to examine how ILC and religiosity influence self-employment across countries and over time. Our binary dependent variable distinguishes self-employed individuals from others. Mixed-level logit models account for both individual and country-level factors (Schade & Schuhmacher, 2022). The analysis encompasses socio-demographic factors, personality traits, and economic indicators such as GDP, bank deposits, corporate tax rate, unemployment rate, and human development indicator. Our findings highlight the interplay between cultural and economic contexts in shaping self-employment in the MENA region. This study provides policy recommendations to promote entrepreneurship and self-employment, addressing socio-political instability and unemployment while supporting UN Sustainable Development Goal 8 (United Nations, 2024).

Literature review

The cultural framework of the MENA region has a significant impact on decision-making and entrepreneurial activities such as self-employment. Culture encompasses personal traits, such as ILC, and beliefs, including religiosity, which influence entrepreneurial actions (Baluku et al., 2018). ILC, in which individuals attribute success to personal effort, is closely linked to risk-taking and autonomy (Mueller & Thomas, 2001). Religiosity, reflecting external attributions such as divine will, can complement or counterbalance internal motivations and drive informal institutions (Miao et al., 2022). These cultural factors influence pathways to self-employment, which is crucial for business development in the diverse sociopolitical contexts of the MENA region.

Locus of control

Locus of control is a key factor in entrepreneurship theories, influencing entrepreneurial intentions and behavior (Asante & Affum-Osei, 2019) as well as self-employment (Baluku et al., 2018; Caliendo et al., 2014; Lee-Ross, 2015). It plays a crucial role in shaping careers and life choices (Asante & Affum-Osei, 2019), with classical theories highlighting the impact of ILC on entrepreneurial behavior.

The Theory of Planned Behavior (TPB) (Ajzen, 1991) is a key framework for understanding entrepreneurial intentions and behavior (Schlaegel & Koenig, 2014). It links locus of control to self-employment, suggesting that behavior is driven by intentions, influenced by attitudes, subjective norms, and perceived behavioral control. In entrepreneurship, attitude reflects evaluations of starting a business, subjective norms relate to social pressures from family and friends, and perceived behavioral control is the self-assessed control over actions (Tseng et al., 2022). A strong ILC enhances perceived behavioral control, boosting entrepreneurial activity (Liñán & Chen, 2009).

Attribution theory explains how individuals attribute causes of success and failure to events (Heider, 1958; Weiner, 1985). It involves three properties: locus of control (internal/external), stability (consistent or situational), and controllability (whether the individual can control the event) (Graham & Chen, 2020). These properties influence psychological and behavioral outcomes. According to the theory, individuals with a higher ILC attribute successes and failures to their actions, motivating them to take on challenges and risks, such as those in self-employment.

This study applies Attribution Theory to examine how beliefs about control influence the likelihood of self-employment. While TPB focuses on perceived behavioral control, Attribution Theory emphasizes perceptions of causality, linking self-employment decisions to internal or external factors. This framework helps understand the role of ILC, religiosity, and self-employment. In our study, ILC represents internal attributions, where individuals attribute their success or failure in self-employment to personal actions and competencies. Attribution Theory suggests that those with ILC view entrepreneurial success as stemming from internal factors like skill, determination, and effort, motivating them to pursue self-employment. Based on this theory, individuals with high ILC believe outcomes are under personal control, which aligns with entrepreneurial risk-taking. This belief is central to venture creation (Gala & Mueller, 2024; Rietveld & Hoogendoorn, 2022).

In contrast, individuals with an external locus of control may attribute setbacks to luck, fate, or divine will, which can encourage or hinder self-employment. Previous studies highlight the significant role of religious beliefs in shaping economic behavior (Di Pietro & Masciarelli, 2021), fostering social trust (Badaoui, 2023), and influencing entrepreneurial decisions (Audretsch et al., 2013; Rietveld & Hoogendoorn, 2022). Giacomin et al. (2023) define religiosity through four dimensions: Belonging, Bonding, Believing, and Behaving. The first two help entrepreneurs build social and networking capital (Hoogendoorn et al., 2016), while the latter influence risk attitudes and financial risk tolerance (Aldhehayan & Tamvada, 2023). Thus, religiosity has a significant impact on entrepreneurial activities, such as self-employment. This paper focuses on the “believing” dimension of religiosity in the MENA region. Religiosity may moderate the relationship between ILC and self-employment by introducing external attributions, potentially reducing the internal attributions of those with strong ILC by incorporating the concept of divine intervention. This combined framework (Figure 1) examines how religious beliefs and personality traits influence self-employment, considering both national and individual factors.

Empirical studies confirm the impact of ILC on self-employment. Based on the TPB framework, Arkorful and Hilton (2022) demonstrate that individuals with high ILC are more inclined towards entrepreneurship and better equipped to address challenges (Zhao & Wibowo, 2021). Gürol and Atsan (2006) find a positive link between ILC and entrepreneurial intention among Turkish university students. Caliendo et al. (2014) show that ILC influences self-employment decisions in Germany, while Lange (2012) links autonomy (related to ILC) to higher job satisfaction in self-employment. Beugelsdijk and Noorderhaven (2005) also identify ILC as a key determinant of self-employment across 13 European countries. However, these studies assume a fixed relationship between ILC and self-employment, neglecting the role of a country's socioeconomic conditions. Additionally, research on the link between ILC and self-employment in non-Western contexts, like the MENA, is limited. Therefore, our first hypothesis is:

H1: ILC has a positive homogeneous influence on self-employment in the MENA region.

Since the effects of ILC on self-employment may be context-dependent on socioeconomic conditions, we control for these factors in our analysis.

Religiosity

Religiosity refers to an individual's commitment to religious beliefs and practices (Fatima et al., 2023). It serves as an informal institution through unwritten social norms and values that govern behavior (Fuentelsaz et al., 2015), providing a moral framework that influences trust and cooperation (Badaoui, 2023). It also affects economic behavior (Di Pietro & Masciarelli, 2021), risk behavior (Aldhehayan & Tamvada, 2023; Hilary & Hui, 2009), civic engagement (Lewis et al., 2013), and entrepreneurial decision-making (Audretsch et al., 2013; Rietveld & Hoogendoorn, 2022).

Informal Institutional Theory has been widely used to explain how unwritten societal norms, especially religious norms, influence entrepreneurial behavior in developing contexts (Eijdenberg et al., 2019; Williams & Shahid, 2016). Unlike formal institutions enforced by legal systems (Miao et al., 2022), informal institutions are implicit social norms transmitted through traditions, culture, and religion (Busch et al., 2020; Miao et al., 2022). These institutions shape social and economic behaviors (North, 1990; Raymond & Weldon, 2014) and become more significant when formal institutions are weak (Miao et al., 2022). In the MENA region, informal institutions, such as religion, operate alongside or in place of weak formal institutions, exerting a significant influence over entrepreneurial motivations and decision-making. This complements our argument that religiosity serves as a moderating informal institutional force affecting the ILC–self-employment relationship (see Hypotheses 2 and 3).

First, religiosity fosters social capital by providing access to informal networks and community support, facilitating the sharing of opportunities and customer information (Hoogendoorn et al., 2016; Van Buren et al., 2020). These networks play a crucial role in shaping entrepreneurial behavior by providing informal support and resources (Van Buren et al., 2020), as well as facilitating job searches and hiring (Dougherty et al., 2019). They also foster trust and collaboration, which are essential for securing capital, identifying opportunities, and cultivating customer relationships (Guiso et al., 2003). Shared religious beliefs foster trust-based networks, which reduce transaction costs and enhance collaboration (Badaoui, 2023; Guiso et al., 2003). Additionally, religiosity enforces informal rules, with social and spiritual sanctions deterring unethical behavior, making religious communities effective governance structures (Iyer, 2016).

Second, religiosity influences individuals’ attitudes toward risk (Aldhehayan & Tamvada, 2023), whereas religious individuals are often risk-averse (Hilary & Hui, 2009). Individuals may view business risks as part of a divine plan, reducing perceived uncertainties (Carswell & Rolland, 2007). In highly religious societies, outcomes may be attributed to divine will, fostering resilience and persistence. In Islamic cultures, community-based funding mechanisms such as zakat and waqf support entrepreneurial ventures (Kuran, 2012). However, Welter et al. (2015) note that religious norms can limit innovation by promoting conformity to traditional practices.

Empirical evidence on the relationship between religiosity and entrepreneurial intentions is mixed. Parboteeah et al. (2015) find a positive link, while other studies (Dana, 2009; Dougherty et al., 2019; Nair & Pandey, 2006) report negative or no relationships. In the MENA region, where religion significantly influences societal norms (Hashim, 2023), understanding its impact on self-employment is crucial, as the effect may vary due to country-specific socio-economic factors. Thus, we propose the following hypothesis:

H2: Religiosity has a positive homogeneous influence on self-employment in the MENA region.

According to the Attribution Theory, in MENA societies where religiosity and external factors are prominent, self-employment decisions may be more influenced by external attributes (Jamali, 2009). While ILC typically promotes self-employment, religiosity can moderate this relationship, either enhancing or hindering entrepreneurial activity. Scholars in psychology suggest that religiosity is often a positive driver of ILC (Coursey et al., 2013; Jackson & Coursey, 1988).

Religiosity, especially in highly religious societies, may attribute outcomes to divine will rather than personal effort, moderating the relationship between ILC and self-employment by either enhancing or diminishing personal control, depending on beliefs. Religious beliefs shape perceptions of control and responsibility, influencing entrepreneurial intentions, particularly in the MENA region, where religion has a significant impact on both personal and professional life. However, empirical evidence on the link between religiosity and entrepreneurial intentions is mixed. Parboteeah et al. (2015) show a positive relationship, while other studies (Dougherty et al., 2019; Nair & Pandey, 2006) report negative or no relationships. Understanding how religiosity interacts with ILC in the MENA region is essential, as shown in Figure 1. Thus, we propose the following hypothesis:

H3: Religiosity moderates the relation between ILC and self-employment.

Data and methodology

Data

The data were sourced from the sixth and seventh waves of the WVS, covering 2010–2022, and the World Bank Database. The WVS is a global, cross-national survey with a nested structure: Individuals are nested within country-years, and country-years are nested within countries. The analysis includes data from 22 country-years and approximately 29,000 individuals. For some countries, two waves were considered, while only one wave was available for others. The countries are Algeria, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Palestine, Qatar, Tunisia, Turkey, and Yemen.

Methodology

Multilevel modeling is chosen due to the hierarchical nature of the WVS data, where individuals are grouped by country-year. This approach enables us to apply different levels of ordinary and logistic regression analyses to estimate both fixed and random effects, while accounting for both between- and within-country variance. The dependent variable (self-employed = 1) is binary, indicating self-employment (=1) or not (=0). The dataset includes individual-level predictors such as socio-demographic factors (gender, age, marital status, education, income), personality traits (ILC, work importance, hard work, imagination, technology, private ownership, competition benefits), and subjective religiosity. Country-year-level predictors include real GDP per capita, bank deposits as a percentage of GDP, corporate tax rate, unemployment rate, and the HDI. Detailed variable definitions are in the online Appendix (Table A1).

We use a two-level logistic regression model to account for the impact of individual and country-year factors on self-employment. The random-intercept model captures baseline variations, while the random-slope model allows the intercepts and slopes to vary across countries.

In the first step, we estimate the following general model:

logit (P (Y_{i, j, t} = 1)) = β_{i, t} + δ_{i, t} Y_{i, j, t} + \sum_{k = 1}^{K} β_{k} X_{k, i, j, t} + \sum_{m = 1}^{M} τ_{m} T_{m, i, j} + ϵ_{i j}

(1)

where i denotes the individual in country $j$ in year $t$ , and k is the number of individual-level predictors (level-one predictors). $Y_{i, j, t}$ represents either ILC or subjective religiosity (Reg)¹. $X_{k, i, j, t}$ represents the rest of the individual-level predictors. $T_{m, i, j}$ represents year dummies added to the model to control for time-specific effects. The intercept $(β_{i, t})$ and the slope $(δ_{i, t})$ combine observed and unobserved country characteristics.

In the second step of the random-slope model, we estimate:

{\hat{β}}_{i, t} = β_{0} + \sum_{c = 1}^{C} α_{c} Z_{j, t} + u_{j, t} .

(2)

and

{\hat{δ}}_{i, t} = δ_{0} + \sum_{c = 1}^{C} α_{c} Z_{j, t} + η_{j, t}

(3)

where ${\hat{β}}_{i, t}$ and ${\hat{δ}}_{i, t}$ are estimates of the country-specific intercept and slope obtained from step 1. $Z_{j, t}$ denotes the country-year-level predictors (level-two predictors). In the random intercept model, we assume that $δ_{i, t} = δ$ in equation (1). Hence, fixed with no random component. In the second step, we estimate equation (2).

If we combine equations (1), (2), and (3), we obtain:

logit (P (Y_{i, j, t} = 1)) = (β_{0} + u_{j, t}) + (δ_{0} + η_{j, t}) Y_{i, j, t} + \sum_{k = 1}^{K} β_{k} X_{k, i, j, t} + \sum_{m = 1}^{M} τ_{m} T_{m, i, j} + \sum_{c = 1}^{C} α_{c} Z_{j, t} + ϵ_{i j}

(4)

where $β_{0}$ is the overall fixed intercept and $u_{j, t}$ is the random intercept at the country-year level. It captures country-year characteristics explained by the country-year predictors. This is crucial for understanding the baseline differences in self-employment activity across countries. If $u_{j, t} > 0$ , then the country-year specific intercept is above the fixed intercept. If $u_{j, t} < 0$ , then the country-year specific intercept is below the fixed intercept. $δ_{0}$ is the average fixed slope, and $η_{j, t}$ is the random slope of either ILC or religiosity. We test the null $η_{j, t} = 0$ using the likelihood ratio test. If the null is not rejected, we conclude that there is no heterogeneity in the impact of $Y_{i, j, t}$ on self-employment across countries.

Results and discussion

Random-intercept models

Table 1 (Models 1–4) and Table 2 (Model 5) show the results of the random-intercept models. We first estimate a random intercept-only model (Model 1) to verify whether multi-level analysis is necessary. We find that the expected probability of self-employment or baseline probability is $Prob = \frac{e^{- 2.148}}{1 + e^{- 2.148}} = 0.1045$ . This implies that individuals have, on average, a 10.45% chance of being self-employed across all countries. However, the log-odds may vary from one country to another. The variance of the random intercept, $var (u_{j}) = 0.333$ , is significant at the 5% level. It indicates that individuals’ chances of being self-employed vary significantly across countries. Moreover, the intra-class correlation of 0.0919 is statistically significant at the 5% level, indicating evidence of clustering at the country-year level (Heck et al., 2014). Furthermore, the likelihood-ratio test comparing this model with ordinary logistic regression is significant at the 1% level with a $χ^{2} (1) = 860.92$ . We conclude that there is variation at the country-year level, underscoring the need for multilevel analysis. In all the other estimated random-intercept models shown in Table 1, the random intercept variance and the intra-class correlation are statistically significant at the 5% level. The likelihood ratio statistics for Models 2, 3, and 4 are, respectively $χ^{2} (1) = 427.41, χ^{2} (1) = 237.53, χ^{2} (1) = 237.51$ , and $χ^{2} (1) = 233.23$ . The tests reject the null hypothesis at a 1% significance level, supporting a two-level logit modeling approach.

Table 1.

Regression results for models 1–4.

Models	Model 1		Model 2		Model 3		Model 3		Model 4
Models	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.	Coeff.	Prob.
Constant $(β_{0})$	−2.148*	(0.000)	−7.820*	(0.000)	−7.238*	(0.000)	−7.351*	(0.000)	−7.341*	(0.000)
Male			1.629*	(0.000)	1.563*	(0.000)	1.561*	(0.000)	1.508*	(0.000)
Age			0.134*	(0.000)	0.123*	(0.000)	0.124*	(0.000)	0.126*	(0.000)
Age square			−0.001*	(0.000)	−0.001*	(0.000)	−0.001*	(0.000)	−0.001*	(0.000)
Married			0.146**	(0.034)	0.170**	(0.021)	0.167**	(0.023)	0.197**	(0.011)
Divorced			0.599*	(0.000)	0.614*	(0.000)	0.609*	(0.000)	0.604*	(0.000)
Widowed			0.157	(0.270)	0.227	(0.148)	0.228	(0.147)	0.236	(0.163)
Children			−0.005	(0.722)	−0.009	(0.577)	−0.009	(0.610)	−0.0003	(0.983)
Middle_Ed			−0.174*	(0.000)	−0.189*	(0.000)	−0.188*	(0.000)	−0.146*	(0.008)
High_Ed			−0.468*	(0.000)	−0.499*	(0.000)	−0.493*	(0.000)	−0.456*	(0.000)
Middle_In			0.178*	(0.000)	0.184*	(0.001)	0.176*	(0.001)	0.204*	(0.000)
High_In			0.726*	(0.000)	0.722*	(0.000)	0.722*	(0.000)	0.748*	(0.000)
ILC					0.031*	(0.001)	0.034*	(0.004)	0.038*	(0.000)
Unimportance of work					−0.297*	(0.000)	−0.301*	(0.000)	−0.285*	(0.000)
Hard work					0.0001	(0.986)	−0.002	(0.801)	−0.002	(0.784)
Imagination					−0.161*	(0.005)	−0.162*	(0.004)	−0.160*	(0.006)
Technology					−0.011	(0.775)	−0.017	(0.665)	−0.025	(0.549)
Benefits of competition					0.013	(0.152)	0.107*	(0.001)	0.101*	(0.002)
Square of benefits of competition							−0.009*	(0.002)	−0.009*	(0.006)
Government ownership					−0.035*	(0.000)	−0.036*	(0.000)	−0.035*	(0.000)
Reg									−0.171*	(0.001)
Country-year variation $(σ_{u_{j, t}}^{2})$	0.333 [0.17;0.61]		0.172 [0.09;0.32]		0.145 [0.07;0.27]		0.145 [0.07;0.27]		0.158 [0.082;0.305]
Intra-class correlation (ICC)	0.09 [0.051;0.158]		0.04 [0.027;0.088]		0.042 [0.02;0.07]		0.042 [0.02;0.07]		0.045 [0.024;0.084]
# of individuals (country-years)	29,001 (22)		27,786 (22)		24,739 (22)		24,739 (22)		22,314 (21)
Year dummies	No		Yes		Yes		Yes		Yes

Note: (i) *, **, and *** denote significance at 1%, 5%, and 10%, respectively. (ii) [] are 95% confidence intervals. (iii) $σ_{u_{j}}^{2}$ is the variance of the random intercept.

In Model 2, we add the socio-demographic variables to Model 1. The results suggest that males are more likely to be self-employed, reflecting potential gender differences in job choices. This aligns with Hashim (2023), who argues that cultural and religious influences in the MENA region lower women's expectations regarding income earning. Age presents an inverse U-shaped relationship with self-employment, while marital status has a positive correlation with it; being married or divorced increases the probability of self-employment. Interestingly, higher education correlates with lower self-employment chances, likely due to better wage employment opportunities. Higher income increases the likelihood of self-employment, likely due to increased capital or risk tolerance. Models 3 and 4 add the respondents’ personality traits, including $ILC$ and $Reg$ . In Table A2 in the online appendix, we run different specifications of Model 3 for robustness check: We add the personality traits individually. The results are very similar to those presented in Table 1.

Models 3 and 4 suggest that an ILC has a positive impact on the likelihood of self-employment. The variable unimportance of work has a significantly negative effect. Our findings imply that the respondents who consider work unimportant are less likely to be self-employed. Unexpectedly, imagination harms self-employment. The benefits of competition have an inverse U-shaped relationship with self-employment. Increased government ownership of business and industry decreases the likelihood of self-employment. Finally, Model 4 shows that subjective religiosity decreases the probability of being self-employed.

We use Model 4 to calculate the marginal effects of $I L C$ on self-employment at specific values for Reg and ILC (Figure 2). Figure 2 shows that the effect of ILC on the probability of self-employment varies according to Reg. Specifically, individuals who self-identify as religious exhibit a lower impact of ILC on self-employment, suggesting that religiosity mitigates ILC's influence. Additionally, the probability of self-employment increases at higher levels of ILC.

In Model 5, we extend Model 4 by adding country-year predictors, such as real GDP per capita (in $l n$ ), banks’ deposits to GDP ratio (in $l n$ ), corporate tax rate, unemployment rate, and HDI (Table 2). When we add country-year predictors, the intra-class correlation decreases and falls below 0.017. A low value for ICC suggests that most of the variance in the random intercept is at the individual level, and country-year differences have little influence on self-employment. Hence, individual factors mainly drive self-employment rather than the country-year's variables.

Table 2.

Regression results for model 5.

	Coeff.	Prob.
Constant $(β_{0})$	−22.252*	(0.000)
Male	1.456*	(0.000)
Age	0.155*	(0.000)
Age square	−0.001*	(0.000)
Married	0.117	(0.215)
Divorced	0.565*	(0.000)
Widowed	0.229	(0.249)
Children	0.007	(0.748)
Middle_Ed	−0.146**	(0.032)
High_Ed	−0.462*	(0.000)
Middle_In	0.123***	(0.100)
High_In	0.733*	(0.000)
ILC	0.024***	(0.059)
Unimportance of work	−0.269*	(0.000)
Hard work	−0.008	(0.412)
Imagination	−0.145**	(0.045)
Technology	−0.021	(0.674)
Benefits of Competition	0.067^&	(0.106)
Square of Benefits of Competition	−0.006	(0.158)
Government ownership	−0.026*	(0.003)
Reg	−0.187*	(0.004)
RGDPPC	−1.401*	(0.000)
Bank deposits	1.789*	(0.000)
Tax rate	0.161*	(0.000)
Unemployment rate	−0.102*	(0.000)
HDI	20.882*	(0.000)
Country-year variation $(σ_{u_{j, t}}^{2})$	0.057 [0.023; 0.142]
ICC	0.007 [0.002; 0.021]
# of individuals	15,484
# of country-years	14
Year dummies	Yes
LR test statistic $χ^{2} (1)$	13.50*

Note: (i) refer to the note under Table 1. (ii) ^& is the significance at 11%. HDI: Human Development Index; ICC: intra-class correlation.

The results in Table 2 show that an increase in the corporate tax rate increases the probability of self-employment. A high corporate tax rate would discourage investment and business expansion. Firms would decrease or stop hiring, pushing individuals toward self-employment as an alternative for labor outcomes. This result is also supported by the negative coefficient for real GDP per capita, although it is not statistically significant.

These findings indicate that individuals are driven into self-employment by necessity, aligning with the concept of necessity-based entrepreneurship as opposed to opportunity-based entrepreneurship (Hechavarria & Reynolds, 2009). Furthermore, a positive relationship is expected between banks’ deposits and self-employment. An increase in banks’ total deposits implies an increase in the banks’ capacity to lend, which is necessary to start businesses. Finally, the HDI has a positive impact on the likelihood of engaging in self-employment. Improving education, health, financial security, and institutions implies a higher HDI and the creation of an environment conducive to self-employment.

Random-slope models

We estimate, using equation 4, two random-slope models that allow the impact of ILC and Reg to be country-specific and time-dependent. We use the likelihood ratio to test the goodness of fit of these models compared to the random-intercept models. In equation (4), we test the null hypothesis $η_{j, t} = 0$ versus the alternative $η_{j, t} \neq 0$ . The likelihood ratio statistic is 3.70 with a p-value equal to 0.0514 (when $Y_{i, j, t}$ is ILC). Hence, we do not reject the null $η_{j, t} = 0$ at a 5% significance level and conclude that there is no evidence of a random slope for ILC. This implies that the effect of ILC on the probability of self-employment does not vary in time between countries. We also find that the variance of the random slope is 0.001. This suggests that the variations around the fixed slope $δ$ are negligible². We also use equation (4) to test the null hypothesis $η_{j, t} = 0$ versus the alternative $η_{j, t} \neq 0$ when $Y_{i, j, t}$ is Reg. The likelihood ratio statistic is 37.89 (p-value = 0.000). Therefore, we conclude at the 1% significance level that there is heterogeneity in the impact of Reg on self-employment across countries. Thus, the effect of subjective religiosity on the odds of self-employment is dependent on both country and time. The results of the random slope model for Reg are shown in Table 3.

Table 3.

Models with a random-slope for “religiosity”.

Models	Model 6		Extended Model 6
Models	Coeff.	Prob.	Coeff.	Prob.
Constant $(β_{0})$	−7.196*	(0.000)	−9.200*	(0.000)
Male	1.509*	(0.000)	1.457*	(0.000)
Age	0.127*	(0.000)	0.156*	(0.000)
Age square	−0.001*	(0.000)	−0.001*	(0.000)
Married	0.191**	(0.013)	0.114	(0.231)
Divorced	0.600*	(0.000)	0.568*	(0.000)
Widowed	0.233	(0.170)	0.236	(0.237)
Children	0.003	(0.853)	0.010	(0.440)
Middle_Ed	−0.146*	(0.008)	−0.144**	(0.035)
High_Ed	−0.454*	(0.000)	−0.451*	(0.000)
Middle_In	0.207*	(0.000)	0.121^&	(0.106)
High_In	0.747*	(0.000)	0.734*	(0.000)
ILC	0.038*	(0.000)	0.025***	(0.052)
Unimportance of work	−0.284*	(0.000)	−0.267*	(0.000)
Hard work	−0.001	(0.825)	−0.007	(0.460)
Imagination	−0.159*	(0.006)	−0.139***	(0.054)
Technology	−0.033	(0.429)	−0.024	(0.621)
Benefits of competition	0.099*	(0.003)	0.066^&	(0.112)
Square of benefits of competition	−0.008*	(0.008)	−0.005	(0.171)
Government ownership	−0.033*	(0.000)	−0.027*	(0.004)
Reg	−0.210**	(0.037)	−0.240**	(0.030)
RGDPPC	-	-	−1.474*	(0.000)
Bank deposits	-	-	1.703*	(0.000)
Tax rate	-	-	0.149*	(0.000)
Unemployment rate	-	-	−0.110*	(0.000)
HDI	-	-	20.904*	(0.000)
Country-year variation $(σ_{u_{j, t}}^{2})$	0.108 [0.050; 0.232]		0.0008 [0.007; 0.138]
Variation in the slope of Reg	0.160 [0.072; 0.355]		0.100 [0.031; 0.032]
ICC	0.031 [0.015; 0.066]		0.0002 [2.16e-10; 0.996]
# of individuals	22,314		15,484
# of countries	21		14
Year dummies	Yes		Yes
LR test statistic $χ^{2} (1)$	271.12*		27.66*

Note: (i) Refer to the note under Table 1. (ii) ^& indicates significance at 12%. HDI: Human Development Index; ICC: intra-class correlation; LR: likelihood ratio.

The magnitude and statistical significance of the estimates of Model 6 in Table 3 are very similar to those of Model 4 shown in Table 1, except for the estimate of Reg. We notice that it is higher in the random-slope model than in the random-intercept model. Interestingly, Model 6 shows that the variance in the random slope of Reg is higher than that of the random intercept. This suggests that deviations from the fixed slope $(δ)$ are more important than deviations from the fixed intercept. It indicates that, across countries, the effect of Reg varies more than the average probability of self-employment. This variability underlines the importance of controlling for country differences when analyzing the likelihood of self-employment. The impact may change based on the country's economic conditions.

Next, we extend Model 6 by adding the country-year predictors (Table 3). We find that RGDP, bank deposits, corporate tax rate, unemployment rate, and the HDI significantly influence the random slope of Reg. Hence, they have an indirect significant impact on the likelihood of self-employment through the random slope. The results are very similar to those of Model 5 in Table 2, and religiosity remains associated with a negative coefficient.

Finally, we extract the random slopes from the extended Model 6. Figure 3 shows that the random slope is negative for counties below the orange line. This implies that Reg has a more significant negative impact in these countries than the common fixed effect $δ = - 0.241$ . The most considerable negative effect was in Libya in 2022. Countries falling between the orange and black lines have a positive random slope. Therefore, Reg has a lower negative impact in these countries than the common fixed effect. Furthermore, a positive value above the black line indicates that Reg has a positive effect on the likelihood of self-employment. This is the case for Turkey in 2018. Finally, all the countries falling on the orange line have an almost zero random slope. Hence, the impact of Reg on the likelihood of self-employment in these countries is equal to the fixed slope $δ = - 0.241$ .

To further explore the two main results related to ILC and Reg, we perform three robustness check exercises using the extended model 6 in Table 3. First, we consider religious salience as an alternative dimension for religiosity. It is a dummy variable equal to 1 if the respondent's answer to the question “How important is God in your life?” is 8, 9, or 10³. Second, we estimate a Bayesian multilevel logit model to check the sensitivity of our results to the estimation approach. Third, we change the model's specification and estimate a multilevel ordered logit model using the Bayesian approach. In this specification, we consider a categorical dependent variable for employment outcome that captures the four job categories in the WVS: 1 = unemployed and no paid employment, including retired individuals, housewives not employed, and students, 2 = part-time employees working less than 30 h per week, 3 = full-time employees working more than 30 h per week, and 4 = self-employed individual working for oneself as a freelance or is the owner of a business. The objective is to check the sensitivity of the results to the model's specification and the choice of the dependent variable. All the estimation results are in Table A3 in the Appendix and are consistent with our previous findings. They show that ILC positively impacts employment while Reg has an average negative impact.

Conclusion and implications

Our study addresses two key questions: (i) Does ILC uniformly influence self-employment across countries, and do country-level factors matter?, and (ii) does religiosity affect self-employment uniformly and moderate the relationship between ILC and self-employment?

Our study examines the impact of ILC on self-employment in the MENA region, an area largely overlooked in prior research, which primarily focuses on Western economies (Baluku et al., 2018; Caliendo et al., 2014) or China (Luo and Chong, 2019). Previous studies have mainly explored the direct effects of ILC, neglecting socio-economic variations and the moderating role of religiosity (Caliendo et al., 2014). This research aims to address these gaps and offer valuable insights into self-employment in the MENA context.

Our first contribution examines the relationship between ILC and self-employment (H1). We find that ILC has a positive influence on self-employment, with this effect being consistent across countries. This suggests that individuals with a strong ILC are more likely to pursue self-employment, as they feel empowered to take entrepreneurial risks (Beugelsdijk and Noorderhaven, 2005; Caliendo et al., 2014). Unlike previous studies, we account simultaneously for country-level and individual-level factors.

Our second contribution emphasizes the role of individual traits and country-level factors in explaining self-employment decisions. The analysis emphasizes the importance of accounting for contextual variation, considering five country-level variables: GDP per capita, bank deposits-to-GDP ratio, corporate tax rate, unemployment rate, and the HDI. These factors significantly affect self-employment, supporting North's Institutional Theory (1990), which underscores the impact of formal and informal institutions on economic outcomes. The negative coefficient of GDP per capita supports prior findings (Gindling & Newhouse, 2014; Jamali, 2009), showing that wealthier countries tend to have lower self-employment rates. The negative effect of the corporate tax rate suggests that higher corporate taxes discourage incorporation, favoring sole proprietorships or self-employment (Long, 1982). However, our results suggest that individual traits play a more significant role than country-level factors in predicting self-employment in the MENA region. This comparison has not been widely explored in the existing literature.

Our third contribution examines the direct and moderating effects of religiosity on self-employment, as outlined in hypotheses H2 and H3. We find that, on average, religious individuals are less likely to engage in self-employment, possibly due to cultural norms, communal support, or conservative behavior (Heubeck, 2024; Rietveld & Hoogendoorn, 2022). We find the impact of religiosity to be heterogeneous across countries, with a positive effect in Turkey and negative effects in other MENA countries. This variation can be attributed to differences in governance and economic conditions. Our results suggest that higher GDP, lower unemployment, and higher HDI amplify religiosity's influence on self-employment.

Our results show that religiosity negatively moderates the relationship between ILC and self-employment. Religious individuals, with a strong belief in divine will, may attribute business success to divine influence rather than personal effort, reducing their entrepreneurial motivation (Gala & Mueller, 2024). This perception can undermine the confidence needed for self-employment, as individuals may not view their actions as the primary driver of success (Jackson & Coursey, 1988; Schieman et al., 2005). As religiosity reduces the impact of ILC, individuals in highly religious cultures, like the MENA region, may avoid self-employment, prioritizing conservation values over openness to change (Rietveld & Hoogendoorn, 2022).

Our findings emphasize the complexity of self-employment in the MENA region. Significant country-level variations in self-employment persist even after controlling for individual characteristics. Countries such as Lebanon, Morocco, Palestine, Tunisia, and Yemen have higher rates of self-employment. In contrast, Algeria, Iran, Jordan, Kuwait, and Libya have lower rates, influenced by economic factors such as GDP per capita, bank deposits, corporate tax rates, unemployment, and human development. Individual traits, such as gender (Caliendo et al., 2014; Fairlie & Robb, 2009), age (Minola et al., 2016), marital status (Livanos, 2009), education (Blanchflower & Oswald, 1998), and income (Lee-Ross, 2015), also influence the likelihood of self-employment. Interestingly, respondents who view work as unimportant are less likely to be self-employed, while imagination negatively impacts self-employment. Additionally, the benefits of competition show an inverse U-shaped relationship with self-employment, and greater government ownership reduces self-employment likelihood.

Policy and managerial implications

Our research suggests that individuals who feel in control of their life outcomes are more likely to pursue self-employment. Policymakers could introduce educational programs that enhance personal agency, self-efficacy, and resilience. To foster these skills, schools and universities can integrate entrepreneurship modules and personality development activities. Since education shapes perceptions of personal success (Weiner, 1985), strengthening self-efficacy and confidence through mindfulness and a growth mindset can empower individuals to take charge of their careers

Our results indicate that religiosity does not support self-employment. Therefore, entrepreneurship programs should be culturally sensitive, aligning initiatives with community values to reduce tensions between personal agency and religious beliefs. Individuals may perceive entrepreneurship as risky or unsuitable when religious views discourage self-employment. Governments can support this by establishing mentorship programs and incubators within religious institutions and promoting cooperative business models that emphasize risk-sharing and collective responsibility.

Our results suggest a negative link between GDP per capita and self-employment, as stable economies tend to encourage wage employment, while low-income economies often push individuals into self-employment out of necessity. To support them, governments could offer microloans and low-cost incubators, easing market entry barriers.

Limitations and future extensions

We did not examine in our study personality traits, such as risk tolerance or attitude toward failure, which may also influence self-employment. Due to the cross-sectional nature of the WVS, we were unable to conduct a panel analysis to track behavioral patterns over time. While our focus is on the MENA region, our findings can be extended to other cultural contexts. Additionally, we considered only subjective religiosity and religious salience; however, future studies could employ broader measures to capture religious complexity. Lastly, our results underscore the need to investigate additional contextual factors that could influence the relationship between religiosity and self-employment.

Supplemental Material

sj-docx-1-iei-10.1177_14657503251400968 - Supplemental material for Personality traits and beliefs on self-employment: An empirical study of the Middle East and North Africa region

Supplemental material, sj-docx-1-iei-10.1177_14657503251400968 for Personality traits and beliefs on self-employment: An empirical study of the Middle East and North Africa region by Arsalan Safari, Charbel Bassil and Mahour Parast in The International Journal of Entrepreneurship and Innovation

Footnotes

ORCID iDs

Arsalan Safari

Charbel Bassil

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Templeton World Charity Foundation, Inc. (grant number 3045) (funder DOI 501100011730) through grant [grant DOI ]. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.

Declaration of conflicting interests

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

Supplemental material

Supplemental material for this article is available online.

Notes

References

Ajzen

(1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50(2): 179–211.

Aldhehayan

Tamvada

(2023) Religiosity and risk-taking: Evidence from Islam. Journal of Management, Spirituality & Religion 20(5): 473–498.

Arkorful

Hilton

(2022) Locus of control and entrepreneurial intention: A study in a developing economy. Journal of Economic and Administrative Sciences 38(2): 333–344.

Asante

Affum-Osei

(2019) Entrepreneurship as a career choice: The impact of locus of control on aspiring entrepreneurs’ opportunity recognition. Journal of Business Research 98: 227–235.

Audretsch

Bönte

Tamvada

(2013) Religion, social class, and entrepreneurial choice. Journal of Business Venturing 28(6): 774–789.

Badaoui

(2023) Which dimensions of religiosity matter for trust? New insights from the MENA region. World Development 167: 106215.

Baluku

Bantu

Otto

(2018) Effect of locus of control on entrepreneurial attitudes and self-employment intentions: The moderating role of individualism. Journal of Enterprising Culture 26(3): 251–283.

Beugelsdijk

Noorderhaven

(2005) Personality characteristics of self-employed: An empirical study. Small Business Economics 24: 159–167.

Blanchflower

Oswald

(1998) What makes an entrepreneur? Journal of Labor Economics 6: 26–60.

10.

Busch

Gassemi

Papastamatelou

, et al. (2020) Perception of formal and informal institutions by entrepreneurs in China, Morocco, and Germany: A cross-cultural pilot study. International Journal of Management and Economics 56(4): 324–338.

11.

Caliendo

Fossen

Kritikos

(2014) Personality characteristics and the decisions to become and stay self-employed. Small Business Economics 42: 787–814.

12.

Carswell

Rolland

(2007) Religion and entrepreneurship in New Zealand. Journal of Enterprising Communities: People and Places in the Global Economy 1(2): 162–174.

13.

Coursey

Kenworthy

Jones

(2013) A meta-analysis of the relationship between intrinsic religiosity and locus of control. Archive for the Psychology of Religion 35(3): 347–368.

14.

Dana

(2009) Religion as an explanatory variable for entrepreneurship. The International Journal of Entrepreneurship and Innovation 10(2): 87–99.

15.

Di Pietro

Masciarelli

(2021) The effect of local religiosity on financing cross-regional entrepreneurial projects via crowdfunding (local religiosity and crowd financing). Journal of Business Ethics 178(2): 429–443.

16.

Dougherty

Johnson

Polson

(2019) Prosperity beliefs and value orientations: Fueling or suppressing entrepreneurial activity. Journal for the Scientific Study of Religion 58(1): 231–249.

17.

Eijdenberg

Thompson

Verduijn

, et al. (2019) Entrepreneurial activities in a developing country: An institutional theory perspective. International Journal of Entrepreneurial Behavior & Research 25(3): 414–432.

18.

Fairlie

Robb

(2009) Gender differences in business performance: Evidence from the characteristics of business owners survey. Small Business Economics 33: 375–395.

19.

Fatima

Qurat-ul-Ain

Aslam

, et al. (2023) Role of religiosity in psychological well-being and subjective happiness among transgender considering social support as a moderator. International Journal of Social Science & Entrepreneurship 3(3): 138–154.

20.

Fuentelsaz

González

Maícas

, et al. (2015) How different formal institutions affect opportunity and necessity entrepreneurship. BRQ Business Research Quarterly 18(4): 246–258.

21.

Gala

Mueller

(2024) Religion and entrepreneurial exits: Divine attributions and eternal time-frames. Journal of Management, Spirituality & Religion 21(2): 156–183.

22.

Giacomin

Janssen

Shinnar

, et al. (2023) Individual religious affiliation, religiosity and entrepreneurial intentions among students in four countries. International Small Business Journal 41(3): 318–346.

23.

Gindling

Newhouse

(2014) Self-employment in the developing world. World Development 56: 313–331.

24.

Graham

Chen

(2020) Attribution theories. Oxford Research Encyclopedia of Education. 10.1093/acrefore/9780190264093.013.892

25.

Guiso

Sapienza

Zingales

(2003) People’s opium? Religion and economic attitudes. Journal of Monetary Economics 50(1): 225–282.

26.

Gürol

Atsan

(2006) Entrepreneurial characteristics amongst university students: Some insights for entrepreneurship education and training in Turkey. Education + Training 48(1): 25–38.

27.

Hashim

(2023) Women entrepreneurs in the Gulf states: Taking stock and moving forward. Entrepreneurship & Regional Development 35(9–10): 841–884.

28.

Hechavarria

Reynolds

(2009) Cultural norms and business start-ups: The impact of national values on opportunity and necessity entrepreneurs. International Entrepreneurship and Management Journal 5: 417–437.

29.

Heck

Thomas

Tabata

(2014) Multilevel modeling of categorical outcomes using IBM SPSS, 2nd ed New York: Routledge.

30.

Heider

(1958) The psychology of interpersonal relations. New York: John Wiley & Sons. 10.1037/10628-000

31.

Heubeck

(2024) Untangling the paradoxical relationship between religion and business: A systematic literature review of chief executive officer (CEO) religiosity research. Journal of Business Ethics 195(1): 191–214.

32.

Hilary

Hui

(2009) Does religion matter in corporate decision making in America? Journal of Financial Economics 93(3): 455–473.

33.

Hoogendoorn

Rietveld

van Stel

(2016) Belonging, believing, bonding, and behaving: The relationship between religion and business ownership at the country level. Journal of Evolutionary Economics 26: 519–550.

34.

Iyer

(2016) The new economics of religion. Journal of Economic Literature 54(2): 395–441.

35.

Jackson

Coursey

(1988) The relationship of god control and internal locus of control to intrinsic religious motivation, coping and purpose in life. Journal for the Scientific Study of Religion: 399–410.

36.

Jamali

(2009) Constraints and opportunities facing women entrepreneurs in developing countries: A relational perspective. Gender in Management: An International Journal 24(4): 232–251.

37.

Kuran

(2012) The long divergence: How Islamic law held back the Middle East. Princeton, NJ: Princeton University Press.

38.

Lange

(2012) Job satisfaction and self-employment: Autonomy or personality? Small Business Economics 38: 165–177.

39.

Lee-Ross

(2015) Personality characteristics of the self-employed: A comparison using the world values survey data set. Journal of Management Development 34(9): 1094–1112.

40.

Lewis

MacGregor

Putnam

(2013) Religion, networks, and neighborliness: The impact of religious social networks on civic engagement. Social Science Research 42(2): 331–346.

41.

Liñán

Chen

(2009) Development and cross-cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice 33(3): 593–617.

42.

Livanos

(2009) What determines self-employment? A comparative study. Applied Economics Letters 16(3): 227–232.

43.

Long

(1982) The income tax and self-employment. National Tax Journal 35(1): 31–42.

44.

Luo

Chong

TTL

(2019) Regional differences in self-employment in China. Small Business Economics 53: 813–837.

45.

Miao

Gast

Laouiti

, et al. (2022) Institutional factors, religiosity, and entrepreneurial activity: A quantitative examination across 85 countries. World Development 149: 105695.

46.

Minola

Criaco

Obschonka

(2016) Age, culture, and self-employment motivation. Small Business Economics 46: 187–213.

47.

Mueller

Thomas

(2001) Culture and entrepreneurial potential: A nine country study of locus of control and innovativeness. Journal of Business Venturing 16(1): 51–75.

48.

Nair

KRG

Pandey

(2006) Characteristics of entrepreneurs: An empirical analysis. The Journal of Entrepreneurship 15(1): 47–61.

49.

North

(1990) Institutions, institutional change and economic performance. Cambridge, UK: Cambridge University Press.

50.

Parboteeah

Walter

Block

(2015) When does Christian religion matter for entrepreneurial activity? The contingent effect of a country’s investments into knowledge. Journal of Business Ethics 130: 447–465.

51.

Pargament

(2001) The psychology of religion and coping: Theory, research, practice. New York: Guilford Press.

52.

Raymond

Weldon

(2014) Mini-symposium introduction: Informal institutions and “intractable” global problems. Political Research Quarterly 67(1): 181.

53.

Rietveld

Hoogendoorn

(2022) The mediating role of values in the relationship between religion and entrepreneurship. Small Business Economics 59: 1–27.

54.

Rotter

(1966) Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied 80(609): 1–28.

55.

Schade

Schuhmacher

(2022) Digital infrastructure and entrepreneurial action-formation: A multilevel study. Journal of Business Venturing 37(5): 106232.

56.

Schieman

Pudrovska

Milkie

(2005) The sense of divine control and the self-concept: A study of race differences in late life. Research on Aging 27(2): 165–196.

57.

Schlaegel

Koenig

(2014) Determinants of entrepreneurial intent: A meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice 38(2): 291–332.

58.

Simoes

Crespo

Moreira

(2016) Individual determinants of self-employment entry: What do we really know? Journal of Economic Surveys 30(4): 783–806.

59.

Tseng

Wang

Lin

, et al. (2022) Relationships between locus of control, theory of planned behavior, and cyber entrepreneurial intention: The moderating role of cyber entrepreneurship education. The International Journal of Management Education 20(3): 100682.

60.

United Nations (2024) Sustainable Development Goals. United Nations. Available at: https://sdgs.un.org/goals (accessed 7 December 2024).

61.

Van Buren

Syed

Mir

(2020) Religion as a macro social force affecting business: Concepts, questions, and future research. Business & Society 59(5): 799–822.

62.

Weiner

(1985) An attributional theory of achievement motivation and emotion. Psychological Review 92(4): 548–573.

63.

Welter

Smallbone

Pobol

(2015) Entrepreneurial activity in the informal economy: A missing piece of the entrepreneurship jigsaw puzzle. Entrepreneurship & Regional Development 27(5–6): 292–306.

64.

Williams

Shahid

(2016) Informal entrepreneurship and institutional theory: Explaining the varying degrees of (in)formalization of entrepreneurs in Pakistan. Entrepreneurship & Regional Development 28(1-2): 1–25.

65.

Zhao

Wibowo

(2021) Entrepreneurship resilience: Can psychological traits of entrepreneurial intention support overcoming entrepreneurial failure? Frontiers in Psychology 12: 707803.

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