ABSTRACT Human capital and social capital are crucial in shaping entrepreneurial decisions, yet their combined effects on entrepreneurship entry remain insufficiently explored. This study

uses data from the China Household Tracking Survey (2010–2018) to examine how the coupling of human and social capital influences entrepreneurship entry. By defining human-social capital

coupling as the interdependence between these two forms of capital, we estimated its nonlinear impact using generalized propensity score matching and analyzed variations across gender and

region. The results revealed that human capital-social capital coupling mediated the relationship between these capitals and entrepreneurship entry, following a significant N-shaped trend

their actions; policymakers are encouraged to consider and enhance the coupling between human and social capital in their entrepreneurship support strategies. SIMILAR CONTENT BEING VIEWED BY

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Article Open access 16 March 2024 INTRODUCTION Entrepreneurship is a central driving force for economic development and it is an important channel for addressing employment problems.

However, there are various factors that influence the decision to start a business. When examining why some individuals choose to become entrepreneurs while others do not, researchers have

highlighted the lack of resources as a significant barrier to entrepreneurship (Mickiewicz et al., 2017). Despite the known benefits of human and social capital, their impacts on

entrepreneurship entry are complex and varied. Research has demonstrated that the effects of human and social capital on entrepreneurship can be positive, negative, or neutral (Davidsson

& Honig, 2003; Meyer et al., 2009; Afandi et al., 2017). This variation suggests that while substantial levels of these resources are advantageous, many entrepreneurs operate with

relatively modest levels of both human and social capital (Backes-Gellner & Moog, 2013). This observation suggests that resource availability alone does not fully explain entrepreneurial

entry. The varied results in the literature may be explained by several factors, including gender, regional differences, cultural contexts, and specific mediators such as cognitive

resources, encouragement dynamics, or knowledge sharing (Sahasranamam & Nandakumar, 2020; Gruber et al., 2024; Boudreaux & Nikolaev, 2019; Santarelli & Tran, 2013; Shan &

Tian, 2022; Salamzadeh et al., 2023). For instance, some studies suggest that social capital mediates the relationship between human capital and entrepreneurship entry, highlighting both

independent and collective effects (Klyver & Schenkel, 2013). Building on this, research into the collective effects of human and social capital on entrepreneurship has identified

several interaction patterns. For example, human and social capital can act as either substitutes or complements, depending on the context (Semrau & Hopp, 2016). The nature of this

interaction—whether substitutive or complementary—affects entrepreneurship entry, particularly in relation to the type of social capital involved (Yavuz, 2024). Additionally, the combination

of high levels of both human and social capital tends to generate greater opportunity novelty, which positively influences entrepreneurship, while lower levels tend to hinder it (Vadnjal,

2020; Bailey, 2016). Backes-Gellner & Moog (2013) introduced the concept of a “human and social capital balance,” suggesting that a well-balanced combination of these resources enhances

the likelihood of entrepreneurship. This balance can be seen as part of the broader systematic effects, where human and social capital when organized with other resources, contributed to new

venture formation. These systematic effects are often analyzed through methods like Qualitative Comparative Analysis (Razmdoost et al., 2020), which helps to understand how different

configurations of resources foster entrepreneurship. Gender and regional contexts further complicate the analysis. For example, in economically underdeveloped areas, social capital may play

a more significant role than human capital, whereas the reverse may be true in more developed regions (Shane & Venkataraman, 2000; Qian, 2018). Gender differences also affect the impact

of social capital on entrepreneurship, with women’s social capital levels being influenced by their levels of human capital (Arregle et al., 2015). To explore these dynamics, China presents

a particularly compelling context. The country’s rapid economic growth, significant government initiatives, and complex socio-economic landscape offer a unique backdrop for studying the

roles of human and social capital in entrepreneurship. In China, supportive policies have transformed the industrial landscape across both urban and rural areas, notably benefiting central

and eastern regions and those emerging from poverty and contributing to reduced income disparities (Liu et al., 2024). The shift towards a more efficient market economy, driven by reforms,

has further enhanced networking and knowledge spillovers, supporting entrepreneurial activities (Zhang & Rodríguez-Pose, 2024). Given China’s dynamic environment, entrepreneurship drives

innovation and economic resilience. In this context, social capital often substitutes for formal institutional support (He et al., 2019), and the relationship between human and social

capital highlights gender and regional differences shaped by China’s unique cultural and institutional contexts (Hemmert et al., 2022; Yin et al., 2020; Xu et al., 2024). In China’s

traditionally patriarchal society, gender disparities in entrepreneurship are evident, with female entrepreneurs frequently facing challenges such as limited access to networks and capital

compared to their male counterparts (Wiig et al., 2023). While previous studies have provided valuable insights, gaps remain. Thresholds for the collective effects of human and social

capital disposition on entrepreneurship entry are not well-defined. Interaction effects vary based on human and social capital levels, while combination effects depend on specific sample

characteristics. Systematic effects involve complex interactions with other factors, making thresholds difficult to determine. Moreover, the collective mediating effects of human and social

capital disposition have not been fully examined. This research addresses these gaps by employing a structural equation model and Generalized Propensity Score Matching to estimate thresholds

and total effects, revealing that human and social capital operates in a coupled approach with threshold effects, which helps explain the observed phenomena. By addressing critical gaps in

the existing literature, it makes a significant contribution to the understanding of human and social capital and their interactions in the context of entrepreneurship. The study provides

insights through the following key contributions: * 1. The research introduces the concept of “ human-social capital coupling” using a novel approach called coupling coordination. This new

framework offers an enhanced perspective on how human and social capital jointly affect entrepreneurship entry. By integrating these resources in a cohesive manner, the study enriches the

scholarly discussion and adds new dimensions to the understanding of entrepreneurship resources. * 2. The paper explores the nonlinear effects of coupling human and social capital on

entrepreneurship entry, identifying specific thresholds at which these effects become significant. This analysis addresses previously observed aberrations and provides explanations for

puzzling phenomena by pinpointing the thresholds where the combined influence of human and social capital transitions. This detailed examination clarifies the complex dynamics involved and

offers practical insights for both entrepreneurs and policymakers. * 3. The research employs data from a Chinese household tracking survey to empirically analyze the effects of human and

social capital dispositions on entrepreneurship entry, considering gender and regional variations. This context-specific analysis reveals how different factors influence the interplay

between human and social capital, providing nuanced insights into how these resources affect entrepreneurial outcomes across various contexts. The study offers detailed evidence of the

heterogeneity of these effects, enriching the theory with empirical data from a diverse setting. Overall, the research advances the theoretical framework of human and social capital by

demonstrating their coupled effects and identifying critical thresholds that explain variability in entrepreneurial behavior. These findings contribute to a deeper understanding of how these

resources interact and offer insights for fostering entrepreneurship entry in different contexts. CONCEPTUAL DEFINITION AND RESEARCH HYPOTHESES CONCEPTUAL DEFINITION HUMAN CAPITAL The

concept of human capital is widely accepted as encompassing a person’s productive attributes, such as knowledge, abilities, skills, experience, and even health or age (Al-Awlaqi et al.,

2023). These attributes are essential because they can generate returns for labor (Bashir & Siddique, 2023). Researchers often measure human capital using either a single index or a

composite index, with years of education and work experience being the most common measures (Marvel et al., 2014). In entrepreneurship research, human capital is generally divided into

general human capital and specific human capital. General human capital is useful across a wide range of jobs and includes indicators such as education level, work experience, training, and

self-efficacy. Specific human capital, on the other hand, is relevant to a particular venture or industry and includes indicators such as experience in business startups or management,

industry-specific experience, and specialized skills and knowledge related to entrepreneurship (Markman & Baron, 2003; Ashourizadeh et al., 2014). In line with this, researchers often

selected multiple indicators to measure human capital, as they thought this approach was more appropriate (Marvel et al., 2014). SOCIAL CAPITAL Social capital includes networks and the

resources embedded within or derived from these networks, which are central to an individual’s social capital (Ferri et al., 2009). Nahapiet and Ghoshal (1998) defined social capital as

having structural, relational, and cognitive dimensions. Lin (1999) distinguished between accessed and mobilized social capital, while other researchers categorized social capital into

bonding–bridging or strong–weak ties (Iantosca et al., 2024; Chen & Li, 2024), as well as characteristics of the network (such as closure, transitivity, and density) and the agent

(including position, identity, social demographics, and group memberships), or other connecting links (Urban & Moetse, 2024). In the context of entrepreneurship, researchers have

selected various dimensions to measure social capital. Salamzadeh et al. (2022) defined it in terms of bonding, bridging, and structural aspects, measuring it through the number of people

and the frequency of interactions in entrepreneurial activities. Santarelli and Tran (2013) measured social capital based on network participation, network usefulness, network intensity,

network size, and network support. In the context of Chinese collectivism and social hierarchy, social capital is often referred to as “guanxi,” which encompasses networks related to

politics, family, and business communities (Su et al., 2022). HUMAN CAPITAL-SOCIAL CAPITAL COUPLING Coupling is a phenomenon in physics where two or more objects interact, and it has been

used to analyze the interdependence between two or more systems in both economics and sociology (Zhan et al., 2023). When the levels of two systems are low and contribute almost equally, or

when the levels are high and contribute significantly, the degree of coupling is likely high but varies in its nature. Therefore, coupling effects are often calculated using a coupling

coordination model to distinguish between low levels of high coupling and high levels of coupling (Wang et al., 2024; Fang et al., 2023). When coupling coordination is high, the two elements

within the system are both high and mutually reinforce each other, leading to a collective effect that exceeds the contribution of either element alone. As the collective effect of the two

elements’ interaction decreases, coupling coordination also decreases, and the mutual constraint effect may increase (Lin & Chen, 2023). In entrepreneurship research, human capital,

social capital, institutions, and other factors are elements within the entrepreneurship ecosystem and should be considered as a cohesive system (Stam, Van de Ven, 2021). The systematic

effect is not necessarily equal to the effect of one or some of the elements alone (Chen & Song, 2024), and the interdependence between elements should not be overlooked (Wurth et al.,

2022). Researchers recommend using fuzzy-set qualitative comparative analysis and coupling coordination to estimate systematic effects (Wang et al., 2023; Tao et al., 2023). While fuzzy-set

qualitative comparative analysis can identify which element dispositions are more beneficial for outcomes, it does not provide the magnitude of the combined effect. Coupling coordination

analysis, however, can determine this magnitude. Previous research has provided evidence of the interdependence between human and social capital. Empirical studies have shown that

individuals with strong cognitive abilities (i.e., human capital) often have better interpersonal skills and can maximize the resources provided by their networks. Consequently, human

capital and social capital can facilitate each other (Zhou & Liu, 2017). Key information and resources needed for entrepreneurial activities are often embedded within social capital

networks (Black & Boal, 1994). However, the identification, capture, and utilization of this information and these resources are filtered through personal experience and knowledge

(Bergmann et al., 2016). This filtering process guides individuals to invest in human capital, resulting in a reciprocal constraint between social capital and human capital. Given the

interdependence between human and social capital, this study defines the collective effect of human capital-social capital disposition as human capital-social capital coupling and uses the

degree of coupling coordination to estimate it. If the degree of coupling coordination is high, it indicates a strong collective effect between human capital and social capital. Conversely,

a low degree of coupling coordination signifies a weaker collective effect. RESEARCH HYPOTHESES HUMAN CAPITAL, SOCIAL CAPITAL, AND ENTREPRENEURSHIP ENTRY Education and cognitive abilities

are two different forms of human capital that have positive impacts on entrepreneurship entry. Higher education is associated with broader and deeper personal knowledge and facilitates the

generation of new knowledge based on existing knowledge (Bradley et al., 2012). This new knowledge provides entrepreneurial opportunities for individuals, and many academic entrepreneurship

opportunities arise from scientific research (Bergmann et al., 2016). People with strong cognitive abilities are found to be better at gathering information, addressing interpersonal

relationships, and solving complex problems (Grégoire et al., 2011). Therefore, cognitive abilities are helpful in promoting entrepreneurship entry. A social network is a form of social

capital that positively impacts entrepreneurship (Afandi et al., 2017). Having a larger social network refers to access to a greater number of resources (Adler & Kwon, 2002). Interacting

with individuals of higher social status is associated with easier access to high-quality resources (Lin, 1999). Such resources are beneficial in promoting entrepreneurship entry. HUMAN

CAPITAL-SOCIAL CAPITAL COUPLING AND ENTREPRENEURSHIP ENTRY The influence of resource combinations is assumed to be greater than the influence of individual resources alone (Han et al.,

2017). For instance, when faced with resources that appear superficially identical, certain individuals possess the ability to recognize entrepreneurial opportunities within them, while

others do not. This distinction may stem from differences in human capital (Adler & Kwon, 2002). Entrepreneurial opportunities do not necessarily translate into entrepreneurial

activities; social capital is needed to support the completion of these activities (Greve & Salaff, 2003). Evidence shows that the interplay between human capital and social capital adds

extra value to entrepreneurial activities (Rezaei et al., 2014). This interplay increases the total amount of resources available to entrepreneurs and can even lead to the development of

new resources (Ciabuschi et al., 2012; Lu et al., 2022), thereby supporting individuals in starting businesses. According to the concept of human capital-social capital coupling used in this

study, when the coupling coordination between human capital and social capital is high, they mutually reinforce each other, resulting in a larger collective effect. This optimal utilization

of both human capital and social capital increases the likelihood of starting a business. Therefore, human capital-social capital coupling is expected to positively influence an

individual’s entrepreneurship entry. For example, the diverse thoughts and ideas generated from human capital can facilitate the use of social capital, while the potential of social capital

can enhance the strength of human capital (Hollenbeck & Jamieson, 2015). High levels of both human capital and social capital generate a synergy effect, which is associated with positive

outcomes. Thus, a high degree of coupling coordination between human and social capital is expected. Further analysis is required to better understand the impact of human-social capital on

entrepreneurial entry. According to the coupling effect of human capital- social capital disposition, it is anticipated that this coupling effect will be low when both human capital and

social capital are lacking and high when they are abundant. In instances where both human and social capital is low, an increase in either is expected to enhance the coupling effect,

potentially leading individuals to pursue entrepreneurship due to limited opportunities for traditional employment. Conversely, when both human and social capital are high, an increase in

either is expected to further improve the coupling effect, as individuals stand to gain more from entrepreneurship. Additionally, when the coupling of human and social capital is moderate,

corresponding levels of human and social capital are expected to provide individuals with some job prospects that mitigate the risks associated with entrepreneurship. Alternatively, when one

is high, and the other is low, individuals are likely to forego entrepreneurial ventures in favor of employment opportunities that leverage their strengths. Consequently, the relationship

between human capital-social capital coupling and entrepreneurial entry is likely nonlinear as the coupling effect increases. GENDER AND REGIONAL DIFFERENCES IN HUMAN CAPITAL-SOCIAL CAPITAL

COUPLING Gender has been found to affect individuals’ entrepreneurship entry through their entrepreneurial perceptions. Females are affected by gender stereotypes and the social division of

labor, and other factors (Gupta et al., 2009). As such, female entrepreneurs have often been categorized as having fewer entrepreneurial resources, poorer entrepreneurial qualifications and

abilities, more conservative management approaches, and inferior performance (Marlow & McAdam, 2013). Women’s self- evaluation of their entrepreneurship abilities has been found to be

lower compared to men, resulting in low entrepreneurship self-efficacy and reducing women’s entrepreneurship entry (Aidis et al., 2007; Anna et al., 2000). The social norm associated with

entrepreneurs is that they are predominantly male. Information asymmetry can exacerbate gender stereotypes (Murphy et al., 2007), which impacts the ability of female entrepreneurs to gain

access to receive entrepreneurship resources and reduces women’s entrepreneurship entry. Female entrepreneurs can attempt to overcome the negative gender effects on entrepreneurship by

showing masculine traits, or by accumulating sufficient resources to improve their entrepreneurship competencies and to signal that competence to society (Busenitz et al., 2014). Compared

with men, female entrepreneurs need a higher coupling effect of human capital-social capital to encourage entrepreneurship entry. Thus, it is expected to have a greater impact on women’s

entrepreneurship entry. Different entrepreneurial environments provide different resources for entrepreneurs (Cerqueiro & Penas, 2017). A more effective entrepreneurial environment is

associated with lower resource constraints on entrepreneurs; this supports individuals’ entrepreneurship entry (Lim et al., 2010). In underdeveloped areas, markets are less advanced,

government support for entrepreneurship is relatively inefficient, and entrepreneurs may face difficulties in obtaining outside resources. As such, they need to rely on their own social

capital to overcome the institutional hole and develop social networks, and on their own experience, ability, and other sources of human capital to maintain their enterprises’ survival and

development (Qian, 2018). Compared with entrepreneurs in economically developed areas, entrepreneurs in economically underdeveloped areas need a higher coupling effect of human

capital-social capital disposition. For this study, entrepreneurs were divided into four groups: women in the Eastern region, males in the Eastern region, women in the Midwest region, and

men in the Midwest region. Female entrepreneurs require access to resources equivalent to their male counterparts and must also contend with the adverse impacts of gender bias stemming from

societal divisions of labor. Compared with the Eastern region in China, the economic development level in the Midwest region is relatively low, the market is inefficient, the market

atmosphere is not inclusive and open, and female entrepreneurs are generally more constrained by gender stereotypes. As such, the coupling effect of human capital-social capital for female

entrepreneurs in the Midwest region needs to be higher, so it has a greater impact on female entrepreneurs in the Midwest region compared to men in the same area. Therefore, the coupling

effect of human capital-social capital is expected to have the greatest impact on women’s entrepreneurship entry in the Midwest region and the smallest impact on men’s entrepreneurship entry

in the Eastern region. There is a gender difference with respect to entrepreneurship motivation, and men’s motivation is generally stronger than women’s (Davidsson & Honig, 2003; Li et

al., 2023). Women in the Eastern region in China have greater advantages compared to men in the Midwest region, but may be less motivated to become entrepreneurs. They are expected to need a

higher coupling effect of human capital-social capital to encourage them, so the coupling effect of human capital-social capital on women’s entrepreneurship entry in the Eastern region is

expected to be greater for them compared to men in the Midwest region. In conclusion, the following hypotheses and assumptions are presented, as shown in Fig. 1. Hypothesis 1: Human

capital-social capital coupling plays a mediating role in how human capital and social capital influence entrepreneurship entry. Hypothesis 2: The effect of human capital-social capital

coupling on entrepreneurship entry is nonlinear. Hypothesis 3: The effect of human capital-social capital coupling on entrepreneurship entry has gender and regional heterogeneity. MATERIALS

AND METHODS SAMPLES AND DATA SOURCES The data for this study came from the China Family Panel Studies (CFPS) for 2010, 2014, and 2018. CFPS is a well-known longitudinal survey conducted in

China, focusing on various aspects of family life, education, employment, health, and other social and economic factors. As such, other researchers studying entrepreneurship (Koomson et al.,

2024, Xiao & Wu, 2021, Wang & Lin, 2019) draw their data from CFPS. Cognitive ability served as a key variable for this study. The CFPS measurements for cognitive ability from the

years 2010, 2014, and 2018 were both consistent and authoritative, thus samples were selected from these three years. The adult and family questionnaires were matched using a family code and

subsequently organized into a mixed panel dataset based on personal identifiers (IDs) and year. The final dataset comprised 101,201 samples, with 33,598 samples from 2010, 37,141 samples

from 2014, and 37,354 samples from 2018. Samples with ages fewer than 16 years or larger than 65 years were excluded from the analysis. Additionally, samples were omitted if key variables,

such as occupational status, human capital, or social capital, were missing, ambiguous, or unavailable. To minimize the impact of outliers, data were truncated at the 1st and 99th

percentiles. Consequently, the final dataset comprised 42,858 observations. Further inspection identified additional samples with missing personal information, which were also removed. Thus,

the final sample size was 42,857, with detailed variable descriptions provided in Table 1. VARIABLES AND MEASURES ENTREPRENEURSHIP ENTRY Entrepreneurship entry served as the dependent

variable in this study and was expressed by whether or not the respondent operated their own business. The data came from the CFPS question: “Do you work for yourself/your own family, or are

you employed by someone else/other family/organization/ unit/company?” If the respondent answered, “Work for yourself/your own family,” it was coded as 1; otherwise, it was coded as 0.

HUMAN CAPITAL Education and cognitive ability were used to represent human capital (Marvel et al., 2014). To express cognitive abilities, the mathematical test and the word test were taken

into account (Zhou & Liu, 2017). The word test provided valuable information about language skills and literacy levels among the survey participants. Primary, junior high, high school,

junior college, and undergraduate education were coded as 6, 9, 12, 15, and 16, respectively; master and doctoral degrees were coded as 19 and 22, respectively; illiteracy was coded as a 0.

Mathematical and language skills have been standardized since 2010, so they can be compared between years. Years of education, math scores, and word scores underwent Z-normalization before

being computed as (1). _x__i_ was a variable; _z___x__i_ was the normalized value of _x__i_; and _u__i_ and _σ_ were the mean and standard deviation of _x__i_, respectively. The Cronbach’s

Alpha was 0.765 in 2010, 0.78 in 2014, 0.778 in 2018. Subsequently, principal component analysis (PCA) was conducted, and principal components were extracted based on eigenvalues exceeding 1

(Bertin & Mavoori, 2022; Afandi et al., 2017). When the three variables from every sampled year (2010, 2014, and 2018) were aggregated into one principal component, the result was

termed human capital. Its cumulative explanation of the variance of variables for the respective years was 85.1%, 85.5%, and 88.8%, the result fitted with statistical standards. The weight

was assigned according to the variance of years of education, math scores, and word scores. Therefore, human capital in 2018 was calculated as (2), 2014 as (3) and 2010 as (4).

$${{\rm{z\_x}}}_{{\rm{i}}}=\frac{{{\rm{x}}}_{{\rm{i}}}-{{\rm{u}}}_{{\rm{i}}}}{{\rm{\sigma }}}$$ (1) $$0.339\times {edu}+0.334\times {math}+0.327\times {word}$$ (2) $$0.343\times

{\rm{edu}}+0.324\times {\rm{math}}+0.333\times {\rm{word}}$$ (3) $$0.340\times {\rm{edu}}+0.326\times {\rm{math}}+0.334\times {\rm{word}}$$ (4) SOCIAL CAPITAL Social capital was another

explanatory variable in this study. Research has demonstrated that family expenditure on gifts and social interactions serves as a means for Chinese individuals to express emotions and

maintain or expand social connections, reflecting an important cultural tradition (Ma & Yang, 2011; Lin et al., 2024). In this study, social capital was measured through data from the

family questionnaire, specifically the item ‘How much has your family spent on personal gifts for social relations in the past 12 months?’ The resulting expenditures were then Z-standardized

for analysis. HUMAN CAPITAL-SOCIAL CAPITAL COUPLING Human capital-social capital coupling served as the mediating variable. The calculation method is described in Pang et al. (2019): human

capital was nondimensionalized as (5), it was termed as _u__n_, _u__i_ was human capital of i individual, _u__min_ was its minimum value and _u__max_ was maximum value. By the same

calculations, social capital was nondimensionalized and termed as \(u\)m. $${{\rm{u}}}_{{\rm{n}}}=0.99\times \frac{{u}_{i}-{u}_{\min }}{{u}_{\max }-{u}_{\min }}+0.01$$ (5) $$D=\sqrt{c\times

T}$$ (6) $${\rm{c}}=\frac{\sqrt{{{\rm{u}}}_{{\rm{n}}}\times {u}_{m}}}{\frac{1}{2}({u}_{n}+{u}_{m})}$$ (7) $${\rm{T}}={\alpha }_{i}\times {{\rm{u}}}_{{\rm{n}}}+{\alpha }_{j}\times {u}_{m}$$

(8) Since human capital and social capital are both crucial, so _α_i and _α_j were set at 0.5. _T_ Denotes the comprehensive coordination index, _c_ denotes the coupling degree where _D_

denotes the coupling coordination degree, _c_ and _D_ assume the value of [0, 1]. The degree of coupling coordination is categorized into different levels: advanced coordination ranges from

0.8 to 1; basic coordination ranges from 0.5 to 0.8; basic imbalance ranges from 0.3 to 0.5; and serious imbalance ranges from 0 to 0.3. These levels indicate the extent to which human

capital and social capital generate a collective effect greater than either human capital or social capital alone, or both combined. In descending order, the lowest level reflects a

situation where human and social capital constrain each other. OTHER VARIABLES Subjects in the sample were divided into four groups: a woman in the Eastern region was coded as 0; a man in

the Eastern region was coded as 1; a woman in the Midwest region was coded as 2; and a man in the Midwest region was coded as 3. In this study, the control variables included marital status,

age (and it’s square), year, and health. Marital status was coded as 1 for married and 0 for all other statuses. The years 2018, 2014, and 2010 were coded as 0, 1, and 2, respectively. Age

was recorded using the ‘actual age’ question from the adult questionnaire. Health was rated on a scale from 1 to 5, where 1 indicated ‘very unhealthy’ and 5 indicated ‘very healthy’.

VARIABLE DESCRIPTION Table 1 presents the variables used in this study. Among the total sample, 22,398 individuals identified as entrepreneurs, comprising 52.26% of the sample, while others

were non-entrepreneurs, making up 47.74%. Males represented 54.02% of the sample, whereas females constituted 45.98%. Participants from the Eastern region accounted for 42.88% of the sample,

while those from the Midwest and Western regions made up 57.12%. Married individuals represented 85.72% of the sample, with all other marital statuses comprising 14.28%. The average degree

of human capital-social capital coupling across all subjects in the sample was 0.57, indicating a medium effect. METHOD GENERALIZED PROPENSITY SCORE MATCHING Individuals make the choice to

engage in entrepreneurship based on personal characteristics and the external environment. The choice is not due to random selection, so the entrepreneurship entry variable is likely

self-selective. Was there a difference in entrepreneurship entry between entrepreneurs and non-entrepreneurs simply due to the human-social capital coupling? Given this background, in this

study, self-selection bias was found to cause bias in estimation. As such, generalized propensity score matching (GPSM) was used to overcome these problems. It estimates the conditional

expectation of the outcome given the observed treatment and the estimated GPSM by calling the routine dose-response model. Then dose-response 2 estimates the average potential outcome for

each level of the treatment in which the user is interested. Entrepreneurship entry served as the dependent variable, while the treatment variable was human-social capital coupling. The step

size was divided into intervals of 0.1. Treatment values were assigned as follows: if they were 0.3 or less, they were set to 0.3; if they were greater than 0.3 but 0.7 or less, they were

set to 0.7; and if they were greater than 0.7 but 1 or less, they were also set to 1. STRUCTURAL EQUATION MODEL The structural equation model (SEM) provides an effective means to evaluate

complex socio-economic phenomena, such as the mediator effect (Bowen & Guo, 2011). Path analysis and multi-group comparisons, which are two techniques within SEM, were utilized in this

paper. Firstly, the study involves multiple influencing mechanisms. The research hypotheses proposed the presence of two independent variables, one mediated variable, and one moderator

variable, necessitating the estimation of a complex relationship. Secondly, major variables were converted into observed variables using principal component analysis (PCA), making path

analysis suitable for the study. Thirdly, it is necessary to estimate the varying effects of human capital and social capital coupling across genders and regions on entrepreneurship, for

which multi-group comparisons are applicable. In this study, SEM was conducted using Stata 17.0 software to estimate the parameters. Initially, the model was estimated without imposing

equality constraints between groups, and its fit with the criteria was assessed. If the model did not meet the fit criteria, adjustments were made by imposing equality constraints on

variables with significant differences in coefficients between groups until the model met the adequacy threshold. RESULTS THE EFFECT OF HUMAN-SOCIAL CAPITAL COUPLING ON ENTREPRENEURSHIP

ENTRY This study examines the impact of human capital-social capital coupling on entrepreneurship entry through generalized propensity score matching, with results presented in Table 2.

Notably, human capital, social capital, marital status, age, and health significantly differed across groups, satisfying the balancing property (_p_ < 0.01). However, the coefficient for

the coupling of human and social capital remained significant at the 0.01 level, indicating the presence of endogeneity. Additionally, the coefficient for this coupling is statistically

significant at the 1% level, suggesting a cubic relationship with entrepreneurship entry. Figure 2a shows the dose-response effect, while Fig. 2b shows the treatment effect of human-social

coupling on entrepreneurship entry. Specifically, the relationship resembles an N-shaped trend in Fig. 2b: it initially increases when the coupling is less than 0.4, then decreases when it

is between 0.4 and 0.7 and increases again when it exceeds 0.7. MODEL SPECIFICATION OF GENDER AND REGION The study employed a structural equation model to assess the impact of gender and

region. In the constrained model, _chi_2(23) = 732.5, _Prob_ > _chi_2 = 0.00. The sample size of 42,857 indicates that using the _x_2/_df_ (Chi-square test) might not be suitable due to

its large value (Lefcheck, 2016). Some standards were recommended to estimate the fit of SEM (Baumgartner & Homburg, 1996; Krieger et al., 2021), these standards in the paper are as

follows: RMSEA = 0.054, SRMR = 0.003 and CFI = 0.993. The RMSEA and SRMR are below the recommended thresholds of 0.08 and 0.05, respectively, and the CFI exceeds 0.95, indicating a good fit

between the model and the data. Table 3 shows the parameters estimated in this paper. In Model _M_a, the coefficient for human and social capital’s contribution to their coupling is

significantly positive at a 1% level. This indicates that improvements in human capital and social capital facilitated the human capital-social capital coupling. In Model _M_b, the

coefficient of human capital-social capital coupling is also significantly positive at the 1% level. These results indicate that human capital and social capital encourage entrepreneurship

entry through human capital-social capital coupling. In addition, Table 3 reveals that the Wald test for the coefficient difference between groups regarding human capital-social capital

coupling yielded a value of 20.947, which was statistically significant at the 1% level. The results indicated that there were gender and regional differences in the role of human

capital-social capital on entrepreneurship entry. The coefficient of human capital-social capital coupling for women in the Midwest region was 0.334, while the coefficient for women in the

Eastern region was 0.164. For men, the coefficient in the Midwest region was 0.171, and in the Eastern region, it was 0.132. Regardless of the region, women’s human capital-social capital

coupling had a higher impact on entrepreneurship entry than men’s. Additionally, the human capital-social capital coupling had a greater impact on entrepreneurship entry in the Midwest

region compared to the Eastern region. This indicates that the impact of human capital-social capital coupling on entrepreneurship entry exhibited both gender and regional differences. THE

EFFECT OF HUMAN-SOCIAL CAPITAL COUPLING AS A MEDIATOR BETWEEN HUMAN CAPITAL AND SOCIAL CAPITAL Mediator effect can be estimated by SEM. Table 3 shows that the impact of human capital and

social capital on human capital-social capital coupling was significant at 1%, so is human capital-social capital coupling on entrepreneurship entry. Therefore, human capital and social

capital affect the entrepreneurship entry through human capital-social capital coupling, so there is mediator effect. CONCLUSIONS AND DISCUSSION CONCLUSIONS Using CFPS data from 2010, 2014,

and 2018, this study examined the mechanisms and heterogeneity by which human capital and social capital influence entrepreneurship entry through the concept of human capital-social capital

coupling. The findings from the propensity score matching method indicate that the influence of human capital-social capital coupling on entrepreneurship entry is robust. The study results

lead to the following key conclusions: THE IMPACT OF HUMAN CAPITAL-SOCIAL CAPITAL COUPLING ON ENTREPRENEURSHIP ENTRY IS NONLINEAR Prior research has explored various dispositions of human

and social capital and their linear impacts on entrepreneurship activities. Some studies identified linear relationships or simple interactions (Vadnjal, 2020; Semrau &Hopp, 2016), while

others observed inverted U-curves or linear curves (Davidsson & Honig, 2003; Meyer et al., 2009; Huang et al., 2024). Our study reveals that the impact of human capital-social capital

coupling on entrepreneurship entry is nonlinear, resembling an N-shaped curve. This finding highlights the presence of multiple thresholds and provides a more nuanced understanding of how

varying levels of these resources influence entrepreneurship. The results complement existing literature by showing both positive and negative effects depending on the levels of human and

social capital, offering a more detailed perspective on entrepreneurial entry dynamics. GENDER AND REGIONAL DIFFERENCES IMPACT HUMAN CAPITAL-SOCIAL CAPITAL COUPLING ON ENTREPRENEURSHIP ENTRY

Our analysis shows significant differences in how human capital-social capital affects entrepreneurship entry across gender and region. The impact is strongest for women in the Midwest,

followed by women in the East, with men in both regions experiencing less pronounced effects. The results in the paper support that there are gender or region differences in how human

capital and social capital influence entrepreneurship entry in China (Li et al., 2023; Wang et al., 2023). These results indicate that human capital-social capital coupling influences female

entrepreneurship more strongly in the Midwest compared to other groups. The study underscores the importance of regional and gender-specific contexts, contributing to the understanding of

how different demographics are impacted by the interplay of human and social capital in China. HUMAN CAPITAL AND SOCIAL CAPITAL INFLUENCE ENTREPRENEURSHIP ENTRY THROUGH COUPLING The study

defines human-social coupling as an interdependent relationship between human and social capital and examines its effect on entrepreneurship entry. While previous research has explored these

factors independently or through various mediators (Shan&Tian, 2022; Gruber et al., 2024), this study focuses on the combined effect of human and social capital coupling, that responds

to a call that their collective effect, even unconventional combining should be studied (Hargadon & Sutton, 1997). The results address gaps in the literature by highlighting nonlinear

effects and differences based on gender and region, offering a comprehensive explanation for the variability in entrepreneurship entry. IMPLICATIONS The findings and conclusions of this

study have significant implications for both theory and practice, particularly in understanding how human capital and social capital interact and affect entrepreneurship entry. IMPLICATIONS

FOR THEORY The study contributes to theories on resource combination in entrepreneurship by introducing the concept of human-social capital coupling. It provides a new perspective on how

these resources jointly impact entrepreneurial activities, enhancing the theoretical understanding of resource interplay. The identification of thresholds and nonlinear effects offers

valuable insights into the systematic effects of human and social capital, addressing previous gaps and expanding the theoretical framework. IMPLICATIONS FOR PRACTICE For individuals,

understanding the thresholds of human-social capital coupling is crucial for making informed entrepreneurship decisions. Evaluating one’s human and social capital can help individuals

identify optimal conditions for entrepreneurship entry. Specifically, those with low coupling should focus on improving their human and social capital before pursuing entrepreneurial

ventures. This is especially relevant for women in the rural Midwest region of China, who face unique challenges and opportunities. For policymakers, it is essential to recognize how

human-social capital coupling influences entrepreneurship. Policies should be tailored to different demographic groups and regions, considering the specific needs of women and those in

underdeveloped areas. Increasing investment in education and providing support for social capital development can foster a more conducive environment for entrepreneurship. LIMITATIONS AND

FUTURE RESEARCH Because of limited data sources, this study did not incorporate personal experiences or professional skills related to human capital variables, nor did it account for

industry-specific variations. Additionally, because some social capital indicators were missing or inadequate, the study used a single indicator with Chinese characteristics to measure

social capital. As data sources continue to expand, future research should delve deeper into how various types of experiences and professional capabilities affect entrepreneurship entry.

Social capital should be measured using more suitable methods to capture its full scope. Additionally, exploring disparities in how the coupling between human capital and social capital

influences entrepreneurship entry across different industries warrants further investigation. DATA AVAILABILITY The data in this study are available from

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references ACKNOWLEDGEMENTS We thank the support provided by the National Social Science Fund (NSSF) General Project “Study on the Mechanism Path and Countermeasures of Labor Market

Flexibility to Promote Common Wealth in China” (Project Number: 23BJL067). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Economics and Management School, Yangtze University, Jingzhou, China

Yanyan Sheng & Chenlu Ye * Hubei Enterprise Culture Research Center, Hubei University of Economics, Wuhan, China Yiping Sun * Hanze University of Applied Sciences, Groningen, The

Netherlands Diederich Bakker Authors * Yanyan Sheng View author publications You can also search for this author inPubMed Google Scholar * Chenlu Ye View author publications You can also

search for this author inPubMed Google Scholar * Yiping Sun View author publications You can also search for this author inPubMed Google Scholar * Diederich Bakker View author publications

You can also search for this author inPubMed Google Scholar CONTRIBUTIONS The author(s) make equal contributions to the paper. CORRESPONDING AUTHOR Correspondence to Yiping Sun. ETHICS

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