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This study exammes the cognitive, socioeconomic, and demographic antecedents of entrepreneurial intention in India, drawing on nationally representative data from the 2021 Global Entrepreneurship Monitor (GEM). By employing binary logistic regression analysis on responses from over 4,100 individuals, the research identifies key predictors of entrepreneurial intention with particular attention to developmental contexts, gender disparities, and inclusive enterprise potential. The findings reveal that self-efficacy, perceived opportunity, and the belief that entrepreneurship is a respected career significantly enhance entrepreneurial intention. Conversely, fear of failure, female gender, and advanced higher education are negatively associated with entrepreneurial propensity, underscoring persistent structural barriers. Subgroup analyses further indicate heterogeneity in predictors across income and gender groups, highlighting how organizational and cultural barriers disproportionately impact marginalized populations. These insights hold considerable implications for policy and practice, particularly in shaping inclusive entrepreneurship ecosystems in emerging economies. The study contributes to developmental entrepreneurship literature by situating intention as a precursor to microenterprise formation, entrepreneurial leadership, and the eventual success-failure boundary in the SME sector.
Introduction
Entrepreneurship is universally recognized as a potent catalyst for inclusive economic growth, employment generation, and grassroots innovation, particularly within the dynamic environments of emerging economies (Acs et al., 2008; Naude, 2010). In the Indian context (characterized by a youthful demographic dividend, persistent structural unemployment, and a heavy reliance on the informal sector) promoting entrepreneurial activity transcends basic economic strategy; it is a critical developmental necessity (Kantis et al., 2020). Small and Medium Enterprises (SMEs) form the backbone of this economic landscape, serving as primary engines for job creation and poverty alleviation. However, the translation of latent economic potential into actual enterprise formation depends fundamentally on entrepreneurial intention, the cognitive commitment and psychological readiness to start a business (Krueger et al., 2000).
While macro-economic factors provide the environment for business creation, the micro-foundations of entrepreneurship dictate who actually steps across the success-failure boundary. The decision to launch a venture is deeply rooted in individual cognitive determinants, such as an internal locus of control, innovativeness, and critically, self-efficacy (Zhao et al., 2005). Despite the introduction of major national policy initiatives designed to stimulate venture creation (such as Startup India, Stand-Up India, and the MUDRA financial schemes) stark disparities in entrepreneurial participation remain entrenched across the country. These initiatives largely focus on easing access to capital, often overlooking the deeper cognitive, behavioural, and structural barriers that inhibit entrepreneurial intent.
Consequently, marginalized segments of the population, including women, the unemployed, and low-income individuals, remain persistently underrepresented in the entrepreneurial ecosystem. This underrepresentation is not merely a product of financial exclusion but is heavily influenced by organizational and societal power dynamics. Deep-seated cultural norms dictate resource allocation, networking opportunities, and the perceived legitimacy of certain groups as business leaders. For instance, women frequently encounter unique structural barriers that erode their perceived self-efficacy and amplify their fear of failure, artificially suppressing their entrepreneurial intention despite possessing comparable recognition of business opportunities (Brush et al., 2009; Kelley et al., 2016).
While entrepreneurship is increasingly championed for its empowerment and social mobility potential, empirical research examining these micro-level antecedents within the Indian context remains fragmented. Existing literature often relies on small, localized samples that fail to capture the immense heterogeneity of the Indian population, or it focuses predominantly on established entrepreneurs rather than exploring intention as a developmental precursor. There is a distinct need for rigorous, large-scale empirical evidence that dissects how cognitive traits interact with demographic variables to drive or inhibit entrepreneurial behaviour.
This study addresses these critical gaps by investigating the cognitive, social, and demographic predictors of entrepreneurial intention in India using the globally recognized and methodologically robust Global Entrepreneurship Monitor (GEM) 2021 dataset. By employing binary logistic regression and targeted subgroup analyses, this research aims to unpack how psychological traits (e.g., self-efficacy and fear of failure), social drivers (e.g., societal recognition), and demographic factors (e.g., gender, education, and income) interact to influence early-stage intention. In doing so, this study responds to broader scholarly calls for context-specific, evidence-based approaches that promote equitable access to enterprise development in the Global South (Bruton et al., 2013; Welter, 2011).
The remainder of this paper is structured as follows: Section 2 reviews the extant literature on developmental entrepreneurship, cognitive determinants, and socioeconomic drivers, culminating in the identification of current research gaps. Section 3 details the data source, variable definitions, and analytical methodology employed. Section 4 presents the empirical results of the logistic regression and subgroup analyses. Finally, Section 5 discusses the implications of these findings for ecosystem builders and policymakers, followed by the conclusion and directions for future research in Section 6.
2. Literature Review
2.1 Entrepreneurial Intention as a Developmental Construct: Entrepreneurial intention (El) is widely acknowledged as the immediate antecedent to entrepreneurial behaviour (Krueger et al., 2000). Grounded in cognitive psychology, models such as Ajzen's (1991) Theory of Planned Behaviour and Shapero and Sokol's (1982) Entrepreneurial Event Model assert that individuals are more likely to start businesses when they believe they can succeed, perceive positive outcomes, and feel that external constraints are manageable. In the developmental entrepreneurship paradigm, intention acquires an added dimension as a proxy for economic empowerment and social mobility among underrepresented groups (Bruton et al., 2013). Recent literature validates that GEM reports provide a robust theoretical foundation for designing causal models that predict such entrepreneurial behaviour at regional and national levels (Martinez-Gonzalez et al., 2022).
2.2 Cognitive Determinants and Entrepreneurial Leadership: Self-efficacy has consistently emerged as the strongest positive predictor of entrepreneurial intention (Linan& Chen, 2009; Zhao et al., 2005). Conversely, fear of failure generally suppresses entrepreneurial behaviour by heightening perceived risk (Cacciotti & Hayton, 2015). In the context of developmental entrepreneurship, these cognitive variables serve as the foundational psychological scaffolding for later-stage entrepreneurial leadership. Intention and self-efficacy do not operate in isolation; they function alongside essential traits such as innovativeness and an internal locus of control. Together, these cognitive drivers act as critical predictors that ultimately dictate the success-failure boundary within India's highly competitive SME sector. When individuals lack self-efficacy or harbour a deep fear of failure, their capacity to enact change management and navigate the complex power dynamics inherent 111 establishing a new venture 1s severely constrained.
2.3 Socioeconomic and Demographic Drivers: Entrepreneurial intention is heavily influenced by socioeconomic variables, with gender playing a deeply entrenched role. Men generally express higher EI than women across cultures due to social role expectations, limited access to resources, and institutional barriers (Brush et al., 2009; Manolova et al., 2012). Research indicates that trait-like tendencies and personality dimensions such as risk-taking vary significantly across genders, complicating intention models (Zisser et al., 2019). However, behavioural intention models that factor in cultural and educational environments remain robust tools for evaluating these disparities (Contreras-Barraza et al., 2021).
Education presents a nuanced picture; while it equips individuals with skills, it can also lead to a preference for formal employment over self-employment (Fayolle &Gailly, 2015). Income also shows a context-dependent relationship, with lower-income individuals often viewing entrepreneurship as a necessity-driven survival mechanism, while higher-income groups approach it as an opportunity-driven choice (Kolvereid, 1996; Xavier et al., 2013).
3. Data and Methodology
3.1 Data Source and Variable Definitions: This study uses data from the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) for India, collected in 2021. The GEM dataset is the world's most comprehensive tool for analysing entrepreneurial attitudes (Bosma et al., 2020). The nationally representative sample comprises 4,107 individuals aged 18 to 64.
The dependent variable, Entrepreneurial Intention, was measured via a binary response regarding the intent to start a business within three years (1 = yes, 0 = no). Independent variables included binary cognitive measures (Self-efficacy, Fear of failure, Perceived opportunity, Entrepreneurship as a good career) and categorical demographic measures (Gender, Age, Educational attainment, Household income, and Employment status).
3.2 Analytical Strategy: The study employs binary logistic regression to assess the likelihood of an individual expressing entrepreneurial intention. The logistic regression model is formally expressed as:
Where Pis the probability of having entrepreneurial intention, Xi represents the explanatory variables, and βi denotes their respective coefficients. Separate subgroup models were estimated to explore gender and income disparities, reinforcing the developmental framing of the research.
4. Results
4.1 Descriptive Profile of Respondents: The demographic characteristics of the 4,107 respondents are presented in Table 1. The sample demonstrated near gender parity, comprising 52.0% male and 48.0% female respondents. The largest age cohort was between 25 and 34 years old (32.0%), followed by the 35-44 age bracket (27.0%). Regarding educational attainment, the highest proportion of respondents held secondary-level qualifications (39.0%), while 24.7% had achieved graduate or postgraduate degrees. Income distribution aligned with broader national patterns, with a significant segment (46.0%) falling into the low-income category. Approximately 27.0% of the total sample expressed entrepreneurial intention.
Table 1. Descriptive Statistics of Sample Respondents
Variable |
Category |
Frequency (n) |
Percentage(%) |
Gender |
Male |
2,136 |
52.0 |
|
Female |
1,971 |
48.0 |
Age Group |
18-24 |
779 |
19.0 |
|
25-34 |
1,314 |
32.0 |
|
35-44 |
1,110 |
27.0 |
|
45-54 |
658 |
16.0 |
|
55-64 |
246 |
6.0 |
Education Level |
Primary or less |
822 |
20.0 |
|
Secondary |
1,602 |
39.0 |
|
Post-secondary |
670 |
16.3 |
|
Graduate and above |
1,013 |
24.7 |
Household Income |
Low |
1,889 |
46.0 |
|
Middle |
1,456 |
35.5 |
|
High |
762 |
18.5 |
Employment Status |
Employed |
2,079 |
50.6 |
|
Unemployed |
594 |
14.5 |
|
Student |
451 |
11.0 |
|
Homemaker |
640 |
15.6 |
|
Retired |
343 |
8.3 |
Entrepreneurial Intention |
Yes |
1,109 |
27.0 |
|
No |
2,998 |
73.0 |
Note. Percentages are rounded to the nearest decimal.
Source: Authors' own work
4.2 Logistic Regression Analysis: Full Model A binary logistic regression was conducted to identify the cognitive, socioeconomic, and demographic predictors of entrepreneurial intention (Table 2). The full model was statistically significant (x2 = 248.67, p < 0.001) and explained approximately 21.9% of the variance in entrepreneurial intention (Nagelkerke R2 = 0.219).
Self-efficacy emerged as the strongest positive predictor, significantly increasing the odds of expressing entrepreneurial intention (OR = 2.14, p< 0.001). Perceived opportunity also demonstrated a substantial positive effect (OR = 1.78, p< 0.01). Conversely, fear of failure significantly reduced the odds of entrepreneurial intention (OR= 0.71, p< 0.05). The perception of entrepreneurship as a respected career exhibited a marginal positive association (OR= 1.26, p< 0.10).
Demographically, male gender was positively associated with intention (OR = 1.59, p< 0.01), highlighting a significant gap where female respondents had lower odds of expressing intent (OR=0.63). Interestingly, advanced educational attainment exerted a negative effect; individuals with post-secondary (OR= 0.73, p< 0.05) and graduate-level education or above (OR= 0.61, p< 0.01) were significantly less likely to express entrepreneurial intent compared to those with primary education or less. Household income, age variations, and employment status did not yield statistically significant results in the full aggregate model.
Table 2. Logistic Regression Results Predicting Entrepreneurial Intention
Predictor Variable |
B |
SE |
Odds Ratio (Exp(B)) |
p-value |
Self-Efficacy |
0.761 |
0.112 |
2.14 |
< .001 *** |
Perceived Opportunity |
0.578 |
0.149 |
1.78 |
< .01 ** |
Fear of Failure |
-0.343 |
0.138 |
0.71 |
< .05 * |
Entrepreneurship as Respected Career |
0.233 |
0.121 |
1.26 |
< .10 t |
Gender (1 = Male) |
0.461 |
0.131 |
1.59 |
< .01 ** |
Age (25-34) |
0.081 |
0.112 |
1.08 |
n.s. |
Age (35-44) |
-0.052 |
0.124 |
0.95 |
n.s. |
Age (45-54) |
-0.218 |
0.138 |
0.80 |
n.s. |
Age (55-64) |
-0.344 |
0.166 |
0.71 |
n.s. |
Education (Secondary) |
-0.134 |
0.115 |
0.87 |
n.s. |
Education (Post-secondary) |
-0.309 |
0.142 |
0.73 |
< .05 * |
Education (Graduate & above) |
-0.486 |
0.163 |
0.61 |
< .01 ** |
Note: Reference categories: Gender = Female; Age = 18-24; Education = Primary or less; Income = Low; Employment = Employed. Model x2(df=17) = 248.67, p < . 001; Nagelkerke R2=0.219. Significance levels: *p < .05; p < .01; ***p < . 001; fp < . 10; n.s. = not significant.
Source: Authors' own work.
4.3 Subgroup Analysis: Gender and Income Effects: To evaluate heterogeneity within the dataset, the sample was disaggregated to examine specific predictive differences across gender and income demographics.
Model A: Gender-Specific Differences: When estimating the model strictly for women, self-efficacy and perceived opportunity remained significant positive predictors of entrepreneurial intention. However, societal value variables (such as entrepreneurship as a respected career) exhibited comparatively weaker effects. Crucially, fear of failure demonstrated a substantially stronger negative impact on women (OR = 0.59) than it did on men (OR = 0.79), indicating disproportionate psychological or structural barriers.
Model B: Income-Specific Differences: The analysis disaggregated by household income revealed divergent drivers of intention. Among low-income respondents, self-efficacy retained its robust effect, whereas perceived opportunity became a less powerful predictor. Conversely, among middle and high-income cohorts, both opportunity perception and the perceived societal legitimacy of entrepreneurship yielded significantly higher predictive power.
5. Discussion
The empirical findings of this study provide a nuanced understanding of the micro-level antecedents that drive entrepreneurial intention in India. By analysing the GEM 2021 dataset through a developmental lens, this research moves beyond aggregated metrics to illuminate how cognitive readiness interacts with structural and demographic realities.
5.1 Cognitive Drivers as the Foundation of Entrepreneurial Leadership: The results confirm that self-efficacy and perceived opportunity are the paramount cognitive catalysts for entrepreneurial intention. Within the dynamic and resource-constrained Indian SME sector, these psychological traits represent far more than a simple desire to start a business; they signify a robust internal locus of control. High self-efficacy equips nascent founders with the psychological scaffolding required to exhibit entrepreneurial leadership and drive innovativeness. Individuals who strongly believe in their capabilities are better positioned to aggressively pursue opportunities and push their ventures across the success-failure boundary.
Conversely, the significant inhibitory effect of the fear of failure highlights a critical cognitive vulnerability. Fear of failure restricts risk-taking and stifles the innovativeness necessary for venture creation (Cacciotti & Hayton, 2015). This suggests that cognitive interventions must prioritize building resilience alongside technical business acumen.
5.2 Gendered Disparities: Institutional Culture and Power Dynamics: A profound insight from the subgroup analysis is the persistent gender disparity. Women not only exhibited lower overall entrepreneurial intention but were also disproportionately constrained by the fear of failure compared to their male counterparts. This finding cannot be attributed solely to individual psychological differences; rather, it reflects the restrictive organizational cultures and entrenched societal power bases within the broader ecosystem.
Women frequently navigate environments where structural power dynamics limit their access to critical networks, mentorship, and venture capital (Brush et al., 2009). The magnified fear of failure among female respondents is a rational response to an ecosystem that penalizes female failure more harshly than male failure. Fostering female entrepreneurship, therefore, requires dismantling these structural power imbalances and promoting an inclusive culture that legitimizes women as capable business leaders.
5.3 The Education Paradox and Ecosystem Mismatch: Interestingly, the data reveals an inverse relationship between advanced educational attainment and entrepreneurial intention. Respondents with post-secondary and graduate-level education were significantly less likely to express intent compared to those with primary education. This "education paradox" suggests a fundamental mismatch between formal higher education outputs and the realities of the entrepreneurial landscape.
In India, higher education often socializes individuals to seek secure, formal employment, framing entrepreneurship as an unstructured, high-risk alternative (Fayolle &Gailly, 2015). To counteract this, universities must embed experiential learning, change management frameworks, and failure-positive environments into their curricula, transforming graduates from passive job seekers into proactive job creators.
5.4 Income Disparities and Navigating Enterprise Distress: The income-disaggregated analysis illuminates the stark dichotomy between necessity-driven and opportunity-driven entrepreneurship. Low-income individuals maintain high self-efficacy but struggle to perceive viable market opportunities, indicating that structural environmental constraints suppress their action even when capability is present. For this demographic, entrepreneurship is heavily necessity-driven, a survival mechanism rather than a strategic pursuit for wealth accumulation (Naude, 2010).
These nascent, necessity-driven ventures often face high mortality rates. Because they operate at the margins of the economy, they are highly susceptible to business sickness. Supporting this demographic requires policies that do more than provide initial micro-loans; they must offer ongoing strategic advisory to help founders manage business distress, execute turnaround strategies, and ultimately manage growth effectively.
5.5 Implications for Policy and Practice: The synthesis of these findings yields critical implications for ecosystem builders. Current developmental entrepreneurship policies, such as the MUDRA scheme, rely heavily on easing financial access. However, this study proves that financial capital is insufficient without cognitive capital. Interventions must be restructured to include targeted capability development, localized mentorship, and initiatives that actively shift the cultural narrative around failure. Success metrics for these policies must evolve to measure not only venture profitability but also improvements in entrepreneurial self-efficacy, equitable participation, and local community resilience.
6. Conclusion and Limitations
6.1 Conclusion: This study advances the developmental entrepreneurship literature by empirically analysing the cognitive, socioeconomic, and demographic antecedents of entrepreneurial intention in India. Leveraging a nationally representative sample from the GEM 2021 Adult Population Survey, the research demonstrates that entrepreneurial intention is deeply mediated by an interplay of psychological readiness and structural accessibility. Self-efficacy and perceived opportunity emerge as the most potent cognitive drivers, serving as the psychological bedrock for early-stage enterprise formation. Conversely, the fear of failure acts as a significant deterrent, restricting the very innovativeness and proactive behaviours necessary to cross the threshold into active business ownership.
Crucially, the findings reveal that entrepreneurial potential is not uniformly distributed. Persistent gender disparities highlight that women face magnified psychological and organizational barriers, resulting in lower entrepreneurial intention despite comparable opportunity recognition. Similarly, the income-disaggregated analysis underscores that while low-income populations possess the internal drive, their lack of structural support often confines them to necessity-driven, vulnerable microenterprises.
Entrepreneurial intention is merely the initial catalyst; translating this intent into sustainable microenterprise formation requires individuals to actively navigate the success-failure boundary inherent in India's SME sector. The initial cognitive scaffolding identified in this study (particularly self-efficacy) ultimately evolves into the entrepreneurial leadership and internal locus of control required to sustain a venture. By centring developmental concerns, this research advocates for a paradigm shift in entrepreneurship policy. Interventions must move beyond mere capital infusion to embrace a holistic ecosystem approach that actively builds cognitive resilience, dismantles entrenched structural power dynamics, and fosters an inclusive culture of venture creation.
6.2 Limitations and Future Research Directions: Despite the robust empirical insights generated by this research, the study is subject to several limitations that offer valuable avenues for future scholarly inquiry. First, the utilization of the GEM 2021 dataset introduces cross-sectional constraints, which inherently restrict the ability to draw definitive causal inferences or track the longitudinal evolution from initial intention to active startup behaviour over time. Because entrepreneurial intentions are dynamic, future research employing longitudinal designs is essential to trace how these early cognitive intentions materialize into concrete firm creation across different socio-economic segments in India.
Furthermore, because this study predominantly focuses on the pre-startup phase, a significant gap remains regarding the long-term lifecycle and survival of the established microenterprises. Future research should explicitly connect early-stage cognitive predictors to post-formation performance metrics. Specifically, investigating how a founder's initial self-efficacy and psychological readiness influence their subsequent capability in managing growth, navigating complex organizational change, or executing strategic turnaround strategies when facing business distress would significantly bridge the theoretical gap between entrepreneurial intention and ultimate firm survival within India's highly competitive SME sector.
Finally, the reliance on self-reported psychological measures within the GEM survey may introduce social desirability bias, particularly regarding sensitive self-perceptions like the fear of failure. Moreover, while the sample is nationally representative, it may mask critical intra-country heterogeneity given India's profound regional, linguistic, and cultural diversity. Future predictive models exploring the SME success-failure boundary would benefit from utilizing state-level entrepreneurial indices and integrating critical omitted variables (such as digital access, family entrepreneurial background, and formal institutional trust) thereby providing a more localized, context-specific assessment of entrepreneurial viability in the Global South.
References