Volume 12, Issue 2 (12-2025)
J Entrepreneurial Strategies Agric 2025, 12(2): 15-30 |
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Mohammadi Khyareh M. (2025). Cognitive, Social, and Demographic Drivers of Agricultural Entrepreneurship: Evidence from Resource-Based Economies Using GEM Data. J Entrepreneurial Strategies Agric. 12(2), 15-30. doi:10.61882/jea.2025.427
URL: http://jea.sanru.ac.ir/article-1-427-en.html
URL: http://jea.sanru.ac.ir/article-1-427-en.html
Department of Economics, Gonbad Kavous University, Gonbad Kavous, Iran
Abstract: (356 Views)
Background: Agricultural entrepreneurship represents a cornerstone of sustainable economic transformation in resource-based economies (RBEs) and factor-driven economies (FDEs). Beyond its direct contribution to agricultural productivity, it plays a pivotal role in promoting rural development, food security, income diversification, and innovation diffusion. In RBEs, agricultural entrepreneurship often emerges as both a necessity and an opportunity, shaped by the interplay between abundant natural resources, labor availability, and institutional weaknesses. However, despite its critical relevance to sustainable development and structural transformation, empirical evidence on the micro-level determinants of agricultural entrepreneurship within such contexts remains scarce. The global entrepreneurship literature has predominantly focused on advanced or efficiency-driven economies, overlooking the cognitive, social, and demographic foundations of entrepreneurial entry in agriculture-dependent regions.
This study addresses this gap by developing and empirically testing an integrated analytical framework that examines how cognitive, social, and demographic factors jointly shape individuals’ decisions to engage in nascent agricultural entrepreneurship. Specifically, it focuses on three cognitive determinants—opportunity perception, self-efficacy, and fear of failure—and a key social variable, networking, while controlling for age, gender, education, and household income. By so doing, the research contributes to a more nuanced understanding of how individual-level attributes interact with structural and institutional features in RBEs to influence entrepreneurial activity. The study also introduces a comparative, cross-country perspective by utilizing harmonized data from the Global Entrepreneurship Monitor (GEM), enabling a broader understanding of heterogeneity across emerging and resource-based settings.
Methods: The analysis employs a quantitative, cross-sectional research design, using pooled data from the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) spanning 2018–2022. The GEM dataset is particularly suited for this inquiry, as it provides standardized measures of entrepreneurial behavior, perceptions, and contextual factors across countries, allowing for robust cross-national comparison. The final sample comprises 1,200 working-age individuals (18–64 years) from five selected resource-based economies: India, Kazakhstan, Iran, Vietnam, and Madagascar. These countries were chosen based on their shared characteristics of natural resource dependency, transitional institutional frameworks, and varying levels of agricultural reliance.
The dependent variable, entry into nascent agricultural entrepreneurship, is operationalized as a binary outcome (1 = engaged in starting an agricultural venture, 0 = otherwise). Independent variables include three cognitive factors—opportunity perception, self-efficacy, and fear of failure—and one measure of social capital, networking, all coded as binary indicators following GEM standards. Demographic control variables comprise age (continuous), gender (binary), education level (four dummy variables: primary, secondary, post-secondary, and university), and household income level (two dummy variables based on national income tertiles).
A binary logistic regression (Logit) model is employed to estimate the probability of entrepreneurial entry. Diagnostic tests, including the Hosmer-Lemeshow goodness-of-fit test and Variance Inflation Factor (VIF) analysis, confirm model adequacy and the absence of multicollinearity. Competitive hypotheses are tested to evaluate the relative strength of cognitive versus social factors, enabling an assessment of whether opportunity recognition or social embeddedness serves as the dominant driver of entrepreneurial engagement in agricultural contexts.
Results: The empirical results underscore the primacy of cognitive drivers in shaping agricultural entrepreneurial entry. Among these, opportunity perception emerged as the most influential factor, surpassing the effect of self-efficacy. This suggests that, in resource-based economies, identifying viable agricultural opportunities constitutes a more decisive trigger for entrepreneurship than the belief in one’s entrepreneurial competence. The finding aligns with the notion that, in environments characterized by institutional fragility and limited access to capital, cognitive alertness and opportunity recognition are critical precursors to action.
Interestingly, fear of failure exhibited no statistically significant relationship with entrepreneurial entry, implying that necessity-driven motives—often rooted in employment scarcity and income insecurity—mitigate the deterrent effect of risk aversion. The analysis also reveals that while networking positively influences the probability of starting an agricultural venture, its effect size remains lower than that of the key cognitive variables. This outcome contradicts the hypothesis that social capital supersedes cognitive drivers in resource-constrained contexts, suggesting that while networks facilitate access to information and informal resources, individual cognitive orientation remains the critical initiating factor.
Among demographic controls, age negatively affects entrepreneurial likelihood, indicating that younger individuals are more inclined toward agricultural start-ups. Gender exerts a significant influence, with males being 1.73 times more likely to engage in agricultural entrepreneurship than females, reflecting enduring structural and cultural barriers to female participation in agribusiness. Education exhibits a positive and significant effect, highlighting that higher educational attainment—particularly university-level education—enhances the capacity to identify and exploit agricultural opportunities. Conversely, household income level does not significantly affect the probability of entrepreneurial entry, suggesting that financial constraints are relatively uniform across the observed sample or that informal financing mechanisms partly offset income disparities.
Conclusion: The findings of this research provide novel empirical evidence on the determinants of agricultural entrepreneurship in resource-based economies. They reveal that entrepreneurial entry in agriculture is predominantly driven by cognitive processes—particularly opportunity recognition—rather than by social embeddedness or fear-based behavioral constraints. This insight challenges conventional views emphasizing the primacy of social capital in entrepreneurship and highlights the importance of cognitive capabilities as the central axis of entrepreneurial decision-making in low- and middle-income settings.
Theoretically, this study advances the literature by illustrating the context dependency of entrepreneurial drivers. It demonstrates that the relative influence of cognitive, social, and demographic factors is contingent upon broader structural features, such as institutional quality, market maturity, and the nature of economic incentives. The integration of cognitive and social perspectives into a unified model offers a more holistic understanding of entrepreneurial behavior in developing and resource-based contexts.
From a policy standpoint, the results carry several important implications. First, strengthening market information systems and digital infrastructure can enhance opportunity recognition by improving access to timely, relevant data on agricultural markets and technologies. Second, embedding entrepreneurship education within agricultural training programs can boost self-efficacy and technical competence, thereby nurturing a more proactive and innovative entrepreneurial culture. Third, targeted interventions are required to overcome gender-specific barriers, including limited access to credit, land, and training opportunities for women. Finally, fostering youth-oriented entrepreneurship initiatives can help channel the dynamism of younger populations into sustainable agribusiness ventures.
This study addresses this gap by developing and empirically testing an integrated analytical framework that examines how cognitive, social, and demographic factors jointly shape individuals’ decisions to engage in nascent agricultural entrepreneurship. Specifically, it focuses on three cognitive determinants—opportunity perception, self-efficacy, and fear of failure—and a key social variable, networking, while controlling for age, gender, education, and household income. By so doing, the research contributes to a more nuanced understanding of how individual-level attributes interact with structural and institutional features in RBEs to influence entrepreneurial activity. The study also introduces a comparative, cross-country perspective by utilizing harmonized data from the Global Entrepreneurship Monitor (GEM), enabling a broader understanding of heterogeneity across emerging and resource-based settings.
Methods: The analysis employs a quantitative, cross-sectional research design, using pooled data from the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) spanning 2018–2022. The GEM dataset is particularly suited for this inquiry, as it provides standardized measures of entrepreneurial behavior, perceptions, and contextual factors across countries, allowing for robust cross-national comparison. The final sample comprises 1,200 working-age individuals (18–64 years) from five selected resource-based economies: India, Kazakhstan, Iran, Vietnam, and Madagascar. These countries were chosen based on their shared characteristics of natural resource dependency, transitional institutional frameworks, and varying levels of agricultural reliance.
The dependent variable, entry into nascent agricultural entrepreneurship, is operationalized as a binary outcome (1 = engaged in starting an agricultural venture, 0 = otherwise). Independent variables include three cognitive factors—opportunity perception, self-efficacy, and fear of failure—and one measure of social capital, networking, all coded as binary indicators following GEM standards. Demographic control variables comprise age (continuous), gender (binary), education level (four dummy variables: primary, secondary, post-secondary, and university), and household income level (two dummy variables based on national income tertiles).
A binary logistic regression (Logit) model is employed to estimate the probability of entrepreneurial entry. Diagnostic tests, including the Hosmer-Lemeshow goodness-of-fit test and Variance Inflation Factor (VIF) analysis, confirm model adequacy and the absence of multicollinearity. Competitive hypotheses are tested to evaluate the relative strength of cognitive versus social factors, enabling an assessment of whether opportunity recognition or social embeddedness serves as the dominant driver of entrepreneurial engagement in agricultural contexts.
Results: The empirical results underscore the primacy of cognitive drivers in shaping agricultural entrepreneurial entry. Among these, opportunity perception emerged as the most influential factor, surpassing the effect of self-efficacy. This suggests that, in resource-based economies, identifying viable agricultural opportunities constitutes a more decisive trigger for entrepreneurship than the belief in one’s entrepreneurial competence. The finding aligns with the notion that, in environments characterized by institutional fragility and limited access to capital, cognitive alertness and opportunity recognition are critical precursors to action.
Interestingly, fear of failure exhibited no statistically significant relationship with entrepreneurial entry, implying that necessity-driven motives—often rooted in employment scarcity and income insecurity—mitigate the deterrent effect of risk aversion. The analysis also reveals that while networking positively influences the probability of starting an agricultural venture, its effect size remains lower than that of the key cognitive variables. This outcome contradicts the hypothesis that social capital supersedes cognitive drivers in resource-constrained contexts, suggesting that while networks facilitate access to information and informal resources, individual cognitive orientation remains the critical initiating factor.
Among demographic controls, age negatively affects entrepreneurial likelihood, indicating that younger individuals are more inclined toward agricultural start-ups. Gender exerts a significant influence, with males being 1.73 times more likely to engage in agricultural entrepreneurship than females, reflecting enduring structural and cultural barriers to female participation in agribusiness. Education exhibits a positive and significant effect, highlighting that higher educational attainment—particularly university-level education—enhances the capacity to identify and exploit agricultural opportunities. Conversely, household income level does not significantly affect the probability of entrepreneurial entry, suggesting that financial constraints are relatively uniform across the observed sample or that informal financing mechanisms partly offset income disparities.
Conclusion: The findings of this research provide novel empirical evidence on the determinants of agricultural entrepreneurship in resource-based economies. They reveal that entrepreneurial entry in agriculture is predominantly driven by cognitive processes—particularly opportunity recognition—rather than by social embeddedness or fear-based behavioral constraints. This insight challenges conventional views emphasizing the primacy of social capital in entrepreneurship and highlights the importance of cognitive capabilities as the central axis of entrepreneurial decision-making in low- and middle-income settings.
Theoretically, this study advances the literature by illustrating the context dependency of entrepreneurial drivers. It demonstrates that the relative influence of cognitive, social, and demographic factors is contingent upon broader structural features, such as institutional quality, market maturity, and the nature of economic incentives. The integration of cognitive and social perspectives into a unified model offers a more holistic understanding of entrepreneurial behavior in developing and resource-based contexts.
From a policy standpoint, the results carry several important implications. First, strengthening market information systems and digital infrastructure can enhance opportunity recognition by improving access to timely, relevant data on agricultural markets and technologies. Second, embedding entrepreneurship education within agricultural training programs can boost self-efficacy and technical competence, thereby nurturing a more proactive and innovative entrepreneurial culture. Third, targeted interventions are required to overcome gender-specific barriers, including limited access to credit, land, and training opportunities for women. Finally, fostering youth-oriented entrepreneurship initiatives can help channel the dynamism of younger populations into sustainable agribusiness ventures.
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