#4745. Predicting the effect of entrepreneurial stressors and resultant strain on entrepreneurial behaviour: an SEM-based machine-learning approach

July 2026publication date
Proposal available till 26-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Business, Management and Accounting (miscellaneous);
Business and International Management;
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Abstract:
Considerable evidence suggests that although they overlap, entrepreneurial and employee stressors have different causal antecedents and outcomes. However, limited empirical data explain how entrepreneurial traits, work and life drive entrepreneurial stressors and create entrepreneurial strain (commonly called entrepreneurial stress). Drawing on the challenge-hindrance framework (CHF), this paper hypothesises the causal effect of hindrance stressors on entrepreneurial strain. The study adopts an SEM-based machine-learning approach. Cross-lagged path models using SEM are used to analyse the data and train the machine-learning algorithm for cross-validation and generalisation. The entrepreneurs completed two self-report surveys over 12 months. The results show that hindrances to personal and professional goal achievement, demand-capability gap and contradictions between aspiration and reality, primarily due to unique resource constraints, characterise entrepreneurial stressors leading to entrepreneurial strain. The study further asserts that entrepreneurial strain is a significant predictor of entrepreneurial behaviour, significantly affecting innovativeness behaviour.This study is one of the first to evaluate the impact of entrepreneurial strain on entrepreneurial behaviour. Using a machine-learning approach is a new possibility for using machine learning for SEM and entrepreneurial strain.
Keywords:
Cross-lagged panel study; Entrepreneurial behaviour; Entrepreneurial strain; Hindrance stressors; Machine learning; Psychological capital; Structural equation modelling

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