To read this content please select one of the options below:

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

Tahseen Anwer Arshi (School of Business, American University of Ras Al Khaimah, Ras al Khaimah, United Arab Emirates)
Sardar Islam (Victoria University, Melbourne, Australia)
Nirmal Gunupudi (Faculty of Business, Majan University College, Ruwi, Oman)

International Journal of Entrepreneurial Behavior & Research

ISSN: 1355-2554

Article publication date: 10 August 2021

Issue publication date: 11 October 2021




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. Furthermore, the study posits that entrepreneurial stressors and the resultant strain affect entrepreneurial behaviour.


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 sample consists of 415 entrepreneurs from three countries: India, Oman and United Arab Emirates. 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. Finally, the finding suggests that psychological capital moderates the adverse impact of stressors on entrepreneurial strain over time.


This study contributes to the CHF by demonstrating the value of hindrance stressors in studying entrepreneurial strain and providing new insights into entrepreneurial coping. It argues that entrepreneurs cope effectively against hindrance stressors by utilising psychological capital. Furthermore, the study provides more evidence about the causal, reversed and reciprocal relationships between stressors and entrepreneurial strain through a cross-lagged analysis. 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.



Arshi, T.A., Islam, S. and Gunupudi, N. (2021), "Predicting the effect of entrepreneurial stressors and resultant strain on entrepreneurial behaviour: an SEM-based machine-learning approach", International Journal of Entrepreneurial Behavior & Research, Vol. 27 No. 7, pp. 1819-1848.



Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

Related articles