Study on Stress Level and Coping Strategies among Women Entrepreneurs through Machine Learning Algorithms

Abstract

Women entrepreneurs play an inevitable role in the economic development of their communities, yet they often face unique challenges that can contribute to increased stress levels. The pandemic turned business activities upside down, impacting global negative growth and causing stress among entrepreneurs. Understanding women's sources of stress among these groups and finding effective coping mechanisms could provide insights for support programs and policy development, thereby helping manage their overall well-being. This research aims to investigate the stress levels experienced by women entrepreneurs in Vellore District, Tamil Nadu, India, and the coping strategies they employ to manage this stress. The results exhibit the highest mean score, role overload, 55.84 (±6.23) on the stress scale among the entrepreneurs and 58.60 (±6.56) in the Acceptance category of coping strategies. Additionally, it indicates that stress and coping strategies could explain around 1% of the variance in health (R2 = 0.010). Based on the p-value, the results revealed the coping strategies as predictors of general health (P = 0.001, beta = 0.093). The results highlight the significance of coping strategies for predicting mental health among women entrepreneurs. Machine learning models trained with the collected information revealed the important factors determining the stress conditions. The best feature subset is selected using the minimum-redundancy maximum-relevance (mRMR) algorithm. The random forest algorithm attained the best accuracy with a higher accuracy rate (93.74%), precision (94.51%), and recall (93.91%), outperforming state-of-the-art methods. The need to prevent stressful effects is an urge; support programs and policies must be developed for entrepreneurial stress.