Empowering Women Entrepreneurs in Pakistan: Unraveling the Mediating Effects of Culture and Gender Bias on the Role of Leadership Skills and Financial Inclusion


  • Zohaib Naeem University of Central Punjab


Family support, Financial inclusion, Leadership skills, Culture, Gender biasness, Success of women entrepreneurship


The study highlights the imperative for a comprehensive comprehension of the socio-cultural and economic factors that impact women's entrepreneurship in Pakistan. This establishes a solid foundation for the development of enhanced policies and practices aimed at nurturing their achievements. This study delves into the realm of women's entrepreneurship in Pakistan, with a particular emphasis on the impact of familial backing, leadership acumen, financial inclusivity, cultural subtleties, and gender prejudices on business efficacy. Drawing upon feminist theory, this analysis underscores the advantageous consequences that arise from embracing a female perspective within the realm of entrepreneurship. The present study delves deeper into the intricate dynamics of cultural and gender biases in mediating these interactions. Surveys were employed to gather data from a cohort of 311 female entrepreneurs. The findings of this study reveal that various factors, including but not limited to family support, financial inclusion, and leadership skills, exhibit a substantial association with the success of women entrepreneurs. In the realm of women's entrepreneurship, the intricate interplay between culture and gender bias assumes a mediating role, as elucidated by the comprehensive study conducted by Andrew Hayes. This study offers valuable insights into the subtle, yet impactful indirect effects exerted by these multifaceted factors. All variables exhibited statistically significant effects, implying that these factors, in conjunction with cultural and gender considerations, possess the potential to augment the triumph of women entrepreneurs. Further investigations may encompass a more extensive array of variables and augmented sample sizes.