Transitioning from subjectivity to objectivity: empowering women in academic leadership

Purpose The study aims to provide a comprehensive understanding of the existing literature on women’s leadership in academia by identifying the existing challenges for their underrepresentation, and proposing a new-age leadership interventions to address the inherent systemic biases and develop foster an equitable academic climate. Design/methodology/approach The study employed bibliometric analysis to map the literature by investigating publication and geographical trends. Techniques like citation, co-citation, bibliographic coupling and co-word analysis identified seminal research and emerging themes, providing insights into research developments and facilitating identification of avenues for future research. Findings Our study highlights how social, organizational and individual barriers disadvantage women academic leaders. Existing enablers for women in leadership, like mentorship, leadership development and family friendly policies, focus on bringing change within the prevailing academic culture, reinforcing the notion “women need support”, overlooking the influence of systemic barriers. Such interventions are often ineffective in bringing sustainable change. We propose integrating AI/machine learning (ML) technologies in leadership selection to reduce bias arising from subjectivity. Research limitations/implications This study contributes to the discourse on gender inequality in academic leadership by offering a robust understanding of the research topic and informing avenues for future research. Practical implications Policymakers and higher education institutions can use the findings of the study to aid the formulation of policies, initiatives and institutional procedures to mitigate the prevalent gender bias in academia and cultivate an inclusive culture for growth of women. Originality/value The paper analyses women’s under-representation as academic leaders and proposes a novel data-driven intervention using gamification, AI and ML, aiming to reshape gender dynamics in academic leadership.