How Women’s Leadership Shapes Firm Performance in the Indian IT Services Sector: A Quantitative Study of Women Leaders

Abstract

 This research work examines how women’s leadership is associated with firm performance in the Indian information technology and IT-enabled services (IT/ITES) context. Using a research survey of 50 women leaders, the study operationalizes five constructs: organizational support, women leadership effectiveness, leadership barriers, organizational effectiveness, and firm performance. Item responses were recorded on a five-point Likert scale, with barrier items reverse coded so that higher construct scores represent fewer perceived barriers. Analysis was conducted in Microsoft Excel using formula-based descriptive statistics, Cronbach’s alpha, Pearson correlation, and ordinary least-squares regression. The results show that women leadership effectiveness is strongly and positively associated with firm performance (r = 0.714, p < .001), explaining approximately 51.0% of the variance in a focal simple regression. Organizational support is also positively associated with firm performance (r = 0.684, p < .001), while organizational effectiveness has a strong positive relationship with firm performance (r = 0.752, p < .001). Leadership barriers show the strongest bivariate relationship in absolute terms and are negatively associated with firm performance (r = -0.824, p < .001). In the simultaneous four-predictor model, the constructs explain approximately 69.6% of the variance in firm performance (adjusted R² = 0.669); however, only leadership barriers makes a statistically significant unique contribution (B = -0.720, p = .001). The findings suggest that the performance value of women’s leadership is closely connected with the organizational conditions surrounding leaders, rather than with leadership presence alone. Given the small, non-probability, self-reported and cross-sectional data, the findings should be interpreted as evidence of association rather than causality and should be replicated with larger, longitudinal and multi-source data.