Gender diversity, education diversity and big data analytics investments - A test of moderation

Abstract

Purpose: This study examines the influence of education diversity in moderating the association between gender diversity and big data analytics investments. Design/methodology/approach: A multimethod approach is employed where the data (161 responses) are drawn from two quantitative data sources. The primary data are obtained from the questionnaire survey, and the secondary data are gathered from the annual reports. Multiple linear regression analysis is used as the analytical method. Findings: This study finds that gender diversity is positively and significantly associated with big data analytics investments. Next, the association between gender diversity and big data analytics investments is negatively moderated by education diversity. The association between gender diversity and big data analytics investments is weakened when the education diversity is higher. Originality: This study is one of the first to examine the intervening processes of how gender diversity affects big data analytics investments using education diversity as a moderating variable. This study is novel in its approach to providing empirical evidence and examining the moderating effect of education diversity. Practical implications: This study shows that gender diversity facilitates big data analytics investments. The findings help policymakers encourage the female director’s role in strategic decision-making. The negative moderating effect of education diversity on gender diversity and big data analytics investments association implies that firms may face challenges accessing resources. The findings help firms promote open debates and effective communication in strategic decision-making to leverage education diversity.