The Role of Environmental Heterogeneity in the Relationship Between Big Data Analytical Capability and Competitive Talent Acquisitions

Abstract

This research aims to establish a clear and empirically supported relationship between big data analytical capability (BDAC) and competitive talent acquisition (TA). It examines a proposed framework to clarify the interactions between these variables, generating both theoretical insights and practical implications for organisations. Environmental heterogeneity (EH) is incorporated as a moderating factor to assess the relationship from multiple perspectives. Despite the growing significance of BDAC, a persistent gap exists in harnessing its potential to generate actionable insights for business value, particularly in talent management. Scholars continue to face challenges in leveraging BDAC to enhance organisational competitiveness. Existing research on dynamic capabilities remains limited, with few studies exploring their alignment with talent acquisition. Moreover, there is a notable scarcity of literature investigating the intersection of BDAC and talent management in the context of organisational talent acquisition. This research seeks to bridge these gaps by integrating academic expertise with empirical insights from industry professionals well-versed in the research variables. The central research question was: “How can BDAC enhance competitive talent acquisition through dynamic capabilities?”. The study was grounded in the knowledge-based view (KBV), complemented by the resource-based view (RBV) and dynamic capabilities view (DCV). Conducted in Abu Dhabi, the research targeted all eligible governmental entities, aiming to optimise the strategic use of advanced technologies and data resources in talent management acquisition. A quantitative, deductive research approach was employed, utilising a survey method. With a sample size of 351 respondents, the study ensures rigorous, valid findings that contribute to both theoretical advancements and practical decision-making in the field.