Unveiling the dynamics of data driven innovation in human resource analytics: A grounded theory of strategic approach to HRM

Abstract

Data-driven innovation (DDI) in Human Resource Analytics (HR Analytics) presents a significant potential for strategic organizational change. A persistent gap exists between the theoretical promises of HR Analytics and its practical, value-generating implementation within organizations. This study aims to develop a conceptual model for the effective implementation of HR Analytics within private-sector organizations in a developing economy (Iran), framed through the lens of DDI. Employing a Grounded Theory (GT) approach, specifically the systematic coding methodology of Strauss and Corbin, this research gathered rich qualitative data through in-depth, semi-structured interviews with HR Analytics and Business Analytics experts operating within the Iranian organizational context. Data analysis, facilitated by MAXQDA, involved rigorous open, axial, and selective coding until theoretical saturation was achieved, with study rigor confirmed via Lincoln and Guba's criteria. The resulting conceptual model elucidates the complex interplay of causal conditions (e.g., transformation in human resource management, data-driven approach), context (e.g., technology adoption, cultural contexts), strategies (pre-adoption, decision-making, adoption phases), intervening obstacles, and possible outcomes. By highlighting the contextual features of the Iranian economic environment, this model offers a coherent framework to assist private enterprises in comparable settings to more effectively navigate their transition toward data-driven HR management, offering insights for organizations aiming to leverage HR Analytics as a driver for strategic innovation and business value enhancement.

The authors' abstract, as published at the source. Technological Forecasting and Social Change, 2026 · DOI ↗