Developing a Human Resource Analytics Adoption Model for Improving Business Performance in Indonesian Companies

Human Resource Analytics (HRA) has increasingly been recognized as a strategic instrument for improving the quality of human resource decision-making and overall organizational performance. Nevertheless, the adoption rate of HRA in many organizations, particularly within developing countries such as Indonesia, remains relatively low and often unsustainable. Previous studies have predominantly conceptualized HRA adoption as a technical or technological phenomenon, overlooking its inherently socio-technical nature. In reality, the adoption of HRA involves complex interactions among technological, organizational, environmental, and human factors, as well as objectives, change mechanisms, and evaluation processes. This study aims to develop a comprehensive understanding of HRA adoption within Indonesian organizations by integrating three complementary analytical approaches: Partial Least Square-Structural Equation Modeling (SEM-PLS), Fuzzy Analytic Hierarchy Process (FAHP), and Interpretive Structural Modeling (ISM). The multi-method design enables the research to simultaneously address three fundamental questions: (1) which factors empirically influence HRA adoption, (2) which elements are perceived by experts as strategically most critical, and (3) how causal relationships and interdependencies among adoption elements are structured within the HRA system. The SEM-PLS analysis reveals that environmental factor – particularly external support and competitive pressure – exert the most significant influence on HRA adoption compared to technological and organizational factors. This finding indicates that, in the Indonesian context, HRA adoption is largely driven by external ecosystem dynamics and the pursuit of competitive legitimacy, rather than solely by internal readiness. Moreover, HRA adoption demonstrates a positive and substantial impact on organizational performance, although the effect is indirect and mediated through the quality of analytical processes and evidence-based decision-making. While SEM-PLS provides robust empirical evidence, it offers limited explanatory power in establishing implementation priorities and hierarchical relationships among adoption elements. To address this limitation, the FAHP approach is employed to determine the relative priority of various factors, actors, objectives, and strategies based on expert judgment. The FAHP findings indicate that a data-driven organizational culture and strong top management commitment are perceived as the most crucial enablers of successful HRA adoption. At the actor level, top executives and HR leaders play dominant roles, confirming that HRA adoption is a strategic organizational agenda rather than merely a technical initiative. To further uncover the causal hierarchy and structural dependencies among key adoption elements, the study utilizes ISM and MICMAC analyses. The results reveal that not all elements form a linear hierarchy. Certain elements – such as program requirements, organizational change, and evaluation metrics – are systemic and interlocked, while others, including stakeholders and key constraints, exhibit clear hierarchical structures. Top management emerges as the primary driving force with the highest driving power and the lowest dependence, whereas limited budgets and analytical competencies serve as foundational structural drivers. The MICMAC analysis reinforces these findings by categorizing elements based on their driving and dependence power. Strategic elements such as leadership, HRA roadmap, Data Literacy training, and the quality and speed of data-driven decision-making consistently appear as dominant drivers. Conversely, HR outcomes and organizational performance are classified as dependent elements, suggesting that the impact of HRA is indirect and cumulative – manifesting as a long-term outcome of effective analytical processes and systemic capability building. The ISM Digraph visualizes the causal mechanism underlying HRA value creation, which follows the logic of “strategic-first, capability-driven, governance-enabled, and technology-last.” This layered process begins with strategic leadership and human capability development, continues with governance and compliance reinforcement, proceeds with technical implementation, and ultimately results in improved HR and organizational performance. Collaboration with external vendors or consultants is shown to be effective only after sufficient internal readiness has been established. Collectively, the integrated results demonstrate that HRA adoption constitutes a complex, non-linear, and context-dependent socio-technical system. Statistically significant factors are not always the principal structural drivers, and elements perceived as strategically important cannot always be directly intervened upon. Therefore, sustainable HRA adoption requires a balanced and systemic approach that harmonizes strategy, human capability, governance, and technology readiness within the organizational context. The main contribution of this study lies in its ability to differentiate between statistically significant, strategically important, and structurally prioritized elements within the HRA adoption process. Academically, this research advances the understanding of HRA adoption through a contextualized multi-method framework tailored to the realities of developing economies. Practically, it offers a structured and sustainable adoption model that provides actionable guidance for organizations in designing, implementing, and institutionalizing HRA initiatives. This model emphasizes strategic leadership, analytical capability development, and integrated data governance as critical levers for enhancing organizational performance in the era of digital transformation