<![CDATA[Digital transformation encourages organizations to manage human resources more strategically through the use of data and analytical technology. People analytics has emerged as a crucial approach to evidence-based decision-making, but its implementation in developing countries still faces various challenges, such as limited data infrastructure, analytical competency, and organizational support. This study aims to analyze the influence of People Analytics Capability on Strategic HR Decision Quality and identify the most dominant capability dimensions in improving the quality of strategic HR decisions. The study used a quantitative approach with an explanatory research design. Data were collected through a survey of 128 respondents consisting of HR Managers, HR Business Partners, and organizational leaders across various industrial sectors in Indonesia. Data analysis was conducted using Structural Equation Modeling Partial Least Squares (SEM-PLS) to test validity, reliability, and causal relationships between variables. The results of the study indicate that People Analytics Capability has a positive and significant effect on Strategic HR Decision Quality (β = 0.763; p < 0.001) with a coefficient of determination (R² = 0.582), indicating the model's predictive ability is in the moderate-strong category. The dimensions that provide the greatest contribution are top management support and HR analytics competency, followed by data quality and data-driven culture. These findings confirm that the success of people analytics implementation is not only determined by technology, but also by the readiness of the organization and its people to utilize data as a basis for decision-making. This study concludes that People Analytics Capability is a strategic capability that can improve the accuracy, speed, and objectivity of HR decisions and support organizational competitiveness in the digital era. Therefore, organizations need to develop an integrated people analytics ecosystem through technology investment, increasing data literacy, and strengthening leadership commitment to evidence-based decision-making.]]>
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