Predictive Analytics in Attendance Systems for Employee Productivity and Accountability - Putri, Indri Mariska; Ramadhani, Destania Putri; Indriyani, Pifin; Aidah, Elsa Nur; Cahyani, Afifah Putri (Pandawan Sejahtera Indonesia, 2025)
The integration of predictive analytics in attendance systems is becoming a critical approach to improving employee productivity and accountability. However, its impact on technology readiness, employee engagement, and attendance regularity remains underexplored, particularly in educational and professional settings. This study aims to evaluate how Predictive Analytics Utilization (PAU) influences Technology Readiness (TR) and Employee Engagement (EE), and how these variables contribute to Attendance Regularity (AR) and overall employee satisfaction. A quantitative approach was employed using Structural Equation Modeling (SEM) with SmartPLS 4.1. Data were gathered via 40 item questionnaires distributed to Information Systems students at Raharja University. Each variable PAU, TR, EE, and AR was measured through 10 questions to ensure robust data collection and analysis. The findings demonstrate a strong model fit, with R² values of 0.895 for AR, 0.701 for EE, and 0.847 for TR. PAU significantly influences TR and EE, which in turn positively affect AR. Higher levels of technology readiness and engagement enhance attendance regularity, reflecting the effectiveness of predictive analytics. This study highlights the pivotal role of predictive analytics in fostering technological readiness, enhancing employee engagement, and improving attendance regularity. Organizations can leverage these findings to optimize their systems and achieve a more productive workforce. Future research should explore diverse population samples, different organizational contexts, and the integration of advanced analytics tools, such as AI and IoT, to further enhance attendance systems and employee outcomes.