Unveiling the Retention Puzzle for Optimizing Employee Engagement and Loyalty Through Analytics-Driven Performance Management: A Systematic Literature Review

Abstract

Disengagement and turnover of employees are significant costs to organizations worldwide. In many organizations, it isn't easy to foster continuous engagement among employees. Analytically-driven performance management aims to capture and analyze workplace data with advanced analytical techniques to develop a sustainable solution. This systematic literature review (SLR) examines and analyzes frameworks proposed for optimizing engagement and retention through performance analytics. Among the forty initial papers screened, twenty-four highly relevant sources were selected and analyzed. Human resources (HR) related key themes included bias issues, text analysis of reviews, personalized HR management, talent assessments, augmenting HR work with Artificial Intelligence (AI), and integration challenges. According to the findings, a reliable emphasis was placed on the balance of human and machine perspectives. While analytics and algorithms offer insightful information, human judgment is needed to contextualize this data. If datadriven methods are the only ones used, complicated personal aspects that influence experience may be overlooked. Consequently, a human-machine strategy working together is crucial. Furthermore, effective integration requires both strategy alignment and cultural preparedness. Longitudinal evaluations and more real-world case studies help close gaps in the literature. Analytics with human-centric frameworks can maximize engagement and performance management.