Browsing by Author "Lim OW"

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    The Mediating Effect of Organizational Prestige on Extrinsic Rewards and Employee Retention
    (SAGE Publishing, 2025-04-17) Anthonysamy L; Tan CN-L; Lim OW; Zainal Z
    Employee turnover intention poses significant challenges for organizations globally, incurring financial costs and productivity losses while disrupting workflows and impeding innovation. Effective human resource practices, including extrinsic rewards such as financial incentives and non-monetary benefits, are critical in retaining high-performing employees. However, the influence of these rewards on employee retention is not always straightforward, and organizational prestige—perceived as an indicator of a company’s reputation and status—may play a crucial mediating role. This study explores the mediating effect of organizational prestige on the relationship between extrinsic rewards and employee retention intention within the Klang Valley region, Malaysia. Utilizing partial least squares structural equation modeling (PLS-SEM), the research investigates how financial incentives, promotion opportunities, and relationships with supervisors and peers’ impact organizational prestige and, consequently, employees’ intention to stay. Findings indicate that while financial incentives do not significantly influence organizational prestige, promotion opportunities, supervisor relations, and peer relations positively affect it. Moreover, organizational prestige significantly mediates the effects of supervisor and peer relations on retention intention, highlighting its critical role in enhancing employee commitment. The study provides valuable insights for organizations seeking to improve retention strategies by emphasizing the importance of fostering a prestigious organizational image and cultivating positive workplace relationships. The limitations of this study include the use of a single geographical region and the focus on specific types of extrinsic rewards, suggesting that future research should explore additional contexts and reward types to generalize findings across different settings.