Machine learning in employee performance evaluation: A HRM perspective

Abstract

This research explores how machine learning, the tech whiz kid, is shaking up the traditional HR world of employee reviews. Companies are hungry for better ways to assess their workforce, and machine learning comes to the table with a buffet of data analysis tools. Productivity metrics, project wins, even that qualitative feedback, all get crunched by these algorithms to dish up comprehensive and objective performance pictures. This research dives into both the pros and cons of bringing this tech titan into HR, aiming to make evaluations not just accurate, but also fair and impactful. It's about finding the sweet spot where technology and human understanding join forces to power up performance reviews for the modern workplace. In the ever-evolving dance of the modern workplace, where performance reigns supreme, the old waltz of subjective evaluations stumbles toward obsolescence. This study steps into the spotlight, exploring the transformative potential of machine learning as the new lead partner in HRM, specifically for crafting nuanced and objective performance assessments. Imagine algorithms like virtuoso musicians, weaving together diverse data melodies – productivity's staccato riffs, project outcomes' triumphant crescendos, even the subtle whispers of qualitative feedback – to paint a vibrant portrait of individual contributions. But this data-driven tango isn't without its tricky steps. This research scrutinizes both the grace and potential missteps of machine learning in HRM, aiming to illuminate a path toward optimized performance evaluations that are not only accurate but also fair and effective. By bridging the gap between technology and human understanding, this study offers a roadmap for organizations waltzing towards a future where performance appraisals unlock the full potential of their workforce.