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Predictive Analytics on Employees Performance
Subject area: Management and Commerce · Area of research: Human Resource
Abstract
This research explores the application of predictive analytics in evaluating and enhancing employee performance in organizations. Using a primary data questionnaire distributed to 150 employees, the study leverages regression analysis, ANOVA, and clustering to identify key predictors of high performance. Results reveal that timely feedback, analytics-based recommendations, and clear, data-driven goals are the most significant drivers of productivity. The study concludes with practical recommendations for HR leaders and future research directions.
References
[1] Davenport, T. H., Harris, J. H., & Shapiro, J. (2010). Competing on Talent Analytics. Harvard Business Review.
[2] Bassi, L., & McMurrer, D. (2007). Maximizing Your Return on People. Harvard Business Review.
[3] Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad.
[4] IBM Smarter Workforce Institute Reports.
How to cite this paper
@article{1708755,
author = {Swati Raj},
title = {Predictive Analytics on Employees Performance},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {1857-1860},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708755.pdf},
abstract = {This research explores the application of predictive analytics in evaluating and enhancing employee performance in organizations. Using a primary data questionnaire distributed to 150 employees, the study leverages regression analysis, ANOVA, and clustering to identify key predictors of high performance. Results reveal that timely feedback, analytics-based recommendations, and clear, data-driven goals are the most significant drivers of productivity. The study concludes with practical recommendations for HR leaders and future research directions.},
month = {May},
}