Abstract

Data mining methods have been used to study a variety of topics in industrial and organizational psychology, including predicting employee performance. With the increased interest in predictive analytics in human resources, the present study aimed to review and explore the application of two commonly used data mining methods, decision trees (DTs) and artificial neural networks (ANNs), for predicting employee performance in organizational settings. Out of 103 studies reviewed, eight studies were retained and used for the meta-analyses. The number of employee performance classifications meta-analyzed was 2430 in total. The results suggested that both data mining methods showed good performance in employee performance prediction, although the difference between the overall effect sizes was not statistically significant. The theoretical and practical implications and the potential limitations were discussed, and recommendations were provided for future research directions. The current study was a first attempt to qualitatively and quantitatively evaluate the effectiveness of the data mining methods in predicting employee performance.

Notes

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Graduation Date

2021

Semester

Spring

Advisor

Su, Shiyang

Degree

Master of Science (M.S.)

College

College of Sciences

Department

Psychology

Degree Program

Industrial and Organizational Psychology

Format

application/pdf

Identifier

CFE0008471; DP0024147

URL

https://purls.library.ucf.edu/go/DP0024147

Language

English

Release Date

May 2022

Length of Campus-only Access

1 year

Access Status

Masters Thesis (Open Access)

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