Evaluating competencies with spoken comments and machine learning

Jana Kim Gutt () and Kirsten Thommes ()
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Jana Kim Gutt: Paderborn University
Kirsten Thommes: Paderborn University

No 130, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: Employees are frequently evaluated on a numerical scale by their supervisors. These numerical assessments inform far-reaching managerial decisions, such as promotions, training opportunities, and dismissals. Yet, they often lack accuracy, are subject to supervisor bias, and do not provide justification for the ratings. In this paper, we address the limitations of numerical ratings by letting individuals provide spoken assessments of others and use a Random Forest algorithm to convert the spoken assessments into numbers (algorithmic ratings). Through this method, we combine the advantages of qualitative feedback and numerical ratings while potentially mitigating common biases. Our results suggest that the algorithmic ratings more accurately reflect the distribution of competencies (as measured by psychometric tests) than assigned numerical ratings (assigned ratings). The algorithmic ratings are considerably more nuanced and less skewed compared to the assigned ratings. Our findings highlight the potential of combining spoken comments with a machine learning model to enhance the accuracy of employee assessments in organizational settings.

Keywords: performance appraisals; rating prediction; machine learning; spoken comments (search for similar items in EconPapers)
JEL-codes: D91 J24 M51 (search for similar items in EconPapers)
Pages: 44
Date: 2025-02
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