APJIS Asia Pacific Journal of Information Systems


The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Seung Hyun Kim

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Current Issue

Date December 2023
Vol. No. Vol. 33 No. 4
DOI https://doi.org/10.14329/apjis.2023.33.4.1118
Page 1118~1134
Title Validity of Language-Based Algorithms Trained on Supervisor Feedback Language for Predicting Interpersonal Fairness in Performance Feedback
Author Jisoo Ock, Joyce S. Pang
Keyword Feedback Delivery, Performance Management, Interpersonal Fairness, Computer-Aided Text Analysis, Machine Learning
Abstract Previous research has shown that employees tend to react more positively to corrective feedback from supervisors to the extent they perceive that they were treated with empathy, respect, and concern towards fair interpersonal treatment in receiving the feedback information. Then, to facilitate effective supervisory feedback and coaching, it would be useful for organizations to monitor the contents of feedback exchanges between supervisors and employees to make sure that supervisors are providing performance feedback using languages that are more likely to be perceived as interpersonally fair. Computer-aided text analysis holds potential as a useful tool that organizations can use to efficiently monitor the quality of the feedback messages that supervisors provide to their employees. In the current study, we applied computer-aided text analysis (using closed-vocabulary text analysis) and machine learning to examine the validity of language-based algorithms trained on supervisor language in performance feedback situations for predicting human ratings of feedback interpersonal fairness. Results showed that language-based algorithms predicted feedback interpersonal fairness with reasonable level of accuracy. Our findings provide supportive evidence for the promise of using employee language data for managing (and improving) performance management in organizations.

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