Impact of Supervisor's Interactional Justice and Interpersonal Affect on Subordinates' Performance Rating: A Signalling Perspective

Sanjay Kumar Singh*, Arup Varma, Pawan S. Budhwar, Prakriti Soral

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The complexity of performance evaluation and the insufficiency of objective measures to make informed performance decisions is an ongoing challenge. We suggest that extracting supportive information from social cues during supervisor–subordinate interactions can aid in navigating these complexities. The current study assesses how signals transmitted during supervisor–subordinate interactions play a crucial role in providing additional information for evaluations. We propose the ‘signalling chain’ concept based on signalling theory, which elaborates on the reciprocal exchange of signals between the sender and receiver, ultimately mitigating information asymmetry for both parties. We collected data from 253 matched supervisor–subordinate dyads to study the proposed relationships and analysed the data using structural equation modelling techniques. The findings show that the supervisor's signals of liking and relational fairness from interpersonal affect and interactional justice positively influence the subordinate's organizational commitment. The findings also suggest that subordinates reciprocate their obligation to the supervisor by being committed to the organization that counter‐signals involvement and identification to supervisors and aid in performance evaluation. We discuss the theoretical and practical implications of our study and offer future research directions.
Original languageEnglish
JournalBritish Journal of Management
Early online date30 Aug 2023
Publication statusE-pub ahead of print - 30 Aug 2023

Bibliographical note

Copyright © 2023, The Authors. British Journal of Managementpublished by John Wiley & Sons Ltd on behalf of British Academy of Management. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License [], which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.


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