Fraught Expectations: A Fairness Heuristic Process Model of the Pros and Cons of CSR for Talent Acquisition

Adam A. Kay, Pavlos A. Vlachos, Konstantinos Tasoulis, Elaine Farndale

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Corporate social responsibility (CSR) is purely an asset when it comes to talent acquisition: that is the dominant narrative among Human Resource Management (HRM) practitioners and scholars alike. Growing evidence, however, gives reason to question this assumption. Accordingly, in this conceptual paper, we develop a process model to articulate both the pros and cons of CSR for recruitment. Using fairness heuristic theory as a central organizing framework, we integrate three theoretical perspectives into the HRM and micro-CSR literatures. First, we leverage dual-processing attribution theory to propose that job seekers process information about CSR through both heuristic and deliberative processes, leading them to attribute employer CSR to substantive or symbolic motives. We explain how CSR attributions represent a fairness heuristic, meaning a proxy for how trustworthy job seekers appraise an employer to be. Second, invoking expectancy violation theory, we propose that the more job seekers attribute employer CSR to substantive (symbolic) motives, the higher (lower) their justice expectations will be, thereby increasing (decreasing) the consequences to employers for violating those expectations. Third, expanding scholarship on the dynamic nature of organizational fairness perceptions, we propose that job seekers update their attributions of employer CSR in a recursive cycle that can improve, but tends to degrade, as the recruitment process unfolds—particularly if they have high expectations to begin with. In so doing, we nudge the talent acquisition literature beyond static, fixed-in-time accounts to a more representative description of the dynamic and dual-sided role of CSR in recruitment over time.

Original languageEnglish (US)
Pages (from-to)465-483
Number of pages19
JournalHuman Resource Management
Volume64
Issue number2
DOIs
StatePublished - Mar 1 2025

All Science Journal Classification (ASJC) codes

  • Applied Psychology
  • Strategy and Management
  • Organizational Behavior and Human Resource Management
  • Management of Technology and Innovation

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