📄 Abstract
Purpose – Artificial intelligence (AI) is moving from recruitment into the most sensitive gateway of an employee’s career: promotion. Existing research on algorithmic fairness treats “the organisation” as a single, undifferentiated context, and draws almost exclusively on Western, large-firm or platform samples. This article asks whether the same AI-assisted promotion system produces the same justice perceptions and the same discretionary effort in a private corporation, a government department and a micro, small or medium enterprise (MSME). Design/methodology/approach – A structured integrative review of organisational-justice, algorithmic-fairness, public-management and small-firm HRM literatures is combined with a comparative sector analysis built on documented examples: IBM’s AI-supported pay-and-promotion workflow (private), India’s SPARROW e-APAR and Departmental Promotion Committee system (government), and the Udyam-registered MSME base of 7.83 crore enterprises (MSME). Five propositions and an illustrative simulation are developed. Findings – The fairness → justice → effort chain is universal, but its weakest link differs by sector. In large private firms the binding constraint is informational justice (explaining a model score); in government it is procedural justice under rule codification, where AI threatens the seniority-cum-merit settlement rather than accuracy; in MSMEs it is interpersonal justice, because an off-the-shelf algorithm interrupts a personal owner–employee exchange. Sector therefore moderates both the fairness → justice path and the justice → discretionary effort slope, which is predicted to be steepest in MSMEs and flattest in government. Originality/value – The article is, to the author’s knowledge, the first to compare AI-assisted promotion fairness across the private–public–MSME triad, and supplies a sector-specific governance agenda for India, where neither the DPDP Act 2023 nor service rules contain an AI-promotion-specific safeguard.
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📚 How to Cite:
Dr. N Subbukrishna Sastry, Dr. Manjula Mallya M , PERCEIVED ALGORITHMIC FAIRNESS IN AI-ASSISTED PROMOTION DECISIONS, ORGANISATIONAL JUSTICE AND DISCRETIONARY EFFORT: A COMPARATIVE ANALYSIS OF PRIVATE, GOVERNMENT AND MSME SECTORS , Volume 14 , Issue 9, October 2026, EPRA International Journal of Climate and Resource Economic Review (CRER) , Pages: 1 - 10 ,