TY  - JOUR
T1  - Algorithmic Psychological Contracts and Fairness Expectations in Artificial Intelligence–Mediated Work
A1  - Olivia Bennett
A1  - Harry Collins
A1  - Jack Foster
A1  - Ethan Wright
JF  - Journal of Applied Organizational Systems and Behavior
JO  - J Appl Organ Syst Behav
SN  - 3108-852X
Y1  - 2024
VL  - 4
IS  - 2
DO  - 10.51847/o95HH2b6Sl
SP  - 103
EP  - 111
N2  - Artificial intelligence increasingly mediates task allocation, performance evaluation, scheduling, selection, and other consequential employment decisions. Yet organizational theory has not fully explained how employees form reciprocal expectations when managerial claims are communicated or enacted through algorithmic systems rather than through direct human interaction. This original conceptual theory article develops Algorithmic Psychological-Contract Theory to explain how organizational claims, prior exchange schemas, system roles, and enacted algorithmic practices may become cues from which employees infer obligations. The theory distinguishes general expectations, fairness judgments, trust, formal policy, and technological functionality from perceived reciprocal obligations attributed to the organization. It proposes that consequential algorithm-mediated encounters activate discrepancy, attribution, fairness, and legitimacy appraisals, producing perceived fulfilment, ambiguity, or breach. These appraisals may subsequently shape emotion, trust recalibration, voice, silence, resistance, withdrawal, and contract updating. Transparency is treated not as disclosure alone but as a possible organizational claim whose promissory force depends on comprehensibility, accountability, contestability, and consequential review. The article also proposes procedural, relational, structural, and participatory repair pathways, while emphasizing that their adequacy should depend on the inferred obligation and perceived source of breach. The contribution is a bounded socio-technical theory that retains organizational accountability without treating algorithms as autonomous contracting parties. Its relationships and propositions remain conceptually plausible rather than empirically validated and require discriminant measurement, longitudinal process research, field experimentation, qualitative tracing, and multilevel tests across technologies, occupations, employment arrangements, and institutional settings.
UR  - https://apsshs.com/article/algorithmic-psychological-contracts-and-fairness-expectations-in-artificial-intelligencemediated-wo-0kpaxahnnqxnytw
ER  -