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Editorial verdict
Methodologically credible dual-study design with internally consistent findings, but the China-only samples, correlational field component, and adapted entitlement scale limit generalisability — treat the theoretical framework as a useful lens, not settled science.
Executive summary
This study investigates how employees respond interpersonally when they observe a coworker producing high or low performance through AI-assisted work. Drawing on social comparison theory, the author (Wang, 2026) proposes that coworker AI-augmented performance functions as a comparison-relevant cue whose interpersonal consequences diverge depending on task interdependence. The core argument is that high task interdependence channels upward social comparison toward approach-oriented knowledge seeking, which in turn improves observer work performance; whereas low task interdependence channels the same comparison toward psychological entitlement, which erodes observer motivation and performance. The model was tested in an online between-subjects experiment (N = 240 full-time employees in China's technology sector) and a three-wave field study of employee–supervisor dyads (N = 360). The experiment tested and supported the two interaction hypotheses, and the field study tested and supported all four: the interaction between coworker AI-augmented performance and task interdependence predicted knowledge seeking and psychological entitlement, and both variables mediated downstream effects on work performance in the predicted directions. The findings suggest that visible AI-augmented performance is neither uniformly beneficial nor harmful across a team, and that work-design structure — specifically task interdependence — is a key contingency shaping whether AI-related performance signals diffuse learning or invite defensive, entitled cognitions.
Key insights
- 1Coworker AI-augmented performance operates as a social comparison cue whose interpersonal consequences are contingent on task interdependence, not uniform across team structures.
- 2Under high task interdependence, observing a high-performing AI-assisted coworker is associated with interpersonal knowledge seeking, which positively predicts observer work performance; under low interdependence, the same cue is associated with psychological entitlement, which negatively predicts observer performance.
- 3As AI use becomes organisationally ordinary, 'whether a coworker uses AI' loses diagnostic value as a social signal; what observers respond to increasingly is the observable quality of AI-assisted performance outcomes rather than AI use per se.
Practical takeaways
- Work-design structure — specifically the degree to which employee tasks are interdependent — appears to shape whether visible AI-augmented performance diffuses learning or generates entitlement-driven disengagement, making interdependence a relevant diagnostic consideration when deploying AI tools across teams.
- In low-interdependence work arrangements, organisations may face unintended motivational costs when AI performance disparities become visible; structured knowledge-sharing mechanisms or rotating collaboration arrangements represent contextual responses the article identifies, without asserting their effectiveness.
Frameworks mentioned
Social Comparison Theory
Festinger's (1954) theory proposing that individuals evaluate their own abilities and opinions by comparing themselves to others; extended here to explain how observers respond to coworker AI-augmented performance through either approach-oriented (knowledge seeking) or defensive (entitlement) pathways depending on relational context.
Self-Evaluation Maintenance Model
Tesser's (1988) model specifying that when an interdependent relationship aligns the interests of observer and comparison target, a high-performing other can reflect positively on the self and encourage emulation; when the relationship is independent or competitive, the same performance constitutes a relative threat to self-standing.
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Source & Provenance
Not specified
Yi Wang
October 30, 2026
Research Study
Asia-Pacific
Original source metadata is preserved. AI analysis is generated separately.
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