Creative or Contrived? How AI Use Shapes the Social Evaluations of Individuals with Creative Reputations

Anand Benegal et al.

Academy of Management Discoveries2026https://doi.org/10.5465/amd.2024.0277article
AJG 3ABDC A*
Weight
0.37

Abstract

A creator’s reputation provides a considerable social advantage, signaling competence and affording greater latitude to take risks and challenge the status quo, such as through unorthodox approaches or adopting novel methods and technologies. Yet as individuals across creative industries increasingly rely on generative artificial intelligence – a technology that can raise doubts about originality and authorship – important questions arise about whether these reputational benefits endure. In two experiments, we examine how AI use in creative work affects social evaluations of creators and whether the buffering effect of an established reputation persists. Focusing on a freelance artistic production context – music composition for a video game – our first experiment shows that regardless of a creator’s existing reputation, third-party evaluators judged those who used AI more negatively, lowering perceptions of reputational prestige and creative competence. Shifting to a traditional organizational context – an employee in advertising developing slogans for a marketing campaign – our second experiment replicates these findings and identifies authenticity of creativity as a key underlying mechanism. We further investigate several boundary conditions, including perceptions of creative credit, evaluators’ prior AI use for creativity, and whether AI was applied to creative versus non-creative tasks. We conclude by discussing the implications of our findings for the reputation literature, creative industries, and human agency and creative credit in human-AI co-creativity contexts.

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@article{anand2026,
  title        = {{Creative or Contrived? How AI Use Shapes the Social Evaluations of Individuals with Creative Reputations}},
  author       = {Anand Benegal et al.},
  journal      = {Academy of Management Discoveries},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.5465/amd.2024.0277},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.