Author ORCID Identifier
Xiaoni Zhang: https://orcid.org/0000-0003-2755-1761
Cata Teuta: https://orcid.org/0009-0004-9456-8664
Abstract
Generative AI (GenAI) feedback is a double-edged sword. Using a sequential mixed-methods design with 350 working MBA students (averaging 5 years' experience) and 42 information systems graduates, we examined how professionals respond to GenAI integration. Structural equation modeling revealed that GenAI feedback simultaneously enhances self-efficacy and motivation while increasing perceived devaluation. Qualitative thematic analysis explained this paradox: participants experience GenAI feedback as both empowering and threatening because it validates competence while demonstrating replaceability, as feedback comes from an entity that can autonomously perform the same tasks.
Our findings also revealed an unexpected result: skill variety, traditionally a key Job Characteristics Theory predictor, did not predict motivation. Qualitative analysis showed GenAI creates "prompt engineering convergence," where diverse tasks reduce to uniform prompting and reviewing activities.
We extend Job Characteristics Theory and Self-Efficacy Theory for GenAI contexts by demonstrating that feedback from capable AI sources creates paradoxical effects through source agency, and that skill variety requires reconceptualization into execution variety versus orchestration variety. We introduce perceived devaluation as a novel construct grounded in Self-Determination Theory capturing professional worth diminishment distinct from job insecurity.
Recommended Citation
Zhang, X., Teuta, C., & Tomeo, M. (In press). The Double-Edged Sword of GenAI Feedback: How Generative AI Influences Employee Motivation and Perceived Devaluation. Communications of the Association for Information Systems, 58, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol58/iss1/96
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