Stimulus-organism-response framework of decision-makers intention to adopt generative AI to replace entry-level jobs: The moderating impact of personality traits

Mohammad Dalvi-Esfahani, Hajar Barati-Ahmadabadi, T. Ramayah, Jason J. Turner, Noorminshah A. Iahad and Nasrin Azar

Technological Forecasting and Social Change, 2025, vol. 219, issue C

Abstract: This study was motivated by the limited research on the adoption of Generative Artificial Intelligence (GenAI) in the workplace. Based on the Stimulus-Organism-Response (S-O-R) framework, we developed a model to assess the factors influencing decision-makers' intention to adopt GenAI as a substitute for entry-level jobs in financial institutions. To test the hypotheses, we collected survey data from 335 respondents in Malaysian financial institutions and analyzed it using partial least squares structural equation modeling. The findings indicate that trust in GenAI significantly affects decision-makers' intention to adopt it as an alternative solution to human positions. Trust, in turn, was found to be positively influenced by constructs from the Theory of Effective Use (transparent interaction, informed action, and representational fidelity) as well as AI literacy, which reflects users' ability to evaluate and interact with AI. The results also show that personality traits, particularly conscientiousness, moderate the relationship between trust and adoption intention, highlighting the importance of individual differences in GenAI usage. Collectively, our findings extend the S-O-R framework by revealing how both cognitive and affective factors shape GenAI adoption behavior. The study also offers practical implications for GenAI stakeholders, especially about the vital role of trust-building strategies in fostering AI adoption.

Keywords: Generative AI; Entry-level jobs; Stimulus-Organism-Response (S-O-R); Theory of Effective Use (TEU) (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:219:y:2025:i:c:s0040162525003221

DOI: 10.1016/j.techfore.2025.124291

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