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The adoption pace of Generative Artificial Intelligence (GAI) is swift, yet the factors specifically affecting GAI acceptance remain underexplored. Building on the long-standing tradition within information systems to elucidate technology acceptance, this paper adopts an exploratory approach given the novelty of GAI. An interview study involving thirteen experienced digital leaders from established companies has been conducted to understand GAI acceptance among human agents. The inquiry has led to developing the GAI Acceptance Model (GAIAM), which highlights trust, hedonic motivation, convenience, efficiency, and effectiveness as antecedent factors. The proposed model demonstrates that performance expectancy and perceived value creation, influenced by context factors, serve as appraisals that ultimately lead to behavioral intention and usage intensity. GAIAM may serve as an initial model for grounding the acceptance of GAI at an individual level of analysis and encourages further research in this area. Additionally, the hypothesized model provides practitioners with guidance on which levers they can proactively manage to increase the intensity of GAI usage among human agents.

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Driving Factors in the Technology Acceptance of Generative Artificial Intelligence: Insights from an Exploratory Interview Study with Digital Leaders

  • 章节

  • 发表日期：

    2025年

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  • Daria Höhener,
  • Benedict Lösser

摘要

The adoption pace of Generative Artificial Intelligence (GAI) is swift, yet the factors specifically affecting GAI acceptance remain underexplored. Building on the long-standing tradition within information systems to elucidate technology acceptance, this paper adopts an exploratory approach given the novelty of GAI. An interview study involving thirteen experienced digital leaders from established companies has been conducted to understand GAI acceptance among human agents. The inquiry has led to developing the GAI Acceptance Model (GAIAM), which highlights trust, hedonic motivation, convenience, efficiency, and effectiveness as antecedent factors. The proposed model demonstrates that performance expectancy and perceived value creation, influenced by context factors, serve as appraisals that ultimately lead to behavioral intention and usage intensity. GAIAM may serve as an initial model for grounding the acceptance of GAI at an individual level of analysis and encourages further research in this area. Additionally, the hypothesized model provides practitioners with guidance on which levers they can proactively manage to increase the intensity of GAI usage among human agents.