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

Daria Höhener () and Benedict Lösser ()
Additional contact information
Daria Höhener: University of St. Gallen
Benedict Lösser: University of St. Gallen

A chapter in Technology-Driven Transformation, 2025, pp 187-203 from Springer

Abstract: Abstract 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.

Keywords: Generative artificial intelligence; Technology acceptance; Interview study (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-01396-5_11

Ordering information: This item can be ordered from
http://www.springer.com/9783032013965

DOI: 10.1007/978-3-032-01396-5_11

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().