Factors Affecting the Adoption of Generative AI Tools Among Information Technology Employees: A UTAUT3, TTF, and SOR Perspective

KDV Prasad; Shivoham Singh; Ved Srinivas; Hemant Kothari; Ankita Pathak; Devendra Shrimali

doi:10.47852/bonviewJCCE52026127

Authors

KDV Prasad Symbiosis Institute of Business Management Hyderabad, Symbiosis International (Deemed University), India https://orcid.org/0000-0001-9921-476X
Shivoham Singh Symbiosis Institute of Business Management Hyderabad, Symbiosis International (Deemed University), India
Ved Srinivas Thiagarajar School of Management, India
Hemant Kothari Pacific Academy of Higher Education and Research, India
Ankita Pathak Institute of Business Management, GLA University, India https://orcid.org/0000-0002-8068-8874
Devendra Shrimali Department of Business Administration, Mohanlal Sukhadia University, India

DOI:

https://doi.org/10.47852/bonviewJCCE52026127

Keywords:

generative AI tools, social influence, trust, effort expectancy, optimism

Abstract

This empirical study examined the components that affect the adoption and actual use of generative AI tools among information technology (IT) employees in Hyderabad. This study also attempted to unravel the SOR, UTAUT3, and TTF models by integrating them for practical application in industry and by IT sector employees. Data were gathered from 470 employees working in several IT industries in Hyderabad by adopting a quantitative method for this investigation. IBM-AMOS was used to test the hypotheses. Thirty-six variables were used to measure the following 10 reflective constructs: optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, task–technology fit, adoption intentions toward generative AI tools, and actual use of generative AI tools. EFA and CFA analyses were conducted to unravel the structural relationships between constructs, and hypotheses were tested using SEM. A 12% variance in the adoption intention of generative AI tools by IT industry employees was explained by optimism, innovativeness, trust, performance expectancy, effort expectancy, hedonic motivation, social influence, and task–technology fit, and a 5% variance in the actual use of generative AI tools was explained by the adoption intentions. The construct trust fully mediated the nexus between adoption intentions to use generative AI tools and actual use. All constructs, except hedonic motivation, statistically significantly influenced the adoption intentions of generative AI tools. In turn, adoption intentions to use generative AI tools influenced the actual use of generative AI tools. A study involving Hyderabad IT industry employees revealed that they could adopt useful technology to improve performance.

Received: 10 May 2025 | Revised: 13 August 2025 | Accepted: 9 September 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available at https://figshare.com/s/cad95b195de4aa53e43a.

Author Contribution Statement

KDV Prasad: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft. Shivoham Singh: Conceptualization, Methodology, Writing – original draft. Ved Srinivas: Formal analysis. Hemant Kothari: Validation, Resources, Writing – review & editing, Supervision, Project administration. Ankita Pathak: Methodology, Investigation, Data curation, Visualization. Devendra Shrimali: Investigation, Resources, Writing – review & editing.