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Generative AI and the Future of Work: A Study on Adoption and Resistance using UTAUT2 and Innovation Resistance Theory
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Orientador(es)
Resumo(s)
This study explores the factors that influence the adoption of Generative AI for work-related
tasks. Due to its ability to follow instructions in natural language, Generative AI is considered
a key technology in digital transformation, transforming processes, tasks, job roles, and
human engagement. The research identifies drivers of adoption and sources of resistance,
developing a model that combines both. Unified Theory of Acceptance and Use of Technology
2 (UTAUT2) was used to analyze enablers, while Innovation Resistance Theory (IRT) helped
identify resistance factors. Both models were combined into a single framework with
behavioral intention and use behavior as outcome variables. This research uses a quantitative
method, gathering data through an online survey via Qualtrics, shared on social media. Out of
286 responses, 203 valid responses were included. The sample consists of participants from
various employment statuses for broader relevance, mostly from Portugal (65%), with a
balanced gender distribution (102 male, 100 female, one undisclosed), an average age of
35.13 years (SD = 12.12), most holding higher education degrees and being employed. The
results supported eight of twelve hypotheses. Performance expectancy, social influence,
hedonic motivation, and habit showed significant positive effects on behavioral intention,
while tradition barrier demonstrated a significant negative effect. Facilitating conditions and
effort expectancy did not show significant effects. Age, as a control variable, had a marginal
influence on Generative AI use. Limitations include a sample mainly from certain countries,
potential measurement issues related to the image barrier construct, and the cross-sectional
design of the study. This research contributes to the literature by merging UTAUT2 and IRT in
the context of Generative AI adoption at work, offering a comprehensive model that considers
both drivers and barriers, and providing valuable insights for academics and practitioners
preparing for the future of work with AI technologies.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Digital Transformation
Palavras-chave
Future of Work Generative Artificial Intelligence IRT PLS-SEM UTAUT2 SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure