Examining employees’ intention to use artificial intelligence: Extending the UTAUT

Date of Award


Document Type



Edith Cowan University

Degree Name

Doctor of Philosophy


School of Business and Law

First Supervisor

Reza Kiani Mavi

Second Supervisor

Denise Gengatharen

Third Supervisor

Mohammad Iranmanesh


The rise of Artificial Intelligence (AI) has prompted organisations to adopt AI systems, aiming to enhance revenue, reduce costs and improve business efficiency. However, AI adoption remains challenging, leading to employee stress and influencing their behaviour towards AI systems. Employee-related factors are crucial for AI success, yet limited research exists on these factors, leaving a gap in understanding the determinants of employees’ behavioural intentions towards adopting new AI systems in organisations. To address this research gap, this study aims to identify the factors influencing employees’ intentions to use AI systems and examine the effects of such intentions. Through an extensive literature review, this study identifies four major themes. The first theme, the AI theme, presents studies focused on AI adoption in organisations. The machine learning theme includes studies that concentrate on the acceptance of AI and machine-learning-based systems in organisations. The UTAUT theme showcases studies that utilised adoption and acceptance models, specifically the UTAUT. The TAM theme primarily represents the studies that employed the TAM to analyse and investigate AI adoption in organisations. Consequently, this study applied content analysis to the identified themes through systematic review, summarising the contents of the reviewed articles within each theme. As a result, the study developed a conceptual model that integrates various technology adoption models to address this research gap.

Further, this study empirically examines factors influencing AI adoption within an organisational context. Data were collected from the Asian electrical and electronics industry through an online survey, resulting in 208 valid responses. Using a hybrid approach of PLS- SEM and fsQCA, the research analysed AI adoption factors' impact on employees' intention to use AI systems. PLS-SEM findings revealed that performance expectancy, social influence, personal innovativeness, resistance to use, and TTF significantly influence AI system intention. Meanwhile, fsQCA results indicated that all factors may influence AI system intention, suggesting nine potential factor combinations leading to increased AI system usage. This study makes theoretical contributions by extending existing AI adoption knowledge within organisations. It uncovers trends, explains AI adoption determinants, and develops an extended UTAUT model for the electrical and electronics industry. These findings provide valuable insights for organisations and leaders, aiding AI adoption procedures, and enhancing understanding of its drivers. The study highlights the importance of individual, social, organisational, technological, and environmental factors in AI adoption, empowering managers to make informed decisions within their specific business environments. This is achieved by leveraging the insights derived from the hybrid approach of PLS-SEM and fsQCA analysis, which elucidates how factors such as performance expectancy, social influence, personal innovativeness, resistance to use, and Task-Technology Fit (TTF) significantly impact employees' intentions to use AI systems. By understanding these nuanced impacts, managers can align AI adoption strategies more effectively with their organisation's and employees' unique needs and culture. Furthermore, the study emphasises the importance of fostering a positive work environment and recognising employee differences as crucial for the successful implementation of AI systems.



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