Assessing perceptions and feelings, and personal characteristics toward the adoption of AI systems

Abstract

This research project, titled “Assessing perceptions and feelings, and personal characteristics toward the adoption of AI systems”, aimed to determine which individual-level factors influence the adoption of artificial intelligence (AI) systems in organisational settings. This is critical, as 70% of AI projects failed to yield anticipated benefits, often due to a lack of employee adoption. The study focused on perception and feeling, including performance expectancy (PE), effort expectancy (EE) and perceived threat (PT), and then personal characteristics, comprising personal development concerns (PDC), personal innovativeness (PI) and self-efficacy (SE). Building on the Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Threat Avoidance Theory (TTAT) and the Integrated AI Acceptance-Avoidance Model (IAAAM), the study extended the IAAAM framework by incorporating PI and SE. A quantitative, cross-sectional survey was conducted with 121 employed individuals, and data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings indicated that PE had a strong, positive and significant effect on AI adoption (β = 0.593 and p = 0.000). Positive development beliefs (PDB), derived from PDC, also showed a moderate, positive and significant influence (β = 0.187 and p = 0.010). However, EE, PT, negative development concerns (NDC), PI and SE did not significantly influence adoption. The research concluded that PE is the most significant factor, suggesting that organisations should tailor their change management strategies to highlight how AI enhances employees’ job performance, efficiency and effectiveness to maximise adoption and achieve return on investment.