|
Factors influencing the acceptance and use of generative artificial intelligence in Thai startup companies |
|---|---|
| รหัสดีโอไอ | |
| Title | Factors influencing the acceptance and use of generative artificial intelligence in Thai startup companies |
| Creator | Kittisak Kanokbusabarn |
| Contributor | Tanatorn Tanantong, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | Generative AI, Artificial intelligence, Technology acceptance, UTAUT, Startups, AI anxiety, Trust in AI, PLS-SEM |
| Abstract | This study investigates the determinants of Generative AI acceptance and usage among personnel within Thai startup companies. The primary objective is to elucidate the critical organisational and psychological factors driving adoption in this specific, high-agility context. To achieve this, the study proposes and tests a conceptual framework by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This extension incorporates three psychological constructs comprising Hedonic Motivation (HM), Trust in AI (TAI), and AI Anxiety (AIA).Employing a quantitative methodology, the research gathered 343 valid responses from employees and founders across the Thai startup ecosystem through a multi-channel strategy comprising online communities, on-site data collection at industry events, and direct outreach. The data was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).The results demonstrate that the model possesses moderate explanatory power, accounting for 49.2% of the variance in Behavioural Intention (BI) and 60.1% of the variance in Use Behaviour (UB). The analysis confirmed five hypotheses where Performance Expectancy (PE), Social Influence (SI), and Hedonic Motivation (HM) were identified as significant positive drivers of Behavioural Intention. Furthermore, Facilitating Conditions (FC) and Behavioural Intention (BI) served as significant predictors of actual Use BehaviourConversely, three key hypotheses were not supported where Effort Expectancy (EE), Trust in AI (TAI), and AI Anxiety (AIA) were found to have an insignificant effect on Behavioural Intention. Consequently, the study concludes that adoption in this context is distinctively pragmatic. Users are motivated primarily by utility (PE), social norms (SI), and enjoyment (HM). These drivers prove sufficiently potent to override low trust and functional anxieties, resulting in a "Pragmatic Adoption" model. This behaviour reinforces a "Human-in-the-Loop" approach, wherein users verify AI output due to a lack of absolute trust. The findings offer practical guidelines for startup leaders, suggesting a strategic pivot in training from the "how" (ease of use) to the "why" (utility), alongside the implementation of formal "Human-in-the-Loop" policies to effectively manage user anxiety. |
