Gender differences in technology acceptance: Exploring UTAUT2 constructs for AI chatbot adoption

This study examines the factors influencing the adoption of LanGTa, an AI-powered chatbot developed to support historical tourism, through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Data were collected from 427 participants (51% male, 49% female) via structured surveys, focusing on core constructs including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT). Structural equation modeling (SEM) was employed to analyze the relationships between these constructs and Behavioral Intention (BI) to adopt LanGTa. The model demonstrated an excellent fit, with indices such as CFI = 1.00, GFI = 1.00, RMSEA = 0.00, and SRMR = 0.02. PE and EE emerged as the strongest predictors of BI, indicating that perceived usefulness and ease of use significantly influence adoption. HM was also a significant factor, particularly among male respondents, suggesting that enjoyment enhances the intention to use the chatbot. SI had a weaker yet statistically significant effect, especially among females, highlighting the influence of peer recommendations. Notable gender differences were observed: females prioritized PE, EE, and SI, whereas males emphasized EE, FC, HM, and PV. These findings underscore the importance of gender-sensitive strategies for promoting AI chatbot adoption. The study contributes to technology acceptance research by extending the UTAUT2 framework to AI chatbots in the tourism sector and offers practical recommendations for developers focused on usability, engagement, and contextual relevance.