Enterprise AI Analysis
Unveiling the Determinants of Tourists' Behavioural Intention to Adopt AI-Powered Chatbots for the Hospitality and Tourism Industry: Revising the UTAUT2 Model
Emerging technologies, such as artificial intelligence (AI), including chatbots, are now transforming the hospitality and tourism industry. Chatbot technology is an excellent tool for enhancing communication, boosting service delivery efficiency, reducing costs, and improving the tourist experience. Despite their potential benefits, the adoption of AI-powered chatbots in Goa's hospitality and tourism industry remains low, underscoring the need to identify the determinants influencing tourists' behavioural intention to adopt this technology and use behaviour. Therefore, this study examines the key determinants influencing tourists' behavioural intentions to adopt AI-powered chatbots in the hospitality and tourism industry. In addition, the study also examines the impact of tourists' behavioural intentions to adopt AI-powered chatbots on use behaviour. For this purpose, a revised UTAUT2 model is assessed by leveraging a quantitative research approach. Structured questionnaires were distributed to a total of 400 inbound and outbound tourists, of which 227 respondents who were aware of Al-powered chatbots were chosen as the respondents for this study based on purposive sampling. The collected data were analysed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0. The findings revealed that attitude, performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived enjoyment significantly influence tourists' behavioural intention to adopt Al-powered chatbots, whereas automation and habit do not significantly influence their behavioural intention to adopt AI-powered chatbots. This study has implications for tourism managers and policymakers in the tourism and hospitality industry, who can gain insights into the factors that can encourage tourists to adopt AI-based facilities.
Executive Impact & Key Findings
This study dives into the factors influencing tourists' intention to adopt AI-powered chatbots in the hospitality and tourism sector, utilizing a revised UTAUT2 model. It highlights crucial determinants for successful AI integration and offers practical guidance for industry stakeholders.
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R² for Behavioural Intention
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R² for Use Behaviour
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Supported Hypotheses (Out of 9)
Deep Analysis & Enterprise Applications
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UTAUT2 Model Revision
The study extends the UTAUT2 model by incorporating additional constructs like attitude, perceived enjoyment, and automation, specifically tailoring it for AI-powered chatbot adoption in tourism. This revision aims to better capture the technological and experiential characteristics relevant to the industry.
Key Determinants
Performance expectancy, effort expectancy, social influence, facilitating conditions, attitude, and perceived enjoyment were found to significantly influence tourists' behavioural intention to adopt AI-powered chatbots. Attitude emerged as the strongest predictor.
Non-Significant Factors
Automation and habit were found not to significantly influence behavioural intention. This suggests that in the early stages of AI chatbot adoption, practical usefulness and attitude are more influential than routine usage or perceptions of task replacement.
Use Behavior Impact
Behavioural intention to adopt AI-powered chatbots significantly impacts actual use behaviour, confirming that users who intend to use the technology are highly likely to engage with it in practice. This underscores the importance of fostering positive intentions.
Practical Implications
The findings offer actionable insights for tourism managers and policymakers. Recommendations include clearly communicating benefits, ensuring user-friendly design, leveraging social influence, providing robust technological infrastructure, and focusing on creating emotionally engaging chatbot experiences.
0.284
Path Coefficient: Attitude → Behavioural Intention (Strongest Predictor)
Path to AI Chatbot Adoption Intention
Performance Expectancy
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Effort Expectancy
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Social Influence
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Facilitating Conditions
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Attitude
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Perceived Enjoyment
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Behavioural Intention to Adopt AI-powered Chatbots
| Hypothesis | Relationship | Path Coeff. | p-Value | Inference |
|---|---|---|---|---|
| H1 | PER → BINT | 0.257 | <0.001 | Supported |
| H2 | EFF → BINT | 0.148 | 0.035 | Supported |
| H3 | SOC → BINT | 0.133 | 0.006 | Supported |
| H4 | FAC → BINT | 0.104 | 0.031 | Supported |
| H5 | ATT → BINT | 0.284 | <0.001 | Supported |
| H6 | AUT → BINT | 0.058 | 0.166 | Unsupported |
| H7 | HAB → BINT | 0.010 | 0.860 | Unsupported |
| H8 | PEE → BINT | 0.122 | 0.032 | Supported |
| H9 | BINT → UBEH | 0.662 | <0.001 | Supported |
Goa: An Ideal Setting for AI Tourism Research
Goa's Unique Tourism Landscape
Goa, a prominent tourist destination in India, serves as an excellent case study due to its diverse inbound and outbound tourist population, rich cultural heritage, and thriving hospitality industry.
The state's rapid technological innovation and competitive service environment make it conducive for studying the adoption of AI-driven solutions like chatbots for travel planning, virtual concierges, and automated booking.
This diverse setting, with varying levels of digital literacy, allows for a comprehensive assessment of user expectations and behavioral intentions towards AI technologies, providing valuable real-world insights for service providers.
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Your Enterprise AI Implementation Roadmap
A typical journey to integrate AI chatbots into your hospitality and tourism operations, designed for maximum impact and smooth transition.
Phase 1: Discovery & Strategy Alignment
Conduct a comprehensive assessment of current pain points, identify high-impact areas for AI chatbot integration, and align AI strategy with overall business objectives. Define clear KPIs and success metrics.
Phase 2: Pilot Program Development & Customization
Develop a tailored AI chatbot solution focusing on initial use cases (e.g., booking inquiries, FAQ automation, basic customer support). Customize chatbot personality, language models, and integration points with existing systems.
Phase 3: Deployment & Employee Training
Launch the AI chatbot in a controlled environment. Provide extensive training for staff on how to leverage the chatbot, manage escalations, and understand its capabilities to ensure a seamless human-AI collaboration.
Phase 4: Performance Monitoring & Iterative Enhancement
Continuously monitor chatbot performance against KPIs. Collect user feedback, analyze interaction data, and implement iterative improvements to enhance accuracy, user experience, and expand its functional scope.
Phase 5: Full-Scale Integration & Strategic Expansion
Integrate the AI chatbot across all relevant customer touchpoints and expand its capabilities to include more complex tasks, personalization, and proactive engagement based on evolving tourist needs and business insights.
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