ChatGPT for tourism: applications, benefits and risks

Analysis

Why This Paper Matters

This paper is significant because it is among the first to systematically explore the implications of ChatGPT and large language models (LLMs) specifically for the tourism and hospitality industry. Given the rapid adoption of generative AI across sectors, understanding its applications, benefits, and risks in tourism—a major global industry—is crucial for both practitioners and researchers. The paper provides a structured overview that can guide strategic decision-making for tourism businesses and inform policy discussions around workforce impacts.

The timing of the publication (March 2023) is notable, as it captures early reactions to ChatGPT's release, making it a foundational reference for subsequent studies. With 469 citations, it has already influenced a growing body of literature on AI in tourism.

Technical Contributions

• Application Mapping: The paper categorizes ChatGPT uses into front-of-house (e.g., customer service chatbots, personalized recommendations) and back-of-house (e.g., data analysis, content generation for marketing) operations.
• Stakeholder Analysis: It considers implications for multiple tourism stakeholders including tourists, businesses, employees, and destinations.
• Benefit-Risk Framework: Provides a balanced assessment of productivity gains versus potential job displacement and ethical concerns.
• Research Agenda: Proposes specific research questions for future empirical studies, such as the impact on service quality and employee well-being.

Results

The paper does not present quantitative results but offers qualitative findings: ChatGPT is likely to streamline customer service and increase operational efficiency. It notes that while some negative human resource consequences are expected, the technology is predicted to mostly enhance tourism employees rather than replace them. The authors emphasize that the full impact will depend on implementation strategies and regulatory frameworks.

Significance

This work has broader implications for AI research by demonstrating how LLMs can be analyzed within a specific industry context. It highlights the need for domain-specific studies that go beyond general AI capabilities to address sectoral nuances. The paper also underscores the importance of considering both opportunities and risks, contributing to responsible AI discourse. For AI practitioners, it provides a template for evaluating LLM applications in service industries and emphasizes the critical role of human-AI collaboration.