A study on artificial intelligence-based differentiation strategies in travel agencies

The primary aim of this thesis is to investigate how travel agencies that employ artificial intelligence (AI)-supported digital applications reflect these tools in their differentiation strategies and how they evaluate them in relation to perceptions of business performance. Within the scope of its sub-objectives, the study examines the specific areas of differentiation strategies in which travel agencies primarily adopt AI-supported applications, the underlying reasons for these strategic choices, the performance perceptions guiding their adoption, and the rationales behind these perceptions. Given the explanatory and in-depth nature of the research objective, a mixed methods design was adopted, with a predominant emphasis on qualitative inquiry. In this way, qualitative and quantitative data collection processes were structured to complement one another, ensuring both consistency and semantic depth across the findings. In the quantitative phase, valid survey data were analyzed from 106 A-group certified travel agencies operating in Antalya city center and providing international services. In the qualitative phase, data were gathered through semi-structured interviews conducted with five travel agency managers. Descriptive statistics regarding AI-supported digital applications revealed that the mean values of variables were at a medium-to-high level. The highest means were observed in the dimensions of image, quality-support, and design differentiation, while the lowest mean was found in the non-differentiation dimension. The findings indicated that participants perceived AI as a factor enhancing differentiation in businesses, whereas perceptions of non-differentiation strategies remained low. Quantitative results further suggested that AI is primarily preferred in travel agencies to reduce costs, ensure operational efficiency, and increase customer satisfaction. Moreover, AI-based differentiation strategies demonstrated significant interrelationships, with the strongest correlations observed between quality-support and design strategies, quality-support and service strategies, and service and image strategies. In contrast, the non-differentiation dimension showed no significant relationship with any other differentiation strategies. When examining the relationships between differentiation dimensions and performance, all relationships except for non-differentiation were found to be positive and significant. The strongest relationship was between service differentiation and performance, followed by quality-support, price, and design differentiation. Word cloud analysis revealed that the most frequently repeated concepts in participants' statements were "customer," "artificial intelligence," "satisfaction," "service," "supported," "efficiency," and "competition." Within the qualitative findings, the first main category identified was "differentiation strategies." Themes under this category included AI and price differentiation, AI and image differentiation, AI and quality-support differentiation, AI and design differentiation, AI and non-differentiation, and AI and service differentiation. The second main category identified was "business performance," with themes including AI and general competition, AI and managerial approach, AI and factors influencing transition, AI and the impact of encountered problems on performance, and AI and contribution to the sector. Findings from the code matrix analysis indicated that within the differentiation strategies category, the theme with the highest proportion was "AI and price differentiation," accounting for the largest share of coding. This was followed by "AI and quality-support differentiation," "AI and service differentiation," "AI and image differentiation," and "AI and design differentiation." The lowest frequency was observed in the "AI and non-differentiation" theme. In the code matrix analysis of the business performance category, the theme with the highest frequency was "AI and contribution to the sector." This was followed by "factors influencing the transition to AI," with both themes strongly emphasizing the impact of AI technologies on sectoral transformation and adoption processes. The originality of this thesis lies in its proposal of a holistic perspective that evaluates AI in conjunction with differentiation strategies specifically within the context of travel agencies. In the existing literature, the role of AI in tourism has largely been examined with reference to different actors, while strategic and performance-based effects at the scale of travel agencies have remained insufficiently addressed. In this respect, the study aims to fill a gap at both theoretical and sectoral levels by explaining the strategic positioning of AI technologies in travel agencies.