ChatGPT의 여행일정수립 서비스품질이 지속사용의도에 미치는 영향 연구 - TAM-TTF 모델을 중심으로 -
The Effect of ChatGPT’s Travel Itinerary Service Quality on Continuous Use Intention - Applying the TAM-TTF Model -
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This study examined the influence of ChatGPT’s travel itinerary planning service quality on task-service fit (TSF), perceived ease of use (PEOU), perceived usefulness (PU), and continuous use intention (CUI). Drawing upon an extensive literature review, three dimensions of ChatGPT’s service quality pertinent to travel itinerary planning were identified: accuracy, consistency, and accessibility. Integrating the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) model, the study hypothesized that these service quality attributes directly impact TSF, which subsequently influences PU, PEOU, and CUI. A survey was administered to individuals possessing prior experience in planning travel itineraries utilizing ChatGPT. Data were collected via an online survey conducted from October 28 to November 11, 2024, and 225 valid responses were subjected to empirical analysis. The findings indicated that accuracy and consistency exerted a significant positive influence on TSF. Secondly, TSF was found to be positively associated with both PEOU and PU. Finally, both PEOU and PU were identified as significant positive antecedents to CUI. Based on these empirical results, theoretical and practical implications are discussed.
Abstract
This study examined the influence of ChatGPT’s travel itinerary planning service quality on task-service fit (TSF), perceived ease of use (PEOU), perceived usefulness (PU), and continuous use intention (CUI). Drawing upon an extensive literature review, three dimensions of ChatGPT’s service quality pertinent to travel itinerary planning were identified: accuracy, consistency, and accessibility. Integrating the Technology Acceptance Model (TAM) and the Task-Technology Fit (TTF) model, the study hypothesized that these service quality attributes directly impact TSF, which subsequently influences PU, PEOU, and CUI. A survey was administered to individuals possessing prior experience in planning travel itineraries utilizing ChatGPT. Data were collected via an online survey conducted from October 28 to November 11, 2024, and 225 valid responses were subjected to empirical analysis. The findings indicated that accuracy and consistency exerted a significant positive influence on TSF. Secondly, TSF was found to be positively associated with both PEOU and PU. Finally, both PEOU and PU were identified as significant positive antecedents to CUI. Based on these empirical results, theoretical and practical implications are discussed.