Role of tourist-chatbot interaction on visit intention in tourism: the mediating role of destination image

ABSTRACT

The current research examined the link between the informativeness of tourist-chatbot interaction, destination image, and visit intention in two studies. In the first study, 111 participants were asked to interact with ChatGPT about a destination (i.e. Batumi) for 5–10 min. The conceptual model was analyzed using the Structural Equation Modelling framework. Findings suggested that the informativeness of tourist-chatbot interaction would increase destination image and visit intention. Destination image was also directly and positively related to visit intention. Specifically, destination image mediated the link between the informativeness of tourist-chat bot interaction and visit intention. A second study (N = 184) was conducted, in which the entire procedure was the same as the first study, to test the replicability of the current findings. Consequently, all results remained consistent with the first study. This is the first study to show the mediating role of destination image in the link between human-machine interaction and visit intention in tourism research. Thus, the findings would expand the current understanding regarding the role of human-machine interaction on attitude and behaviour.

KEYWORDS:

• Tourist-chatbot interaction
• human-machine interaction
• destination image
• visit intention
• structural equation modeling
• Turkey

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The study data can be accessed via the following anonymized link: https://osf.io/u6cjz/?view_only=7e6b6459d4c64d15926c8ac147623dc8

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 Based on similar research (see Orden-Mejía & Huertas, 2022a), the power analysis parameters were as follows: the number of indicators (10), the number of latent variables (3), power level (0.80), and the expected effect size (0.3).