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The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

This paper aims to address the issue of privacy leakage indirectly caused by non-private data shared by travellers on location-based social media (LBSM) in data mining.

This paper proposes a privacy-preserving location data collection approach based on local differential privacy (LDP) and validates the feasibility of the approach through experiments on three real-world public check-in datasets.

The experimental results demonstrate the effectiveness of our proposed approach, which preserves privacy while retaining over 90% of the data utility.

This research has the potential to assist tourism practitioners in establishing improved collaborations with trusted third parties, enabling the exploration of user location data insights without undue concerns regarding indirect privacy breaches during data mining, thereby enhancing travellers’ experience and aiding businesses’ decision-making.

This paper is likely to be the first to represent a promising solution for addressing privacy leakage concerns related to non-sensitive data in data mining. It provides a location data collection technology that strikes a balance between user privacy and data utility for tourism practitioners.

The surge in the older population has gained significant interest within the tourism sector worldwide. Little empirical research has been conducted to examine the role of tourism experiences on older adults’ healthy aging. Based on Rowe and Kahn’s (1987) successful aging model, this study utilized a qualitative approach to examine the health benefits that tourism can play for seniors.

A total of 19 interviews were conducted with senior adults living in Macau aged over 60. Thematic analysis was employed for data analysis.

The results demonstrate that the tourism experience contributes to healthy aging in three ways: first, nature-based tourism benefits seniors’ physical health; second, the social interactions with friends, relatives, locals and fellow travelers during tourism activities benefit seniors' emotional well-being and third, transcendent tourism experiences can exhibit positive spirituality among seniors.

This paper not only enriches the theoretical and empirical studies on tourism and healthy aging but also puts forward relevant empirical suggestions to destination marketing organizations that target the senior tourist market.