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The Collaborative Interaction with Pokémon-Go Robot uses Augmented Reality technology for Increasing the Intentions of Patronizing Hospitality

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Abstract

The aim of the present study is to present a new research model which includes both extrinsic and intrinsic determinants that influence Pokémon-Go robot users’ behavioral intention to patronize hospitality firms that draw visitors with Pokémons. Pokémon-Go Robot uses Augmented Reality technology in this study. Survey data collected from 261 usable questionnaires were tested against the research model using the structural equation modeling approach. The results present that all the proposed variables were found to be critical factors significantly influencing Pokémon-Go robot users’ patronizing intention. The application area of proposed theoretical model is new; very sparse research has been undertaken on exploring Pokémon-Go robot users’ intentions toward visiting hospitality firms attracting guests by virtual monsters. The present study mainly contributes to an understanding of the factors that actually influence Pokémon-Go robot users’ intention to patronize hospitality firms using virtual monsters to boost their sales.

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Acknowledgments

This study was supported by grants from the course project for thinking about politics approved by the Teaching Affairs Office of Yulin Normal University in 2020. The name of the course project is the Internet of Things control technology. The course project number is No. 2020KCSZ21. The manager of the course project is Jian-Hong Wang. The source of funding for the course project is the construction funding of the course. This study also was supported by the scientific research funds of Yulin Normal University. The authors acknowledge the partial support for this research by the Shandong Provincial Natural Science Foundation No. ZR2020MG015.

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Wang, HY., Wang, JH., Zhang, J. et al. The Collaborative Interaction with Pokémon-Go Robot uses Augmented Reality technology for Increasing the Intentions of Patronizing Hospitality. Inf Syst Front 26, 107–119 (2024). https://doi.org/10.1007/s10796-021-10200-1

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