From Engagement to Immersion: A Self-Determination Theory and Approach to Gamified Cultural Tourism
Open Access
Article
Conference Proceedings
Authors: Lusha Huang, Yuxiang Wang
Abstract: In a rapidly growing economy, contemporary tourists are increasingly drawn to unique cultural encounters; this highlights the significance of innovative approaches in promoting cultural tourism. This paper, which relies on self-determination theory, adopts a mixedmethods approach, amalgamating text mining with quantitative and qualitative methodologies, to dissect the interplay of gamification in enhancing engagement within immersive cultural tourism, with a concentrated lens on the “Wizarding World of Harry Potter”. Immersive experiences, intricately crafted from profound narratives, engender deep-seated connections between participants and the embedded tales. Concurrently, the strategic deployment of gamification, while leveraging game mechanics, acts as a potent catalyst in bolstering engagement levels, serving as a conduit to heightened immersion. Rooted in motivational psychology, the tenets of self-determination theory emerge as indispensable when applied to game mechanics, fostering a richer, more holistic engagement and experience.This harmonious confluence of immersive narratives, gamification techniques, and self-determination principles not only augments engagement but, as underscored in this study, propels it toward deeper immersion, satisfying the intricate psychological cravings of tourists. This research provides an illustrative case study that contributes to the ongoing academic and industry discourse through a detailed analysis of selected immersive cultural tourism exemplars. In doing so, the paper paves the way for a more synchronized trajectory in cultural tourism, emphasizing the transition "From Engagement to Immersion" and underscoring the pivotal role of self-determination theory in gamified cultural tourism endeavours.
Keywords: Self-determination Theory, Immersive Experiences, Gamification, Cultural Tourism, Text mining
DOI: 10.54941/ahfe1004428
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