ABSTRACT:
Understanding metaverse gaming experience value (MGEV) is crucial for the success of metaverse games. However, prior research lacks context-specific theoretical knowledge of MGEV dimensions, roles, and practical guidance for enhancing player experience value. To address this gap, we conducted an abductive, multi-study investigation to develop a multi-dimensional typology of MGEV, demonstrate its nomological validity, and propose a player-centered approach for its investigation. First, we performed qualitative manual coding of online reviews to identify six MGEV dimensions. We developed a two-axis typology that categorizes these dimensions by motivation- (intrinsic vs. extrinsic) and activeness-focused (active vs. reactive) perspectives. Second, we developed a deep-learning classification model to automate coding and create a panel dataset. We validated the relationships between MGEV dimensions and players’ word-of-mouth (WOM) by analyzing the panel dataset. Third, we applied a player-centered approach with cluster analysis to the coded data. We uncovered three player groups with distinct MGEV profiles (i.e. intrinsic value-dominated, extrinsic value-dominated, and active value-insensitive), metaverse gaming participation characteristics, and WOM. This paper identifies the context-specific and fine-grained dimensions of MGEV, establishes a valid MGEV framework, and reveals distinct player groups with unique MGEV profiles.
Key words and phrases: Metaverse gaming, experience value, deep learning, player-centered approach, online games, online players