本研究旨在透過分析通路豐富度對科技焦慮及顧客參與的中介效果，最終塑造參與者對於元宇宙的在訪意願以及抗拒的結果，並考慮資訊超載的調節效果。本研究主要根據通訊管道擴張理論及層級化的顧客參與理論，藉由所提出的五階層模型架構探討潛在變數如何影響元宇宙慶典參與者。透過亞馬遜土耳其機器人蒐集448份問卷樣本，以偏最小平方結構方程模型進行分析。研究結果顯示相關經驗對於豐富度認知有顯著影響，且對於科技焦慮及顧客參與亦有顯著影響效果，隨後分別對於參與者的再訪意願及抗拒有顯著影響等假設。另外，結果亦顯示資訊超載對於行為型參與有顯著負向調節效果。本研究為以元宇宙平台作為通訊管道進行探討的先驅性研究，除了著重參與者豐富度認知之外，也結合不同面向的顧客參與（認知型、情感型及行為型），並證實其相關性。此外，本研究同時將正向（再訪意願）和負向（抗拒）的結果變數納入模型，藉以對參與者對於使用元宇宙的態度有更宏觀的瞭解。;The purpose of this research is to assess the effects of channel richness on technology anxiety and the hierarchical nature of customer engagement under the contingency of information overload in the usage of the metaverse, subsequently affecting the revisit intention and the resistance to the metaverse. Drawing on mainly Channel expansion theory and the notion of customer engagement, this research proposes a five-level framework to evaluate the focal forces and underlying processes related to the participants of the metaverse festival. The study utilizes the Partial Least Squares Structural Equation Modeling Method (PLS-SEM) to analyze the relationships under investigation. The dataset consists of responses from 448 participants who completed questionnaires gathered through Amazon Mechanical Turk (MTurk). The findings support the impact of relative experiences on the perception of channel richness. This, in turn, exerts influence over technology anxiety and customer engagement, subsequently shaping customer engagement toward revisiting or resisting metaverse festivals in the future. Regarding moderating factors, information overload is found to negatively affect behavioral engagement. Current research marks a pioneering contribution to the emerging field of metaverse festivals by investigating the determinants of perceived channel richness and exploring the relationships between customer engagement (cognitive, emotional, and behavioral) within the metaverse platform. Notably, it adopts a holistic perspective by simultaneously examining both positive (revisit intention) and negative (resistance) outcomes, thereby enriching our comprehensive comprehension of metaverse usage dynamics. Furthermore, conditional insight is given as the research is studied under the contingency of information overload.
