Unpacking active participation and customer loyalty in metaverse retailing: an integrated means-end chain and cognitive-affective system approach
本研究整合手段-目的链和认知-情感系统理论，通过调查和实验发现元宇宙零售场景的设计美学和可试用性通过认知吸收和情感卷入影响用户的主动参与和重访意愿，为优化场景设计提供依据。
Purpose Metaverse retailing (MR) aims to transform users from passive receivers to active participants, yet there is a lack of active participation and willingness to return among users. This study employs the means-end chain (MEC) and cognitive-affective system (CAS) theory to investigate a realization model that uncovers how MR scene attributes (i.e. design aesthetics and trialability) translate to downstream outcomes (i.e. active participation and revisit intention). Design/methodology/approach A survey collected 320 responses that were analyzed using partial least squares structural equation modeling (PLS-SEM), and a 2 × 2 factorial experimental design with 172 participants was conducted using analysis of variance to test the research hypotheses. Findings Firstly, design aesthetics positively impact both cognitive absorption and affective involvement, while trialability primarily fosters cognitive absorption. Secondly, affective involvement is the dominant driver of active participation, whereas cognitive absorption exhibits a stronger direct influence on revisit intention. This clarifies a dual-channel mechanism where affection fuels immediate participation, and cognition underpins long-term loyalty. Finally, the casual link from active participation to revisit intention is affirmed, and the interactive effects between design aesthetics and trialability further inform the cognitive and affective responses. Practical implications These findings recommend optimizing MR scene design to enhance performance and nurture sustainable metaverse ecosystems. Originality/value By combining MEC and CAS frameworks, this study offers insights into the foundational drivers and mechanisms behind fostering active user engagement and loyalty in MR environments. The proposed theoretical framework unveils how environmental and technical attributes shape user responses in MR settings.