ArticlePsychology research and behavior management2023
Unraveling the Influential Mechanisms of Social Commerce Overloads on User Disengagement: The Buffer Effect of Guanxi.
Article in Psychology research and behavior management, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The effects of responsiveness, perceived warmth, and anthropomorphism on university students' use of conversational AI for learning support: a chain mediation analysis based on S-O-R framework.Frontiers in psychology · 2026Article
- How Does Interactivity Shape Users' Continuance Intention of Intelligent Voice Assistants? Evidence from SEM and fsQCA.Psychology research and behavior management · 2024Article
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Authors and funding
3 authors.
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Abstract
Purpose: Although user engagement has been paid increasing attention, the work on user disengagement is scarce, and little is understood about how overloads elicited by excessive social commerce activities affect user disengagement. Based on the stimulus-organism-response (SOR) framework and psychological reactance theory (PRT), the authors aimed to investigate the effects of social commerce overloads (SCOs) on user disengagement, its influential mechanism, and the buffer effect of guanxi. Participants and Methods: The authors conducted an online survey to collect the data and then examined our theoretical model and hypotheses. This study employed SPSS 20.0 software and Amos 24.0 software to examine the hypothesized relationships and the model. Results: Social commerce overloads (ie, information overload (IO), social overload (SO), and communication overload (CO)) positively impact reactance via inferences of manipulative intent (IMI) and compulsive perception (CP); IMI and CP positively influence reactance; IMI, CP, and reactance positively affect user disengagement (ie, neglecting behavior and blocking behavior); guanxi has the buffer effect on the relationship between IMI (CP) and user disengagement, negatively moderates the impacts of IMI on user disengagement (ie, neglecting behavior and blocking behavior), and negatively moderates the effects of CP on blocking behavior but not neglecting behavior. Conclusion: The findings of this study contribute to the literature on PRT and user disengagement by displaying the effects of excessive social commerce activities on user disengagement and uncovering the buffer effect of guanxi, which can help social e-commerce practitioners better reduce the negative effect of social commerce overloads.
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