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Understanding the psychological mechanisms of platform-enabled green disposal behavior: A hybrid SEM-ANN study on
Xingjun Ru1, Bo Gong1, Min Chen2
1School of Management, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang Province, People's Republic of China.
Abstract:
As global textile waste continues to rise, digital secondhand trading platforms have increasingly become key arenas for promoting clothing circularity. However, existing research across countries often treats these platforms as external conduits, with limited exploration of how their technological and social designs reshape users' psychological constructs and decision-making mechanisms. To address this gap, this study employs Affordance Theory as its foundation. It first reconstructs core constructs of the Theory of Planned Behavior (TPB) through scenario-based semi-structured interviews, then quantitatively validates these constructs using 1520 questionnaires combined with Partial Least Squares Structural Equation Modeling and Artificial Neural Networks. Findings reveal: Digital eco-identity amplifies effects across multiple psychological pathways; Perceived economic risk not only directly suppresses disposal intentions but also generates systemic negative effects by eroding trust in various platform affordances; Additionally, platform literacy exhibits a "high literacy-high concern" paradox, where higher literacy enhances identity transformation while amplifying risk sensitivity. This study makes three distinct contributions: Theoretically, this study advances beyond prior technology-augmented TPB research by embedding psychological structures within a socio-technical enlightenment framework, explaining how technology actively generates novel behavioral mechanisms. Methodologically, the integration of qualitative exploration with SEM-ANN enhances construct validity and predictive power. Practically, these findings provide actionable insights for platform design (e.g., strengthening disposal support features, establishing identity mechanisms) and policy (e.g., improving algorithm transparency and differentiated user support).
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