Growing global concern over the environmental impact of fast fashion and consumerism has spurred interest in sustainable development and the circular economy. The secondhand clothing market is expanding rapidly, reaching $895.6 billion globally in 2020 and projected to reach nearly $420 billion in China by 2025. However, China's clothing recycling rate remains low, with most used clothing handled by recycling companies with low utilization rates. Online secondhand clothing platforms are increasing the recycling rate, but it lags behind the total amount of used clothing. Over half of Chinese consumers without prior secondhand trading experience are considering future participation. The textile and garment industry is among the world's most polluting, with China being a major producer and exporter. Approximately 26 million tons of used clothing are discarded annually in China, with less than 1% reused. While previous research has explored secondhand clothing consumption in developed nations primarily using the Theory of Planned Behavior (TPB), this study addresses a gap by focusing on reuse intention and practices in a developing context like China and incorporating emotional factors.

This study's theoretical foundation is the Theory of Interpersonal Behavior (TIB), which considers cognitive/attitude, social factors, and affect in shaping intentions. Unlike the TPB, which focuses on rational decision-making, TIB incorporates emotional factors, aligning with research suggesting the influence of affect on pro-environmental behavior. Existing literature supports the validity of TIB in predicting pro-environmental intentions and behaviors. The study modifies TIB by incorporating environmental factors: perceived values on sustainability (PVS), problem awareness (PRA), ascription of responsibility (ASR), sense of community (SOC), and anticipated guilt (ATG). Hypotheses were formulated to explore the relationships between these factors and reuse intention (RUI) and reuse practices (RUB). The hypotheses propose positive relationships between PVS, PRA, ASR, SOC, and ATG with RUI, and a positive relationship between RUI and RUB.

A cross-sectional study using convenience sampling collected quantitative data from 514 Chinese adults (18+) through an online survey using WJX. The sample size was determined using G*Power and followed recommendations for PLS-SEM analysis. Data collection ran from June 14th to July 29th, 2022, with initial 888 questionnaires screened to remove invalid responses. The questionnaire included sections on demographic information and measurement items adapted from existing literature for PVS, PRA, ASR, SOC, ATG, RUI, and RUB, all measured using a seven-point Likert scale. The questionnaire was translated and reviewed by two specialists. A pilot study was conducted prior to the main data collection. Common method bias was assessed using the variance inflation factor (VIF), with all values being below 5, indicating an acceptable level. Multivariate normality was tested using Mardia's multivariate skewness and kurtosis. PLS-SEM was employed for data analysis using SmartPLS, assessing both the measurement and structural models. Convergent validity, discriminant validity (using Fornell-Larcker criterion and HTMT), and internal consistency (Cronbach's alpha, composite reliability) were examined. Hypotheses were tested using bootstrapping with 5000 iterations. Multigroup analysis (MGA) was conducted to investigate differences based on gender, age, education, and income groups.

The demographic profile of the respondents included a relatively even distribution across genders, age groups, education levels, marital status, location, and various other demographic details. The internal consistency and convergent validity of the measurement model were established as Cronbach's alpha, composite reliability, and AVE values for all constructs exceeded the acceptable thresholds. Discriminant validity was confirmed via Fornell-Larcker criterion and HTMT values. The measurement model demonstrated substantial predictive power for RUI (R² = 0.401) and RUB (R² = 0.365). Hypothesis testing revealed that PVS (β = 0.106, p < 0.05), ASR (β = 0.126, p < 0.01), SOC (β = 0.206, p < 0.01), and ATG (β = 0.248, p < 0.01) significantly and positively influenced RUI, while PRA did not (p > 0.05). RUI significantly and positively predicted RUB (β = 0.604, p < 0.01). Multigroup analysis revealed significant differences in the relationship between PRA and RUI, ASR and RUI, as well as PVS and RUI, across different demographic groups (gender, education level, and income).

The study's findings demonstrate the importance of PVS, ASR, SOC, and ATG in shaping reuse intention of secondhand clothing. The significant influence of PVS highlights the value consumers place on sustainable practices. The impact of ASR emphasizes the role of moral responsibility in pro-environmental behaviors. SOC's influence showcases the role of social norms and community influence. The strong effect of ATG underscores the importance of emotional factors in driving sustainable choices. The insignificant role of PRA suggests that simply increasing awareness of environmental issues may not be enough to encourage reuse behaviors. This aligns with research suggesting that simply providing information about environmental problems is insufficient for changing behavior. The strong relationship between RUI and RUB confirms that intention is a significant predictor of behavior. The findings of the MGA suggest that marketing efforts should consider demographic factors to tailor messaging effectively. This will improve the efficiency of marketing campaigns.

This study extends existing research by examining the intention to sell or swap secondhand clothing in China using a modified TIB model that includes environmental factors. It reveals that perceived values related to sustainability, ascription of responsibility, sense of community and anticipated guilt are important drivers of reuse intention, highlighting the value of emotional and social factors in promoting sustainable practices. Future research could explore additional disposal methods (renting, redesigning), incorporate moderating variables, and utilize longitudinal designs to capture dynamic changes. The study provides valuable insights for policymakers and practitioners in promoting the circular economy of secondhand clothing.

The cross-sectional design limits the ability to infer causality. Convenience sampling may not fully represent the entire Chinese population. The focus on selling/swapping might not encompass the full spectrum of secondhand clothing disposal methods. While MGA identified demographic differences, the study did not delve into the specific mechanisms of these influences. Future research should consider addressing these limitations.