Understanding consumers’ continuous-use intention of crowdsourcing logistics services: Empirical evidence from China

• Main Authors: Liu, Yu, Shang, Meng, Jia, Chunjie, Lim, Xin-Jean, Ye, Ye
• Format: Article
• Language: English
• Published: Elsevier 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/112808/1/112808.pdf; http://psasir.upm.edu.my/id/eprint/112808/; https://www.cell.com/heliyon/fulltext/S2405-8440(24)05850-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS240584402405850X%3Fshowall%3Dtrue
• Institution: Universiti Putra Malaysia

Crowdsourcing logistics-based O2O (online to offline) has been increasingly implemented to help individuals or merchants tackle down the problem of intra city instant delivery in China. However, since insufficient control is imposed on free couriers, consumers are subjected to certain risks generated by the uneven service quality provided by free couriers, such that the continuous-use intention to adopt crowdsourcing logistics may be affected in an unexpected manner. A sampling survey was carried out in China's first- and second-tier cities, with 292 valid questionnaires collected. On that basis, the corresponding hypotheses were tested using the partial least squares (PLS) method. The findings of this study revealed that trust, perceived value, and satisfaction positively contributed to continuous-use intention, where trust contributed the most. Perceived risk exerted a significant negative effect on continuous use intention. Trust is capable of notably reducing perceived risk. Crowdsourcing logistics service quality is the critical driving variable of perceived value and satisfaction. Perceived risk has a negative moderating effect on satisfaction-continuous-use intention relationship, showing that the higher the perceived risk, the weaker the effect of satisfaction on continuous-use intention. Given perceived risk, a conceptual model was built by integrating e-CSI model (e-Customer Satisfaction Index Model) and PAM-ISC model (Post-acceptance Model of IS Continuance Model). From the integration, the findings of this study are expected to provide decision-making basis for crowdsourcing logistics platforms to help solve the “last mile” delivery problem. © 2024