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

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.

Empirical analysis of the impact of the digital economy on the green transformation of manufacturing: Evidence from China.

In this study, the entropy method and the Super-SBM model with unexpected output are used at first to calculate the digital economy development index and the level of green transformation in manufacturing. Then, a range of multi-dimensional empirical methods, including fixed effects models, threshold models, and mediation models, are applied to analyze the characteristics shown by the impact of digital economy development on the green transformation of manufacturing. The research results are obtained as follows. Firstly, the digital economy contributes significantly to promoting the green transformation of manufacturing after excluding the macro-system environmental effects, conducting such robustness tests as stepwise regression and introducing instrumental variables. Secondly, there is a nonlinear relationship between the development of the digital economy and the green transformation of manufacturing with an increasing marginal effect. Lastly, it is revealed through mechanism analysis that the digital economy promotes the green transformation of manufacturing by enhancing the capabilities of green technological innovation and rationalizing industrial upgrading, with the partial mediation effects reaching 21.2% and 21.8%, respectively. Despite the contribution of digital economy to the advanced upgrading of industries, there is no mediation effect exhibited. In addition to confirming the path of achieving the green transformation of manufacturing through the digital economy, these results also guide the government on how policies can be formulated and improved to grow the digital economy and promote the green transformation of manufacturing.