The social currency, the existing potential resource in the social networks and communities of an individual, has become more significant in the era of information technology. Meanwhile, the rapid development of Internet service, especially its application on mobile devices, brings many new contents of prosocial behaviors (PBs), which benefits both individuals and communities. Specifically, social currency plays a positive role in promoting PB, forming positive personalities, promoting positive social adaptation, and contributing to human survival and social development. However, the theoretical research in this field still lags far behind the development of practice, and the research on the impact of social currency on PB remains exceedingly scarce. Grounded in the social exchange theory (SET), the present study collects a total of 497 WeChat user questionnaires, constructs a model for the influence mechanism of social currency on PB, and tests the hypothesis through hierarchical regression. According to the results obtained, it was found that the social currency is positively associated with PB, and users with a more social currency are more likely to act prosocially on their own. The PB of an individual is then influenced by the social currency generated through collective self-esteem (CS) so that the perception of CS can be considered as a mediating variable. Moreover, the communication network heterogeneity (CNH) moderates the above relationship, and the degree of heterogeneity will have different effects on the relationship of CS. The obtained conclusions enrich the previous theoretical results of PB and provided new insights for social managers to enhance the prosocial-related behaviors for the group, organization, and society.

Purpose: Nowadays, some online retailing platforms emerge to integrate transport capacity to provide standard distribution service for sellers. Such an integrated form of service is defined as delivery alliance (DA). To have a better understanding of how to price the service, this study aims to fixate on the seller’s problems and builds a series of profit maximization models in accordance with the two-sided market pricing theory within a platform business model.Design/methodology/approach: In the present study, some optimization models are built in the two-sided market type and the optimal solutions are found in a three-dimensional decision space. By using the basic model as the benchmark, some optimization problems of DA in realistic situations are discussed. Particularly, a power-law-distribution model is established to deal with the uncertainty in forecasting. Also, a price-sensitive model and a loss-aversion model are presented to describe the various reactions of sellers to charging modes. Finally, some combined situations are discussed and the strategies are compared under the mentioned models.Findings: By selecting the basic model as the benchmark, the specific pricing strategies are found for each context to yield the optimal profits. The flexibility of pricing strategy in the basic model and rigid pricing strategies in extended models, are discussed. As a result, the guidelines for the online retailing platforms are developed on designing and pricing the DA service.Research limitations/implications: First, it would be interesting to expand the pricing plan of the platform. For instance, menu pricing and quantity discount have not been considered, which are common in practice. The time discounting has also been ignored. If the time value were calculated, the contract fees would be more critical due to the earliest of collecting money. Finally, those joiners who have huge order sizes are crucial for the ecosystem indeed, but arouse no attention. While in reality, they may have more power to bargain with the platform. Thus, how the platform competition affects the pricing strategies needs future research.Originality/value: The optimal pricing strategies under these models are analytically found out, and it is shown that the presented models result in the same scale of joiners and profits in optimization. This suggests that DA works well in various behavioral contexts. This also suggests that DA is a significant controller in service quality improvement. Then, the optimal pricing strategies are compared among all the models. During this, it is discovered that the realistic contexts might reduce the profit, whereas an appropriate pricing strategy can pull this back without loss of service quality.