PurposeUpstream suppliers attempt to outsource product after-sales services to midstream third-party service providers while selling the product directly to downstream sellers, forming a networked supply chain. However, a problem of information asymmetry in the market demand among supply chain members exists. The authors investigate the impact of demand information asymmetry among third-party service providers, upstream suppliers and downstream sellers in the supply chain on the supplier's contract selection under the networked framework.Design/methodology/approachThe authors establish a model in which the supplier can use a wholesale price contract and facilitate a signaling game between the third-party service provider and the seller. Conversely, the supplier could use a menu contract to establish an incentive mechanism to solve information asymmetry. The authors propose heuristic algorithms to quickly estimate a supplier's optimal profit.FindingsThe results show that when the demand forecasting bias is relatively small, the use of a menu contract by the supplier could eliminate information asymmetry; when the demand forecasting bias is large enough, the signaling mechanism between the third-party service provider and the seller could alleviate the double marginalization effect in the supply chain. Although it is common to solve the asymmetric information problem by establishing incentive mechanisms, the authors found that in the latter case, the supplier is better off when no incentive mechanisms are implemented in the networked supply chain.Originality/valueThis study compares screening and signaling effects and compares firms' profits in both cases.

In the era of big data, data-driven services (DDS) have become critical competitive strategies for digital platform-based enterprises. This paper considers two operational modes of e-commerce platforms, which are self-operated and third-party modes, respectively, and they each lead a platform system. The Hotelling model is adopted to describe the competitive market of both platforms. We characterize their system performance functions. The optimization models are built using game theory to discuss the DDS and price decisions. We obtain the implementation conditions of DDS strategies for both platforms and the dominant situations of their respective DDS levels. We find that a platform adopting the price reduction strategy can improve the performance of its platform system while reducing the competitor’s system performance. From the system performance perspective, continuous improvement of the DDS level may appear “harming others may not benefit oneself”; that is, continuously improving the DDS level leads to a decrease in the competitor’s system performance but not necessarily an increase in its system performance. Further, consumer welfare within both platform systems shows the law of “as one falls then another rises”. As the big data industry matures, self-operated platforms would demonstrate the advantages of service level, profit, and system performance. In contrast, third-party platforms would have an advantage in consumer welfare. These conclusions have important implications for e-commerce platforms developing data-driven operations-based strategies.