Abstract
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.