Workshop’s Topic: The theory of platform envelopment rests on network effects as the key mechanism for value creation, which nonetheless receives mixed support for its efficacy in determining competitive outcomes. We argue that the value of network effects depends on matching quality, which is a function of platform-specific algorithm technology and market-level data-driven learning. In formalizing these conceptualizations, we analyze a model demonstrating how an entrant with a superior algorithm technology may outcompete an incumbent possessing a user base advantage, a strategy we call "algorithm envelopment". By delving into specific characteristics of data-driven learning, our analysis leads to propositions regarding the entry barriers for the enveloper, illuminating how learning may overshadow or interact with network effects in affecting the enveloper’s market selection decisions. We also show that market selection may be contingent on whether algorithm envelopment is instituted through competition or mergers, suggesting an interdependence between "where to enter" and "how to enter". Finally, we explore the welfare effects of algorithm envelopment. We extend the recent debate on "data network effects" and showdata-driven learning, and algorithm technology in how teasingapart network effects,envelopment attacks can generate implications for incumbency advantages, yield novel insights into platform diversification, and inform antitrust policymaking.

Time and Location: 10:00-11:30 AM (GMT+8), Room A523 (School of Management)

Language: Bilingual (Chinese and English)