Algorithm Envelopment in Platform Markets

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 that demonstrates how an entrant with a superior algorithm technology may outcompete an incumbent possessing a user base advantage, a strategy we call “algorithm envelopment.” By considering 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 impacting 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 show how teasing apart network effects, data-driven learning, and algorithm technology in envelopment attacks can generate novel implications for incumbency advantages, yield insights into platform diversification, and inform antitrust policymaking.