Algorithm envelopment in platform markets
Publication Type
Journal Article
Publication Date
12-2024
Abstract
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.
Keywords
platform, network effects, envelopment, data-driven learning, artificial intelligence
Discipline
Operations and Supply Chain Management | Strategic Management Policy
Research Areas
Strategy and Organisation
Publication
Academy of Management Review
ISSN
0363-7425
Identifier
10.5465/amr.2023.0156
Publisher
Academy of Management
Citation
CHEN, Liang; ZHOU, Zhou; and CHAN, Lester.
Algorithm envelopment in platform markets. (2024). Academy of Management Review.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7647
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5465/amr.2023.0156
