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ALGORITHM ENVELOPMENT IN PLATFORM MARKETS.

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Τίτλος: ALGORITHM ENVELOPMENT IN PLATFORM MARKETS.
Συγγραφείς: CHEN, LIANG (AUTHOR) liangchen@smu.edu.sg, ZHOU, ZHOU (AUTHOR) zhou_zhou@fudan.edu.cn, CHAN, LESTER T. (AUTHOR) lesterchantl@gmail.com
Πηγή: Academy of Management Review. Jan2026, Vol. 51 Issue 1, p187-206. 20p. 4 Color Photographs.
Θεματικοί όροι: *Network effect, *Digital platforms, *Algorithms, *Value creation, Antitrust guidelines
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of Academy of Management Review is the property of Academy of Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Business Source Index
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  Data: ALGORITHM ENVELOPMENT IN PLATFORM MARKETS.
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  Data: <searchLink fieldCode="AR" term="%22CHEN%2C+LIANG%22">CHEN, LIANG</searchLink> (AUTHOR)<i> liangchen@smu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22ZHOU%2C+ZHOU%22">ZHOU, ZHOU</searchLink> (AUTHOR)<i> zhou_zhou@fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22CHAN%2C+LESTER+T%2E%22">CHAN, LESTER T.</searchLink> (AUTHOR)<i> lesterchantl@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Academy+of+Management+Review%22">Academy of Management Review</searchLink>. Jan2026, Vol. 51 Issue 1, p187-206. 20p. 4 Color Photographs.
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  Data: *<searchLink fieldCode="DE" term="%22Network+effect%22">Network effect</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+platforms%22">Digital platforms</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Value+creation%22">Value creation</searchLink><br /><searchLink fieldCode="DE" term="%22Antitrust+guidelines%22">Antitrust guidelines</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Academy of Management Review is the property of Academy of Management and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 187
    Subjects:
      – SubjectFull: Network effect
        Type: general
      – SubjectFull: Digital platforms
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Value creation
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      – SubjectFull: Antitrust guidelines
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              Text: Jan2026
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              Y: 2026
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