Lee, Vicky (Younjung) and Han, Tian (2025) Manager-oriented competition networks: a machine-learning approach. In: Academy of Management (AOM) 85th Annual Meeting, 25th - 29th 2025, Copenhagen, Denmark. (In Press)

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Abstract

Identifying competitors from a managerial standpoint is pivotal for deciphering the factors that shape major corporate decisions and subsequent performance outcomes. This paper employs a supervised machine learning approach to identify a focal firm’s competitors from a managerial perspective, constructing a large-scale machine-learning-trained competitor networks (MLCN) of US public firms. To evaluate its effectiveness, we analyze how MLCN-identified competitors impact a focal firm’s decisions on strategic decisions including product changes, mergers and acquisitions, and strategic alliances. We then compare MLCN’s performance to that of non-manager-oriented competition networks, TNIC. Our findings show that MLCN is more robust than TNIC at predicting competitor influence on critical corporate decisions, suggesting its empirical applicability to a broad range of topics involving industry phenomena and managerial decisions.

Item Type:	Conference or Conference Paper (Paper)
Uncontrolled Keywords:	competitor identification, machine learning, strategic mimicry
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > School of Accounting, Finance and Economics
Last Modified:	24 Feb 2025 13:06
URI:	http://gala.gre.ac.uk/id/eprint/49832

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