Competitor identification with memory in a dynamic financial transaction network

Competitor identification (CI) is an essential step in establishing an effective competitive business strategy. For complex business environments, network-based CI methods have been studied in the literature, aiming to shed light on the blind spots in managers’ radars. Typically, CI is based on netw...

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Bibliographic Details
Published inAnnals of operations research Vol. 341; no. 1; pp. 349 - 374
Main Authors Choi, Jeongsub, Kim, Byunghoon, Lee, Ho-shin
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2024
Springer Nature B.V
Subjects
Business and Management
Business competition
Combinatorics
Commonality
Competition
Luminous intensity
Managers
Operations Research/Decision Theory
Original Research
Theory of Computation
Competitor identification
Multimarket competition
Dynamic network
Financial transaction
Competitive analysis
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Summary:Competitor identification (CI) is an essential step in establishing an effective competitive business strategy. For complex business environments, network-based CI methods have been studied in the literature, aiming to shed light on the blind spots in managers’ radars. Typically, CI is based on networks without temporal information, despite the dynamic changes in business environments. Alternatively, the temporal information is considered in CI by simply accumulating the intensities of synchronous interfirm competition evaluated over time. As a result, competitors’ actions in the past are overlooked in evaluations of interfirm competition, although such actions remain in managers’ memories. In this study, we propose a new method for CI incorporating memories of the past transactions of competitors in a dynamic network. The proposed method measures the interfirm competition between firms based on their resource similarity and market commonality in a dynamic financial transaction network. The proposed method facilitates capturing the asynchronous competition from suppliers and demanders taken by competitors. We evaluate the proposed method on a toy network and on a case of interfirm transactions in Korea from 2011 to 2014. The results show that the temporal information in dynamic networks and memory about past transactions improves the predictive accuracy in CI with the proposed method.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-023-05552-7