JOURNAL ARTICLE
Measuring Deterrence Motives in Dynamic Oligopoly Games.
Published In: Management Science (INFORMS), 2024, v. 70, n. 6. P. 3527 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Fang, Limin; Yang, Nathan 3 of 3
Abstract
This article develops a novel decomposition approach to measure deterrence motives in dynamic oligopoly games, providing a formal, scale-free metric that quantifies the proportion of entry motives attributable to deterrence. The framework distinguishes between direct effects (immediate profit impacts) and strategic effects (influences on rivals' future behavior), defining deterrence broadly as aggressive entry aimed at preventing rival entry or forcing rival exit. An empirical application to the Canadian coffee chain industry in Toronto (1989–2005), involving Coffee Time, Country Style, Starbucks, and Tim Hortons, reveals that deterrence motives are significant—especially for Starbucks, whose deterrence motives can account for up to 43% of entry motives in certain market types. Counterfactual simulations show that eliminating deterrence motives reduces Starbucks' outlet growth and market share, while increasing presence of smaller rivals, illustrating the impact of deterrence on market structure and industry dynamics. The paper also compares its counterfactual approach to existing methods, emphasizing its preservation of direct competition effects while removing deterrence incentives in future periods.
Additional Information
- Source:Management Science (INFORMS). 2024/06, Vol. 70, Issue 6, p3527
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4864
- Accession Number:177878302
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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.)
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