Modeling the evolution of competitive market structure via competitive group dynamics

Amirali Kani, Duncan K.H. Fong, Wayne S. DeSarbo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: This paper aims to examine the evolution of a competitive market structure over time through the lens of competitive group membership dynamics. Design/methodology/approach: A new hidden Markov modeling approach is devised that accounts for the three sources of competitive heterogeneity involving managerial strategy, corporate performance and the impact of strategy on performance. In addition, some observed “entry” and “exit” states are considered to model firms’ entry into and exit from the market. The proposed model is illustrated with an investigation of the US banking industry based on a data set created from the COMPUSTAT database. This paper estimated the model within the Bayesian framework and devised a reversible jump Markov chain Monte Carlo estimation procedure to determine the number of latent competitive groups and uncover the characteristics of each group. Findings: This paper shows that the US banking industry, contrary to the prior findings of having a relatively stable structure, has, in fact, gone through dramatic changes in the past number of decades. Originality/value: Contrary to prior work that has primarily focused on managerial strategy to study market evolutions, the competitive groups perspective accounts for all three sources of intra-industry competitive heterogeneity. In addition, unlike prior research, the analysis is not limited to firms remaining in the panel of study for the entire observation period. Such limitation results in missing the various changes that occur in the competitive market structure because of the new entrants or the struggling firms that do not survive in the market.

Original languageEnglish (US)
Pages (from-to)457-479
Number of pages23
JournalJournal of Modelling in Management
Volume18
Issue number2
DOIs
StatePublished - Mar 31 2023

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Strategy and Management
  • Management Science and Operations Research

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