Modeling the evolution of competitive market structure via competitive group dynamics
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".