Optimal Distinctiveness in the Console Video Game Industry: An Exemplar-Based Model of Proto-Category Evolution
Bibliographic record
Abstract
In this paper, we develop an exemplar-based model of the emergence and evolution of proto-categories—new groupings of products that are only weakly entrenched but have the potential to become widely institutionalized—and examine how different positioning strategies of new entrants vis-à-vis the exemplar of a proto-category affect entrant performance. Empirically, we study the U.S. console video game industry where proto-categories frequently emerge and evolve around exemplary hit games. Analyzing a proprietary database of 6,544 games comprising 78 such proto-categories, we find that, in the early stages of proto-category emergence, conformity with the exemplar’s features is positively associated with new entrants’ sales. As a proto-category evolves, a moderate level of differentiation becomes important for enhancing sales. We also find that this temporal dynamic is driven by the changing competitive intensity in the proto-category and strongly mediated by critics’ reviews. Moreover, the mediating effect of critics’ reviews on entrant sales becomes increasingly salient with the evolution of a proto-category. Finally, we show that accounting for the influence of emerging prototypes does not diminish the explanatory power of the exemplar model we propose. We conclude the paper by discussing the implications of our findings for research on categorization and optimal distinctiveness.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".