Competitive Intensity and Its Two-Sided Effect on the Boundaries of Firm Performance
Bibliographic record
Abstract
The new perspective emerging from strategy’s value-capture stream is that the effects of competition are twofold: competition for an agent bounds its performance from below, while that for its transaction partners bounds from above. Thus, assessing the intensity of competition on either side is essential to understanding firm performance. Yet, the literature provides no formal notion of “competitive intensity” with which to make such assessments. Rather, some authors use added value as their central analytic concept, others the core. Added value is simple but misses the crucial, for-an-agent side of competition. The core is theoretically complete but difficult to interpret and empirically intractable. This paper formalizes three, increasingly general notions of competitive intensity, all of which improve on added value while avoiding the complexity of the core. We analyze markets characterized by disjoint networks of agents (e.g., supply chains), providing several insights into competition and new tools for empirical work. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2737 . This paper was accepted by Bruno Cassiman, business strategy.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".