Competitive Rationales: Beneath the Surface of Competitive Behavior
Bibliographic record
Abstract
Competitive dynamics research has focused on studying whether rivals are able and likely to carry out competitive actions, typically by examining indirect reasons such as characteristics of the actions themselves, the firms involved, or the competitive context. We explore why rivals initiate a specific competitive action at a particular time and situation. Drawing from the philosophy of action literature, we introduce the concept of competitive rationales to examine the primary reasons that cause tactical actions. Given the rapid exchanges characterizing tactical competitive dynamics, we conducted an inductive, multicase study to explore the reasons behind over 800 discrete tactical decisions carried out by 9 professional basketball coaches during 15 basketball games. To garner insight, we develop a conceptual framework revealing their types and scope. Even during intense head-to-head rivalry, most rationales were not rivalrous but were instead organizational-to optimize resource use, strategic consistency, and reputation-or social-to manage relationships. Moreover, the three main types of rationales varied in scope, extending beyond immediate competitive situations and rivals to address longer term, strategic outcomes, and assorted stakeholders. Thus, our analysis reveals these rationales to be complex and potentially difficult for rivals to decipher. It also recasts each component of the dominant awareness-motivation-capability (AMC) model of rivalry, suggesting that awareness is challenged by subtle rationales, motivation drives not only action but also forbearance, and capability is both a requirement and product of action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".