Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine what distinguishes bet-the-company decisions from strategic decisions in general, what forms do they take and under what circumstances do firms make them. Design/methodology/approach – From a comprehensive review and synthesis of relevant literature and media coverage since 1980, the author identifies 42 cases of bet-the-company decisions, from which the conclusions are drawn. Findings – The author identify four characteristics, three distinct types and four drivers of bet-the-company decisions. Research limitations/implications – The author draws conclusions from a qualitative analysis of 42 cases mentioned in prominent business media, supplemented by a literature review. Identifying the underlying processes that result in betting the company would require a more in-depth series of case studies. Acquiring statistical evidence would require more formal meta-analysis that this paper lacks. Practical implications – The paper identifies common characteristics, types and drivers of bet-the-company decisions that decision-makers could use to judge to recognize whether they also face such a situation. Originality/value – Executives are very sensitive to decisions with extreme consequences. This paper offers the first review of bet-the-company decisions, decisions that lie at the upper extreme of the range of strategic decision consequences.
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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".