Strategic choices of exploration and exploitation alliances under market uncertainty
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a better understanding of what drives firms’ choice between exploration alliances and exploitation alliances by examining the role of organizational slack and its interaction with market uncertainty. Design/methodology/approach An empirical study is conducted based on 1,614 alliances formed by 581 US biotechnology firms, and the hypotheses are tested using a zero-inflated multilevel Poisson model. Findings The results indicate that firms’ strategic choice to pursue exploration or exploitation alliances is a reflection of organizational intention and adaptation to environmental turbulence. More specifically, firms with more financial slack tend to form more exploration alliances and fewer exploitation alliances. However, under high market uncertainty, firms with financial slack tend to establish more exploitative partnerships and avoid exploration collaborations. Originality/value This paper contributes to the literature on exploration–exploitation alliances, which tends to fall short of providing an understanding of why organizations pursue such alliances. By identifying the impact of organizational slack and its interaction with market uncertainty, this study shows that organizations are able to respond to environmental change, and those with capabilities are likely to craft their strategic choice configurations based on their own characteristics.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".