Alliance or Acquisition? A Mechanisms‐Based, Policy‐Capturing Analysis
Bibliographic record
Abstract
Research summary : While alliance researchers view prior partner‐specific alliance experience as influencing firms' subsequent alliance or acquisition decisions, empirical evidence on the alliance versus acquisition decision is surprisingly mixed. We offer a reconciliation by proposing and testing an analytical framework that recognizes prior partner‐specific experiences as heterogeneous along three fundamental dimensions: partner‐specific trust, routines, and value certainty. This allows us to use a policy‐capturing methodology to rigorously operationalize and test our mechanism‐level predictions. We find that all three mechanisms can increase the likelihood of a subsequent alliance or acquisition, and in terms of the comparative choice between alliances versus acquisitions, partner‐specific trust pulls towards alliances, and value certainty pulls towards acquisitions. We conclude with a discussion of the theoretical and empirical implications of our approach and method . Managerial summary : This study focuses on an important corporate decision: When a firm has had an alliance with another firm, how would that experience affect the likelihood of a future alliance or acquisition with that same firm? We first suggest that it will depend on three factors: the level of trust that existed in that prior alliance, the extent to which specific work routines were developed, and the degree to which the firm was able to confidently assess the value of the partner firm's resources. We then find that trust is a particularly strong predictor of future alliances, while confidence regarding value more strongly predicts future acquisitions. In this way, we demonstrate more precisely how past corporate choices can affect (consciously or unconsciously) future ones . © 2017 The Authors. Strategic Management Journal Published by John Wiley & Sons Ltd.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".