The effectiveness of contractual and trust‐based governance in strategic alliances under behavioral and environmental uncertainty
Bibliographic record
Abstract
Research summary: We examine the interplay of behavioral and environmental uncertainty in shaping the effectiveness of two key governance mechanisms used by strategic alliances: contractual and trust‐based governance. We develop and test hypotheses, using a meta‐analytic dataset encompassing over 15,000 strategic alliances across 82 independent samples. We find that contractual governance works best under low to moderate levels of behavioral uncertainty and moderate to high levels of environmental uncertainty, while it is detrimental to alliance performance when both types of uncertainty are low or high. Trust‐based governance is most effective at high levels of behavioral uncertainty and low levels of environmental uncertainty. It suffers a large loss of usefulness at high behavioral uncertainty as environmental uncertainty increases . Managerial summary: Strategic alliances allow firms to gain greater efficiency and create value. Yet, many such alliances fail because they are not able to deal with the twin challenges posed by behavioral and environmental uncertainty. Findings from our meta‐analysis imply that under conditions of high behavioral uncertainty and low‐to‐moderate levels of environmental uncertainty, the use of trust‐based governance alongside contractual governance might enhance the latter's effectiveness. The combined effectiveness of contractual and trust‐based governance under high levels of both behavioral and environmental uncertainty is not obvious. When both behavioral and environmental uncertainty are high, contractual governance hurts alliance performance while trust‐based governance does not function at its best either. Under these conditions, it might be better for firms to turn to hierarchy or vertical integration . Copyright © 2015 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 | 0.055 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".