Opportunism and unilateral commitment: the moderating effect of relational capital
Bibliographic record
Abstract
Purpose This paper focuses on the decision by firms to commit and to invest unilaterally. It is concerned with the intriguing question as to whether unilateral commitments are mechanisms that help a firm manage risks in alliance relationships in a proactive manner. Design/methodology/approach The hypotheses are tested with survey data on 344 alliance relationships of European biotechnology small and medium‐sized enterprises (SMEs). Findings In this paper it is shown that unilateral commitments have a positive effect on perceived opportunistic behavior. However, the evidence suggests that, by creating a basis for exchange, relational capital moderates the relationship between unilateral commitments and the perception of opportunistic behavior. Research limitations/implications This research adopts a static perspective. It is known that alliances evolve, and develop. Consequently, future research could extend and modify this study along this dimension and analyze the evolution of unilateral commitments through longitudinal research. Practical implications From a managerial point‐of‐view, this paper shows that motivation for commitments is different and their effects on risk perception can be contradictory according to the level of relational capital in the inter‐organizational relationship. Originality/value This paper is one of the few empirical studies that explored the concept of unilateral commitments and provided empirical evidence to highlight the significance of some managerial practices such as building trust.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".