The moderating effect of corporate reputation on inter-firm alliance impact on company performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to untangle the impacts of a firm’s corporate reputation and its alliance partners’ social capital on its financial performance, drawing on the relational and the network points of view. Design/methodology/approach This paper explored the moderating effect of corporate reputation on the relationship between partners’ social capital (e.g. resource heterogeneity, structural relations and partners’ social ties) and a focal firm’s performance. An OLS three-step regression model (controls, main effects and interaction effects) was used to test the proposed hypotheses based on 265 US joint ventures. Findings The influence of partners’ social capital on a focal firm’s performance is negatively moderated by the focal firm’s reputation at the firm and network levels; larger and more prestigious firms listed inFortunedatabase tend to choose partners with a higher level of resource heterogeneity, whereas smaller firms tend to choose partners in similar industries to increase economies of scale. The social capital factors of the partners will have different effects on the focal firm performance. Originality/value The value of this paper is in providing insight into the importance and nuances of corporate reputation in offsetting the advantages of inter-firm alliances and their impact on firm performance. In particular, the performance benefits of inter-firm alliance partners’ social ties and heterogeneous resources are negatively affected by the corporate reputation of a firm.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".