Value cocreation and wealth spillover in open innovation alliances
Bibliographic record
Abstract
In this study, we investigate the economic and strategic value of open innovation alliances (OIAs), in which collaborators and competitors integrate in the pursuit of the codevelopment of technological innovations. Given that OIAs differ substantially from traditional, closed alliances in many aspects, including their strategic scope and scale, governing mechanisms, and member composition, it is important to understand and assess the potential value inherent in these new modes of collaboration. Furthermore, OIAs evolve over time as the participating members are free to enter and leave at will. Therefore, we also examine the on-going value creation and wealth spillover that result from changes in membership. Moreover, we investigate how a firm's participation in an IT-based open alliance alters the market value of its rivals operating within the same marketplace. To gain additional insight into the factors that moderate the market valuation of OIA participation, several contextual factors, including the degree of partner heterogeneity, innovation type, and degree of openness of the OIAs are used to account for variability in abnormal returns. Based on 194 observations, we found that allying firms realize significant positive abnormal returns when their entry into an OIA is made public. The results also suggest that substantial excessive returns accrue to the allying firms with the belated entry of a market leader firm. Furthermore, we discovered that a firm's entry into an OIA increases, rather than decreases, the market valuation of its rivals. Interestingly, an incumbent rival that did not participate in the alliance appears to gain greater free-riding benefits from the OIA, as compared to peer rivals. Innovation type and openness were significantly associated with the amount of abnormal returns accruing to allying firms, while no significance was found for partner heterogeneity. Finally, we conclude with a discussion of the implications of our findings for research and practice with respect to value cocreation in multifirm environments.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".