Process Capabilities and Value Generation in Alliance Portfolios
Bibliographic record
Abstract
This paper develops a multidimensional, process-based conceptualization of alliance portfolio management capability. Arguing that such a capability consists of organizational processes to proactively pursue alliance formation opportunities, engage in relational governance, and coordinate knowledge and strategies across the portfolio, we examine the impact of such a capability on organizational outcomes in the context of formal structure (alliance function) and strategy (portfolio diversity). Using data from 235 firms, we find that these three processes have a positive effect on a firm's alliance portfolio capital, and some of these effects are conditioned by a formal alliance function and diversity of the portfolio. Whereas the ability of proactive formation and relational governance processes to create value is further strengthened in the presence of an alliance function, that of the coordination dimension is weakened. Furthermore, the benefits of relational governance are strengthened for firms with diverse portfolios, whereas the benefits of coordination processes are weakened. We discuss implications of these findings for the alliance and network literature, and in general, for firm heterogeneity. In summary, we find evidence that variance in process-based capabilities to manage alliance portfolios can explain performance heterogeneity among firms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".