Innovations in a relational context: Mechanisms to connect learning processes of absorptive capacity
Bibliographic record
Abstract
Companies increasingly regard relationships with other companies as a source of competitive advantage. Relationships constitute a context in which the firm may learn and build absorptive capacity. This study provides an in-depth explanation of the key mechanisms that interlace the different learning processes leading to innovations in a relational context. A theoretical elaboration of these mechanisms precedes their empirical study within four customer-supplier dyads, centred on two focal customer organizations.The article contributes by discussing how the mechanisms act and interact to create absorptive capacity for a focal firm across relationships. We find that structural learning mechanisms, while necessary are not sufficient to explain variation in the presence of absorptive capacity across different learning contexts. Cultural, psychological and policy learning mechanisms complement the picture. From the empirical analysis we derive propositions to guide further research into the creation of absorptive capacity in a relational context.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".