Innovations in a relational context: Mechanisms to connect learning processes of absorptive capacity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it