Absorptive capacity, relational learning and organizational culture in a knowledge management context: three essays on their influences in the innovation outcomes.
Bibliographic record
Abstract
RACAP)-and analyzes their influence on innovation outcomes (IO) in project teams.We also examine potential absorptive capacity as an antecedent of realized absorptive capacity.In addition, we propose that relational learning (RL) will play a moderator role reinforcing the PACAP-RACAP link Chapter 5 encloses the paper entitled "Absorptive capacity, innovation and cultural barriers: A conditional mediation model" (Journal of Business Research, 2014).On the basis of Zahra and George's (2002) conceptualization of absorptive capacity, this paper addresses these two dimensions -PACAP and RACAP-separately, and analyzes their influence on innovation outcomes (IO) within organizations.This study also examines the mediating role of RACAP in the relationship between PACAP and IO.We therefore posit that the link between PACAP and IO is mediated by RACAP.Furthermore, the paper contains a discussion on the moderating role of cultural barriers (CB) in decreasing the PACAP-RACAP and the RACAP-IO links.Finally, Chapter 6 exposes the overall discussion of the results as well as the conclusions, implications -both at the academic and the managerial level-, and limitations of this study.The chapter ends establishing several lines of research that we aim to develop in the future in order to enhance and improve this thesis. REFERENCES.Grant, R. M. (1996).Toward a knowledge-based theory of the firm.Strategic
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".