How does organizational learning contribute to corporate social responsibility and innovation performance? The dynamic capability view
Bibliographic record
Abstract
Purpose Innovative organizations are increasingly facing challenges in a dynamic market to address corporate social responsibility (CSR) issues; however, research on how organizational learning (OL) contributes to organizations’ social responsibility and innovation remains sparse. This study aims to bridge the gap in previous research and examines how OL and dynamic capabilities (DCs) act as drivers of CSR performance (CSRP) and innovation performance. Design/methodology/approach This study is survey-based and uses time-lagged, multisource data from 151 pharmaceutical industry-related companies in Iran. Structural equation modeling was applied to test the validity of the measurement model and hierarchical regression was used to test the key hypotheses. Findings DCs mediate the relationship between OL and CSRP. Moreover, CSRP significantly mediates the relationship between OL and innovation. Originality/value Drawing on the perspective of DCs, this research is among the first to offer new insights in a new context on what antecedent conditions lead to the successful implementation of organizational CSRP and how CSRP would, in turn, lead to subsequent innovation performance improvement.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 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".