Supply chain relationships as a context for learning leading to innovation
Bibliographic record
Abstract
Purpose – The purpose of this paper is to integrate the literature on learning in the context of boundary spanning innovation in supply chains. A two-dimensional framework is proposed: the learning stage (exploration, assimilation, exploitation) and the learning facet (structural, cultural, psychological and policy). Supply chain management (SCM) practices are examined in light of this framework and propositions for further empirical research are developed. Design/methodology/approach – In total, 60 empirical papers from the major journals on supply chain relationships published over an 11-year time span (2000-2010) were systematically analyzed. Findings – The paper reveals a comprehensive set of best practices and identifies four gaps for future research. First, assimilation and exploitation are largely ignored as mediating learning stages between exploration and performance. Second, knowledge brokers and reputation management are key mechanisms that foster assimilation. Third, the iteration from exploitation back to exploration is critical though underdeveloped in efficiency seeking supply chains. Fourth, the literature stresses structural mechanisms of learning, at the expense of a more holistic view of structural, cultural, psychological and policy mechanisms. Research limitations/implications – The search could be extended to other journals that report on joint learning and innovation. Practical implications – The framework provides guidelines for practitioners to develop learning capabilities and leverage the knowledge from supply chain partners in order to continuously or radically improve boundary spanning processes and products. Originality/value – The study is multi-disciplinary; it applies a model developed by learning scholars to the field of SCM.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.008 |
| 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".