Conceptualizing the relative openness of supply chain and its impact on organizational performance
Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop a framework conceptualizing the relative openness of a supply chain and its impact on organizational performance. Design/methodology/approach – The literature on system theory and the attributes of supply chain management are used to develop a framework describing the relative openness of a supply chain. Findings – Different supply chain terminologies – such as adaptive supply chain network, best value supply chain, and open inter-organizational system – partially draw upon the basic premises of an open system. The relative openness of a supply chain and, consequently, the dynamics of different supply chain attributes remain understudied. This supports the idea that an open system perspective of the supply chain is imperative to improve the understanding of the influence of supply chain openness on organizational performance. Originality/value – The conceptual framework posits that different supply chain attributes affect the openness of supply chain to a varying degree which ultimately influences the organizational performance. The proposed framework and research propositions will serve as a springboard for conducting future empirical studies.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".