Bibliographic record
Abstract
Purpose Despite the extensive body of research on the relationship between trust and performance in a supply chain environment, the concepts and the relationship between them has not been fully understood. The purpose of this paper is to develop a model that links the antecedents of trust, trust itself and firm outcome success. Design/methodology/approach A questionnaire survey was conducted to gather the data for this study. Statistical analysis included factor analysis with reliability and validity tests, and partial least square of structural equation modeling. Findings The data suggest that trust is built principally through supplier centric traditional performance metrics such as delivery reliability and product quality conformance. However, contrary to the extant literature, the people oriented trust enablers (e.g. personnel exchange, interpersonal contacts) have no bearing on the establishment of trust. Research limitations/implications The research limitation is the relatively small sample size. However, this study can be perceived as a directional one for further research. Practical implications The results can be used by the managers to improve their understanding on the relationship with other parties in the supply chain. Originality/value The significant value of this research can be retained by buying firm managers. The results are particularly important for them to improve their understanding in how they allocate time and resources in managing their supply chains and partner firms.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".