A dyadic perspective on supplier–buyer relationship through the digitalization of suppliers’ manufacturing process
Bibliographic record
Abstract
Purpose This study empirically investigates the impact of the digitalization of suppliers’ manufacturing processes on their relationship with buyers, focusing on credibility and relationship duration. It highlights suppliers’ digital traceability and managerial competence in digitalization as key factors in strengthening supplier-buyer relationships. Design/methodology/approach We test our hypotheses by analyzing 103 supplier–buyer dyads using the PLS-SEM approach in the context of the Smart Factory scheme for small and medium-sized enterprises (SMEs) in South Korea. Findings We highlight that even at nascent stages, digitalization can improve supplier–buyer relationships by strengthening suppliers’ credibility and extending relationship duration, primarily through enhanced traceability of products and manufacturing processes. Moreover, a supplier’s managerial competence in digitalization reinforces the positive relationship between digital traceability and credibility. Originality/value Drawing on the resource-based view (RBV) and social exchange theory (SET), this study theorizes and empirically demonstrates the importance of digital traceability and managerial competence in digitalization for strengthening buyer-supplier relationships in the digital era.
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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 | 0.007 |
| 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 | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".