Supplier service quality in supply chains of Indian SMEs: A dual direction dyadic perspective
Bibliographic record
Abstract
This paper investigates the role played by service quality at supplier-manufacturer dyad in small-medium manufacturing units, and presents a model to establish that contribution of both the supplier and manufacturer towards service quality could lead to satisfaction followed by loyalty.The research design for this study includes a combination of literature survey, exploratory interviews with practitioners, and a questionnaire survey conducted through interview schedule from 120 respondents working in different small-medium manufacturing units of North India.Structural equation modeling (SEM) is used for data analysis.The paper develops dual directional scales to evaluate service quality at supplier-manufacturer dyad and tests a set of four propositions.A model showing linkages of manufacturer (manufacturing unit's) service quality with supplier service quality leading to satisfaction and loyalty is also developed.The model is empirically tested and is found to be fit.This study would be of interest to SME managers particularly engaged in 'purchase' function and researchers working on inter-firm supply chains in such units.This study recommends forming strong collaborative relationships with suppliers to achieve a win-win situation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".