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Record W2898745996 · doi:10.5267/j.uscm.2018.8.002

Supplier service quality in supply chains of Indian SMEs: A dual direction dyadic perspective

2018· article· en· W2898745996 on OpenAlexvenueno aff
Surjit Kumar Gandhi, Anish Sachdeva, Ajay Gupta

Bibliographic record

VenueUncertain Supply Chain Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsDual (grammatical number)BusinessPerspective (graphical)Supply chainService qualityQuality (philosophy)Service (business)Industrial organizationMarketingOperations managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.017
GPT teacher head0.275
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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