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Record W2560128555 · doi:10.1108/jrme-11-2014-0029

Distributor orientation and channel profitability for manufacturing-centered SMEs

2016· article· en· W2560128555 on OpenAlexaff
Chiquan Guo, Yong Wang, Ying Zhu

Bibliographic record

VenueJournal of Research in Marketing and Entrepreneurship · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsDistributorProfitability indexBusinessOriginalityMarketingOrientation (vector space)Industrial organizationChannel (broadcasting)PsychologyComputer scienceTelecommunicationsSocial psychologyMechanical engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

Purpose This study aims to explore how the distributor orientation of manufacturing-centered small and medium-sized enterprises (MCSMEs) influences relationship building outcomes, distributor satisfaction, and channel profitability. Design/methodology/approach This study examines the moderating role of competitive intensity and coordinative culture in the association between distributor orientation and relationship building outcomes. Findings Empirical results from 115 MCSMEs reveal a strong positive relationship between distributor orientation and distributor satisfaction. The findings also show a positive relationship between distributor orientation and channel profitability. Furthermore, although competitive intensity strengthens the positive relationship between distributor orientation and the two relationship building outcomes, coordinative intensity weakens the positive relationship between distributor orientation and the two relationship outcomes. Practical implications Managerial implications and future research opportunities were discussed. Originality/value The research contributes to the literature on the management of small and medium-sized enterprises and offers practical implications for manufacturers and distribution channel managers.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.341
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2016
Admission routes1
Has abstractyes

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