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Record W2767940513 · doi:10.5539/ibr.v10n12p159

The Effectiveness of Strategic Relationship Marketing: Exploring Relationship Quality towards Customer Loyalty

2017· article· en· W2767940513 on OpenAlexvenueno aff
Liew Chee Yoong

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsLoyalty business modelRelationship marketingMarketingCustomer satisfactionBusinessService qualityLoyaltyContext (archaeology)Customer retentionStructural equation modelingQuality (philosophy)Customer delightPerspective (graphical)Customer advocacyService (business)Marketing managementComputer science

Abstract

fetched live from OpenAlex

This study examines customer satisfaction, trust, control mutuality and communication as determinants of relationship quality and customer loyalty in relationship marketing. The study focuses on Malaysian telecommunication industry in the business-to-customer context. The structural equation modelling technique is used to empirically test the proposed hypotheses based on the sample size of 405 customers collected by a questionnaire survey. Trust had the greatest positive influence on relationship quality, followed by satisfaction. Subsequently, there was no significant effect of control mutuality and communication on relationship quality. Customer loyalty was significantly affected by relationship quality. The contribution of this paper is twofold. From a theoretical perspective, the social exchange theory is validated and it offers both a conceptual foundation and empirical-based evaluation of customer loyalty through the context of relationship quality. In the practical perspective, the findings proposed useful information to the telecommunication service providers in developing more effective relationship marketing strategies to build better relationship quality and customer loyalty.

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)0.023
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0000.001
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 head0.382
GPT teacher head0.434
Teacher spread0.051 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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