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The Relationship Between Extrinsic Attributes of Product Quality with Brand Loyalty on Malaysia National Brand Motorcycle/Scooter

2010· article· en· W2102050260 on OpenAlexvenueno aff
Mohd Rizaimy Shaharudin, Anita Abu Hassan, Suhardi Wan Mansor, Shamsul Jamel Elias, Etty Harniza Harun, Nurazila Abdul Aziz

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProduct (mathematics)AdvertisingQuality (philosophy)MarketingBrand loyaltyPerceptionBrand managementBrand extensionPromotion (chess)Brand awarenessLoyaltyBrand equityPsychologyMathematicsPolitical science

Abstract

fetched live from OpenAlex

This study is about the discoveries on the relationship between extrinsic attributes of product quality with brand loyalty. It helps to extend the understanding of a commitment to re-purchase a product, due to the feelings and effects formed as a result of the perception of quality. Results obtained in this study with the earlier literature are consistence to confirm that although the product in study was different, the product quality based on the perceived quality (extrinsic attribute) was still found to have significant influence on the brand loyalty. This happened because the customer has developed perceptions that derived from high level of customer awareness, good image from marketing activities such as advertising, sales promotion and etc. Such perceptions may increase the consumer’s desire to buy the product. Future research should focus on the similar study of product quality and brand loyalty to the other brands being the competitor to Malaysia National Brand Motorcycle/Scooter in the market. By doing this only the gap can be closed with a clearer picture on the extended scope of market environment which can be further examined. Keywords: Product Quality; Brand Loyalty; Intrinsic Attributes; Extrinsic Attributes; Perceived Quality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
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 head0.055
GPT teacher head0.295
Teacher spread0.239 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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