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Record W2052872672 · doi:10.5539/ass.v9n4p14

Evaluation of Malaysian Retail Service Quality

2013· article· en· W2052872672 on OpenAlexvenueno aff
Zalina Ibrahim, Linda Dana, Ahmad Faisal Mahdi, Mohamad Zaid Mohd Zin, Mohd Anuar Ramli, Mohd Roslan Mohd Nor

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingBusinessQuality (philosophy)Service qualityService (business)Test (biology)Product (mathematics)Reliability (semiconductor)Government (linguistics)Modernization theoryEconomicsMathematics

Abstract

fetched live from OpenAlex

In retailing different requirement of service quality in product or services environment required. Some of the retail stores have lack on service quality whereas the salesperson is not well trained, unknowledgeable and rude. This study aims to evaluate the retail service quality from a supermarket in Kuching, Malaysia and sets out to determine what influence customers to shop. This research looks at the five dimensions of retail service quality: the physical aspect, reliability, personal interaction, problem solving and policy. A survey of 200 respondents was conducted and reliability test is measured to represent the dimensions whereas regression test and Pearson correlation is used to answer the research questions and testing the hypotheses. The overall finding confirms the importance of quality service, the need to upgrade services to remain competitive, and consideration by government on modernization of retail outlets using the credit card will bring burden to the consumers as against the benefits. In addition, implications of this study as well as directions for future research are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.329
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2013
Admission routes1
Has abstractyes

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