MétaCan
Menu
Back to cohort
Record W1556753799 · doi:10.5539/ass.v11n16p88

Examining Customer Satisfaction at the Point-of-Purchase Phase: A Study on Malaysian e-Consumers

2015· article· en· W1556753799 on OpenAlexvenueno aff
Noorshella Che Nawi, Abdullah Al Mamun, Nursalihah Ahmad Raston

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCustomer satisfactionMarketingPaymentService qualityDatabase transactionClothingService (business)Quality (philosophy)Point of saleOrder (exchange)Computer science

Abstract

fetched live from OpenAlex

Customer satisfaction is considered the essence of enterprise success, especially in the competitive online business environment. This study aimed to examine how the selected key factors, i.e., general belief, information quality, website design, merchandise attributes, payment transaction, security and privacy, delivery service, and customer service, contribute to overall satisfaction among the customers of small online apparel businesses in Malaysia. This study used a cross-sectional design and complete data was collected from 765 customers who purchased apparel online at the point-of-purchase. The finding reveals that general belief, information quality, website design, payment transaction, security and privacy, delivery service, and customer service have a positive significant effect on overall satisfaction. In order to sustain in the competitive online business environment, small online apparel businesses in Malaysia should therefore focus on operational strategies on the dimensions of service quality, which would lead to an improvement in overall customer satisfaction.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.329
Teacher spread0.256 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicCustomer Service Quality and LoyaltyFrench-language works237,207