MétaCan
Menu
Back to cohort
Record W2118335484 · doi:10.1108/08876040310501241

Services quality dimensions of Internet retailing: an exploratory analysis

2003· article· en· W2118335484 on OpenAlexaff
Zhilin Yang, Robin T. Peterson, Shaohan Cai

Bibliographic record

VenueJournal of Services Marketing · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsLakehead University
Fundersnot available
KeywordsCredibilityMarketingBusinessService qualityThe InternetQuality (philosophy)Competence (human resources)Dimension (graph theory)Context (archaeology)Service (business)Source credibilityRealmAdvertisingPsychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The purpose of this article is to extend what is know about service quality in realm of the context of Internet retailing. As a result of content analyzing 1,078 consumer anecdotes of online shopping experiences, 14 service quality dimensions representing 42 items were identified. The unique contents of each service quality dimension relate to Internet commerce are examined and discussed. Further, the analysis uncovered a number of contributors to consumer satisfaction and dissatisfaction. The most frequently‐mentioned service attributes resulting in consumer satisfaction were responsiveness, credibility, ease of use, reliability, and convenience. On the other hand, different dimensions including responsiveness, reliability, ease of use, credibility, and competence, were likely to dissatisfy online consumers. Finally, this paper provides various managerial implications and recommendations which may suggest avenues for improving service quality in Internet retailing and, as a corollary, expanding experiences by consumers.

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)0.002
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.030
GPT teacher head0.279
Teacher spread0.249 · 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

Citations274
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Services MarketingSame topicCustomer Service Quality and LoyaltyFrench-language works237,207