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Record W4412533507 · doi:10.5267/j.ijdns.2024.8.006

Investigating the effect of e-service quality on customer loyalty within the online marketplace during the covid-19 pandemic

2025· article· en· W4412533507 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Data and Network Science · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicBusinessLoyaltyService qualityLoyalty business modelQuality (philosophy)MarketingSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakService (business)AdvertisingVirologyMedicineInternal medicinePhysics

Abstract

fetched live from OpenAlex

This study examines the effect of the quality of e-services provided by e-commerce on customer satisfaction and loyalty during the COVID-19 pandemic. This study used a quantitative approach to involve 118 respondents who traded in online markets. The sampling method used is purposive sampling. The data were analyzed using a structural equation model using the partial least squares technique. Research results indicate that out of the five hypothetical relationships raised by the researcher, one relationship related to remuneration was found to lack a statistically significant positive effect. Besides that, the remaining four hypothetical relationships, including efficiency, confidentiality, accountability, and customer satisfaction, demonstrated positive and statistically significant. The implication of this study highlights the need to cultivate and improve the quality of e-services in the online market, especially in the context of the COVID-19 pandemic. In this way, businesses can provide consumers with a rich shopping experience, enhancing customer satisfaction and laying the foundation for long-term 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.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.053
GPT teacher head0.371
Teacher spread0.318 · 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