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Record W3023567468 · doi:10.5267/j.msl.2020.4.034

The influence of electronic service quality on relationship quality: Evidence from tourism industry

2020· article· en· W3023567468 on OpenAlexvenueno aff
Arwa Hisham Rahahleh, Sana’a Nawaf Al-Nsour, Monira Abdallah Moflih, Zaid Alabaddi, Bilal Ali Yaseen Al-Nassar, Nour Al-Nsour

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTourismService qualityQuality (philosophy)BusinessMarketingService (business)UsabilityTertiary sector of the economyPopulationReliability (semiconductor)GeographyComputer scienceMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The purpose of this study is to offer better understanding to the dimensions of e-service quality and relationship quality by building on previous literature on e-service quality of tourism sector in Jordan. Moreover, the study also aimed to study the influence of Electronic Service Quality on relationship quality within the tourism sector in Jordan from the customer's perspective. The Electronic Service Quality is represented by information quality, ease of use, reliability, privacy and responsiveness. The population of the study consisted international tourists, who visited (Dead Sea) Jordan during summer 2019. A convenient random sample was taken amounted (400) participants and PLS was used to examine the study hypotheses. The researchers found that there was statistically significant influence of the Electronic Service Quality on relationship quality. The study also indicates that ease of use, privacy and responsiveness had significant positive influence on relationship quality. The researchers recommended the use of electronic services and focus on the dimensions of e-service quality on tourism electronic services especially in Jordan.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.445
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.292
Teacher spread0.241 · 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.

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

Citations15
Published2020
Admission routes1
Has abstractyes

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