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Record W4392107903 · doi:10.35536/ljb.2023.v11.i1.a5

Driving Customer Loyalty through Customer Satisfaction inOnline Shopping: The Role of Brand Image, Price, Trust andWebsite Quality

2023· article· en· W4392107903 on OpenAlexaff
Rimsha Shafiq, Muhammad Zeeshan

Bibliographic record

VenueLahore Journal of Business · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsBishop's University
Fundersnot available
KeywordsBusinessMediationAdvertisingCustomer satisfactionMarketingLoyalty business modelContext (archaeology)LoyaltyCustomer retentionBrand loyaltyBrand managementBrand imageCustomer delightPath analysis (statistics)Service qualityComputer scienceSociologyService (business)Geography

Abstract

fetched live from OpenAlex

The relationship between brand image and loyalty in online shopping has gained significant attention in the marketing literature. Despite this attention, the specific path linking brand image to customer loyalty remains unclear. This study delves into the sequential and parallel mediations between brand image and customer loyalty, aiming to identify the effects of brand image, price, trust and website quality on customer loyalty through the mediation of customer satisfaction. Employing a parallel and sequential mediation model, this research addresses the practical implications for emerging e commerce businesses. For data collection, an online questionnaire survey distributed to 120 respondents yielded a 65 percent response rate. The findings of the study affirm that brand image, price and trust positively influence customer satisfaction and loyalty, establishing direct and indirect relationships among these constructs. This study contributes to the validation of the proposed research model in the specific context of a developing country, i.e., Pakistan.

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)0.002
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 score0.139
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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