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Record W2613367129 · doi:10.1108/bfj-11-2016-0567

Specialty food retailing: examining the role of products’ perceived quality

2017· article· en· W2613367129 on OpenAlexaff
Cristina Calvo-Porral, Jean-Pierre Lévy-Mangín

Bibliographic record

VenueBritish Food Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsMarketingContext (archaeology)Quality (philosophy)Structural equation modelingBusinessLoyaltyPerceptionOriginalityProduct (mathematics)SpecialtyLoyalty business modelConceptual modelCustomer satisfactionSample (material)AdvertisingMarket segmentationService qualityPsychologyComputer scienceCreativityService (business)Social psychologyMathematics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to address the following issue: “Does the products’ perceived quality influences the consumer behaviour in the specialty retailing setting?” Design/methodology/approach For this purpose, the authors propose and empirically test a conceptual model on the creation of consumer satisfaction and loyalty in specialty retailing, to examine the influence of products’ quality perception and its potential moderating role. Data were analysed through structural equation modelling on a sample of 592 consumers Findings The findings show that the store-based attributes have different influence on customer satisfaction and loyalty, according to the quality perception of products, and suggest the moderating role of products’ perceived quality. Practical implications Retailing managers may use the product’s perceived quality as a segmentation variable in the specialty food retailing context. Originality/value The major contribution of this paper is the empirical analysis of one subjective customer-based variable in the specialty retailing setting.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
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 head0.066
GPT teacher head0.270
Teacher spread0.205 · 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.

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

Citations20
Published2017
Admission routes1
Has abstractyes

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