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Record W2996965250 · doi:10.2478/jeb-2019-0017

Attractiveness Modeling of Retail on Emotional Fatigue of Consumers

2019· article· en· W2996965250 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSouth East European Journal of Economics and Business · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsTransport Canada
Fundersnot available
KeywordsAttractivenessBusinessMarketingBoosting (machine learning)Quality (philosophy)Competitive advantagePsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Demand for high-quality shopping service has seen continuous growth in the recent years, allowing retail chains to achieve sustainable competitive advantage, increase number of loyal customers. This in-turn results in demand boosting and image of the firm. To analyze and achieve this emotional reactions of customers while shopping becomes important. The paper attempts to evaluate the effect of emotional fatigue on purchase process and uses neuromarketing tool – Galvanic skin reaction analysis to do so. Changes in the buyer emotional reaction of consumers was observed through more than 150 experiments at 15 different retailers. The results showed that retailer selection depended on emotional fatigue of the customer. Different types of retailers create different emotional fatigue which affects the footfall.

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)0.001
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 score0.032
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.220
Teacher spread0.149 · 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