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Record W4389473060 · doi:10.31542/tge3ez60

Heating Up the Market

2023· article· en· W4389473060 on OpenAlexvenueno aff
Shuang Wu, Connor Smith, Randy Rolf

Bibliographic record

VenueMacEwan University Student eJournal · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsProduct (mathematics)Affect (linguistics)TraitPerceptionAdvertisingConsumption (sociology)MarketingRisk perceptionPsychologyBusinessDemographySociologyMathematics

Abstract

fetched live from OpenAlex

The study was conducted for a locally based company that wished to expand their consumer base and market reach. The relationship between consumer attitudes and brand perception was examined to identify potential marketing approaches. Results were gathered through a questionnaire (n= 136), in order to model consumer profiles and analyze their affect on product perception. The resulting multivariate regression model indicated that the difference in receptiveness to Kaiso’s branding between the Black/African demographic and the Caucasian demographic was significant. The average Black/African participant perceived the product more negatively than the average Caucasian participant. The same observation applied to South Asian demographics. Consumers’ self-reported likelihood to purchase was positively correlated with an increase in consumption rate across all participant profiles. Notably, aspects that provide opportunities to improve in the peer sauce industry are associated with traditional spicy cuisine, which is a trait correlated with the desire for perceived authenticity over other product attributes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0960.015

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 head0.031
GPT teacher head0.250
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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