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Record W2122953658 · doi:10.5539/ijms.v4n5p108

Research on Consumer Purchase Behavior Diffusion across Market Using Cellular Automata

2012· article· en· W2122953658 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Marketing Studies · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsCellular automatonDiffusionMarket segmentationInnovation diffusionProduct (mathematics)Construct (python library)Consumer behaviourComputer scienceMarketingAutomatonBusinessMicroeconomicsIndustrial organizationEconomicsArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The goal of this article is to offer new theoretical propositions to advance consumer behavior diffusion research and to provide a cellular automata method for diffusion modeling. We construct a cellular automata model (CAD) that captures the consumer learning mechanism in the purchase behavior diffusion which result in the new product diffusion. The results show that the cellular automata model successfully examined the purchase behavior diffusion on different products both in China and America to compare consumer behavior in different segmentation market for implication on new product marketing.

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)0.056
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0560.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.401
GPT teacher head0.551
Teacher spread0.150 · 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