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Record W2475869564 · doi:10.5539/ijms.v8n4p87

An Extended Model of Behavioural Process in Consumer Decision Making

2016· article· en· W2475869564 on OpenAlexvenueno aff
Nagasimha Balakrishna Kanagal

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsConsumer behaviourDecision-makingMarketingDecision-making modelsProcess (computing)Decision processStimulus (psychology)BusinessPsychologyEconomicsComputer scienceManagement scienceSocial psychologyCognitive psychologyPurchasing

Abstract

fetched live from OpenAlex

The stimulus response model of consumer behaviour is useful to understand the buying behaviour of individual consumers in the context of individuals buying consumer products. An extended stimulus-response model of behavioural processes in consumer decision making is proposed that serves to integrate the influences and interlinkages of buyer psychology, various buyer characteristics, and the impact of the buyer decision process on consumer decision making. The model proposes that the behavioural process of consumer decision making be as a result of the interaction of three aspects of individual buyer behaviour: communication sensitivity; enculturated individuality; and rational / economic decision making. The paper addresses the flip side of the consumer decision making process in terms of the five stages of decision making from need recognition to post-purchase satisfaction. An aggregate level framework of behavioural process in consumer decision making has been provided, that could lead to a richer analysis of micro level factors and relationships influencing consumer decision behaviour.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.078
GPT teacher head0.362
Teacher spread0.284 · 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 designSimulation or modeling
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

Citations27
Published2016
Admission routes1
Has abstractyes

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