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Motivation Research

2010· other· en· W3190667226 on OpenAlexaff
Robert V. Kozinets

Bibliographic record

VenueWiley International Encyclopedia of Marketing · 2010
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyMetaphorSubconsciousPurchasingConsumer researchProjective testSocial psychologySentenceAdvertisingMarketingPsychoanalysisComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Abstract Motivation research is a term used to refer to a selection of qualitative research methods that were designed to probe consumers' minds in order to discover the subconscious or latent reasons and goals underlying everyday consumption and purchasing behaviors. Motivation research was derived from an application of Sigmund Freud's psychoanalytic personality theories and became the premier consumer research method in the 1950s. It has had a lasting influence on the areas of advertising and consumer research, as well as advertising practice. Dr. Ernest Dichter, a trained psychoanalyst, was the first and foremost practitioner of motivation research. Critics of motivation research disliked its small sample sizes, its subjective interpretations, its basis in clinical methods, and its exotic explanations. Despite its controversial past, motivation research is still regarded as an important technique by marketers who want to gain a deeper understanding into consumer motivations. Current techniques in wide use derive directly from motivation research. These methods include projective tests, metaphor analysis, storytelling, word‐association tasks, sentence‐completion tasks, picture generation, and also photo sorts. Some of the most prominent contemporary marketing researchers such as Gerald Zaltman and Clotaire Rapaille use techniques that are recognizably affiliated with motivation research.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.224
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0210.001

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 head0.039
GPT teacher head0.301
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2010
Admission routes1
Has abstractyes

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