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Record W2120213669 · doi:10.1509/jppm.22.2.170.17641

When Profit Equals Price: Consumer Confusion about Donation Amounts in Cause-Related Marketing

2003· article· en· W2120213669 on OpenAlexaff
G. Douglas Olsen, John W. Pracejus, Norman Brown

Bibliographic record

VenueJournal of Public Policy & Marketing · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConfusionDonationProfit (economics)MarketingBusinessActuarial scienceEconomicsPsychologyMicroeconomics

Abstract

fetched live from OpenAlex

A series of five studies examine potential consumer confusion associated with the “percentage of profit” wording often used to describe cause-related marketing in which money is donated to a charity each time a consumer makes a purchase. The initial four studies demonstrate that (1) expressing the donation amount as a percentage of profit leads to widespread confusion and near universal overestimation of the amount being donated, (2) even consumers who have had formal accounting training are susceptible to this bias, (3) participant motivation in an experimental setting cannot account for these results, and (4) people report higher attitudes toward a company and express stronger purchase intentions as a function of the percentage value of the donation but not as a function of whether it is a percentage of profit or price. The authors conclude with a study that explores several potential affirmative disclosures for the percentage-of-profit problem.

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)0.020
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.111
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.376
Teacher spread0.327 · 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 designObservational
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

Citations162
Published2003
Admission routes1
Has abstractyes

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