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Record W4408178873 · doi:10.1080/00218499.2025.2464288

Does Sadness Sell? The Use of Negative Emotions in Fundraising Appeals: Fundraising Strategies for For-profit and Nonprofit Organizations

2025· article· en· W4408178873 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Advertising Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsSadnessBusinessFor profitMarketingProfit (economics)Public relationsAdvertisingFinanceEconomicsPsychologyMicroeconomicsSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Fundraising appeals frequently feature sad victims. This research postulates that the evaluation of fundraising appeals by consumers and their willingness to donate are contingent upon the congruence between organizational stereotypes (warm vs. competent) and the intensity of the negative emotion expressed in the appeal. Results show that when for-profit organizations employ negative emotional narratives, rather than non-emotional factual appeals, evaluations are less favorable (Experiment 1). Additionally, highly negative emotional appeals featuring multiple sad children designed to evoke compassion do not increase donations in for-profit fundraising campaigns (Experiment 2). These findings suggest that negative emotional appeals may backfire on for-profit organizations.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.139
GPT teacher head0.445
Teacher spread0.306 · 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