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Record W2888552364 · doi:10.2308/bria-52233

Individual Donor Support for Nonprofits: The Roles of Financial and Emotional Information

2018· article· en· W2888552364 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

VenueBehavioral Research in Accounting · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsBrock University
Fundersnot available
KeywordsDonationEmotional intelligenceAgency (philosophy)FinanceBusinessAccountingPsychologyMarketingActuarial sciencePublic relationsSocial psychologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT The ability to attract donor funding is important to many nonprofit organizations' success in achieving their goals. Prior literature indicates that the emotional response of potential donors to the mission of these organizations as well as the assessment of the financial information provided impacts donation decisions. However, prior literature has examined either the effect of the emotional response or financial information, but not both. Using an experiment, our paper fills this gap in the literature by investigating both factors in the same study. Furthermore, we investigate the potential moderating effect of emotional intelligence. The results indicate that, under some circumstances, the emotional response of the potential donor and the donor's emotional intelligence impact both the decision to donate and the size of the donation. However, the financial information, as compiled by the Better Business Bureau, a business rating agency that also rates charities, impacts only the size of the donation.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.132
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.154
GPT teacher head0.452
Teacher spread0.298 · 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