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Record W4413222670 · doi:10.1002/nvsm.70026

The Role of Crowdfunding in Philanthropy: Donor Motivations From a Self‐Determination Theory and Cultural Lens

2025· article· en· W4413222670 on OpenAlexaboutno aff
Jonathan Bezalel

Bibliographic record

VenueJournal of Philanthropy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
FundersUniversity of Leicester
KeywordsSociocultural evolutionIncentiveMarketingSelf-determination theoryPublic relationsPsychologySocial psychologyBusinessSociologyPolitical scienceEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT This study examines how intrinsic and extrinsic motivations influence monetary support in crowdfunding through the lens of self‐determination theory and cultural background, offering implications for fundraising strategies. Using controlled lab experiments across varied crowdfunding contexts—art, music, small business, and nonprofit campaigns—we explore the overjustification effect and the role of sociocultural factors in shaping donor behavior. Rewards are categorized as extrinsic (gift and recognition) or intrinsic (participation and influence). Findings reveal that removing gifts slightly increases average contributions, whereas eliminating both gifts and recognition reduces support. Donor behavior also varies by country: US‐born participants contribute the most overall, UK‐born donors prefer gift‐free scenarios, and Canadian‐born donors are more generous when both gifts and recognition are absent. These results challenge conventional interpretations of the overjustification effect and highlight the importance of culturally responsive reward structures. We offer practical recommendations for campaign designers and nonprofit organizations to optimize crowdfunding strategies using targeted incentives and social proof. By anchoring donor motivations within a self‐determination framework, this study contributes to the literature on philanthropic marketing and behavioral economics.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.009
GPT teacher head0.251
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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