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Record W4406296218 · doi:10.1177/27533743241313462

From Intent to Inaction: Factors Conditioning Cultural Institutions From Embracing Crowdfunding as a Fundraising Tool

2025· article· en· W4406296218 on OpenAlexaff
Anders Rykkja, Lluís Bonet

Bibliographic record

VenueJournal of Alternative Finance · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsQueen's University
FundersNorges Forskningsråd
KeywordsConditioningBusinessEquity crowdfundingPublic relationsMarketingPolitical scienceFinanceSeed money

Abstract

fetched live from OpenAlex

Purpose The article explores factors explaining cultural institutions’ intentions to adopt and circumstances that lead to inaction in terms of launching a crowdfunding campaign. Study Design The study is a single-case study of a Norwegian museum consortium. It combines thematic analysis of qualitative data from interviews and non-parametric tests of quantitative survey data to examine differences between four categories of employees: leadership, curators, technical staff, and support staff. Findings The study identifies three key factors contributing to the inaction and failure to launch the crowdfunding campaign: (1) the added value of funding and non-monetary benefits; (2) legitimacy concerns surrounding crowdfunding; and (3) the leadership and management of implementing what proved to be a non-routine activity. Contributions The paper provides insights into the drivers and barriers that influence the intention to use institutional cultural crowdfunding. It adds to the literature by demonstrating that cultural institutions’ internal organisational dynamics influence decision-making. The derived propositions provide a basis for further empirical research into how cultural institutions approach crowdfunding. Implications The findings hold practical implications for cultural institutions, policymakers, and scholars. They underscore the importance of leadership in navigating legitimacy concerns, fostering intra-organisational collaboration, and supporting institutional cultural crowdfunding efforts from intent to action.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.575
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.312
Teacher spread0.270 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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