From Intent to Inaction: Factors Conditioning Cultural Institutions From Embracing Crowdfunding as a Fundraising Tool
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".