The Role of Campaign Descriptions and Visual Features in Crowdfunding Success: Evidence from Africa
Bibliographic record
Abstract
Crowdfunding has gained popularity among entrepreneurs who seek funding for their business projects on crowdfunding platforms. The success of these campaigns largely depends on the ability to attract and convince backers to support the fundraising initiative. Drawing on signalling, persuasion, and attribution theories, this study examines how campaign descriptions and visual features specifically word description length, spelling errors, images, frequently asked questions (FAQs), number of backers, funding target, flexible funding, and campaign duration. The study utilised econometric techniques such as ordinary least squares and logistic regression models on a dataset consisting of 854 small and medium enterprises and entrepreneurial projects collected from Kickstarter, Indiegogo, and Fundraised databases. The probability of success is significantly increased by the length of project descriptions, the inclusion of images, and an increased number of backers. On the other hand, higher funding targets and flexible funding models decrease the probability of success. These results support the attribution and persuasion theories, indicating that detailed project descriptions can address information gaps and improve the project’s credibility and trustworthiness. This study contributes to the literature by providing an empirically grounded understanding of how textual and visual elements influence crowdfunding outcomes in the African context and offers practical guidance for entrepreneurs and investors on designing effective campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".