Determinants of Crowdfunding Success in Africa: An Exploratory Perspective on Incentive Rewards and Beyond
Bibliographic record
Abstract
This study aims to determine the role of reward incentives in crowdfunding success in Africa. Reward incentives seem to play an essential role in the success of a crowdfunding project. Therefore, understanding how distinct types of incentive rewards influence the backer’s engagement and viability of crowdfunding campaigns is essential. Drawing from secondary cross-section data from Kickstarter and Indiegogo, this research uses the probit regression method to analyse and test the hypotheses. The findings revealed that flexible funding negatively influences a crowdfunding campaign, diminishing the probability of success. In contrast, a more significant number of backers positively affects a crowdfunding campaign, boosting its chances of success. Rewards promised to potential backers increase the probability of success. These findings influence how crowdfunding campaigns are launched, allowing them to build and achieve their financing targets. The findings provide knowledge that entrepreneurs could develop far more attractive, culturally appropriate reward schemes to boost their crowdfunding campaigns’ chances of success. It provides a valuable understanding regarding the potential of crowdfunding as an alternative tool for economic development in Africa among policymakers and development agencies. Lastly, it adds to the limited literature on crowdfunding in Africa, especially within the context of reward incentives, and provides a foundation for further studies in this area.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".