An Integrated Online/Offline Social Network-Based Model for Crowdfunding Support in Developing Countries: The Case of Nigeria
Bibliographic record
Abstract
This paper is one of the first attempts to address the fundamental barriers to the adoption of online crowdfunding mechanisms in a developing country by offering a new online/offline fundraising model. The focus is on Nigeria as a typical example of an environment that, unlike that in the developed world, is not fully conducive to social networking as a crowdfunding platform due to both economic and technological issues. Using a mixed research method, the study first compares the state of the art in crowdfunding in a developed and developing economy by interviewing two groups of 20 entrepreneurs from the UK and Nigeria, respectively. The differences between those in terms of crowdfunding facilitation are identified, and propositions for the Nigerian market are formulated. These are then tested statistically by surveying 160 randomly selected Nigerian fundraisers. Based on the outcomes of the thematic analysis and statistical modelling, a unique integrated online/offline crowdfunding model is proposed. It is particularly aimed at supporting entrepreneurial activities and related policymaking that can have a key impact on further social and economic development of these countries. The proposed model can be considered as an alternative novel fundraising instrument in regions where socioeconomic and technological challenges inhibit the adoption of traditional crowdfunding approaches.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".