Ramification of crowdfunding on Bangladeshi entrepreneur’s self-efficacy
Bibliographic record
Abstract
The novel funding sources turn out to be important for the Small and Medium Enterprises (SMEs) sector all over the world especially after 2007-2008 world financial crisis. Thus, to develop a new business idea and start-ups, SMEs need a sufficient amount of capital. However, after the financial crisis in 2008, SME sector faced the challenges of attracting new capital. Therefore, an innovative method of fundraising for SME was introduced as crowdfunding. Crowdfunding indicates financing a project or an idea via the internet owing the help from the many investors or donors of a society. Since there are limited works about the influence of Crowdfunding on Entrepreneur Self-efficacy (ESE), hence, to minimize the research gap and to achieve the objective of the study, we conduct a quantitative research among 190 entrepreneurs in Bangladesh using crowdfunding based on Social Cognitive theory. The data were analyzed using Structural Equation Modeling (SEM) in IBM-SPSS-Amos 25.0 and the stated hypotheses were tested. The study found a direct and positive effect of Crowdfunding on Entrepreneur's Self-efficacy (=0.924, P=.001). Overall, the result has landed support for crowdfunding, which indicates that it can influence on self-efficacy of entrepreneur. In order to determine the need of crowdfunding, we have explained and statistically demonstrated how crowdfunding can provide a supplementary channel where firms can gain finance and to create self-efficacy of entrepreneurs through exploiting the potential of internet.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".