A Fuzzy Ecosystem Benchmarking for Crowdfunding in Transport Sector
Bibliographic record
Abstract
The global crowdfunding (CF) market was valued at 10.2 billion US$ in 2018 and is expected to almost triple in size by 2025. The CF is evolving as a major and easy source of fundraising methods for various industries. Still, this acceptability is not widely accepted in transportation activities due to various limitations and low awareness among policymakers. The present research analyzes the factors contributing to the growth of market acceptability of CF, divided into three different research phases: identifying barriers from the literature, interviews with transport industry experts at two stages, and designing an ISM model in a fuzzy environment. The identification phase led to selecting 16 factors from the past literature and suggesting industrial experts. The Interpretive Structural Modelling (ISM) analysis was used to understand the impact and linkage of identified barriers on seven levels of the fuzzy scale. The factors are classified into four major categories based on the fuzzy matrix's drive and dependence power using Fuzzy MICMAC. The sixteen identified growth factors for CF have been distributed in 5 levels in the ISM designed model. All the factors had fallen in only two quadrants of MICMAC based on the fuzzy scale matrix. Except for No or Low in regulation, the selected fifteen factors fall in the linkage quadrant, with high dependency and driving power. Such relation of all variables is the precise reason for storm growth in the field. “No or Low in regulation” is one of the most significant factors to the growth and acceptance of this innovative fundraising method by common investors but cannot be controlled directly by the associated crowdfunding members in the transport industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".