The role of trust and e-WOM in the crowdfunding participation: the case of equity crowdfunding platforms in financial services in Iran
Bibliographic record
Abstract
Purpose The aim of this research is to examine the roles of trust and electronic word-of-mouth (e-WOM) in crowdfunding (CF) participation for equity CF by taking into account the following antecedents of trust and e-WOM: intrinsic motivation (IM), extrinsic motivation (EM), deterrents, venture quality (VQ), third-party seal (TPS), value congruence (VC) and perceived accreditation (PA). Design/methodology/approach In this research, a survey among 408 active and potential funders in Iran was conducted. The statistical analysis used partial least squares structural equation modeling (PLS-SEM). Findings The results of this research revealed a significant influence of trust and e-WOM on participation in CF for equity CF. Extrinsic motivation had the greatest impact on trust and VC had the greatest impact on e-WOM. Originality/value This research extends the equity CF research area to CF success and considers the effects of some parameters on CF participation. This research provides many theoretical and practical implications.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".