The Effect and Impact of Signals on Investing Decisions in Reward-Based Crowdfunding: A Comparative Study of China and the United Kingdom
Bibliographic record
Abstract
When traditional financial institutions faced difficulties in the task of assisting micro, small and medium-sized enterprises (MSMEs) with capital allocations, crowdfunding can upsurge as an innovative and vibrant vehicle that can support and assist the activity of such MSME’s, by financing their activity and instrumenting the process of risk-sharing. Simultaneously with its enormous growth and popularity, crowdfunding is faced by several key challenges, one of biggest such challenges referring to the problem of information asymmetry that can exist between fundraisers and potential backers. Based on the signaling theory, a research taxonomy has been developed for a comparative analysis between China and the UK. This has been accomplished by retrieving secondary data from the following crowdfunding platforms: Dreamore (Chinese platform) and Crowdfunder (UK platform). The objective of the study is to investigate both the effect and the impact that signals (goal setting, project comments and updates) have upon mitigating the problem of information asymmetry, in order to make the project successful. We have thus deployed an Ordinary Least Square (OLS) regression and validated the models through a robustness check. The findings reveal that signals actively mitigate the problem of information asymmetry in both countries, but this varies in the sense that higher goal setting has a more positive/impactful relationship with project success in the UK than it does in China. Project comments are more positively associated with project success in China as compared to the UK, whereas project updates are more negatively related to project success in China as compared to the UK. These findings demonstrate the importance that signals have upon successful crowdfunding activities/campaigns, highlighting the theoretical and practical influence and relevance for potential fundraisers in the two aforementioned economies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".