Do Big Words Make a Big Difference in Funding Outcomes in Equity Crowdfunding?
Bibliographic record
Abstract
Venture descriptions are a pivotal means of communication on crowdfunding platforms. The general advice for entrepreneurs is to keep the language of venture descriptions short and simple. However, knowledge about the conditions under which language in crowdfunding communications impacts investment behavior is limited. Using data from 886 actual equity crowdfunding ventures, we show that descriptions with a higher proportion of complex words are positively associated with higher funding outcomes and greater campaign success. Further, using our investor level panel data comprising of 78,141 investments made by 26,965 unique investors we show that the effect of complex language on amount invested at any given investment occasion is particularly strong for less sophisticated investors. However, our results indicates that simultaneous use of complex language and innovative language in venture descriptions can attract more sophisticated investors. Our work contributes to the research on communication in crowdfunding by highlighting the significance of lexical complexity in influencing funding behaviour. We also provide actionable insights for entrepreneurs and recommend that the use of big words especially in tandem with innovative language.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".