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Record W4387668534 · doi:10.1080/13691066.2023.2265565

Lexical sophistication and crowdfunding outcomes

2023· article· en· W4387668534 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVenture Capital · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSophisticationOptimal distinctiveness theorySample (material)PsychologyPerceptionMarketingBusinessSocial psychologySociology

Abstract

fetched live from OpenAlex

It is advised that entrepreneurs should 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. We propose that the use of sophisticated language in crowdfunding venture descriptions creates perceptions of venture distinctiveness that, in turn, increase the amount invested. We test this proposed mechanism across a controlled experiment and present evidence from 886 crowdfunding campaigns that increasing the linguistic sophistication of campaign descriptions can result in a 20% increase in the amount raised by a campaign and greater campaign success. Furthermore, we show that the impact of lexical sophistication on the amount raised is moderated by investor experience whereby experienced investors invest more in ventures with sophisticated language compared to inexperienced investors. Our work contributes to research on communication in crowdfunding by highlighting the significance of lexical sophistication in influencing funding behaviour.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.019
GPT teacher head0.233
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it