Sample overview
| Authors | Platforms and countries | Methodologies and techniques | Dependent variables | Investment speed | Panel data set |
|---|---|---|---|---|---|
| Ahlers et al. (2015) | Australian Small Scale Offerings Board (ASSOB) (Australia) | Univariate: mean differences, multivariate: zero-inflated negative binomial regressions, OLS, survival analysis (exponential hazard models) | Fully funded, Number of investors, Funding amount, Speed investment | Yes | |
| Agrawal et al. (2016) | AngelList (USA) | Qualitative | |||
| Li et al. (2016) | Dajiatou (China) | ELM,independent-samplet-test,K-means cluster, linear regressions | Ratio of fundraising completion, Fundraising speed, Number of followers | Yes | |
| Lukkarinen et al. (2016) | Invesdor (FIN) | Multiple linear regressions | Amount raised, Number of investors | ||
| Vismara (2016) | Crowdcube, Seedrs (UK) | Negative binomial regression; OLS | Percentage of funding, Number of investors | ||
| Vulkan et al. (2016) | Seedrs (UK) | Linear probability model, OLS, quantile regression | Success dummy, Percentage raised, Shares of goal covered in Week 1 | Yes | |
| Löher (2017) | Companisto, Fundsters, Innovestment, Seedmatch and Bergfürst | Qualitative: semistructured interviews | |||
| Block et al. (2018) | Seedmatch and companisto (GER) | Fixed effects negative binomial, OLS panel regression | Number of investments, Capital raised | Yes | Yes |
| Löher et al. (2018) | Companisto, Fundsters, Innovestment and Seedmatch (GER) | Quali-quantitative: interviews, OLS | Funding level (percentage of funding) | ||
| Malaga et al. (2018) | USA | Exploratory analysis | |||
| Mamonov and Malaga (2018) | 16 platforms from the USA | Logistic regression | Success binary | ||
| Motylska-Kuzma (2018) | Beesfunds, Crowdway, Findfunds (POL) | Nonparametric correlation tests | Amount of raised funds, Success rate, Number of contributors | ||
| Piva and Rossi-Lamastra (2018) | SiamoSoci (ITA) | Probit | Success binary, Percentage of funding, Number of investors | ||
| Barbi and Mattioli (2019) | Crowdcube (UK) | Univariate and multivariate models (OLS) | Capital raised, Number of investors | ||
| Cumming et al. (2019a, 2019b) | Crowdcube (UK) | First stage: bivariate, probit regression. Second stage: generalized structural equation model (GSEM) | Success binary | ||
| Kleinert and Volkmann (2019) | Crowdcube (UK) | Qualitative: codebook; quantitative: Poisson regression | Funding raised | Yes | |
| Mamonov and Malaga (2019) | Crowdfunder (Los Angeles) | Logistic regression models | Success, Partial success | Yes | |
| Nitani et al. (2019) | Crowdcube (UK), Invesdor (FIN), Companisto (GER) and FundedByMe (SWE) | OLS, logistic regression and survival models (proportional hazards models) | Fundraising success (binary), Funding speed, Capital raised | Yes | |
| Rossi et al. (2019) | 185 platforms | Negative binomial regressions | Platform success | ||
| Usman et al. (2019) | Crowdfunder (UK) | Logistic regression, Tobit regression | Success binary, Number of backers, Funding amount | ||
| Vismara (2019) | Crowdcube and Seedrs (UK) | Probit regressions, negative binomial regression | Success binary, Number of investors, Presence of professional investors | ||
| De Crescenzo et al. (2020) | Crowdcube (UK) | Fuzzy-set qualitative comparative analysis | Success binary, Failure binary | ||
| Kleinert et al. (2020) | Crowdcube (UK) | Negative binomial and logit regressions | Success binary, Number of investors | ||
| Ralcheva and Roosenboom (2020) | Crowdcube and Seedrs (UK) | Logistic regressions | Success binary | ||
| Xiao (2020) | AngelCrunch (China) | Qualitative: interviews | |||
| Lim and Busenitz (2020) | Crowdfunder (Los Angeles) | Zero-inflated negative binomial and normal negative binomial regressions | Funding Raised | ||
| Shafi (2021) | Crowdcube (UK) | Probit regressions, OLS | Success, Amount raised | ||
| Andrieu et al. (2021) | Wiseed, Smart Angels, Sowefund, Anaxago (FRA) | OLS regression, iteratively reweighted least squares, propensity score matching | Percentage of funding | ||
| Dority et al. (2021) | Alchemy Global, AngelList, Crowdfunder, EarlyShares, EquityNet, MicroVentures, OneVest, OurCrowd, Return on Change, Seed Equity, SeedInvest, WeFunder (USA) | Sentiment analysis; Tobit regression | Percentage of funding | ||
| Meoli and Vismara (2021) | EquityCrowd (name disguised, country unknown) | Probit regression (other empirical settings also: panel Poisson, panel negative binomial regressions) | Success binary | ||
| Vrontis et al. (2021b) | 21 Italian platforms | Social network analysis, panel pooled OLS regression | Success ratio of platforms | Panel | |
| Coakley et al. (2022) | Crowdcube, Seedrs, SyndicateRoom (UK) | OLS, probit regressions | Success binary, Capital raised, Overfunding |
| Authors | Platforms and | Methodologies and | Dependent | Investment | Panel data |
|---|---|---|---|---|---|
| Australian Small Scale Offerings Board (ASSOB) (Australia) | Univariate: mean differences, multivariate: zero-inflated negative binomial regressions, OLS, survival analysis (exponential hazard models) | Fully funded, Number of investors, Funding amount, Speed investment | Yes | ||
| Agrawal | AngelList (USA) | Qualitative | |||
| Dajiatou (China) | ELM,independent-sample | Ratio of fundraising completion, Fundraising speed, Number of followers | Yes | ||
| Invesdor (FIN) | Multiple linear regressions | Amount raised, Number of investors | |||
| Crowdcube, Seedrs (UK) | Negative binomial regression; OLS | Percentage of funding, Number of investors | |||
| Seedrs (UK) | Linear probability model, OLS, quantile regression | Success dummy, Percentage raised, Shares of goal covered in Week 1 | Yes | ||
| Companisto, Fundsters, Innovestment, Seedmatch and Bergfürst | Qualitative: semistructured interviews | ||||
| Seedmatch and companisto (GER) | Fixed effects negative binomial, OLS panel regression | Number of investments, Capital raised | Yes | Yes | |
| Companisto, Fundsters, Innovestment and Seedmatch (GER) | Quali-quantitative: interviews, OLS | Funding level (percentage of funding) | |||
| USA | Exploratory analysis | ||||
| 16 platforms from the USA | Logistic regression | Success binary | |||
| Beesfunds, Crowdway, Findfunds (POL) | Nonparametric correlation tests | Amount of raised funds, Success rate, Number of contributors | |||
| SiamoSoci (ITA) | Probit | Success binary, Percentage of funding, Number of investors | |||
| Crowdcube (UK) | Univariate and multivariate models (OLS) | Capital raised, Number of investors | |||
| Crowdcube (UK) | First stage: bivariate, probit regression. Second stage: generalized structural equation model (GSEM) | Success binary | |||
| Crowdcube (UK) | Qualitative: codebook; quantitative: Poisson regression | Funding raised | Yes | ||
| Crowdfunder (Los Angeles) | Logistic regression models | Success, Partial success | Yes | ||
| Crowdcube (UK), Invesdor (FIN), Companisto (GER) and FundedByMe (SWE) | OLS, logistic regression and survival models (proportional hazards models) | Fundraising success (binary), Funding speed, Capital raised | Yes | ||
| 185 platforms | Negative binomial regressions | Platform success | |||
| Crowdfunder (UK) | Logistic regression, Tobit regression | Success binary, Number of backers, Funding amount | |||
| Crowdcube and Seedrs (UK) | Probit regressions, negative binomial regression | Success binary, Number of investors, Presence of professional investors | |||
| Crowdcube (UK) | Fuzzy-set qualitative comparative analysis | Success binary, Failure binary | |||
| Crowdcube (UK) | Negative binomial and logit regressions | Success binary, Number of investors | |||
| Crowdcube and Seedrs (UK) | Logistic regressions | Success binary | |||
| AngelCrunch (China) | Qualitative: interviews | ||||
| Crowdfunder (Los | Zero-inflated negative binomial and normal negative binomial regressions | Funding Raised | |||
| Crowdcube (UK) | Probit regressions, OLS | Success, Amount raised | |||
| Wiseed, Smart Angels, Sowefund, Anaxago (FRA) | OLS regression, iteratively reweighted least squares, propensity score matching | Percentage of funding | |||
| Alchemy Global, AngelList, Crowdfunder, EarlyShares, EquityNet, MicroVentures, OneVest, OurCrowd, Return on Change, Seed Equity, SeedInvest, WeFunder (USA) | Sentiment analysis; Tobit regression | Percentage of funding | |||
| EquityCrowd (name disguised, country unknown) | Probit regression (other empirical settings also: panel Poisson, panel negative binomial regressions) | Success binary | |||
| 21 Italian platforms | Social network analysis, panel pooled OLS regression | Success ratio of platforms | Panel | ||
| Crowdcube, Seedrs, SyndicateRoom (UK) | OLS, probit regressions | Success binary, Capital raised, Overfunding |