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For the case of many content features, This paper aims to investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models.

With 1,368 features extracted from 15,195 Kickstarter campaigns in the USA, the authors compare base models such as logistic regression (LR) with tree-based homogeneous ensembles such as eXtreme gradient boosting (XGBoost) and heterogeneous ensembles such as XGBoost + LR.

XGBoost shows higher prediction accuracy than LR (82% vs 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g. founders) are more important than visual objects (e.g. products). In both spoken and written language, words related to experience (e.g. eat) or perception (e.g. hear) are more important than cognitive (e.g. causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see and compare) to complement visual content are also effective and positive tone matters in speech.

This research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception and future time). Also, in a multimodal context, complementary cues (e.g. speech aids) across different modalities help. Furthermore, the noncontent parts of speech such as positive “tone” or pace of speech are important.

Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g. goal, duration and reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended.

Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

Social interaction in comment sections has become a key factor for backers' decision making in crowdfunding platforms. However, current research on the two-way social interaction in crowdfunding is insufficient, and there exist inconsistent conclusions. This study focuses on the social interaction between creators and backers and explores its influence on the successful exit of crowdfunding projects.

The extended Cox model is used for the empirical analysis of 1,988 crowdfunding projects on the Modian (www.modian.com) platform, a crowdfunding platform for cultural and creative projects in China. The two-way social interaction is reflected in comment quantity and sentiment, as well as reply rate.

Results reveal an inverted U-shaped relationship between comment quantity/sentiment and the successful exit of crowdfunding projects. This relationship is strengthened by high reply rate.

This study focuses on comment quantity and sentiment. The inverted U-shaped results reconcile previous conclusions. Replies from creators are regarded as a separate factor, and their moderating role is explained. The study research proves the importance of social interaction in crowdfunding platforms and provides suggestions for backers, creators and platform managers.

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).

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).

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.

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.

The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

Crowdfunding allows enterprises or individuals to collect funds from numerous other individuals. This study applies the anchoring effect and range theory in reward-based crowdfunding to explore how different pledge option designs affect the backers' final pledge amount. Moreover, this study examines whether showing the current average amount pledged in the fundraising process has an anchoring effect on the subsequent backers' pledge amount.

Online experiments were conducted, and data were analyzed using the Kruskal–Wallis test and Spearman rank correlation analysis.

Results show that among the three pledge option designs, employing the “bolstering range offer” has the highest backing amount. However, presenting the current average amount pledged in the fundraising process has a reversed anchoring effect on subsequent backers' pledge amount only in the case of a crowdfunding project in the physical goods category with a “point offer.”

To the best of authors’ knowledge, no reward-based crowdfunding platform has yet provided the pledge option design of a “bolstering range offer.” This study reveals that the “bolstering range offer” can significantly increase the amount pledged. This study extends the crowdfunding research area to crowdfunding success and suggests a novel way to set up pledges.

This study aims to investigate how the language used in US Title II equity crowdfunding campaign descriptions relates to campaign success.

Data on >3,200 equity offerings from 12 Title II platforms was obtained from 2013 to 2016. The aspects of the campaign descriptions that are focused on are tone and two measures of readability: information quantity – the amount of information available to the investor and information quality – the ease of understanding of the passage of text. Tobit regressions with sector-clustered standard errors are used for estimation while controlling for company-specific variables, market sentiment and platform, regional, sector and time effects. Results are robust to alternative estimation approaches.

Inverse U-shaped relationships exist between information quantity, information quality and tone and Title II equity crowdfunding campaign success. Overall, less is more as it appears that an intermediate level of information – quantity, quality and tone – is optimal in terms of being a factor that contributes to equity crowdfunding campaign success.

Extends the use of textual analysis to the equity crowdfunding environment in the USA where such analysis is lacking and provides empirical evidence that the language used (e.g. sentiment) in US Title II equity-based crowdfunding campaign descriptions does influence campaign success. It provides empirical evidence of and extends the concept of information overload to the entrepreneurial finance sub-field and indicates tone may be an additional information attribute to consider in this context as contributing to overload.

Serial crowdfunding is becoming a common phenomenon as entrepreneurs repeatedly return to online crowdfunding to raise capital. In this study, the authors focus attention on serial crowdfunders, that is, entrepreneurs who experience launching more than one crowdfunding project. The purpose of this paper is to investigate the role of past experience on subsequent crowdfunding performance. This study also examines whether initial success vs initial failure leads serial crowdfunders to engage in more explorative behaviors (i.e. switching industry) and to take exploitative actions (i.e. adjusting campaign strategies in terms of goal setting and funding option).

Data on serial crowdfunding projects was retrieved from Indiegogo platform. The logistic regression models are estimated to assess the impact of past entrepreneurial experience on subsequent crowdfunding decisions, and to estimate the effects of the three strategies on subsequent funding performance.

The results show that serial creators who experienced successful initial crowdfunding are more likely to explore a new industry or product category in the crowdfunding market and to set a higher target capital for the subsequent campaign when they change a project category.

Despite the fact that there are a considerably large number of serial crowdfunders in crowdfunding market, relatively little research has been conducted to investigate the presence of learning benefits from a previous to a subsequent crowdfunding project. Two competing hypotheses, drawn from the attribution theory and hubris theory of entrepreneurship, were tested in this study to determine the impact of prior success vs failure experience on both subsequent crowdfunding decisions and funding performance.

Despite the growing research exploring the possibility and feasibility of financing socially oriented projects through crowdfunding, relatively little research examines which crowdfunding model is better to serve such purpose. The purpose of this paper is to offer novel insights to mitigate this research gap.

A unique data set collected from the largest Chinese crowdfunding platform is used to test the hypotheses. To solve the perceived self-selection problem, the propensity score matching method is adopted in this paper. Based on this approach, the results of similar prosocial campaigns in two different models (pure donation and hybrid donation) are compared.

The empirical results show that the hybrid donation model is negatively associated with the status of success and the extent of success of prosocial campaigns. Specifically, compared to the pure donation model, hybrid donation model leads to a lower probability of success, fewer contributors, a lower funding amount and a lower completion ratio.

The paper contributes to a relatively understudied theme in crowdfunding, namely, donations. It does so by introducing the concepts of pure vs hybrid donation models and investigates the model selection problem in financing social projects through crowdfunding based on cognitive evaluation theory.

Fierce competition in the crowdfunding market has resulted in high failure rates. Owing to their dedication and efforts, many founders have relaunched failed campaigns as a second attempt. Despite the need for a better understanding, the success of campaign relaunches has not been well-researched. To fill this research gap, this study first theorizes how founders’ learning may enhance their competencies and influence investors’ attribution of entrepreneurial failure. The study then empirically documents the extent and conditions under which such learning efforts impact campaign relaunch performance.

This study examines 5,798 Kickstarter-relaunched campaigns. The founders’ learning efforts are empirically captured by key changes in campaign design that deviate from past business practices. Word movers’ distances and perceptual hashing algorithms (pHash) are used separately to measure differences in campaign textual descriptions and pictorial designs.

Differences in textual descriptions and pictorial designs during campaign failure–relaunch are positively associated with campaign relaunch success. The impacts are further amplified when the previous failures are more severe.

This study is one of the first to examine the success of a campaign relaunch after an initial failure. This study contributes to a better understanding of founders’ learning in crowdfunding contexts and provides insights into the strategies founders can adopt to reap performance benefits.