• The Journal of Industrial Distribution & Business
• /
• v.10 no.4
• /
• pp.25-31
• /
• 2019

Purpose - A framework is suggested in this paper which will help crowdfunding platforms to match projects according to expectations of funders, leading to successful campaigns and thus increase the profitability of the crowdfunding platform. Research design, data, and methodology - The paper is theoretical and conceptual in nature which proposes a model for crowdfunding platforms to match expectations of crowds with project fundraisers. Results - Crowdfunding platforms are going through incremental innovations in order to match customer (funders and fundraisers) expectations. Leading crowdfunding platforms like Kickstart holds benchmark for other players in the market but the secret of success lies in matching quality projects with the appropriate funders. Crowdfunding platforms have to securitize the projects and allow only quality projects but also provide a wide range of options for funders. Thus, to manage this trade-off between quality and quantity of options, a framework is proposed. Conclusions - Crowdfunding platforms have to adopt a model which will help them in providing a perfect match between crowds and fundraisers. Each member of the crowd and every project will be assigned a category and rating based on the past records. Securitization of projects will help to entertain only demanded projects which will reduce the number of failing campaigns.

• Journal of Service Research and Studies
• /
• v.11 no.3
• /
• pp.1-16
• /
• 2021

Recently, crowdfunding has been receiving increased attention due to the variety of novel ways it provides for connecting funders and fundraisers. Concurrently, however, it has been criticized on the grounds of information asymmetry. We are interested in whether information collected from an asymmetric information source significantly influences crowdfunding success. Through this study, we would like to establish a social field of convergence pursued by Serve Science and to conduct practical research and practice together. First, we investigated the importance of interaction between funders and fundraisers within the crowdfunding platform. In the "comments" section, communication between funders and fundraisers reduces the degree of asymmetric information. In the aspect of presumable funders, they should make the best of the "comments" section to collect more solid information without losing return on their investment. Second, we analyze sentiment-based optimistic and pessimistic information from the "story" (asymmetric source) and "comments" sections. In conclusion, it shows that the optimistic and pessimistic information of the "comments" section has a great impact on crowdfunding success and that the influence of the "story" section is limited to pessimistic information under certain conditions. The crowdfunding platform is an innovative and productive way for startups and entrepreneurs to start a business, and since information an important role in the success of crowdfunding, It can be seen that it is essential to focus on establishing the best communication methods.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.231-242
• /
• 2023

Crowd funding faces a number of significant obstacles despite its rapid growth and popularity, with the main one being the possible asymmetric information between fundraisers and potential supporters. A study taxonomy based on signalling theory has been created to compare projects originating from Taiwan and India. This was made possible by obtaining a dataset from the crowd funding website, Kickstarter (Global platform). To make the project effective, the study's goal is to look into how signals (e.g., goal-setting, comments, and updates) might be used to reduce the problem of information asymmetry. Thus, we applied an Ordinary Least Squares (OLS) regression. Both Taiwan and India demonstrated signal mitigation of information asymmetry, but Taiwan showed a stronger relationship between ambitious goals and successful projects than India. The relative importance of project comments has been found to be stronger in Taiwan than in India; the relative importance of project updates has been found to be weaker and negatively correlated with project success in India, in contrast to Taiwan. Notably, our findings provide a theoretical and practical framework for understanding and using signals in successful crowd funding campaigns and activities in these two emerging countries.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.45-66
• /
• 2018

Crowdfunding has become more popular than angel funding for fundraising by venture companies. Identification of success factors may be useful for fundraisers and investors to make decisions related to crowdfunding projects and predict a priori whether they will be successful or not. Recent studies have suggested several numeric factors, such as project goals and the number of associated SNS, studying how these affect the success of crowdfunding campaigns. However, prediction of the success of crowdfunding campaigns via non-numeric and unstructured data is not yet possible, especially through analysis of structural characteristics of documents introducing projects in need of funding. Analysis of these documents is promising because they are open and inexpensive to obtain. We propose a novel method to predict the success of a crowdfunding project based on the introductory text. To test the performance of the proposed method, in our study, texts related to 1,980 actual crowdfunding projects were collected and empirically analyzed. From the text data set, the following details about the projects were collected: category, number of replies, funding goal, fundraising method, reward, number of SNS followers, number of images and videos, and miscellaneous numeric data. These factors were identified as significant input features to be used in classification algorithms. The results suggest that the proposed method outperforms other recently proposed, non-text-based methods in terms of accuracy, F-score, and elapsed time.