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

• Information Systems Review
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• v.25 no.4
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• pp.265-290
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• 2023

Even though crowdfunding has become popular as a novel means of raising capital for early-stage ventures and startups through an Internet-based platform, it is unclear how a funder's characteristics, such as motivation and ability, influence their information processing and pledging decision. This study aims to propose and test a research model for determining the relationships between a funder's personal attributes, information processing style, and funding intention. To test the research model, we collected data from 139 Amazon Mechanical Turk participants through an online questionnaire survey. The findings indicate that a funder's self-efficacy has a positive effect on heuristic processing but has no significant effect on systematic processing. By contrast, a funder's personal relevance positively influences both systematic and heuristic processing. Furthermore, heuristic processing, as well as perceived value and perceived risk, influence pledging intentions positively. Our findings potentially contribute to improving the design of crowdfunding platforms to better support a funder's information needs. Based on our findings, we discuss the implications of our study as well as the directions for future research.

• The Research Journal of the Costume Culture
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• v.25 no.6
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• pp.893-912
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• 2017

Crowdsourcing models in which organizaions obtain needed product ideas and services from a crowd in a network-based society are rising as a global industry trend. The purpose of this study was to figure out the types and characteristics of crowdsourcing design shown in the domestic fashion brands, and to provide implications for design strategies using crowdsourcing. This study was based on qualitative research which was brand case studies on crowdsourcing design in the fashion industry from January 2006 to July 2017. Also, quantitative analysis using frequency and percentage was applied. The results were as follows: First, crowdsourcing design was used in almost all types of fashion brands, such as sports and outdoor wear, men's wear, women's wear, men's and women's casual wear, shoes, bags, school uniforms, jeans, accessories, etc. Crowdsourcing design in the fashion industry was classified into three types: crowdsourcing graphics and artwork; crowdsourcing customized designs; and crowdsourcing product designs. Of the three types, crowdsourcing graphics and artwork was used most. There were four methods to choose the best crowsourced design: review only by experts, voting by crowd and review by experts, crowdvoting, and crowdfunding. Second, the characteristics of crowdsourcing design were openness, participation, reward and acknowledgement, sharing and interaction, and individualized collective intelligence. Crowdsourcing design could be used as an open innovation strategy in the fashion industry, which could collect new and creative design ideas for product development, resulting in the satisfaction of consumers and benefitting the company.