• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.175-193
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• 2022

Although there is paradigm shift in art industry and interdisciplinary convergence between art and entrepreneurship, little has been done in "art entrepreneurship." First, this study organized the concepts of art entrepreneurship and conducted literature reviews on the trends of international and domestic research. Second, this paper aimed to understand the concept of art platform business. To do so, authors reviewed the general concept of business model and special features of platform business. Third, this paper categorized and introduced 11 art platform businesses from the based on the purposes of companies (① rental & selling, ②commercialize & selling, ③crowdfunding, ④information sharing & digital exhibition). Forth, this study provided two frameworks (①business model components, ②platform controllability and customers' information asymmetry) and applied them into 11 cases. By systematically reviewing the previous studies, this paper expects to increase scholarly understanding of the field of art entrepreneurship where two different areas (art and entrepreneurship) have studied separately. In addition, introduction and analyses of 11 online art platform have practical implications.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.