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作者：Levallet, Nadege; Denford, James S.; Chan, Yolande E.
作者单位：University of Guelph; Royal Military College - Canada; Queens University - Canada
摘要：Research questions surrounding phenomena in information systems (IS) are growing more complex and less amenable to simple explanations. Although many encourage the use of multiple research methods, we go further, advocating for leveraging research methods, analytic approaches, and theoretical perspectives (coined MAP) to better examine complex IS phenomena. The use of multi-MAPs benefits from the complementarity between different theoretical perspectives, that is, variance, process and systems...
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作者：Dong, Yan; Song, Sining; Venkataraman, Sriram; Yao, Yuliang
作者单位：University of South Carolina System; University of South Carolina Columbia; University of Tennessee System; University of Tennessee Knoxville; Lehigh University
摘要：Using a data set on mobile technologies and mobile money in the emerging markets from 2000 to 2014, we examine the demand patterns of mobile technologies and mobile money when multiple generations of mobile technologies coexist in the market and each generation of the technologies may be bundled with mobile money. Using a structural model, we estimate the own and cross price elasticities for both mobile technologies and mobile money, and the demand effects between mobile money and mobile techn...
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作者：Clarke, Jonathan; Chen, Hailiang; Du, Ding; Hu, Yu Jeffrey
作者单位：University System of Georgia; Georgia Institute of Technology; University of Hong Kong; Massachusetts Institute of Technology (MIT)
摘要：Does fake news in financial markets attract more investor attention and have a significant impact on stock prices? We use the U.S. Securities and Exchange Commission (SEC) crackdown of stock promotion schemes in April 2017 to examine investor attention and the stock price reaction to fake news articles. Using data from Seeking Alpha, we find that fake news stories generate significantly more attention than a control sample of legitimate articles. We find no evidence that article commenters can...
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作者：Fu, Runshan; Huang, Yan; Singh, Param Vir
作者单位：Carnegie Mellon University; Carnegie Mellon University
摘要：Big data and machine learning (ML) algorithms are key drivers of many fintech innovations. Although it may be obvious that replacing humans with machines would increase efficiency, it is not clear whether and how machines can improve human decisions. We answer this question in the context of crowd lending, in which decisions are traditionally made by a crowd of investors. Using data from Prosper.com, we show that a reasonably sophisticated ML algorithm predicts listing default probability more...
