3期

最新文章

• Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research AI翻译
  作者：Agarwal, Ritu; Dhar, Vasant
  作者单位：University System of Maryland; University of Maryland College Park; New York University
  摘要：We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally different, the strengths that the information systems (IS) community brings to this discourse, interesting research questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging area should be evaluated for contribution and signifi...
• Discriminant Analysis with Strategically Manipulated Data AI翻译
  作者：Zhang, Juheng; Aytug, Haldun; Koehler, Gary J.
  作者单位：University of Massachusetts System; University of Massachusetts Lowell; State University System of Florida; University of Florida
  摘要：We study the problem where a decision maker uses a linear classifier over attribute values (e. g., age, income, etc.) to classify agents into classes (e. g., creditworthy or not). Sometimes the attribute values are altered and/or hidden by agents to obtain a favorable but undeserved classification. Our main goal is to develop methods to thwart agents from hiding or distorting attribute values to obtain a favorable but incorrect classification. Intentionally altered attributes to obtain strateg...
• Latent Growth Modeling for Information Systems: Theoretical Extensions and Practical Applications AI翻译
  作者：Zheng, Zhiqiang (Eric); Pavlou, Paul A.; Gu, Bin
  作者单位：University of Texas System; University of Texas Dallas; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Arizona State University; Arizona State University-Tempe
  摘要：This paper presents and extends Latent Growth Modeling (LGM) as a complementary method for analyzing longitudinal data, modeling the process of change over time, testing time-centric hypotheses, and building longitudinal theories. We first describe the basic tenets of LGM and offer guidelines for applying LGM to Information Systems (IS) research, specifically how to pose research questions that focus on change over time and how to implement LGM models to test time-centric hypotheses. Second an...