Yi-Jen (Ian) Ho

Color portrait of Yi-Jen (Ian) Ho

Assistant Professor of Information Systems

Department Supply Chain & Information Systems
Office Address 465 Business Building
Phone Number 814-865-0678
Email Address ian.ho@psu.edu

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Yi-Jen (Ian) Ho is an assistant professor of Information Systems at the Smeal College of Business, Pennsylvania State University. He received his Ph.D. in Information Systems in 2016 at the Merage School of Business, the University of California, Irvine. He is mainly interested in understanding the impacts of information technologies on consumer choice. His research focuses on location-based services and advertising, online platforms, and artificial intelligence. He applies various methods to obtain insights and identify casualties, including game-theoretic modeling, econometrics, randomized experiments, and machine learning. He has published in premier business journals, such as Information Systems Research and Production and Operations Management. He was the winner of the 2022 Gordon B. Davis Young Scholar Award and the 2017 Nunamaker-Chen Dissertation Award (by INFORMS Information Systems Society). His research also won the best paper award or nominations at major conferences, including INFORMS Information Systems and eBusiness Sections, WISE, and WEB. He serves the community as a special-issue senior editor at Production and Operations Management and top-tier journal reviewer, and a cluster co-chair, associate editor, and program committee member for leading conferences.

Expertise

Location-based service, online platform, artificial intelligence

Education

PhD, Information Systems, University of California, Irvine, 2016

MS, Management Information Systems, University of Arizona, 2008

BBA, Management Information Systems, National Central University, 2003

Courses Taught

BA 841 – BUS INTELLIGENCE (3)
Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data, also called "Big Data". Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets.They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and tools that can facilitate such analysis and discovery. Students will work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting, and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms. Upon successful completion of this course, students will have:- acquired the tools and techniques of data cleaning and preparation, data mining, and data visualization- become competent in analyzing both structured and unstructured data- developed an understanding of, and an appreciation for, the complexities of mining unstructured data such as text data includingdocuments, web pages, emails, etc.- developed an understanding of social networks as well as mobile and location-based analytics

SCIS 596 – Individual Studies (Variable)
Creative projects, including nonthesis research, that are supervised on an individual basis and which fall outside the scope of formal courses. A specific title may be used in each instance and will be entered on the student's transcript.

MIS 441 – Bus Intelligence (3)
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.

Selected Publications

Ho Y., Liu S., Pu J., Zhang D., "Is it about You or Your Driving? Designing IoT-enabled Risk Assessment." Production and Operations Management Journal, vol. 31, no. 11, 2022, pp. 4205-4222, doi.org/10.1111/poms.13816
Mao S., Dewan S., Ho Y., "Personalized Ranking at a Mobile App Distribution Platform." Information Systems Research, 2022, doi.org/10.1287/isre.2022.1156
Ho Y., Liu S., Wang L., "Fun Shopping: A Randomized Field Experiment of Gamification." Information Systems Research, 2022, doi.org/10.1287/isre.2022.1147
Ho Y., Sanjeev D., Ho Y., "Distance and Competition in Mobile Geofencing." Information Systems Research, vol. 31, no. 4, 2020, pp. 1037-1492, doi.org/10.1287/isre.2020.0953
Ho Y., Ho Y., Tan Y., "Online Cash-back Shopping: Implications for Consumers and e-Businesses." Information Systems Research, vol. 28, no. 2, 2017, pp. 250–264, doi.org/10.1287/isre.2017.0693
Dewan S., Ho Y., Ramasprasad J., "Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community." Information Systems Research, vol. 28, no. 1, 2017, pp. 117-136, doi.org/10.1287/isre.2016.0654
Chen H., Li X., Chou M., Ho Y., Tseng C., "Using Open Web APIs in Teaching Web Mining." IEEE Transactions on Education, vol. 52, no. 4, 2009, pp. 482-490, ieeexplore.ieee.org/document/5152946

Editorships

Production and Operations Management, Other, March 2022 - Present
International Conference of Information Systems, Associate Editor, January 2017 - Present