MIS Distinguished Speaker Series

Temple University

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Big Data

Mar 19 – Jungpil Hahn to present “The Impact of End-User Privacy Enhancing Technologies (PETs) on Firms’ Analytics Performance”

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The Impact of End-User Privacy Enhancing Technologies (PETs) on Firms’ Analytics Performance

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Jungpil Hahn

Associate Professor
Head, Department of Information Systems and Analytics
School of Computing
National University of Singapore

Friday, Mar 19

9 – 10 am | Zoom

(send an email to ayabakan@temple.edu to get the Zoom link)

Abstact:

Big data analytics in digital commerce requires vast amounts of personal information from consumers, but this gives rise to major privacy concerns. To combat the threat of privacy invasion, more and more individuals are proactively adopting privacy enhancing technologies (PETs) to protect their personal information. Consumers’ adoption of PETs may hamper firms’ big data analytics capabilities and performance but our knowledge of how PETs impact firms’ data analytics is rather limited. This study proposes a theoretical framework to better understand how consumers’ use of PETs will affect firms’ analytics performance by way of inducing measurement error and/or missing values with regards to entities, attributes and relationships in firms’ customer databases. However, the impact of specific end-user PETs may vary by analytics use cases. We conduct a computational study to investigate and quantify the impact of consumer PET use on product recommendation performance. Our simulation experiments find that consumers’ adoption characteristics (adoption rate and pattern) and PETs characteristics (protection mechanism and intensity) significantly affect the performance of product recommendation systems.

April 26 – Balaji Padmanabhan to Present “Showing to be Seen: Using Data Science to Discover TV Programs for Public Health Announcements”

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Showing to be Seen: Using Data Science to Discover TV Programs for Public Health Announcements

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Balaji Padmanabhan

Professor, Information Systems & Decision Sciences

Muma College of Business, University of South Florida

Friday, April 26, 2019

10:30 AM – noon

Speakman Hall Suite 200

 

Abstract

Television is a prominent channel for educating the public about chronic health conditions. This study presents a methodology for selecting TV programs for public health campaigns targeted to the at-risk individuals. Through high-dimensional analysis of a large dataset on TV viewership of the entire U.S. panel in 2016 the methodology first inductively discovers programs whose popularities are correlated with eight chronic conditions and risk factors. A series of nonparametric tests then examine the robustness of findings and verify that a significant portion of the correlations is genuine—that is, not all the discovered correlations are accidental due to the “curse of dimensionality.” We then use Facebook’s split testing platform and run a series of online experiments to compare the effectiveness of targeting the shows discovered by the methodology with those that were targeted by the major 2016 public health campaigns. The experimental results corroborate the potential value of the methodology, which opens up a potentially new set of programs for public health officials to consider in their efforts to combat a range of conditions that are significantly expensive both in human lives and cost to the economy.