The jury of the CIONET European Research Paper of the Year award has elected the 2 finalists for the 2017 award, from a short list of 27. Congratulations to David Martens from the University of Antwerp and Wolfgang Ketter from the Erasmus University, as well as the other authors of the two papers!
Read the abstracts of the two papers hereunder, and make sure to discuss more in depth with them on their major findings at the digital leadership conference CIOCITY in Amsterdam, June 26-27, where the award ceremony takes place. Register here if you didn’t do so yet (if you need a code for registration please contact firstname.lastname@example.org)
See you there!
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics
- Martens, David – University of Antwerp
- Provost, Foster – New York University
- Clark, Jessica – New York University
- Junqué de Fortuny, Enric – Erasmus University Rotterdam
Organizations increasingly have access to massive, fine-grained data on consumer behavior. Despite the hype over “big data,” and the success of predictive analytics, only a few organizations have incorporated such fine-grained data in a non-aggregated manner into their predictive analytics. This paper examines the use of massive, fine-grained data on consumer behavior—specifically payments to a very large set of particular merchants—to improve predictive models for targeted marketing. The paper details how using this different sort of data can substantially improve predictive performance, even in an application for which predictive analytics has been applied for years. One of the most striking results has important implications for managers considering the value of big data. Using a real-life data set of 21 million transactions by 1.2 million customers, as well as 289 other variables describing these customers, the results show that there is no appreciable improvement from moving to big data when using traditional structured data. However, in contrast, when using fine-grained behavior data, there continues to be substantial value to increasing the data size across the entire range of the analyses. This suggests that larger firms may have substantially more valuable data assets than smaller firms, when using their transaction data for targeted marketing.
MIS Quarterly, December 2016, p. 869-888
Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics
- Ketter, Wolfgang – Erasmus University
- Peters, Markus – Erasmus University
- Collins, John – University of Minnesota
- Gupta, Alok – University of Minnesota
Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex sociotechnical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating these problems requires research methods that scale beyond the traditional areas of inquiry of information systems (IS) individuals, organizations, and markets and that deliver solutions in addition to insights. We describe an approach to address these challenges through competitive benchmarking (CB), a novel research method that helps interdisciplinary research communities tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.
MIS Quarterly, December 2016, p. 1058-1089