2021 - Working Papers: Technology and Information Systems
The Joker Product –Product Features that Lead to Online Purchases, 8 pp.
S. Reichman
(Working Paper no. 2/2021)
Research no.: 01920100
NO ABSTRACT
Using Predictive Analytics to Reduce Uncertainty in Enterprise Risk Management, 10 pp.
H. Ghasemkhani, S. Reichman and G.Westerman
(Working Paper no. 3/2021)
Research no.: 01921100
Traditional economic and business forecasting about corporate credit has relied on statistics from government agencies, annual reports and financial statements. These statistics are often published with significant delay, which limits their usefulness for predicting changes in creditworthiness. Yet, a delay in responding to changes in a company’s credit rating can have significant financial and risk consequences. With the widespread adoption of search engines, social media and related information technologies, it is possible to obtain data on literally trillions of economic decisions almost the instant that they are made. In this study, we investigated the power of these online activity data, combined with data on firms’ business ecosystems, to predict the likelihood of counterparty credit downgrade risk. The research offers a novel approach that contributes to the fields of information systems, finance, and social science by providing new insights on the role of these data types on firms’ financial risk.
Online news consumption, subscription and churn, 35 pp.
S. Bar-Gill & L. Spivak
(Working Paper no. 10/2021)
Research no.: 06021100
Churn is one of the main challenges for subscriber-based business models, specifically when conversion rates (the rate at which users are converted to paying subscribers) are low. Prior work shows that churn is associated with consumption patterns, yet the role of subscription utilization over time and its relevance to consumers’ decisions to unsubscribe from digital media services has not yet been explored. Using observational data from a U.S based news website, I investigate the dynamics of decreasing utilization of a paywall subscription leading up to churn, further evaluating the predictive power of different subscription utilization states on churn. Utilization is defined with respect to a metered paywall for news articles, which sets a threshold number of articles that can be accessed free of charge, while above threshold reading requires a paid subscription. Above threshold reading represents subscription utilization, whereas below threshold reading by subscribers implies underutilization of the subscription. My preliminary results show that utilization levels are informative for predicting churn offering significant improvements over the predictive power of user attributes, and general consumption patterns. This finding is consistent across prediction methods (Logistic Regression, Classification Tree, and Random Forest). I find that underutilization levels in a certain month are strongly associated with decreasing utilization levels and churn in the following month, while utilization (i.e., above threshold consumption) is a stable state. I am extending this study to examine the casual effect of subscription utilization on churn in collaboration with a leading Israeli newspaper with paywalled access to articles. I am currently designing a field experiment to test randomly assigned interventions at different underutilization levels to increase utilization, with the goal of decreasing subscriber churn. I believe that a better understanding of utilization and churn may improve consumer retention efforts and contribute to the current stream of literature on paywalls, subscription utilization, and churn.
Cancellation policy as a signal of trust and quality in the sharing economy: The case of Airbnb, 47 pp.
L. Zalmanson
(Working Paper no. 11/2021)
Research no.: 06320100
We study the effect of cancellation policy settings on listing demand in the context of Airbnb, a popular home-sharing platform. We employ a difference in differences strategy to show that, contrary to previous findings in the traditional accommodation industry, listings with a strict cancellation policy have, on average, four percentage points higher demand than listings with a more lenient policy. We complement these findings with a survey of real Airbnb users aimed at understanding the mechanisms behind our results. We find that Airbnb guests perceive listings with a strict cancellation policy to be of higher quality and their host to be more trustworthy. These results suggest that the cancellation policy of a listing can act as a signal of quality and, in turn, can increase the listing demand. Overall, our findings suggest that sharing economy platforms like Airbnb do not only offer a new business model and value proposition, but they also have the potential to affect consumer behavior and their decision-making process in ways that are different from more traditional settings.
