Location
260-005, Owen G. Glenn Building
Start Date
12-15-2014
Description
Using a unique panel data set detailing individual-level mobile app consumption, this study develops a utility theory-based structural model for multiple discrete/continuous choices in app use. We identify the dynamics and inter-dependencies between mobile apps and jointly quantify consumers’ app choice and satiation simultaneously. The results suggest that mobile users’ baseline utility is the highest for communication apps, while the lowest for personal financing apps. In addition, users’ satiation level is the highest for the personal financing apps and the lowest for the game apps. However, a substantial heterogeneity in baseline utility and satiation is observed across diverse users. Furthermore, both positive and negative correlations exist in the baseline utility and satiation levels of mobile web and app categories. Consequently, the proposed frameworks could open new perspectives for handling large-scale, micro-level data, serving as important resources for big data analytics in general and mobile app analytics in particular.
Recommended Citation
HAN, SANG PIL; park, sungho; and Oh, Wonseok, "An Empirical Analysis of Consumption Patterns for Mobile Apps and Web: A Multiple Discrete-Continuous Extreme Value Approach" (2014). ICIS 2014 Proceedings. 16.
https://aisel.aisnet.org/icis2014/proceedings/DecisionAnalytics/16
An Empirical Analysis of Consumption Patterns for Mobile Apps and Web: A Multiple Discrete-Continuous Extreme Value Approach
260-005, Owen G. Glenn Building
Using a unique panel data set detailing individual-level mobile app consumption, this study develops a utility theory-based structural model for multiple discrete/continuous choices in app use. We identify the dynamics and inter-dependencies between mobile apps and jointly quantify consumers’ app choice and satiation simultaneously. The results suggest that mobile users’ baseline utility is the highest for communication apps, while the lowest for personal financing apps. In addition, users’ satiation level is the highest for the personal financing apps and the lowest for the game apps. However, a substantial heterogeneity in baseline utility and satiation is observed across diverse users. Furthermore, both positive and negative correlations exist in the baseline utility and satiation levels of mobile web and app categories. Consequently, the proposed frameworks could open new perspectives for handling large-scale, micro-level data, serving as important resources for big data analytics in general and mobile app analytics in particular.