How Is Mobile User Behavior Different? — A Hidden Markov Model of Mobile Application Usage Dynamics

37 Pages Posted: 19 Nov 2018 Last revised: 25 Nov 2018

See all articles by Shaohui Wu

Shaohui Wu

Tsinghua University, School of Economics & Management, Students

Yong Tan

University of Washington - Michael G. Foster School of Business

Yubo Chen

Tsinghua University, School of Economics & Management

Yitian (Sky) Liang

Tsinghua University - School of Economics and Management

Date Written: November 14, 2018

Abstract

Mobile application usage is becoming an essential activity in many people’s daily lives. Compared with PC internet, mobile internet usage is ubiquitous, temporally fragmented, and more context-dependent. Thus far research is limited on the mechanism of mobile app usage and the effects of context, even as usage continues to grow. In this paper, we aim to develop a framework to capture the underlying mechanism of mobile app usage, taking into account its unique time-fragmented feature and the possible impacts of contextual factors. To this end, we propose a hidden Markov model (HMM) and calibrate it using a consumer panel that contains real-time app usage information. We find three hidden states driving mobile app usage: utilitarian, social, and hedonic. Consumers have multiple intentions in the utilitarian state but are relatively single-minded in either the social or hedonic state. The state dynamic is volatile: Chains of continuous utilitarian states and hedonic states intercommunicate frequently with densely intermittent social states. Such a volatile state dynamic provides an antecedent for the fragmented-time mobile usage phenomena. Contextual factors—in particular, location and time of day—influence the state dynamic, e.g., its volatility varies across different locations and times of day. In sum, our analysis depicts the following picture: In the mobile internet era, consumers take advantage of relatively long free-time windows for functional and entertainment activities, while exploiting short micro-moments for social activity.

Furthermore, with the help of mobile internet technology, consumers seem to:

(1) utilize more micro-moments outside the office or in the morning as a complement to work, and

(2) reserve more micro-moments in the evening for relaxation and entertainment, all at the expense of social activities.

Keywords: mobile internet, mobile application usage, hidden Markov model, micro-moment, context

JEL Classification: M15, M31, L86

Suggested Citation

Wu, Shaohui and Tan, Yong and Chen, Yubo and Liang, Yitian (Sky), How Is Mobile User Behavior Different? — A Hidden Markov Model of Mobile Application Usage Dynamics (November 14, 2018). Available at SSRN: https://ssrn.com/abstract=3284269 or http://dx.doi.org/10.2139/ssrn.3284269

Shaohui Wu

Tsinghua University, School of Economics & Management, Students ( email )

Beijing
China

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

Yubo Chen (Contact Author)

Tsinghua University, School of Economics & Management ( email )

Beijing, 100084
China

Yitian (Sky) Liang

Tsinghua University - School of Economics and Management ( email )

Wei Lun Building 543
Tsinghua University
Beijing, 100084
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
376
Abstract Views
1,602
rank
114,499
PlumX Metrics