Paper Info
Title
Predicting Mobile Trading System Discontinuance: The Role Of Attention
Abstract
As mobile devices have become people's first go-to informational source, they are becoming critical for ecommerce companies in understanding how mobile trading devices influence their businesses. This study involves a collaboration with a nationwide financial services company in Korea to examine the role of mobile attention in predicting mobile stock trading system discontinuance. Employing XG-Boost and an artificial neural network, we analyze the complete transaction history, as well as the usage and login patterns data from 2017 to 2018 for 25,822 mobile trading application users. We find that mobile attention has significant statistical power over traditional trade-related metrics such as recency, frequency, and monetary value (RFM) in predicting subsequent mobile trading system discontinuance. Moreover, the new prediction methodology, augmented by incorporating mobile attention into the RFM framework and utilizing up-to-date machine learning techniques, consistently outperforms benchmarks in the empirical literature. Thus, this study sheds new light on the postadoption information system usage literature and furnishes practical guidance to those companies whose business hinges on mobile systems.
Year
DOI
Venue
2020
10.1016/j.elerap.2020.101008
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
Keywords
DocType
Volume
Mobile trading system, Discontinuance, Attention, Field study, Machine learning
Journal
44
ISSN
Citations 
PageRank 
1567-4223
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Dongyeon Kim121.37
Kyuhong Park200.34
Dongjoo Lee318212.87
Yongkil Ahn400.34