e-Loyalty: How to Keep Customers Coming Back to Your Website
e-Loyalty: How to Keep Customers Coming Back to Your Website
Design and e-loyalty across cultures in electronic commerce
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Predicting navigation patterns on the mobile-internet using time of the week
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Design aesthetics leading to m-loyalty in mobile commerce
Information and Management
Why Do Internet Users Stick with a Specific Web Site? A Relationship Perspective
International Journal of Electronic Commerce
A Long Interval Method to Identify Regular Monthly Mobile Internet Users
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
NBiS '08 Proceedings of the 2nd international conference on Network-Based Information Systems
ICMB '09 Proceedings of the 2009 Eighth International Conference on Mobile Business
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The Mobile Internet is becoming a first-class citizen of the Internet in many advanced countries It is also increasing its broadband capabilities with 3G penetration As increased penetration and bandwidth leverage mobile application business opportunities, it is important to identify methodologies for serving mobile-specific demands Regularity is one of the important means of retaining and enclosing easy-come, easy-go mobile users It is known that users with multiple visits in one day and a long interval have a higher possibility of revisiting in the following month than others The author applies this empirical law to his investigation of the mobile broadband services The result shows that the method proposed for the text-based mobile services is also applicable to mobile video services in the mobile broadband context The author shows that the method based with 2 bits per day can provide results that can be used for classifying monthly-scale regular users in the case study.