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The aim of this paper is to show that online learning behaviors are dictated by both personal characteristics and regional differences.

Data were collected from 16,133 users in 25 regions of Taiwan. The paper examined usage behaviors by looking at 11 items of categorical variables about online learning. This study implemented a multi‐level latent class model to investigate online learning behavior patterns that exhibit regional differences.

The results showed that online learning patterns do exhibit regional differences, as the regional segments are dictated by the individual segments of different use patterns. For instance, the urban area segment comprised a higher proportion of members who are good at using the internet. The rural area segment made up a higher proportion of members who occasionally use the internet. Interestingly, rural users went online more often than urban users when in search of e‐learning or entertainment. On the other hand, the individual segments are dictated by users' personal characteristics. For instance, younger people are good at employing online learning and entertainment services. Moreover, those who use many types of online applications pay less respect to intellectual property rights than those who only use a few types of applications.

By using a massive amount of survey data to show regional differences in online learning behavior patterns, the findings herein will help internet service providers form an applicable guideline for developing service strategies of higher service satisfaction between products and users' needs.

The purpose of this article is to investigate urban and rural differences for online activities and e‐payment behavior patterns.

This study applied the MLCA model to investigate Internet usage patterns from 11 online applications among 10,909 Taiwan residents in 25 different regions.

The results showed that online behavior patterns exhibited regional differences, as the regional segments affected the individual segments of different use patterns. For instance, the urban area comprised a higher proportion of members who were accustomed to internet applications and skilled in online shopping by using a credit card. The rural area made up a higher proportion of members who only occasionally used online services. Moreover, rural region residents used other payment methods (excluding credit cards) more often than urban region residents. As expected, users’ personal characteristics also dictated the online behavior pattern. For instance, people with higher‐level income spent relatively more money for online shopping and often used various internet applications than others.

The findings herein should help Internet service providers form an applicable guideline for developing service strategies of higher service satisfaction regarding products and users’ needs.

This study implemented a multilevel latent class model to investigate online behavior patterns that exhibited urban and rural differences, with the goal of providing service providers an understanding and mastery of their target users.