Multi-Level Latent Class Analysis of Online Behavior Patterns

• Main Author: 謝翠娟
• Other Authors: 楊千
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/74657768144883256871

博士 === 國立交通大學 === 資訊管理研究所 === 99 === This research confirms that online behaviors are dictated by both personal characteristics and areas of people reside. This study has applied the MLCA model to investigate Internet usage patterns from seven online applications among 10,909 Taiwan residents who live in one of 25 different regions. The results showed that online behavior patterns do exhibit regional and gender 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, non-metropolitan area users went online more often than those in metropolitan area users when using e-learning or online shopping. Service providers can offer an appropriate collocation of online shopping, online financing and delivery services to attract purchases from non-metropolitan area residents. If a service provider is trying to target metropolitan area residents, then it should enhance security and could use pre-introduction or a trial together with a promotion on an interactive and mobile service. On the other hand, the individual segments are dictated by users’ personal characteristics. For instance, younger people were good at various online services, as they had more employing blog and instant message services than others. Gender difference depends on various/heterogeneity application. Females used the Internet more often for online shopping application than males. People aged 21-40 were the major users of online applications, and websites could offer these users appropriate discounts of customization to attract their purchases. By using a massive amount of survey data to show regional and gender differences in online behavior patterns, the findings herein will help Internet service providers to form an applicable guideline for developing service strategies of higher service satisfaction between products and users’ needs.