An Exploratory Study of Personalization and Learning Systems Continuance

Learning systems are widely adopted by institutions worldwide in the new millennium. The challenge on utilization of learning systems is switched from users’ pre-acceptance behaviours (whether they are likely to adopt learning systems) to post-acceptance behaviours (whether they will continue to use the learning systems). It is commonly expected that successfully adopted learning systems that have, at one time, been perceived as being useful and easy to use would likely achieve a high rate of user continuance. However, reality can be different as user continuance is often not as high as expected. The continuance of learning systems draws our attention because the investment in institutionalizing a learning system is huge. There is also a theoretical gap between technology acceptance and system continuance for which continuance behaviour cannot be explained by traditional technology acceptance models. This study extends a post-adoption model on habit and IS continuance to investigate the effect of personalization (which includes personal content management, personal time management and privacy control) on learning system continuance. Empirical results suggest that personalization has a positive influence on perceived usefulness and habit, but does not directly influence continuance intention

Modeling Online Service Discontinuation with Nonparametric Agents

The internet and world wide web are an increasingly important resource, both as a market and as an information source, to both individual users and business entities. An estimated 120 million active web users exist in the United States alone. Access to these electronic marketplaces and information sources is accomplished through either a direct internet connection or through a service provider. Internet service providers (ISPs) enable internet and web access for most of these users either via dial-up modems (62.2 percent), or DSL connections (17 percent). Customers of ISPs frequently switch or discontinue service. The model selection perspective is used to extend previous work in this area through the development of a multi-agent system with neural network wrappers. The nonparametric (neural network) agents identify over 92 percent of those users that either stop or change service, which is a 15 percent increase over previous models