Online recommendation systems, which are becoming increasingly prevalent on the Web, help reduce information overload, support quality purchasing decisions, and increase consumer confidence in the products they buy. Researchers of recommendation systems have focused more on how to provide a better recommendation system in terms of algorithm and mechanism. However, research which has empirically documented the link between customers' motivations and intentions to use recommendation systems is scant. Therefore, the aim of this study attempts to explore how consumers assess the quality of two types of recommendation systems, collaborative filtering and content-based by using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Specifically, the under-investigated concept of trust in technological artifacts is adapted to the UTAUT model.

In addition, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommendation systems. A total of 51 participants completed an online 2 (recommendation systems) x 2 (products) survey. The quantitative analysis of the questionnaires was conducted through multiple regression and path analysis in order to determine relationships across various dimensions.

Results of this study showed that types of recommendation systems and products did have different effects on behavioral intention to use recommendation systems. To conclude, this study may be of importance in explaining factors contributing to use recommendation systems, as well as in providing designers of recommendation systems with a better understanding of how to provide a more effective recommendation system.

In addition, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommendation systems. A total of 51 participants completed an online 2 (recommendation systems) x 2 (products) survey. The quantitative analysis of the questionnaires was conducted through multiple regression and path analysis in order to determine relationships across various dimensions.

Results of this study showed that types of recommendation systems and products did have different effects on behavioral intention to use recommendation systems. To conclude, this study may be of importance in explaining factors contributing to use recommendation systems, as well as in providing designers of recommendation systems with a better understanding of how to provide a more effective recommendation system.