Online retailers are providing large amount of products over internet for potential customers. Given the opportunity of accessing vast amount of products online, customers usually encounter difficulties to choose the right product or service for themselves. Obtaining advice from internet is both time consuming and most of time not reliable. Therefore, intelligent software is needed to act on behalf of customer in such situations. Recommender systems (agents) are intelligent software providing easily accessible, high-quality recommendations for online consumers. They either track online customer behavior implicitly or obtain information from the customer explicitly and provide the products or services in which customer might be interested. By utilizing such systems, online retailers not only increase their sales but also assist their customers in finding the products or services. This study has assessed the influence of knowledge-based recommender systems on online-consumer decision making process. Shopping duration, purchase of intended item, effort spent in searching intended product and decision quality of online consumer have been assessed by exposing the participants to knowledge-based recommender system which has been integrated to one of the online shopping systems developed in the scope of this study. Only objective measures have been utilized in this research; that is, shopping system log data have been used to measure the influence of recommender agents on consumer decision making process. Study findings have showed that knowledge-based recommender systems improve consumer decision making process by reducing the shopping duration and effort spent in searching suitable products. Also, it has been found that decision quality and the number of consumers who purchase the intended item increase in the existence of such systems. Results of this study provide additional evidences of the potential benefits of integrating such systems to online web stores.
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Via Georgina Rodriguez