Online retailers are providing a large amount of products over the Internet for potential customers. Given the opportunity of accessing vast amounts of products online, customers usually encounter difficulties to choose the right product or service for themselves. Obtaining advice from the Internet is both time consuming and most of the time unreliable. Therefore, some kind of intelligent software is needed to act on behalf of customers in such situations. Recommender agents are intelligent software providing easily accessible, high-quality recommendations for online consumers. They either track online customer behaviour implicitly or obtain information from the customer explicitly and provide the products or services in which the 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 they seek. This study assessed the influence of knowledge-based recommender agents on the online-consumer decision-making process. Shopping duration, purchase of desired item, effort spent in searching for the desired product and the decision quality of online consumers were assessed by exposing the participants to a knowledge-based recommender system which has been integrated into 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 has been used to measure the influence of recommender agents on the consumer decision-making process. Study findings have shown that knowledge-based recommender agents improve the consumer decision-making process by reducing the shopping duration and effort spent in searching for suitable products. Also, it was found that decision quality and the number of consumers who purchase the desired item increase with their use of such systems. The results of this study provide additional proof of the potential benefits of integrating such systems into online web stores.