ارزیابی تاثیر عوامل توصیه گر بر رفتار خرید برنامه ریزی نشده مصرف کننده آنلاین

Recommendation agents (RAs) have been used by many Internet businesses such as Amazon and Netflix. However, few authors have studied how consumer behavior is affected by those that make suggestions to online consumers based on their recent shopping behavior. Fewer still have examined the role that RAs play in influencing impulse purchasing decisions online. Our study developed a theoretical model to illustrate the impact of RAs on online consumer behavior. The model was tested through an online shopping simulation which used a collaborative filtering based product RA. Particular attention was paid to the effects of an RA on consumer behavior; we found that it increased promotion and product search effectiveness, user satisfaction with the website, and unplanned purchases. Results showed that our model provided insights into the impact of an RA on online consumer behavior and thus provided suggestions for implementing effective systems.

B2C electronic commerce has become a large and important segment of the new digital economy. Online retailers like Amazon.com and service providers like Netflix.com have come to dominate their market segments online. One of the tools used on the websites of these online powerhouses is the recommendation agent (RA): it provides a customized online shopping experience. Many researchers have speculated that RAs provide an opportunity for online merchants to influence customers’ behavior [8], [14] and [24]. Several studies have suggested ways in which RAs may influence online consumer behavior [20] and [22]. Felfernig and Gula [6] proposed that RAs may persuade the customer that some product attributes are more important than others or make the consumer more satisfied with their online shopping experience. A complementary stream of research from the marketing literature has examined the process by which consumers shop for and purchase goods and services. Several models describing consumer purchase behavior have emerged over the years. Now that consumers are buying products online via the Internet, researchers and practitioners have become interested in how the technological aspects of online shopping affect consumer behavior. The point at which online shopping, the use of intelligent software agents, and consumer behavior theory come together was thus the primary focus of our study. Previous studies have provided a theoretical foundation that helps identify online buying process factors important in measuring the RA's impact. While several researchers have identified areas of consumer behavior theory where RAs seem to logically fit into the purchasing process [1], none has attempted to link or examine the impact that RA usage has on the various phases of the consumer buying behavior model. A few studies have examined the impact of intelligent software agents on the product selection and merchant selection processes, but little research has been done in other areas. One very important area involves assessing how software agents affect consumers during the initial phase of the buying process; i.e., when they realize that they want a particular product. Most of the research deals with the later stages in the purchase process where a consumer is trying to decide which one of the set of alternatives to purchase [7]. Very few studies have examined the role of impulse purchasing, even though such activities have been shown to be a very large component of shopping behavior [19]. Fewer have examined the role of RAs in influencing satisfaction with website and impulse purchasing decisions. The objective of our research was to test the impact of the use of RAs on online consumer purchase behavior of unplanned purchases on line, as well as consumer affective reaction to product promotion, product search, and satisfaction with the website. If the use of RAs can be shown to produce a significant positive impact on any of these commercially important variables, companies should increase their efforts to use more effective RAs. Moreover, understanding the effects of intelligent web technologies on consumers’ online behavior is essential for the success of any web-based businesses. To accomplish our objectives, we examined the influence of the use of an RA and created a simulated online shopping environment in which some data could be collected to study the impact of the use of RAs in an online retail environment.

We developed a theoretical model which explains the relationships between the various constructs in the online shopping process. Our model showed clear relationships between the use of an RA and product promotion effectiveness, product search effectiveness, customer satisfaction with the website, and unplanned purchases. We conclude that using an RA to aid customer online shopping can have a positive impact on businesses. Our results indicated that the use of the RA enhances online consumers’ satisfaction with the website, provides a vehicle for better product promotion, a more effective product search process, and increases consumers’ unplanned purchases.