Zhang, T., Agarwal, R., & Lucas Jr, H. C. (2011). The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets. MIS Quarterly-Management Information Systems,35(4), 859.

The Internet is capable of bombarding its users with information, resulting in an information overload or choice overload. Adapting information to the needs of individual consumers alleviates this information overload. Information personalization is practiced to present the product information that individual consumers want to see in the appropriate manner and at the appropriate time (Pierrakos et al., 2003). Online retailers have widely adopted  personalization as a means to enhance the shopping experience of their customers in order to build and maintain a strong customer relationship. Online retailers can implement information personalization by offering real-time personalized product recommendations (PPRs) to their customers.

This article investigated whether PPRs generate value for online retailers, and if so, how. The authors looked at the effects of online retailer learning (in the form of higher quality PPRs) on consumer store loyalty. In their research they manipulated the quality of the retailers’ learning, resulting in varying qualities of PPRs offered.

Below are the conceptual model of the mechanism through which personalized services affect consumer store loyalty (figure 1) and the research model (figure 2) including the tested hypotheses.

Figure 1.

Figure 2.

H1a: Higher quality PPRs are associated with lower consumer product screening cost.

H1b: Higher quality PPRs are associated with higher consumer product evaluation cost.

H1c: Higher quality PPRs are associated with higher consumer decision-making quality.

H2a: Higher website knowledge is associated with lower consumer product screening cost.

H2b: Higher website knowledge is associated with higher consumer decision-making quality.

H3a: Lower consumer product screening cost is associated with higher consumer store loyalty.

H3b: Lower consumer product evaluation cost is associated with higher consumer store loyalty.

H3c: Higher decision-making quality is associated with higher consumer store loyalty.

The authors designed the experiment as a two-phase task. The subjects’ first task was to rate a list of Amazon.com’s DVDs. Their second task was to pick two DVDs from the website.

The authors’ findings show strong support for the proposed model. They find that, indeed, higher quality PPRs are positively associated with consumers’ online product brokering efficiency: higher decision-making cost and lower product screening cost, and ultimately repurchase intention.

The insights derived from this article could serve as guidelines for online retailers to better adjust their IT strategies to improve customer retention. PPRs have the potential to create a virtuous cycle: the more purchases made by consumers, the higher the level of input to the recommender system, the higher the quality of PPRs received by consumers, the higher the consumers’ online product brokering efficiency, the higher decision-making quality and the lower the product screening cost, and, finally, the higher consumers’ repurchase intentions. This brings sustained competitive advantage, because it becomes increasingly more difficult for competitors to imitate. When consumers switch to another store, their shopping efficiency will suffer.

References:

Pierrakos, D., Paliouras, G., Papatheodorou, C., and Spyropoulos, C. D. 2003. “Web Usage Mining as a Tool for Personalization: A Survey,” User Modeling and User-Adapted Interaction (13:4), pp. 311-372.