“I see you are looking at our infinite range of stuffed animals, may I help you find what you need?” They are the salespeople of the online retailers; recommendation agents (RA’s). By capturing our perceived preferences based on browsing patterns or interests, RA’s aim to understand our needs. Not an unnecessary luxury of any sort, as the complexity and amount of information we are confronted with often exceeds our limited information-processing capacities and thus the benefits of RA’s can turn into costs. (Dabholkar, 2006; West et al., 1999). If there would be a Maslow pyramid for online shopping needs, it would be the bottom layer; a basic need, indeed.
However, one recommendation agent does not fit all. Different websites use different types of RA’s and the extent to which we can interact with these systems is heavily influenced by the interface design and its dialogue initiation process. Ranging from extensive questionnaires to not even a “hello, I’m here”, the possibility to participate in a two-way dialogue depends on the online salesperson you have encountered. But does the quality and quantity of customers’ input really matter?
In their lab based experiment, using existing RA’s in a controlled setting, Dabholkar and Sheng (2011) show that greater consumer participation in using RA’s leads to more satisfaction, greater trust and higher purchase intentions with respect to the recommended products and the system itself. Existing research already elaborates on the effects of participation in decision making on satisfaction, trust and purchase intentions in the offline and online context (Driscoll, 1978; Chang et al., 2009; Yoon 2002). In addition, much research has been conducted in the RA field, but upon this point failed to combine these two topics.
A great strength in Dabholkar and Shengs’ research, is the fact that there is a significant importance in understanding these relationships in the RA field as they are of huge strategic importance to online marketers. Therefore this topic is highly relevant. Moreover, by adding the dimension of financial risk, the authors are able to also identify that higher product prices moderate the need of participation in the RA context. This gives marketers insight for which products their recommendation agents should have high/low levels of possible interaction and therefore are able to personalize their RA’s per product and possibly increase purchases.
But, there are also a few limitations that need to be taken into account. One could argue that the used sample is non-representative for the online shopping population, as it completely consisted of college students with an average age of 21.91. Although the authors highlight the fact that the largest share of the Internet population is aged 18-32, it is not unthinkable that a student’s perception of financial risk differs from a middle aged person with substantially more spending power. Besides, students perceptions of trust in the online shopping context may be not completely representative, as they grew up with the Internet.
Summarizing, Dabholkar and Sheng give great insights in the effects of consumer participation in RA’s on satisfaction, trust and even purchase intentions. However, generalizability at this point is questionable, so further research across different age groups needs to be conducted to validate these results. But for now; Does your customer base primarily consist of students? Then it is time to revaluate your online salespeople. Get them to communicate with us, we would love to talk!