This study is the first, so the authors claim (and I have no reason to suspect otherwise), to test "whether a consumer's prepurchase trust impacts post-purchase satisfaction through a combined model of consumer trust and satisfaction developed from a longitudinal viewpoint." It is one of the few studies that observe all three phases of the purchase process -- pre-purchase, decision to purchase, and post-purchase. Finally, it is relatively unique in collecting data both from those who have decided to buy and those who decided not to buy.
The model is beautiful, if one can use that term to describe a model:
Customer trust affects willingness to purchase directly and indirectly through perceived risk and perceived benefit. That is, increasing trust reduces the customer's perceived risk and increases the customer's perceived benefit, and the combination of trust, risk, expectations, and benefit combine to increase willingness to purchase. The willingness to purchase affects the decision to purchase. After the purchase, confirmation of expectations is affected by the expectations themselves (the greater the expectation, the less likely it will be confirmed) and the perceived performance of the website in effecting the sale. Confirmation, expectation, and trust all affect satisfaction, which in turn affects loyalty. All relationships are statistically significant!
While the model is beautiful, one has to question its value. None of these relationships is unexpected, or even interesting. Every seller and website designer understands the need to increase customer trust, reduce risk to the extent possible, offer the greatest benefit possible, and set high expectations. Interestingly, these variables explain less than 50% of the variance in willingness to purchase. Readers should certainly be interested in knowing what other factors affect willingness to purchase. Furthermore, willingness to purchase explains only 21% of the variance in the decision to purchase. Readers should ask, why did consumers who had high willing to purchase fail to do so; and why did consumers who had low willingness to purchase actually decide to purchase? Readers should also want to understand why one site engendered trust while other sites did not. These are the types of questions that case studies, rather than statistical studies, can answer. It is only through a deeper understanding of the independent variables affecting the purchase decision that sellers and website designers can extract value from such a study.
At this point I have to disclose a personal bias. Those who know me know that I have a strong belief in case study research as opposed to statistical research and am somewhat of a crusader for applying case study methodologies. Also, I am Editor-in-Chief of a journal that accepts only case study research: JITCAR, the Journal of Information Technology Case and Application Research (http://www.jitcar.org). So, I am, perhaps, on a soapbox here, expounding on my favorite topic, using an information systems study as a case in point (a case study, if you will).
Of course, a case study would have to be designed differently. This study asked student consumers to visit at least two B2C retailers to comparison shop for an item of their choice. There was no control over what sites they visited or the item they chose to buy. A case study design would most likely have to limit the sites and/or the item purchased. But, by asking more open ended questions and conducting interviews, it would result in much more nuanced understanding of what factors created or destroyed trust and how they entered into the purchase decision. Admittedly, the results might not be generalizable to sites selling different products or, perhaps, retailers of different size (or other characteristics) than those used for the case study. But, sellers reading the study could determine whether or not their particular application was sufficiently represented by the case study to be of value in their design decisions. Case studies suffer from a lack of generalizability, but they have value for at least some readers, while statistical studies leave readers without knowledge about where they stand in relation to the norm.
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