In order to configure individual products according to their own preferences, customers are required to know what they want. While most research simply assumes that consumers have sufficient preference insight to do so, a number of psychologically oriented scholars have recently voiced serious concerns about this assumption. They argue that decades of consumer behavior research have shown that most consumers in most product categories lack this knowledge. Not knowing what one wants means being unable to specify what one wants—and therefore, they conclude, the majority of customers are unable to use configuration toolkits in a meaningful way. In essence, this would mean that mass customization should rather be termed “niche customization” as it will be doomed to remain a concept for a very small minority of customers only. This pessimism stands in sharp contrast to the optimism of those who herald the new possibilities enabled by advances in communication and production technologies as the dawn of a new era in new product development and business in general.
Which position is right? In order to answer this question, this research investigates the role of the configuration toolkit. Implicitly, the skeptic position assumes that the individual customers' knowledge (or absence of knowledge) of what they want is an exogenous and constant term that does not change during the interaction with the toolkit. However, learning theories suggest that the customers' trial-and-error interaction with the configuration toolkit and the feedback information they receive should increase their preference insight. If this was true and the effect size strong, it would mean that low a priori preference insight does not impede customers to derive value from mass customization.
Three experiments show that configuration toolkits should be interpreted as learning instruments that allow consumers to understand their preferences more clearly. Even short trial-and-error self-design processes with conventional toolkits bring about substantial and time-stable enhancements of preference insight. The value of this knowledge is remarkable. In the product category of self-designed watches, the 10-minute design process resulted in additional preference insight worth 43.13 euros on average or +66%, measured by incentive-compatible auctions. A moderator analysis in a representative sample shows that the learning effect is particularly strong among customers who initially exhibit low levels of preference insight.
These findings entail three contributions. First, it becomes evident that the interaction with mass customization toolkits not only triggers affective reactions among customers but also has cognitive effects—a response category not investigated before. Second, it suggests that the pessimism regarding the mass appeal of these toolkits is not justified—mass customization has the potential to truly deserve its name. The prerequisite for this, and this normative conclusion is the final contribution, is that the toolkit should not be interpreted as a mere interface for conveying preexisting preferences to the producer. Rather, it should be treated as a learning instrument. Several suggestions are made for how firms employing this innovative business model could design their toolkits towards this end.