Using Neuroimaging to Understand Multi-Attribute Product Preference Judgments Involving Sustainability
Using user data to accurately collect, model, and predict consumer preferences continues to be a critical part of the engineering design process. To understand consumer behavior, engineering design teams utilize a wide array of both qualitative and quantitative methods. However, despite their power, both qualitative and quantitative research methods are ultimately limited by the fact that they all rely on direct input from the user themselves. This can be problematic due to the fact that potential users may not accurately represent their own true preferences. Furthermore, these individuals may be unable to express what they are truly thinking, feeling, or desiring.
Some preference judgments are particularly difficult to obtain accurate data for. For example, this is evident when dealing with preference judgments that involve difficult choices, such as sustainability. Sustainable products have largely underperformed in the consumer marketplace, and the reasons for this are unclear, especially due to the fact that consumer studies have shown that individuals desire environmentally smart products. Previously, work in our research group from Goucher-Lambert and Cagan showed that when a product is evaluated with its environmental impact present, users tend to perceive functional attributes to be more important, and aesthetics to be less important.
One way that researchers are exploring complex decision-making scenarios at the time of judgment is through the use of neuroimaging techniques. These methods provide a powerful set of tools to capture the neural processes underlying brain functions—including choice decisions. In our group we have begun exploring functional magnetic resonance imaging (fMRI) to explore engineering design research questions. This project seeks to utilize fMRI to understand the brain functions, and areas of brain activation associated with multi-attribute preference decisions involving sustainability.