Consumer decision support systems for novice buyers – a design science approach

Abstract

For business-to-consumer e-commerce to have a significant impact, consumer decision support systems (CDSS) must accommodate a wide variety of online buyers. The popular parameter-based CDSS (PCDSS) are limited by their inability to help novice consumers in their purchase of highly differentiated products. Drawing on Cognitive Fit Theory, we propose that a needs-based CDSS (NCDSS) that derives product selections based on user specified usage context is suitable for buyers without sufficient expertise in the product category. We adopt a design science paradigm to develop several approaches that serve this purpose. The effectiveness of our design is empirically evaluated through a user study in a simulated scenario. The results show that in general, the proposed needs-based decision supporting mechanisms significantly outperform the conventional PCDSS when applied to aid e-buyers to make purchase decisions. Moreover, they can also be utilized as the complement of the popular PCDSS in order to enable purchase decision support for online shoppers at various expertise levels.