Dynamic Interaction in Decision Support: Effects on Perceived Diagnosticity and Confidence in Unstructured Domains
The evolution of eCommerce over the past decade has resulted in a wide range of tools that enable consumers to make better decisions about the products or services that they are purchasing. One class of tools that are now widely used in a variety of eCommerce domains are mashups, which combine disparate sources of information (e. g., price, product reviews, and seller reviews) to support buyer decision making. Previous academic studies that examined decision support tools for eCommerce domains have focused on the impacts on information search, consideration set size, and decision quality. This paper discusses dynamic interaction, namely, the degree to which a user can revisit and revise their inputs and consider alternative solutions during a decision. The relationships between dynamic interaction, diagnosticity, confidence, and intention were investigated in an experiment. The results of the study indicated that increasing dynamic interaction increased the perceived diagnosticity (i.e., the extent to which the user believes that the tool is useful to evaluate a product) of the decision support tool and the overall confidence in the decision. In addition, a post hoc analysis of decision quality suggests that increased levels of dynamic interaction also improve the overall quality of the decision made.
dynamic interaction perceived diagnosticity confidence calibration web 2.0 mashups end-user programming