Using Effort, Accuracy and Technology Acceptance to Predict Decision Confidence in Online Shopping

In business-to-consumer (B2C) electronic commerce, the conversion rate of lookers-to-buyers averages 2%, 2 buyers for every 100 lookers. We believe that this rate is due, in part, to decision aids that are not designed to fit the large search space faced by online shoppers. A Decision Aid (DA) is a software tool designed to help decision makers, e.g., online shoppers. We investigate whether some sequences of DAs help shoppers more than other sequences. We develop a shopping model, which combines Effort, Accuracy and the Technology Acceptance Model (TAM) (Davis 1989) to support the shopper with different electronic store (e-store) designs, which are sequences of two or three DAs. The shopper’s goal is consistent with the Effort-Accuracy Model (EAM): maximize accuracy and minimizing effort. Our integrated shopping model shows that TAM extends EAM to better predict Decision Confidence (DC). We use a controlled experiment on 116 subjects and treatments that are four different e-store designs. We use exploratory second-generation Structural Equation Modeling, namely Partial Least Squares Regression. The analysis helps us determine the best experimental treatment, i.e., e-store design. There are two key findings: 1) some e-store designs minimize effort, maximize accuracy, or maximize DC significantly more than others, and 2) several TAM-related variables are important predictors of DC. This research could have direct implications for electronic commerce decision aid designers who are trying to increase revenues. The designers could have their decision aids dynamically detect the current task complexity and either recommend or impose a particular decision aid. The dynamic detection could be tailored to the individual’s customer profile or real-time behavior.