USING EFFORT, ACCURACY AND TECHNOLOGYACCEPTANCE TO PREDICT DECISIONCONFIDENCE IN ONLINE SHOPPING

摘要

In business-to-consumer (B2C) electronic commerce, the conversion rate of lookers-to-buyers averages 2%,rn2 buyers for every 100 lookers. We believe that this rate is due, in part, to decision aids that are not designedrnto fit the large search space faced by online shoppers. A Decision Aid (DA) is a software tool designed to helprndecision makers, e.g., online shoppers. We investigate whether some sequences of Das help shoppers more thanrnother sequences.rnWe develop a shopping model, which combines Effort, Accuracy and the Technology Acceptance Model (TAM)rn(Davis 1989) to support the shopper with different electronic store (e-store) designs, which are sequences ofrntwo or three Das. The shopper’s goal is consistent with the Effort-Accuracy Model (EAM): maximize accuracyrnand minimizing effort. Our integrated shopping model shows that TAM extends EAM to better predict DecisionrnConfidence (DC).rnWe use a controlled experiment on 116 subjects and treatments that are four different e-store designs. We usernexploratory second-generation Structural Equation Modeling, namely Partial Least Squares Regression. Thernanalysis helps us determine the best experimental treatment, I.e., e-store design. There are two key findings:rn1) some e-store designs minimize effort, maximize accuracy, or maximize DC significantly more than others,rnand 2) several TAM-related variables are important predictors of DC.rnThis research could have direct implications for electronic commerce decision aid designers who are tryingrnto increase revenues. The designers could have their decision aids dynamically detect the current taskrncomplexity and either recommend or impose a particular decision aid. The dynamic detection could be tailoredrnto the individual’s customer profile or real-time behavior.