A DEMATEL based network process for deriving factors influencing the acceptance of tablet personal computers

Abstract

The tablet personal computers (Tablet PCs) emerged recently as one of the most popular consumer electronics devices. Consequently, analyzing and predicting the consumer purchasing behaviors of Tablet PCs for fulfilling customers' needs has become an indispensable task for marketing managers of IT (information technology) firms. However, the predictions are not easy. The consumer electronics technology evolved rapidly. Market leaders including Apple, ASUS, Acer, etc. are also competing in the same segmentation by providing similar products which further complicated the competitive situation. How the consumers' acceptance of novel Tablet PCs can be analyzed and predicted have become an important but difficult task. In order to accurately analyze the factors influencing consumers' acceptance of Tablet PCs and predict the consumer behavior, the Technology Acceptance Model (TAM) and the Lead User Method will be introduced. Further, the differences in the factors being recognized by both lead users as well as mass customers will be compared. The possible customers' needs will first be collected and summarized by reviewing literature on the TAM. Then, the causal relationship between the factors influencing the consumer behaviors being recognized by both the lead users as well as the mass customers will be derived by the DEMATEL based network process (DNP) and the Structural Equation Modeling (SEM) respectively. An empirical study based on the Taiwanese Tablet PC users will be leveraged for comparing the results being derived by the DNP and the SEM. Based on the DNP based lead user method, the perceived usefulness, perceived ease of use, attitude and behavioral intention are perceived as the most important factors for influencing the users' acceptance of Tablet PCs. The research results can serve as a basis for IT marketing managers' strategy definitions. The proposed methodology can be used for analyzing and predicting customers' preferences and acceptances of high technology products in the future. © 2011 Springer.Verlag Berlin Heidelberg.