Blockchain Technology Adoption

Abstract

Identifying and quantifying the drivers for adopting blockchain technologies are important for developing effective launch plan. Technology Acceptance Model (TAM) and its derivatives have been used for this purpose. However, some of these models only use a few standardized, predetermined independent variables to collectively represent the drivers. Low predictive power of TAM leads to questions on whether this restriction may detrimentally constrain the exploration of other driving factors. Some other extended models with higher R2 are considered impractical and lack of theoretical foundations. This paper demonstrates that reasonable predictive power can be achieved even with simple, practically implementable model when research targets are sampled and segmented properly. By employing a more fundamental theory, this study has also included additional variable that would normally not be considered in TAM.