Description:
Research on digital businesses builds upon a well-established set of theories, models, and frameworks. A frequently used approach within the paradigm of common variance theory is to first create a theory-based model and then test it using a multivariate method. Cross-sectional data is often used to test the overall fit of the model and the significance of its parameters, yet this approach fails to take into account the important role of temporal dynamics and the potential change of parameters over time, which constitutes an important research gap. For example, the popular Technology Acceptance Model (TAM) provides a snapshot in time, but cannot explain the postulated temporal patterns of the Diffusion of Innovations (DOI) theory. In this exploratory paper, we call for a theoretical and methodological paradigm shift and introduce process theory as a complement to the popular variance theory. This theory considers how entities change over time and create important network effects. Additionally, we illustrate how agent-based modeling (ABM) in combination with Structural Equation Modeling (SEM) can help to create models that combine the rigor of multivariate statistical methods with the capability of process theory to account for changes over time. Using ABM within the process theory paradigm can help to create meaningful models that are capable of incorporating temporal dynamics.