The nature of quantitative research in information systems has been dominated by variance theories. Variance theories comprise constructs or variables and propositions or hypotheses linking them. Typically, researchers identify independent variables and a dependent variable and collect data to verify the hypothesized relationship between the two sets of variables. One of the major shortcomings of such an approach is that the temporal dimension is often lost because data are collected at a given point in time. In this paper, we present a research method that operationalizes process theory. Process theory recognizes that variables change over time and interact with each other. This approach is particularly useful to study the conversion of IT investments into IT assets, or the conversion of IT assets into organizational value. This conversion process, which is often subsumed into the black box that lies between the input (independent) variables and output (dependent) variable in variance theories, is recognized and formalized in process theory. We show how systems dynamics modeling can be used to operationalize process theory in the context of IS use. We demonstrate how we can study complex IS problems by developing dynamic hypotheses and then using systems dynamics modeling. The approach that we employ incorporates both qualitative (soft) and quantitative aspects and complements variance theory. We conclude by highlighting the contribution of this approach and the study results to both theory and research. Specific theoretical contributions lie in developing and communicating archetypal patterns of IS use as well as the ability to incorporate the effects of feedback in the context of IS use. An important contribution to research lies in the ability to explicitly relate IS use to productivity. The implication of such contributions to both theory and research is that practitioners can benefit from directly applicable results, especially when it comes to deciding management policies and strategies.