The illusory diffusion of innovation: An examination of assimilation gaps

Abstract

Innovation researchers have known for sometime that a new information technology may be widely acquired, but then only sparsely deployed among acquiring firms. When this happens, the observed pattern of cumulative adoptions will vary depending on which event in the assimilation process (i.e., acquisition or deployment) is treated as the adoption event. Instead of mirroring one another, a widening gap—termed here an assimilation gap—will exist between the cumulative adoption curves associated with the alternatively conceived adoption events. When a pronounced assimilation gap exists, the common practice of using cumulative purchases or acquisitions as the basis for diffusion modeling can present an illusory picture of the diffusion process—leading to potentially erroneous judgments about the robustness of the diffusion process already observed, and of the technology's future prospects. Researchers may draw inappropriate theoretical inferences about the forces driving diffusion. Practitioners may commit to a technology based on a belief that pervasive adoption is inevitable, when it is not. This study introduces the assimilation gap concept, and develops a general operational measure derived from the difference between the cumulative acquisition and deployment patterns. It describes how two characteristics—increasing returns to adoption and knowledge barriers impeding adoption—separately and in combination may serve to predispose a technology to exhibit a pronounced gap. It develops techniques for measuring assimilation gaps, for establishing whether two gaps are significantly different from each other, and for establishing whether a particular gap is absolutely large enough to be of substantive interest. Finally, it demonstrates these techniques in an analysis of adoption data for three prominent innovations in software process technology—relational database management systems (RDBs), general purpose fourth generation languages (4GLs), and computer aided software engineering tools (CASE). The analysis confirmed that assimilation gaps can be sensibly measured, and that their measured size is largely consistent with a priori expectations and recent research results. A very pronounced gap was found for CASE, while more moderate—though still significant—gaps were found for RDBs and 4GLs. These results have the immediate implication that, where the possibility of a substantial assimilation gap exists, the time of deployment should be captured instead of, or in addition to, time of acquisition as the basis for diffusion modeling. More generally, the results suggest that observers be guarded about concluding, based on sales data, that an innovation is destined to become widely used. In addition, by providing the ability to analyze and compare assimilation gaps, this study provides an analytic foundation for future research on why assimilation gaps occur, and what might be done to reduce them.