The decision processes surrounding investments in innovative information technology (IT) platforms are complicated by uncertainty about expected payoffs and irreversibilities in the costs of implementation. When uncertainty and irreversibility are high, concepts from real options should be used to properly structure the evaluation and management of investment opportunities, and thereby capture the value of managerial flexibility. However, while innovation researchers have posited that option value can influence the motivations of early adopters, and options researchers have identified emerging IT as a promising area for application of options valuation techniques, there has yet to be a systematic theoretical integration of work on IT innovation and real options. This paper seeks to fill this gap by developing a model of the determinants of option value associated with investments in innovative IT platforms. In so doing, the model addresses a central question in the innovation field: When should a firm take a lead role in innovation with emerging technologies? The analysis begins with an explanation of real options analysis and how it differs from conventional approaches for evaluating new technologies. Then a set of 12 factors—drawn from 4 complementary perspectives on organizational innovation (technology strategy, organizational learning, innovation bandwagons, and technology adaptation)—is synthesized into a model of the option value of IT platform investments. Rationales are provided to explain the direct effects of these factors on option value, and selected interactions among the factors are also considered. Finally, the implications of the model are presented in three areas: predicting IT platform initiation and adoption, valuing IT platform options, and managing IT platform implementation.

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.