Innovation diffusion theory provides a useful perspective on one of the most persistently challenging topics in the IT field, namely, how to improve technology assessment, adoption and implementation. For this reason, diffusion is growing in popularity as a reference theory for empirical studies of information technology adoption and diffusion, although no comprehensive review of this body of work has been published to date. This paper presents the results of a critical review of eighteen empirical studies published during the period 1981-1991. Conclusive results were most likely when the adoption context closely matched the contexts in which classical diffusion theory was developed (for example, individual adoption of personal-use technologies) or when researchers extended diffusion theory to account for new factors specific to the IT adoption context under study. Based on classical diffusion theory and other recent conceptual work, a framework is developed to guide future research in IT diffusion. The framework maps two classes of technology (ones that conform closely to classical diffusion assumptions versus ones that do no0 against locus of adoption (individual versus organizational), resulting in four IT adoption contexts. For each adoption context, variables impacting adoption and diffusion are identified. Additionally, directions for future research are discussed. 1.

and Knowledge Transfer, the special editor and two anonymous referees. The author would also like to acknowledge Himanshu Sheth’s help with statistical analysis. Financial support was graciously provided by the Reginald Jones Center and by the Hunstman Center at the Wharton School of the University of Pennsylvania. Errors and omissions are solely the author’s responsibility. 1 THE PROCESS OF KNOWLEDGE TRANSFER: A DIACHRONIC ANALYSIS OF STICKINESS Even though intra-firm transfers of knowledge are often laborious, time consuming and fraught with difficulty, extant conceptions treat them essentially as a costless and instantaneous exploit. When at all acknowledged, difficulty is an anomaly in the way transfers are modeled rather than a characteristic feature of the transfer itself. One first step towards incorporating difficulty in the analysis of knowledge transfer is to recognize that a transfer is not an act, as typically modeled, but a process. This paper offers a process model of knowledge transfer. The model identifies stages of transfer and factors that are expected to correlate with difficulty at different stages of the transfer. The general expectation is that factors that affect the opportunity

Introduction The task of deciding when and how to innovate is not an easy one. Consider the following managerial quandaries: . A CIO has joined a firm that lags in the adoption of emerging information technologies. He wonders: just how innovative should this firm be going forward, and what can be done to position it to be more willing and able to assume the challenge of early adoption? . A VP of marketing resides in a firm that generally leads in IT innovation, and must decide whether to endorse the immediate adoption of a particular innovation with major implications for marketing strategy. She wonders: are her firm's needs in this area and "readiness" to adopt sufficient to justify taking the lead with this specific innovation? If so, how should the assimilation process be managed? . A product manager must design a deployment strategy for an innovative software development tool. He wonders: how fast can this technology diffu

Based on a study of knowledge transfer within more than 15 industries, across three forms of governance, and between both domestic and international R&D partners, knowledge transfer success was found to be associated with several key variables, and to hinge upon (a) both R&D units’ understanding where the desired knowledge resides within the source, (b) the extent to which the parties share similar knowledge bases, and the extent of interactions between the source and the recipient to (c) transfer the knowledge and (d) participate in an articulation process through which the source’s knowledge is made accessible to the recipient.

The literature reports inconsistent findings for the effect of management support on the successful implementation of information systems innovations. This study proposes a contingent model that begins to explain this variance in the extant research findings. Conducting a meta-analysis of the IS implementation literature, we show, as hypothesized, that task interdependence moderates the effect of management support on implementation success and that there is no main effect of management support on implementation success. The implications for theory and practice are discussed. 1.

Abstract not found

Given a choice, individuals may experiment with certain types of office automation and accept them wholeheartedly, while others may reject them. This article develops models to characterize the probability of continued use of an office information system. It then tests these models with usage patterns of 210 study participants of the IBM PROFS Professional Office System during a twoyear period at the University of California at Berkeley. Two types of longevity analyses were performed, one using survival functions and the other using proportional hazard models. The survival function analysis showed that users did not try PROFS out and then immediately quit. Nevertheless, over a two-year period, 50 % stopped using the system. Subjective analysis of the data suggests a considerable number of these users moved to other systems rather than completely stopping use of any office automation software product. Proportional hazard models were employed to determine how the probability of terminating use related to user characteristics. It was found that the longer individuals had access to the system, the lower was the dropout rate. No relation was found between dropout rate and gender, age, or years working for the university. There was a direct relation between the probability of discontinuance and the individual’s departmental affiliation, and that a participant was a faculty member. The data did not support conclusions about the relation between other job classifications and discontinuance of use.

at Boston University, ETH Zürich,

Extending the workday to the home into the night and weekends is nothing new; however, the wide array of technological tools that facilitate an anytime-anywhere connectedness of employees to their employers is a recent phenomenon. Technology-assisted supplemental work (TASW) is the practice of lengthening working time by remaining connected to work, coworkers, supervisors, and other organizational stakeholders from home via advanced digital information technology (i.e., personal and handheld computers, cellular phones, or pagers). Although previous research offers insight into some aspects of this work form (Venkatesh & Vitalari, 1992), we present a new, broader theoretical framework that explains how organizational climate and employee characteristics promote the performance of TASW, and how both perceived usefulness of technology and satisfaction with adopted technology affect the strength of these relationships. We also explain the linkages among TASW and job performance, career success, and work-tofamily conflict and incorporate media richness and time and boundary management as moderators of these relationships. We present propositions, offer a general research strategy, and discuss the framework’s theoretical and managerial implications. © 2004 Wiley Periodicals, Inc.

This chapter explores the role end user perceptions in information technology adoption from the perspective of innovation diffusion theory. It is based on empirical data from a three-year longitudinal study of an information system implementation in an engineering organization. Data were collected on six different applications and their adoption by four categories of end users: engineering managers, project engineers, professionals, and secretaries. The data indicate a substantial variance across time, user categories, and applications in terms of adoption rates and perceptions of technology. The managerial implications of the results are that differentiated implementation strategies focused on specific end user categories are likely to be more successful than a single broadbrush strategy for all users. The results also suggest a framework for predicting technology adoption in the long run, based on initial adoption rates and user perceptions of technology.