In this paper we combine three theories of attitude and behavior change in an attempt to inform the under-studied concept of sustained technology usage over time. We address two broad research questions (1) what specific processes act to drive behavior change? and (2) does the route of persuasion accepted by the recipient affect the long-term behavior of the recipient, i.e. are the changes enduring? We use Kelman’s (1958) processes of attitude change (i.e. compliance, identification, and internalization) as the three mechanisms through which the change can occur. We use the elaboration likelihood model (ELM) to provide a theoretical underpinning for understanding the cognitive elaboration that information recipients use when they are subject to persuasive messages. Finally, social learning theory helps us identify supervisors, work groups, and self as salient referents for behavioral modeling. We test our conceptual model using longitudinal data from a field study of users of a new customer relationship management system in a large financial services institution. Our results show that individuals are influenced to use technology by multiple processes including compliance, identification and Clusters are groups of separate firms that collaborate for business purposes. They are an important government strategy to increase economic development. Government agencies attempt to provide the initial impetus for clusters to develop. These agencies base their programs on reviewing naturally occurring clusters (clusters that develop without government intervention), the advice of cluster consultants, and relevant theory (for example, Porter, 1998; Porter, 2000). The success of these government initiatives has been mixed. This paper examines the factors that affect cluster adoption by developing a model based on the theories of innovation diffusion and the resource-based view of the firm. The research model was tested using a survey-based approach. The results indicated that relative advantage, as measured by resource value, immobility, and heterogeneity, has a positive effect on the extent of cluster adoption. Use of information and communication technologies and complexity were found to affect relative advantage. Cluster compatibility was found to reduce the perceived complexity of clusters. The results imply that future innovation-diffusion research should consider the effects of innovation attributes on each other, as well as innovation adoption. The paper also affects practice. It provides guidance to consultants and government agencies on how to formulate strategies to increase cluster adoption.