The research method

There are many systems implementation models in the information systems literature. As a starting point, the Payton and Ginzberg (2001) model, developed to explore the implementations of multiple health care information networks, provided a broad base from which to begin the analysis. The Payton and Ginzberg model, based on the diffusion work of Cooper and Zmud (1990), although not developed in the data warehouse context, does provide a broad perspective of adoption across organizations. This inter-organizational context where many organizations must work together is analogous to the case studied here, where one company is seeking to adopt a CDW across different functional areas. Additionally, the model offered a broad framework from which to investigate the data warehouse implementation from many perspectives in the organization, including organizational dynamics.

The dependent variable in this model as shown in Figure 1 is the success of the implementation effort. Three factor clusters are defined: push/pull factors, behavioral factors and shared systems topologies (Payton and Ginzberg, 2001). Push or pull factors are elements that can influence an organization's willingness to adopt a given technology, strategy, and/or change initiative. These factors include competitive pressures and perceived economic benefits from the system (Cooper et al., 2000).

Behavioral factors in this model are those factors that stand to impact and/or influence stakeholders and include end-user support, organizational autonomy and control, as well as vendor and top management support (Wixom and Watson, 2001). Political factors are those factors that arise from conflicting personal and organizational objectives among stakeholders. Political factors will tend to impede rather than facilitate implementation progress.

Shared or integrated systems topologies represent certain aspects of the infrastructure needed for a data warehouse. These factors include arrangements for cooperation and information sharing as well as for assuring information quality. Both elements of shared system topologies, information sharing and information quality were predicted to have favorable impacts on implementation progress.

As the degree of information sharing among internal departments increased, the quality of information available was also expected to increase, thereby fostering successful implementation (Cooper et al., 2000; Wixom and Watson, 2001). Others (Wixom and Watson, 2001) offered the suggestion that implementation success impacts perceived systems success, which can be defined here as the quality of data warehouse and the data that is extracted from the system. This model would imply that information quality is a central measure of the success or failure of a data warehouse to sustain CRM initiatives. Although our results support the importance of data quality in CDW adoption, several other implementation factors are highlighted by this research in the marketing context.

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