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Abstract. Enterprise Architecture (EA) is increasingly being used by large organizations to get a grip on the complexity and inflexibility of their business processes, information systems and technical infrastructure. Although seen as an important instrument to help solve major organizational problems, effectively applying EA seems no easy task. Efficient collaboration between architects and EA stakeholders is one of the main critical success factors for EA. The basis for efficient collaboration between architects and EA stakeholders is mutual understanding. In EA research, there has been much focus on the role of the architect; there is little research on the EA stakeholder. In this article we present the cognitive structure of four EA stakeholder groups, revealing how they expect the EA function to help them achieve their goals. With this we gain understanding of the EA stakeholder and provide the basis for better collaboration between architects and EA stakeholders.

Current architecture assessment models focus on either architecture maturity or architecture alignment, considering the other as an explaining sub-variable. Based on an exploratory study, we conjecture that both alignment and maturity are equally important variables in properly assessing architecture organizations. Our hypothesis is that these variables conceptually differ, correlate, but do not explain one another. In this paper we describe our Multi-dimensional Assessment model for architecture Alignment and architecture Maturity (MAAM), which contains six main interrelated sub-variables that explain both alignment and maturity. We used existing models, literature from business and IS domains, and knowledge gained from previous research to identify the explaining variables. We constructed MAAM using structured modeling techniques. We are currently using a structured questionnaire method to construct an Internet survey with which we gather data to empirically validate our model. Our goal is to develop an architecture assessment process and supporting tool based on MAAM.