| Summary: | Most large modern enterprises comprise different departments, subsidiaries, and divisions internally, and each of these will typically operate multiple, interdependent, information technology systems. Externally, all enterprises face dynamic and sometimes turbulent environments, with ongoing changes in laws and regulations, technologies, competition, customer preferences, and marketplace changes. These ongoing external dynamics will impact on the enterprise’s goals and strategies, and thus on their IT systems and processes. Enterprise architecture management (EAM) frameworks have proven to be a valuable and widespread means of representing the internal IT systems of enterprises, and of representing links of these systems to the organization’s goals and strategies. But how well do EAM frameworks cope with the dynamic envi-ronments that organizations face? It turns out not well. Indeed, common EAM frameworks are mostly static. In practice, enterprises opt to build their own adapted approach on top of standard frameworks. Stakeholders use their capacity and attempt to incorporate implicit knowledge and business behaviour specific to their own enterprise. Based on an action research case study undertaken in a large, complex business enterprise in Saudi Arabia, we propose a methodology for managing changing business behaviour. This builds on selecting existing and well-established approaches in line with EAM frameworks. This is achieved by an extended meta-model offering further capacity with new constituents enabling the representation of time-knowledge for changing sources of information, and new constituents enabling constant maintenance of enterprise architecture (EA) models. In addition, the incorporation of the changing business behaviour is facilitated via guidelines for the modelling of different stakeholders’ collective-thinking/mental-modelling in order to offer a shared understanding of business behaviour. Furthermore, we propose a number of techniques relying on the ex-tended meta-model to facilitate the constant maintenance of the EA landscape. These techniques use the capacity of the extended meta-model to represent multiple states of the EA reflecting changing elements to compliment the architectural development method (ADM) of the open group architecture framework (TO-GAF). Our methodology is driven by action research to ensure the applicability and real-world relevance of our solution, which is itself a novel approach in the EAM field.
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