Utilization of Augmented Reality for Enterprise Architecture Decision-Making – An Empirical Investigation

Enterprise Architecture Management (EAM) is a management discipline that deals with constant organizational change and the alignment between business and IT in organizations. EAM develops, maintains, and refines Enterprise Architectures (EAs), which are time-dependent high-level representations of an organization’s business and IT structure and how these relate to each other. These insights are visualized in the form of text, numbers, tables, graphs, mod- els, and diagrams to support stakeholders in decision-making processes. Particularly in the current era of business process digitalization and the rapid pace of technology innovations, which in turn require a variety of capabilities and adoption measures, EAM plays a significant role in managing digitalization efforts by providing decision-makers and subject-matter experts with fact-based rational arguments. Yet despite theory and practice substantiating and demonstrating EAM’s relevance for today’s organizations, the successful application of EAM artefacts remains moderate.

This cumulative dissertation is composed of a set of five articles with the overarching research objective of shedding light on current challenges in EAM in practice, and that propose artefacts to overcome these challenges. Based on a comprehensive review of the literature on the goals, decision-making tasks, and employed EA artefacts in organizations, this dissertation suggests a positive influence of Virtual Reality (VR) and Augmented Reality (AR) affordances on the quality of EA decision-making, and hence, on EAM effectiveness. This claim has been supported by developing two 3D EA visualizations in the form of a three-layer EA model and an EA city model. In-depth evaluations with business experts and decision-makers in the form of case studies, structured interviews, and comprehensive usability testing resulted in sophisticated EA artefacts that can be applied in practice. The results indicate especially that less experienced EA decision-makers benefit the most from these innovative EA visualizations. Finally, this dissertation further prepares the ground for future comparability evaluations by providing a taxonomy for this purpose.

All in all, this work is part of the efforts to further develop the EAM discipline by investigating empirically the application of AR in EA decision-making scenarios. Consequently, the results contribute to research by both providing conceptual and empirical insights. The designed artefacts provide insights and recommendations into how organizations can utilize AR for EA decision-making to increase EAM effectiveness in real-world settings.


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