2016

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Enterprise engineering and management at the crossroads

The article provides an overview of the challenges and the state of the art of the discipline of Enterprise Architecture (EA), with emphasis on the challenges and future development opportunities of the underlying Information System (IS), and its IT implementation, the Enterprise Information System (EIS). The first challenge is to overcome the narrowness of scope of present practice in IS and EA, and re-gain the coverage of the entire business on all levels of management, and a holistic and systemic coverage of the enterprise as an economic entity in its social and ecological environment. The second challenge is how to face the problems caused by complexity that limit the controllability and manageability of the enterprise as a system. The third challenge is connected with the complexity problem, and describes fundamental issues of sustainability and viability. Following from the third, the fourth challenge is to identify modes of survival for systems, and dynamic system architectures that evolve and are resilient to changes of the environment in which they live. The state of the art section provides pointers to possible radical changes to models, methodologies, theories and tools in EIS design and implementation, with the potential to solve these grand challenges.

A multidimensional Classification of 55 Enterprise Architecture Frameworks

Many enterprises are still in the early stage of research and exploration into the concrete practice of enterprise architecture. Under the milieu of globalization, it is increasingly necessary for an enterprise to improve cognition and practices of informatization construction. Also crucial for enterprises is the ability to exploit and develop a myriad of architecture frameworks in order to remain creative and dynamic in this field in order to determine which EAF suits them best. Enterprises should do so based on the understanding of not only business strategy and process, but also on the meanings of EA frameworks. This paper defines a classification system which is used to analyze 55 different EA frameworks.

Factors Influencing the Engagement Between Enterprise Architects and Stakeholders in Enterprise Architecture Development

The development of Enterprise Architecture (EA) is facing several challenges. The highly referenced challenges in literature are related to enterprise architects and stakeholders. The enterprise architects and the stakeholders are the main actors in EA development. However, there are limited studies that cover the relationship of the enterprise architects and the stakeholders. The purpose of this paper is to identify the factors characterizing the engagement of enterprise architects and the stakeholders in EA development. The study used a systematic literature review (SLR) as a method to identify the factors and proposing an initial engagement model. The SLR revealed 12 factors that influence the engagement between the enterprise architect and the stakeholders. These factors are organized using the multiple perspective theory under three perspectives namely; technical, organizational and personal that comprise the initial engagement model. The study is contributing by shedding the light on the key aspects of engagement factors between the enterprise architects and the stakeholders in the development of EA. Furthermore, it is an initial step towards developing the engagement framework by comprehending these key aspects.

An Application of Semantic Techniques to the Analysis of Enterprise Architecture Models

Enterprise architecture (EA) model analysis can be defined as the application of property assessment criteria to EA models. Ontologies can be used to represent conceptual models, allowing the application of computational inference to derive logical conclusions from the facts present in the models. As the actual common EA modelling languages are conceptual, advantage can be taken of representing such conceptual models using ontologies. Several techniques for this purpose are widely available as part of the semantic web standards and frameworks. This paper explores the use of the aforementioned techniques in the analysis of enterprise architecture models. Namely, two techniques are used to this end: computational inference and the use of SPARQL. The aim is to demonstrate the possibilities brought by the use of these techniques in EA model analysis.

Organizational Subcultures and Enterprise Architecture Effectiveness: Findings from a Case Study at a European Airport Company

This paper studies how organizational subcultures influence the effectiveness of the enterprise architecture (EA) function. It provides findings from a case study in a European airport company. We find specific subcultural differences that can lower EA effectiveness. In addition, we discover that not only subcultural differences but also subcultural similarity can reduce EA effectiveness. For instance, the preference for working isolated of some business departments results in a lack of communication between those departments, which lowers EA effectiveness. Also, our data suggest that the subcultural influence is indirect. We identify, amongst others, communication defects as an important intermediary variable.

A Learning Perspective on Enterprise Architecture Management

Enterprise architecture management (EAM) has long been propagated in research and practice as an approach for keeping local information systems projects in line with enterprise-wide, long-term objectives. EAM literature predominantly promotes strictly governed and centralized coordination mechanisms to achieve the promised alignment contributions. Notwithstanding the increasing maturity levels in practice, organizations still struggle with the successful establishment of EAM, mainly due to the inherent challenges of a firmly centralized approach in complex organizational settings. This study opts for cooperative learning as a theoretical lens to afford a distinctive, non-centralized conceptualization of EAM. We empirically demonstrate EAM as a stage-wise learning process in which knowledge acquisition and cooperative interactions among individuals contribute to project performance on the local level. Projects that benefit from this particular learning process, in turn, are found to significantly leverage enterprise-wide performance.