@article {10.3844/jcssp.2025.595.611,
article_type = {journal},
title = {Validating and Evaluating a Dynamic Enterprise Architecture Model: A Case Study},
author = {Ettahiri, Imane and Rassam, Latifa and Doumi, Karim and Zellou, Ahmed},
volume = {21},
number = {3},
year = {2025},
month = {Feb},
pages = {595-611},
doi = {10.3844/jcssp.2025.595.611},
url = {https://thescipub.com/abstract/jcssp.2025.595.611},
abstract = {In today’s rapidly changing business environment, adapting to evolving circumstances is a fundamental capability for organizations. Enterprise Architecture (EA) provides a holistic view of an organization’s structure, processes and technology, ensuring alignment with strategic objectives. However, traditional EA frameworks often lack the flexibility to dynamically respond to both predictable and unpredictable internal and external changes. This study proposes a novel, multidimensional evaluation framework for assessing the dynamic aspects of EA, structured around four axes: Dimensions (e.g., flexibility, Modularity), the six-step dynamic EA process, architectural layers and categories of dynamic metrics. The framework leverages the Goal-Question-Metric (GQM) methodology, allowing for a structured, goal-driven approach to measuring the adaptability and responsiveness of EA. We applied the proposed framework to a use case involving a Multinational Corporation (MNC) operating in a highly dynamic environment to validate it. This real-world scenario demonstrates the framework's ability to identify strengths and areas for improvement in the organization's architecture, particularly regarding flexibility, Extensibility and strategic alignment. Additionally, we discuss the framework's limitations, including the complexity of managing multiple axes and the challenges of continuous data collection. Nevertheless, the proposed model provides a robust, actionable tool for evaluating EA dynamism, with significant potential for guiding organizations in making goal-oriented improvements. Future research will focus on refining the framework's application and improving data collection methods to enhance scalability and generalizability.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}