Uppsats

Enterprise Architecture research communities and trends

Kandidat-uppsats

Stockholms universitet/Institutionen för data- och systemvetenskap

Publicerad: 2026

Språk: Engelska

Sammanfattning

Introduction Enterprise Architecture is an established multidisciplinary term but with many definitions. This thesis creates an overview of Enterprise Architecture research between 2010 and 2025. This is to better understand the current research landscape and how the focus of research has shifted over time in relation to technological and organisational change. Research Question The research question is: What research communities and trends can be identified and visualised in Enterprise Architecture research from 2010 to 2025? Method This study applied a data-driven systematic literature review combined with bibliometric network analysis. Bibliometric metadata was collected from Scopus and analysed through author graphs, citation relationships and Louvain community detection. This made it possible to identify structures and trends within the EA research field. Results The analysis collected 95990 article records, authored by 6745 authors and associated with 152205 citations. The results indicate that nine EA research communities were found between the years 2010 and 2025 using the Louvain community detection (Modelling and design tools, Management and Value, Governance and Operations, Implementation, Framework Methods, Digital Transformation, Enterprise Engineering, Model Engineering and Smart Cities). Discussion These findings suggest that EA research is positioned between established modelling and framework traditions and newer applications connected to digital transformation and organisational change. The study contributes with an updated overview of the EA research landscape. The limitations of the study implies that future research include more than Englishlanguage Scopus-indexed publications and focus on newer publications to hopefully analyse emerging EA fields.

Information

Lärosäte / institution
Stockholms universitet/Institutionen för data- och systemvetenskap
Publiceringsdatum
2026
Uppsatstyp
Kandidat-uppsats
Språk
Engelska

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