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<p>Modeling for information systems (IS) analysis and design offers broad insights into the advances and challenges of enterprise, business process, software, and conceptual modeling. In celebration of its 30th edition, this paper presents a data-driven retrospective analysis of studies published at the <i>Exploring Modeling Methods for Systems Analysis and Development (EMMSAD)</i> working conference from 2005 to 2024. EMMSAD has long been a key venue for research on Information Systems (IS) Modeling, covering areas such as conceptual modeling, enterprise modeling, and model-driven engineering, as well as the evaluation of modeling techniques and tools. Using machine learning, specifically Dynamic Topic Modeling (DTM) with BERTopic, this study identifies recurring topics, emerging trends, and shifts in research focus within the IS modeling community. The findings highlight key areas of alignment between IS modeling and the broader modeling landscape, providing insights into the field’s evolution and future research opportunities.</p>

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Exploring modeling methods for information systems analysis and design: a data-driven retrospective

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月27日

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  • Iris Reinhartz-Berger,
  • Adir Solomon,
  • Jelena Zdravkovic,
  • John Krogstie,
  • Henderik A. Proper

摘要

Modeling for information systems (IS) analysis and design offers broad insights into the advances and challenges of enterprise, business process, software, and conceptual modeling. In celebration of its 30th edition, this paper presents a data-driven retrospective analysis of studies published at the Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) working conference from 2005 to 2024. EMMSAD has long been a key venue for research on Information Systems (IS) Modeling, covering areas such as conceptual modeling, enterprise modeling, and model-driven engineering, as well as the evaluation of modeling techniques and tools. Using machine learning, specifically Dynamic Topic Modeling (DTM) with BERTopic, this study identifies recurring topics, emerging trends, and shifts in research focus within the IS modeling community. The findings highlight key areas of alignment between IS modeling and the broader modeling landscape, providing insights into the field’s evolution and future research opportunities.