Towards a Conceptual Model on Causal Loop Diagram

• Júlia de Souza Borges UFES
• Thiago Felippe Neitzke Lahass UFES
• Amanda Brito Apolinário UFES
• Monalessa P. Barcellos UFES

DOI: https://doi.org/10.5753/sbsi.2026.248566

Resumo

Context: Information systems are increasingly complex, shaped by interdependent technical, organizational, and human factors. Understanding this environment is crucial for developing and using information systems effectively. Systems Thinking (ST) offers tools that help in this matter. In ST, a system comprises elements and interconnections coherently organized to produce characteristic behaviors. ST tools aid in modeling complex systems and improving the understanding of their dynamics. Causal Loop Diagrams (CLDs), one of the most used ST modeling tools, represent cause-and-effect relationships and feedback structures, supporting the identification of problem causes, behavioral patterns, and enabling informed decision-making. Problem: Despite its potential, the lack of knowledge and supporting tools has limited CLD adoption. Solution: To structure knowledge about CLD, we developed a conceptual model representing its main elements, relationships, and constraints. IS Theory: This work relates to the organizational knowledge creation theory. A conceptual model provides an abstract representation of a domain, serving as a theoretical foundation that captures essential entities, their properties, and relationships. It enables knowledge formalization and supports communication among stakeholders. Method: To evaluate the model clarity and correctness, it was assessed by domain experts. To demonstrate its applicability, it was used to develop a supporting tool for CLD creation and interpretation. Results: Findings indicate that the conceptual model adequately captures essential concepts for understanding CLDs and provides structured knowledge useful for building them. The model can also serve as a reference model to aid tool development and has potential applications in semantic annotation and interoperability. Contributions and Impact to IS area: For researchers, this work advances knowledge about CLD, providing a formal representation of its conceptualization. For practitioners, it offers a model that aids in CLD understanding and communication, and in developing computational solutions. It also offers a tool to support CLD creation and interpretation, fostering its use in practice.

Referências

Andersson, C., Karlsson, L., Nedstam, J., Host, M., and Nilsson, B. I. (2002). Understanding software processes through system dynamics simulation: a case study. In Proceedings Ninth Annual IEEE International Conference and Workshop on the Engineering of Computer-Based Systems, pages 41–48. IEEE.

Antonelli, D., Chiabert, P., and Romagnoli, V. (2013). Information system and systems thinking: a compulsory marriage? IFAC Proceedings Volumes, 46(9):1780–1785. 7th IFAC Conference on Manufacturing Modelling, Management, and Control.

Apolinário, A., Lahass, T., Borges, J., Júnior, P. S., and Barcellos, M. (2025). Calmo: A tool to support the use of causal loop diagram in software engineering. In Anais do XXXIX Simpósio Brasileiro de Engenharia de Software, pages 969–975, Porto Alegre, RS, Brasil. SBC.

Arnold, R. D. and Wade, J. P. (2015). A definition of systems thinking: A systems approach. Procedia computer science, 44:669–678.

Binder, T., Vox, A., Belyazid, S., Haraldsson, H., and Svensson, M. (2004). Developing system dynamics models from causal loop diagrams. In Proceedings of the 22nd International Conference of the System Dynamic Society, pages 1–21.

Borges, J., Lahass, T., Apolinário, A., Júnior, P. S., and Barcellos, M. (2024). Unveiling the landscape of system thinking modeling tools use in software engineering. In Anais do XXXVIII Simpósio Brasileiro de Engenharia de Software, pages 47–57, Porto Alegre, RS, Brasil. SBC.

Boscarioli, C., de Araujo, R. M., and Maciel, R. S. P. (2017). I grandsi-br grand research challenges in information systems in brazil 2016-2026.

Burge, S. (2015). The systems thinking tool box. Burge, Hughes, Walsh, pages 1–5.

Checkland, P. (1988). Information systems and systems thinking: Time to unite? International Journal of Information Management, 8(4):239–248.

Checkland, P. (1999). Systems Thinking, Systems Practice. John Wiley & Sons.

Ching, P. M. and Mutuc, J. E. (2018). Evaluating agile and lean software development methods from a system dynamics perspective. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pages 1–6. IEEE.

Ciancarini., P., Farina., M., Kruglov., A., Succi., G., and Thapaliya., A. (2023). A reflection on the use of systemic thinking in software development. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE, pages 521–529. INSTICC, SciTePress.

Clancy, T. (2018). Systems thinking: Three system archetypes every manager should know. IEEE Engineering Management Review, 46(2):32–41.

Dhirasasna, N. and Sahin, O. (2019). A multi-methodology approach to creating a causal loop diagram. Systems, 7(3):42.

dos Santos Júnior, P. S., Barcellos, M. P., and Calhau, R. F. (2022). First step climbing the stairway to heaven model-results from a case study in industry. Journal of Software Engineering Research and Development, 10:5–1.

Drack, M. and Apfalter, W. (2007). Is paul a. weiss’ and ludwig von bertalanffy’s system thinking still valid today? Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 24(5):537–546.

Fatema, I. and Sakib, K. (2017). Factors influencing productivity of agile software development teamwork: A qualitative system dynamics approach. In 24th Asia-Pacific Software Engineering Conference (APSEC), volume 2017-December, pages 737–742. IEEE.

Guizzardi, G., Almeida, J., Guarino, N., and Carvalho, V. (2015). Towards an Ontological Analysis of Powertypes. In The Joint Ontology Workshops at the International Join Conference on Artificial Intelligence.

Haraldsson, H. V. (2004). Introduction to system thinking and causal loop diagrams. Department of chemical engineering, Lund University Lund, Sweden.

Huang, T. and Fang, C.-C. (2022). Applying system dynamics approach for optimizing software release decisions. In 2022 2nd International Conference on Computation, Communication and Engineering (ICCCE), pages 54–57. IEEE.

Kim, D. (1993). System archetypes i: Diagnosing systemic issues and designing highleverage interventions.

Kim, D. (2018). Introduction to System Thinking. Pegasus Communications.

Kim, D. H. (1995). Systems thinking tools: a user’s reference guide. Toolbox reprint series. Pegasus Communications.

Kim, D. H. and Anderson, V. (1998). Systems archetype basics. Waltham, Mass, Pegasus Communications Inc.

Lawrence, M. (2024). Causal loop diagrams handbook. Technical Paper 2024-3, Cascade Institute.

Meadows, D. H. (2008). Thinking in systems: A primer. chelsea green publishing.

Mylopoulos, J., Guizzardi, G., and Guarino, N. (2025). Conceptual modeling: Foundations, a historical perspective, and a vision for the future. Data & Knowledge Engineering, page 102483.

Senapathi, M. and Drury-Grogan, M. L. (2021). Systems thinking approach to implementing kanban: A case study. Journal of Software: Evolution and Process, 33(4):e2322.

Sterman, J. (1994). Learning in and about complex systems. systems dynamics review, 10, 1994.

Sterman, J. (2010). Business Dynamics: Systems Thinking And Modeling For The Complex World. Tata McGraw Hill Education Private Limited.

Tip, T. (2011). Guidelines for drawing causal loop diagrams. Systems Thinker, 22(1):5–7.