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Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks: A Guiding Framework

Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks: A Guiding Framework

José Sousa, João Barata
Copyright: © 2021 |Pages: 20
ISBN13: 9781799867135|ISBN10: 1799867137|EISBN13: 9781799867159
DOI: 10.4018/978-1-7998-6713-5.ch002
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MLA

Sousa, José, and João Barata. "Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks: A Guiding Framework." Handbook of Research on Autopoiesis and Self-Sustaining Processes for Organizational Success, edited by Małgorzata Pańkowska, IGI Global, 2021, pp. 38-57. https://doi.org/10.4018/978-1-7998-6713-5.ch002

APA

Sousa, J. & Barata, J. (2021). Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks: A Guiding Framework. In M. Pańkowska (Ed.), Handbook of Research on Autopoiesis and Self-Sustaining Processes for Organizational Success (pp. 38-57). IGI Global. https://doi.org/10.4018/978-1-7998-6713-5.ch002

Chicago

Sousa, José, and João Barata. "Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks: A Guiding Framework." In Handbook of Research on Autopoiesis and Self-Sustaining Processes for Organizational Success, edited by Małgorzata Pańkowska, 38-57. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-6713-5.ch002

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Abstract

Organizations worldwide are supporting their processes and decisions with enterprise systems (ES). Large amounts of data are produced and reproduced in these increasingly complex sociotechnical systems, opening new opportunities for the adoption of self-supervised learning techniques. Complex networks are viable solutions to create models that learn from data. This chapter presents (1) a review on the possibilities of networks for self-supervised learning, (2) three cases illustrating the potential of complex networks to address the autopoietic nature of ES (adoption of enterprise resource planning, web portal development, and healthcare data analytics), and (3) a framework to mine sociotechnical patters uncovering the entanglement of human practice and information technologies. For theory, this chapter explains the potential of complex networks to assess enterprise systems dynamics. For practice, the proposed framework can assist managers in establishing a strategy to continuously learn from their data to support decision-making in self-adapting scenarios.

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