2014•Unpublished venueOpen access

Towards Real-time Static and Dynamic Profiling of Organisational Complexity

Kon Shing Kenneth Chung

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Abstract

In this position paper, I argue that although the definition and quantifiable metric for organisational complexity may still be controversial, it is possible to capture structural aspects of complexity in both static and dynamic forms. Based on Kannampallil’s theoretical framework for computing complexity, it is proposed here that complexity, in an aggregate sense, can be evaluated in terms of (i) the number of components (NoC) there are within a socio-technical organisation and (ii) the degree of interrelatedness (DoI) between these components. Given these variables, it is then possible to characterise complexity in terms of simple, complicated, relatively complex and complex profiles. These profiles serve as useful toolkits for indicating the complexity level a team, a department or the entire organisation is at for useful interventions or insights to be made. Adapting the ideas of Pentland, I also argue that with technological advances in Information Systems, organisations are now able to capture relational or social network data with relative ease, to construct useful network and complexity maps of individuals, teams and organisations in real time.

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What this paper is about

In this position paper, I argue that although the definition and quantifiable metric for organisational complexity may still be controversial, it is possible to capture structural aspects of complexity in both static and dynamic forms. Based on Kannampallil’s theoretical framework for computing complexity, it is proposed here that complexity, in an aggregate sense, can be evaluated in terms of (i) the number of components (NoC) there are within a socio-technical organisation and (ii) the degree of interrelatedness (DoI) between these components. Given these variables, it is then possible to characterise complexity in terms of simple, complicated, relatively complex and complex profiles. These profiles serve as useful toolkits for indicating the complexity level a team, a department or the entire organisation is at for useful interventions or insights to be made. Adapting the ideas of Pentland, I also argue that with technological advances in Information Systems, organisations are now able to capture relational or social network data with relative ease, to construct useful network and complexity maps of individuals, teams and organisations in real time.

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Available abstract

In this position paper, I argue that although the definition and quantifiable metric for organisational complexity may still be controversial, it is possible to capture structural aspects of complexity in both static and dynamic forms. Based on Kannampallil’s theoretical framework for computing complexity, it is proposed here that complexity, in an aggregate sense, can be evaluated in terms of (i) the number of components (NoC) there are within a socio-technical organisation and (ii) the degree of interrelatedness (DoI) between these components. Given these variables, it is then possible to characterise complexity in terms of simple, complicated, relatively complex and complex profiles. These profiles serve as useful toolkits for indicating the complexity level a team, a department or the entire organisation is at for useful interventions or insights to be made. Adapting the ideas of Pentland, I also argue that with technological advances in Information Systems, organisations are now able to capture relational or social network data with relative ease, to construct useful network and complexity maps of individuals, teams and organisations in real time.

Key concepts: Computer science, Construct (python library), Aggregate (composite), Complexity management, Complexity theory and organizations, Data science, Metric (unit), Structural complexity

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