Organisational Cybernetics


The concept presents a definition and description of organisational cybernetics and provides the capabilities and limitations of this approach, together with its business applicability and useful industry examples.

Technique Overview

Organisational Cybernetics

Organisational Cybernetics Definition

Organisational Cybernetics is a systems complexity theory approach based on the principles of communication and control and the cybernetics created by Wiener (1948). These principles were transferred to the design of a system aimed to provide the nesesary and sufficient conditions of communication and control to create viable organisations through the study and design of structures, roles, communication and information systems, of which the theoretical and methodological foundations were developed by Stafford Beer (1979, 1985).

Organisational Cybernetics Description *

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Business Evidence

Strengths, weaknesses and examples of Organisational Cybernetics *

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Business Application

Implementation, success factors and measures of Organisational Cybernetics *

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Professional Tools

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Further Reading

Organisational Cybernetics web and print resources *

Organisational Cybernetics references (4 of up to 20) *

  • Beer, S. (1979) The Heart of Enterprise. John Wiley, Chichester.
  • Beer, S. (1985) Diagnosing the System for Organizations. John Wiley, Chichester.
  • Cardoso, P. (2011) Facilitating Self-organization in Non-hierarchical Communities: A Methodology for Rural Regeneration Programs. PhD Thesis, University of Hull, UK.
  • Chapman, R.E. (1999) Benefits and Costs of Research: A Case Study of Cybernetic Building Systems. Building and Fire Research Laboratory, US Department of Commerce.

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Related Concept: Collating and Formatting Data

Before any analysis can happen, data needs to be collated from correct sources and formatted so it follows clear organisational standards. Research shows that inconsistent structures and formats make data harder to combine, process and trust (Jagadish et al., 2014). Collating and formatting ensure the dataset is clean, consistent and ready for use.