A Sustainable Future for Corporate Governance Theory and Practice

  • Shann Turnbull


This chapter shows how the natural “science of control and communications in the animal and the machine” identified by Wiener in 1948 can be applied to social organizations to establish a science of governance. The science of governance provides a sustainable future for corporate governance theory and practice. Good governance is defined as the ability of organizations in the private, public and non-profit sectors to achieve their purpose in the most efficacious manner while minimizing the need for laws, regulations, regulators, courts or codes of so called “best practices” to protect and further the interests of their stakeholders and society. Evidence is provided that current best practices: (a) did not prevent firms failing to create the 2008 financial crisis; (b) are not based on theory or conclusive empirical evidence; and (c) are inconsistent with common sense. Systemic problems arising from organizations governed by a single board are identified. These include the absolute power of directors to manage their own conflicts of interest to allow the corruption of themselves and the organization. Examples of organizations with over a hundred boards show how network governance provides: (a) division of powers; (b) checks and balances; (c) distributed intelligence; (d) decomposition in decision making labor; (e) cross checking communication and control channels from stakeholder engagement; (f) integration of management and governance to further self-regulation and self-governance with: (g) operating advantage and sustainability. The examples illustrate how an ecological form of network governance could reduce the size, scope, cost and intrusiveness of government and their regulators while improving economic efficiency, resiliency and enriching democracy with widespread citizen stakeholder engagement.


Corporate Governance Audit Committee Stakeholder Engagement Transaction Cost Economic Network Governance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.International Institute for Self-governanceSydneyAustralia

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