Simple or single-loop reflection refers to increasing efficiency of an objective; ‘Are we doing things right?’ (Flood and Romm, 1996) – it is task oriented and is about the design of the process to retain reliability. It is simple reflection that may challenge assumptions and strategies to alter the plan of action but always ‘in ways that leave the values of a theory of action unchanged’ (Brockbank and McGill, 1998). Courtney et al. (1998) describe this as low-level as it involves keeping to a set of rules and is simply error correction. This reflection and learning are viewed as valuable for day-to-day activities and are necessary for progress to be made within the established frameworks (Brockbank and McGill, 1998). To illustrate this sort of reflection, Dooley (1999) uses the example of a buggy whip manufacturer in the early 21st century improving the manufacturing process in order to make finer buggy whips. The manufacturer does not look beyond his immediate task to take in the strategic perspective of, for example, the long term viability of buggy whip manufacturing. Similarly, an organisation may upgrade a transaction processing system to handle processes more efficiently without the effectiveness or even necessity of the processes being examined.

Double-loop reflection is described by Argyris and Schon (1996), who use the term ‘double loop learning’, and Courtney et al. (1998) as a higher level of reflection than single-loop reflection – it incorporates the first loop (that centres on finding the best means of achieving an end) together with a second loop. This second loop centres on the examination and reflection of the theory or perspective in use. It is recognised that the action and consequences striven for in the first loop may not be valid – that there may be different perspectives regarding what the outcome should be and therefore assumptions, premises and context are questioned. Consequently, double-loop learning asks, ‘Are we doing things right AND are we doing the right things’ (Flood and Romm, 1996). While it is advised that the ‘gate’ into the second loop should be used frequently, it cannot be sustained constantly (Brockbank and McGill, 1998) as the required paradigmatic shift that the second loop requires is often disruptive to everyday activities. Dooley (1999) gives as an example of double-loop learning when, in the 1980s, Royal Dutch Shell delayed its plans for acquisition of oil fields when it foresaw the drop in oil prices and the demise of the Soviet Union. It examined what it was doing, as well as how it was doing it. See Figure 9.2, “Schon’s double-loop reflection.” for an illustration of the combination of the two loops to form double-loop learning as depicted in Encyclopedia/Forum (2004).

Morris and Moore (2000) maintain that research has shown that ‘the way individuals make sense of experienced outcomes is greatly determined by thoughts of what could have been, by comparisons of actual outcomes to counterfactual alternatives’. When preparing for an action, ‘upward counterfactual comparisons’ as anticipatory reflection lead to enhanced outcomes. Therefore, reflection against an ideal, or counterfactual alternatives, when anticipating or reviewing action has been shown to improve the outcome of the process.

There is a double movement in all reflection: a movement from the given partial and confused data to a suggested comprehensive (or inclusive) entire situation; and back from this suggested whole – which, as suggested, is a meaning, and idea – to the particular facts, so as to connect these with one another and with additional facts to which the suggestion has directed attention (Dewey, 1997, p. 80).

Dewey (1997, p. 12) warns that it is impossible to reflect against an ideal if one has not had experience in a similar situation: ‘But unless there has been experience in some degree analogous, which may now be represented in imagination, confusion remains mere confusion. There is nothing upon which to draw in order to clarify it’.

‘It is a theory of experience that assigns to our observations the equally modest and almost equally important role of tests which may help us in the discovery of our mistakes. Though it stresses our fallibility it does not resign itself to scepticism, for it also stresses the fact that knowledge can grow and that science can progress – just because we can learn from our mistakes.’ Popper (1969, p. vii)

Negative feedback in this paper is approached from the self-organisation perspective. The concept of reflection involves criticising one’s conjectures, ideas, theories or past actions with a view to invalidating them or, in the case of action, to improve performance by learning from mistakes in previous action. Jung (1950), as cited in Fortune and Peters (1997), asserts that ‘little or nothing is learnt from successes … while failures are, on the other hand, priceless experiences in that they not only open up the way to a deeper truth, but force (one) to change views and methods’. Popper (1969) concurs, maintaining that mistakes indicate a gap in knowledge and that learning occurs when those mistakes are rectified.

Fortune and Peters (1997) stress that the main deterrent to learning from failure is ‘post-event rationalisation’ where frequently the desire to discover the reasons for failure cause investigations to be carried out hastily; this leads to a false picture of the failure, its cause and the context in which it occurred. They cite the cases of the Bhopal chemical disaster and the British European Trident Papa India air crash as examples of hastily formed first impressions that led to completely erroneous findings. The writers advocate using the Systems Failure Method whose goal is ‘a systemic interpretation of a failure and its context’ (Fortune and Peters, 1997, p. 64) to learn from any failure or mistake. This applies systems thinking concepts to reflection – the focus of reflection is viewed as a ‘system’ and, when reflection is taking place, the boundary, environment, purpose and different perspectives are taken into account to describe the system in which the failure occurred. An idealised model of the system is designed, and then this and the failed system are compared. This comparison is used to identify or explain the causes of failure. The knowledge created from this reflection is used to recommend actions to improve the situation.