The Impact of Technology Insertion on Organisations

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HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 Attewell [172] argues that ‘It is important to understand that because <strong>of</strong> the last three decades <strong>of</strong> research and the importance <strong>of</strong> context, many distinguished scholars <strong>of</strong> technology avoid the term ‘technology impact’. Using this term in framing the question is viewed by some as indicating an ignorance <strong>of</strong> the body <strong>of</strong> scholarship in technology studies. For them, the term ‘impact’ connotes a kind <strong>of</strong> technological determinism that is very dated and widely discredited. As a result, the more recent models <strong>of</strong> technology are descriptive <strong>of</strong> the various forces and triggers involved in organisational change and are not normally intended as accurate tools for predicting specific technological impacts or organisational outcomes. 7.1.5 Chaos <strong>The</strong>ory and Complexity <strong>The</strong>ory Complexity theory, which has developed from Chaos theory, looks at how very simple things can generate very complex outcomes that could not be predicted by just looking at the parts by themselves. Chaos is sometimes viewed as extremely complicated information, rather than as an absence <strong>of</strong> order. A key difference between the two theories is that chaos theory remains deterministic, i.e. with perfect knowledge <strong>of</strong> the initial conditions and <strong>of</strong> the context <strong>of</strong> an action, the course <strong>of</strong> this action can be predicted. Complexity theory is non-deterministic, and gives no way whatsoever to predict the future. Indeed it was developed to help researchers from a range <strong>of</strong> disciplines to develop theories for situations too complex to be explained by earlier principles [174]. Scott [175] even suggests that complexity theory has brought ‘new vitality to many areas <strong>of</strong> science where a more typical reductionist strategy has fallen short.’ Complexity theorists purport that we cannot predict what a complex system will evolve into. <strong>The</strong>y argue that ‘all life from the smallest cell to the largest animals are complex systems’ and have developed the term ‘emergence’ to describe the way complex systems and patterns, such as those that form a hurricane, arise out <strong>of</strong> a multiplicity <strong>of</strong> relatively simple interactions. Examples <strong>of</strong> emergence include ‘intelligence in the field <strong>of</strong> AI, or agents in distributed artificial intelligence’, emergence is central to the physics <strong>of</strong> complex systems and yet very controversial. According to many complexity theorists, socio-cognitive systems, including humans, groups and organisations, are all complex by their nature. <strong>The</strong> study <strong>of</strong> socio-cognitive complexity, a relatively new domain in systemics, is beginning to look at complexity and emergence within organisations. Complexity theory has been used extensively in the field <strong>of</strong> strategic management and organisational studies, sometimes called 'complexity strategy' or 'complex adaptive organisation'. Broadly speaking, complexity theory is used in these domains to understand how organizations or firms adapt to their environments. <strong>The</strong> theory treats organisations and firms as collections <strong>of</strong> strategies and structures. When the organisation shares the properties <strong>of</strong> other complex adaptive systems - <strong>of</strong>ten defined as consisting <strong>of</strong> a small number <strong>of</strong> relatively simple and partially connected structures - they are more likely to adapt to their environment and, thus, survive. One <strong>of</strong> the major contributions to early complexity theory is the distinction between the human capacity to predict the behaviour <strong>of</strong> simple systems and its capacity to predict the behaviour <strong>of</strong> complex systems through modelling [176]. Hayek believed that economics and the sciences <strong>of</strong> complex phenomena in general, ‘including biology, psychology, and so on, could not be modeled in the same manner as sciences that deal with essentially simple phenomena like physics’. Hayek notably explains that complex phenomena, through modeling, can only allow pattern predictions, compared with the precise predictions that can be made out <strong>of</strong> non-complex phenomena [176]. 37
HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 Complexity theory may prove to be a useful approach to studying technology insertion and the impacts that this can have upon organisations. If researchers feel they have reached the point where it is impossible to predict organisational outcomes, due to the complexity <strong>of</strong> the system, this approach may well be a valuable one. As complexity theorists believe organisms, such as humans, and large organisations to be ‘complex systems’ then it may be sensible for us to admit that, whilst we accept that there are many factors that influence outcomes, it is impossible to predict with any accuracy the actual final outcome. This <strong>of</strong> course does not mean that we should throw the baby out with the bath water, so to speak. Decades <strong>of</strong> research tell us that some approaches work better than others. Simply saying ‘it’s a complex system, let’s stand back and let it sort itself out’ would clearly be foolish as we know a great deal about the effective implementation <strong>of</strong> technologies and how to minimise unpleasant consequences. What we do not know with certainty is how all the actors in an organisation will interact, nor are we able to predict the emergence <strong>of</strong> all organisational behaviours. Perhaps, in the future, complexity theory will support more realistic ambitions by encouraging researchers to follow Hayeck’s view and explore pattern predictions in complex systems rather than continuing to look for precise predictions. 38
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