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Theories in technology evaluation
Theories in technology evaluation

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1.3.1 CCTV and crime reduction

The early 1990s saw increasing interest in the UK in the argument (underpinned by various theories) that the installation of CCTV in towns and cities would improve the detection of criminal activity and therefore lead to a reduction in crime. This view was promoted heavily by various interest groups (and rather vociferously by the makers of the technology) at a time when ‘fear of crime’ was a particularly high-profile political issue. The consequence of this was that a number of CCTV projects were established in areas where crime had been identified as a problem.

Post-implementation/summative evaluations of the CCTV schemes showed that crime in these areas had fallen. Therefore (using the input–output model of causality) various interest groups concluded that CCTV (the input) caused the reduction (the output). More significantly, as a number of CCTV initiatives had been implemented over a relatively short period of time, and evaluations showed that crime fell in each area, it was not long before these findings were used to generalise that there was a technological solution to a significant social problem. In other words, the regularities in the findings led to the inference that there was a direct causal relationship between a reduction in crime and CCTV.

What seemed like a straightforward and logical conclusion soon came under attack when other evaluators and researchers (who often subscribed to a different causal paradigm, such as forms of realism or systems thinking) queried these conclusions. Their particular focus was on the context in which CCTV was implemented and what occurred through various processes and mechanisms in that context over the period between the implementation of the CCTV and the evaluation of the outcome. This demonstrated, for example, that CCTV projects got significant publicity in the local and national press, and that in areas where cameras were installed large signs were often placed on lampposts over extensive areas (often larger than the area covered by the cameras) warning of the presence of CCTV.

These ‘mechanisms’ functioned in at least two ways. They raised the awareness of crime amongst local people. And they educated people in the importance of adopting simple security measures, such as making sure cars were locked and valuables out of sight. They also alerted would-be criminals to the likelihood that the environment in which they operated might have changed. In short, in addition to the technology there were other social and structural mechanisms that appeared to have contributed to the observed outcome of a reduction in crime. Furthermore, it appeared that ‘awareness raising’ alone (i.e. where no CCTV was actually present) led to a reduction in crime. Consequently, deciding which of the technological or non-technological mechanisms was the most significant to the observed outcome was far from clear.

The example of CCTV clearly illustrates how conclusions based on a particular view of the relationship between cause and effect can be partial (both in terms of being incomplete and favouring one particular argument) and misleading. Nevertheless, in the UK this did not stop local government and those with a commercial interest in the technology engaging in a headlong dash into the implementation of CCTV systems across the UK. This led Beck and Willis (two of the leading scholars of the subject at the time) to conclude that ‘The burgeoning use of CCTV appears to be a product of its seductive appeal as a ‘‘high-tech fix’’, where a strong – albeit ill-founded – belief about its value in controlling crime is all that is necessary to secure its deployment’ (1995, p. 5).

Figure 1 portrays the input–output approach to evaluation well. The project or programme becomes a ‘black box’ which remains unexplored. However – and this is the crucial point to grasp – contrary to what you may be familiar with before doing this course, alternative paradigms do exist for understanding causality. Systems thinking, which features in many Open University courses, is one example. Another, often referred to as ‘realist’, is CMO [or C + M = O] context plus mechanism(s) equals outcome (Pawson and Tilley, 1997). In the example above, CCTV was simply a mechanism contributing to an outcome, not the mechanism. In other words, it is not a technology, project or programme per se that leads to an outcome, but the interaction between whatever these consist of (e.g. technology, resources) and people in a particular context.

Figure 1 Weaknesses of the ‘black box’ approach to evaluation (Bamberger, et al., 2006)
Figure 1 Weaknesses of the ‘black box’ approach to evaluation (Bamberger, et al., 2006)

From the perspective of a practising evaluator, what is probably of most significance from the discussion above is that the model of causality we apply to an evaluation or assessment has a significant implication for the actions of evaluators and the design and (most importantly) outcome of an evaluation. The CCTV case demonstrates how useful the input–output model was to individuals and groups who had vested interests in that technology (manufacturers, suppliers, installers, etc.), whereas employing a different model of causality may not have produced an evaluation that was as favourable.