The Impact of Technology Insertion on Organisations

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HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 • <strong>Impact</strong>/loss the risk can cause <strong>The</strong> next stage is then to define risk as the measure: Risk = Probability x <strong>Impact</strong> This standard ‘utility’ measure <strong>of</strong> risk ‘is quite useful for prioritising risks (the bigger the number the ‘greater’ the risk)’ but according to Fenton et al [223] ‘it’s normally meaningless’. More importantly, you ‘cannot get the numbers you need to calculate it’. Fenton goes on to say that the problems with this type <strong>of</strong> risk number is that ‘we cannot get the Probability number because the probability <strong>of</strong> any risk is conditional on a number <strong>of</strong> other control events and trigger events. It makes no sense to assign a direct probability without considering the events it is conditional on. In general it makes no sense (and would in any case be too difficult) for a risk manager to give the unconditional probability <strong>of</strong> every ‘risk’ irrespective <strong>of</strong> relevant controls, triggers and mitigants’. Fenton et al also argue that it does not give a ‘useful <strong>Impact</strong> number as we cannot say what the impact is without considering the possible mitigating events Risk score is meaningless. It does not tell us what we really need to know.’ <strong>The</strong> argument is that the rational way to think <strong>of</strong> risks is ‘in terms <strong>of</strong> causal models (risk maps) with trigger events, control events, risk events, mitigant events and consequence events.’ Coincidentally, causal models <strong>of</strong> risk can also be used to support decision making under uncertainty and it is this combination <strong>of</strong> risk assessment and decision support that we turn to next in order to consider their potential application to TI problems. 11.5.2 Decision Support Given the number <strong>of</strong> factors that influence whether a technology project is a success, many technology insertion decisions, even those apparently straightforward ones, are difficult to make with any degree <strong>of</strong> confidence. We have also seen the implications that poor quality, inexperienced, uninformed or simply unfortunate technology decisions can have upon organisations, individuals and systems. Consequently, it seems reasonable to suggest that the development <strong>of</strong> a decision support toolkit would be a valuable investment <strong>of</strong> research time and effort. <strong>The</strong>re is considerable research in the area <strong>of</strong> decision making under uncertainty, and numerous tools have been developed to support decision makers [223]. <strong>The</strong>se tools have primarily been developed because, as we have discussed, humans are not perfect probabilistic decision makers, as they are influenced heavily by their own experiences and biases. Probabilistic decision-making techniques such as Bayesian networks, have been found to be useful in supporting people to make difficult decisions, especially where there is a degree <strong>of</strong> uncertainty over the best course <strong>of</strong> action. <strong>The</strong>re is a wealth <strong>of</strong> literature within Psychology and Human Factors suggesting that the quality <strong>of</strong> decision making can diminish under time pressure and stress, two aspects which are clearly evident in many technology insertion programmes. Literature also finds that under stress decision makers will focus their attention very narrowly on the information that first comes to hand. Such natural human responses serve a useful purpose in fight or flight survival situations, but unfortunately do not always help effective decision making. <strong>The</strong> provision <strong>of</strong> an effective decision support tool would encourage the evaluation <strong>of</strong> different options, which may not have otherwise been considered and reduce the risk <strong>of</strong> key issues being ignored. 65
HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 12.1 Summary and Follow on 12 Direction <strong>of</strong> Future Work This scoping study has shown that there are many risks to technology insertion projects, which are both difficult to identify and a challenge to manage. <strong>The</strong> study has also found that whilst technology does impact upon organisations, the influence is neither deterministic nor unidirectional. Organisation, culture and a host <strong>of</strong> other factors such as context, also influence technology outcomes. <strong>The</strong> findings from this study suggest that a probabilistic risk identification toolkit could have the potential to support uncertain and high-risk technology insertion decisions. <strong>The</strong> existence <strong>of</strong> basic s<strong>of</strong>tware tools to support practitioners in aspects <strong>of</strong> technology insertion programmes suggest that developing a toolkit would be a feasible goal. It is suggested that follow-on work from this task focuses on developing requirements and methods to guide the development <strong>of</strong> a toolkit to support technology insertion practitioners. 66
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The Impact of Technology Insertion on Organisations

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