The Impact of Technology Insertion on Organisations

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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.
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