The Impact of Technology Insertion on Organisations

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HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 Furthermore, KM relies heavily upon something called Case Based Reasoning (CBR), as data is too scarce for statistical relevance. CBR, simply put, is the process <strong>of</strong> solving new problems based on the solutions <strong>of</strong> similar past problems. <strong>The</strong> use <strong>of</strong> CBR as a method <strong>of</strong> KM has been criticised as it is an approach that accepts anecdotal evidence as its main operating principle. Without statistically relevant data for backing and implicit generalisation, there is no guarantee that any generalisation is correct. KM has also been criticised by those who debate the usefulness <strong>of</strong> experience [192]. <strong>The</strong> argument goes that ‘much <strong>of</strong> our experience is <strong>of</strong> no value for future work’ because: • Changing conditions makes history invalid for future predictions. Sometimes the experience itself changes the conditions so much that the experience cannot be mapped on to other cases. • It is <strong>of</strong>ten impossible to compare what actually happened with what would have happened if the actual actions were not taken. Establishing cause and effect is, therefore, very difficult in one <strong>of</strong> a kind development projects. • Experience is context dependent and much <strong>of</strong> the context is hard to describe. Perhaps the biggest challenges to KM is that techniques rely upon the generation <strong>of</strong> perceived risks or the interpretation <strong>of</strong> captured knowledge from colleagues and their work on previous projects. This sort <strong>of</strong> knowledge capture is open to reporting inaccuracies due to a number <strong>of</strong> cognitive biases which may, unintentionally, influence people’s reported experiences. <strong>The</strong>se biases <strong>of</strong>ten occur subconsciously as a result <strong>of</strong> normal human cognitive processes. To provide some background, a cognitive bias is any <strong>of</strong> a wide range <strong>of</strong> observer effects identified in cognitive science and social psychology including basic statistical, social attribution, and memory errors that are common to all human beings. Biases drastically skew the reliability <strong>of</strong> anecdotal and legal evidence. <strong>The</strong>re exist a number <strong>of</strong> biases, which have been consistently recorded in the literature. Some <strong>of</strong> which will be described in the following paragraph. This is not an exhaustive list, rather it includes those biases which are likely to influence reports <strong>of</strong> people’s own performance on technology projects. Biases perhaps are inevitable in a context where people are evaluated based on their own performance, and where they may be asked to justify errors that might influence their career progression. • Choice-supportive bias – the tendency to remember one's choices as better than they actually were. • Confirmation bias – the tendency to search for or interpret information in a way that confirms one's preconceptions. • Focusing effect – a prediction bias occurring when people place too much importance on one aspect <strong>of</strong> an event; causes error in accurately predicting the utility <strong>of</strong> a future outcome. This bias can cause error in accurately predicting the utility <strong>of</strong> a future outcome. • Anchoring or focalism – a cognitive bias that describes the common human tendency to rely too heavily, or ‘anchor’, on one trait or piece <strong>of</strong> information when making decisions. During normal decision making, individuals anchor, or overly rely, on specific information or a specific value and then adjust to that 45
HFIDTC/2/12.2.1/1 Version 3 / 21 November 2007 value to account for other elements <strong>of</strong> the circumstance. Usually once the anchor is set, there is a bias toward that value. This is particularly likely to occur in KM where a practitioner has considered others’ experience and is basing their decisions and actions based on this ‘anchor’. • Attentional bias – neglect <strong>of</strong> relevant data when making judgments <strong>of</strong> a correlation or association. • Neglect <strong>of</strong> prior base rates effect – the tendency to fail to incorporate prior known probabilities which are pertinent to the decision at hand. • Selective perception – different people perceive the same events differently. Studies show that what we perceive is heavily influenced by what we expect and want to find. • Hindsight bias: When people know the actual outcome <strong>of</strong> a process, they tend to regard that outcome as having been fairly predictable all along – or at least more predictable than they would have judged before knowing the outcome. Experience from s<strong>of</strong>tware projects collected after their completion can be strongly impacted by hindsight bias (‘I-knew-it-from-the-beginning...’). Unfortunately, it is not enough to inform people about hindsight bias and encouraging them to avoid it [192]. Another challenge for Knowledge Management is that few studies have been done to establish whether there is an empirical relationship between Knowledge and Business performance [195]. Perhaps one <strong>of</strong> the reasons for this is that KM metrics have been notoriously difficult to develop due to the intangible nature <strong>of</strong> the ‘knowledge resource’ [196]. As we have seen, ‘knowledge’ is difficult to define and has multiple interpretations that make it difficult to value and measure. As a result <strong>of</strong> the complexity <strong>of</strong> assessing organisational initiatives in general, research [197] and practice [198] on the assessment <strong>of</strong> KM initiatives and knowledge management systems (KMS) is not well developed. 8.2.2.2 Learning From Experience Learning From Experience (LFE), one approach to managing knowledge, has been implemented within the MoD procurement and project management processes. LFE as described on the MoDs Acquisition Management Site (AMS) [199] ‘identifies lessons learned from past projects and how you can contribute to future learning’. LFE is a key element <strong>of</strong> Knowledge Management (KM), concentrating on the knowledge sharing aspect. <strong>The</strong> AMS goes on to say that ‘Learning From Experience (LFE) is one <strong>of</strong> the Project Delivery Assurance Criteria within the Project Review and Assurance (PR & A) Process, seen as a key driver across all disciplines’. This LFE Guide has been ‘written to help projects meet their business targets by applying LFE tools and techniques. It is an effective Key Risk Management Tool, which helps with identifying potential risks and issues.’ According to the MoD’s ‘Procurement Development Group Learning From Experience Information and Process Guide’, there are a great number <strong>of</strong> Benefits from using LFE: • It will develop individual staff by improving skills and knowledge. 46
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The Impact of Technology Insertion on Organisations

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