Decision making in unfamiliar problem domains: evidence from the investment banking industry - McGrath, Michael Peter (Cranfield University, 2004)
This research explores the determinants of risk behaviour when an organisation operates outside its normal operational domain. Organisations are being forced outside their normal operational domains with ever-increasing frequency. Through studying a banking acquisition, an area which has not been studied before, the research identifies the risks faced by the organisation, the apparent irrational management of the risks, and the reasons for this behaviour. The research applies multiple research methods, which include the review of company documentation, interviews with key managers and external experts, a modified Delphi technique, case studies and statistical analysis. Through these methods, the risks faced by the organisation are identified and evaluated in terms of probability, impact, and degree of mitigation. Four risks are investigated in detail, and based on these, six propositions are put forward, four of which are support by statistical tests. The research shows that where the organisation had a successful outcome history in managing a given risk, or could manage the risk using normal management controls, the risk tended to be managed disproportionately well compared to its significance. Where those conditions do not apply the management of the risk tends to be proportionately lacking. There is also evidence to suggest that the existence of industry-specific regulation in relation to a risk results in the risk being better mitigated. Organisations wishing to improve their risk response in unfamiliar operational domains should therefore consider day-to-day controls as one route to improvement. Also, where possible, they should try to create a history of successful outcomes in dealing with the risk types they are likely to face in unfamiliar problem domains. Regulatory bodies need to consider the impact that their regulations will have in order to help organisations exhibit better behaviours in unfamiliar problem domains.