Validation of a biases model in strategic security decision making

ISSN： 0968-5227

Source： Information Management & Computer Security, Vol.20, Iss.2, 2012-06, pp. : 52-70

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Abstract

Purpose - Funding agencies such as the Office of Naval Research, Department of Homeland Security, and others, have reduced funding for non-tactical operations. Simultaneously, organizations are squeezing their overhead budgets (where security initiatives fall) and are focusing more on revenue generation given current economic climates. Thus, in both governmental sectors and in commercial settings, there are reasons to believe that strategic security initiatives are being sacrificed, and those that survive must be compelling. To assist organizational leaders with these difficult choices, it is critical to understand biases that affect decisions about strategic security initiatives. The purpose of this paper is to validate and empirically test the predictability of a theoretical model, from which implications can be made for research and practice. Design/methodology/approach - Using behavioral decision theory, a randomized longitudinal study was conducted over three years with a multinational corporation with headquarter-offices in the UK and the USA, and regional offices in India, Germany and France. From these data, a model was developed and tested for fit with a confirmatory factor analysis and its predictive ability was tested using structured equation modeling. Findings - It was found that risk aversion, overconfidence, adjustment of cognitive anchors, and expected utility biases affected whether managers and other stakeholders continued or terminated strategic security initiatives. Originality/value - Prematurely terminating or over commitment to a strategic initiative can be costly if not significantly damaging to an organization or government military or intelligence agency. Understanding how biases factor into these decisions can help strategic initiative decision makers improve their decisions and assist them in recognizing normative rules or optimal (straddle point) solutions.