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Supporting Decision Making During a Pandemic: Influence of Stress, Analytics, Experts, and Decision Aids
Published in Jay Liebowitz, The Business of Pandemics, 2020
Gloria Phillips-Wren, Jean-Charles Pomerol, Karen Neville, Frédéric Adam
Research has shown that an individual’s perceived stress impacts the quality of their decisions. Decision quality should be evaluated considering both the process of decision making and outcomes from the decision itself (Phillips-Wren et al., 2004). Stressed decision makers show impaired performance in terms of decision quality (Ahituv et al., 1998), generate fewer alternatives (Mann & Tan, 1993; Svenson & Maule, 1993), ignore crucial information and use inefficient strategies (Lehner et al., 1997; Svenson & Edland, 1987), offer solutions too early before considering all available alternatives and are not systematic when scanning alternatives (Keinan, 1987), and become extremely alert to discrediting evidence (Wright, 1974). The impact of stress on people during COVID-19 has become more urgent as the months of social distancing and shutdowns of economic and social activities continue (Mayo Clinic, 2020). The use of decision support technologies such as analytics can overcome some of these shortcomings by structuring the decision environment to improve process and the search for alternatives while providing data and analytical models to help assess potential future states—if people will use them under stressful conditions (Adya & Phillips-Wren, 2019; Phillips-Wren & Adya, 2020).
DECAS: a modern data-driven decision theory for big data and analytics
Published in Journal of Decision Systems, 2021
Nada Elgendy, Ahmed Elragal, Tero Päivärinta
Drawing on Simon’s approach, the data-driven decision-making process starts with identifying problems and opportunities, then defining strategic objectives and criteria for success, followed by developing and evaluating alternatives, and finally prioritising and selecting one or more of these alternatives. However, in each step, big data technologies, analytics, and machines are essential, since they enable the effective capturing, integration, and analysis of data, which in turn improves the accuracy, sophistication, and completeness of the rational analysis and final decision (Cao & Duan, 2015). Moreover, analysing the large volumes of data, whether internal or external, may create descriptive value, by summarisation of the data and describing current or historic events, predictive value, through predictions about the future based on historic data, and/or prescriptive value, by suggesting optimised courses of action and descriptions of the consequences (Strand & Syberfeldt, 2020). Additionally, data-driven decision making is said to lead to more informed, quality decisions, since more knowledge about the data, the analytics, the relationships among variables, and the resulting information all add to enhancing the decision quality (Janssen et al., 2017).
Decision making under stress: the role of information overload, time pressure, complexity, and uncertainty
Published in Journal of Decision Systems, 2020
Gloria Phillips-Wren, Monica Adya
Decision quality has been variously defined in different studies. In general, decision quality can be characterised in terms of two aspects: the process of decision making (i.e. how the decision was derived) and the outcome (i.e. goals of the decision problem) (Phillips-Wren et al., 2009). Many authors focus on only one of these aspects such as optimal performance on a specific task (Marsden et al., 2006; Speier et al., 1999b, 1999a). By comparison, Todd and Benbasat (1992) defined decision quality in terms of deviation of the decision from a normative solution that maximises value or utility. They considered both the effort required from the decision maker and the decision alternative selected in evaluating decision quality. Since perceived stress can affect both the process of, and outcome from decision making, we accept this definition. It is possible to develop a single metric for decision quality using a multi-criteria approach such as ranked attribute weights (Barron & Barrett, 1996) or the Analytic Hierarchy Process (Phillips-Wren et al., 2009).