This paper discusses necessary and sufficient preference conditions for utility models based on a risk-value framework. These conditions provide additional insights into traditional utility models regarding decision making by risk-value tradeoffs, and can help decision makers identify specific functional forms of utility measure in practice.

This paper outlines an application of multiattribute utility theory to the selection of a technology for the disposition of surplus weapons-grade plutonium. The analysis presented evaluated thirteen alternatives, examined the sensitivity of the recommendations to the weights and assumptions, and quantified the potential benefit of the simultaneous deployment of several technologies. The results were used by the Department of Energy to support its recommendation of an alternative.

This paper uses a simulation approach to investigate how different attribute weightingtechniques affect the quality of decisions based on multiattribute value models. The weightingmethods considered include equal weighting of all attributes, two methods for using judgmentsabout the rank ordering of weights, and a method for using judgments about the ratios of weights.The question addressed is: How well does each method perform when based on judgments ofattribute weights that are unbiased but subject to random error? To address this question, weemploy simulation methods. The simulation results indicate that ratio weights were either betterthan rank order weights (when error in the ratio weights was small or moderate) or tied with them(when error was large). Both ratio weights and rank order weights were substantially superior tothe equal weights method in all cases studied. Our findings suggest that it will usually be worththe extra time and effort required to assess ratio weights. In cases where the extra time or effortrequired is too great, rank order weights will usually give a good approximation to the trueweights. Comparisons of the two rank-order weighting methods favored the rank-order-centroidmethod over the rank-sum method.

Based on our previous work on the standard measure of risk, this paper presents twoclasses of measures for perceived risk by considering a two-dimensional structure, the mean of alottery and its standard risk. One of the classes of our risk measures presumes that there is no riskif and only if there is no uncertainty involved and the other allows that different degeneratelotteries may be evaluated with different values of "risk". The former has more prescriptiveappeal in risky decision making and the later has more descriptive power for subjective riskjudgments. Our risk measures can also take into account the asymmetric effects of losses andgains on perceived risk. It is demonstrated that our perceived risk models unify a large body ofempirical evidence regarding risk judgments and can be incorporated into preference models (i.e., based on risk-value tradeoffs) in a clear fashion.

We show that some of the most common beliefs aboutcustomer-perceived quality are wrong. For example, 1) it isnot necessary to exceed customer expectations to increasepreference, 2) receiving an expected level of bad service doesnot reduce preference, 3) rational customers may rationallychoose an option with lower expected quality, even if all nonqualityattributes are equal, and 4) paying more attention toloyal, experienced customers can sometimes be counterproductive.These surprising findings make sense in retrospect,once customer expectations are viewed as distributions,rather than simple point expectations. That is, eachcustomer has a probability density function that describes therelative likelihood that a particular quality outcome will beexperienced. Customers form these expectation distributionsbased on their cumulative experience with the good or service.A customer's cumulative expectation distribution maybe conceptualized as being a predictive density for the nexttransaction.When combined with a diminishing returns (i.e., concave)utility function, this Bayesian theoretical framework resultsin predictions of: (a) how consumers will behave over time,and (b) how their perceptions and evaluations will change.In managerial terms, we conclude that customers considernot only expected quality, but also risk. This may help explainwhy current measures of customer satisfaction (whichis highly related to expected quality) only partially predictfuture behavior. We find that most of the predictions of ourtheoretical model are borne out by empirical evidence fromtwo experiments. Thus, we conclude that our approach providesa useful simplification of reality that successfully predictsmany aspects of the dynamics of consumer response toquality.These findings are relevant to both academics and managers.Academics in the area of customer satisfaction andservice quality need to be aware that it may be insufficientto measure only the point expectation, as has always beenthe standard practice. Instead it may be necessary to measurethe uncertainty that the customer has with respect to the levelof service that will be received. Due to questionnaire lengthconstraints, it may not be practical for managers to includeuncertainty questions on customer satisfaction surveys. Neverthelessit is possible to build a proxy for uncertainty bymeasuring the extent of experience with the service/good,and this proxy can be used to partially control for uncertaintyeffects.The findings of the study were obtained using 1) an analyticalmodel of customer expectation updating, based on aset of assumptions that are well-supported in the academicliterature, and 2) two behavioral experiments using humansubjects: a cross-sectional experiment, and a longitudinal experiment.Both the analytical model and the behavioral experimentswere designed to investigate the effects that distributionsof expectations might have, and especially theeffects that might deviate from the predictions that wouldarise from a traditional point expectation model. The behavioralexperiments largely confirmed the predictions of theanalytical model. As it turned out, the analytical model correctly(in most cases) predicted behavioral effects that contradictsome of the best-accepted "truisms" of customersatisfaction.It is now clear that a more sophisticated view of customerexpectations is required-one that considers not only thepoint expectation but also the likelihood across the entiredistribution of possible outcomes. This distinction is not "justacademic," because it results in predictable behavior that deviatessignificantly from that which was traditionally expectedbased on simpler models.