贾建民
主要研究领域为决策理论与应用、消费者选择模型、顾客关系管理、市场风险分析、以及中国市场营销。
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 姓名：贾建民
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博士生导师， 国家杰出青年科学基金获得者
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管理理论
 研究兴趣：主要研究领域为决策理论与应用、消费者选择模型、顾客关系管理、市场风险分析、以及中国市场营销。
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【期刊论文】Preference Conditions for Utility Models: A RiskValue Perspective*
贾建民， James S. Dyer， Jianmin Jia
RiskValue Study Series Working Paper No.5，0001，（）：
1年11月30日
This paper discusses necessary and sufficient preference conditions for utility models based on a riskvalue framework. These conditions provide additional insights into traditional utility models regarding decision making by riskvalue tradeoffs, and can help decision makers identify specific functional forms of utility measure in practice.
Utility models， measures of risk， riskvalue tradeoffs

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贾建民， James S. Dyer， Thomas Edmunds， John C. Butler， Jianmin Jia
，0001，（）：
1年11月30日
This paper outlines an application of multiattribute utility theory to the selection of a technology for the disposition of surplus weaponsgrade 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.
Multiattribute Utility Theory， Multicriteria Decision Making， Decision Analysis in Public Policy

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贾建民， Jianmin Jia *， Gregory W. Fischer †， and James S. Dyer ‡
Journal of Behavioral Decision Making, in press May 1993 Revised April 1997，0001，（）：
1年11月30日
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 rankorder weighting methods favored the rankordercentroidmethod over the ranksum method.
attribute weights， multiattribute utility， decision quality， rankorder weights， preference uncertainty， response error， simulation， uncertain weights

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【期刊论文】MEASURES OF PERCEIVED RISK
贾建民， Jianmin Jia†， James S. Dyer* and John C. Butler*
RiskValue Study Series Working Paper No.4，0001，（）：
1年11月30日
Based on our previous work on the standard measure of risk, this paper presents twoclasses of measures for perceived risk by considering a twodimensional 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 riskvalue tradeoffs) in a clear fashion.
Perceived risk， risk measurement， riskvalue tradeoffs

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贾建民， Roland T. Rust， J. Jeffrey Inman， Jianmin Jia， Anthony Zahorik
Marketing Science/Vol 18, No.1, 1999 pp. 7792，0001，（）：
1年11月30日
We show that some of the most common beliefs aboutcustomerperceived 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 wellsupported in the academicliterature, and 2) two behavioral experiments using humansubjects: a crosssectional 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 bestaccepted "truisms" of customersatisfaction.It is now clear that a more sophisticated view of customerexpectations is requiredone 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.
Quality， Customer Satisfaction Measurement， Customer Expectations， Customer Retention， Bayesian Updating， Customer Lifetime Value

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贾建民， Fischer， G. W.， Jia， J. & Luce， M. F.
The RandMAU Model Management Science 46, 5, 669684, 2000，0001，（）：
1年11月30日
This paper extends the behavioral results reported in Fischer et al. (2000) by developing amodel addressing preference uncertainty in multiattribute evaluation. The model is motivatedby two hypotheses regarding properties of multiattribute profiles that lead to greater preferenceuncertainty. Our attribute conflict hypothesis predicts that greater withinalternativeconflict (discrepancy among the attributes of an alternative) leads to more extremity (veryhigh or low attribute values) leads to less preference uncertainty. To provide a deeperexplanation of attribute conflict and extremity effects, we develop RandMAU, a family ofadditive (RandAUF) and multiplicative (RandMUF) random weights multiattribute utilitymodels. In RandMAU models, preference uncertainty is represented as random variation inboth the weighting parameters governing tradeoffs among attributes and the curvatureparamters governing singleattribute evaluations. Simulation results show that RandMAUsuccessfully predicts both the attribute conflict and attribute extremity effects exhibited bythe experimental participants in Fischer et al. (2000). It also predicts an outcome valueeffect on error whose form depends on the shape of singleattribute functions and on thetype of multiattribute combination rule.
Attribute Conflict， Preference Uncertainty， Random Multiattributie Utility

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【期刊论文】Generalized Disappointment Models
贾建民， Jianmin Jia†， James S. Dyer*， John C. Butler*
RiskValue Study Series Working Paper No.6，0001，（）：
1年11月30日
Based on our riskvalue framework, this paper presents generalizations and extensions for the disappointment models that were proposed by Bell (1985) and Loomes and Sugden (1986). We provide explicit functional forms for modeling the effect of disappointment on risky choice behavior. Our generalized disappointment models can explain a number of decision paradoxes, and offer additional insights into nonexpected utility preferences based on the intuitive notions of disappointment and riskvalue tradeoffs.

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