A CONJUNCTIVE-COMPENSATORY APPROACH TO THE SELF-EXPLICATION OF MULTIATTRIBUTED PREFERENCES*

Consumer choice among multiattributed products is modeled as a two-stage process in which a conjunctive stage (that eliminates products with one or more “totally unacceptable” attribute levels) is followed by a compensatory stage (that trades off remaining products on multiple attributes). A self-explicated preference measurement procedure based on the two-stage model yielded a slightly larger predictive validity compared to conjoint analysis.     

CHOICE-CONSTRAINED CONJOINT ANALYSIS

Choice-constrained conjoint analysis (CCCA) is a new method for metric conjoint analysis studies. It computes part-worth utility functions that account for “revealed preference”—those products a respondent actually selects in an independent choice situation. CCCA uses an iterative penalty function estimation procedure that successively modifies initial regressionderived part worths so that respondent choices (either actual or intended) of real brands are predicted as accurately as possible. The paper first describes the motivation and rationale for CCCA and presents the mathematics of the algorithm. As an illustration, it applies the CCCA model and penalty function estimation procedure to a limited set of synthetic data. A second application of the technique is presented that uses data obtained by a major telecommunications firm that used conjoint analysis to examine the importance of several features of residential communication devices. The paper also discusses potential extensions of the CCCA model and the kinds of marketing applications for which it might be useful.     

Assessing Trade‐offs among Multiple Objectives for Humanitarian Aid Delivery Using Expert Preferences

Humanitarian aid agencies deliver emergency supplies and services to people affected by disasters. Scholars and practitioners have developed modeling approaches to support aid delivery planning, but they have used objective functions with little validation as to the trade‐offs among the multiple goals of aid delivery. We develop a method to value the performance of aid delivery plans based on expert preferences over five key attributes: the amount of cargo delivered, the prioritization of aid by commodity type, the prioritization of aid by delivery location, the speed of delivery, and the operational cost. Through a conjoint analysis survey, we measure the preferences of 18 experienced humanitarian logisticians. The survey results quantify the importance of each attribute and enable the development of a piecewise linear utility function that can be used as an objective function in optimization models. The results show that the amount of cargo delivered is the most valued objective and cost the least important. In addition, experts prioritize more vulnerable communities and more critical commodities, but not to the exclusion of others. With these insights and the experts’ utility functions, better humanitarian objective functions can be developed to enable better aid delivery in emergency response.     

基于证据推理的不确定多属性决策方法

针对基本属性权重的不确定性,以及基本属性与广义属性评价集的不一致性等问题,提出一种基于证据推理的不确定多属性决策方法,将证据推理算法推广到更一般的决策环境中.根据决策矩阵的信息熵客观地获得属性的权系数;而对于基本属性与广义属性评价集不一致的情况,则通过对基本属性分布评价的模糊化及模糊变换,合理地实现到广义分布评价的统一形式;最后应用证据推理算法得到整个方案集的排序.实例结果表明,该方法是可行的、有效的.     

基于属性重要度的产品定制程度测量模型及定制策略

定制程度的高低反映了制造商对顾客个性化需求响应的程度。运用组合分析法建立基于效用的顾客属性定制偏好测量模型和基于属性重要度的产品定制程度测量模型;以笔记本电脑装配定制为研究对象,运用K-means聚类将样本分成5个不同的属性偏好群体,测量个体与5个属性偏好群体满意度,比较分析多样化生产方式与定制化生产方式下顾客满意度的差异,建立不同偏好群体定制程度与顾客满意度之间的回归模型。研究表明,按照定制属性重要度从大到小依次进行定制生产比细分市场的多样化生产能更有效地提高顾客满意度。     

Hierarchical fuzzy integral stated preference method for Taiwan's broadband service market

The stated preference method (or conjoint analysis) has been a popular method for measuring buyer tradeoffs among multi-attribute products or services. In addition, the multi-nomial logit (MNL) model is a popular model for the stated preference method, although it relies on the assumption that its attributes have no correlations. However, in social problems, attributes are often correlated with each other. Recently, the Choquet integral has been used to solve non-additive problems. This study combines the Choquet integral and the stated preference method to propose the hierarchical fuzzy integral stated preference (HFISP) method, and then analyze the narrowband service users’ preferences for broadband service. The results demonstrate that the hierarchical fuzzy integral stated preference method performs better than the partitioned fuzzy integral multi-nomial logit (PFIMNL) model. The HFISP method is effective and can be applied to deal with real life problems since it solves correlated attribute problem in discrete choice behavior.     

Optimizing Service Attributes: The Seller's Utility Problem*

Service designers predict market share and sales for their new designs by estimating consumer utilities. The service's technical features (for example, overnight parcel delivery), its price, and the nature of consumer interactions with the service delivery system influence those utilities. Price and the service's technical features are usually quite objective and readily ascertained by the consumer. However, consumer perceptions about their interactions with the service delivery system are usually far more subjective. Furthermore, service designers can only hope to influence those perceptions indirectly through their decisions about nonlinear processes such as employee recruiting, training, and scheduling policies. Like the service's technical features, these process choices affect quality perceptions, market share, revenues, costs, and profits. We propose a heuristic for the NP‐hard service design problem that integrates realistic service delivery cost models with conjoint analysis. The resulting seller's utility function links expected profits to the intensity of a service's influential attributes and also reveals an ideal setting or level for each service attribute. In tests with simulated service design problems, our proposed configurations compare quite favorably with the designs suggested by other normative service design heuristics.     

REPLY TO: “ON THE SENSITIVITY OF BRAND-CHOICE SIMULATIONS TO ATTRIBUTE IMPORTANCE WEIGHTS”

Our reply to Curry, Louviere, and Augustine's critique of our earlier paper focuses on differences in motivation between our research and theirs. Our interest in the problem relates to the possible incorporation of self-explicated evaluations in conjoint data collection methods; subsequent to the appearance of our original paper, we have developed hybrid models that combine elements of self-explicated (compositional) and conjoint (decompositional) data collection procedures. As far as we can surmise from their critique, Curry, Louviere, and Augustine are concerned with much broader strategic issues relating share of choices in the consumer population to changes in the shape of attribute weight distributions, shape of the Pareto tradeoff boundary, and so on.     

基于愿景满意度函数的多属性群决策方法

针对定性和定量属性相混合的多属性群决策问题，提出基于愿景满意度函数的多属性群决策方法。该方法从损失和收益的角度考虑问题，通过计算标准化的愿景值和属性值之间的距离，建立考虑专家不同风险态度的愿景满意度函数，求得专家各自的满意度，最后应用简单加权算子集结专家的排序结果。通过案例分析和算法对比说明了该算法可适用于不同风险态度的专家进行决策，而风险态度和愿景值的分析结果表明当属性不确定性较大时，专家看重损失或者收益取决于专家的风险态度和愿景值的高低，该方法可为参与决策的双方提供有价值的参考意见。     

Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions

Laypeople's perceptions of health and safety risks have been widely studied, but only a few studies have addressed perceptions of ecological hazards. We assembled a list of 39 attributes of ecological hazards from the literatures on comparative risk assessment, ecological health, environmental conservation and management, environmental psychology, and risk perception. In Study 1, 125 laypeople evaluated 83 hazards on subsets of this attribute set. Factor analysis of attribute ratings (averaged over participants) revealed six oblique factors: ecological impacts, human impacts, human benefits, aesthetic impacts, scientific understanding, and controllability. These factors predicted mean judgments of overall riskiness, ecological riskiness, acceptability, and regulatory strictness. In Study 2, 30 laypeople each evaluated 34 hazards on 17 attributes and 3 dependent variables. Aggregate-level factor analysis of these data replicated the appropriate portion of the factor solution and yielded similar regression results. Parallel analyses at the individual-participant level yielded factors that explained less variance in judgments of overall riskiness, ecological riskiness, and acceptability. However, the decrease in explanatory power was much less than is often reported for disaggregate-level analyses of psychometric data. This discrepancy illustrates the importance of distinguishing between the level of analysis (aggregate versus disaggregate) and the focus of analysis (distinctions among hazards versus distinctions among participants). In a hybrid analysis, aggregate-level factor scores predicted individual participants' riskiness judgments reasonably well. Psychometric studies such as these provide a sound empirical basis for selecting attributes of ecological hazards for use in comparative risk assessment.     

ON THE USE OF VERBAL PROTOCOLS IN CONJOINT ANALYSIS STUDIES

A recent study by Olshavsky and Acito helped gain insight into (a) the decision process used by respondents when asked to do a conjoint task, (b) the predictive validity of conjoint models compared to models derived from verbal protocols, and (c) the reasons for prediction errors. In this article, the author argues that the Olshavsky and Acito results do not lead one to believe that the compensatory conjoint models can be potentially more misleading than models that try to represent more accurately the underlying evaluation process with noncompensatory and compensatory rules. Hence, it does not seem necessary to try to identify noncompensatory rules in a conjoint study.     

Betriebsformen im Automobilhandel �C Resultate einer empirischen Untersuchung

Whereas most retail industries are characterized by a great diversity of competitive retail formats, the automotive industry largely relies on one single retail format, the authorized car dealer. However, both automobile manufacturers and car dealers are trying to expand sales activities for automobiles by implementing new and innovative retail formats. A focal point raised within this context is the question about (potential) customers?? expectations regarding retail formats in the automotive industry. To be able to analyze these expectations, eleven attributes which are specific to automotive retail formats have been derived from a literature overview. The resulting taxonomy of attributes has then been used to systematically identify current and future retail formats in this particular industry. Using an hierarchical adaptive conjoint analysis model, the attributes describing different automotive retail formats have being extensively analyzed. By developing this specific conjoint analysis model, it is possible to both analyze single attributes and entire retail formats regarding their impact on the automotive market as well as particular market segments.     

基于三角直觉模糊数Choquet积分算子的多属性决策方法

定义了三角直觉模糊数的Hamming距离,给出了属性独立情形下的三角直觉模糊数算术集成算子,并运用模糊测度和Choquet积分,定义了三角直觉模糊Choquet积分算子,并分析其有关性质。针对方案评价信息为三角直觉模糊数的关联多属性决策问题,利用三角直觉模糊Choquet积分算子集成得到方案的综合属性值,建立多目标优化模型并转化为线性目标规划求解,客观地确定了属性集的模糊测度,进而提出了相应的决策方法。实例分析表明了方法的有效性和合理性。     

Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility Models

Conventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deterministic, which may be unrealistic because assessment of such elements can be imprecise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We propose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgeworth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic dominance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a standard normal density as the base function will be sufficient for approximating an aggregate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illustrated using a vendor selection problem.     

ASSESSING INFORMATION SYSTEM VALUE: AN EXPERIMENTAL STUDY

Linear models of information system value that are a function of information attributes are empirically studied by having managers in two firms make assessments of the importance of attributes, the level of satisfaction produced by various levels of each attribute, and the overall value of total systems. The results verify that such models may be quite useful in information system design. It is also shown that ordinally ranked attributes work as well as the interval-scaled attributes in the linear model. Since ordinal measures are easier to obtain, this suggests that “linear ordinal” models may be the most practical method of quantifying information systems value.     

Consistency and Accuracy in Decision Aids: Experiments with Four Multiattribute Systems*

There have been a number of multiattribute decision aids developed to aid selection problems. Multiattribute value theory and the analytic hierarchy process are two commonly used techniques. Different systems can result in radically different conclusions if they inaccurately and inconsistently reflect the preference structure of decision makers, or if they are based on inappropriate theoretical models. This study examines the impact of the underlying theoretical model, the method in which preference information is elicited, and the structure of alternatives as influences on the results from using various decision aids. It was found that two systems based on the multiattribute value theory model were just as diverse in their conclusions as were results between AHP and the multiattribute value theory models. Therefore, accuracy of information reflecting decision maker preference is an important consideration. Feedback capable of assuring the decision maker that information provided is consistent is a necessary feature required of decision aids applied to selection problems. The study also found that the way in which information is elicited influenced the result more than did the underlying model. Exact numerical data for complex concepts such as attribute importance and alternative performance on attributes is not necessary, and elicitation procedures that are more natural for the user are likely to be more accurate.     

基于Hawkes因子模型的股价共同跳跃研究

本文首先比较了三种目前主流的共跳检验方法：基于LM检验的共跳检验、BLT共跳检验和FHLL共跳检验,结果表明,三种方法在识别共跳数量上差距明显,但三者结果的重合部分基本属于市场暴涨暴跌行情,说明共跳识别对市场剧烈波动的聚集性较为敏感。基于跳跃、共跳存在的聚集性问题,本文将Hawkes过程引入跳跃和共跳的研究,构建了基于Hawkes过程的因子模型,结果显示,基于Hawkes因子模型的MJ统计量、CJ统计量和实证数据的拟合程度较好,表明因子模型能够更好地描述跳跃和共跳的聚集性。     

A three-stage hybrid approach for weight assignment in MADM

How to determine weights for attributes is one of the key issues in multiple attribute decision making (MADM). This paper aims to investigate a new approach for determining attribute weights based on a data envelopment analysis (DEA) model without explicit inputs (DEA-WEI) and minimax reference point optimisation. This new approach first considers a set of preliminary weights and the most favourite set of weights for each alternative or decision making unit (DMU) and then aggregates these weight sets to find the best compromise weights for attributes with the interests of all DMUs taken into account fairly and simultaneously. This approach is intended to support the solution of such MADM problems as performance assessment and policy analysis where (a) the preferences of decision makers (DMs) are either unclear and partial or difficult to acquire and (b) there is a need to consider the best "will" of each DMU. Two case studies are conducted to show the property of this new proposed approach and how to use it to determine weights for attributes in practice. The first case is about the assessment of research strengths of EU-28 member countries under certain measures and the second is for analysing the performances of Chinese Project 985 universities, where the weights of the attributes need to be assigned in a fair and unbiased manner.     

A Meta‐Analysis of Carbon Nanotube Pulmonary Toxicity Studies—How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes

This article presents a regression‐tree‐based meta‐analysis of rodent pulmonary toxicity studies of uncoated, nonfunctionalized carbon nanotube (CNT) exposure. The resulting analysis provides quantitative estimates of the contribution of CNT attributes (impurities, physical dimensions, and aggregation) to pulmonary toxicity indicators in bronchoalveolar lavage fluid: neutrophil and macrophage count, and lactate dehydrogenase and total protein concentrations. The method employs classification and regression tree (CART) models, techniques that are relatively insensitive to data defects that impair other types of regression analysis: high dimensionality, nonlinearity, correlated variables, and significant quantities of missing values. Three types of analysis are presented: the RT, the random forest (RF), and a random‐forest‐based dose‐response model. The RT shows the best single model supported by all the data and typically contains a small number of variables. The RF shows how much variance reduction is associated with every variable in the data set. The dose‐response model is used to isolate the effects of CNT attributes from the CNT dose, showing the shift in the dose‐response caused by the attribute across the measured range of CNT doses. It was found that the CNT attributes that contribute the most to pulmonary toxicity were metallic impurities (cobalt significantly increased observed toxicity, while other impurities had mixed effects), CNT length (negatively correlated with most toxicity indicators), CNT diameter (significantly positively associated with toxicity), and aggregate size (negatively correlated with cell damage indicators and positively correlated with immune response indicators). Increasing CNT N₂‐BET‐specific surface area decreased toxicity indicators.     

Individual differences and leadership: Contributions to a third tipping point

This commentary describes two tipping points in the history of research on leader individual differences, and suggests the approach of a third. This third tipping point reflects the use of more multivariate (e.g., multiple leader attributes; multistage models; pattern and profile approaches) perspectives to individual differences and leadership. The four papers in the special issue are described as examples of these perspectives.