SOME CAUTIONARY NOTES ON THE USE OF CONJOINT MEASUREMENT FOR HUMAN JUDGMENT MODELING*

Conjoint measurement has been suggested as a methodology that might be useful in assisting research concerned with the identification of the structural form of a judge's model. This paper synthesizes the results of some recent research that examined the robustness of this methodology. This research suggests that conjoint measurement has three major weaknesses: (1) certain biases exist when diagnosing model structure, (2) model diagnosis is limited to a small set of potential models, and (3) error substantially compromises conjoint measurement's ability to diagnose model structure. An empirical example that demonstrates some of the difficulties of using this methodology with experimental data is also presented.     

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.     

The Internal Validity of the Trade-Off Method of Conjoint Analysis

The discovery of consumers' preferences for multiattribute products/services has recently become possible with the development of conjoint measurement techniques. Conjoint analysis uses an individual's preference ranking or rating of some deliberately manipulated constructs of a concept to determine his/her numerically valued preferences for levels of those attributes that comprise the concept. Generally speaking, there are two methods of data collection for conjoint analysis: the full profile and the trade-off methods. This paper calls into question the internal validity of the preferences obtained via the trade-off method. An empirical analysis, which is based on the subjects' preferences for selecting a hospital, clearly shows that the estimates derived from the trade-off method may not reflect consumers' true preferences.     

Applications and Implementation DETERMINING THE REWARDS SALESPEOPLE VALUE: A COMPARISON OF METHODS*

Although salesmanagers can attempt to motivate salespeople by manipulating their rewards, these manipulations will fail if salespeople do not value the rewards and their levels. Methods for determining valued rewards can therefore be important to those charged with their determination. This study compares three measurement schemes for assessing salespeople's valences for various rewards: functional measurement, conjoint analysis, and thermometer scaling. All three schemes produce similar conclusions as to how rewards are valued and the acceptable tradeoffs among them. Thermometer scaling outperformed the others with respect to the time it took to secure the judgments and the palatability of the task, while conjoint measurement performed least well in these respects.     

USING CONJOINT ANALYSIS TO PREDICT TRIAL AND REPEAT-PURCHASE PATTERNS OF NEW FREQUENTLY PURCHASED PRODUCTS*

A new approach is proposed to evaluate new-product opportunities. This approach uses the distribution of brand-purchase probability of the new product over a population of potential customers and the outputs from conjoint analysis. The heterogeneous distribution of brand-purchase probability is expressed by a beta binomial brand-choice model compounded with a negative binomial product-class purchase-incidence model. The resulting model provides a way to predict trial and repeat-purchase patterns of new-product concepts. The paper discusses the development of the model. It also discusses issues of measurement, estimation, testing, and implementation of the proposed approach based on actual empirical data.     

Effect of Level of Disaggregation on Conjoint Cross Validations: Some Comparative Findings*

Early formulations of conjoint models focused on part-worth estimation at the individual level. As the methodology's popularity grew so did industry demands for increasingly larger numbers of attributes and levels. In response to these demands, new approaches, based on partial or full data aggregation (such as clusterwise/latent class conjoint and choice-based conjoint), have appeared. This paper suggests that pooled-data models will often be successful in predicting market shares when researchers employ monotonic attributes. In these cases more of a good attribute (or less of a bad attribute) is always more preferred. In the more realistic case, in which some of the attributes may be nonmonotonic, we find that data aggregation does not predict holdout sample preferences as well as individual part-worth models.     

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.     

Consistency issues in the best worst method: Measurements and thresholds

The Best-Worst Method (BWM) uses ratios of the relative importance of criteria in pairs based on the assessment done by decision-makers. When a decision-maker provides the pairwise comparisons in BWM, checking the acceptable inconsistency, to ensure the rationality of the assessments, is an important step. Although both the original and the extended versions of BWM have proposed several consistency measurements, there are some deficiencies, including: (i) the lack of a mechanism to provide immediate feedback to the decision-maker regarding the consistency of the pairwise comparisons being provided, (ii) the inability to consider the ordinal consistency into account, and (iii) the lack of consistency thresholds to determine the reliability of the results. To deal with these problems, this study starts by proposing a cardinal consistency measurement to provide immediate feedback, called the input-based consistency measurement, after which an ordinal consistency measurement is proposed to check the coherence of the order of the results (weights) against the order of the pairwise comparisons provided by the decision-maker. Finally, a method is proposed to balance cardinal consistency ratio under ordinal-consistent and ordinal-inconsistent conditions, to determine the thresholds for the proposed and the original consistency ratios.     

Measuring Job Satisfaction with CUB Models

In this paper we present two statistical approaches for discussing and modelling job satisfaction based on data collected in the Survey on Household Income and Wealth (SHIW) conducted by the Bank of Italy. In particular, we analyse these data by means of a mixture model introduced for ordinal data and compare results with the Ordinal Probit model. The aim is to establish common outcomes and differences in the estimated patterns of global job satisfaction, but also to stress the potential for curbing the effects of measurement errors on estimates by using CUB models [a Combination of discrete Uniform and (shifted) Binomial distributions], allowing control for the effect of uncertainty and shelter choices in the response process.     

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.     

Theory of integer-valued data envelopment analysis under alternative returns to scale axioms

Kuosmanen and Kazemi Matin [Theory of integer valued data envelopment analysis. European Journal of Operational Research 2009;192:658–67] developed an axiomatic foundation for a data envelopment analysis (DEA) model that assumes subsets of input and output variables to be integer valued. In this paper we extend and generalize the axiomatic foundation for the integer DEA under variable, non-decreasing and non-increasing returns to scale. These model variants are achieved by introducing new axioms of natural convexity and natural augmentability. We also develop mixed integer linear programming (MILP) formulations for computing efficiency scores in these environments. An empirical example illustrates the approach.     

The Measurement of Intellectual Influence

This paper examines the problem of measuring intellectual influence based on data on citations between scholarly publications. We follow an axiomatic approach and find that the properties of invariance to reference intensity, weak homogeneity, weak consistency, and invariance to splitting of journals characterize a unique ranking method. This method is different from those regularly used in economics and other social sciences.     

组合拍卖竞胜标确定问题的优化方法综述

由于当今组合拍卖在电子商务的理论和实践中具有极其重要意义,因此,国外关于组合拍卖竞胜标确定问题优化方法的研究近年来有了较大的发展。本文根据收集到的丰富资料,对求解这一问题的精确算法、近似算法和启发式算法的最新发展进行综述,并提出了该问题未来的研究方向。     

Estimating Willingness‐to‐pay with Choice‐based Conjoint Analysis – Can Consumer Characteristics Explain Variations in Accuracy?

Knowing consumers' willingness to pay (WTP) is crucial for making effective pricing decisions. We assess the accuracy of choice‐based conjoint analysis (CBCA), a method strongly supported by behavioural theory, in the context of WTP measurement at the individual level. Furthermore, we analyse whether variations in the accuracy of WTP estimates derived by CBCA can be explained by consumers' involvement, brand awareness and the strength of consumer preferences. The results show that CBCA does not provide accurate WTP estimates and, on average, grossly overestimates the true WTP of consumers. No empirical evidence can be found that consideration of the above‐mentioned consumer characteristics results in more accurate WTP values.     

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.     

大数据行为研究趋势:一个“时空关”的视角

本文从"时空关",即时间、空间、关联3个维度来分析近十余年大数据行为研究的进展,总结近年来本课题组的相关研究结果,并探讨未来发展趋势。各种时空大数据的广泛采集和应用取得了突破性的进展,推动了有关人类流动行为的研究;与此同时,社会网络和社会媒体大数据的发展把人们关联行为的研究推向新高度。流动性和关联性以及这两个方向的交融构成了大数据行为研究的基本范畴,而通过"时空关+"不同内容和场景形成了大数据应用的广泛格局。大数据行为研究面临的主要挑战是如何解决大数据分析中的内生性问题,未来的研究将更加重视通过随机实验、田野实验或者自然实验等方法来做因果推断。大数据行为研究的"时空关+"为探讨人类行为的基本规律以及各种应用场景提供了一个崭新的视角。     

超网络初探

对近年来出现的超网络的理论和应用进行了述评。超网络乃是高于而又超于现存网络的网络,或者说是网络组成的网络,存在于物流网络、信息网络和资金网络等相交织的复杂系统之中。对复杂网络的特征和定义、已有的基于变分不等式与基于超图的超网络分析方法作了介绍,并对今后的发展与有待解决的问题进行了探讨。     

THE LEARNING CURVE: HISTORICAL REVIEW AND COMPREHENSIVE SURVEY

The use of the learning curve has been receiving increasing attention in recent years. Much of this increase has been due to learning curve applications other than in the traditional learning curve areas. A comprehensive survey of developments in the learning curve area has never been published. The closest thing to a survey was by Asher in 1956. His study focused exclusively on military applications during and immediately after World War II. This paper summarizes the learning curve literature from World War II to the present, emphasizing developments since the study by Asher. Particular emphasis is given to identifying the new directions into which the learning curve has made recent inroads and identifying fruitful areas for future research.     

Choice Without Beliefs

We provide an axiomatic foundation for decision making in a complex environment. We do not assume that the decision maker has complete structural knowledge of the environment. Instead the agent knows the set of actions he can take, he formulates preferences directly on the actions, and chooses according to these preferences. On the basis of experience he modifies these preferences according to a systematic procedure. Our axioms are imposed on this procedure, rather than directly on the choice itself. The axioms consist of a group of natural structural restrictions and a group of independence axioms. Our main result is an axiomatic foundation for a set of simple adaptive learning procedures that include the replicator dynamic.