Conjoint models of family decision making

The objective of this research is to develop viable approaches to modeling joint decisions. Using conjoint-analysis-type preference data, three methods are developed to combine individual preferences to approximate joint preferences and predict joint decisions. The first is an equal weighting model, which is a simple average of individual members' part-worth utilities. The second is a relative influence model, which combines individual utility functions using a measure of derived influence. The third is a conflict resolution model, which combines utility functions using a measure of conflict. In addition to these three combination models, individual member models and a joint model based on the joint preferences are available.The application area in which the models are operationalized is family decision making. The decision involves choice of a job by MBA students and spouses at a major private university. The models are first calibrated using preference data on hypothetical jobs from MBAs, spouses, and couples and then evaluated on their ability to predict the actual job chosen.     

Testing for the utility maximization hypothesis of consumers using the revealed preference theory

The objective of this paper is to test the utility maximization hypothesis in various Western countries using the non-parametric approach based on the revealed preference theory. The data employed are annual time series covering the period 1964 to 1992 of per capita consumption and prices from Germany, France, the United Kingdom, Spain, Sweden and Canada. The results indicate that all sample countries are consistent with the weak, strong and generalized axioms of the revealed preference theory. This implies that the observed behaviour of these consumers is consistent with the condition of utility maximization or, in other words, that the hypothesis of stability of individual preferences is accepted.     

Modeling Group Preferences Using a Decompositional Preference Approach

This article examines an extension of the decompositional, conjoint, or stated-preference approach to model group decisions. In the conventional approach, only one member is chosen to be the group's representative and provide answers for the group as a whole. In this study, all group members are brought together and asked to jointly complete a conjoint preference experiment. The hypothesis is tested that this joint group approach predicts group behavior better than the conventional approach with representatives. The paper presents the estimated part-worth utilities of the group model and compares preference structures of individual group members and groups. Finally, group preference models are tested to determine whether they outperform representative-based preference models in terms of the ability to correctly predict the group preferences for new alternatives. These analyses are performed in the context of residential preferences of co-ops, which are groups of young people, usually not partners, who live together in owner-occupied houses.     

Family Members' Projections of Each Other's Preference and Influence: A Two-Stage Conjoint Approach

Two important sources of error that may limit the accuracy of individual family members' projections of joint family preferences are: (i) misperceptions of other members' preferences, and (ii) misperceptions of other members' influence in joint family evaluations. We propose a two-stage conjoint approach to study these potential errors. Stage one compares family members' projections of each other's preferences to members' self-reported preferences. Stage two compares family members' projections of each other's influence to observed influence in joint family preferences. An empirical illustration shows that family members are relatively poor predictors of preference, but fairly accurate predictors of influence, in the area of family holiday preferences.     

Do Consumers Really Know How Much They Are Willing to Pay?

Previous research reveals willingness‐to‐pay (WTP) obtained through consumer surveys and experiments is often influenced by arbitrary information that, from a normative perspective, should have no influence on WTP. This article utilizes data collected from a split‐sample online survey to determine whether the bias induced from arbitrary anchors can be mitigated with a preference elicitation approach designed to facilitate the preference‐discovery process by revealing tradeoffs and consequences implied by stated WTP. The approach forces internal consistency between statements of value and preference orderings. We compare the WTP elicited using the new approach to two more simplified elicitation devices by determining sensitivity to an arbitrary anchor and by determining predictive validity relative to a holdout choice. Our results indicate that the imposition of internal consistency yields more stable estimates of WTP; however, there is no evidence to support the hypothesis that the method provides WTP estimates that lead to a higher degree of predictive validity .     

Comparing stated and realized preferences for shade‐grown vs. conventionally grown coffee

We used both stated preference and revealed preference data to estimate willingness to pay (WTP) for shade‐grown coffee as compared with conventionally grown coffee. Stated preference data was collected using contingent valuation studies. Revealed preference data came from an experiment where all survey participants received a personally identifiable voucher redeemable for a free bakery item when the holder purchased a coffee. We compared estimates of mean and median WTP a price premium for shade‐grown coffee from stated preference data with similar estimates from revealed preference data. We used a logit model to evaluate the effect of explanatory variables (measures of environmental attitudes, personal norms for pro‐environmental behaviour and demographic variables) on respondents’ WTP a price premium for shade grown coffee. Model parameters were estimated using the maximum likelihood approach. Respondents with higher scores on measures of environmental attitudes and personal norms for pro‐environmental behaviour were, on average, willing to pay more for shade‐grown coffee. While this paper examined a specific case, purchase of shade‐grown coffee, our results confirmed that stated environmental concern was a good predictor of pro‐environmental behaviour. We found that mean and median WTP estimates from stated preference methods were higher, but not significantly different than mean and median WTP estimates from actual purchases, indicating convergent validity between stated and realized preference methods. The majority of individuals both stated WTP a price premium and purchased shade‐grown coffee at a price premium. We did, however notice some interesting behaviour at the individual level where stated preferences under‐predicted realized preferences at low price‐premia and over‐predicted realized preferences at high price premia.     

Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors that is free from potential distortion resulting from the analysis of preference data confounded with the effects of consumer-specific brand unfamiliarity. An application based on preference and familiarity ratings for ten luxury car models collected from 240 consumers who intended to buy a luxury car within a designated time frame is presented. The results are compared with those obtained from MDPREF, a popular metric vector MDS model used for the scaling of preference data. In particular, we find that the consumer preference vectors obtained from the proposed methodology are substantially different in orientation from those estimated by the MDPREF model. The implications of the methodology are discussed.The authors gratefully acknowledge helpful comments from the editor and two anonymous reviewers, and also from Michael D. Johnson and Robert J. Meyer. They also thank Michael Kusnick and Robert Kleinbaum for their assistance in conducting the survey.     

Household preference revisions and decision making: The role of disconfirmation

In this paper, we examine the role of disconfirmation as an important determinant of household preference revisions and decision making. Although prior research has examined decision influence and information exchange in household decision making, the impact of disconfirmation on individual family member preferences and the joint household preference has not been explicitly studied. Analogous to the main types of social influence found within groups, we identify two types of disconfirmation that impact individual preferences arising from family interaction or discussion. These two types of disconfirmation are informational disconfirmation, related to new product information revealed in family discussion being different than one's prior beliefs, and preference disconfirmation, where a family member has incorrect expectations of the preferences of other influential household members. An empirical study involving a household vacation decision demonstrates significant disconfirmation effects in explaining individuals' post-discussion preferences, as well as the joint household preference and decision. Based on the empirical results, we discuss various implications and suggest future research to study the specific consequences of disconfirmation in household decisions.     

社会网络对农民工生育观念的影响——来自深圳调查的发现

利用西安交通大学人口与发展研究所2005年深圳市农民工调查数据,从社会网络视角实证分析了流动后农民工生育观念的影响因素。生育讨论网的弱关系和网络成员的生育行为,以及个体在城镇的滞留时间、初次流动时的年龄、受教育程度等因素对流动后农民工的生育观念产生了影响。     

孤独感对体验消费意愿的影响机理

随着经济的发展和社会的进步,体验消费的形式日益多样化,并逐渐在消费市场中占据重要地位。鉴于现有研究大多关注体验消费的结果变量,而缺乏对其前因变量如心理因素的探索,通过在线问卷调查收集有效样本1014份,并利用层次回归分析法、拔靴法等实证分析孤独感与体验消费意愿之间关系,探讨社交需求和自我建构在其中的作用。研究结果表明,孤独感对个体体验消费意愿具有显著正向影响,即个体感受到的孤独感越强,其为摆脱孤独感及相关负面情绪影响而进行体验消费的意愿越强;社交需求在孤独感对个体体验消费意愿的影响中起中介作用,即孤独感会激发个体产生社交需求,进而提高个体进行体验消费的意愿;自我建构调节了社交需求的中介作用,且相比于独立型自我建构个体,孤独感对依存型自我建构个体社交需求的影响更大,依存型自我建构个体进行体验消费的意愿更强。因此,为进一步推动体验消费产业发展,对企业而言,应积极关注孤独感等消费者内在心理因素,根据消费者心理与个人特质制定相关营销策略,满足消费者社交需求,缓解消费者孤独感;对商家而言,应积极向目标消费者传递体验产品社交价值,激发消费者依存型自我建构,满足消费者情感诉求;对政府而言,应关注居民心理状态,积极引导居民进行体验消费,加强社会成员心理健康建设。     

Sequential choice designs to estimate the heterogeneity distribution of willingness-to-pay

Two prominent approaches exist nowadays for estimating the distribution of willingness-to-pay (WTP) based on choice experiments. One is to work in the usual preference space in which the random utility model is expressed in terms of partworths. These partworths or utility coefficients are estimated together with their distribution. The WTP and the corresponding heterogeneity distribution of WTP is derived from these results. The other approach reformulates the utility in terms of WTP (called WTP-space) and estimates the WTP and the heterogeneity distribution of WTP directly. Though often used, working in preference space has severe drawbacks as it often leads to WTP-distributions with long flat tails, infinite moments and therefore many extreme values. By moving to WTP-space, authors have tried to improve the estimation of WTP and its distribution from a modeling perspective. In this paper we will further improve the estimation of individual level WTP and corresponding heterogeneity distribution by designing the choice sets more efficiently. We will generate individual sequential choice designs in WTP space. The use of this sequential approach is motivated by findings of Yu et al. (2011) who show that this approach allows for superior estimation of the utility coefficients and their distribution. The key feature of this approach is that it uses Bayesian methods to generate individually optimized choice sets sequentially based on prior information of each individual which is further updated after each choice made. Based on a simulation study in which we compare the efficiency of this sequential design procedure with several non-sequential choice designs, we can conclude that the sequential approach improves the estimation results substantially.     

Analysis of brand preference segments

Market segments are defined in this article on the basis of a vector of preference levels for alternative brands. The proposed analytical procedure does not require the usual assumption of homogenous perceptions. The resulting segments are shown to differ in perceptions of the brands, correlations of attribute-specific perceptions with preference, and product-oriented AIO measures. A common joint space developed from a common discriminanr configuration of brand perceptions is compared with separate joint spaces for two of the segments. The results reveal unique brand configurations and attribute orientations for these segments, and the fit of preference vectors in these unique spaces is superior to that in the common joint space. These findings lead to the conclusion that the commonly made assumption of homogenous preceptions is unwarranted and can lead to erroneous managerial implications.     

A simple method for estimating preference parameters for individuals

This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100–200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule.     

Estimating Discrete Joint Probability Distributions for Demographic Characteristics at the Store Level Given Store Level Marginal Distributions and a City-Wide Joint Distribution

This paper provides a solution to the problem of estimating a joint distribution using the associated marginal distributions and a related joint distribution. The particular application we have in mind is estimating joint distributions of demographic characteristics corresponding to market areas for individual retail stores. Marginal distributions are generally available at the census tract level, but joint distributions are only available for Metropolitan Statistical Areas which are generally much larger than the market for a single retail store. Joint distributions over demographics are an important input into mixed logit demand models for aggregate data. Market shares that vary systematically with demographics are essential for relieving the restrictions imposed by the Independence from Irrelevant Alternative property of the logit model.We approach this problem by formulating a parametric function that incorporates both the city-wide joint distributional information and marginal information specific to the retail stores market area. To estimate the function, we form moment conditions equating the moments of the parametric function to observed data, and we input these into a GMM objective. In one of our illustrations we use four marginal demographic distributions from each of eight stores in Dominicks Finer Foods data archive to estimate a four dimensional joint distribution for each store. Our results show that our GMM approach produces estimated joint distributions that differ substantially from the product of marginal distributions and emit marginals that closely match the observed marginal distributions. Mixed logit demand estimates are also presented which show the estimates to be sensitive to the formulation of the demographics distribution.The views expressed are not purported to reflect those of the United States Department of Justice     

Do family members agree on family purchase decision? – an empirical study among families in Hong Kong

Family decision making is one of the most important consumer decisions. It is complicated because all family members can be involved in the decision‐making process. The current study examined the impact of perceived buying preferences of individual family member on perceived family buying preferences. A new family decision‐making model with family members’ buying preference is proposed based on resources theory, social learning theory and family system theory. It is found that there is a synergy effect in a family decision‐making process. The synergy effect is expressed as positive correlations between individual family members buying preferences. Quota sampling was adopted to collect primary data in Hong Kong using triadic approach. Managerial implications and future research directions are suggested.     

Augmenting discrete-choice data to identify common preference scales for inter-subject analyses

Discrete-choice experiments are commonly used to measure subjects?? preference structures and are often preferred to other measurement methods because they better align with actual choice behavior and avoid some of the well-documented biases inherent in alternative elicitation methods. A limitation of discrete-choice methods is the loss of inter-subject comparability because preference estimates are invariant to linear transformations necessitating indentifying constraints that remove a common, between-subjects utility scale. This constraint limits the application of discrete-choice results to situations where within-subject comparisons are meaningful. They enable one to sort options for each subject but not to sort subjects according to the relative intensity of their preferences. This paper uses auxiliary data to recover a common preference scale for between-subject comparisons. The model combines discrete-choice data with ratings data while adjusting for response biases due to method effects. The joint model moves the identification constraints from the sub-model for the discrete-choice data to the sub-model for the ratings data. The proposed methodology is complementary to willingness-to-pay computations when studies lack price or its economic foundation is untenable.     

Multi level categorical data fusion using partially fused data

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.     

A support to consensus reaching in group decision

This paper outlines an interactive aid that facilitates consensus in cooperative decision making or policy recommendation. The approach draws on a previous result which shows that a multiple ranking can be derived from a single relational preference system. In other words, the preferences expressed individually by each member of a group may lead to either unanimous or a majority consensus, with or without concession from individual members.     

A Note on Linguistic Hybrid Arithmetic Averaging Operator in Multiple Attribute Group Decision Making with Linguistic Information

In this paper, we propose a linguistic hybrid arithmetic averaging (LHAA) operator, which is based on linguistic weighted arithmetic averaging (LWAA) operator and extended ordered weighted averaging (EOWA) operator, and study some desirable properties of the LHAA operator. The LHAA operator can not only reflect the importance degrees of both the given argument and its ordered position, but also relieve the influence of unfair arguments on the decision results by weighting these arguments with small values. Based on the LWAA and LHAA operators, we develop a practical approach to multiple attribute group decision making under linguistic environment. The approach first aggregates the individual linguistic preference values into a collective linguistic preference value for each alternative by using the LWAA and LHAA operators (it is worth pointing out that the aggregation process does not produce any loss of linguistic information), and then orders the collective linguistic preference values to obtain the best alternative(s). Finally, an illustrative example is also given to verify the approach and to demonstrate its feasibility and practicality.