An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria

Organizations often require decisions to be made by a group, and decision makers often have fuzzy preferences for alternatives and individual judgments when attempting to reach an optimal solution. In order to deal with the fuzziness of preference of decision makers, this paper proposes an integrated fuzzy group decision-making method. This method allows group members to express fuzzy preferences for alternatives and individual judgments for solution selection criteria. It also allowed for the weighting of group members. The method then aggregates these elements into a compromise group decision which is the most acceptable for the group as a whole. This method has been implemented and tested. An example is presented to illustrate the method.     

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.     

A Comparison of Full Profile and Hierarchical Information Integration Conjoint Methods to Modeling Group Preferences

This paper presents a comparative test of full profile (FP), original hierarchical information integration (HII-O), and integrated hierarchical information integration (HII-I) conjoint methods for modeling group preferences. It is hypothesized that in settings where groups need to decide about complex multi-attribute alternatives, HII-O will predict holdout profiles better than FP, and that HII-I will perform better than HII-O. The predictive ability of the three methods is tested for the case of housing preferences of housing co-ops, which are groups consisting of three to five people who jointly own a house. The results confirm that HII-I outperforms the other two methods and further suggest that FP and HII-O perform equally well. In addition, two variations of HII-I are developed. One of these provides independent estimates of the relative influence of group members on the decision outcomes, however at the expense of requiring larger designs. The two HII-I variations are also tested and found to be equivalent in predictive ability.     

Evaluations of Consumers’ Preference Structure for Locally-Produced Beef

This study assesses how locality of production, method of production, and price of beef and beef products influence purchasing preferences. Consumers were surveyed for demographic, economic, and taste and preference characteristics. A conjoint analysis is implemented to analyze the effect of various product attributes on consumers’ beef purchases. The results reveal that the most important of the attributes for consumers is the distance of origin. Consumers consider beef locally grown if the beef is produced within an 85-mile radius, and the distance of origin accounts for 60% of the total importance from all attributes.     

Have the Mobile Coupon Preferences of Millennials Changed?

ABSTRACT

In 2007, the authors delivered a web-based survey to a sampling frame of 18 to 30 year olds. Choice-based conjoint analysis was used to determine their local coupon preferences. The findings indicated that Millennials, in general, were resistant to receiving coupons for local businesses through their cell phones. In this article, the same survey was delivered to a sampling frame of Millennials in 2014. Choice-based conjoint analysis was again used to examine the couponing preferences of Millennials and statistical analysis was conducted to determine if these preferences have significantly changed over the last seven years.     

Segmentation Analysis of the Volunteering Preferences of University Students

The purpose of this article is to explore how universities can increase volunteering among their students. To accomplish this objective, conjoint anlaysis was undertaken to determine the different underlying needs and preferences that drive the volunteering behavior of students. From this data, four need‐based segments are identified and discussed. Strategies are then formulated to meet the needs and preferences of each segment as a means to increase volunteering on campuses.     

Better preference prediction with individualized sets of relevant attributes

Conjoint studies are aimed at analysing individual preferences based on product profiles. The findings are used either for exploratory or for predictive purposes.One basic paradigm states that the analysis should have an individual orientation. Therefore, the part-worth utilities are estimated on an individual level. The set of relevant attributes, however, is usually supposed to be unique for all respondents. In contrast to this traditional approach, which is only partially individualized, we developed a completely individualized conjoint approach based upon an EDP-assisted information handling system. In addition, we have tested the quality of this completely individualized analysis against the partially individualized analysis.In regard to predictive validity, the completely individualized analyses were, as far as the correct first choice probability criterion is concerned, significantly better than the partially individualized analyses. Thus, we conclude that for commercial analyses conjoint analyses should be based on the individualized conjoint approach.     

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.     

Enhancing Consumer Choice: Are We Making Appropriate Recommendations?

To reduce information search costs, consumers often use choice-making aids. If these aids are to help consumers make choices consistent with their own preferences, as well as reduce search effort, they must be based on choke models consistent with consumers' true preference sets. This study used conjoint analysis to identify these ideal choice models. Thirty-six percent of the subjects were found to use compensatory choice models and 64 percent noncompensatory models. The results suggest a need to base choice-making aids on ideal choice models if the aid is to lead consumers to decisions consistent with true preferences.     

The Joint identification of utility and discount functions from stated choice data: An application to durable goods adoption

We present a survey design that generalizes static conjoint experiments to elicit inter-temporal adoption decisions for durable goods. We show that consumers’ utility and discount functions in a dynamic discrete choice model are jointly identified using data generated by this specific design. In contrast, based on revealed preference data, the utility and discount functions are generally not jointly identified even if consumers’ expectations are known. The separation of current-period preferences from discounting is necessary to forecast the diffusion of a durable good under alternative marketing strategies. We illustrate the approach using two surveys eliciting Blu-ray player adoption decisions. Both model-free evidence and the estimates based on a dynamic discrete choice model indicate that consumers make forward-looking adoption decisions. In both surveys the average discount rate is 43 percent, corresponding to a substantially higher degree of impatience than the rate implied by aggregate asset returns. The estimates also reveal a large degree of heterogeneity in the discount rates across consumers, but only little evidence for hyperbolic discounting.     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.     

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.     

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.     

Analysis of Consumer Preferences for Table Olives—The Case of Albanian Urban Consumers

The table olive production sector is undergoing rapid changes, as the government is undertaking an ambitious program supporting the expansion of olive grove plantations. Despite the increase in domestic production, imports of table olives are still high, due to constraints in quantity and quality of domestically supplied olives. In the context of import substitution strategy, embraced by producers and policy-makers, it is important to analyze the consumer preferences for table olives. The objective of this paper is to segment the table olive market according to preferences for table olives attributes, and assess willingness to pay for the main product attributes, applying Conjoint Choice Experiment (CCE) and Latent Class Analysis to collect and analyze the data. The research results show a strong consumer preference for domestic table olives whereas preferences for other attributes and willingness to pay vary between consumer groups.     

Do Self-Reported Strategies Match Actual Behavior in a Social Preference Experiment?

According to many models, social preferences are influenced by properties of payoff distributions like the payoff to the group member who is worst off, or higher payoffs to other members possibly causing envy. In this paper, we explore if subjects in a social preference experiment consciously take these elements into account. To study this question, we performed an experiment in which subjects stated indifference values of unequal payoff distributions, and explicitly reported on the strategies they used in stating these values. This approach allows us to obtain a cardinal measure of their social preferences, and to study the impact both of subject characteristics and stated strategies on these values. Our results indicate that although experimental factors, like group composition or field of study of subjects, are significantly related to self-reported strategies, they barely have a statistically significant impact on the indifference values given. On the other hand, we find highly significant relationships between self-reported strategies and actual behavior. Furthermore, our results indicate that the salience of different payoff distribution characteristics, and the impact of different motives on actual decisions, is context dependent.     

Investigating brand preferences across social groups and consumption contexts

Using a unique dataset on U.S. beer consumption, we investigate brand preferences of consumers across various social group and context related consumption scenarios (??scenarios??). As sufficient data are not available for each scenario, understanding these preferences requires us to share information across scenarios. Our proposed modeling framework has two main building blocks. The first is a standard continuous random coefficients logit model that the framework reduces to in the absence of information on social groups and consumption contexts. The second component captures variations in mean preferences across scenarios in a parsimonious fashion by decomposing the deviations in preferences from a base scenario into a low dimensional brand map in which the brand locations are fixed across scenarios but the importance weights vary by scenario. In addition to heterogeneity in brand preferences that is reflected in the random coefficients, heterogeneity in preferences across scenarios is accounted for by allowing the brand map itself to have a discrete heterogeneity distribution across consumers. Finally, heterogeneity in preferences within a scenario is accounted for by allowing the importance weights to vary across consumers. Together, these factors allow us to parsimoniously account for preference heterogeneity across brands, consumers and scenarios. We conduct a simulation study to reassure ourselves that using the kind of data that is available to us, our proposed estimator can recover the true model parameters from those data. We find that brand preferences vary considerably across the different social groups and consumption contexts as well as across different consumer segments. Despite the sparse data on specific brand-scenario combinations, our approach facilitates such an analysis and assessment of the relative strengths of brands in each of these scenarios. This could provide useful guidance to the brand managers of the smaller brands whose overall preference level might be low but which enjoy a customer franchise in a particular segment or in a particular context or a social group setting.     

Channel coordination under retailer's (sub)conscious preferences of loss aversion and fairness

This study examines the impact of retailers' conscious and subconscious preferences on the coordination of a supplier-retailer channel. By evaluating various preference models, the research finds that a wholesale price contract is effective only when the retailer exhibits a conscious fairness preference without any conscious loss aversion preference. Additionally, the study indicates that retailers possessing both a conscious fairness preference and a subconscious loss aversion preference are more receptive to coordination efforts. This research provides valuable insights for managers, enabling them to promote collaboration and maximum channel utility without compromising retailers' interests.     

Using a Community of Knowledge to Build Intelligent Agents

The modeling of individual consumer preference can be aided by incorporating others' opinions which contain information above and beyond identified product attributes. The value of others' opinions is tested using two empirical data sets. The results indicate that incorporating others' opinions into an attribute-based model can reduce systematic error and increase predictive accuracy by serving as a proxy for missing information (e.g., undiscovered attributes or attribute interactions, sensory or experiential aspects of the product, as well as advertising or word of mouth effects). Additionally, modeling individual preference based on others' opinions alone is shown to predict as well or better than traditional multiattribute models thus bypassing the need for defining a product attribute space.     

A Conjoint Analysis of Quality Wine

Abstract

Weighted least squares approach for conjoint analysis is used to examine buyer preferences towards wines produced in different Spanish regions. Two producer regions are considered, Na-varra and Aragon, and three wine attributes: price, origin and grape vintage year (wine age). In both regions, differences between urban and rural consumers' preferences are tested. Results indicate that the origin of the grapes is the most important attribute for the wine consumer. Rural consumers prefer locally produced wines while Rioja wines are better considered by urban consumers. The price and the age of the wines are secondary attributes in consumer preferences. Finally, some market segments are formed based on individual consumer preferences and some of their sociodemographic characteristics in order to address differentiated marketing strategies to these segments.