The no-choice option and dual response choice designs

Choice set designs that include a constant or no-choice option have increased efficiency, better mimic consumer choices, and allow one to model changes in market size. However, when the no-choice option is selected no information is obtained on the relative attractiveness of the available alternatives. One potential solution to this problem is to use a dual response format in which respondents first choose among a set of available alternatives in a forced-choice task and then choose among the available alternatives and a no-choice option. This paper uses a simulation to demonstrate and confirm the possible gains in efficiency of dual response over traditional choice-based conjoint tasks when there are different proportions choosing the no-choice option. Next, two choice-based conjoint analysis studies find little systematic violation of IIA with the addition/deletion of a no-choice option. Further analysis supports the hypothesis that selection of the no-choice option is more closely related to choice set attractiveness than to decision difficulty. Finally, validation evidence is presented. Our findings show that researchers can employ the dual response approach, taking advantages of the increased power of estimation, without concern for systematically biasing the resulting parameter estimates. Hence, we argue this is a valuable approach when there is the possibility of a large number of no-choices and preference heterogeneity.     

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

Designing Pareto optimal stimuli for multiattribute choice experiments

Full factorial designs have long been used in designing multiattribute stimuli (e.g., hypothetical job applicants) for use in policy capturing and functional measurement models. More recently, marketing researchers have employedfractional factorial designs in multiatribute preference models, such as those used in conjoint analysis.Occasions arise where the researcher also desires the stimulus profiles to be Pareto optimal. This paper addresses some conceptual and methodological issues associated with Pareto optimal choice sets. In particular, we discuss the problem of determining the expected number of dominant-entry pairs. We then consider the task of deriving Pareto optimal choice sets from fractional factorial designs. A heuristic for accomplishing this is described and applied to an illustrative set of main effects and main effects plus interactions designs.     

Market segmentation with choice-based conjoint analysis

Choice-based conjoint analysis has increased in popularity in recent years among marketing practitioners. The typical practice is to estimate choice-based conjoint models at the aggregate level, given insufficient data for individual-level estimation of part-worths. We discuss a method for market segmentation with choice-based conjoint models. This method determines the number of market segments, the size of each market segment, and the values of segment-level conjoint part-worths using commonly collected conjoint choice data. A major advantage of the proposed method is that current (incomplete) data collection approaches for choice-based conjoint analysis can still be used for market segmentation without having to collect additional data. We illustrate the proposed method using commercial conjoint choice data gathered in a new concept test for a major consumer packaged goods company. We also compare the proposed method with ana priori segmentation approach based on individual choice frequencies.     

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.     

Setting prices in mixed logit model designs

We investigate different procedures to set prices in designs for choice-based conjoint analysis using the mixed logit model which captures latent consumer heterogeneity. Besides discrete attributes, we include a linear price term in the deterministic utility function thereby treating price as continuous variable. We consider two different price intervals and several price sets which contain either two or three prices. We compare these alternatives to set prices by simulating choices for different constellations on the basis of the mixed logit model. Furthermore, we generate ten designs simultaneously instead of just one. Using these simulated choices, we estimate the parameters of the mixed logit model in the next step. To reduce the needed sample size and computation time caused by accounting for latent consumer heterogeneity, we apply Halton draws and set a minimum potential design for prior draws. ANOVA with root mean squared error between estimated and true price coefficient values of individual consumers as dependent variable shows that using more extreme prices as interval bounds and one intermediate price positioned to the right of the interval performs best.     

HEV Choice Models as a Search Engine for the Specification of Nested Logit Tree Structures

This paper illustrates how the heteroskedastic extreme value (HEV) model can serve as an effective search engine for identifying appropriate tree structures in hierarchical choice models, particularly the nested logit. This use of the HEV model exploits its ability to estimate unique variances, and hence unique scale parameters, for each alternative in a choice set. The analysis of variance can reveal tree structures that may not be obvious to analysts who tend to base their search strategy on intuitive tree structures. The reliance on behavioural intuition may miss out on the identification of the 'best' tree in an econometric sense. This note illustrates how the HEV model is used to search for the hierarchical domain in which a statistically preferred nested logit model is positioned.     

Dynamic assortment in the presence of brand heterogeneity

In this paper, we study the dynamic assortment planning problem in the presence of heterogeneous brands. Over a limited selling season, the retailer sells heterogeneous products from one store brand and one national brand. We use the nested multinomial logit (NMNL) framework to model consumer choice process, in which consumers choose first which brand to buy and then a product within that brand. We formulate this problem using the finite-horizon dynamic programming approach. Using available sales transaction data, we estimate the consumer choice behavior and empirically demonstrate existence of brand heterogeneity. Further, our results suggest that ignoring brand heterogeneity will make the retailer’s expected revenues significantly overestimated, and the potential revenue overestimation depends on initial inventories and prices of products of two brands. We finally show that the retailer will benefit from dynamic assortment optimization with the estimated consumer choice model.     

The use of multinomial choice analysis in international business research

Many strategic decisions made by firms involve a choice among several discrete alternatives. International Business (IB) scholars are often interested in modelling the factors that potentially influence these (multinomial) choices: these factors might include not only characteristics of the firms making the choices but also attributes of the alternative choices. This paper provides a succinct and intuitive introduction to the possible applications of multinomial choice models in IB research. We briefly outline the theory behind discrete choice modelling, and then explain how multinomial choice models may be estimated (including how the datasets need to be formatted) and how the significance of the coefficient estimates as well as the diagnostic statistics may be tested and interpreted.     

Eliciting and estimating reservation price: A semi-compensatory approach

This study proposes a two-stage method to elicit consumers' price acceptability range. The method combines a conjunctive stage to elicit price acceptability limits with a utility-based stage to choose a preferred product variation. The method is efficient in choice situations entailing many multi-attribute product variations under partial information conditions. A semi-compensatory model complements the method by jointly representing the conjunctive stage with multiple ordered-response models and the choice stage with a multinomial logit model. A case study of ceiling reservation price (CRP) elicitation for students' rental apartment choice shows (i) CRP distribution for different product variations, (ii) model estimation unraveling CRP determinants, and (iii) linkage between CRP and transaction price.     

Modelling pedestrian go-home decisions: A comparison of linear and nonlinear compensatory,and conjunctive non-compensatory specifications

This paper presents the main findings of an application of several models to predict the go-home decision of pedestrians in shopping streets. Two compensatory multinomial logit models, one with a linear utility function of time and the other with a nonlinear utility function of time, and a non-compensatory conjunctive model are specified. Data about pedestrian behaviour in a major shopping street in Beijing served as input for model estimation. The conjunctive model performs best, suggesting that pedestrians use simplifying heuristics to decide when to end the shopping trip and go home. In addition, the nonlinear multinomial logit model outperforms the linear model, indicating that marginal utility of time decreases with increasing time.     

知情选择对中国避孕行为的影响分析

利用近三十年来的避孕抽样调查数据,考察知情选择的逐步展开对中国已婚育龄人群避孕行为的影响。育龄人群的避孕行为已随知情选择发生较大变化,短效自控型避孕措施选择的可能性在逐步升高,长效医控型避孕措施选择的可能性在逐步降低,避孕模式呈自由选择多元化发展态势。知情选择强化了个体因素在避孕措施选择中的决定作用,为育龄个体提供了更为自由的避孕选择空间。避孕行为受子女性别影响的社会事实长期存在,知情选择在一定程度上增加了性别偏好对避孕措施选择的影响。     

Assessing Industrial Buyer Preferences: Using the Swait-Louviere Test to Test the Key Informant Assumption

When using professional buyers to study an organizations buying behavior an important consideration is whether their preferences reflect those of the organization. Since this is a key informant problem, the present article focuses on the issue of the degree to which key informants can be used to provide insights into their own organizations preferences. We conduct a direct test of the key informant assumption using the Swait-Louviere test. In this test preferences from a choice experiment using actual buyers, and from market decisions made by the organization, are estimated separately, then jointly in multinomial logit models. We found that buyers experimental preferences were similar to estimates obtained from the market decisions. Buyers preferences were closer to the intuitive preferences of the organizations top executives than the estimates based on past market decisions, although a model based on the combined data outperformed either. We discuss the implications of these results for industrial buying research.This revised version was published online in May 2005 with a corrected cover date.     

The Effect of Government Ideology on an Exchange Rate Regime: Some International Evidence

This paper comprehensively investigates the effect of government ideology on the type of exchange rate regime that a country implements via multinomial logit and multinomial probit models for 147 countries in the period 1974–2009. Our results clearly indicate that a left‐wing government increases the likelihood that a country implements a flexible regime in the classifications of exchange rate regimes. Nevertheless, evidence is weaker when using the de jure IMF course classification, which is set up by Ilzetzki et al. ( 2008 ). In a deeper investigation, we find that left‐wing governments are more likely to choose a flexible regime relative to a fixed one in our sample of OECD, non‐OECD and non‐Eurozone countries, as the impacts from government ideology on the determinant of the choice of exchange rate regime in Eurozone countries disappear. More importantly, we present many explanations for exchange rate regime choices when macroeconomic conditions, political constraints and institutions impact the choice of exchange rate regime.     

Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model

In this paper, we study the impact of customer stochasticity on firm price discrimination strategies. We develop a new model termed the Bayesian Mixture Scale Heterogeneity (BMSH) model that incorporates both parameter heterogeneity and customer stochasticity using a mixture model approach, and demonstrate model identification using extensive simulations. We estimate the model on yogurt scanner data and find that compared to the benchmark mixed logit and multinomial probit models, our model shows that markets are less price elastic, and that a majority of customers exhibit stochasticity in purchases; our model also obtains better prediction and more profitable targeting strategies.     

Current Issues in Discrete Choice Modeling

Until recently, computational constraints forced researchers in thediscrete choice area to limit themselves to very simple statistical models,such as the multinomial logit (MNL), in which choice probabilities could beevaluated quickly on a computer. But the MNL only makes sense as abehavioral model under very special circumstances. Recent advances incomputation make it possible to estimate richer behavioral models thatgenerate very complex choice probability expressions. This paper discusses anumber of possible avenues for future research in the discrete choice areain light of these developments.     

Accounting for heterogeneity in shopping centre choice models

Random utility models are a tool of great interest in the study of shopping centre choice. However, most research that has used these models employed the most basic specification: the multinomial logit model. This model presents two main drawbacks. Firstly, it is assumed that sensitivity to variables of attraction and dissuasion can be considered the same for all consumers. Secondly, this model can only be applied to situations in which alternatives from which you can choose are totally independent. In this paper, we present two specifications which allow us both to introduce heterogeneity and relax the assumption of independence of irrelevant alternatives, a nested logit model and a random effects model. Data collected in three European cities of the same region are used to implement these models. Results attained from these models reveal the existence of different segments of consumers. This provides the potential for retail managers to gain better insight into the way in which different consumers choose between a set of shopping centres in a certain area.     

A forecast simulation analysis of the next‐generation DVD market based on consumer preference data

Just as standards wars over formats had characterized VCRs upon introduction to the market, the next‐generation DVD standards war between Blu‐Ray and HD‐DVD lasted 6 years before Blu‐Ray won the contest. Beginning with stated preference data drawn from a structured conjoint survey conducted before Blu‐Ray became the de facto standard of the next‐generation DVD format, we estimate consumer preferences on digital video players. A Bayesian mixed‐logit model is used and market share simulations are conducted under various scenarios based on the estimated parameters from Bayesian mixed‐logit model to surmise the future South Korean digital video‐player market. Results indicate that consumers feel that network size and title availability are more important than hardware‐related facets of the product, such as definition and storage capacity. The level of title availability and price of the Blu‐Ray player for Blu‐Ray's dominance over DVD will dramatically vary by the penetration rate of DVD players.     

Tradeoffs Between Price and Quality: How a Value Index Affects

The present research examines how the availability of information about the value of a product, expressed as a ratio of the quality received per dollar, influences preference formation. This index, similar to unit price which provides information about how much quantity is received per dollar, presents consumers with information regarding the quality received per dollar. An experiment that compares consumers' preferences inferred from a choice task with their preferences inferred from a conjoint task was conducted. In the choice task consumers selected the most attractive alternative from a set of options, while in the conjoint task the attractiveness of each option was rated. Consumers, presented with an index of quality received per dollar paid (the value index), are more likely to choose a lower priced, higher value option rather than a higher priced, higher quality option compared to consumers presented with only price and quality information. Clearly, how a consumer chooses to “get the best for his or her money” depends on the ease with which information about the choices can be processed.     

Investigation of the ordering behavior of a retailer in the revenue sharing and buyback contracts considering round number bias

This study investigates the ordering behavior of a boundedly rational retailer who purchases a product from a rational supplier under a revenue sharing or buyback contract. We develop a behavioral model considering random error, learning effect and anchoring bias using a logit choice framework which also includes a new behavioral paradigm known as round number bias. Experimental data and theoretical analysis are employed to demonstrate the differences between retailer order quantity and related optimal decisions using the suggested behavioral model. We found that round numbers are enticing anchors in retailer ordering decisions. The findings further reveal that the adaptive learning model better explains and predicts the retailer's ordering behavior than the time trend learning model. Moreover, the results indicate that the prediction accuracy of the model is higher for revenue sharing compared to buyback contracts.