The role of fundamental motivations in willingness-to-pay online

This study aims to determine whether an understanding of chronic fundamental consumer motivations can help determine the mechanisms of willingness-to-pay for products online. To do so, it employs a simulated buying task on a fictional e-commerce site for a consumer product (branded either as a “new arrival” or a “classic”) to investigate the effects of two fundamental motivations (mate acquisition vs. self-protection) on willingness-to-pay for the product online. The primary focus of the paper to investigate the capacity of mate acquisition and self-protection motives to moderate the relationship between attitude toward the product and willingness-to-pay, as well as, the effects of the motives on willingness-to-pay are considered. Through regression and interaction effect analyses, it is shown that chronic fundamental motivation for mate acquisition is directly correlated with an increased willingness-to-pay for both product types, and it moderates the relationship between attitude toward a product and willingness-to-pay. Self-protection motivation increases willingness-to-pay for classic products but not new arrivals. By offering a rare look at chronic fundamental motivation in the consumer context and potentially being the first investigation of the moderating effects of fundamental motivations, the results mostly support the notion of predictable motivation induced behavioral tendencies.     

Incorporating choice dynamics in models of consumer behavior

This paper presents a framework for organizing and discussing factors influencing consumer choice dynamics, how these factors may be incorporated into models of buyer behavior and problems that may arise in estimating such models. The paper identifies research issues and delineates possible approaches. Proceedings of Session on Choice Dynamics at the Banff Symposium on Consumer Decision-Making and Choice Behavior. All authors share equally in content and remaining errors.     

Estimation bias in choice models with last choice feedback

The study compares two estimation methods for choice models with last choice feedback, using simulated and real data. The first method ignores the impact of unobserved heterogeneity on observed choices via presample choices, while the second method approximates this impact by a stochastic relationship. In panels with less than 10 choices per panelist, the first method overstates the impact of last choice on current choice and understates the impact of intrinsic preferences (i.e., brand intercepts). The second method performs significantly better than the first method. Under both methods, an increase in the number of heterogeneous coefficients in the model tends to increase the bias in the estimates. The largest bias occurs when lagged choice coefficients are heterogeneous.     

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.     

Quick and easy choice sets: Constructing optimal and nearly optimal stated choice experiments

In this paper we compare a number of common strategies for constructing discrete choice experiments. Two of the strategies, including one based on theoretical constructions for optimal discrete choice experiments, produce designs that are better than those that come about from random grouping and from using the L^MA construction. A simple account of this theoretical construction is given.     

Advances in computation,statistical methods,and testing of discrete choice models

This paper gives a brief overview of recent developments in computation, estimation, and statistical testing of choice models, with marketing applications. Topics include statistical models for discrete panel data with heterogeneous decision-makers, simulation methods for estimation of high-dimension multinomial probit models, specification tests for model structure and for brand and purchase clustering, and innovations in numerical analysis for estimation and forecasting. In collaboration with Denis Bolduc, David Bunch, Michael Keane, Don Kridel, and Steve Stern.     

Efficient choice set designs for estimating availability cross-effects models

Batsell and Polking proposed a discrete choice model which incorporates the availability (presence or absence) of competing brands into the utility of each brand under study. The information on relative impacts of adding or deleting brands is of strategic interest, and models that do not incorporate such effects may be misleading. The designs suggested by Batsell and Polking have 2^m–m–1 choice sets. Even with as few as 10=m brands, this requires over 1000 choice sets. In this paper we provide a catalog of designs for estimating cross effects models in as few as 2m–1 choice sets. This will make cross effects modelling practical in a wide range of academic and commercial settings.     

Consideration set influences on consumer decision-making and choice: Issues,models, and suggestions

This paper affords a stylized view of individual consumer choice decision-making appropriate to the study of many marketing decisions. It summarizes issues relating to consideration set effects on consumer judgment and choice. It discusses whether consideration sets really exist and, if so, the factors that affect their composition, structure, and role in decision-making. It examines some new developments in the measurement and modeling of consideration set effects on decision-making. The paper concludes with suggestions for needed research. The authors wish to acknowledge the numerous ideas and perspectives contributed by the other members of the Banff Symposium workshop:Mukesh Bhargava (University of Alberta),Bill Black (Louisiana State University),Gary Gaeth (University of Iowa),Hotaka Katahira (University of Tokyo, Japan),Gilles Laurent (Centre HEC-ISA, France),Irwin Levin (University of Iowa),David Midgley (Australian Graduate School of Management),Thomas Novak (Southern Methodist University), andJames Wiley (University of Alberta). This paper has benefited greatly from their contributions.     

A risk worth taking: Perceived risk as moderator of satisfaction,loyalty, and willingness-to-pay premium price

This research investigates the moderating role of perceived risk in the relationship between satisfaction, loyalty, and willingness to pay premium price (WTP). A total of 364 respondents were recruited from Amazon's Mechanical Turk panel. Financial, social, performance, and psychological risks have significant negative effects on the relationship between satisfaction and WTP, whereas only financial risks have significant negative effects on the relationship between loyalty and WTP. Using moderated mediation analysis, we found that the mediating effects of loyalty diminish significantly in high social risk conditions and diminish completely in high financial risk conditions.     

Random regret minimization for consumer choice modeling: Assessment of empirical evidence

This paper introduces to the field of marketing a regret-based discrete choice model for the analysis of multi-attribute consumer choices from multinomial choice sets. This random regret minimization (RRM) model, which has recently been introduced in the field of transport, forms a regret-based counterpart of the canonical random utility maximization (RUM) paradigm. This paper assesses empirical results based on 43 comparisons reported in peer-reviewed journal articles and book chapters, with the aim of finding out to what extent, when, and how RRM can form a viable addition to the consumer choice modeler's toolkit. The paper shows that RRM and hybrid RRM–RUM models outperform RUM counterparts in a majority of cases, in terms of model fit and predictive ability. Although differences in performance are quite small, the two paradigms often result in markedly different managerial implications due to considerable differences in, for example, market share forecasts.     

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.     

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.     

Advances in random utility models report of the workshop on advances in random utility models duke invitational symposium on choice modeling behavior

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian estimation. This paper summarizes these developments and discusses their implications for practice.     

A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best–worst scaling

We review the measurement of product attribute importance, and find little consensus in definition or measurement methods. We compare four measurement methods: 1) two direct methods whereby respondents report the importance of attributes using best–worst scaling or constant sum scales, and 2) two indirect methods derived from discrete choice experiments. Our comparisons rely on previous findings that choice experiments are externally valid to use as the standard. We find high agreement within direct or indirect methods, but less agreement between direct and indirect methods. Our results also demonstrate that inferences derived from indirect measures appear to be susceptible to context effects related to the particular attributes a researcher chooses to investigate. We discuss implications for current and future research.     

Information processing and innovative choice

In this paper we examine how the information processing of subjects who make an innovative choice (innovators) differs from that of subjects who make a noninnovative choice (noninnovators). The task involves selection of an alternative within a range of prerated product category innovativeness. We propose that subjects who seek 1) impersonal/uncontrollable sources, 2) higher levels of information, 3) more detailed (versus summary) data, and 4) noncomparative (versus comparative) data are more likely to make innovative choices. The research method is a computerized process tracing experiment utilizing Search Monitor (Brucks 1988).The authors wish to express their gratitude to Merrie Brucks for the use of and help with Search Monitor and to Eric Johnson, Dave Schmittlein, and Mita Sujan for helpful comments on an earlier version of this paper.     

Non-normal simultaneous regression models for customer linkage analysis

Simultaneous systems of equations with non-normal errors are developed to study the relationship between customer and employee satisfaction. Customers interact with many employees, and employees serve many customers, such that a one-to-one mapping between customers and employees is not possible. Analysis proceeds by relating, or linking, distribution percentiles among variables. Such analysis is commonly encountered in marketing when data are from independently collected samples. We demonstrate our model in the context of retail banking, where drivers of customer and employee satisfaction are shown to be percentile-dependent.      

Positive affect moderates the impact of assortment size on choice satisfaction

The present research proposes that positive affect mitigates choice overload. Results from four studies show that whereas people in neutral affect feel less satisfied when choosing from a large relative to a small assortment, people in positive affect do not experience a decline in satisfaction and may even feel more satisfied when choosing from a large, relative to a small assortment. It is proposed that positive affect has these effects by shifting people's attention from the difficulty of the choice task to the quality of the assortment, as a basis for judgment of choice satisfaction.     

Estimating time-varying parameters in brand choice models: A semiparametric approach

Nowadays, brand choice models are standard tools in quantitative marketing. In most applications, parameters representing brand intercepts and covariate effects are assumed to be constant over time. However, marketing theories, as well as the experience of marketing practitioners, suggest the existence of trends or short-term variations in particular parameters. Hence, having constant parameters over time is a highly restrictive assumption, which is not necessarily justified in a marketing context and may lead to biased inferences and misleading managerial insights.In this paper, we develop flexible, heterogeneous multinomial logit models based on penalized splines to estimate time-varying parameters. The estimation procedure is fully data-driven, determining the flexible function estimates and the corresponding degree of smoothness in a unified approach. The model flexibly accounts for parameter dynamics without any prior knowledge needed by the analyst or decision maker. Thus, we position our approach as an exploratory tool that can uncover interesting and managerially relevant parameter paths from the data without imposing assumptions on their shape and smoothness.Our approach further allows for heterogeneity in all parameters by additively decomposing parameter variation into time variation (at the population level) and cross-sectional heterogeneity (at the individual household level). It comprises models without time-varying parameters or heterogeneity, as well as random walk parameter evolutions used in recent state space models, as special cases. The results of our extensive model comparison suggest that models considering parameter dynamics and household heterogeneity outperform less complex models regarding fit and predictive validity. Although models with random walk dynamics for brand intercepts and covariate effects perform well, the proposed semiparametric approach still provides a higher predictive validity for two of the three data sets analyzed.For joint estimation of all regression coefficients and hyperparameters, we employ the publicly available software BayesX, making the proposed approach directly applicable.     

Determinants of retail bank choice in Nigeria: a focus on gender-based choice decisions

As the Nigerian retail banks customers become more sophisticated, it is now very important that retail banks determine the factors that are important and relevant to the customers’ retail bank choice decisions. By using a survey of retail bank customers, this study evaluates the relative importance attached to retail banks’ choice criteria used by male and female customers in Nigeria. The results show that there are some differences in choice factors used by male and female customers in selecting a retail bank for patronage. The recommendation is that bank managers should take both female and male market segments into consideration when making retail bank strategic planning in order to become competitive. The findings can assist retail bank managers in refining their marketing strategies as a means of overcoming the intense competition that exists in the Nigerian banking system.