The importance of atmospherics in the choice of hypermarkets and supermarkets

Are the atmospheric variables equally relevant for supermarket and hypermarket customers? As a contribution to the customers' choice of the store format, firstly this work aims to determine the perception of differences between hypermarkets and supermarkets in terms of store attributes. Second, it sheds light on the role played by atmospherics in the customers' decision process. This study uses a logit approach to model the probabilistic choice of the customers' store format using atmospheric and other marketing variables as important attributes of the grocery store. The model's estimation is based on personal interviews conducted during the shopping process in urban supermarkets and hypermarkets belonging to the same retail chain. The findings show that although the atmospheric variables are significantly important for both store formats, the customers perceive differently the importance of each retail atmospheric cue and other marketing variables, when buying in a hypermarket or in a supermarket: the choice of buying in hypermarket is explained by the significant importance given to the latent variables named pleasant atmosphere and accessibility and responsiveness; in contrast, the choice of buying in supermarkets is explained by the significant importance given to the latent variable nice decoration and empathy with the staff. The other latent variable designated by other marketing variables is only significantly important in the decision of choosing supermarkets.     

A Cross-Validity Comparison of Conjoint Analysis and Choice Models at Different Levels of Aggregation

Several (ratings-based) conjoint analysis and experimental choice (choice-based conjoint) models are compared on their ability to predict both aggregate choice shares among the sample and individual choices in an availability validation task. While there was a weak relationship between validations at the individual and aggregate levels, several models stand out. In general, models capturing individual differences validated well at both the individual and aggregate level. The hierarchical Bayes choice and conjoint models validated particularly well.Among choice models, the hierarchical Bayes choice model had the highest aggregate and individual level-validations. It was followed by the hybrid and seven segment latent segment choice models. Overall, the highest validating ratings-based conjoint model was the hierarchical Bayes model. However, the seven segment latent segment conjoint model produced better aggregate choice share validations than any other conjoint model. These results indicate that validations can be improved either by using benefit segment models and/or merging different types of data to estimate more individualized models.In most cases, rescaling improved the ratings-based, but not the choice-based choice share validations. This suggests that one might adjust for differences between ratings and choice tasks before making choice share predictions.     

Integrated mixed logit and latent variable models

Extending the traditional discrete choice model by incorporating latent psychological factors can help to better understand the individual’s decision-making process and therefore to yield more reliable part-worth estimates and market share predictions. Several integrated choice and latent variable (ICLV) models which merge the conditional logit model with a structural equation model exist in the literature. They assume homogeneity in the part-worths and use latent variables to model the heterogeneity among the respondents. This paper starts from the mixed logit model that describes the heterogeneity in the part-worths and uses the latent variables to decrease the unexplained part of the heterogeneity. The empirical study presented here shows these ICLV models perform very well with respect to model fit and prediction.     

On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths

An exciting development in modeling has been the ability to estimate reliable individual-level parameters for choice models. Individual partworths derived from these parameters have been very useful in segmentation, identifying extreme individuals, and in creating appropriate choice simulators. In marketing, hierarchical Bayes models have taken the lead in combining information about the aggregate distribution of tastes with the individual's choices to arrive at a conditional estimate of the individual's parameters. In economics, the same behavioral model has been derived from a classical rather than a Bayesian perspective. That is, instead of Gibbs sampling, the method of maximum simulated likelihood provides estimates of both the aggregate and the individual parameters. This paper explores the similarities and differences between classical and Bayesian methods and shows that they result in virtually equivalent conditional estimates of partworths for customers. Thus, the choice between Bayesian and classical estimation becomes one of implementation convenience and philosophical orientation, rather than pragmatic usefulness.     

A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data

We consider the problem of simultaneously performing market segmentation and predicting product choice for a given population of consumers. This behaviour-based segmentation model uses several types of information characterizing individual consumers and their market context: sociodemographic, psychographic and product choice data. These data are combined in a normative conceptual framework that posits the formation of latent segments, each of which displays differing degrees of sensitivity to relevant product attributes. This permits results obtained by the proposed model structure to differ significantly from those of traditional segmentation techniques, which usually produce a set of groups characterized by smaller within-group than between-group variation in sociodemographic and/or psychographic variables. By making the segments behaviour-based, it is argued that the segments produced by the model will be more actionable and more directly relevant to management decision-making. The proposed model is applied to a data set involving choice of a beauty product offered by five national brands. The results show that the explanatory power of the latent segmentation and choice model is far greater than that of the MNL model, which is nested within our more general specification. The types of results available from the model are illustrated through the application.     

Process and context in choice models

We develop a general framework that extends choice models by including an explicit representation of the process and context of decision making. Process refers to the steps involved in decision making. Context refers to factors affecting the process, focusing in this paper on social networks. The extended choice framework includes more behavioral richness through the explicit representation of the planning process preceding an action and its dynamics and the effects of context (family, friends, and market) on the process leading to a choice, as well as the inclusion of new types of subjective data in choice models. We discuss the key issues involved in applying the extended framework, focusing on richer data requirements, theories, and models, and present three partial demonstrations of the proposed framework. Future research challenges include the development of more comprehensive empirical tests of the extended modeling framework.     

Location choices of multinational firms in Europe: The role of EU cohesion policy

Using data on 5509 foreign subsidiaries established in 50 regions of 8 EU countries over the period 1991-1999, we estimate a mixed logit model of the location choice of multinational firms in Europe. In particular, we focus on the role of EU Cohesion Policy in attracting foreign investors from both within and outside Europe. We find that, after controlling for the role of agglomeration economies as well as a number of other regional and country characteristics and allowing for a very flexible correlation pattern among choices, Structural and Cohesion funds allocated by the EU to laggard regions have indeed contributed to attracting multinationals. These policies as well as other determinants play a different role in the case of European investors as opposed to non-European ones.     

我国典型物流公共信息平台商业模式的比较研究

文章从六个角度对我国四个典型物流公共信息平台的商业模式进行了比较研究,总结了不同类型平台的商业模式。研究表明,平台类型是其选择商业模式的基础,只有那些与平台自身类型相适应的商业模式才能保证平台的可持续发展。最后归纳出平台发展的几点建议,即以中小企业为主、采取灵活的价格撮合机制、确立政府协会企业联合运营模式、完善隐私保护和信用机制、提供差异化的服务以及利用云计算进行流程整合是平台的生存之道。     

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 Graph Model for Conflict Resolution: Past,Present, and Future

The Graph Model for Conflict Resolution is a methodology for the modeling and analysis of strategic conflicts. An historical overview of the graph model is presented, including the basic modeling and analysis components of the methodology, the decision support system GMCR II that is now used to apply it, and the recent initiatives that are currently in various stages of development. The capacity of this simple, flexible system to provide advice to decision-makers facing strategic conflicts is emphasized throughout, and illustrated using a real-life groundwater contamination dispute.     

A nested logit model of brand choice incorporating variety-seeking and marketing-mix variables

We model the effects of variety-seeking and marketing-mix variables on consumers' purchases of coffee using a nested logit model. We premise that on any given purchase occasion, the utilities of brands other than the one purchased on the previous occasion may be correlated due to the consumer's tendency to seek variety or to avoid variety. This results in a two-level hierarchical model where choice on any purchase occasion is conditioned on the brand purchased on the immediately preceding occasion. Such a structure accounts for variety seeking and inertia tendencies of consumers and is consistent with a hierarchical decision process, where consumers first decide whether or not to make a repeat purchase and then decide which brand size to purchase. The assumed hierarchical structure is shown to be consistent with observed coffee purchase behavior, and the model is shown to outperform a nonhierarchical logit model in predicting consumers' brand choices.     

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.     

Logit模型在市场营销定量研究中的应用——消费者对家电品牌选择的实证分析

市场营销的定量研究以及如何开启消费者的行为黑箱,一直以来都是国外市场营销研究者们长期并将继续深入探讨的核心主题。在此过程当中,Logit模型是他们比较青睐和广泛采用的一类研究工具。通过对Logit模型的原理的介绍,以及将这一模型用于国内消费者对某一家电品牌选择的实证研究,从而表明Logit模型能够用于分析市场营销研究中的诸多问题,并且能够揭示出更多有用的量化信息,而这对于市场营销的学术研究和市场营销管理的实践都无疑是大有裨益的。     

Models and optimal designs for conjoint choice experiments including a no-choice option

In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life, the respondent does not always make a choice: often he/she does not prefer any of the options offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature, three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model, and the nested no-choice multinomial logit model. We develop optimal designs for the two most appealing of these models using the D-optimality criterion and the modified Fedorov algorithm and compare these optimal designs with a reference design, which is constructed while ignoring the no-choice option, in terms of estimation and prediction accuracy. We conclude that taking into account the no-choice option when designing a no-choice experiment only has a marginal effect on the estimation and prediction accuracy as long as the model used for estimation matches the data-generating model.     

A multi-category customer base analysis

Customer base analysis is an essential tool to measure and develop relationships with customers. While various models have been proposed in a noncontractual setting, they focus primarily on analyzing transactional patterns associated with a single product category or a firm-level activity, such as the times at which purchases are made at a particular retailer. This research proposes a modeling framework for customer base analysis in a multi-category context. Specifically, we model the time between a customer's purchases at the firm and the product categories that comprise her shopping basket arising from multi-category choice decisions. The proposed model uses a latent space approach that parsimoniously captures the dynamics of multi-category shopping behavior due to the interplay between purchase timing and shopping basket composition. We also account for interdependence among multiple categories, temporal dependence across category choices, and latent customer attrition. Using category-level transaction data, we show that the proposed model offers excellent fit and performance in predicting customer purchase patterns across multiple categories. The forecasts and inferences afforded by our model can assist managers in tailoring marketing efforts across categories.     

Investment Decisions for Retirement Savings

We conducted a choice experiment to investigate whether retirement savers follow simple portfolio theory when choosing investments. We modeled experimental survey data on 693 participants using a scale-adjusted version of the latent class choice model. Results show that underlying variability in response was explained by age and “risk profile” score and that preferences varied with income and age. Younger individuals were conventionally risk averse, but older, higher-income individuals may react positively to both higher returns and increasing risk, when risk is presented as widening ranges of possible outcomes. Respondents tended to choose among a few similar investment options.     

Extended Framework for Modeling Choice Behavior

We review the case against the standard model of rational behavior and discuss the consequences of various anomalies of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.     

Optimal checkout strategies for online retailers

Growth in online retailing has driven retailers to focus on optimizing the consumers’ shopping journey. One of the most important aspects of online shopping is the checkout process offered by the retailer. This paper focuses on factors influencing retailers’ choice of providing either a flexible checkout or a restricted checkout option to consumers. We define a checkout strategy as flexible when consumers can purchase items in their shopping cart either as a guest or by logging into their account. In contrast, with a restricted checkout strategy, the consumers must log in to the account to make purchases. With a game-theoretic model and duopolistic framework, the current study identifies conditions in which online retailers might adopt symmetric strategies and those in which two ex-ante symmetric retailers might prefer asymmetric strategies. The analysis suggests that the relative proportion of privacy-conscious (PC) vs. convenience-conscious consumers (CC), additional utility due to account registration, reduction in transaction cost, and additional revenue due to targetability are the crucial determinants of the strategies adopted by online retailers.Specifically, we show that retailers adopt a restricted checkout strategy when additional revenues due to targeted advertising are relatively high. Retailers adopt a flexible checkout strategy when the proportion of CC consumers and additional revenues due to targeted advertising are relatively lower. Furthermore, an asymmetric equilibrium may also exist when the proportion of CC consumers is relatively high and additional revenues due to targeted advertising are in the intermediate range. Our modeling framework provides a consumer demand-based (rather than cost-based) justification as a plausible explanation for why we observe ex-ante identical retailers offering distinct checkout strategies.     

Consumer Heterogeneity in the Willingness to Pay for Local and Organic Food

Due to growth and changing distribution channels for organic food in Germany, there is some concern that organic food is losing authenticity. Contrarily, local food production is seen as a new trend and is gaining market share. This article analyzes whether those alternative concepts of organic and local food production rather support or threaten each other in consumers’ choice. Results of a Bavarian survey, including a choice experiment for bread, beer, and milk on the attributes price, brand, local, and organic, are analyzed using a mixed logit model. Willingness-to-pay estimations confirm the importance of local production to the surveyed consumers, especially in interaction with organic production, leading to the conclusion that the two production methods can support each other in achieving price premiums. The estimated standard deviations show significant heterogeneity of the parameters for all three products for most attributes.