Cahiers de recherche

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Départements : Marketing

It is becoming increasingly easier for researchers and practitioners to collect eye tracking data during online preference measurement tasks. We develop a dynamic discrete choice model of information search and choice under bounded rationality, that we calibrate using a combination of eye-tracking and choice data. Our model extends the directed cognition model of Gabaix et al. (2006) by capturing fatigue, proximity effects, and imperfect memory encoding and by estimating individual-level parameters and partworths within a likelihood-based, hierarchical Bayesian framework. We show that modeling eye movements as the outcome of forward-looking utility maximization improves out-of-sample predictions, enables researchers and practitioners to use shorter questionnaires, and allows better discrimination between attributes.

Mots clés : Preference Measurement, Incentive Compatibility, Eye Tracking, Dynamic Discrete Choice Models


Départements : Marketing, GREGHEC (CNRS)

This paper models payment evasion as a source of profit by letting the firm choose the price charged to paying consumers and the fine collected from detected payment evaders. The consumers choose whether to purchase, evade payment, or refrain from consumption. We show that payment evasion allows the firm to charge a higher price to paying consumers and to generate a higher profit. We also show that higher fines do not necessarily reduce payment evasion. Finally, we provide empirical evidence which is consistent with our theoretical analysis, using comprehensive micro data on fare dodging on the Zurich Transport Network.

Mots clés : Payment Evasion, Pricing, Fine, Self-Selection


Départements : Marketing, GREGHEC (CNRS)

This web appendix has three main purposes. First, we provide a more or less 'stand-alone' technical appendix that describes the estimation algorithm for the proposed attribute model using Markov Chain Monte Carlo techniques (sections A1 and A2). The reversible jump (RJ) algorithm (Green, 1995) is also described in detail for the (vector) finite mixture regression model. We first give a discussion of priors and a general description of the reversible jump algorithm; then we present details of the estimation schema for the standard finite mixture regression model. We subsequently extend these details for the attribute model. As we will show, the algorithms and equations for the attribute model are similar to the results for the vector model due to a simple transformation. This similarity makes the coding of the attribute model straightforward once computer code for the vector model (with reversible jump steps) is developed. Furthermore, we discuss how the algorithms should be modified to estimate a standard choice model (e.g. a probit model). Second, we briefly discuss in section A3 the benchmark models for heterogeneity considered in the main document and their implementation, including the mixture of normals model (Allenby et al. 1998, Lenk and DeSarbo 2000) and the Dirichlet Process Priors (Ansari and Mela 2003, Kim et al. 2004). Third, we present the results of an additional simulation experiment where the traditional (vector) finite mixture model is used to generate the data in section A4, which augments the Monte Carlo experiment in the main document.

Mots clés : heterogeneity, mixture models, hierarchical Bayes, conjoint analysis, reversible jump MCMC, segmentation


Départements : Marketing, GREGHEC (CNRS)

Modeling consumer heterogeneity helps practitioners understand market structures and devise effective marketing strategies. In this research we study finite mixture specifications for modeling consumer heterogeneity where each regression coefficient has its own finite mixture, that is, an attribute finite mixture model. An important challenge of such an approach to modeling heterogeneity lies in its estimation. A proposed Bayesian estimation approach, based on recent advances in reversible jump Markov Chain Monte Carlo (MCMC) methods, can estimate parameters for the attribute-based finite mixture model, assuming that the number of components for each finite mixture is a discrete random variable. An attribute specification has several advantages over traditional, vector-based, finite mixture specifications; specifically, the attribute mixture model offers a more appropriate aggregation of information than the vector specification facilitating estimation. In an extensive simulation study and an empirical application, we show that the attribute model can recover complex heterogeneity structures, making it dominant over traditional (vector) finite mixture regression models and a strong contender compared with mixture-of-normals models for modeling heterogeneity.

Mots clés : Segmentation, Mixture Models, Hierarchical Bayes, Conjoint Analysis, Reversible Jump MCMC


Départements : Marketing, GREGHEC (CNRS)

The structure of a social network, characterized by the connections between members of that network can significantly affect how a marketing process plays out on the network. Many social networks of relevance to marketers are large, complex, or hidden which makes it prohibitively expensive to work with the entire network in marketing applications. Instead, marketers need to work with a sample (i.e., a subgraph) of the population network. In this paper we evaluate the efficacy of nine different sampling methods in recovering the underlying structural characteristics of population networks. In particular, we focus on recovery of four characteristics of importance for marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, each of which is relevant for certain marketing processes. Via extensive simulations, we find that sampling methods differ substantially in their ability to recover population network characteristics. Traditional sampling procedures, such as random node sampling, result in poor subgraphs. When the focus of a marketing research project is on understanding local network effects (e.g., peer influence) then forest fire sampling with a medium burn rate performs the best, i.e., it is most effective for recovering the distributions of degree and clustering coefficient. When the focus is on broader network effects (e.g., speed of diffusion, or the “multiplier” effects of network seeding), then random-walk sampling (i.e., forest-fire sampling with a low burn rate) performs the best, and it is most effective for recovering the distributions of betweenness and closeness centrality. Also, of great relevance for marketers, sample size has only a minimal impact on sampling performance unless the sample is very small relative to population size. We validate our findings on four different networks, including a Facebook network and a co-authorship network, and conclude with recommendations for practice.

Mots clés : Social Networks, Word-of-Mouth Marketing, Sampling, Graph Sampling


Départements : Marketing, GREGHEC (CNRS)

Endogeneity problems in demand models occur when certain factors, unobserved by the researcher, affect both demand and the values of a marketing mix variable set by managers. For example, unobserved factors such as style, prestige, or reputation might result in higher prices for a product and higher demand for that product. If not addressed properly, endogeneity can bias the elasticities of the endogenous variable and subsequent optimization of the marketing mix. In practice, instrumental variables estimation techniques are often used to remedy an endogeneity problem. It is well known that, for linear regression models, the use of instrumental variables techniques with poor quality instruments can produce very poor parameter estimates, in some circumstances even worse than those that result from ignoring the endogeneity problem altogether. The literature has not addressed the consequences of using poor quality instruments to remedy endogeneity problems in nonlinear models, such as logit-based demand models. Using simulation methods, we investigate the effects of using poor quality instruments to remedy endogeneity in logit-based demand models applied to finite-sample datasets. The results show that, even when the conditions for lack of parameter identification due to poor quality instruments do not hold exactly, estimates of price elasticities can still be quite poor. That being the case, we investigate the relative performance of several nonlinear instrumental variables estimation procedures utilizing readily available instruments in finite samples. Our study highlights the attractiveness of the control function approach (Petrin and Train 2010) and readily-available instruments, which together reduce the mean squared elasticity errors substantially for experimental conditions in which the theory-backed instruments are poor in quality. We find important effects for sample size, in particular for the number of brands, for which it is shown that endogeneity problems are exacerbated with increases in the number of brands, especially when poor quality instruments are used. In addition, the number of stores is found to be important for likelihood ratio testing. The results of the simulation are shown to generalize to situations under Nash pricing in oligopolistic markets, to conditions in which cross-sectional preference heterogeneity exists, and to nested logit and probit-based demand specifications as well. Based on the results of the simulation, we suggest a procedure for managing a potential endogeneity problem in logit-based demand models.

Mots clés : Choice Models, Endogeneity, Econometric Models, Instrumental Variables


Département Marketing

Word of mouth marketing — the intentional influencing of consumer-to-consumer communications — is an increasingly important technique. The authors overview and synthesize extant word of mouth theory and present a study of a marketing campaign in which mobile phones were seeded with prominent bloggers. Eighty-three blogs were followed for six months. Findings reveal the complex cultural conditions through which marketing “hype” is transformed by consumers into the “honey” of relevant, shared communications. Four word of mouth communication strategies are identified — evaluation, embracing, endorsement and explanation. Each is influenced by communicator narrative, communications forum, communal norms and the nature of the marketing promotion. An intrinsic tension between commercial and communal interests plays a prominent, normative role in message formation and reception. This “hype-to-honey” theory shows that communal word of mouth does not simply increase or amplify marketing messages. Rather, marketing messages and meanings are systematically altered in the process of embedding them. The theory has implications for how marketers should plan, target and benefit from word of mouth and how scholars should understand word of mouth in a networked world.


Département Marketing

Consumers’ tendency to choose the option in the center of an array and the process underlying this effect is explored. Findings from two eye tracking studies suggest that brands in the horizontal center receive more visual attention. They are more likely to be chosen. Investigation of the attention process revealed an initial central fixation bias, tendency to look first at the central option, and a central gaze cascade effect, progressively increasing attention focused on the central option right prior to decision. Only the central gaze cascade effect was related to choice. An offline study with tangible products demonstrated that the centrally located item within a product category is chosen more often, even when it is not placed in the center of the visual field. Despite wide-spread use, memory based attention measures were not correlated with eye tracking measures. They did not capture visual attention and were not related to choice.

  • 935
  • Is the luxury industry really a financier's dream?
  • Jean-Noël Kapferer
  • Télécharger

Département Marketing

  • 933
  • Does marketing and sales integration always pay off? Evidence from a social capital perspective
  • Dominique Rouziès, John Hulland, Donald Barclay
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Département Marketing


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