Michel Wedel

A cross-national investigation into the individual and national cultural antecedents of consumer innovativeness

The authors examine antecedents of consumer innovativeness in a cross-national context. They propose a framework that distinguishes individual difference variables and national cultural variables. ...

Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects

The three key ad elements (brand, pictorial, and text) each have unique superiority effects on attention to advertisements, which are on par with many commonly held ideas in marketing practice. This is the main conclusion of an analysis of 1363 print advertisements tested with infrared eye-tracking methodology on more than 3600 consumers. The pictorial is superior in capturing attention, independent of its size. The text element best captures attention in direct proportion to its surface size. The brand element most effectively transfers attention to the other elements. Only increments in the text elements surface size produce a net gain in attention to the advertisement as a whole. The authors discuss how their findings can be used to render more effective decisions in advertising.

Designing conjoint choice experiments using managers prior beliefs

The authors provide more efficient designs for conjoint choice experiments based on prior information elicited from managers about the parameters and their associated uncertainty. The authors use a Bayesian design procedure that assumes a prior distribution of likely parameter values and optimizes the design over that distribution. The authors propose a way to elicit prior information from managers and show in Monte Carlo studies that the procedure provides more efficient designs than the current procedures. The authors provide an empirical application, in which they elicit prior information on the parameter values and the associated uncertainty from managers. Here, the Bayesian design provides 30%-50% lower standard errors of the estimates and an expected predictive validity that is approximately 20% higher.

Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction

An important aspect of the new orientation on customer relationship marketing is the use of customer transaction databases for the cross-selling of new services and products. In this study, we propose a mixed data factor analyzer that combines information from a survey with data from the customer database on service usage and transaction volume, to make probabilistic predictions of ownership of services with the service provider and with competitors. This data-augmentation tool is more flexible in dealing with the type of data that are usually present in transaction databases. We test the proposed model using survey and transaction data from a large commercial bank. We assume four different types of distributions for the data: Bernoulli for binary service usage items, rank-order binomial for satisfaction rankings, Poisson for service usage frequency, and normal for transaction volumes. We estimate the model using simulated likelihood (SML). The graphical representation of the weights produced by the model provides managers with the opportunity to quickly identify cross-selling opportunities. We exemplify this and show the predictive validity of the model on a hold-out sample of customers, where survey data on service usage with competitors is lacking. We use Gini concentration coefficients to summarize power curves of prediction, which reveals that our model outperforms a competing latent trait model on the majority of service predictions.

A MIXTURE LIKELIHOOD APPROACH FOR GENERALIZED LINEAR-MODELS

A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this approach handles many of the existing latent class regression procedures as special cases, as well as a host of other parametric specifications in the exponential family heretofore not mentioned in the latent class literature. As such we generalize the McCullagh and Nelder approach to a latent class framework. The parameters are estimated using maximum likelihood, and an EM algorithm for estimation is provided. A Monte Carlo study of the performance of the algorithm for several distributions is provided, and the model is illustrated in two empirical applications.

An investigation into the association pattern technique as a quantitative approach to measuring means-end chains

Abstract Means-end chain theory links products to consumers by postulating hierarchical relations between attributes of the product, consequences of product use and values of consumers. It has served as an important conceptual framework for studies in marketing. The authors investigate the association pattern technique (APT) as a supplement to laddering, the most popular, qualitative measurement methodology in means-end chains research. APT is a structured method for measuring means-end chains, suitable for large-scale surveys. It can be used in personal as well as quantitative mail interviews. APT separately measures the attribute-consequence, and the consequence-value links. The independence of attribute-consequence, and consequence-value links is crucial to the validity of APT. Using loglinear models, we investigate this assumption for empirical data on four different products. Consistent support for independence is found. In addition, we use loglinear models to test the convergent validity of APT and laddering with respect to the content and structure of the means-end chains network that they reveal. The results show that the content of the APT and laddering networks differs. This result is explained from the different task formats. Most importantly, the hypothesis that the structure of APT and laddering networks is the same could not be rejected.

Adoption of a service innovation in the business market: An empirical test of supply-side variables

The objective of this article is to assess the influence of variables over which suppliers have control (supply-side variables) on the adoption of innovations in addition to adopter-side variables. The empirical study focused on the adoption of electronic banking in the Dutch business market. A quantitative study was conducted to test hypotheses. The results show that the extent to which a supplier has pursued a strategy aimed at positioning the innovation in the marketplace or has focused on reducing the risk of adoption has a positive and significant effect on the probability of innovation adoption. The evidence corroborates that not only adopter-side variables significantly influence innovation, but supply-side variables as well.

Heterogeneous Conjoint Choice Designs

Previous conjoint choice design construction procedures have produced a single homogeneous design that is administered to all study participants. In contrast, this article proposes to construct a limited set of different designs. The principle of heterogeneous designs is applicable to a variety of types of models. This article illustrates this principle for Bayesian designs, taking into account prior uncertainty about the parameter values, and for mixed logit designs that accommodate respondent heterogeneity. The authors develop and investigate a computational procedure that enables quick and easy implementation. Although the number of different designs in the optimal set is small, the authors use a Monte Carlo study to demonstrate that their heterogeneous design achieves substantial gains in efficiency compared with homogeneous designs.

Latent Class Metric Conjoint Analysis

A latent class methodology for conjoint analysis is proposed, which simultaneously estimates market segment membership and part-worth utilities for each derived market segment using mixtures of multivariate conditional normal distributions. An E-M algorithm to estimate the parameters of these mixtures is briefly discussed. Finally, an application of the methodology to a commercial study (pretest) examining the design of a remote automobile entry device is presented.

The Effectiveness of Customized Promotions in Online and Offline Stores

This study investigates the effectiveness of customized promotions at three levels of granularity (mass market, segment specific, and individual specific) in online and offline stores. The authors conduct an empirical examination of the profit potential of these customized promotion programs with a joint model of purchase incidence, choice, and quantity and through optimization procedures for approximately 300 conditions. They find that (1) optimization procedures lead to substantial profit improvements over the current practice for all types of promotions (customized and undifferentiated); (2) loyalty promotions are more profitable in online stores than in offline stores, while the opposite holds for competitive promotions; (3) the incremental payoff of individual-level over segment- and mass market–level customized promotions is small in general, especially in offline stores; (4) for categories that are promotion sensitive, individual-level customized promotions can lead to a meaningful profit increase over segment- and mass market–level customized promotions in online stores; and (5) low redemption rates are a major impediment to the success of customized promotions in offline stores. Optimal undifferentiated promotions should be the primary promotion program in this channel, and firms can benefit from offering a combination of optimal undifferentiated and customized promotions for suitable categories in offline stores.