Tammo H. A. Bijmolt

Meta-analysis in marketing when studies contain multiple measurements

Most meta-analyses in marketing contain studies which themselves contain multiple measurements of the focal effect. This paper compares alternative procedures to deal with multiple measurements through the analysis of synthetic data sets in a Monte Carlo study and a re-analysis of a published marketing data set. We show that the choice of procedure to deal with multiple measurements is by no means trivial and that it has implications for the results and for the validity of the generalizations derived from meta-analyses. Procedures that use the complete set of measurements outperform procedures that represent each study by a single value. The commonly used method of treating all measurements as independent performs reasonably well but is not preferable. We show that the optimal procedure to account for multiple measurements in meta-analysis explicitly deals with the nested error structure, i.e., at the measurement level and at the study level, which has not been practiced before in marketing meta-analyses.

Children's Understanding of TV Advertising: Effects of Age, Gender, and Parental Influence

Parents, consumer organizations, and policy makers are generally concerned about effects of TV advertising directed towards children. These effects might be mediated by childrens understanding of TV advertising, that is their ability to distinguish between TV programmes and commercials and their comprehension of advertising intent. In this paper, we investigate childrens understanding of TV advertising, using verbal and non-verbal measurements. The sample consists of 153 Dutch children, ranging from 5 to 8 years old, and their parents. The results based on non-verbal measures suggest that most children are able to distinguish commercials from programmes and that they have some insight into advertising intent. The results based on verbal measures are not as conclusive; the percentage of children who show understanding of TV advertising is then substantially lower. Effects of age, gender, and parental influence are assessed using MURALS, a regression analysis technique for categorical and continuous variables, and CHAID, a technique for identifying homogeneous segments on the basis of the relationship between categorical dependent and explanatory variables. The age of a child turns out to have a positive effect. The effects of gender and parent- child interaction are rather small, both for verbal and for non-verbal measures of understanding of TV advertising. A high level of parental control of TV viewing may result in lower understanding of TV advertising. Implications for consumer policy and directions for future research are discussed.

Importance and similarity in the evolving citation network of the International Journal of Research in Marketing

Abstract The citation network of the International Journal of Research in Marketing (IJRM) is examined from 1981 to 1995. A time-heterogenous log-multiplicative model is estimated to examine simultaneously the importance and similarity of journals in the network over time. Two distinct types of journal similarity, cohesion and structural equivalence, are considered and modeled in an integrative fashion. The findings show that the overall importance of IJRM in its network is growing rapidly albeit from a low base. The importance of psychology journals in the network appears to be decreasing. Clear cohesive and structurally equivalent groups of core marketing, methodology, managerial and psychology journals with distinct functions in the network are identified. Recommendations for future citation research are offered as well.

Judgments of brand similarity

This paper provides empirical insight into the way consumers make pairwise similarity judgments between brands, and how familiarity with the brands, serial position of the pair in a sequence, and the presentation format affect these judgments. Within the similarity judgment process both the formation of a consumers mental similarity perception and the mapping of the judgment on the scale are examined. We investigate the occurrence of different response patterns, i.e. the distribution of judgments on the scale, in two studies: one using an input–output approach, and one using a process-tracing approach. In study 1, judgments of brand similarity are obtained for soft drinks and magazines from 240 subjects. In study 2, 36 subjects judged, while thinking aloud, the similarity between soft drink brands. In both studies, the presentation format is varied between subjects, and familiar and unfamiliar brands are included. Familiarity with the brands, and to a lesser extent, the presentation format affect the response patterns, whereas serial position has no effect. Implications for similarity data collection, statistical modelling of similarity data, and conceptual models based on brand similarity are discussed.

Assessing the effects of abstract attributes and brand familiarity in conjoint choice experiments

Abstract The multinomial logit model for conjoint choice experiments is extended to include effects of abstract brand attributes. The proposed CONFOLD model describes consumers utility for the alternatives in conjoint choice experiments as a (weighted) sum of two components: one pertaining to the concrete attributes used in the design of the choice alternatives, and the other pertaining to abstract attributes underlying the evaluation of brand names. An extension of the models allows the weights of both of these components may depend on the familiarity of consumers with the brands. An application to a conjoint study of automobiles is provided.

Strategic marketing research

This article introduces the term “strategic marketing research” for the collection and analysis of data in support of strategic marketing management. In particular, strategic marketing research plays an important role in defining the market, analysis of the environment, and the formulation of marketing instrument strategies. The concept of strategic marketing research allows research on important topics such as segmentation, positioning, new product development, and product/service quality to be placed into a broader framework.

Incorporating context effects in the multidimensional scaling of `pick any/N' choice data

This paper presents a multidimensional scaling model that is estimated on pick any/N choice data, and accommodates a broad range of context effects. The methodology estimates a set of parameters capturing the direction and magnitude of the context effects, as well as the locations of brands and consumers ideal points in a joint multidimensional space. Numerical simulation suggests that our approach can recover the true configuration of brands and ideal points in the presence of context effects with greater accuracy than a `baseline model that does not incorporate any context effects. To illustrate the methodology and its implications, we provide an empirical application based on choice of supermarkets, from a consumer study conducted in the Netherlands.

A multidimensional scaling model accommodating differential stimulus familiarity

We introduce a multidimensional scaling procedure that attempts to derive a spatial representation of stimuli unconfounded by the effect of subjects degrees of familiarity with these stimuli. The proposed model assumes that stimulus unfamiliarity produces a tendency for a subject to anchor his/her dissimilarity judgments towards a reference value on the response scale. The input data needed to perform such analyses are the degree of stimulus familiarity along with the dissimilarity judgments for all pairs of stimuli. In a Monte Carlo study, we investigate the extent to which the procedure recovers known parameters. Furthermore, empirical applications; of the model to positioning studies of magazines and banks in the Netherlands are provided.

A Spatial Interaction Model for Deriving Joint Space Maps of Bundle Compositions and Market Segments from Pick-Any/J Data: An Application to New Product Options

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure simultaneously extracts market segment and product option positions in a joint space map such that the closer a product option is to a particlar segment, the higher the likelihood of its being chosen by that segment. A segment-level threshold parameter is estimated that spatially delineates the bundle of product options that are predicted to be chosen by each segment. Estimates of the probability of each consumer belonging to the derived segments are simultaneously obtained. Explicit treatment of product and consumer characteristics are allowed via optional model reparameterizations of the product option locations and segment memberships. We illustrate the use of the proposed approach using an actual commercial application involving pick-any/J data gathered by a major hi-tech firm for some 23 advanced technological options for new automobiles.

Mixed Tree and Spacial Representations of Dissimilarity Judgments

Whereas previous research has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, focussing on the comparative fit at the aggregate level, we investigate whether there is heterogeneity among subjects in the extent to which their dissimilarity judgments are better represented by ultrametric tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data, that is formulated in a stochastic context, and entails a representation and a measurement model component. The latter involves distributional assumptions on the measurement error, and enables estimation by maximum likelihood. The representation component allows dissimilarity judgments to be represented either by a tree structure or by a spatial configuration, or a mixture of both. In order to investigate the appropriateness of tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate tree and spatial models, as well as with mixtures of pure trees and mixtures of pure spaces, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments at the individual level.