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The complexity of business markets, resulting from different levels of organization and the ways social constructs combine across levels, means no clear theory of relational coordination has been developed. The relationship and network framework of the industrial marketing and purchasing (IMP) group provides a means of handling this complexity. This paper proposes a collaborative interest model (CIM) of relational coordination, which partially overcomes these problems by examining the coordination process within a context that accounts for levels of organization. This conceptualization of coordination processes resulting from firm intentions to achieve relationship performance presents a new way to empirically examine relational coordination. An empirical test of the model using structural equation methodology shows relational coordination (i.e., commitment and trust) explains relationship performance better than market coordination mechanisms.

Business ethics is an area difficult to operationalise. The literature proposes a number of models explaining ethical behaviours but these are seldom of the kind which can be used by marketers in their day-to-day decision making. In this study, based on data collected from 166 firms operating on overseas market, a conjoint clusterwise regression approach is used to predict firms’ ethical tolerance while at the same time homogenising the profiles of firms within clusters. The analysis is based on simple and visible organisational variables, allowing an ’ethical scoring’ of firms and the identification of meaningful models which may be used for present as well as future dealings with other firms. The resulting clusters are interpreted both in terms of the model which they follow as well as in terms of their profiles.

In business science, the studied objects are often groups of partners rather than independent firms. Extending classical segmentation to these polyads raises conceptual problems, principally: defining what should be consid- ered as common or specific at the partners' and at the segment levels. The general approaches consist either in merging partners characteristics and performances into a single macro-object, thus loosing their specific contributions to each partner's performance, or in modelling partners' performance as if their models were inde- pendent. As a step to understanding, how partnership influences firms' perform- ance, the dyadic (i.e. two partners') case is studied. First, some theoretical issues concerning the degrees of individual and contributive interest in a dyadic popula- tion are discussed. Next, partnership's conceptualisation is based upon two models for each firm: a "self-model" that reflects how the firm's characteristics explain its own performance, and a "contributive-model" model that reflects how these characteristics influence the partner's performance. This allows definition of three relationship modes: merging, teaming and sharing. Subsequently, dyad segmenta- tion strategies are discussed according to their capacity to reflect the modes of part- nership and a dyadic clusterwise regression method, based on a genetic algorithm, is presented. Finally, the method is illustrated empirically using actual data of busi- ness partners in the software market.

A bio-mimetic approach of multiple goal problem solving is proposed. The approach relies on the stochastic ope-rators (reproduction, selection) of the Genetic Algorithms (GA). The method is presented in the case of a three-goal management problem: market segmen-tation and compared empirically with the two-goal bio-mimetic algorithm (TYPREN) from which it is derived and with the statistical Regression Mixture Model (GLIMMIX).

Foreword. 1. The Potential of Neural Networks Evaluated within a Taxonomy of Marketing Applications S. Pandelidaki, A.N. Burgess. 2. Complexity Control and Generalization in Multilayer Perceptrons P. Gallinari, T. Cibas. 3. Application of Neural Networks to Bond Rating and House Pricing H. Daniels, et al. 4. Connexionist Approach and Corporate Distress Diagnosis: A Contribution to the Processing of Incomplete Information J.-F. Casta, B. Prat. 5. Comparison of the Predictivity of a Neural Network with Backpropagation with Those Using Linear Regression, Logistic and A.I.D. Methods for Direct Marketing Scoring P. Desmet. 6. Comparison of Discriminant Analysis and Neural Networks Application for the Detection of Company Failures M. Bardos, Wenhua Zhu. 7. Using Artificial Neural Nets to Specify and Estimate Aggregate Reference Price Models M. Natter, H. Hruschka. 8. Towards Connectionist Merchandising: A Conceptual and Operational Definition Ph.R. Demontrond, D. Thiel. 9. A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-Selling Opportunities A.N. Burgess, S. Pandelidaki. 10. Optimization by a Genetic Algorithm of Stochastic Linear Models of Time Series R. Bone, et al. 11. A Bio-Mimetic Clusterwise Regression Algorithm for Consumer Segmentation J.-M. Aurifeille. 12. Hybrid Genetic Learning of Hidden Markov Models for Time Series Prediction M. Slimane, et al. 13. Learning How to Regulate a Polluter with Unknown Characteristics: An Application of Genetic Algorithms to a Game of Dynamic Pollution Control T. Vallee, C.Deissenberg.