Jacques-Marie Aurifeille

A collaborative interest model of relational coordination and empirical results

Abstract 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.

A Conjoint Clusterwise Regression Analysis of Business Ethical Tolerance

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.

Measuring and optimizing the validity of Means-End data

The validity of means-end data is discussed from a conceptual and empirical perspective. A dynamic programming approach (Markov chain) is then proposed that enables to measure the validity and reliability of any group of means-end data (item, hierarchical level, whole data basis), thus enabling to purify it and to have empirical insight on debated topics such as the number of means-end levels to be considered and the validity of the means-end data collection methods.