A dyadic segmentation approach to business partnerships

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 considered 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 independent. As a step to understanding, how partnership influences firms' performance, the dyadic (i.e. two partners') case is studied. First, some theoretical issues concerning the degrees of individual and contributive interest in a dyadic population 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 segmentation strategies are discussed according to their capacity to reflect the modes of partnership and a dyadic clusterwise regression method, based on a genetic algorithm, is presented. Finally, the method is illustrated empirically using actual data of business partners in the software market.Jacques-Marie Aurifeille and Christopher John Medli

Dimensions of inter-firm trust: benevolence and credibility

Past research on inter-firm trust has noted two dimensions, benevolence and credibility. The credibility dimension of trust has been operationalized variously as a combination of honesty, reliability and expectancy; while benevolence has rarely been examined as a unique dimension. We examine the two trust dimensions with empirical data of inter-firm relationships in the software industry and find that benevolence is strongly associated with relationship performance. No association is found between credibility and relationship performance, when discriminant validity is imposed. This result has important implications, as almost all of the inter-firm empirical research on trust has been based on the credibility dimension or a global measure combining the two dimensions. The research in this chapter suggests that benevolence, where managers perceive the other firm willing to look after their firm’s interests and so the collective interests of both firms moving forward, is the key to business relationship performance

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

Destination images, holistic images and personal normative beliefs: Predictors of intention to revisit a destination

This research examines the complex relationship between components of images of destinations and behavioral intentions, incorporating two pivotal constructs that have not been explored in the related literature, namely holistic image and personal normative beliefs (PNBs). Previous studies incorporating destination images as predictors of intention to revisit have mostly investigated their direct effect. This research integrates holistic image as a mediator and PNBs as a moderating variable. The findings verify the mediating role of holistic image for predicting tourists’ intentions to revisit a destination, supporting a model that incorporates a partial effect and two indirect mediations. Interestingly, only affective and conative images contribute to the prediction of tourists’ intentions to revisit a destination through the holistic image towards this destination. Moreover, PNBs moderate the effect that conative destination images have on tourists’ holistic images. Practically, the research sheds light to factors that affect tourists' tendency to select a tourism destination, which can serve as a basis for tailoring the effective positioning of destinations

L'évolution des valeurs et ses implications en marketing : Une enquête en Russie

International audienceLa Russie a été choisie pour étudier le processus d'évolution des valeurs terminales et instrumentales. De cette étude, à caractère essentiellement exploratoire, deux observations se dégagent:D'une part, la stabilité globale des systèmes de valeurs : la régression de certaines valeurs est compensée par la progression d'autres valeurs. Ceci pose la question de la pertinence des stratégies marketing, notamment « moyens-fins », fondées sur une unique valeur des consommateurs.D'autre part, on observe une plus grande stabilité des valeurs instrumentales que des valeurs terminales; par suite, il semble que plus d'attention devrait être accordée aux valeurs instrumentales quand des stratégies durables de segmentation et de positionnement sont recherchées

A dyadic approach to the relationship performance of international business partners

As a step to understand how business partner's differences influence their relationship performance, a new quantitative methodology for examining partnerships is proposed. It is based upon two models for each firm: a “self-model” that reflects how a firm’s characteristics explain its perspective of relationship performance, and a “contributive-model” that reflects how they influence the partner’s view of relationship performance. A clusterwise regression approach is then used to analyse the similarities/dissimilarities among the models at work in “dyads”, that is: partnerships composed of two firms. This allows definition of four interaction modes: merging, teaming, sharing-mutual and sharing-dominated. The method is tested empirically on international dyads with “relationship performance” as the dependent variable and relationship variables as the predictors (ie. trust, commitment, time perspective, relationship experience and economic goal). As expected the main results indicate that some similarities among partners management models are more profitable. Such a result suggests that as firms use business relationships to globalise they should be careful about the types and degree of standardisation they seek to impose.Jacques-Marie Aurifeille and Christopher John Medlinhttps://www.novapublishers.com/catalog/product_info.php?products_id=621

A bio-mimetic approach to marketing segmentation: Principles and comparative analysis

A regression algorithm is proposed for partitioning a population into clusters characterised by homogeneous models and predictions. This algorithm, Typren, is based on the hybridisation of a genetic algorithm with linear regression. An empirical illustration is provided, using real marketing data, which compares Typren with the fuzzy clustering approach, Glimmix, based on a regression mixture model. Typren provides better predictivity and better within-cluster homogeneity of predictions. However, the results are slightly less robust compared to Glimmix

Proposition d'une méthode de mesure du halo affectif en marketing

International audienceCet article propose une méthode de mesure du halo affectif en marketing. Le concept de halo affectif et l'intérêt de sa mesure sont d'abord exposés. Les principales difficultés rencontrées par les travaux antérieurs sur le filtrage et la mesure du halo en marketing sont ensuite discutées. Puis une méthode originale de mesure est proposée, avec une illustration pratique de l'influence du halo affectif sur les attitudes

A distributed Genetic Algorithm for the clusterwise regression problem: Comparison with a classical GA and with a regression mixture model

Jacques-Marie Aurifeille and Christopher Medli