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While metrics are becoming increasingly important for marketing’s relevance, there is also a need to understand how they, as enablers of learning, affect marketing’s adaptive capabilities that ensure its long-term success. Therefore, this study aims to test the association of marketing and financial metrics use and the metric-based orientations of training and compensation, with two key marketing routines – exploitation, i.e. the perfecting of existing activities while allowing for incremental adaptations and exploration or experimentation accompanied by radical adaptation.

The study gathers data from 205 managers and uses partial least squares structural equation modeling to test the hypothesized relationships.

Marketing metrics encourage both forms of marketing adaptation. Financial metrics use discourages exploration. Market orientation and long-term orientation strengthen (weaken) the positive (negative) relationship between marketing (financial) metrics use and marketing exploration. Metric-based training is more positively associated with both adaptive capabilities than a metric-based compensation orientation, albeit weakly.

The study’s central proposition – that different metrics or metric orientations are associated with distinct types of knowledge, interpretations, mindsets, motivations and cultural contexts – provides a deeper theoretical understanding of the pathways by which a metric emphasis affects marketing adaptation.

Marketing managers should emphasize marketing metrics and training more than compensation, to promote marketing exploitation/exploration, while exercising caution in overstressing financial metrics given their negative association with exploration. This latter negative relationship can be weakened (as can the positive one between marketing metrics and exploration be strengthened) with increased market orientation and long-term orientation.

This study addresses the research gap regarding the relationship between metrics as a configurational element of marketing organization and marketing adaptation.

This paper aims to explore the spillover effect of social responsibility (SR) activity at the product brand level on the full brand portfolio. Extant research has established that SR activity can be beneficial to companies by influencing consumers’ SR associations with the company and its product brands. However, most studies only look at the outcomes of SR implemented at the corporate level (i.e. corporate social responsibility [CSR]). This paper provides a new and expanded perspective by exploring how SR at the product brand level reverberates throughout the full brand portfolio. Drawing on associative network theory, the authors propose a conceptual model that predicts when and how SR associations with a product brand spillover to corporate brand and other product brands and the consequences of this spillover.

Two experiments were conducted to test the conceptual model. The authors used utilitarian products (frozen yogurt, ice cream, and soft drink) in the first experiment and value-expressive products (running shoes, T-shirt and watch) in the second experiment.

Both experiments found support for the proposed spillover effect. The moderating impact of corporate branding strategy and product category fit on the strength of spillover effect were also examined.

The findings will help managers make better decisions about which brands (product and corporate level) should be involved in SR activity.

This research offers a new perspective to look at the consequences of SR activity and reveals a larger picture than extant research on CSR by indicating the impact of a product brand’s SR initiative on the whole brand portfolio.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.