Reflections on assessing academic quality in marketing, and the UK REF

Purpose – The purpose of this editorial is to comment on the paper by Saunders and Wong in this issue. In doing so, the paper reflects on the notion of academic quality within marketing research, along with the systems in place to evaluate and reward it. Design/methodology/approach – The paper takes a reflective, discursive approach. Findings – The author finds that, while Saunders and Wong make a number of pertinent observations, and come up with interesting solutions, the notion of academic quality espoused in their paper is based on a logically flawed set of arguments. Research limitations/implications – The paper is primarily a personal view, and thus does not rely on any empirical research. Practical implications – There are key implications for many parties involved in the creation and assessment of marketing knowledge. In particular, scholars would be well advised to consider notions of quality in relation to their own work, rather than rely unquestioningly on existing definitions. Policy makers and research managers (e.g. business school deans) also need to consider what quality in academic research really is, and how to appropriately direct and reward it. Originality/value – The paper provides another perspective on the well-established debate regarding quality, and thus it is hoped will stimulate further thinking.

A soft-computing-based method for the automatic discovery of fuzzy rules in databases:uses for academic research and management support in marketing

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentation of an analytical method based on fuzzy logic and evolutionary algorithms, developed to analyze marketing databases and uncover relationships among variables. A detailed implementation on a pre-existing data set illustrates the method. © 2012 Published by Elsevier Inc.