Decomposing Market Shares Methodological Foundations and Empirical Application

This book presents both the methodological foundations and empirical applications of a decomposition approach developed by the author, which enables the comparative analysis of dependent variables within analytical models. This approach is compatible with various empirical methods, including regression analysis and structural equation modeling, and allows for the decomposition of causal relationships between independent and dependent variables into distinct components. Using the example of market shares of national brands, private labels, and retail chains, Philipp Brüggemann demonstrates how conventional market share analyses can be enhanced by decomposing them into components based on regular and promotional pricing. The method is characterized by its high transferability to various questions in both academic and practical contexts. This innovative method can be applied not only to market share analysis but also to numerous other relative and absolute key performance indicators in empirical models. The Author Dr. Philipp Brüggemann is a research associate with Univ.-Prof. Dr. Rainer Olbrich at the Chair of Business Administration, especially Marketing, at the FernUniversität in Hagen. His research focuses on topics related to retail, technology, e-commerce, and sustainability. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. This book includes methodological foundations as well as several empirical applications of the novel method for the decomposition of market shares developed by the author. Philipp Brüggemann demonstrates, using the example of market shares of national brands and private labels as well as retail chains, how previous market share analyses can be improved through decomposition by regular and promotional prices. The method is characterized by its high transferability to various questions in both academic and practical contexts. This innovative method can be applied not only to market share analysis but also to numerous other relative and absolute key performance indicators. Using regression analysis or structural equation modeling, this method can be used to break down causal relationships between independent and dependent variables into components.