Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Abstract

Exploring Determinants of the Marketing Budget Allocation Process across Countries using Neural Network Classification: Japan, Germany, United States

Author(s): David J. Smith

As firms continue looking for new ways to optimize expenditures, marketing managers have been forced to examine the transitory targets of efficient allocation levels and effective firm performance. Budget optimization has become the driving factor for marketing and sales expenditures given these optimal expectations. Although numerous studies exist addressing the relationship between marketing expenditures and sales performance, the impact of this knowledge has been slowly applied. Furthermore, very little previous research examines marketing budget allocation optimization in varying product categories or differing geographic regions. Moreover, there appears to be little consensus as to the identification of consistent input firm or customer level variables consistently associated with favorable outcomes and good practice. Therefore, this study will examine organizational, regional and performance determinants and their relationship to the marketing contribution performance in a crosscultural context. The proposition is a firm level examination of variables to confirm impact on marketing performance across cultural settings. Specifically, a sample of 770 retail trade firms from Japan, Germany and the United States are empirically investigated in an attempt to answer the following primary questions: (a) Does a common set of high-ranking determinants for Maximum Net Marketing Contribution exist among retail trade firms from the examined countries, combined? (b) Does a unique set of high-ranking determinants for Maximum Net Marketing Contribution exist within the retail trade firms from each country, individually? To confirm the classification capability, the variables examined employ both a non-linear probabilistic neural network (PNN) and a linear multiple discriminant analysis.

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