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Towards Identifying an Optimized Distribution System

Abstract

Previous research on the drivers of inflow streams for mutual funds has mainly centered on product-related characteristics, such as past investment performance or price components. Questions regarding the organizational choice and design for marketing mutual funds to private investors have been mainly treated as a black-box so far. With the aspiration to shed light on this blind spot, this enquiry largely draws on NIE-arguments of strategic marketing as well as perspectives from the realm of organizational design theories for the derivation of an appropriate framework of analysis. By applying a comparative multi-case study methodology, this empirical study is confident of securing an adequate level of external validity for its findings. From a sampling point of view, although inspired by the NIE-typical distinction, this enquiry claims to be able to capture the representative spectrum mirroring the significant developments in the German private investor market by focusing the analysis on the elaboration of case studies of “hierarchy”, “partnership”, and “market”. Thus, through applying this comparative type of research methodology as strictly as possible, the enquiry is confident of arguing that the diverging results on the framework’s task variables can be ascribed to a systematic variation in the organizational choice and design, against a somewhat uniform context. This essentially favors this study’s ambition to translate the empirical findings into general guidelines on how to accomplish optimized distribution systems for mutual funds in Germany. Hence, in the light of the enquiry’s practical-normative perspective, the intention is now to establish optimization-relevant guiding principles for the choice and design problem confronting asset management firms from a rather meta-theoretical point of view.

Keywords

Mutual Fund Institutional Arrangement Coordination Mode Private Investor Ideal Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Gabler | GWV Fachverlage GmbH, Wiesbaden 2008

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