بینش روش شناختی از پارتیشن بندی داده ها
Abstract
1- Introduction
2- Data partitioning
3- Conclusion
References
Abstract
Data management is an integral part of knowledge engineering in business-to-business (B2B) marketing experiments. This article introduces the concept of data partitioning as a fresh and useful form of data management in the process of knowledge engineering in B2B marketing experiments; articulates the method for partitioning data in B2B marketing experiments; and discusses the implications of data partitioning in the form of data and resource maximization for B2B marketing experiments. It is the hope of the authors that this article will encourage greater visibility and contribute to the advancement of resource-efficient B2B marketing experiments.
Introduction
Experimental research is undertaken to engineer new knowledge1 about cause-and-effect relationships. In the field of business-to-business (B2B) marketing, the experimental method of research allows B2B marketers to control and manipulate one or more independent marketing variables and measure the corresponding changes in dependent marketing variables in B2B settings. Some B2B marketers qualitatively measure, through interviews, the outcomes of control and manipulation of B2B independent marketing variables (e.g. task manipulation; Laursen & Andersen, 2016; Van Bockhaven & Matthyssens, 2017). However, most B2B marketers, including the authors of this article, choose to do so quantitatively (e.g. Bonney, Plouffe, & Wolter, 2014; Ruiz & Kowalkowski, 2014). The results of statistical analyses used by quantitative experimenters to interpret cause-and-effect relationships are more narrowly defined. This makes them more reliable and valid than their qualitative counterparts. A narrow definition of the research and experimentation is encouraged as it allows other researchers to easily replicate the study and validate the results. This is becoming increasingly visible among elite business journals (Babin, Lopez, Herrmann, & Ortinau, 2018; Harzing, 2016), including Industrial Marketing Management (Laplaca, Lindgreen, & Vanhamme, 2018). Thus, the discussion of B2B marketing experiments will focus on quantitative experimental research.