Applying intelligent cultural networks to marketing analysis

Abstract

PurposeIn the real world, an occurrence of an event is often affected by a large number of potential factors. The purpose of this paper is to identify causal factors hidden in the data and discover the underlying causality from the observed data.Design/methodology/approachThis paper suggests an integration of system dynamics and association mining for identifying causality between attributes in a cultural analysis. The framework gives an improved description of the target cultural system represented by a database; it can also improve strategy selection and other forms of decision making. Such a combination extracts important dynamic causality.FindingsComplicated cultural issues can be identified and managed through a causal relation network. This type of causal relation is very common in daily life. For example, “an increase in productivity in a factory might cause an increase in pollution in the environment” and “the increasing pollution will cause a decreasing level of human health and welfare”.Practical implicationsThis paper presents a methodological framework for studying, understanding and managing cultural differences in a marketing environment. This framework provides a foundation for characterizing the causality representations and relations distributed among members of cultural groups.Originality/valueThis framework is being developed as an approach to improve the management of a dynamic environment, such as an international marketing environment, where participants (marketers, sales manager, etc.) are asked to communicate, bargain, analyse and collaborate with other participants who have a different cultural background or understanding. The knowledge employed can be extracted from data gathered from previous cases, from which the models can be developed.