Applying intelligent cultural networks to marketing analysis

Abstract

Purpose

In 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/approach

This 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.

Findings

Complicated 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 implications

This 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/value

This 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.

Citation

Wang, Y. (2009), "Applying intelligent cultural networks to marketing analysis", International Marketing Review, Vol. 26 No. 4/5, pp. 542-553. https://doi.org/10.1108/02651330910972020