Improving the Effectiveness of English Cross-Cultural Business Communication Based on Fuzzy Logic

Authors

  • Xiaoning Zhang School of Foreign Languages, Zhanjiang University of Science and Technology, Zhanjiang 524000, Guangdong, China

Abstract

With the advancement of globalization, cross-cultural business communication has become an important factor for the success of enterprises. Communication between people from different cultural backgrounds can lead to misunderstandings, conflicts, and difficulties in conveying information accurately. These issues can affect the international development of enterprises. In order to improve the effectiveness of cross-cultural communication, this paper builds a new cross-cultural model based on fuzzy logic theory to evaluate the effectiveness of communication. The fuzzy reasoning method is used to comprehensively analyze and optimize communication efficiency by taking into account cultural fitness, communication success rate, language articulation and other variables. Firstly, the communication data for different cultural backgrounds are collected, and the fuzzy logic model is used to predict and evaluate the effectiveness of cross-cultural communication. The model’s effectiveness in handling uncertainty and ambiguity from cultural differences is validated by comparing the model-predicted communication effectiveness scores with observed outcomes from real cross cultural business interactions, operationalized as communication success rate, clarity/accuracy of information transfer, cultural adaptability ratings, and participant satisfaction. The results show that the fuzzy logic model can accurately indicate the complex factors in cross-cultural communication, and provide theoretical support and practical guidance for improving communication efficiency.

Keywords: cross-cultural communication; fuzzy logic; communication effectiveness; cultural differences; business communication

Cite As

X. Zhang, "Improving the Effectiveness of English Cross-Cultural Business Communication Based on Fuzzy Logic", Engineering Intelligent Systems, vol. 33 no. 5, pp. 555-564, 2025.

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Published

2025-09-01

Issue

Vol. 33 No. 5 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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