Prof. Lazim Abdullah
Universiti Malaysia Terengganu, Malaysia

Lazim Abdullah is a Professor of Computational Mathematics at the Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu. He received his Ph.D (Information Technology) from the Universiti Malaysia Terengganu, in 2004. His research and expertise focus on fuzzy set theory of mathematics, decision making models, applied statistics, and their applications to environment, health sciences and technology management. His research findings have been published in more than 395 publications including refereed journals, conference proceedings, chapters in book, monographs, and textbooks. He has been ranked among the world’s top 2% scientists by Stanford University in the field of artificial intelligence and image processing since 2018. Prof Lazim is a member of the IEEE Computational Intelligence Society, and a member of International Society on Multiple Criteria Decision Making.

Speech Title: "An Integrated Bipolar Fuzzy-DEMATEL for Elucidating Factors Influencing Customers Choice: A Case of Life Insurance Companies"

Abstract: Multi-criteria decision-making (MCDM) methods have gained substantial traction across various scientific disciplines, with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method being particularly prominent. This study advances the DEMATEL framework by incorporating bipolar fuzzy sets to better handle complex, uncertain decision environments. The primary objectives are twofold: (1) to propose an integrated Bipolar Fuzzy-DEMATEL model and (2) to apply the model to identify key factors influencing customer choice in life insurance companies. The model introduces a novel linguistic scale for bipolar fuzzy sets, allowing simultaneous evaluation of positive and negative membership degrees across truth, falsity, and uncertainty dimensions. A sensitivity analysis was also conducted to assess the robustness of the findings. Results indicate that the cause factors influencing customer choice include F1, F2, F7, F8, and F9, while F3, F4, F5, F6, and F10 are classified as effect factors. Among them, ‘F2|: Competitive pricing and clear terms’ emerged as the most influential. The sensitivity analysis confirmed the model’s robustness, showing minimal impact of weight variations on factor rankings. The stability of top-ranked factors under changing conditions highlights the model’s reliability and its practical relevance for strategic decision-making in the insurance sector.