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.