The current research applies a work-based strategic decision-making approach using fuzzy logic to evaluate electric vehicles (EVs) attractiveness in today’s digital economy. Effective and practical decision support systems that consider economic, environmental and technical elements are critical as businesses negotiate complexities surrounding the adoption of green transportation options. This study is introduced through a conceptual fuzzy logic model planned to help managers and lawmakers evaluate EV adoption based on key factors, including cost, infrastructure and environmental impact. This study offers a work-applied framework that enhances decision clarity and supports real-world decision-making by embedding a flexible and scenario-driven approach within strategic management.
A conceptual model based on fuzzy logic is proposed to evaluate EV attractiveness. The fuzzy logic case study combines qualitative and quantitative aspects, allocating importance correspondingly to market realities and customer viewpoints. The methodology follows a multiple-criteria decision-making (MCDM) approach, utilizing fuzzy inference and defuzzification schemes to manage uncertainty. The implementation of the procedure shows its applicability in analyzing situations relating to electric vehicle (EV) acceptance and generating informative outputs to aid strategic business planning, investments and policymaking to foster sustainable transport. The study shows that fuzzy logic strengthens decision-making by structuring complex trade-offs in EV adoption.
The study shows that fuzzy logic strengthens decision-making by structuring complex trade-offs in EV adoption. Results indicate that although high costs remain a barrier, technological advances and sustainability incentives substantially increase EV attractiveness. The model integrates multiple criteria – cost, environmental performance and technology – highlighting fuzzy logic’s adaptability in uncertain contexts. Findings emphasize its potential in guiding business strategies, investment planning and public policy. The approach also supports scenario-based analysis, enabling stakeholders to anticipate market trends and consumer behavior. The study provides a clear response to the research question, demonstrating how FAHP can be applied to systematically structure and support EV evaluation under uncertainty, balancing economic, environmental and technological considerations.
The present research advances work-applied strategic management by introducing a fuzzy logic decision-support framework tailored to EV adoption. Unlike deterministic approaches, it accounts for uncertainty, subjectivity and dynamic conditions. Integrating multi-criteria evaluation offers a novel way to assess EV attractiveness, filling gaps in current decision-support tools. The study offers actionable insights for mobility strategists, including managers, policymakers and investors. This study also highlights the potential for AI and ML approaches to be enhanced by fuzzy logic, especially in interpretability and assistance in transparent strategic planning in sustainable technology adoption.
