## 1 Introduction
According to [18], a model based on stakeholder knowledge and perceptions could be useful for addressing various challenges, especially those involving stakeholder opinions. Many studies have utilized multi-criteria decision-making methods such as AHP, ANP, TOPSIS, ELECTRE, and SWOT analysis [19; 20; 21]. However, these decision-making models often overlook the interconnectedness of various smart port strategies, such as operational, financial, technical, socio-environmental, and security strategies. They mainly focus on comparing strategies based on individual criteria. For example, AHP and TOPSIS provide structured methodologies for ranking alternatives based on predefined criteria, but they lack the ability to model causal relationships and dynamic feedback loops between factors,limiting their applicability in complex, evolving environments like smart ports. Additionally, AHP breaks down complex problems into hierarchical components but suffers from subjectivity in expert judgments and lacks the ability to model dynamic interactions between the criteria [22]. TOPSIS ranks alternatives based on their proximity to an ideal solution but assumes that criteria weights remain constant over time, limiting its adaptability in dynamic port environments [22]. Lastly, ELECTRE is effective in handling conflicting criteria but struggles with computational complexity and a lack of clear interpretability in results [23]. To overcome these limitations, especially the last one, there is a growing emphasis on collaborative decision-making frameworks that integrate diverse stakeholder perspectives. Effective decision-making in a smart port environment requires input from various stakeholders, including port authorities, logistics companies, environmental regulators, and technology suppliers. Recent research emphasizes the need for collaborative frameworks capable of collecting and reconciling diverse expert perspectives critical for strategic alignment and long-term success in smart port operations [24]. Historically, managerial choices were mainly based on subjective judgments and human competence to address diverse issues. However, in actual project circumstances, human experience often contains inherent uncertainties and ambiguity necessitating systematic approaches that leverage stakeholder input for strategic alignment and sustainable outcomes. Fuzzy cognitive maps (FCMs) have been utilized to describe and model the behavior of a system in decision analysis and operational research. They serve as a symbolic representation for the description and modeling of a complex system. In this context, the scholars in [25] propose a framework that uses FCMs to model and analyze business performance metrics from both an internal and external organizational perspective, while acknowledging the strategic potential of information technology (IT). Business metrics are crucial as they offer insights into business performance and suggest IT solutions to enhance operations. For instance, by measuring key performance indicators (KPIs) such as revenue, profitability, customer satisfaction, and operational efficiency, companies can pinpoint areas for improvement. They can also leverage IT solutions to improve operations, optimize resource allocation, and overall performance. Furthermore, metrics play a vital role in risk management, customer experience management, and driving continuous improvement projects, all of which contribute to the organization's long-term performance and competitiveness in a dynamic business environment. While FCMs have been widely used for modeling complex systems, they have significant limitations. One key disadvantage is their reliance on expert knowledge without accurately measuring the degree of influence across factors, which can result in biases and inconsistencies in decision-making [26]. Furthermore, FCMs struggle to capture dynamic interactions over time, limiting their ability to provide long-term strategic insights [27].