## 5 Conclusions and Future Work
### Concluding Remarks
Our methodology examines the relationships between KPIs used to identify the association between the goals and strategic orientation of the smart port in terms of its performance assessment. NCMs are considered in this analysis, taking into account the aforementioned factors, and a quantitative analysis is applied based on the static and dynamic analysis provided by the use of NCMs. To illustrate the NCM methodology, we present an empirical application for modeling the factors that influence the performance of a smart port. We outline the key aspects of an NCM scenario aimed at gathering stakeholder perspectives on smart ports to determine the most effective strategy for improving performance. Through this process, we identify the highest priority dimension among the main pillars identified. An integrated model is developed by combining individual NCMs created by participants from academia and industry. Graph theoretical indices are calculated using the individual NCMs and the collective NCM resulting from this combination. The results demonstrate the functionality of this approach and show that NCMs are a reliable and efficient tool for this purpose. The proposed method, which combines static and dynamic analysis of NCMs, provides a comprehensive approach to evaluating smart port performance, taking into consideration both immediate relationships and long-term system dynamics. Static analysis finds KPIs and uses centrality measures to evaluate their importance, whereas dynamic analysis simulates the changing interactions between components over time, allowing for the discovery of feedback loops and hidden patterns. This dual approach allows for a more depth understanding of the complex, interdependent connections that occur in smart port systems, which helps decision-makers deal with uncertainty, anticipate future challenges, and prioritize development areas. This capability bridges a critical gap in the literature by addressing the interplay between short-term decisions and long-term operational objectives. Finally, the method creates a flexible, adaptable, and robust framework for the continuous optimization of smart port operations. A comparison with existing MCDM approaches based on fuzzy ANP and fuzzy DEA reveals the suggested method's strengths. Unlike existing methods that struggle with indeterminacy and do not integrate dynamic behavior, the suggested NCM-based method excels in addressing uncertainty and causal factors, giving a more flexible, dynamic, and customizable framework for evaluating smart port performance. In summary, we believe that the proposed technique represents a significant advancement in the field of smart port performance evaluation, providing a powerful, adaptable, and comprehensive decision-making tool that can be easily tailored to unique demands and situations. This method's dynamic and static analytic capabilities, as well as its incorporation of indeterminacy, make it an excellent tool for decision-makers looking to optimize port operations in the face of uncertainty and complex interdependencies across performance measures. The case study illustrates how the suggested approach may provide actionable information to support the development and enhancement of smart ports, helping to achieve the overarching goal of improving port efficiency and sustainability in an increasingly complex global marine environment.