## 2 Literature Review
### Exploring Factor-Related Methods  
Multi-criteria decision-making methods, particularly DEMATEL and ISM, have emerged as effective tools for analyzing relationships between factors across various industries. ISM has been successfully applied in several contexts: examining sustainable supply chain management factors in the oil and gas industry [43], analyzing barriers to 6 Sigma certification in SMEs through integration with structural equation modeling [44], investigating obstacles to information modeling in construction [45], and exploring Industry 4.0 implementation challenges when combined with the Analytic Network Process [46]. Similarly, DEMATEL has demonstrated versatility in different applications. It has been used to assess smart city development factors and, in its grey variant, to explore key factors in green business failure [47]. The method has also been integrated with other analytical tools: it has been combined with Best Worst Method (BWM) and Bayesian networks for security management decisions [48] and adapted into fuzzy DEMATEL to analyze human resource challenges in start-ups [49]. [PERSON] et al. [50] enhanced safety management decision-making by combining DEMATEL with multi-criteria decision-making tools and Bayesian networks. [PERSON] et al. [51] used DEMATEL-ISM method to analyze the key factors affecting risk tolerance in project-oriented firms. This widespread application of DEMATEL and ISM across diverse fields demonstrates their effectiveness as analytical tools for identifying and understanding key factors and their relationships. However, past research has mostly used crisp numbers, but this approach often fails to accurately represent cognitive results with a single value due to insufficient information being available to decision-makers. Additionally, when the members of a decision-making team come from different units or backgrounds, they may have different perspectives due to variations in their experiences or positions. Previous research has mostly adopted averaging methods for analysis, but this approach can easily lead to data loss and fails to reflect the importance of individual decision-makers. Therefore, the use of grey numbers combined with the Aczel-Alsina function proposed in this research can effectively improve upon these shortcomings. DEMATEL has the following advantages [20]: (1) Structural analysis: It can help to analyze the relationship among factors in a complex system. (2) Influence assessment: It can assess the degree of influence of each factor on the system and help decision-makers prioritize the factors that have a greater influence on the system. (3) Causality analysis: It can reveal the causal relationship among factors and help determine which factors are dominant so as to formulate an effective decision-making plan. (4) Graphical presentation: Graphical presentation of the results makes complex analysis results more intuitive and easier to understand. (5) Decision support: It provides tools for a comprehensive analysis of the system, which can provide decision-makers with objective data support and help them make informed decisions. ISM was proposed by [PERSON] [52] and has the following advantages: (1) Hierarchical structure: ISM uses a hierarchical structure to represent the interrelationships among factors, which helps users to clearly understand the hierarchical structure among the factors and thus better understand the problem. (2) Expert participation: It encourages experts to participate in the modeling process, to make full use of their knowledge and experience to improve the accuracy and credibility of the modeling. (3) Causality analysis: ISM can help analyze the causality among factors, reveal the mechanism of interaction among factors, and help identify the main factors and key influencing factors. (4) Decision support: ISM provides a systematic approach to analyzing problems and making decisions, which can provide an objective reference for decision-makers and assist them in making wiser decisions.