## 5 Research Questions
### Construction Sector
In the development of the MCDM method, a hybrid structure is proposed, consisting of the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) with cosine similarity and the Neuro-Fuzzy Analytic Hierarchy Process (NFAHP) to support handling uncertainty in the risk assessment process for production services in general [302]. As a result, critical factors that had not been identified in previous methods emerged [193; 303; 304]. Regarding neural networks, techniques based on ANN [193] or mathematical and algorithmic models [99; 305] are proposed. Finally, regarding genetic algorithms and machine learning, there has been no significant progress in the field of safety. Techniques based solely on mathematical models or Markov functions have come to light [222; 304; 305; 306]. Which countries engage the most with the four techniques relative to their workforce? To answer this question, two factors must be acknowledged. These acknowledgments are necessary to minimize the complexity of the problem and to obtain quick initial insight. The first acknowledgment is how developed the country is according to the OECD, and the second acknowledgment is how much that country invests in technological development and advancement. Participation in such advanced technologies often depends on factors like the level of technological infrastructure, research funding, and the overall focus on scientific and technological advancements. Traditionally, countries with strong research institutions, advanced technology sectors, and noteworthy investments in research and development are likely to be leaders in these areas. According to OECD data, such countries include the United States, China, Japan, South Korea, Germany, Finland, Denmark, Sweden, and the United Kingdom. However, this is not reflected in the research data presented in this article, as will be analyzed below. China ranks first, followed by the United States. The rankings drastically change for the next positions. Turkey ranks third with 25 articles, compared to 27 articles for the United States in second place. Italy is fourth with 23 articles, followed by Iran and India. Similarly, the United Kingdom ranks seventh, and Germany ranks twelfth. This shift occurs because these countries have experienced significant growth in recent years, and the legislation regarding data and personal data access is relatively lax or non-existent [7; 31; 307; 308]. To fully answer the research question, the number of articles must be correlated with the workforce of each country. There are several websites that report the workforce of each country. The most reliable ones, based on both the data and their use by major organizations and businesses, are \"tradingeconomics.com\", \"theglobaleconomy.com\", and \"TheWorldFactbook.com\". These three organizations agree on the workforce figures for each country, with any differences being fewer than a thousand, which does not affect the result of the _d_-index. More specifically, the total number of articles originating from a country should be divided by the total workforce of the respective country. The _d_-index is a frequency index. It shows the engagement of a country's scientific personnel in relation to the safety of its workforce. \[\text{More specifically}\quad d=\frac{\sum p}{\sum e}\times 10^{6} \tag{1}\]  
Here, \(p\) denotes the number of articles, and \(e\) denotes the workforce of the respective country. The result of the division is multiplied by 106 because the number that results is too small to be manageable by data processing programs like Excel and SPSS. Using this formula, the resulting rankings are completely different. The country with the most engagement of its scientific personnel relative to the workforce being examined is Taiwan, according to the _d_-index. Italy ranks second, Slovenia third, Finland fourth, and Switzerland fifth. It is worth noting that the next three positions, with very little difference compared to Switzerland, are held by Serbia, Greece, and Portugal.