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The primary purpose is to empower financial institutions in AI integration decisions. By combining QSFS and the Golden Cut technique, the study establishes a robust foundation for assessing AI progress effects, aligning implementation with performance goals, and promoting technical innovation. Dimensions explored include AI-related workforce competency, technological adaption, and ethical AI practices, crucial components within the BSC framework for technological innovation.

This study employs a distinctive approach, integrating the Balanced Scorecard (BSC) framework with Quantum Spherical Fuzzy Sets (QSFS) and the Golden Cut approach to explore the dynamic landscape of AI deployment. The integration addresses uncertainties, enhancing impact assessment accuracy amid ambiguity associated with AI outcomes. QSFS and the Golden Cut technique together facilitate precise identification of thresholds and crucial values.

The research delves into the intricate relationship between enduring financial stability and AI progress, recognizing technology's crucial influence on financial decision-making. Findings underscore technology's significant impact on financial institutions' AI integration decisions. This novel approach provides a strong quantitative basis, offering insights into workforce competency, technological adaption, and ethical AI practices.

Despite valuable contributions, the study acknowledges limitations, such as potential biases and generalizability concerns, emphasizing the need for cautious interpretation and suggesting future research directions. Recognizing the research's boundaries and complexities in studying AI deployment in financial institutions underscores the need for ongoing exploration.

The research's originality lies in presenting an innovative methodology, integrating BSC, QSFS, and the Golden Cut, providing a unique perspective for decision-making. Contributions extend beyond academia, offering practical insights to enhance AI strategic implementation in the financial industry. This novel approach enriches the technology and finance discourse, fostering theoretical and practical advancements.

The aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.

This paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.

Eco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.

The innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.

The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.

Analysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

This study aims to investigate the strategic priorities of green building projects and analyze energy consumption alternatives in green residence projects using two innovative methods.

This study uses two methods, decision-making trial and evaluation laboratory (DEMATEL) to measure strategic priorities and golden-cut quantum spherical fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) to analyze energy consumption alternatives.

The study reveals that sustainability and atmosphere are the most significant factors in determining the priorities of green residence projects, whereas innovation has a limited impact on addressing environmental challenges in the building sector. The ranking of energy use alternatives shows that sustainability issues and atmosphere quality of space heating and cooking are the top priorities, whereas other factors like white goods, water heating, lighting and space cooling are ranked lower.

This paper offers a significant contribution to the understanding of green buildings by introducing innovative methodological approaches. Theoretically, it uses the DEMATEL to enhance traditional analytical frameworks, marking a novel effort in understanding green residence projects. In addition, the golden-cut quantum spherical fuzzy TOPSIS method is introduced, offering a comprehensive decision-making framework for green projects, considering factors like energy consumption and economic feasibility. This combination of methodologies provides a holistic evaluation, emphasizing sustainability in green building construction. This study reveals untapped potential for environmental sustainability and energy efficiency, enriching the existing knowledge base.

Health technologies are an issue that directly affects the sustainability and quality of health services. Due to budget constraints, it is not financially possible for businesses to apply comprehensive improvement strategies to all these criteria. In this case, it is possible for businesses to implement more priority strategies. Accordingly, the main purpose of this study is to evaluate the important performance indicators of health technology investments.

Firstly, with the help of the artificial intelligence system, a decision matrix is established. Secondly, spherical fuzzy total order of preference decision-making trial and evaluation laboratory methodology is taken into consideration for weighting the criteria. Thirdly, emerging seven countries are ranked by using spherical fuzzy MultiAtributive Ideal-Real Comparative Analysis (MAIRCA).

The findings demonstrate that the criteria of health policies and research and development are defined as the most significant factor in this regard. China and Turkey are also found to be the most successful emerging countries with respect to the performance of health technology investments.

The main contribution of this study is that a novel decision-making model is generated by integrating artificial methodology into the spherical fuzzy sets.

Nuclear energy investments contribute significantly to the energy independence of countries. This situation helps countries to develop both economically and socially. Therefore, countries need to develop strategies to increase their nuclear energy investments. However, radioactive waste generated in nuclear reactors is one of the most important disadvantages of this type of energy. Due to this situation, the wastes generated must be disposed of effectively for nuclear energy investments to be effective. In this study, it is aimed to create optimal strategies for the effective waste managements generated in nuclear power plants. Within this framework, four different balanced scorecard perspectives are considered as the criteria. In the analysis process, Analytical Hierarchy Process (AHP) methodology is taken into consideration. It is identified that learning and development is the most critical factor for the effective radioactive waste management created in nuclear power plants. On the other side, internal factors are the second most important criterion in this framework. Hence, it is understood that nuclear energy investors should give priority to the technological development for effective waste management in nuclear power plants. With the help of these technological improvements, the wastes can be disposed more successfully. As a result, environmental sustainability can be provided much easily.