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Introduction: Decision making is always based on several factors which may affect the possible outcomes, especially in financial markets. Instead of having many criteria which may be required for decision making, “Multiple Criteria Decision Making” (MCDM) models might be used as a tool to reduce all criteria into a single one.

Purpose: The aim of this study is to measure the financial performance of commercial banks listed on Borsa Istanbul (BIST) by the MCDM.

Method: To this end, data from 15 different financial ratios from 11 commercial banks were used between the periods of 2002 and 2018. Both TOPSIS and gray relational analysis (GRA) models were used, which are commonly used in the literature for detecting the financial performance of listed banks in BIST based on their consolidated financial statements.

Results: According to the TOPSIS method, while the best bank is QNB Finansbank, HALKB, a public bank, was determined as the best bank using the GRA method. There is no significant correlation between financial performance indicators and market returns obtained by either method, with exceptions. There is no generally significant correlation detected between financial ratios and market returns. Accordingly, it is concluded that the bank stock prices in the study are shaped by the influence of external factors and expectations. The study results include information that can be used for different purposes among bank managers, academics and financial investors.

The model and existing practice of the construction supply chain (CSC) in the United Kingdom (UK) and Australia was presented in this chapter. The policies and reports that support the practice of the CSC were examined in both countries. It was discovered from the review of literature that the UK has a more detailed report targeted at improving the CSC than Australia. However, both countries have a common factor affecting their CSC which originates from fragmentation experienced within their supply chain. Construction stakeholders in the UK and Australia believe that collaboration and integration are vital components for improving performance. The majority of the contractors in both countries embrace collaborative working for the sole purpose of risk sharing, access to innovation and response to market efficiency. However, most of the models developed for managing the CSC in the UK are built around building information modelling (BIM). Also, the reviewed studies show that supply chain management practice will be effective following the following principle: shared objectives, trust, reduction in a blame culture, joint working, enhanced communication and information-sharing. Finally, the UK has a more established framework and more CSC models compared to Australia.

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.

This chapter empirically investigates the main drivers of the circular economy (CE) and sustainable development (SD) of European countries. The European Union (EU) legislation imposes equal rules for the members who should be followed to achieve CE and SD. This chapter gives a critical overview of the related literature on this topic. The second part focuses on measuring the efficiency of EU countries in achieving CE and SD via a nonparametric approach. Furthermore, the results from the efficiency evaluation are used as a dependent variable in determining which economic, social, institutional, and other factors have the greatest influence on CE and SD achievements. The nonparametric approach consists of selected models of data envelopment analysis (DEA), as this is a methodology useful in constructing a ranking system based on selected criteria. The results indicate that on average, the most efficient countries were (besides Malta and Luxembourg) the Netherlands, Poland, Germany, Sweden, Denmark, France, and the United Kingdom. The worst performing ones were Cyprus, Spain, Greece, Belgium, Portugal, and Croatia. The second part of the research indicates that the resource production and corruption perception index has the greatest effect on the efficiency scores, followed by education attainment. The research and development (R&D) variable is not significant in the observed sample. Based on these results, specific policy recommendations are given at the end of this chapter.

This chapter focused on presenting the result of the Delphi study from the questionnaire distributed to the experts. The Delphi technique was used for modelling the construction supply chain management (CSCM) practice in the fourth industrial revolution (4IR) era. The technique was also used to predict the supply chain management's (SCM) possible trends in the construction industry. A total of 15 experts were selected for this study based on their working experience. The Delphi study also validated the gaps (organisational culture and 4IR component) identified from the existing CSCM model. The findings from the Delphi study revealed that organisational culture has a significant impact on the practice of CSCM in the 4IR era. Regarding adopting the 4IR component for the CSCM in Nigeria, the Delphi study revealed that smart management and virtualisation are the most adopted. Unfortunately, the cyber-physical system, the heartbeat of the 4IR, is yet to be fully implemented for CSCM practice in the Nigerian construction industry.

The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The construction industry's application of supply chain management (SCM) principles is confronted with numerous Gordian Knots ranging from late delivery, fragmentation and others. The challenges could be attributed to multiple factors. The most crucial amongst them are adopting management ideas rooted in the second and third industrial revolution without taking consideration of the present industrial revolution. Evidence from literature and practice revealed that we are presently in the fourth industrial revolution (4IR). This chapter calls for developing a model that supports construction supply chain management (CSCM) in tandem with the principles of 4IR. This chapter presents the idea behind the conception, development and benefit of this research book to construction stakeholders and academia. The various shortcomings in the existing model for CSCM were also discussed in this chapter extensively.