Technology-mediated client behaviors have emerged as critical determinants of organizational effectiveness and competitive positioning in the financial services landscape. This study examines multi-criteria client risk assessment within financial institutions, exploring the key facets that drive organizational capability development in managing digital transformation challenges. Using logistic regression and mediation analysis, we conducted an in-depth analysis based on a sample of 2,824 client profiles and comprehensive social media behavioral validation using 53,187 Reddit posts. Our findings reveal that technology usage assessment capabilities, age-based segmentation strategies, and behavioral motivation evaluation are the primary factors influencing organizational effectiveness in client risk management. In particular, systematic technology assessment emerged as the most critical determinant, underscoring the importance of developing sophisticated behavioral analytics capabilities to address evolving digital client behaviors. The implications of our findings extend to organizational strategy, innovation, and future research directions in financial services management, offering valuable insights to improve institutional effectiveness and competitive positioning against evolving technology-mediated challenges.

The development of the Islamic State (IS) in Southeast Asia creates changes in the social order in a direct and indirect manner. This study aims to identify the factors that influence the development of the Islamic State (IS) and analyze the influence of its development on social vulnerability in Southeast Asia. This study employed a mixed-method supported by the Interpretive Structural Modeling (ISM), Analytical Hierarchy Process (AHP), and System Dynamics (SD). Based on the results of research from relevant experts, this study uncovered seven the most dominant and structured problems. Furthermore, there are fourteen elements related to the social vulnerability of the Islamic State (IS) in Southeast Asia. The social vulnerability value is 0.01 and is categorized as Low Vulnerability. The aspects that influence the development of Islamic State indicate that the existing social system in Southeast Asia is strong enough in encountering the influence of ideology and the development of the Islamic State.

Already faced with tight competition and low profit margins, the airline industry is going through major changes in the wake of the current pandemic resulting in travel restrictions and slump demands, prompting airlines to curtail services and investments in every aspect of business. To that end, developing a comprehensive method of improving airline performance measures is crucial. However, this type of problem is complex to solve due to a large number of factors, requiring a systematic approach. It entails taking into account a multitude of conflicting, or sometimes interrelated criteria, hence becoming an inherently multiple criteria decision making problem. This study is aimed to assess the competitiveness of airlines and evaluate their financial and operational performances in relation to such criteria. We test FAHP, TOPSIS, and a hybrid method of combining FAHP and TOPSIS methods. In particular, regarding the hybrid method, FAHP is employed to determine the influential weights of criteria that are utilized in TOPSIS for preference values among alternatives. We demonstrate the applicability of the proposed methods to solving a MCDM problem of airline performance assessments using real data sets. Further, this study focuses on examining the relationship between financial and operational performance criteria, as well as gleaning insights for airlines to build an evaluation system that would aid in understanding their strength and weakness in the performance metrics. The computational experiment results of our hybrid FAHP-TOPSIS model support the efficacy of incorporating fuzzy values concerning influential weight criteria. By judiciously distributing criteria weights that are specific to the airline industry, our proposed model captures preference scores reflective of industry-related and concurrent measures. This modeling framework can help airlines better evaluate the systematic influential relation structure among criteria in critical financial and operational dimensions.

E-learning websites evaluation and selection is extremely important for the establishment of ef-fective E-learning. The E-learning website selection has crucial importance for the educational sector. The selection of E-learning website problem is generally considered as a Multi-Criteria Decision Making (MCDM) problem which mainly consists of both qualitative and quantitative criteria. The development of an E-learning website mainly depends on the success of the E-learning website selection along with various alternatives. So, for the effective evaluation and se-lection of E-learning websites, a set of selection criteria should be obtained. This paper consists of two steps, the first step is the identification of E-learning website selection criteria, second step provides the linguistic variables against the selection criteria and then fuzzy set theory (FST) is adopted for the calculation of the priority weights of each selection criteria. To show the rela-tive importance of each selection criteria, they ranked according to their global weights.

The purpose of this paper is to propose an integrated multiple criteria decision making (MCDM) approach to analyze interrelationships among sustainability criteria, weight them, and then evaluate suppliers according to these criteria. Data were collected through interviewing with seven experts who are involved in the procurement department of the case. Interpretive structural modelling (ISM), and pairwise comparison questionnaire are used to elicit the existence, strength of relationships among criteria and then weight them by using Fuzzy (Decision Making Trial and Evaluation Laboratory (FDEMATEL), Fuzzy Preference Programming (FPP), and Analytical Network Process (ANP). Then, Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed to evaluate suppliers based on identified and analyzed sustainability criteria. This study provides several implications for practitioners and scholars. First, the results show the efficiency of the proposed approach in practice. Second, respondents state that the proposed approach was very useful in decision making based on interrelationships among criteria, alternatives and policies. Third, findings validate the significant difference in rankings with or without considering interdependencies among criteria.

Efficient and stable performance of the banking system underpins sustainable growth of any economy. Of late, several economic reforms have been initiated in India for facilitating growth and withstanding dynamics of global economy. In this context, the current study compares the performance of the selected private and public sector banks in India on a five year time horizon in order to discern any changes in the performance over a period of time. First, the performance of the selected banks are examined in terms of management efficiency perspective using a Multi-Criteria Decision Making (MCDM) technique such as Combinative Distance-based Assessment (CODAS) when an Entropy method is also employed for determining criteria weight. The study also applies k-Means Clustering for checking consistency of performance based ranking with asset management efficiency. Finally, the paper delves into the relationship between financial and market performance. The study has found consistent results and observed private sector banks perform better than the public sector.

This study develops an effective method to measure value chain performance and rank them based on qualitative criteria and to determine the ranking order of the various forms of performance under study. This approach integrates the advantage of grey systems theory and TOPSIS to evaluate and rank value chain performance. Grey-TOPSIS approach has been applied to measure and rank the value chain performance of various firms. The results indicate that the proposed model is useful to facilitate multi-criteria decision-making (MCDM) problem under the environment of uncertainty and vagueness. The model also provides an appropriate ranking order based on the available alternatives. The Grey-TOPSIS approach that will be useful to the managers to use for solving the similar type of decision-making problems in their firms in the future has been discussed. Even though, the problem of choosing a suitable performance option is often addressed in practice and research, very few studies are available in the literature of Grey-TOPSIS decision models. Also, Grey-TOPSIS model application in the tea processing firms is non-existence hence this study is the very first to apply this model in evaluating value chain performance in the tea processing firms.

Nowadays, selecting the most appropriate location for hub is one of the most significant issues not only in road, rail and air transportations, but also in maritime. Transshipment is the fastest growing segment of the marine container market; it increases traffic flow of marine container and scope of this type of marine carriage, accordingly. In this way, determining a movement loop for the voyages of a shipping company, probes identification of container hub ports by considering different operational factors including distance to the destinations. The focus of this paper is to locate the best location for container transshipment hub in southern seas of Iran. In this paper, an MCDM model is proposed for evaluating and selecting the marine container transshipment hub port. Finally, the utilization of the proposed model is demonstrated with a real case study of Iranian main ports. The results show that the MCDM model can be used to explain the evaluation and decision-making procedures of a proper marine container hub location selection.

The 20th century was the age of an industry-based as well as knowledge-based economy. In a knowledge-based economy, knowledge plays an essential role to produce wealth compared with other tangible and physical assets. The purpose of this research is to identify and rank different aspects of knowledge management based on the Hicks model using the fuzzy TOPSIS technique for one of the most prestigious universities in Iran. The proposed model considers four main criteria of knowledge including creation, distribution, storage, and application along with 17 sub-criteria. The Chi-square correlation test indicates a positive and meaningful correlation between four mentioned criteria and knowledge management implementation. Using the fuzzy TOPSIS technique, the results also indicate that “Need for new and updated information and knowledge” was selected as the most important sub-criterion and “Sharing or distribution of knowledge” was selected as the most important main criterion on Hicks model.

This article introduces a scientometric investigation regarding the use of Multi-Criteria Decision-Making (MCDM) techniques in the area of portfolio optimization. The study, which employs a carefully selected dataset of 108 scholarly articles that are drawn from the Scopus database, covers the period 2003 to 2026, and maps the intellectual landscape, identifies the leading methodologies, and tracks the trends of migration. We perform a systematic analysis of the occurrence, impact, and application domains of 20 different MCDM methods, which include Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), TOPSIS and PROMETHEE, among others. The study employs key indicators such as the number of publications, the number of citations, the geographical distribution of authors, as well as the dispersion of journals, to assess the impact and uptake of the various techniques. The analysis demonstrates the dominance of DEA as a method, which is often combined with other MCDM methods and metaheuristics. A trend towards hybridization, which includes the combination of MCDM with fuzzy set theory, machine learning, and evolutionary algorithms, has been recognized as one of the main factors contributing to the innovation of recent techniques. In addition, the study points out the increasing adoption of Environmental, Social, and Governance (ESG) factors and big data analytics into the portfolio selection process. The survey presents a quantitative picture of the domain and provides researchers and practitioners with valuable insights through the identification of the established pillars, the emerging hotspots, and the future research trajectories in the field of MCDM-based portfolio optimization.