Aug 2026· Journal of Intelligent Decision Making and Information Science· Vol 3, pp. 228-249· 0 citations· 51 references
TL;DR
This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes and proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness.
Abstract
Multi-Criteria Decision-Making (MCDM) has become an indispensable analytical approach for supporting organizational performance evaluation and strategic management in increasingly complex and uncertain business environments. Modern organizations operate under multiple, often conflicting objectives involving financial performance, operational efficiency, sustainability, innovation, stakeholder satisfaction, risk mitigation, and competitive positioning. Conventional single-criterion evaluation techniques frequently fail to capture these multidimensional relationships, necessitating structured decision-making frameworks capable of integrating qualitative and quantitative information. MCDM methodologies enable systematic prioritization of strategic alternatives by incorporating diverse evaluation criteria, expert judgments, uncertainty considerations, and organizational objectives into a unified decision-support process. Recent developments integrating hybrid optimization techniques, fuzzy environments, and data-driven analytics have significantly enhanced decision transparency, robustness, and adaptability across industries. This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes. The study further proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness. The findings are expected to provide valuable theoretical insights and practical guidance for researchers, policymakers, and organizational leaders seeking evidence-based strategic management solutions.
A new AI assisted Fuzzy Multi-Criteria Decision-Making model is presented to assess the performance of an organization and to assist in making strategic management decisions to enhance the decision consistency, transparency of decision making, strategic alignment and managerial responsiveness.
Mukul Agarwal, M. Gupta, Martina Kant et al.· Journal of Intelligent Decis...· 0 citations
This study introduces an integrated decision-support framework to aid early-stage planning for building adaptation. It aims to support structured decision-making and priority-setting within organizations by combining scenario development, stakeholder evaluation and AI-enhanced communication.
The framework integrates cross-impact balance (CIB) analysis, analytic hierarchy process (AHP), Fuzzy-TOPSIS and generative AI techniques for scenario communication and visualization. It was applied within a Paris-based social housing association through participatory workshops with internal stakeholders, including architects, sustainability officers and project managers.
The integrated framework produced 21 internally consistent scenarios, prioritized them through stakeholder-weighted objectives and identified high-performing adaptation pathways. Results revealed that strong CIB-based scenario filtering substantially conditioned downstream MCDA behaviour, producing relatively robust but convergent ranking outcomes across structurally distinct scenarios. AI-generated narratives and visuals further supported communication of complex trade-offs and exploratory planning within the organizational context.
Application was limited to a single case and stakeholder group. Future research should test the method in broader multi-actor settings, incorporate participant validation and explore automation of CIB construction and weighting to improve scalability and reduce resource demands.
The approach helps decision-makers co-develop and compare building adaptation pathways aligned with organizational goals. Its modular design supports integration into asset management and planning systems.
This is the first study to combine CIB-based scenario planning with MCDA for building adaptation, enhanced with AI-supported scenario communication. Beyond methodological integration, the study contributes new insights into how upstream scenario-space conditioning influences downstream ranking behaviour, evaluative convergence and discriminatory capacity within exploratory decision-support systems.
Brian van Laar, Angela Greco, Hilde Remøy et al.· Smart and Sustainable Built...· 0 citations
Decision-making is one of the most fundamental activities within various organizations, and human resources are consistently involved in it, either individually or collectively. Decisions are always made in alignment with achieving organizational objectives; therefore, their quality directly or indirectly affects the entire organization and its human capital. Consequently, identifying and ranking the factors influencing decision-making plays a crucial role in enhancing organizational performance. In this regard, the present study aims to identify and rank the sub-criteria affecting the decision-making process using the Fuzzy Analytic Hierarchy Process (FAHP) technique. The findings indicate that the individual criterion exerts the highest influence, while the external organizational criterion is the least influential in the decision-making process. Among the sub-criteria, emotions and the alignment between competencies and job content rank the highest. This underscores their significance in performance evaluations and competency assessments during recruitment, selection, appointment, or strategic decision-making processes within organizations. Therefore, it is imperative for organizations to invest in these criteria to strengthen their decision-making processes.
Unknown authors· Management, Education and De...· 0 citations
Digital transformation has increased organizational reliance on big data analytics (BDA) to support strategic decisions and improve business performance. This study synthesizes evidence on how BDA contributes to strategic decision-making, organizational performance, and innovation through a systematic literature review. The review followed the PRISMA 2020 framework and searched Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language journal and conference publications from 2021 to 2026. After identification, screening, and full-text eligibility assessment, 33 studies were included and examined using thematic analysis. The findings show that BDA strengthens decision quality and speed by combining analytics capability, predictive modeling, artificial intelligence, and data-driven insights. BDA is also associated with operational efficiency, project success, organizational agility, customer personalization, competitive advantage, sustainability, and innovation capability. The dominant themes were strategic decision-making, business performance, sustainability and innovation, and artificial intelligence with predictive analytics. However, the literature provides limited evidence on explainable and ethical artificial intelligence, human-AI collaboration, real-time analytics, and BDA adoption among small and medium-sized enterprises and organizations in developing economies. The review contributes an integrated view of BDA as a socio-technical and strategic capability and recommends transparent, scalable, and human-centered analytics governance.
Amelia Contesa, Ilzi Adrolis, Wenni Syafitri et al.· Business System & Innova...· 0 citations
This study examines the Warehouse Management System (WMS) evaluation in digitalized logistics operations as a multidimensional decision-making problem. By integrating the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), the study develops a framework that links technology acceptance considerations with operational and strategic software evaluation criteria. The fuzzy Best–Worst Method (BWM) was applied to prioritize the criteria under uncertainty, using evaluations obtained from logistics professionals, software experts, warehouse managers, and academics. The findings show that Software Architecture and Flexibility is the most important main criterion, followed by Security and Performance and System Integration Capability. At the sub-criteria level, Compliance with Standard Protocols, Modularity, Scalability, ERP/TMS/CRM Integration, and Mobile Compatibility emerged as the five most critical determinants. The results indicate that logistics firms prioritize standard compliance, architectural flexibility, scalability, system integration, and mobile compatibility over user-centered criteria. The study contributes to the WMS evaluation literature by combining TAM and IDT with a fuzzy multi-criteria decision-making approach and provides managers with a structured framework for aligning WMS evaluation with operational fit and digital transformation goals.
Sustainable stormwater management requires strategic decision-making that balances environmental, technical, economic, and social considerations while strengthening organizational capacity and human resource readiness. This study aimed to develop a strategic decision-making framework for selecting sustainable stormwater management strategies using a Multi-Criteria Decision-Making (MCDM) approach.
The study employed the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate five alternative stormwater management strategies: conventional drainage, green infrastructure (GI), low-impact development (LID), nature-based solutions (NbS), and integrated stormwater management systems. Data were collected from 15 experts through structured questionnaires and semi-structured interviews. Four decision criteria—environmental, technical, economic, and social—were incorporated into the evaluation framework.
The AHP analysis showed that environmental criteria received the highest priority weight (38.2%), followed by economic (27.9%), technical (21.4%), and social (12.5%) criteria. TOPSIS results identified green infrastructure as the preferred strategy with the highest preference score (0.918), demonstrating superior overall performance across the evaluated criteria. Low-impact development and nature-based solutions also performed well, whereas conventional drainage ranked lowest due to its limited long-term sustainability. Sensitivity analysis confirmed the robustness of the ranking results.
The findings demonstrate that green infrastructure represents the most suitable strategy for sustainable stormwater management because it simultaneously improves environmental performance, enhances urban resilience, and provides broader social and economic benefits. Successful implementation, however, depends not only on technical feasibility but also on organizational readiness and the development of Green Human Resource Management (GHRM) competencies to support long-term sustainability.
Diah Pranitasari, S. H. Nugroho, Sri Harini et al.· Frontiers in Sustainability· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
