Decision Making: Applications in Management and Engineering
type: channel
description: Decision Making: Applications in Management and Engineering (DMAME) publishes high-quality scientific papers that contribute significantly to the fields of operational research and management science. The material published is of high quality and relevance, written in a manner that makes it accessible to all of this wide-ranging readership. Preference will be given to papers with decision-making implications for the practice of management and engineering. Aims and Scope: The principal aim of the journal is to bring together the latest research and development in various fields of decision making. We would like to highlight that papers should refer to Aims and scope, but they are not limited to. Publication Frequency: This journal is published two times a year. ----------------------------------------------------------------------------------------------------------- Open Access This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. ----------------------------------------------------------------------------------------------------------- DMAME's acceptance rate is 14% of the more than 450 major manuscripts it receives annually. In 2023, the median time for an initial editorial decision for submitted manuscripts was 10 days; the median time from submission to acceptance for all articles was 80 days, and 15 days from acceptance to online publication.
publisher: Scientific Oasis
language: en-US
publication name: Decision Making: Applications in Management and Engineering
issn: 2560-6018
items: Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models
items: Predictive Decision Analytics for Membership Retention and Expansion in Martial Arts Organisations
items: A Game-Theoretic and Multimodal Interaction Framework for Collaborative Robots in Smart Manufacturing
items: Digital Twin-Enhanced MCDM Framework for Circular Construction Dynamic Lifecycle Optimization of Hybrid Concrete Mix Design
items: A Decision-Support Framework for Composite Curved Beam Design under Moving Loads: Dynamic Modelling and Multi-Criteria Evaluation
items: Human-AI Decision Dynamics: How Risk Propensity and Trust Impact Choices Through Decision Fatigue, Conditional on AI Understanding
items: Decision-Making in Natural Disaster Response: A Comprehensive Review of Strategies, Models, and Technological Advancements
items: A Structured Decision-Making Approach for Supplier Performance Evaluation in the Restaurant Sector
items: A Decision Framework for Course Recommendation Using Basic Uncertain Linguistic Information Soft Sets
items: How does digitalization enhance sustainable economic development? The case of Azerbaijan and Hungary
items: Simulation-Based Decision Support for Call Centre Staffing Optimisation: A Case Study from the Palestinian Telecom Sector
items: How to Evaluate New Quality Productive Forces for Chinese Express Delivery Enterprises: A Hybrid DEMATEL-BBWM-CoCoSo Method
items: A Supportive Decision-Making Methodology Incorporating a Cost-Accounting System Tailored to Import Firms
items: Reducing Train Delays with Machine Learning-Based Predictive Maintenance for Railways
items: The Influence of Managerial Myopia on Strategic Differentiation: A Decision-Making Perspective
items: Financial Decision-Making as a Moderator between Corporate Governance, Audit Quality, and Financial Reporting Quality
items: A Decision-Support Approach to International Natural Gas Price Forecasting Using Machine Learning Models
items: Career Decision Making: Role of Family- Peer Belonging, Emotional Regulation and Academic Motivation
items: Factors Shaping Project Manager Salaries in Hungary: The Role of Certification, Experience, and Other Key Attributes
items: Enhancing Investors’ Decision-Making through Financial Risk Management: Role of Corporate Governance and Financial Communication Transparency
items: Decision-Making In Crisis: Insights From Hungarian SMES And Households During The Covid-19 Pandemic
items: Business Intelligence, technology anxiety and Strategic Learning as Drivers of Strategic Decision-Making: The Mediating Role of Decision-Making Agility
items: Role of Total Quality Management and Shared Decision-Making in Enhancing Operational Performance and Enterprise Risk Management Effectiveness
items: Carbon Nanotube-Titanium Composites-based Antennas for 6G Wireless Networks: Performance Evaluation
items: An Integrated AHP–DEMATEL Decision-Support Framework for Sustainable Closed-Loop Apparel Supply Chain Design and Stakeholder Alignment
items: From Training to Transformation: How Human-Centred Practices Shape Smart Manufacturing Outcomes
items: A Hybrid Statistical–Fuzzy Decision Framework for Housing and Rental Price Dynamics: Evidence from Spain
items: Enhancing ASEAN Competitiveness in Indonesia's Halal F&B Industry: Comparative AHP-ANP Analysis of SMEs and Large Enterprises
items: Achieving Climate Neutrality in Hungary by 2050: Application of the Pathways Explorer 2050 Model
items: A Decision-Support Model for Enhancing Innovative Business Performance: The Role of Technological Opportunism, Supervisory Support and Innovation Culture
items: Decision-Oriented Framework on Detecting and Countering Finance Management Fraud Amid Big Data Times: Characteristics, Risk Assessment and Technology-Driven Countermeasures
items: A Decision-Support Framework for Village Cultural Heritage Revitalization: Integrating Digital Twins, Optimization, and Participatory Planning Models
items: The Influence of Neural Computing Engineering and AI on Social Media User Behavior and Decision Making
items: Research on Transformation Efficiency of Scientific and Technological Achievements in Henan Province of China Based on Three-Stage DEA Model
items: Towards Efficient Ship Design: A Review of Methods and Attributes for Decision-Making in Ship Selection
items: Advancing Sustainable Material Selection in Construction: A Systematic Review of Multi-Criteria Decision-Making Applications
items: Estimating Future Stock Return for the pharmaceutical sector using Bayesian Vector Auto regression modeling and artificial Intelligence
items: A Decision-Support Framework for Economic Growth Forecasting under Smart Finance: Integrating Dynamic GMM, Threshold Regression, and Machine Learning Optimization
items: A Decision-Support Framework for Enhancing Industrial Chain Resilience through Green Finance: Evidence from China’s Sports Industry
items: A Hybrid Framework for Stock-Market Prediction Using COVID-19 Tweets through Nature-Inspired Algorithms and Machine Learning
items: Evaluating Service Quality in Infant and Childcare Institutions: A Decision-Making Framework Using Interval Type-2 Fuzzy MCDM
items: Design of a Cognitive Decision Support System Using Knowledge Graphs and Deep Reinforcement Learning for Real-Time Emergency Response
items: Application of an Optimized Version of Modified Elman Neural Network Using Improved Pelican Optimizer for Energy System Marginal Price Forecasting
items: Decision-Making and Risk Management in School Bullying Risk: Serial Mediation of Peer Relationships and Internet Addiction between Negative Parenting Styles and Adolescent Bullying
items: Emotion Regulation as a Determinant of Decision-Making Efficiency among Engineering and Administrative Sciences Students
items: Deep Learning for Structural Health Monitoring of Pavements for Improving Road Maintenance and Management
items: An Intelligent Decision-Support Framework Using Mobile and Virtual Reality Technologies for Optimising Intangible Cultural Heritage Management
items: An Integrated WENSLO-WASPAS Model to Prioritize Factors Contributing to Learned Helplessness in Export Processes
items: Enhancing Supply Chain Resilience in Disruption Caused by Catastrophes Such as Pandemics: An Integrated Grey Analysis-Stochastic Optimization Model
items: A Multi-Criteria Decision-Support Framework for Optimizing Digital Marketing Strategies in Indonesian SMEs Using Fuzzy AHP–TOPSIS
items: A Robust Hybrid Multi-Criteria Decision-Making Framework for Sustainable Transportation Planning Under Uncertainty

Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models
type: item
description: Synthesis of solutions based on a set of models is a modern trend in the field of multi-criteria choice. It is assumed that a solution based on many methods increases the reliability of the decisions made. One of the important tasks is to select an independent set of models. Comparison of various multi-criteria methods is performed using two lists: rank and rating. To compare the rating of alternatives obtained using different MCDM models, the article uses the Relative Performance Indicator (RPI). Using RPI, six identical methods for aggregating private attributes of alternatives are established: Weighted Sum Model (WSM), Ratio System approach (RS), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) with L1 metric, Multi Atributive Ideal-Real Comparative Analysis (MAIRCA) and Ranking of Alternatives with Weights of Criterion (RAWEC) provided that each aggregation method combines the same method of linear normalization of attributes. This allows avoiding duplication of equivalent methods in the Multi-Method Model (3M) approach combining different MCDM models. When solving MCDM problems, it is recommended to use the simplest and most easily interpreted of them: WSM. The presented methodology is recommended as mandatory for the analysis of new or hybrid MCDM methods to eliminate duplication of existing methods. A synthesis of a solution based on ratings obtained in different MCDM models within the 3M approach is proposed. The method includes coordinating the common goal of several models and bringing the ratings obtained in different MCDM models to a common scale, which allows comparing and aggregating the ratings. The resulting rating is more informative than a rating based on ranks, such as Borda rules or similar, since it reflects the real proportions of the effectiveness of alternatives in different models.
creator: Irik Z. Mukhametzyanov
creator: Dragan Pamucar
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 8
number: 2
starting page: 1
ending page: 20
doi: 10.31181/dmame8220251473

Predictive Decision Analytics for Membership Retention and Expansion in Martial Arts Organisations
type: item
description: A martial arts organisation that effectively retains its existing members while simultaneously attracting new ones is more likely to experience sustainable growth. Recent studies indicate that Artificial Intelligence (AI)-driven predictive decision analytics can significantly enhance member retention by evaluating training performance and movement precision. The system assesses martial arts competencies through the application of Kernel Principal Component Analysis (KPCA) for reducing spatial-temporal dimensions of contour features, in conjunction with Time Series Analysis (TSA) and human action recognition (HAR) methodologies. This assessment framework employs a hybrid classification model that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. The BiLSTM component interprets temporal sequences, while the CNN performs spatial analysis, facilitating a comprehensive evaluation of training movements. The operational structure comprises two sequential stages: initially, it detects standardised movements from expert demonstrations; subsequently, it evaluates learner performance through practical tests, establishing benchmarks for comparison. By monitoring patterns in skill progression, student dedication, and dropout risks, the system enables the provision of personalised training interventions aimed at improving retention rates. The empirical outcomes demonstrate that the model not only enhances training quality and member engagement but also leverages data insights to inform strategic decisions for expanding membership. Developers of this approach employ AI and predictive analytics to innovate martial arts training processes, thereby reducing short-term enrolments while fostering long-term organisational stability
creator: Liang Song
creator: Rabiatul-Adawiah Binti Ahmad Rashid
creator: Yong Li
creator: Bin Wang
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 8
number: 2
starting page: 21
ending page: 35
doi: 10.31181/dmame8220251474

A Game-Theoretic and Multimodal Interaction Framework for Collaborative Robots in Smart Manufacturing
type: item
description: Industrial assembly settings have encountered novel challenges following the integration of collaborative robots (cobots), particularly in maintaining a balance between effective human-robot interaction and operational efficiency. Interpreting and implementing complex human behaviours remains a significant difficulty, especially in conventional operations conducted under dynamic and unpredictable conditions. A critical requirement has emerged for the development of intelligent interfaces capable of autonomously regulating systems while facilitating seamless collaboration between human operators and robotic agents. This study focuses on a multimodal perception approach enhanced by game-theoretic optimisation, which has been shown to improve cobots’ responsiveness and strategic flexibility. Researchers have chosen this method due to its capacity to model interactive behaviours in variable environments. By integrating vision, auditory, and tactile sensing technologies, cobots are equipped to accurately interpret human communicative cues, even when operating with limited capabilities. The proposed framework utilises game theory to model the strategic and dynamic interactions between humans and robots, thereby addressing the dual challenges of task allocation and decision-making. This system supports optimal coordination and prompt conflict resolution, while enabling real-time behavioural adjustments based on utility-driven outcomes. Its efficacy has been validated through both simulated and practical implementations in industrial assembly scenarios, confirming its potential to enhance collaborative efficiency and safety while maintaining adaptability. Overall, this research presents a strategic design framework for cobot interaction that emphasises perceptual processing, contributing to substantial advancements in the evolution of intelligent manufacturing systems.
creator: Dongdong Chu
creator: Shulan Yu
creator: Yuhang Ling
creator: Yang Zhao
creator: Jianhong Zhang
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 8
number: 2
starting page: 36
ending page: 52
doi: 10.31181/dmame8220251475

Digital Twin-Enhanced MCDM Framework for Circular Construction Dynamic Lifecycle Optimization of Hybrid Concrete Mix Design
type: item
description: This study introduces an integrated, intelligent framework aimed at optimising hybrid concrete mix design by employing Digital Twin (DT) modelling, dynamic decision-making processes, and real-time optimisation. The DT model is developed to continuously mirror and update the changing physical characteristics of hybrid concrete materials, thereby facilitating predictive simulations and lifecycle assessments. Data quality is ensured through pre-processing and feature extraction procedures, which involve cleaning, normalisation, and parameter selection focused on compressive strength, recyclability, embodied carbon, and degradation indicators. A retained carbon emission accounting model is utilised to dynamically monitor environmental impacts throughout the construction lifecycle, thereby enabling the formulation of low-carbon mix design strategies. The framework incorporates recycled aggregates, biochar, and industrial waste materials, aligning with sustainability objectives and circular economy principles. A dynamic multi-criteria decision-making (MCDM) process, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), is employed to assess mix design alternatives. This process evaluates various criteria including cost factors, performance indicators, and environmental impacts, with adaptable weight adjustment mechanisms tailored to the specific phase of the project. Optimisation of mix designs is conducted using PSO, which dynamically integrates data derived from MCDM procedures and Decision Tables (DT). A real-time feedback and decision-making interface allows operators to actively oversee operations, enabling proactive adjustments and informed decisions. The integration of these components results in an adaptive and resilient system for sustainable hybrid concrete construction, balancing structural performance with environmentally conscious practices. Future investigations may expand the framework by incorporating AI-based predictive mai…
creator: Hongling Jiang
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 8
number: 2
starting page: 53
ending page: 70
doi: 10.31181/dmame8220251476

A Decision-Support Framework for Composite Curved Beam Design under Moving Loads: Dynamic Modelling and Multi-Criteria Evaluation
type: item
description: Behavior of curved beams subjected to moving loads has specific application in the structural design of bridges, railways, as well as machining frames. With the growth in applications of the composite material in structural members, in the form of beams, plates, as well as in columns, the stringent analysis of their transient loading behavior has been made inevitable. This paper investigates the dynamics of a moderately deep, orthotropic curved composite beam subjected to moving mass loads. Analysis is based on the first-order shear deformation theory in combination with Hamilton’s principle, for the identification of the governing partial differential equations in the spatial as well as time domains. These equations are then reduced to ordinary differential equations in the spatial domain using Navier’s method, while the temporal aspect is resolved using Newmark’s numerical integration scheme. The accuracy of the model's natural frequency predictions is validated through comparison with both straight and curved composite beam configurations, as well as with cases involving moving mass loads, demonstrating strong agreement with previously published results. In practical engineering scenarios, particularly in high-speed railway systems and aerospace structures, selecting the optimal configuration of curved composite beams necessitates careful consideration of factors such as stiffness, weight, manufacturability, and resilience to dynamic loading. The study reveals that increasing the mass and length of the beam leads to a notable amplification in its dynamic response. Additionally, the stacking sequence and orientation of the composite layers play a decisive role in influencing vibrational behaviour. Nonetheless, the configuration of the layers profoundly affects the dynamic responding related to the beam curved counting on a moving mass, with the amplitude of structural oscillations being maximized for layers and minimized for layers. To support optimal design strat…
creator: Nihayat Hussein Ameen
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-21
publication date: 2025-08-21
volume: 8
number: 2
starting page: 71
ending page: 95
doi: 10.31181/dmame8220251479

Human-AI Decision Dynamics: How Risk Propensity and Trust Impact Choices Through Decision Fatigue, Conditional on AI Understanding
type: item
description: This research investigates how trust in AI, risk-taking propensity, decision fatigue, and knowledge of AI interact to shape human-AI decision-making processes in organizational settings. With AI systems now central to decision-making, it is vital to understand the psychological and cognitive underpinnings behind their adoption and performance. This study seeks to examine these interplays and emphasize how these variables combine to determine decision results. Quantitative research design was used, which gathered data from 244 workers from different organizations. Structured questionnaires with previously validated measures were used. ADANCO software was utilized to analyze the data, where Structural Equation Modeling (SEM) was applied to examine the hypothesized associations between variables. The findings substantiated all six hypothesized paths. Decision making was positively affected by trust in AI and risk propensity, while decision fatigue negatively affected it. Decision fatigue mediated and AI understanding moderated many paths, affirming its key position within decision dynamics. The model provided strong explanatory power for AI-integrated decision contexts. The research has theoretical contribution by synthesizing psychological concepts with AI interaction scholarship. At a practical level, it provides tactical guidance for managers to develop AI decision systems to fit human cognitive traits and behavioral inclinations.
creator: Sheriff Y. Ahmed
creator: Jamshid Pardaev
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-10
publication date: 2025-08-10
volume: 8
number: 2
starting page: 96
ending page: 113
doi: 10.31181/dmame8220251484

Decision-Making in Natural Disaster Response: A Comprehensive Review of Strategies, Models, and Technological Advancements
type: item
description: Natural disasters such as hurricanes and earthquakes cause extensive damage, requiring effective decision-making frameworks during emergency responses. Disaster management traditionally involves four phases: Mitigation, Preparation, Response, and Recovery. This study focuses on the Response phase, aiming to evaluate and categorise existing disaster response models to enhance decision-making and address challenges during this critical period. Employing bibliometric analysis and systematic content review of 86 scholarly works, the study identifies key patterns, research gaps, and challenges in disaster response. Bibliometric methods reveal global research trends and collaboration networks, while content analysis uncovers recurring themes in decision-making, resource allocation, and communication. The findings highlight a shift towards technology-driven frameworks, including IoT, remote sensing, augmented reality, and blockchain, which aid real-time decisions and resource management. However, limitations remain in developing adaptive decision-support systems and fostering multi-stakeholder collaboration. Furthermore, low international co-authorship—especially among researchers from disaster-prone regions—indicates scope for enhanced global cooperation. The study recommends greater adoption of emerging technologies and strengthened international partnerships to improve disaster response effectiveness. Its systematic classification of response models offers a practical tool for advancing evidence-based decision-making and capacity building in emergency management.
creator: Vian Ahmed
creator: Zied Bahroun
creator: Omar Abdulkarim
creator: Hamdan Alteneji
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-10
publication date: 2025-08-10
volume: 8
number: 2
starting page: 114
ending page: 144
doi: 10.31181/dmame8220251490

A Structured Decision-Making Approach for Supplier Performance Evaluation in the Restaurant Sector
type: item
description: In the contemporary, highly competitive restaurant sector, securing high-quality food raw materials is pivotal to achieving sustained business success. An organised and systematic supplier selection process is fundamental for ensuring operational efficiency, cost control, and consistent service standards. The present study introduces a structured framework designed to assess and rank restaurant raw material suppliers by integrating subjective evaluations across multiple criteria. The proposed methodology follows a two-phase process. The first phase entails the identification of essential supplier evaluation criteria, utilising rank-based weighting methods to determine their relative significance from the perspective of restaurateurs in Thailand. In the second phase, the fuzzy technique for order preference by similarity to the ideal solution (fuzzy TOPSIS) is applied to assess and rank potential suppliers based on their aggregate performance. A practical case study is presented involving an independent full-service restaurant in Thailand that was required to select from four candidate suppliers. This case is used to demonstrate the effectiveness of the framework. Furthermore, a sensitivity analysis is conducted to examine the impact of different weighting approaches on the final supplier rankings. The results highlight eight key evaluation criteria: quality level, product cost, responsiveness speed, communication systems, delivery performance, lead time, fill rate, and process flexibility. Findings from the sensitivity analysis indicate that supplier rankings vary according to the weighting methodology applied, underscoring the necessity for adaptable decision-making models. Such flexibility is crucial as the relative importance of these criteria can shift in response to market dynamics or specific operational priorities. For restaurant operators, adopting a systematic approach is vital to address the inherent complexity and uncertainty of supplier assessments. Inco…
creator: Sirawadee Arunyanart
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-06-29
publication date: 2025-06-29
volume: 8
number: 2
starting page: 145
ending page: 164
doi: 10.31181/dmame8220251493

A Decision Framework for Course Recommendation Using Basic Uncertain Linguistic Information Soft Sets
type: item
description: The aim of this paper is to provide fundamental theoretical studies on basic uncertain linguistic information soft set (BULISS). Firstly, the combination of basic uncertain linguistic information and soft set is introduced. Next, set operations and similarity measure on basic uncertain linguistic information soft sets and their properties are discussed. A novel application of basic uncertain linguistic information soft set to multi-criteria group decision making is put forward, in which the similarity measure between any two BULISSs is developed. A group decision algorithm by utilizing traditional decision procedure of soft set theory (or fuzzy soft set theory) and optimization method is given. Finally, a case study relating to curriculum recommendation is shown to illustrate feasibility and validity of the developed group decision making approach.
creator: Peitao Qin
creator: Zhifu Tao
creator: Dragan Pamucar
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-10
publication date: 2025-08-10
volume: 8
number: 2
starting page: 165
ending page: 184
doi: 10.31181/dmame8220251494

How does digitalization enhance sustainable economic development? The case of Azerbaijan and Hungary
type: item
description: Understanding the economic consequences of digitalisation is essential for designing policies that foster inclusive and sustainable development, particularly within emerging and transition economies. This research explores the influence of digitalisation on sustainable economic progress in Azerbaijan and Hungary, utilising annual data spanning from 2000 to 2023. Employing a hybrid methodological approach, the study integrates conventional econometric analysis (VECM) with supervised machine learning techniques (ARIMA and XGBRegressor) to provide a comparative assessment of the economic impacts resulting from digital transformation. The empirical results indicate that, in the short term, variables associated with information and communication technology (ICT) do not exert a statistically significant effect on economic growth. This finding suggests that the economic benefits of digitalisation may take time to materialise. Conversely, in the long term, digitalisation demonstrates a notable influence on GDP per capita in both nations. Specifically, in Azerbaijan, a 1 percent rise in Computer, communications, and other services (CCS) correlates with a decrease of $173.68 in GDP per capita, while Hungary experiences a reduction of $516.28 under similar conditions. Additionally, mobile subscriptions (MSC) and the contribution of high-tech manufacturing value-added (MHTMV) are associated with adverse effects on Azerbaijan’s economic development. Forecasts generated through machine learning further predict economic expansion in Hungary over the coming five years, whereas Azerbaijan is projected to encounter economic contraction. The study concludes by underscoring the importance of long-term policy measures centred on digital infrastructure, innovation potential, and human capital enhancement in order to optimise the economic outcomes of digital transformation.
creator: Mirvari Gazanfarli
creator: Mónika Garai-Fodor
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-10
publication date: 2025-08-10
volume: 8
number: 2
starting page: 185
ending page: 208
doi: 10.31181/dmame8220251496

Simulation-Based Decision Support for Call Centre Staffing Optimisation: A Case Study from the Palestinian Telecom Sector
type: item
description: This research introduces a decision support model based on simulation, designed to enhance staffing strategies and elevate operational performance within a technical support call centre operated by a prominent Palestinian internet service provider. Call centres frequently encounter challenges arising from fluctuating customer demand, often resulting in extended waiting times and elevated abandonment rates, which adversely affect customer satisfaction. To mitigate these issues, a discrete event simulation was developed through the ProModel© platform, integrating statistically fitted probability distributions for call arrival patterns, call handling durations, and customer abandonment tendencies. Following validation, the simulation was utilised to assess various staffing alternatives, with particular focus on reallocating personnel across different shifts. The findings revealed that moving a single agent from the morning shift (8:00 AM to 4:00 PM) to the high-demand afternoon shift (4:00 PM to 12:00 AM) yielded significant enhancements in key performance indicators. This adjustment resulted in a 15% reduction in average customer waiting times and a 27% decrease in call abandonment rates, achieved without increasing the overall staffing level. The study underscores the practical utility of simulation modelling as an evidence-based approach to resource allocation in service-oriented environments. The adopted methodology aligns with established standards in call centre simulation and presents a transferable framework suitable for similar applications within emerging economies.
creator: Tamer Haddad
creator: Ramiz Assaf
creator: Siraj Zahran
creator: Mohammad Kanan
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-10
publication date: 2025-08-10
volume: 8
number: 2
starting page: 209
ending page: 224
doi: 10.31181/dmame8220251497

How to Evaluate New Quality Productive Forces for Chinese Express Delivery Enterprises: A Hybrid DEMATEL-BBWM-CoCoSo Method
type: item
description: The integration of New Quality Productive Forces (NQPF), characterised by innovation, advanced technology, operational efficiency, and high-quality outputs, is transforming express delivery enterprises in China. Conventional performance evaluation systems are increasingly inadequate for assessing the evolving requirements associated with NQPF. To address this gap, this study proposes a comprehensive evaluation framework encompassing three principal dimensions: strategic leadership, technological innovation, and development support. A hybrid Multi-Criteria Decision Making (MCDM) approach, incorporating DEMATEL, Best–Worst Method (BBWM), and Combined Compromise Solution (CoCoSo), is employed to account for interdependencies among evaluation indicators, improve the precision of weighting, and maintain consistency in group decision-making, thereby enhancing the reliability of performance assessments. A case study of five major A-share listed Chinese express delivery firms illustrates the practical application of the proposed framework. The methodology provides a rigorous decision-support tool for evaluating NQPF and yields strategic insights to facilitate sustainable development within the express delivery sector. By aligning the advancement of NQPF with broader industry quality improvements, the study delivers actionable recommendations for modernising China’s logistics industry through innovation-driven productivity transformation.
creator: Junchi Ma
creator: Zhenheng Hu
creator: Qiaoyuan Zhang
creator: Siyue Zhao
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-06-29
publication date: 2025-06-29
volume: 8
number: 2
starting page: 225
ending page: 244
doi: 10.31181/dmame8220251501

A Supportive Decision-Making Methodology Incorporating a Cost-Accounting System Tailored to Import Firms
type: item
description: Import firms bound by long-term, fixed-price agreements are exposed to considerable risks arising from cost fluctuations over time, particularly within unstable global markets. This research seeks to design a systematic decision-support methodology that assists such firms in determining whether to accept or decline prospective delivery contracts. The proposed framework comprises two stages. The first stage involves the development of a cost-accounting system specifically aligned with import activities. This system integrates dynamic factors, including exchange rate variations, freight charges, and commodity price changes, in order to estimate both the anticipated profit and its associated standard deviation for each contract. The second stage introduces a mathematical decision-support model that applies a probability-based acceptance criterion. This criterion is established through a risk-preference survey completed by the firm’s decision-makers. The framework is demonstrated through a practical case study of a major Israeli import company (hereafter referred to as Tile-Art), which specialises in importing porcelain tiles and sanitary ware. Drawing on historical contract records, scenario simulations are conducted under different levels of forecasting precision, enabling a comparison between the proposed methodology, conventional approaches, and idealised benchmarks. The findings indicate that the suggested model substantially lowers the number of unprofitable agreements accepted by the firm, particularly when market volatility is high and contracts extend over longer periods. Overall, the evidence reveals that the tool produces more stable and dependable outcomes than models that overlook either risk preferences or cost variability. This study therefore contributes a novel and adaptable instrument for enhancing risk-conscious decision-making in import-oriented sectors.
creator: Dmitry Tsadikovich
creator: Avi Herbon
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-06-29
publication date: 2025-06-29
volume: 8
number: 2
starting page: 245
ending page: 264
doi: 10.31181/dmame8220251513

Reducing Train Delays with Machine Learning-Based Predictive Maintenance for Railways
type: item
description: The railway network constitutes a vital component of public transportation in many countries, serving millions of passengers and transporting significant volumes of freight. Nevertheless, a persistent challenge within this system is the frequent occurrence of train delays, which arise from diverse causes and result in financial losses, passenger dissatisfaction, and diminished trust among users. Consequently, enhancing operational efficiency and minimising delays has become a central objective for transportation planners and policymakers. In addressing this issue, the present study applies machine learning algorithms (MLAs), specifically multilayer perceptron (MLP) neural networks and the adaptive neuro-fuzzy inference system (ANFIS), to predict potential defects in railway vehicles and improve maintenance and repair strategies within the Iranian railway network. The findings reveal that ANFIS achieves superior predictive accuracy. Building on this, a mathematical model in combination with the Particle Swarm Optimization (PSO) algorithm was developed to optimise train allocation across stations and generate schedules aimed at reducing delays. The employed algorithms proved to be highly effective for predictive maintenance and repair of railway vehicles, ultimately contributing to delay reduction within the railway system.
creator: Mohammad Khalilzadeh
creator: Dragan Pamucar
creator: Ali Heidari
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-07-22
publication date: 2025-07-22
volume: 8
number: 2
starting page: 265
ending page: 284
doi: 10.31181/dmame8220251514

The Influence of Managerial Myopia on Strategic Differentiation: A Decision-Making Perspective
type: item
description: In highly complex and evolving business environments, the methodological soundness of corporate strategic decision-making plays a pivotal role in shaping both organizational risk resilience and competitive positioning. This research integrates Upper Echelons Theory with Strategic Balance Theory to construct a decision science framework that examines the interrelationship among managerial language, cognitive orientation, and strategic behaviour. Employing text mining techniques, the study quantifies executive myopic tendencies within Management Discussion & Analysis (MD&A) disclosures and systematically assesses their influence on corporate strategic differentiation. The analysis utilises panel data from A-share listed companies on the Shanghai and Shenzhen stock exchanges over the period 2014–2022. Based on the outcomes of the Hausman test, a fixed-effects model is applied as the principal estimation method. To enhance methodological robustness, Propensity Score Matching (PSM) is implemented to address potential sample selection bias, while Two-Stage Least Squares (2SLS) estimation is used to correct for endogeneity concerns. Empirical results indicate that managerial myopia exerts a significantly negative impact on strategic differentiation, with findings remaining consistent after controlling for firm age, size, profitability, and other relevant covariates. Mediation analysis reveals that risk-taking propensity serves as a complete mediator, whereas moderation tests indicate that the effect is intensified in state-owned enterprises and firms with limited analyst coverage. These findings carry important implications for strategic decision-making: organisations are encouraged to adopt mechanisms that mitigate cognitive biases through decision-support systems and enhanced executive performance metrics. Policymakers may consider implementing stricter disclosure regulations, promoting analyst engagement for under-covered firms, and facilitating state-owned enterprise r…
creator: Tianjiao Zhang
creator: Qingqing He
creator: Yanan Gao
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-08-13
publication date: 2025-08-13
volume: 8
number: 2
starting page: 285
ending page: 298
doi: 10.31181/dmame8220251516

Financial Decision-Making as a Moderator between Corporate Governance, Audit Quality, and Financial Reporting Quality
type: item
description: The study aimed to investigate the influence of corporate governance mechanisms and audit quality on financial reporting quality with the moderating effect of financial decisions in the Saudi Arabian non-financial sector. For this purpose, longitudinal data were collected from annual reports of Saudi Arabian non-financial companies from 2018 to 2024. The regression results revealed that board size, gender diversity, board independence, directors’ expertise, audit committee effectiveness, and audit quality significantly and positively influence FRQ. Furthermore, the results indicate that financial leverage, as a measurement of financing decisions, negatively moderates the relationship between governance mechanisms and FRQ, suggesting that excessive reliance on debt financing can weaken governance effectiveness. However, financial leverage positively moderates the association between audit quality and FRQ, implying that prudent financial policies strengthen the monitoring role of auditors and improve reporting integrity. The study's theoretical contributions to agency, resource dependence, and stakeholder theories by showing how governance mechanisms interact with financing structures to shape reporting outcomes in an emerging market context. Practically, the findings provide actionable insights for firms and policymakers, emphasizing the need to optimize board structures, promote gender diversity, enhance independence, and engage high-quality auditors while aligning financing strategies with governance frameworks. By integrating sound governance with prudent financial decision-making, Saudi non-financial firms can raise transparency, enhance investor trust, and accelerate their integration into global capital markets.
creator: Mohammed Abdullah Ammer
creator: Tuygunoy Mamadjanova
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-04
publication date: 2025-09-04
volume: 8
number: 2
starting page: 299
ending page: 318
doi: 10.31181/dmame8220251518

A Decision-Support Approach to International Natural Gas Price Forecasting Using Machine Learning Models
type: item
description: Accurate forecasting of international natural gas prices is essential for effective decision-making within the context of a volatile energy market, as unpredictable responses to price fluctuations often arise from non-stationary behaviour. Traditional econometric approaches frequently encounter limitations in capturing market volatility, nonlinear dynamics, and structural breaks, which diminishes their practical utility for strategic planning and operational decisions. This study seeks to conduct a comparative analysis of multiple machine learning (ML) techniques, including linear regression, support vector machines, decision trees, random forests, neural networks, and ensemble methods, in forecasting international natural gas prices using datasets obtained from the International Energy Agency (IEA), Global Data, and the Bloomberg terminal. The findings indicate that more sophisticated ML models, particularly ensemble methods and neural networks, outperform conventional forecasting approaches in terms of accuracy and reliability. Furthermore, the study highlights that forecasts generated through ML can significantly enhance decision-making processes for key stakeholders in the natural gas sector, including government policymakers, investors, and oil and gas producers, by informing structured risk management, optimising resource allocation, and supporting long-term strategic planning
creator: Peifeng Wu
creator: Yaqiang Chen
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-04
publication date: 2025-09-04
volume: 8
number: 2
starting page: 319
ending page: 337
doi: 10.31181/dmame8220251519

Career Decision Making: Role of Family- Peer Belonging, Emotional Regulation and Academic Motivation
type: item
description: The study aimed to test the influence of family and peer belongings on the career decision-making difficulties through academic motivation and emotional regulation. For this objective, quantitative data were collected from 264 students of Saudi Arabian colleges. Hypothesis results showed that family and peer belongings have a significant impact on emotional regulation and academic motivation. On the other hand, emotional regulation also has a significant impact on career decision-making difficulties. While academic motivation has an insignificant effect on career decision-making difficulties. The study with these findings contributed that strong family and peer support played an integral role in increasing the students ' emotional and academic motivations, which leads to improved career decision making of students. Therefore, career guidance programs should integrate family peer dynamics and focus on building these psychological strengths to better support students’ choices. Research limitations to explore the new research framework
creator: Yusra Zaki Aboud
creator: Rommel Al Ali
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-05
publication date: 2025-09-05
volume: 8
number: 2
starting page: 338
ending page: 356
doi: 10.31181/dmame8220251522

Factors Shaping Project Manager Salaries in Hungary: The Role of Certification, Experience, and Other Key Attributes
type: item
description: Research on the determinants of wages remains relatively limited, particularly within Central and Eastern Europe. This study addresses this gap by examining the personal factors that influence both salary levels and overall remuneration of project managers. The investigation employed two perspectives, one centred on corporate positions and the other on the project management discipline. Data were collected from Hungarian project managers using the 11th edition of the Project Management Institute’s Salary Survey questionnaire. A total of 257 valid responses were analysed through descriptive statistics and multivariate methods. The results revealed several variables with a significant effect on salary and total compensation. In relation to salary, eight factors were identified: possession of a PMP® certification (13%), length of professional experience (2% per year), years of project management experience (1–2% per year), along with five specific job positions (21–33%). For total compensation, six key determinants were found: PMP® certification (15–16%), work experience (2% per year), project management experience (2% per year), weekly working hours (0.6% per hour), portfolio management competence (12%), non-predictive approaches such as agile, iterative or incremental methods (10–13%), and risk management competence (13–14%). These insights are valuable for project management practitioners when negotiating remuneration and also provide a basis for researchers and human resource specialists to enhance salary and benefits estimation models.
creator: Bálint Blaskovics
creator: Ágnes Csiszárik-Kocsir
creator: János Tóth
creator: László Kremmer
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-01
publication date: 2025-09-01
volume: 8
number: 2
starting page: 357
ending page: 375
doi: 10.31181/dmame8220251526

Enhancing Investors’ Decision-Making through Financial Risk Management: Role of Corporate Governance and Financial Communication Transparency
type: item
description: Financial communication transparency is a foundation of modern corporate governance, as it enhances stakeholder trust, reduces information asymmetry, and ensures market efficiency. Within the investor decision-making perspective, a lack of transparent communication restricts access to reliable information, which leads to uncertainty that can result in poor investment choices that could weaken firm performance. To address this issue, the present study aimed to investigate the mediating role of financial risk management in the relationship between corporate governance mechanisms and financial communication transparency. The researchers utilized the secondary annual reports data from the large-scale manufacturing companies in Saudi Arabia covering the period 2015 to 2024. The direct effect regression results show that board size, board independence, board diversity, and audit committee effectiveness significantly enhance financial communication transparency, while CEO duality and ownership structure negatively affect financial communication transparency. Mediating analysis further demonstrated that financial risk management partially mediates the positive impacts of board size, diversity, independence, and audit committees on FCT, while fully mediating the negative effects of CEO duality and ownership structure on financial communication transparency. This suggests that effective risk management systems absorb governance weaknesses and reinforce transparency, which is important to increase investors' positive decision-making in their investment. The study contributed theoretically by extending agency, stakeholder, and resource dependence theories and provides practical implications for boards, regulators, and policymakers, emphasizing the importance of robust governance and financial risk management frameworks to achieve sustainable transparency and stakeholder confidence in their investment decisions.
creator: Mohammed Abdullah Ammer
creator: Abdisamat Sattarov
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
volume: 8
number: 2
starting page: 376
ending page: 394
doi: 10.31181/dmame8220251527

Decision-Making In Crisis: Insights From Hungarian SMES And Households During The Covid-19 Pandemic
type: item
description: This study aims to explore the transformations in consumer behaviour in Hungary resulting from the COVID-19 pandemic and the corresponding responses by businesses. The research incorporated semi-structured, in-depth interviews conducted with Hungarian enterprises between late February and early March 2023. The participants comprised owner-managers of 25 domestic small and medium-sized enterprises (SMEs), whose insights offered an understanding of organisational adaptability and resilience during times of crisis. In preparation for the quantitative phase of consumer research, twelve focus group interviews were carried out in February 2023 with young adult participants. These discussions sought to understand consumer responses to the crisis, shifts in purchasing and consumption behaviours, the perceived impacts of the pandemic, their personal experiences, and potential coping mechanisms. Subsequently, in November 2023, a nationally representative survey was administered to Hungarian consumers aged 18 and above, aiming to assess the behavioural shifts triggered by the coronavirus pandemic during 2020–2021. Data gathered from 1,000 respondents were analysed using cross-tabulation (Pearson’s chi-square test) and correspondence analysis to identify the key determinants influencing consumers’ financial wellbeing and the extent to which newly formed shopping habits were retained. The principal outcome of the investigation indicates that, although the pandemic engendered widespread economic instability and altered consumption dynamics, individuals' subjective experiences of financial difficulty often exceeded the realities reflected in macroeconomic indicators. Resilience appeared to vary across demographic segments, with most behavioural adaptations occurring out of necessity rather than choice. These insights underscore the imperative for tailored public policy interventions to assist SMEs in navigating economic volatility. Such measures may include financial support mecha…
creator: Titanilla Oravecz
creator: Éva Pólya
creator: Zoltán Máté
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
volume: 8
number: 2
starting page: 395
ending page: 413
doi: 10.31181/dmame8220251538

Business Intelligence, technology anxiety and Strategic Learning as Drivers of Strategic Decision-Making: The Mediating Role of Decision-Making Agility
type: item
description: This study examines the relationships between business intelligence (BI), strategic learning (SL), technology anxiety (TA), decision-making agility (DMA), and strategic decision-making (DM) in the Iraq’s IT sector. A quantitative research design has been considered and employed, and survey data were collected from 224 top officials from the targeted sector. The analysis was conducted using Smart PLS 4.0 to test both direct and mediating effects. However, the study also focuses on testing the measurement model before applying the structural equation modelling technique. The results show that DMA has a positive impact on strategic DM, and BI significantly enhances DMA but not DM on direct grounds. Moreover, it is observed that strategic learning tends to exert a positive influence on both DMA and DM, while TA shows no significant effect on the given constructs. Mediation analysis also additionally claiming that DMA plays a significant mediating role between SL and DM, and between BI and DM, respectively. These findings highlight the importance and organizational need of agility as a key mechanism through which learning and intelligence improve strategic decision-making in technology-driven organizations specifically in the Iraq region and in generally for the other regional economies. Besides, the limitations and future directions are also discussed.
creator: Ahmed Sameer. N. Al-Thabit
creator: Murtadha Abdul Ghafoor Nsaif
creator: Anmar Shihab Ahmed
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-07
publication date: 2025-09-07
volume: 8
number: 2
starting page: 414
ending page: 424
doi: 10.31181/dmame8220251539

Role of Total Quality Management and Shared Decision-Making in Enhancing Operational Performance and Enterprise Risk Management Effectiveness
type: item
description: This study examines how total quality management (TQM) contributes to enterprise risk management (ERM) effectiveness, with a focus on the mediating role of operational and quality performance and the moderating role of shared decision making. The research aims to provide insights into how quality-driven practices enhance both organizational performance and resilience against risks. A quantitative, cross-sectional design was adopted, using a structured questionnaire administered to a sample of 275 employees engaged in quality and risk-related functions. Total quality management was measured across four key dimensions, while operational and quality performance, enterprise risk management effectiveness, and shared decision making were assessed using established scales. Data were analyzed using SmartPLS to evaluate both the measurement and structural models. The results reveal that TQM has a significant positive effect on both ERM effectiveness and operational and quality performance. Operational and quality performance significantly mediates the TQM–ERM relationship, while shared decision making strengthens the link between TQM and operational and quality performance. This study integrates quality and risk management research, demonstrating that TQM’s impact on ERM is enhanced through performance improvements and participatory governance, offering both theoretical advancement and practical guidance for building resilient organizations.
creator: Jehad Abdallah Atieh Afaneh
creator: Khaled Mohammed Ahmed Alqasa
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-05
publication date: 2025-09-05
volume: 8
number: 2
starting page: 425
ending page: 443
doi: 10.31181/dmame8220251542

Carbon Nanotube-Titanium Composites-based Antennas for 6G Wireless Networks: Performance Evaluation
type: item
description: The development of innovative communication systems employing carbon nanotube–titanium (CNT–Ti) composite-based antennas remains a challenging task, particularly with regard to optimising their performance. Consequently, the design phase assumes a critical role in advancing these antennas. In this context, the performance of CNT–Ti composite antennas was assessed for their potential application in future 6G wireless communication networks. These antennas exhibited notable characteristics, including high efficiency, wide bandwidth, considerable flexibility, and improved impedance features, rendering them well-suited for 6G networks and high-speed data transmission. Furthermore, the analysis of CNT–Ti composite antennas revealed superior properties relative to conventional antennas, such as enhanced electrical conductivity, low loss tangent, minimal energy dissipation, high signal efficacy, broad bandwidth, and reduced voltage standing wave ratio (VSWR). Although antennas with specialised directional patterns, such as those used in satellite communications, may face constraints in widespread application due to their large size and high manufacturing costs, the study emphasised that further research should focus on optimising the production processes, cost-efficiency, design scalability, and long-term reliability of these antennas. Such investigations would facilitate their sustainable integration into large-scale wireless network technologies.
creator: Jafaar Fahad A. Rida
creator: Basim Abood
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
volume: 8
number: 2
starting page: 444
ending page: 464
doi: 10.31181/dmame8220251555

An Integrated AHP–DEMATEL Decision-Support Framework for Sustainable Closed-Loop Apparel Supply Chain Design and Stakeholder Alignment
type: item
description: To establish an effective closed-loop apparel supply chain (CLASC), it is essential to achieve a workable balance across environmental, social, and economic priorities while also addressing the varied expectations of participating actors. This study proposes an integrated application of the Analytic Hierarchy Process (AHP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to support consensus-building during the planning and design stages of Community-Led Action for Sustainable Communities (CLASC). Within the AHP stage, specialist panels evaluate and prioritise the principal criteria from the standpoint of manufacturers, retailers, consumers, and regulatory authorities. The DEMATEL phase then clarifies how these criteria interact, particularly in terms of influence and dependence among system elements. Considering the insights jointly derived from both models enables stakeholders to recognise the underlying factors and relational pathways that shape the functioning and performance of the closed-loop structure. To verify the usefulness of the framework, empirical evidence and professional perspectives from supply chain practitioners were incorporated. The findings indicate that environmental regulation adherence, well-organised reverse logistics, effective stakeholder coordination, and consumer awareness are central considerations when establishing a CLASC. Additionally, DEMATEL exposes key causal linkages and dominance patterns that must be managed so that the priorities of various supply chain participants can be harmonised. The combined AHP–DEMATEL approach therefore strengthens decision clarity and promotes unified strategic direction. It contributes to circular economy research by offering a practical multi-criteria decision-support tool tailored for the apparel industry, and provides valuable guidance for both supply chain managers and policy developers aiming to implement sustainability-oriented, stakeholder-driven closed-loop arrangements.
creator: Yunfan Zhang
creator: Eakachat Joneurairatana
creator: Jirawat Vongphantuset
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
volume: 8
number: 2
starting page: 465
ending page: 479
doi: 10.31181/dmame8220251557

From Training to Transformation: How Human-Centred Practices Shape Smart Manufacturing Outcomes
type: item
description: This study investigates the influence of continuous learning and upskilling, ergonomics and safety, and the optimisation of human-cyber-physical interfaces on workforce adaptability and the performance of smart factories. Furthermore, it explores the mediating role of workforce adaptability within this context. Data were collected from a sample of 316 participants employed in the manufacturing sector of Saudi Arabia and analysed using the statistical software JASP version 0.95.4.0. The findings reveal that optimisation of human-cyber-physical interfaces exerts a significant effect on both workforce adaptability and smart factory performance. Regarding continuous learning and upskilling, results indicate a significant relationship with workforce adaptability, yet no direct association with smart factory performance was observed. Similarly, ergonomics and safety practices significantly affect workforce adaptability, but do not directly influence smart factory performance. Workforce adaptability itself was found not to have a direct impact on smart factory performance. Nonetheless, the analysis confirmed that workforce adaptability serves as a mediating factor between continuous learning and upskilling, ergonomics and safety integration, human-cyber-physical interface optimisation, and smart manufacturing performance. The empirical model proposed in this study represents a novel and valuable addition to existing literature. Additionally, the study offers practical recommendations for enhancing smart manufacturing performance within the Saudi Arabian context.
creator: Hamza Ghulman
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-09-29
publication date: 2025-09-29
volume: 8
number: 2
starting page: 480
ending page: 495
doi: 10.31181/dmame8220251558

A Hybrid Statistical–Fuzzy Decision Framework for Housing and Rental Price Dynamics: Evidence from Spain
type: item
description: Real estate markets demand a nuanced comprehension of the evolving interplay between housing prices and rental rates. Traditional economic theory posits that rents drive property prices; however, contemporary evidence indicates a reversal, suggesting that fluctuations in housing prices can, in fact, influence rent levels. This study introduces a decision-support analytics framework to address this phenomenon, combining fractional integration (ARFIMA), fractional cointegration (FCVAR), and time–frequency domain causality analysis. Examination of the Spanish housing market yields three key insights. First, housing prices display persistent responses to shocks, causing rental rates to revert to equilibrium only over intermediate periods. Second, causality analyses reveal that property values significantly influence rental dynamics, confirming a directional effect. Third, long-term cointegration is evident, implying that rental adjustments facilitate the realignment of the housing–rental system towards equilibrium. The proposed framework equips real estate investors, policymakers, and urban planners with enhanced tools for decision-making, supporting refined risk assessment, optimal investment timing, and evidence-based housing affordability strategies. Overall, the findings advance management and economic policy-making by demonstrating how sophisticated time-series methodologies can inform sustainable housing market interventions.
creator: Manuel Monge
creator: Rafael Hurtado
creator: Juan Infante
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-10-20
publication date: 2025-10-20
volume: 8
number: 2
starting page: 496
ending page: 508
doi: 10.31181/dmame8220251560

Enhancing ASEAN Competitiveness in Indonesia's Halal F&B Industry: Comparative AHP-ANP Analysis of SMEs and Large Enterprises
type: item
description: This research explores the distinctions in strategic competitiveness between large enterprises and small and medium-sized enterprises (SMEs) within Indonesia’s halal food and beverage sector, specifically in the context of the ASEAN market, through a comparative Analytical Hierarchy Process–Analytical Network Process (AHP–ANP) methodology. Although Indonesia possesses significant demographic advantages, its participation in global halal exports remains relatively modest. The analysis reveals that SMEs regard regulatory adherence as the most crucial element of competitiveness, highlighting the necessity for more streamlined certification and regulatory frameworks. In contrast, large enterprises concentrate on market expansion, focusing on understanding consumer preferences, differentiating halal offerings, and ensuring alignment with global benchmarks. Both SMEs and larger firms acknowledge the importance of product innovation and supply chain efficiency in enhancing competitive capabilities. Technological advancements, including blockchain-based traceability and digital marketing tools, are gaining prominence across the sector; however, their implementation is notably more advanced among large enterprises. The findings indicate that policy interventions should aim to simplify halal certification processes and encourage technology adoption among SMEs, whereas larger firms should prioritise branding strategies and international market penetration. By systematically ranking strategic priorities, this study contributes to the existing body of knowledge on halal industry competitiveness and offers differentiated policy and managerial recommendations based on enterprise scale. Prospective studies are encouraged to build upon this framework through longitudinal and multi-country investigations.
creator: Donald Crestofe Lantu
creator: Sri Herliana
creator: Sudrajati Ratnaningtyas
creator: Mia Rosmiati
creator: Inayati Fadhilah
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-10-20
publication date: 2025-10-20
volume: 8
number: 2
starting page: 509
ending page: 533
doi: 10.31181/dmame8220251567

Achieving Climate Neutrality in Hungary by 2050: Application of the Pathways Explorer 2050 Model
type: item
description: This research utilises the Climate 2050 Pathways Explorer (PE) model, which incorporates Hungary’s social and economic context, to explore pathways for achieving climate neutrality by 2050. The study integrates historical data, sectoral reports, and strategies outlined by the Hungarian government, employing a freely accessible online simulation model. The PE model provides an economy-wide analysis, examining interactions across nearly one hundred adjustable parameters. Dynamic and static scenarios are compared to assess the extent and nature of emissions reductions across sectors such as energy, transportation, construction, food, agriculture, forestry, industry, and waste management, in response to various policy interventions. The findings indicate that Hungary’s current National Energy and Climate Plan (NECP) is insufficient to achieve the 2050 climate neutrality target. Alternative scenarios are proposed, ranging from projections based on historical trends to strategies necessitating transformative changes, innovation, and significant investment. The study underscores the importance of improving energy efficiency, expanding renewable energy sources, reducing dependence on fossil fuels, and decarbonising critical sectors such as transportation and agriculture through technological advancements and policy measures. The analysis highlights the necessity of adopting advanced technologies over an extended timeframe and implementing long-term policies supported by comprehensive investment programmes. These measures are essential for enabling Hungary to align with global efforts to combat climate change and transition to a low-carbon economy.
creator: Robert Santa
creator: Monika Rajcsanyi-Molnar
creator: Istvan Andras
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-11-27
publication date: 2025-11-27
volume: 8
number: 2
starting page: 534
ending page: 553
doi: 10.31181/dmame8220251568

A Decision-Support Model for Enhancing Innovative Business Performance: The Role of Technological Opportunism, Supervisory Support and Innovation Culture
type: item
description: This research examines the factors influencing innovative business performance by analysing how technological opportunism, supervisor support for innovation, innovation engagement, and organisational innovation culture affect managerial and organisational decision-making in innovation contexts. Employing a decision-analytic perspective and structural equation modelling using Smart PLS 3, quantitative data from the manufacturing industry were analysed. The findings indicate that technological opportunism impacts innovative business performance both directly and indirectly, highlighting the critical role of technology-related decision-making and adoption strategies. Additionally, the results demonstrate that supervisor support for innovation plays a crucial role in encouraging employees to participate in innovation activities, thereby enhancing overall innovation outcomes. The study also emphasises that an organisational culture promoting experimentation and inclusive decision-making cultivates an environment supportive of creativity and innovation. By combining insights from innovation management and decision sciences, the research suggests that effective innovation performance relies not solely on resource availability but on the quality of technological, supervisory, and cultural decision-making embedded within the organisation.
creator: Mohamed Alkashami
creator: Umair Ahmed
creator: Md. Lazim Mohd Zin
creator: Ammar Ahmed
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 554
ending page: 570
doi: 10.31181/dmame8220251571

Decision-Oriented Framework on Detecting and Countering Finance Management Fraud Amid Big Data Times: Characteristics, Risk Assessment and Technology-Driven Countermeasures
type: item
description: The objective of this study was to examine how data governance maturity, artificial intelligence (AI) integration, internal audit frequency, employee training, access control robustness, and the implementation of anomaly detection systems affect the occurrence of financial fraud. The investigation focused on business organisations operating in China. The dataset consisted of 218 organisations, with each organisation serving as the unit of analysis. Data analysis was performed using RStudio with R programming, applying exploratory factor analysis and regression modelling techniques to the organisational data. The findings indicate that AI integration, employee training, and access control robustness have no significant effect on financial fraud occurrence. In contrast, data governance maturity, internal audit frequency, and the application of anomaly detection systems were found to significantly influence the incidence of financial fraud within business organisations. These results provide valuable insights for Chinese businesses aiming to reduce the likelihood of financial fraud.
creator: Yilin Lv
creator: Youyou Shang
creator: Qianhui Yu
creator: Tianyue Zhao
creator: Shuti Li
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-05
publication date: 2025-12-05
volume: 8
number: 2
starting page: 571
ending page: 585
doi: 10.31181/dmame8220251574

A Decision-Support Framework for Village Cultural Heritage Revitalization: Integrating Digital Twins, Optimization, and Participatory Planning Models
type: item
description: In many local settings, cultural heritage frequently suffers from minimal civic engagement, fragmented contextual understanding, and progressive deterioration of tangible heritage elements. Conventional preservation frameworks tend to neglect both the potential of digital transformation and the shifting expectations of resident communities. Addressing this gap, the present project proposes a comprehensive preservation strategy that incorporates Digital Twins (DT) with a participatory Decision Support System (DSS) to support sustainable revitalisation of heritage sites in rural areas. To construct the DT, structural, spatial, and environmental information is captured through drone surveys and embedded sensor networks. The acquired datasets undergo pre-processing through z-score standardisation and median filtering to suppress measurement noise. Principal Component Analysis (PCA) is subsequently employed to derive features that reflect the historical, artistic, and socio-cultural significance of the sites. Furthermore, an enhanced optimisation methodology based on a Convolutional–Long Short-Term Memory Network (CLSMNet) is introduced to model dynamic site conditions and project future patterns of material decay and conservation requirements. Within this framework, DTasDO-CLSMNet integrates the temporal-spatial analytical capability of CLSMNet with DTasDO parameter adjustment, enabling continuous fine-tuning for predictive accuracy and decision reliability. Empirical findings indicate increased simulation precision in forecasting heritage degradation behaviour (96.87%), as well as strong sensitivity (95.65%), specificity (96.41%), and F1 performance (95.22%). Additionally, participatory engagement assessments reveal a high stakeholder satisfaction level (95.03%) with the joint simulation and decision-making processes. Overall, the proposed DSS-based digital model supports not only preservation but also adaptive and community-led reuse, delivering an intelligent, transp…
creator: Chen Chen
creator: Xing Yang
creator: Yu-pei Kuo
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 586
ending page: 599
doi: 10.31181/dmame8220251575

The Influence of Neural Computing Engineering and AI on Social Media User Behavior and Decision Making
type: item
description: The objective of this study was to investigate the impact of received personalization of content, perceived algorithm transparency and perceived ethical concerns on user decision-making behaviour on social media (SM). For this purpose, quantitative data from the primary resources was collected and this study used RStudio for robust analysis. The population of this study was SM users from USA, Canada, United Kingdom, Brazil, India, Australia, Japan and Germany. A sample of 395 respondents was analysed, and the findings of this study highlight the impact of received personalization of content, perceived algorithm transparency and perceived ethical concerns on user decision-making behaviour on SM is positive and significant. The study has significant implications for the body of knowledge where the identified gaps were addressed. In addition, this study provides practical implications how the users can avoid the influence of SM to influence on their decision-making behaviour.
creator: Muna Nama Thweny
creator: Hamid Sh. Aldulami
creator: Laith M Fawzi
creator: Ammar Abdullatif Hadi
creator: Zeyad Ibrahim Bakri
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 600
ending page: 612
doi: 10.31181/dmame8220251576

Research on Transformation Efficiency of Scientific and Technological Achievements in Henan Province of China Based on Three-Stage DEA Model
type: item
description: The transformation of scientific and technological achievements (TSTA) constitutes a central mechanism for advancing an innovation-led development agenda and plays a vital role in strengthening innovation capacity within Henan Province. To support the long-term improvement of the transformation efficiency of scientific and technological achievements in Henan and comparable urban areas (TESTA), this study applies a combined three-stage Data Envelopment Analysis (DEA) and Malmquist Index (MI) approach to assess both the static and dynamic characteristics of TESTA across 18 cities in the province. In addition, the Dagum Gini coefficient is utilised to measure disparities in TESTA across regions and to decompose their respective contributions. The main outcomes indicate the following: (1) TESTA levels in Henan are strongly shaped by governmental investment in science and technology, the prevailing research climate, and overall economic conditions within each locality. (2) Once external environmental influences and stochastic disturbances are filtered out, the TESTA of most cities shows a marked reduction relative to baseline results, accompanied by substantial variability across regions. (3) Analysis of the MI and its decomposed indices demonstrates that shifts in technological progress (Tech) serve as the principal endogenous force underpinning TESTA. (4) Findings from the Dagum Gini coefficient reveal that variations between regions are the major source of inequality in the provincial development of TSTA. Collectively, these insights offer practical guidance for decision-makers seeking to strengthen TESTA, highlighting the importance of enhancing scale efficiency, accelerating technological advancement, and adopting region-specific development pathways.
creator: Pan Yang
creator: Panpan Liu
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 613
ending page: 634
doi: 10.31181/dmame8220251579

Towards Efficient Ship Design: A Review of Methods and Attributes for Decision-Making in Ship Selection
type: item
description: With the increasing sophistication of naval systems and the rising imperative for environmentally sustainable solutions, it becomes crucial to understand how multi-criteria decision-making (MCDM) models facilitate informed decision-making to ensure vessel performance and sustainability throughout their life cycle. This research undertakes a systematic literature review of MCDM applications across various stages of the ship life cycle from a systems engineering standpoint. Particular attention is given to the design phase, where MCDM approaches are employed to determine the most appropriate ship configuration, considering factors such as operational capabilities, effectiveness, and performance metrics. A total of 131 studies were examined following the PRISMA methodology, with data sourced from Scopus and the Web of Science. The review concentrated on design methodologies, hierarchical structuring, and the assessment of essential decision-making criteria, including cost, effectiveness, and risk. The results demonstrate that MCDM models not only support the identification of optimal ship configurations but also assist in evaluating their effects on operational efficiency and environmental sustainability. The study underscores the importance of incorporating a range of performance measures, including Measures of Effectiveness (MOE), Measures of Performance (MOPs), Technical Performance Measures (TPMs), and Key Performance Parameters (KPPs), to strengthen decision-making in both design and operational contexts. Finally, a framework is proposed to guide the selection of vessels that are both operationally efficient and environmentally compliant, in line with contemporary regulatory requirements.
creator: Edwin Giovanny Paipa- Sanabria
creator: George Fernández-Alonso
creator: Daniel Gonzales Montoya
creator: Jairo Coronado- Hernandez
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 635
ending page: 662
doi: 10.31181/dmame8220251580

Advancing Sustainable Material Selection in Construction: A Systematic Review of Multi-Criteria Decision-Making Applications
type: item
description: Choosing sustainable materials for construction projects is essential to achieving global sustainability goals, as environmental challenges and economic and social issues continue to escalate. The research applies PRISMA methodology to identify and select of MCDM applications in sustainable material selection studies through a systematic review. The research analyzed 120 peer-reviewed papers to identify four main categories which included Site Selection, Infrastructure Planning, Sustainability Assessment, Performance Indicators and Supplier and Material Selection Decision Support Systems, Sustainable Materials and Construction Methods. The research shows that hybrid MCDM methods like AHP-WASPAS, MLCAQ and fuzzy TOPSIS are being used increasingly due to their stronger performance and ability to handle complex evaluation scenarios. Bibliometric analysis of co-authorship shows that there is close collaboration among researchers. New research directions are moving toward combining Internet of Things technologies with Artificial Intelligence systems. Future research agendas for sustainable construction should focus on the development of coherent sustainability assessment frameworks using practical tools that help professionals deliver sustainable construction projects with positive environmental effects.
creator: Abdulrahman Yaghmour
creator: Ahmed Yousry
creator: Hussein Kaya
creator: Ibrahim Alowais
creator: Zied Bahroun1* Karam Al-Assaf
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 663
ending page: 683
doi: 10.31181/dmame8220251583

Estimating Future Stock Return for the pharmaceutical sector using Bayesian Vector Auto regression modeling and artificial Intelligence
type: item
description: The process of forecasting stock returns in the pharmaceutical industry is quite complex and risky because of such issues as the decision making process by regulatory authorities, the results of the research and development, and the variability in the macroeconomic conditions. The paper constructs a decision-support model by evaluating two modern forecasting models namely Bayesian Vector Auto regression (BVAR) model and Artificial Neural Network- Radial Basis Function (ANN-RBF) to predict stock returns of three stock exchange listed pharmaceutical companies based on Amman Stock Exchange. The given framework is based on the methodology where the two models are trained using time series data during the time frame 20192024 and the predictions are made during 20252030. The results show that BVAR, which includes the incorporation of time-dependences of macroeconomic variables, gives better forecasts of returns that possess the features of stability and linearity but ANN-RBF has better forecasts of returns that have volatility and nonlinearity. These findings prompt the need to consider such characteristics in the forecasting studies in order to produce meaningful results. In addition to methodological inputs, the research provides feasible guidelines to investors, corporate leaders, and other policy makers that seek to cope with the risk, make long-term investments, and guide financial decision-making in the pharmaceutical firms. Also, the research incorporates methods of applied mathematics, artificial intelligence, and finance, which supports the importance of forecasting models as evidence-based decision-making tools in conditions with high risks.
creator: Noor Sabah Hameed Al-Dahaan
creator: Mohammed Faez Hasan
creator: Hussein Hadi Abdulameer
creator: Haider Yonis Kadhem
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-06-29
publication date: 2025-06-29
volume: 8
number: 2
starting page: 684
ending page: 700
doi: 10.31181/dmame8220251584

A Decision-Support Framework for Economic Growth Forecasting under Smart Finance: Integrating Dynamic GMM, Threshold Regression, and Machine Learning Optimization
type: item
description: Forecasting economic growth in the context of smart finance necessitates decision-support systems that can synthesise diverse, high-frequency, and nonlinear financial indicators. This research introduces a hybrid decision-support frame-work that integrates Dynamic System GMM, threshold re-gression, and a machine learning optimisation procedure em-ploying Lasso–EM algorithms, aiming to improve both the precision and interpretability of macroeconomic forecasts. Employing panel data from Chinese provinces spanning 2010 to 2023, the proposed model addresses endogeneity con-cerns, identifies structural thresholds within the smart fi-nance–growth relationship, and performs rigorous variable selection to enhance decision-making efficacy. Empirical results indicate that smart finance substantially fosters eco-nomic growth; however, its effects display nonlinear thresh-old behaviour contingent on the extent of digital infrastruc-ture and the capacity for financial innovation. The incorpora-tion of Lasso–EM optimisation further reinforces predictive robustness, mitigating overfitting and stabilising model per-formance. Consequently, the framework provides an ad-vanced tool for policymakers and economic planners, facili-tating evidence-based resource allocation, early detection of economic fluctuations, and the optimisation of fiscal and financial strategies within smart finance contexts. This study contributes to decision science by linking econometric ap-proaches with machine learning optimisation, offering a scal-able methodology for economic forecasting and policy de-sign in digitalised economies.
creator: Xiaoning Lan
rights: Copyright (c) 2025 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 701
ending page: 717
doi: 10.31081/dmame8220251585

A Decision-Support Framework for Enhancing Industrial Chain Resilience through Green Finance: Evidence from China’s Sports Industry
type: item
description: To examine the decision-making implications of green finance (Gre) development mechanisms in China on the resilience of the sports industry chain (SICR), this study utilises a sample drawn from 24 provincial-level regions within the country. Initially, evaluation index systems for both Gre and SICR are established. Subsequently, the study investigates the complex linear and non-linear interactions between the development of green finance and SICR in China, providing a decision-support framework aimed at optimising the allocation of green financial resources and enhancing SICR. The findings indicate that: (1) advancements in the level of green financial development exert a significant positive influence on the resilience of SICR; (2) Gre affects SICR partially through the channel of sports industry innovation, which functions as a partial mediator, with this mediation pathway accounting for 23.8% of the total effect; (3) the impact of Gre on SICR is characterised by non-linear dynamics, whereby Gre can bolster SICR only when regional economic level, digital economy development, market maturity, and residents’ consumption capacity surpass specific threshold values.
creator: Dangsheng Wang
creator: Qiuying Li
creator: Wenxiang Zhou
creator: Laibing Lu
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 718
ending page: 743
doi: 10.31081/dmame8220251593

A Hybrid Framework for Stock-Market Prediction Using COVID-19 Tweets through Nature-Inspired Algorithms and Machine Learning
type: item
description: The COVID-19 outbreak generated severe disturbances across international financial systems, intensifying volatility within equity markets. Stock market behaviour during this period exhibited pronounced instability, shaped by broader economic conditions as well as collective investor sentiment. This study investigates the effect of COVID-19 related discourse on Twitter on market behaviour and proposes a hybrid modelling framework aimed at enhancing the accuracy of stock price movement forecasts. The proposed framework incorporates nature inspired optimisation techniques, including genetic algorithms, Harris hawk’s optimisation, particle swarm optimisation, and differential evolution. The methodological focus lies in embedding social media sentiment into predictive models to uncover latent relationships that are typically overlooked by conventional statistical approaches. Empirical results demonstrate that all hybrid configurations consistently surpassed traditional forecasting methods. Among them, the support vector regression model optimised using Harris hawk’s optimisation achieved superior performance, recording an MSE of 0.00014, an RMSE of 0.00214, and an MAE of 0.00170. These findings underscore the substantial influence of public sentiment on financial market dynamics during periods of global disruption and highlight the effectiveness of hybrid predictive approaches. Such models offer valuable support for stock market forecasting and provide actionable insights for decision makers operating under heightened economic uncertainty.
creator: Muhammad Nauman Khan
creator: Saman Iftikhar
creator: Daniah Al-Madani
creator: Umer Zaheer
creator: Danish khan
creator: Kiran Fatima
creator: Ammar Saeed
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-01
publication date: 2025-12-01
volume: 8
number: 2
starting page: 744
ending page: 767
doi: 10.31081/dmame8220251600

Evaluating Service Quality in Infant and Childcare Institutions: A Decision-Making Framework Using Interval Type-2 Fuzzy MCDM
type: item
description: The study presents a comprehensive framework for evaluating service quality in public childcare institutions by employing an integrated Interval Type-2 Fuzzy Multi-Criteria Decision-Making (IT2F-MCDM) approach. This framework is designed to systematically address uncertainty inherent in human judgement by converting expert linguistic evaluations into Interval Type-2 Fuzzy Numbers (IT2FNs). Decision alternatives, represented by childcare institutions, and evaluation criteria, including safety, staff qualifications, emotional support, and infrastructural conditions, are identified through a combination of expert consultation and an extensive review of existing literature. Expert opinions are aggregated using the Fuzzy Weighted Averaging (FWA) operator, following the normalisation of criterion weights, which are likewise expressed in IT2FN form. The assessment process produces an overall fuzzy performance value for each childcare institution, derived from expert evaluations across the specified criteria. These fuzzy performance values are subsequently defuzzified into crisp scores using the centre-of-gravity technique, enabling more transparent interpretation and practical applicability of the results. Based on the resulting crisp values, childcare institutions are ranked according to their relative levels of service quality. To examine the robustness and stability of the proposed framework, a sensitivity analysis is performed by systematically varying the weights assigned to the evaluation criteria. This analysis provides insight into how changes in criterion importance influence institutional rankings. Furthermore, the reliability of the IT2F-MCDM results is verified through comparative analysis with established decision-making techniques, including Fuzzy TOPSIS and Fuzzy VIKOR. The consistency observed across these methods confirms the validity of the proposed approach. Overall, the framework functions as a dependable decision-support tool capable of assisting child…
creator: Wen Zheng
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
volume: 8
number: 2
starting page: 768
ending page: 784
doi: 10.31081/dmame8220251602

Design of a Cognitive Decision Support System Using Knowledge Graphs and Deep Reinforcement Learning for Real-Time Emergency Response
type: item
description: Emergency response scenarios are inherently complex and rapidly evolving, necessitating immediate and well-informed decision-making. Traditional decision support systems, however, often struggle to effectively integrate heterogeneous data sources and adapt to continuously changing conditions. To address these challenges and enhance both the speed and quality of decision-making in emergency contexts, this study proposes a Cognitive Decision Support System for Emergency Response (CDSS-ER), which combines Knowledge Graphs (KGs) with Deep Reinforcement Learning (DRL), specifically Deep Q-Networks (DQN). The system constructs a dynamic KG by aggregating and semantically aligning data from multiple emergency-related sources, thereby capturing contextual and relational information in real time. These structured knowledge representations are then vectorised to depict the current state of the emergency environment. Leveraging these representations, the DQN component determines optimal response policies through iterative trial-and-error interactions, continuously refining its strategies based on real-time feedback. Experimental results demonstrate that CDSS-ER substantially outperforms conventional rule-based systems with respect to both the efficiency of resource allocation and the accuracy of decisions. The framework provides a scalable and adaptive solution for emergency management and holds promise for application in other domains requiring real-time cognitive decision support.
creator: Wei Yang
creator: Wenjun Wang
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
volume: 8
number: 2
starting page: 801
ending page: 815
doi: 10.31081/dmame8220251605

Application of an Optimized Version of Modified Elman Neural Network Using Improved Pelican Optimizer for Energy System Marginal Price Forecasting
type: item
description: Following the liberalisation of the electricity sector and its transition to a competitive market structure, price volatility has intensified, as electricity tariffs are now formed through market-based pricing mechanisms. Electricity price time series exhibit complex properties, including pronounced instability, strong non-linearity, and substantial fluctuations. In response to these characteristics, this study concentrates on the development of a day-ahead system marginal price (SMP) forecasting framework based on an enhanced Elman Neural Network (ENN). To improve the predictive capability of the proposed ENN, a modified pelican optimisation algorithm is employed to optimise its parameters. For the selection of short-term input variables, the Pearson correlation coefficient is applied to identify the most relevant factors. In contrast, long-term input variables are determined by incorporating the discrete Fréchet distance, alongside seasonal attributes, day-type information, forecasted load, and historical SMP values. A comprehensive dataset spanning fifteen years is used to evaluate the effectiveness of the proposed model. The empirical results demonstrate that the suggested approach achieves superior forecasting accuracy compared with conventional SMP prediction methods, confirming its effectiveness for electricity market price forecasting.
creator: Yuanchun Tang
creator: Shichun Chen
creator: Zhuolin Chen
creator: Ying Shi
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
volume: 8
number: 2
starting page: 816
ending page: 835
doi: 10.31081/dmame8220251606

Decision-Making and Risk Management in School Bullying Risk: Serial Mediation of Peer Relationships and Internet Addiction between Negative Parenting Styles and Adolescent Bullying
type: item
description: Understanding the underlying causes of school bullying is essential for developing effective prevention strategies. This study aims to investigate the impact of negative parenting styles on school bullying behaviors among adolescents, emphasizing the serial mediation role of peer relationships and internet addiction. In addition, the study frames school bullying as a decision-making and risk management challenge within organizational and governance contexts, highlighting how evidence-based models can guide sustainable operations management. A survey was administered to 756 randomly sampled students from junior and senior high schools in Henan, China. Assessment tools included the Parenting Styles Scale, Peer Relationships Scale, Internet Addiction Questionnaire, and School Bullying Behaviors Questionnaire. (1) Significant pairwise correlations were found between any two of the studied variables: negative parenting styles, peer relationships, internet addiction, and school bullying behavior. (2) Parental rejection and overprotection are two dimensions of negative parenting, and both were positive predictors of school bullying behaviors. (3) The impact of negative parenting styles on school bullying behaviors was mediated by three pathways: parental rejection → peer relationships → school bullying, parental overprotection → peer relationships → school bullying, and parental rejection → peer relationships → internet addiction → school bullying. Viewing bullying as an organizational risk, we map the mechanisms into a PDCA loop: identification via brief screeners to create risk profiles; intervention through parenting support, classroom‑climate routines, and tiered digital‑behavior programs; and evaluation using operational indicators (peer‑relationship means, ≥4 internet‑addiction proportion, bullying incidence). Digital‑behavior programs are prioritized when parental rejection is salient, whereas autonomy‑support and peer‑climate strengthening dominate under overprotec…
creator: Huafeng Si
creator: Dandan Zhang
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-29
publication date: 2025-12-29
volume: 8
number: 2
starting page: 836
ending page: 847
doi: 10.31081/dmame8220251613

Emotion Regulation as a Determinant of Decision-Making Efficiency among Engineering and Administrative Sciences Students
type: item
description: The basic aim of the research was to examine the role of emotion regulation as factor of decision-making efficiency among the administrative and engineering students. This study collected data from 300 students studying in universities of Emirates of Sharjah by using convenience sampling. The study used descriptive analytical approach through questionnaires designed to measure cognitive and behavioral aspects of emotion regulation. This research analyzed collected data through T-tests, standard deviations and arithmetic means to explore the differences on the basis of academic specialization and level of emotional regulation. The findings of the study showed that students in administrative sciences demonstrate a high level of emotion regulation, which significantly contributes to decision-making efficiency. Students in administrative sciences exhibited stronger emotional control compared to their counterparts in engineering. These findings highlight the effect of academic specializations on skills of emotional management. This improved emotional regulation helps students to keep composure under academic pressure, make well-considered decisions in situations that are challenging and approach problems logically. The results concluded that emotional regulation is important factor in the influence of decision-making efficiency of students of engineering groups. This study recommends using training and support programs with purpose to support engineering programs to enhance student’s skills of emotional regulation, expand research in terms of more universities, complementary methods and disciplines such as interviews and observations to provide comprehensive understanding of relationship between decision making efficiency and emotion regulation.
creator: Bushra Ahmad Alakashee
creator: Ali Ahmad Al-Barakat
creator: Abdalla Falah El-Mneizel
creator: Rommel Mahmoud Al Ali
creator: Abdelrahim Fathy Ismail
creator: Ali K. Abdullatif
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 848
ending page: 862
doi: 10.31081/dmame8220251621

Deep Learning for Structural Health Monitoring of Pavements for Improving Road Maintenance and Management
type: item
description: Durable and secure roadway networks underpin sustainable mobility systems and urban growth. In recent years, Artificial Intelligence models have increasingly been employed to estimate the structural condition of roads and pavements. Such approaches enable agencies to organise maintenance schedules more effectively and to channel resources based on evidence-driven priorities. When combined with continuous sensor streams and traffic information, these systems support anticipatory and financially efficient road management, thereby strengthening decision reliability. This investigation develops machine learning frameworks to evaluate pavement structural condition. Variables including asphalt temperature and pavement layer depth are incorporated to forecast overall pavement performance. The resulting model offers a cost-efficient, unobtrusive technique for assessing current pavement status and anticipating emerging defects, which subsequently aids maintenance planning and resource distribution. Linking this modelling approach with wider smart city and smart mobility platforms facilitates real time surveillance of pavement behaviour, enhances road safety, and reinforces transport networks that favour long-term sustainability. The study compares long short-term memory techniques with alternative machine learning methods. The outcomes reveal that the long short-term memory model demonstrates superior behaviour during both training and validation, showing high predictive precision and stronger generalisation when applied to unseen datasets. Consequently, it is identified as the most suitable predictor for the data employed in this research. The results indicate that transport authorities can utilise Artificial Intelligence-based structural health monitoring systems for continuous pavement evaluation. These agencies are also encouraged to integrate sensor outputs and traffic information within unified data environments. Furthermore, policy measures that advance predictive mai…
creator: Mohamadreza Azodinia
creator: Kazizat Iskakov
creator: Orman Indira
creator: Sina Ardabili
creator: Nader Karballaeezadeh
creator: Amir Mosavi
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 863
ending page: 880
doi: 10.31081/dmame8220251629

An Intelligent Decision-Support Framework Using Mobile and Virtual Reality Technologies for Optimising Intangible Cultural Heritage Management
type: item
description: Effective administration of intangible cultural heritage (ICH) increasingly faces challenges arising from intricate prioritization demands, constrained resources, and sustainability obligations. Conventional approaches to ICH management are predominantly manual and rely heavily on expert judgement, limiting their scalability and their ability to integrate data across multiple modalities. This study introduces an intelligent decision-support framework that incorporates interactive mobile interfaces, virtual reality (VR), and a novel Adaptive Convolutional Synthesized Intelligent Memory Network (ACSIMN) to facilitate precise ICH prioritization and optimise resource distribution through adaptive learning of multimodal features and cross-modal reasoning. Data are gathered from diverse sources, including images and videos of cultural artefacts, audio recordings of traditional music and oral histories, and textual materials detailing historical and cultural contexts. Modality-specific pre-processing procedures are applied, such as resizing visual inputs for images and videos, generating spectrograms from audio signals, and tokenising and cleansing textual information. Feature extraction is conducted using Scale-Invariant Feature Transform (SIFT) for visual inputs, Mel-Frequency Cepstral Coefficients (MFCCs) for audio data, and Bidirectional Encoder Representations from Transformers (BERT) for text. These features are then integrated into a unified latent space, enabling coherent multimodal correlation and informed joint decision-making. The principal innovation resides in the ACSIMN model, which harmonises adaptive convolutional neural network (ACNN) learning for visual interpretation with intelligent long short-term memory (ILSTM) for temporal and semantic sequence modelling, complemented by VR-enabled visualisation to improve situational comprehension. Implemented in Python, the proposed framework attains an accuracy of 98.4%, supporting both accurate recognition and re…
creator: Chen Mao
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 881
ending page: 897
doi: 10.31081/dmame8220251631

An Integrated WENSLO-WASPAS Model to Prioritize Factors Contributing to Learned Helplessness in Export Processes
type: item
description: In the contemporary global economy, nations compete vigorously to strengthen their market positions, sustaining continuous production across industrial sectors to enhance comparative advantage. Within this competitive landscape, the present study examines the underlying causes of repeated export failure among firms by employing advanced multi-criteria decision-making techniques. Specifically, it analyses why certain firms, after experiencing multiple unsuccessful export attempts, refrain from independently re-engaging in international trade activities, conceptualising this phenomenon through the lens of learned helplessness within the export context. The empirical component involves seven experienced Decision-Makers (DMs) who had previously encountered recurrent export failures before ultimately establishing themselves as professional exporters. Primary data were obtained using a structured expert assessment instrument grounded in linguistic importance scales. The determinants associated with export process failure were identified through the application of the Weights by Envelope and Slope (WENSLO) method. Subsequently, the Weighted Aggregated Sum Product Assessment (WASPAS) technique was employed to prioritise policy alternatives designed to mitigate export-related failure. The findings reveal that “Customs Clearance and Logistics Operations” constitute one of the most influential contributors to export process breakdown. Furthermore, the most effective policy intervention is determined to be the formulation of sector-specific advisory mechanisms and electronic training modules focused on documentation preparation and customs procedures. The integrated analytical framework developed in this study provides a structured decision-support mechanism for policymakers and export promotion agencies, enabling the systematic identification of critical bottlenecks in export activities and the strategic prioritisation of corrective policy measures.
creator: Sinan Çizmecioğlu
creator: Ahmet Çalık
creator: Abdulaziz Alshalfan
creator: Erfan Babaee Tirkolaee
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 898
ending page: 925
doi: 10.31081/dmame8220251645

Enhancing Supply Chain Resilience in Disruption Caused by Catastrophes Such as Pandemics: An Integrated Grey Analysis-Stochastic Optimization Model
type: item
description: This research presents a comprehensive decision-making model for optimizing global supply chain operations during disruptions caused by catastrophes, e.g., pandemics, making a significant contribution to the supply chain management literature. The model integrates Grey optimal ranking and employs a scenario-based stochastic Mixed-Integer Programming approach to enhance resilience. It addresses the 'ripple effect' of regional disruptions by modelling simultaneous impacts on supply, demand, and logistics. Computational examples, based on a real-world case during pandemic, validate the model's effectiveness in minimizing costs and ensuring availability, quality, and emission levels. The study reveals the numerical significance of traditional resilience measures, such as pre-positioning Recovery Materials Inventory and utilizing recovery supplies. Results demonstrate the model's capacity to mitigate the impacts of multi-regional disruptions. In essence, the research provides a quantifiable and practical contribution, offering insights for resilient supply chain management in the face of pandemic disruptions.
creator: Fady Ahmed
creator: Ibrahim Abdelfadeel Shaban
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 926
ending page: 954
doi: 10.31081/dmame8220251649

A Multi-Criteria Decision-Support Framework for Optimizing Digital Marketing Strategies in Indonesian SMEs Using Fuzzy AHP–TOPSIS
type: item
description: Digital marketing has emerged as a critical determinant of competitiveness for small and medium enterprises (SMEs) operating within emerging economies. A comparable situation is observed in Indonesia, where the identification of suitable marketing strategies has become increasingly intricate due to multiple interacting constraints. These include limitations associated with financial resources, performance expectations, and other operational restrictions. Despite this complexity, a considerable proportion of SMEs continue to depend on intuition-based judgement rather than adopting systematic and model-driven approaches. To address this shortcoming, the present study develops a multi-criteria decision support framework integrating Fuzzy AHP and TOPSIS for the optimisation of digital marketing strategy selection among Indonesian SMEs. The proposed framework assesses alternative strategies using key evaluation dimensions, including economic outcomes such as return on investment (ROI) and conversion rates, levels of customer engagement, brand equity development, and associated operational expenditures. Empirical evidence was obtained from marketing professionals and SME practitioners located in major urban centres of Indonesia. The findings indicate that strategies centred on influencer-led content, when combined with performance-oriented advertising mechanisms, achieve the highest aggregated evaluation scores under the simultaneous consideration of financial and branding criteria. The developed model offers a systematic and data-oriented mechanism for prioritising marketing alternatives and determining an efficient portfolio under conditions of uncertainty, thereby minimising reliance on subjective judgement. This research advances the domain of decision science by presenting a structured analytical framework that supports improved allocation of strategic resources and strengthens the digital competitiveness of SMEs. Furthermore, it provides actionable insights for both…
creator: Bunga Aditi
creator: Tapi Rondang Ni Bulan
creator: Jufrizen
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 955
ending page: 973
doi: 10.31081/dmame8220251671

A Robust Hybrid Multi-Criteria Decision-Making Framework for Sustainable Transportation Planning Under Uncertainty
type: item
description: This study develops a sustainability oriented multi-criteria decision-making (MCDM) framework to evaluate and compare smart participation and transportation chain initiatives across major cities in Saudi Arabia. The framework integrates several dimensions, including citizen engagement, the incorporation of renewable energy, multimodal transport systems, artificial intelligence driven traffic management, and circular economy practices. To operationalise the assessment, a three stage MCDM approach is employed. The Analytic Hierarchy Process (AHP) is applied to determine the relative importance of the evaluation criteria, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is utilised to assess alternatives based on their closeness to the ideal solution, and the Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) is used to establish the final ranking of the cities. The sustainability assessment indicates that Riyadh achieves the highest performance level with a score of 0.80, followed by Jeddah with 0.74 and Dammam with 0.68. A sensitivity analysis is conducted to examine the robustness of the framework, confirming the stability of the outcomes, as variations in the weighting of criteria do not produce notable changes in the ranking order. The results emphasise the strong digital participation environment in Riyadh, along with its policy readiness and effective integration of renewable energy initiatives. The proposed framework offers valuable insights for policymakers and transportation planners seeking to advance sustainable urban mobility strategies aligned with the objectives of Saudi Vision 2030.
creator: Karim Soliman
creator: Bandar Altubaishe
creator: Khaled EL Sakty
rights: Copyright (c) 2026 Decision Making: Applications in Management and Engineering https://creativecommons.org/licenses/by/4.0
license: https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
volume: 8
number: 2
starting page: 974
ending page: 994
doi: 10.31081/dmame8220251683