Decision Making: Applications in Management and Engineering
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.
Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models
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.
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Predictive Decision Analytics for Membership Retention and Expansion in Martial Arts Organisations
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
A Game-Theoretic and Multimodal Interaction Framework for Collaborative Robots in Smart Manufacturing
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.
Digital Twin-Enhanced MCDM Framework for Circular Construction Dynamic Lifecycle Optimization of Hybrid Concrete Mix Design
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 maintenance and adaptive learning algorithms for real-time modelling of material behaviour. Furthermore, extending the system’s scalability to accommodate large-scale infrastructure projects may enhance its applicability within smart construction environments.
A Decision-Support Framework for Composite Curved Beam Design under Moving Loads: Dynamic Modelling and Multi-Criteria Evaluation
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 strategies, a Multi-Criteria Decision-Making (MCDM) framework is introduced, enabling a structured evaluation of trade-offs among structural stiffness, mass, cost, and vibration performance. The findings underscore the necessity of such a decision-making tool in selecting optimal beam configurations under realistic dynamic loading conditions, considering variables such as load velocity, curvature, and anisotropic material behaviour. This research offers practical guidance for engineers in choosing suitable laminate architectures and materials to reduce vibration and enhance durability in transportation and civil engineering infrastructure. The study contributes to informed decision-making in high-speed rail and aerospace applications, where control of dynamic performance is of critical importance.
Human-AI Decision Dynamics: How Risk Propensity and Trust Impact Choices Through Decision Fatigue, Conditional on AI Understanding
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.
Decision-Making in Natural Disaster Response: A Comprehensive Review of Strategies, Models, and Technological Advancements
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.
A Structured Decision-Making Approach for Supplier Performance Evaluation in the Restaurant Sector
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. Incorporating multiple weighting techniques can strengthen the robustness of selection processes. This study offers practical guidance for enhancing procurement strategies and competitive advantage, while also providing suppliers with valuable insights into industry requirements and expectations.
A Decision Framework for Course Recommendation Using Basic Uncertain Linguistic Information Soft Sets
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.
How does digitalization enhance sustainable economic development? The case of Azerbaijan and Hungary
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.
Simulation-Based Decision Support for Call Centre Staffing Optimisation: A Case Study from the Palestinian Telecom Sector
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.
How to Evaluate New Quality Productive Forces for Chinese Express Delivery Enterprises: A Hybrid DEMATEL-BBWM-CoCoSo Method
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.
A Supportive Decision-Making Methodology Incorporating a Cost-Accounting System Tailored to Import Firms
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.
Reducing Train Delays with Machine Learning-Based Predictive Maintenance for Railways
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.
The Influence of Managerial Myopia on Strategic Differentiation: A Decision-Making Perspective
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 reform via mixed-ownership strategies to foster system-wide strategic innovation.
Financial Decision-Making as a Moderator between Corporate Governance, Audit Quality, and Financial Reporting Quality
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.
A Decision-Support Approach to International Natural Gas Price Forecasting Using Machine Learning Models
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
Career Decision Making: Role of Family- Peer Belonging, Emotional Regulation and Academic Motivation
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
Factors Shaping Project Manager Salaries in Hungary: The Role of Certification, Experience, and Other Key Attributes
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.
Enhancing Investors’ Decision-Making through Financial Risk Management: Role of Corporate Governance and Financial Communication Transparency
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.
Decision-Making In Crisis: Insights From Hungarian SMES And Households During The Covid-19 Pandemic
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 mechanisms and initiatives promoting digital transformation. Moreover, fostering consumer confidence and stabilising market conditions through strategies such as adaptable tax policies and targeted stimulus programmes is essential. Investment in digital infrastructure and broader access to e-commerce platforms also emerges as a critical factor for enhancing long-term economic resilience and competitiveness.
Business Intelligence, technology anxiety and Strategic Learning as Drivers of Strategic Decision-Making: The Mediating Role of Decision-Making Agility
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.
Role of Total Quality Management and Shared Decision-Making in Enhancing Operational Performance and Enterprise Risk Management Effectiveness
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.
Carbon Nanotube-Titanium Composites-based Antennas for 6G Wireless Networks: Performance Evaluation
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.
A ResNet-Enhanced Framework for Modelling Aesthetic Preferences in User-Centred Decision Systems
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.
From Training to Transformation: How Human-Centred Practices Shape Smart Manufacturing Outcomes
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.
A Hybrid Statistical–Fuzzy Decision Framework for Housing and Rental Price Dynamics: Evidence from Spain
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.
Enhancing ASEAN Competitiveness in Indonesia's Halal F&B Industry: Comparative AHP-ANP Analysis of SMEs and Large Enterprises
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.
Achieving Climate Neutrality in Hungary by 2050: Application of the Pathways Explorer 2050 Model
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.