Decision Analysis Research Articles

Feasibility of All Terrain Vehicle (ATV) Investment in MSMEs Tridea Hills Porame Tourism Village

The purpose of this study is to analyze investment decisions in the All Terrain Vehicle (ATV) rental business. This study uses quantitative descriptive analysis methods and qualitative descriptive analysis methods. The data used in this study are primary data with data collection techniques carried out by observation, interviews, and documentation. The analysis of investment decisions for the All Terrain Vehicle (ATV) rental business uses NPV (Net Present Value), IRR (Internal Rate Return), Net B/C Ratio, and PP (Payback Period) analysis. The results of the ATV rental business study at UMKM Tridea Hills, Porame Village, obtained a Net Present Value (NPV) of Rp82,248,651; Internal Rate of Return (IRR) of 46%; Net B/C Ratio of 1.87; and Payback Period (PP) of 3.7, which means that the investment value invested will be returned for 3 years and 7 months of operation. These investment decision analysis indicators confirm that this ATV rental business is feasible to be pursued and developed.

Using Multicriteria Decision Analysis to Assess Stakeholders Motivation toward Sustainable Packaging in the Fruit and Vegetable Value Chains of Rural Uganda

The use of better protective packaging is often viewed as a low-cost alternative for short-run reduction of post-harvest losses in the fruit and vegetable supply chains of sub-Saharan Africa. We present the exploratory results of mapping the fruit and vegetable value chain actors in rural Uganda and analyzing their influence on the adoption of packaging. We propose a decision-aiding model based on multicriteria analysis to obtain a motivation score for assessing the degree of interest of the value chain actors toward packaging. Because it combines quantitative and qualitative information, our approach constitutes a valuable tool in the context of planning for an appropriate implementation of an appropriate packaging technology. The results suggest that distributors are the most favorable for adoption of packaging, followed by collectors, farmers, and customers, in that order. By demonstrating the use of a multicriteria decision approach for identifying the actors most likely to adopt packaging, this work suggests that the proposed approach could be used by potential regulators and manufacturers to focus their efforts on developing a well-targeted packaging deployment strategy.

Prioritizing vaccine introduction to China’s National Immunization Program: a multi-criteria decision analysis

BackgroundTo prioritize the introducing of new vaccines into China’s National Immunization Program (NIP) among 10 candidate vaccines across four classes.MethodsWe developed a vaccine value framework using Multi-Criteria Decision Analysis (MCDA) to simulate the introduction of new vaccines into NIP, covering 21 criteria encompassing six dimensions: safety, effectiveness, economy, innovation, accessibility, and appropriateness. Two decision scenarios were considered: Scenario One prioritized the four classes of vaccines, while Scenario Two identified specific vaccines within each class.ResultsIn the vaccine value framework, safety received the highest weight, while innovation received the lowest. The Human Papillomavirus (HPV) vaccine was identified as a top priority for inclusion in NIP, followed by Pneumococcal Conjugate Vaccine (PCV), Haemophilus Influenzae Type B (HIB), and Rotavirus (RV) vaccines. The specific types are domestic bivalent HPV vaccine, imported 13-valent PCV vaccine, domestic HIB vaccine, and domestic RV vaccine. The results of the one-way and probabilistic sensitivity analysis demonstrated robustness.ConclusionsThis study provides a transparent, comprehensive, and quantifiable vaccine value framework to aid decision-making for introducing new vaccines into China’s NIP. According to the MCDA results, HPV should be considered the top vaccine candidate for the NIP.

Use of multicriteria decision analysis in transmission line routing: reflections on the inclusion of sustainability in decision making process

Use of multicriteria decision analysis in transmission line routing: reflections on the inclusion of sustainability in decision making process

Computing pth root of a transition matrix with a deep unsupervised learning approach

Transition matrices are a tool used to describe transition probabilities of a Markovian process. From an applied perspective, transition matrices are, for example, used in the fields of credit risk modeling and medical decision analysis. In these domains, it is possible to estimate matrices according to the frequency of data observations, but for modeling purposes, it may be necessary to calculate transition matrices for shorter and more restricted time intervals. From a mathematical perspective, this requirement translates into calculating the pth root of a stochastic matrix through a constrained optimization problem. This article focuses on computing pth root of transition matrices and proposes a novel approach using deep learning-based algorithms, particularly Convolutional Neural Networks. Comparisons with traditional algorithms reveal that, in certain contexts, deep learning models offer computational advantages, especially regarding time efficiency. The applicability of the models is verified through a case study built on Fitch Ratings data.

Prioritisation of infectious diseases from a public health perspective: a multi-criteria decision analysis study, France, 2024.

BackgroundWithin the International Health Regulations framework, the French High Council for Public Health was mandated in 2022 by health authorities to establish a list of priority infectious diseases for public health, surveillance and research in mainland and overseas France.AimOur objective was to establish this list.MethodsA multi-criteria decision analysis was used, as recommended by the European Centre for Disease Prevention and Control. A list of 95 entities (infectious diseases or groups of these, including the World Health Organization (WHO)-labelled 'Disease X') was established by 17 infectious disease experts. Ten criteria were defined to score entities: incidence rate, case fatality rate, potential for emergence and spread, impact on the individual, on society, on socially vulnerable groups, on the healthcare system, and need for new preventive tools, new curative therapies, and surveillance. Each criterion was assigned a relative weight by 77 multidisciplinary experts. For each entity, 98 physicians from various specialties rated each criterion against the entity, using a four-class Likert-type scale; the ratings were converted into numeric values with a nonlinear scale and respectively weighted to calculate the entity score.ResultsFifteen entities were ranked as high-priorities, including Disease X and 14 known pathologies (e.g. haemorrhagic fevers, various respiratory viral infections, arboviral infections, multidrug-resistant bacterial infections, invasive meningococcal and pneumococcal diseases, prion diseases, rabies, and tuberculosis).ConclusionThe priority entities agreed with those of the WHO in 2023; almost all were currently covered by the French surveillance and alert system. Repeating this analysis periodically would keep the list updated.

A Declarative Modeling Framework for Intuitive Multiple Criteria Decision Analysis in a Visual Semantic Urban Planning Environment

The magnitude and multiformity of recorded and readily available urban data from independent sensors and mobile user devices have paved new possibilities for city environment planning and evolution. In previous works, a visual semantic decision support system for urban planning was presented, and the capability of machine learning approaches, ranging from random forests to graph-based convolutional neural networks, to infer preferable directions for future development was explored, extrapolating upon previous opted locations and selected alternatives for publicly commercial services. In this work, the anterior decision-making process leading to establishment choices is addressed with the same sets of criteria and samples within the same environment. A Declarative Modeling shell is proposed, and Multiple Criteria Decision Analysis processes are adopted to encompass the DM’s/DMs’ rationale, solely relying on methodologies close to human intuition. To this end, outranking representatives from the PROMETHEE family as well as weighted sum approaches are employed, fueled by the interpretation of the declarative description of decision parameters on behalf of the DM(s), exploring the ability to achieve classifications in a straight synthetic manner, i.e., in the absence of previous knowledge, thus exhibiting the potential of decision analysis methodologies, enhanced by Declarative Modeling, to be used as powerful intuitive tools in similar paradigm contexts.

A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure.

Comparative Analysis of Common Structural Systems for Pedestrian Bridges in Kuwait

Introduction Pedestrian bridges are crucial urban infrastructure, providing safe passage over roads, railways, and waterways. Different structural systems like trusses, steel girders, and reinforced concrete beams are used, but more research is needed to determine the best design for specific regional contexts. Methods This research paper presents a comparison of various structural systems for pedestrian bridges, with a focus on a practical case study in Kuwait. The study evaluates five common pedestrian bridge designs: steel, concrete, and three truss arrangements. The designs were assessed based on multiple criteria, such as cost, construction time, structural weight, carbon emissions, and vibration performance. Using a decision-making matrix (DMM) and engineering judgment, Truss 3 (X-bracings without verticals) was identified as the optimal design. It features a unique bracing system that enhances its properties. Truss 3 was found to be the lightest design at 561.6 kN, with moderate CO2 emissions of 64.2 tons and a cost of 4288.5 KD per span. Detailed design and safety checks were conducted using ETABS software. The final design was detailed and presented using Autodesk Revit. Results The findings highlight the importance of using integrated frameworks and multi-criteria decision analysis (MCDA) to select suitable structural systems that meet technical and regional sustainability goals. Conclusion This research aims to provide a robust solution tailored to the unique conditions of Kuwait, ensuring safety, efficiency, and sustainability for pedestrian bridge infrastructure.

Attention-guided deep learning for effective customer loyalty management and multi-criteria decision analysis

Attention-guided deep learning for effective customer loyalty management and multi-criteria decision analysis

Impacts of Brand, Price, and Promotion on Modest Fashion Purchases

This research investigates the influence of Brand Image, Price, and Promotion on Purchase Decisions for Gamis Pelangi Hijab in Driyorejo, employing a descriptive quantitative approach. Utilizing multiple linear regression analysis on data collected from 96 respondents via purposive sampling, the study reveals significant effects: (1) Brand Image positively influences purchasing decisions; (2) Price has a significant impact on purchasing choices; (3) Promotional activities affect purchase decisions. Through regression analysis using SPSS 22, the study elucidates the pivotal role of these factors in consumer behavior, providing insights for marketers aiming to enhance consumer engagement and strategic marketing initiatives in the modest fashion industry. Highlight: Quantitative analysis of purchase decisions for modest fashion. Sample selection based on non-probability purposive sampling technique. Multiple linear regression analysis conducted using SPSS version 22. Keywoard: Gamis Pelangi Hijab, Purchase Decisions, Brand Image, Price, Promotion

Selection of photovoltaic panels for floating systems: an analysis based on Entropy, CRITIC and TOPSIS

ABSTRACT In the context of sustainable energy use, multiple criteria are involved in the decision to select the best energy generation projects, as well as its installation location. However, despite the widespread use of decision-making techniques, there is a noticeable gap due to the lack of a systematic process for selecting the best projects for energy transition. This paper evaluates alternatives of photovoltaic panels for energy generation in floating systems and proposes a procedure to select the best project using a multiple-criteria decision analysis. The Entropy method was used to determine the weight of eight criteria, including cost, number of cells, efficiency, area, panel weight, and power characteristics, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to make the selection. A sensitivity analysis was conducted considering different weighing methods. Among the 20 photovoltaic panels analysed, the method proved to be effective in determining the most successful one for installation in floating systems. The chosen panel maintained the best performance in all scenarios tested. This paper provides a systematic approach for selecting the most suitable photovoltaic panel for floating energy systems, contributing to researchers to refine decision-making methodologies and practitioners to optimise project implementation in sustainable energy initiatives.

Analysis of Investment Factors and Decisions among Generation Z and Generation X in Indian Capital Market

This study highlights the disparate behaviours, motives, and methods of these two unique generations as it investigates the investment fervour of Generation Z and Generation X in the Indian capital market. Due in large part to historical financial practices and economic volatility, Generation X favours conservative investment strategies, placing a higher value on stability and long-term growth through well-established asset classes like real estate and mutual funds. As a result of growing up in a digitally first world, Generation Z, on the other hand, has a more daring attitude when it comes to investing and is frequently drawn to high-risk chances like cryptocurrencies and Capitals that are influenced by social media trends. The study utilises a mixed-methods approach, integrating quantitative surveys and qualitative interviews to evaluate the investing choices and behaviours of 393 participants, split evenly between the two groups. The results show notable variations in financial literacy, risk tolerance, and how technology affects investment choices. The report also emphasises how crucial it is to match financial services to the distinct requirements and values of every generation, especially as Generation Z’s emphasis on ethical and sustainable investing is changing the industry. Financial institutions may build a more knowledgeable and empowered investor community by better engaging with their clients by taking into account these generational disparities.

Assessing and Prioritizing Perceived Service Quality in Healthcare Settings in Nepal: A Multi-Criteria Decision Analysis Using Analytical Hierarchy Process (AHP)

This study uses the Analytical Hierarchy Process (AHP) to compare and assess different private healthcare facilities in Nepal according to perceived service quality. For methodology, first, the criteria for the study were established and rated. Then sensitivity analysis was carried out to evaluate the decision using AHP to rank healthcare according to the characteristics of service quality and the relative positions. The data were collected from eleven hospitals in Kathmandu, Nepal. SERVQUAL dimensions were used in order to comprehend the Perceived Service Quality. The analytical procedure was carried out using Expert Choice software. The study shows that Private Teaching Hospitals have the highest Perceived Service Quality among patients, followed by Public Teaching Hospitals and that patients value tangibility over other service aspects. The study provides insights into the characteristics of service quality in Nepalese hospitals. The findings help healthcare managers to understand the factors that patients consider important to improve service quality.

Screening of pregnant women for foetal neonatal alloimmune thrombocytopenia: A cost-utility analysis.

Foetal and neonatal alloimmune thrombocytopenia (FNAIT) results from maternal platelet-directed antibodies and can result in severe intracranial haemorrhage (ICH) in foetuses and newborns. Screening for human platelet antigen-1a (HPA-1a)-directed antibodies during pregnancy could allow timely intervention with antenatal treatment and prevent ICH. We assessed the cost effectiveness of HPA-1a typing and anti-HPA-1a-screening as part of the prenatal screening programme. Different HPA-1a screening scenarios were tested in a decision analysis model and assessed for diagnostic, treatment, intervention and lifetime costs and prevention effects compared to the current situation without screening in the Netherlands. Model parameters were based on available data, literature and expert opinions. One-way sensitivity analysis and probabilistic sensitivity analysis were performed. Adding screening for anti-HPA-1a antibodies to the current antenatal screening programme of the Netherlands will lead to an additional cost of €4.7 million per year and a gain of 226 quality-adjusted life years (QALYs) per year, indicating an incremental cost-effectiveness ratio (ICER) of €20,782 per QALY gained. One-way sensitivity analysis showed that the uncertainty around the incidence of ICH, lifetime costs of disabled children and the probability of having antibody quantitation >3.0 IU/mL at 20 weeks had the highest effect on the ICER. Antenatal anti-HPA-1a screening might be cost effective. To obtain more knowledge and thereby to improve risk stratification, a pilot screening programme is warranted.

More robust offshore wind energy planning through model ensembling

This research performs an ex-ante assessment of the 19 high potential areas for offshore wind energy (HPA-OWE) allocated in four maritime spatial planning subdivisions of Spain. A 39 geo-statistical criteria pool was developed and categorized into five planning tiers (coexistence, socio-ecological, spatial-efficiency, energy-equity, technical/technological). An ensemble of three multi-criteria decision analysis (MCDA) techniques coupled with a Monte Carlo method based on a large, uniform number of randomly distributed criteria weights is applied for more robust priority rankings of HPA-OWE. The co-existence tier indicates that HPA-OWE should be prioritized in the North Atlantic and in the Levantine–Balearic planning subdivision. The application of machine learning on the MCDA results identified criteria that most influence the rank of each HPA-OWE at planning subdivision. The outcomes highlight the need to include place-based data to better take into account spatial inequalities in coastal regions and re-balance them with socio-economic and energetically privileged coastal territories.

A Framework for Case Simulation Surveys: Advancing Clinical Diagnosis Decision-Making

Introduction: Case simulation surveys enhance diagnostic criteria. This review presents an approach to developing such surveys, highlighting the need for standardized methods in studying rare and complex pathologies to improve patient outcomes. Methods: An in-depth literature review was conducted using PubMed with search terms: "Decision Analysis," "Forced-Choice," and "Classification Criteria". These terms were chosen to cover a broad range of relevant literature on decision-making frameworks and diagnostic criteria development. Studies on systemic sclerosis and systemic lupus erythematosus were selected to illustrate complex conditions with which case simulation surveys are effective. The methodology includes: 1) Reviewing literature to identify clinical characteristics; 2) Designing a representative base case; 3) Developing case variations; 4) Piloting the survey with experts; and 5) Analyzing results statistically. Results: This approach was effectively applied in studies of systemic sclerosis and systemic lupus erythematosus. From these examples, we identified key elements and best practices that contributed to developing a more standardized approach. This included designing a base case, systematically developing case variations, and piloting the surveys with expert audiences. This iterative process addressed challenges such as case specificity and the oversight of rare presentations, resulting in a more reliable methodology. This paper discusses these advancements, demonstrating how the standardized methodology enhances the consistency and applicability of case simulation surveys in clinical research. Conclusion: Systematically developed case simulation surveys are powerful tools for improving diagnostic techniques and classification criteria. They enable researchers to study clinical decision-making in controlled environments, significantly contributing to refining diagnostic criteria and treatment protocols.

Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security

This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p < 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.

Scourge of replacing contemporary work environment with artificial intelligence (AI-dark-side): the role of capacity development in quality of work-life and organisational performance

Purpose The emergence of artificial intelligence (AI) which operates through technology and digital workspace has proven to transform organisations in recent times. However, there has been key concern over its efficiency among the workforce on how it may replace human intelligence in the contemporary work environment. This study aims to investigate the drawbacks otherwise known as the dark side of AI and its effect on employee quality of work−life and organisational performance through the lens of employee capacity development in reducing its shortcomings. Design/methodology/approach This study used a descriptive research design using a cross-sectional survey approach to administer the research instrument to 1,847 customer service officers of banks, customer agents of telecoms, customer care of retail organisations in Nigeria business environment across various units were respondents of this study, however, 862 participants were finally used. A simple random strategy was used to survey the study participants, and existing scales were adopted to form a new research instrument. A partial least square (PLS) based structural equation model (SEM) was adapted to analyse the collected data from the respondents. Findings The outcome of the study indicated that AI lacks creativity and has a negative impact on both employee quality of work−life and overall organisational performance. The outcome of the study demonstrated the drawbacks and the dark sides of AI as lack of emotional intelligence, lack of in-depth contextual knowledge, over-reliance on data quality and lack of ethical and moral decision analysis are the possible dark side of AI which adversely affect quality of work−life and overall performance of the organisations. The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side. AI cannot function effectively beyond what is programmed in the system. Originality/value This study has offered a novel trajectory against the efficiency and possible benefits of AI that people are familiar with. It has changed the understanding of the researchers, policymakers and organisations that AI cannot replace human intelligence in the workplace without improvement on those established AI dark sides.

Tail-End Evaluation Based on the Cost-Effectiveness of Bridge Life Cycles

The number of bridges in China is increasing year by year. The contradiction between the declining performance of bridge structures and the increasing traffic volume in China is becoming increasingly prominent, causing a concentrated upsurge in operation and maintenance needs during the service life of the bridges. In accordance with the goal and vision of the comprehensive construction of the Bridge Structural Health Monitoring (BSHM) system, the study of bridge cost-effectiveness now focuses on the installation, operation, and maintenance costs of the BSHM system, the renovation costs of bridge structure repair and maintenance, and the benefits of the BSHM system in assisting in expansion of the service life of bridges. In this study, a new cost-effectiveness framework is proposed that could be used for the operation and maintenance period. The cost-effectiveness framework is constructed from five perspectives: installation, operation, and maintenance costs of the BSHM system; repair and renovation costs during the operation and maintenance period; demolition costs; operation benefits during the operation and maintenance period; and the recovery benefits. The difference between costs and benefits is calculated to evaluate the cost-effectiveness, the service life, and the structural reliability of bridges, in order to provide auxiliary decision making for bridges. A bridge in China is used as an example; the difference between the reliability and operation of the bridge and the maintenance benefits and costs corresponding to different operating times is calculated, and maintenance decision analysis is performed. This can greatly extend the service time of the bridge without major repairs when the bridge reliability meets the requirements. Therefore, the BSHM operation and maintenance cost-effectiveness framework reduces resource waste, reduces operation and maintenance costs, and maximizes resource utilization and benefits, while ensuring the safety of the bridge structure and extending the service life of the bridge.