Decision Analytics Journal最新文献

{"title":"An integrated bibliometric analysis of Benefit of the Doubt composite indicators for policy and decision analysis","authors":"Thyago Nepomuceno ,&nbsp;Flávia Barbosa ,&nbsp;Hermilio Vilarinho ,&nbsp;Ana Camanho","doi":"10.1016/j.dajour.2025.100672","DOIUrl":"10.1016/j.dajour.2025.100672","url":null,"abstract":"<div><div>The Benefit of the Doubt (BoD) is a non-parametric frontier model derived from Data Envelopment Analysis (DEA), used to construct composite indicators in various sectors of economic activity, with a particular focus on macroeconomic assessments. Based on documents published in the Web of Science from 1991 to 2025, we conduct a systematic bibliometric review on this topic, proposing future research directions derived from the bibliographic coverage of the most recurrent concepts, areas, and problems addressed in the current BoD literature. We identify core publication networks for non-parametric frontier composite indicators, highlighting trends, hot topics, and clusters of applications. As a result, we offer three different and comprehensive BoD research agendas based on a practical knowledge discovery exercise from expert knowledge and Large Language Models (LLM), highlighting attractive topics, theoretical contributions, concepts, methods, and potential applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100672"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimal investment strategy for maximizing the expected value of utility accumulation across capital levels","authors":"José Cerda-Hernández ,&nbsp;Anna Sikov ,&nbsp;Alberto Ramos","doi":"10.1016/j.dajour.2026.100681","DOIUrl":"10.1016/j.dajour.2026.100681","url":null,"abstract":"<div><div>This paper examines an optimal investment problem for an insurance company under the Cramer–Lundberg risk model, where investments are allocated between risky and risk-free assets. In contrast to models focusing on optimal investment and/or reinsurance strategies to maximize the expected utility of terminal wealth within a given time horizon, this study considers the expected value of utility accumulation across all intermediate capital levels of the insurer. We employ the dynamic programming principle and prove a verification theorem showing that any solution to the Hamilton–Jacobi–Bellman (HJB) equation solves our optimization problem. We establish the existence of the optimal investment strategy subject to some regular conditions for the solution of the HJB equation. Finally, we present numerical examples to illustrate the applicability of the theoretical findings.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100681"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable transformer framework for sentiment analysis in aviation workforce data","authors":"Sovon Chakraborty ,&nbsp;Protiva Das ,&nbsp;Fahmid Al Farid ,&nbsp;Fuyad Hasan Bhoyan ,&nbsp;Farig Yousuf Sadeque ,&nbsp;Jia Uddin ,&nbsp;Hezerul Abdul Karim","doi":"10.1016/j.dajour.2026.100693","DOIUrl":"10.1016/j.dajour.2026.100693","url":null,"abstract":"<div><div>Aviation is one of the predominant sectors that contribute significantly to the global economy. With the advent of technology, this industry is witnessing a paradigm shift towards data-driven approaches. The morale of the airline employees is barely noticed, which causes fatigue and depression. Furthermore, these mental health issues can be active reasons for destructive accidents. In this research, the authors are focused on collecting insightful information on aviation employees from Glassdoor.com. Moreover, the authors focus on analyzing the sentiments of the employees of renowned aviation companies. Primarily, the authors scraped necessary data from Glassdoor.com and created a dataset named JetJobJoy (JJJ). Data quality is measured with the Inter Annotator Agreement (IAA), in which three experts from the concerned domain ensure the credibility of the dataset. An extensive Exploratory Data analysis is performed to extract essential factors from the dataset. The dataset contains several attributes, such as the company’s rating, the job’s pros and cons along with the feedback of the employees regarding their workplace. The feedback comments are furthermore preprocessed properly and fed into numerous sequence-to-sequence and transformer-based architectures. Furthermore, an improvised architecture of ModernBERT has been proposed with a lesser number of encoders that outperforms other state-of-the-art architectures in terms of performance metrics (95.69% F1-score) and sustainability. The model is also utilized to perform on other datasets for detecting cyberbullying and shows promising results. Finally, the authors have undertaken the diligent effort to interpret the model with the LIME Explainable AI model.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100693"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated resampling and machine learning framework for predictive analytics of large wildfires","authors":"Luís Camacho ,&nbsp;Filipe X. Catry ,&nbsp;Fernando Bacao","doi":"10.1016/j.dajour.2026.100674","DOIUrl":"10.1016/j.dajour.2026.100674","url":null,"abstract":"<div><div>Accurately predicting the final burned area of wildfires at the moment of ignition is a challenging problem with important implications for disaster management and resource allocation. In this study, we apply recently developed resampling techniques, combined with machine learning algorithms, to improve predictive accuracy for rare, high-impact wildfire events. While the study focuses on wildfires, the methodology addresses a broader challenge in decision analytics: generating reliable predictions from imbalanced datasets to support informed managerial decisions. Our results demonstrate that these techniques can enhance the robustness of predictive models for extreme events, offering insights that may be relevant to other domains where accurate forecasts of rare outcomes are critical.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100674"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cloud-enabled predictive analytics model for assessing health risks under climate variation","authors":"S. Sheeja Rani,&nbsp;Raafat Aburukba","doi":"10.1016/j.dajour.2026.100687","DOIUrl":"10.1016/j.dajour.2026.100687","url":null,"abstract":"<div><div>The relationship between climatic conditions and disease spread is a crucial aspect of public health. Factors such as temperature, humidity, precipitation, seasonal changes, and air pollution directly and indirectly influence the transmission of infectious diseases, including respiratory illnesses, heat-related disorders, vector-borne diseases like malaria and dengue, and foodborne infections. Large-scale climate variations can further intensify these impacts. To address these challenges, effective prediction models are essential for minimizing health risks and supporting timely decision-making. This paper introduces a novel method called the Weighted Iterative Piecewise Regression–based Improved Deep Multilayer Perceptron Classifier (WIPR-IDMPC) for predicting health impacts under varying climatic conditions using cloud data centers. The primary objective of the proposed model is to enhance prediction accuracy while reducing time consumption and error rates. The framework consists of three phases: data acquisition, feature selection, and classification. In the data acquisition phase, IoT devices collect large volumes of weather and health-related data, which are transmitted to the cloud for processing. The cloud employs an improved deep multilayer perceptron classifier consisting of an input layer, multiple hidden layers, and an output layer. The Weighted Piecewise Regressive Relief method is used to select relevant features by removing redundant and irrelevant attributes, thereby reducing computational time. The selected features are fed into the classifier, which processes training and testing samples to categorize health impact levels such as very low, low, medium, high, and very high. The performance analysis indicates improvements of accuracy by 98.24% and precision by 98.18% and recall by 98.69%. Additionally, the proposed method achieves a significant reduction in RMSE by 1.75, along with a decrease in prediction time by 68.38 ms compared to traditional approaches.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100687"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analytical framework for uncovering risk interdependencies in sustainable supply chains using Fuzzy DEMATEL","authors":"Labaran Isiaku ,&nbsp;Wesam Shishah","doi":"10.1016/j.dajour.2026.100676","DOIUrl":"10.1016/j.dajour.2026.100676","url":null,"abstract":"<div><div>This study employs the Fuzzy DEMATEL method to investigate the interdependencies and causal dynamics among 12 key risk factors in Sustainable Supply Chain Management (SSCM). By integrating expert evaluations with fuzzy logic and applying the CFCS defuzzification method, the analysis captures both direct and indirect relationships across economic, operational, environmental, and social dimensions. A quadrant-based mapping of total influence (D+R) and net effect (D-R) distinguishes between causal and effect factors, identifying Transportation Disruptions (<strong>R5</strong>), Labor Strikes (<strong>R10</strong>), and Regulatory Restrictions (<strong>R3</strong>) as high-priority causal risks. Sensitivity analysis confirms the robustness of these rankings across varying influence thresholds. The findings are further contextualized through real-world supply chain disruptions and tailored managerial recommendations, enhancing the practical utility of the results. This research contributes theoretically by extending causal risk modeling in SSCM and methodologically by validating the application of Fuzzy DEMATEL as a comprehensive MCDM tool under uncertainty. It also delivers actionable insights for supply chain managers aiming to build resilient, sustainable, and adaptive operations in increasingly volatile environments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100676"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intuitionistic fuzzy analytics approach for examining factors in decentralized renewable energy adoption in emerging economies","authors":"Mohammad Mynul Islam Mahin ,&nbsp;Md Jawad Bin Rouf ,&nbsp;Sheak Salman ,&nbsp;Shah Murtoza Morshed ,&nbsp;Mohammad Morshed","doi":"10.1016/j.dajour.2025.100662","DOIUrl":"10.1016/j.dajour.2025.100662","url":null,"abstract":"<div><div>In many developing countries, heavy reliance on centralized, non-renewable energy sources poses threats to both energy security and the environment. However, there is still a lack of structured evidence on which factors should be prioritized to accelerate the adoption of decentralized renewable energy systems (DRES) in emerging economies such as Bangladesh. This study intends to determine, rank, and examine the factors affecting the adoption of DRES and extends fuzzy MCDM applications by integrating Interval Valued Type 2 Intuitionistic Fuzzy (IVT2IF) with Weighted Influence Non-linear Gauge System (WINGS). The integrated approach provides a way to handle specialist judgments under uncertainty and to reveal both the strength and the direction of relationships among the factors. A total of 15 factors were chosen after a comprehensive review of the existing literature and subsequent specialist validation. The analysis reveals that “Dependability and economic feasibility,” “Supportive policy and regulatory frameworks,” and “Community empowerment and local governance” are the most critical factors, with total engagement values of 0.1587, 0.1585, and 0.1520, respectively. The causal ranking further indicates that “Accessibility of renewable resources” and “Supportive policy and regulatory frameworks” are the most influential factors within the cause group, with total positional values of 0.0047 and 0.0035. The findings suggest that strengthening policy design, improving economic conditions for projects, and empowering local communities are central levers for accelerating DRES deployment. The outcomes provide a practical decision support basis for stakeholders and policymakers who seek to prioritize interventions and enhance the sustainability and resilience of the energy sectors of emerging economies.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100662"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analytical framework for causal decision-making in international trade","authors":"Juan Gabriel Vanegas-López ,&nbsp;Carlos Andrés Pérez-Aguirre ,&nbsp;Diego Alejandro López-Cadavid","doi":"10.1016/j.dajour.2026.100686","DOIUrl":"10.1016/j.dajour.2026.100686","url":null,"abstract":"<div><div>In international trade, selecting an Incoterm is a critical decision impacting costs and risks. Previous quantitative studies relied on traditional econometric models imposing strict linearity, limiting causal interpretation. This study addresses this gap by applying a causal inference framework to identify Incoterm choice drivers among Colombian exporters. The methodology combines (i) directed acyclic graphs to encode causal assumptions; (ii) nested, cross-validated random forests to capture complex, nonlinear relationships; and (iii) G-computation to estimate unbiased causal effects. Results show that transactional features dominate selection. Transitioning from bulk to containerized cargo increases the probability of selecting an E/F-group term-such as Ex Works (EXW), Free On Board (FOB), or Free Carrier (FCA), where the seller’s obligations end upon delivery to the buyer-by 13.4 percentage points. Additionally, shipment weight and value exhibit nonlinear threshold effects, where E/F terms become significantly more probable only after shipments surpass specific size thresholds-a pattern missed by prior linear models. Relational factors also matter; sharing a common language with the trade partner increases E/F-term probability by 6.5 percentage points. This framework provides managers with a tool to predict Incoterm choices and quantify the causal impact of altering shipment profiles, enabling better-aligned, lower-risk trade contracts.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100686"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-guided analytical framework for assessing the value of additional variables in heart transplant decision making","authors":"Ali Dag ,&nbsp;Hamidreza A. Dolatsara ,&nbsp;Abdullah Asilkalkan ,&nbsp;Joseph Ekong ,&nbsp;Kamil Ciftci ,&nbsp;Ugur Kucuk ,&nbsp;Ozlem Cosgun","doi":"10.1016/j.dajour.2026.100690","DOIUrl":"10.1016/j.dajour.2026.100690","url":null,"abstract":"<div><div>Heart transplantation is a life-saving procedure for patients with end-stage heart failure. The United Network for Organ Sharing (UNOS), which administers the US organ allocation system, substantially expanded the number of clinical and demographic variables collected in its database in 2004. This study examines whether these newly added variables improve the ability to predict survival outcomes for patients on the heart transplant waiting list. An information-gain-based feature selection approach, supported by an extensive review of prior studies, was combined with survival analysis and regularized regression to identify the most influential predictors. Using the selected variables, several classification models, including tree-augmented Naïve Bayes, logistic regression, support vector machines, decision trees, and random forests, were developed. Class imbalance was addressed through random under-sampling and cost-sensitive modeling. The results show that prediction accuracy for short-term, medium-term, and long-term survival (one month, one year, and five years) does not improve substantially when the new variables are included. The findings suggest that the expanded data collection introduced in 2004 adds limited incremental value for predicting survival among patients awaiting heart transplantation.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100690"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An accuracy-level method for robust evaluation in predictive analytics","authors":"Mety Agustini ,&nbsp;Kartika Fithriasari ,&nbsp;Dedy Dwi Prastyo","doi":"10.1016/j.dajour.2025.100661","DOIUrl":"10.1016/j.dajour.2025.100661","url":null,"abstract":"<div><div>Evaluation metrics are essential in selecting the most appropriate predictive model. Conventional metrics such as R-squared, root mean squared error (RMSE), and mean absolute percentage error (MAPE) rely on arithmetic averages, making them sensitive to outliers, undefined values, and scale differences. This study introduces a novel accuracy-level approach for evaluating continuous data models, using a formula derived from the combination of the “counted” concept in metrics for discrete data and the “error” concept in metrics for continuous data. This formula has not been proposed in other metrics for continuous data, which typically rely on arithmetic or average-based approaches. The second novelty is the introduction of an accuracy-level method, derived from error thresholds for each level. Unlike robust metrics, which trim some observations from the dataset, the proposed metrics incorporate all observations in the dataset, offering a more inclusive evaluation. Four novel metrics are introduced: counted squared error (CSE), counted absolute error (CAE), counted absolute percentage error (CAPE), and symmetric counted absolute percentage error (SCAPE). These metrics assess model performance based on the accuracy value for each level, providing a more robust and interpretable evaluation. We compare the proposed metrics not only with conventional metrics but also with robust alternatives, including median absolute error, trimmed mean squared error, Huber loss, and quantile loss. In regression models with ten outlier scenarios, the proposed metrics show accuracy patterns consistent with robust metrics and different from average-based metrics. In imputation models, both proposed and robust metrics consistently identify predictive mean matching (PMM) as the optimal model, while average-based metrics yield inconsistent results. Interestingly, in time series models, robust metrics misleadingly favor ARIMA, whereas both the proposed and conventional metrics select Holt-Winters as the most appropriate. Overall, this is the first study to extend counted accuracy from discrete to continuous predictive models, introducing a robust, outlier-resistant, and universally interpretable 0%–100% framework for model evaluation.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"18 ","pages":"Article 100661"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}