Decision Analytics Journal最新文献

{"title":"An analytical study of structural equation modeling on organizational resilience and financial performance in Ecuadorian SMEs","authors":"Juan Diego Ochoa Crespo ,&nbsp;José Manuel Feria Domínguez ,&nbsp;Diego Marcelo Cordero Guzmán","doi":"10.1016/j.dajour.2025.100575","DOIUrl":"10.1016/j.dajour.2025.100575","url":null,"abstract":"<div><div>Organizational resilience is vital for the sustainability of SMEs in emerging economies like Ecuador. This study proposes and validates a conceptual model integrating reactive and organizational-financial resilience to assess their impact on financial performance. Based on data from 333 SMEs, findings show that employee commitment, leadership, and social capital significantly drive reactive resilience, which serves as a precursor to broader resilience. Organizational resilience, in turn, strongly influences financial outcomes, confirming its strategic relevance in volatile contexts. Although information systems had a modest impact, cohesive human and social capital proved essential. Surprisingly, general business practices showed no significant effect on resilience, indicating the need for more focused strategies. This research bridges a literature gap by offering an evidence-based framework for SMEs, guiding managerial action and policy formulation.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100575"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863983","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 machine learning approach for text pattern diagnosis in mental health consultations","authors":"Safitri Juanita ,&nbsp;Anisah Hasratniwati Daeli ,&nbsp;Mohammad Syafrullah ,&nbsp;Wiwik Anggraeni ,&nbsp;Mauridhi Hery Purnomo","doi":"10.1016/j.dajour.2025.100572","DOIUrl":"10.1016/j.dajour.2025.100572","url":null,"abstract":"<div><div>Online health consultation services are crucial for mental health support, particularly in densely populated areas. However, the heavy reliance on human expertise often leads to delays, necessitating more efficient and automated solutions. This study developed a machine learning framework to automate doctor response patterns for mental health questions — focusing on anxiety, depression, and stress — using clinically validated data from an Indonesian Online health consultation platform. We performed comprehensive text preprocessing, including duplicate removal, special character elimination, case folding, stopword removal, tokenization, lemmatization, and part-of-speech tagging, and evaluated four feature extraction methods: Word2Vec, Bag-of-Words, N-Gram, and Global Vectors for Word Representation. Five machine learning algorithms — Naïve Bayes, K-Nearest Neighbors, Random Forest, Neural Network, and Gradient Boosting — were tested, along with hybrid models combining Bagging Classifier or Genetic Algorithm. The results showed that Gradient Boosting achieved the highest accuracy (0.842) among standalone models, with high precision (0.858) and F1-score (0.864) for depression prediction, and recall (0.850) and F1-score (0.856) for stress prediction. The Gradient Boosting-Bagging Classifier hybrid matched this accuracy (0.842), while the Gradient Boosting-Genetic Algorithm hybrid showed superior performance for anxiety prediction (precision: 0.888, recall: 0.816). N-Gram and Bag-of-Words methods and the 90:10 and 70:30 train–test splits consistently produced optimal results. This work demonstrates that machine learning can automate mental health responses at scale, with Gradient Boosting balancing accuracy and efficiency. Future research will explore transformer-based models and multilingual validation to improve broader implementation.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100572"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898988","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 dynamic pharmaceutical inventory investment management model during pandemics using metaheuristic algorithms","authors":"Vinita Dwivedi,&nbsp;Mamta Keswani","doi":"10.1016/j.dajour.2025.100570","DOIUrl":"10.1016/j.dajour.2025.100570","url":null,"abstract":"<div><div>This study presents a comprehensive approach to managing pharmaceutical inventory during pandemics. The study focuses on optimizing investment strategies for promoting COVID-19 medicine across various price ranges while carefully preserving pharmaceutical products. We develop a customized inventory model that accounts for item degradation, considering factors such as price, infection rate, and preservation methods. This model is adaptable to three pandemic scenarios, with the deterioration rate influenced by the level of investment in preservation technology. Our approach employs optimal control theory to dynamically adjust investment rates, maximizing the effectiveness of resource allocation. We also utilize advanced optimization algorithms, including Ant Colony and Cuckoo Search Algorithms, to optimize pricing, preservation strategies, and replenishment schedules. Through numerical experiments, we demonstrate the efficacy of our dynamic investment approach, providing empirical evidence of its effectiveness. Additionally, sensitivity analysis on key parameters offers valuable insights for decision-makers, highlighting the importance of dynamically managing pharmaceutical inventory during pandemics. Our study provides practical solutions and managerial insights for informed pharmaceutical inventory decisions during the pandemic.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100570"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829028","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 Stackelberg game-based logistics cooperation model for agricultural product supply chains in live streaming e-commerce","authors":"Lijun Shi ,&nbsp;Hailong Cheng","doi":"10.1016/j.dajour.2025.100569","DOIUrl":"10.1016/j.dajour.2025.100569","url":null,"abstract":"<div><div>The rapid growth of live streaming e-commerce (LSEC) has revolutionized agricultural product sales, but the perishable nature of these products poses significant challenges to supply chain logistics. Logistics is vital to the agricultural products live streaming e-commerce supply chain. This study analyzes four logistics decision models using the Stackelberg game approach: independent of the third-party logistics (TPL) provider, cooperation with the farmer, cooperation with the LSEC platform, and integrated cooperation among the players. We develop a decision model for the agricultural products live streaming e-commerce supply chain that takes into account logistics service efforts and compares and discusses the sales price, the level of logistics service effort, and market demand under different models. We use numerical examples to study the choice of logistics cooperation model for players in the agricultural products live streaming e-commerce supply chain. The study results show that logistics cooperation can effectively share the logistics service costs, reduce the sales price, and improve the level of logistics service effort. The logistics cooperation model improves the cooperating players and supply chain profits and demonstrates that reasonable profit distribution is the key to successful cooperation among supply chain players. The logistics service efforts improve supply chain profits and benefit all cooperative players. Moreover, a reasonable profit distribution threshold enhances cooperation and profitability in agricultural supply chains. This study provides a reference for the logistics cooperation among players in the agricultural products live streaming e-commerce supply chain.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100569"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848658","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 consumer behavior analytics model for commercial district marketing using network-structured stamp rally data","authors":"Yuya Ieiri ,&nbsp;Shao Tengfei ,&nbsp;Osamu Yoshie","doi":"10.1016/j.dajour.2025.100567","DOIUrl":"10.1016/j.dajour.2025.100567","url":null,"abstract":"<div><div>Marketing strategies should target entire commercial areas, not just individual stores. This study highlights the data collected from stamp rally events as cross-sectional consumer behavior data. Although stamp rally data have been analyzed as tabular data, this approach should capture the complexity of consumer behavior observed during such events. This study focused on the co-occurrence relationships between the pairs of stores identified through data. Moreover, the study proposed a novel method to analyze consumer behavior by representing these relationships in a network structure to address this challenge. Two events were held in 2023. In one event, data were collected from 621 participants in one event, and in the other event, data were collected from 1040 participants. The collected data were analyzed using conventional frequent pattern mining methods applied to tabular data and the proposed network-based method. Consequently, the proposed method identified community hub stores that could be used as catalysts for marketing to new consumer groups beyond the community.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100567"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808705","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 machine learning and explainability-driven methodology for identifying winning strategies in Rugby Union","authors":"Arnaud Odet ,&nbsp;Thomas Bechard ,&nbsp;Pierre Moretto ,&nbsp;Sebastien Dejean ,&nbsp;Cristian Pasquaretta","doi":"10.1016/j.dajour.2025.100568","DOIUrl":"10.1016/j.dajour.2025.100568","url":null,"abstract":"<div><div>Interest in predicting sports match outcomes has grown significantly, driven by advancements in machine learning techniques and widespread adoption. However, the utilization of these predictive models in enhancing tactical team performance remains relatively limited. We propose a methodology that combines machine learning and algorithm explainability techniques, which were demonstrated through a case study on Rugby Union. Our study unfolds in two phases: first, we identify the most suitable modeling approach for our data by establishing a prediction model based on performance indicators observed during games. Subsequently, we applied an analysis based on SHapley Additive exPlanations (SHAP) values to interpret the predictions of this model. Our findings serve three primary purposes: (i) from a global standpoint, identifying performance indicators that primarily determine match outcomes; (ii) from an aggregated point of view highlighting strengths and weaknesses of any given team; and (iii) from a local perspective, offering technical staff diagnostic analyses of past games.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100568"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation of Frequentist and Ensemble Bayesian-aided techniques for prioritizing anomaly detection methods in time-series data","authors":"Vignesh Divakaran ,&nbsp;Vipasha Rana","doi":"10.1016/j.dajour.2025.100566","DOIUrl":"10.1016/j.dajour.2025.100566","url":null,"abstract":"<div><div>Accurately detecting anomalous points in time-series data is critical, as false positives can mislead business stakeholders, waste valuable resources, and diminish the overall impact of the detection system. While various statistical and machine learning techniques are employed to flag potential anomalies, the challenge lies in evaluating the significance of each approach and refining the results to isolate definitive anomalies. This paper examines multiple anomaly tagging techniques and introduces novel weightage assignment methods to prioritize the most effective approaches, filtering out less reliable ones. Specifically, we explore two methods: simple Frequentist approach and Ensemble Bayesian-aided approach, with an emphasis on why the latter is particularly well-suited for anomaly detection. The proposed methodology is validated both theoretically and empirically on time-series datasets. Our findings demonstrate that the Ensemble Bayesian-aided approach significantly improves detection accuracy by accounting for future uncertainty and addressing edge case fallacies inherent in individual tagging methods. This research provides a robust framework for anomaly detection, offering a powerful solution that enhances precision and reliability across diverse applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100566"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A predictive modelling approach to decoding consumer intention for adopting energy-efficient technologies in food supply chains","authors":"Brintha Rajendran,&nbsp;Manivannan Babu,&nbsp;Veeramani Anandhabalaji","doi":"10.1016/j.dajour.2025.100561","DOIUrl":"10.1016/j.dajour.2025.100561","url":null,"abstract":"<div><div>The transition towards energy-efficient practices in the food supply chain (FSC) is essential for addressing the dual imperatives of sustainability and cost-effectiveness. As consumers become increasingly aware of the environmental impact of their food choices, their willingness to support energy-efficient technologies (EET) has become a critical factor in shaping the future of sustainable FSC. This study empirically investigates consumer intention and desire to pay for food products characterized by a reduced energy footprint, utilizing machine learning (ML) algorithms to predict consumer preferences within the FSC. Association rule mining (ARM) was employed to uncover key patterns in consumer intentions, while multiple ML algorithms were compared to identify the most effective algorithm for predicting willingness to pay. The results reveal that the Random Forest algorithm achieved the highest accuracy at 82%, significantly outperforming other models. These findings underscore the potential of ML to refine marketing strategies and operational decisions, facilitating the broader adoption of EET within the FSC (EET-FSC). The study offers valuable implications for industry professionals seeking to enhance sustainability efforts through data-driven decision-making. The research contributes to optimizing FSC through improved decision-making, resource allocation, and sustainability initiatives. Future research directions include expanding the dataset scope, exploring advanced ML techniques, and examining the economic impacts of EET-FSC.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100561"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning and predictive analytics approach for enhancing credit assessment in manufacturing","authors":"Abdul Razaque ,&nbsp;Aliya Beishenaly ,&nbsp;Zhuldyz Kalpeyeva ,&nbsp;Raisa Uskenbayeva ,&nbsp;Moldagulova Aiman Nikolaevna","doi":"10.1016/j.dajour.2025.100560","DOIUrl":"10.1016/j.dajour.2025.100560","url":null,"abstract":"<div><div>The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities, and less favorable client conditions. To overcome this issue, a comprehensive credit assessment scoring system should be implemented to increase importers’ confidence. The article proposes a predictive-based reinforcement learning (PRL) model to help manufacturers and importers acquire more accurate and dependable credit scores while avoiding default risk. Furthermore, the proposed PRL model enhances decision-making, system efficiency, and risk-tolerant financial conditions. To attain these cutting-edge objectives, the proposed PRL model combines three algorithms. Algorithm 1 collects and aggregates data to indicate areas for improvement if credit scoring is poor. Algorithm 2 uses reinforcement learning to validate and enhance bank scores. Algorithm 3 focuses on predictive modeling for bank scoring, ensuring that the credit decision-making system is operational and constantly improving. Furthermore, reinforcement learning leverages the features from local interpretable model-agnostic explanations (LIME) and shapely additive explanations (SHAP) to generate locally reliable explanations and attribute the contribution of each feature for determining the output of the model. The Python platform tests the proposed PRL to achieve the objectives. Based on the results, The PRL model markedly enhances credit assessment precision, achieving an accuracy of over 99.5%, which outstrips current methodologies such OCLA (96.12%), PSML (84.12%), and EMPCC (91.67%). Furthermore, the PRL model augments leverage ratios, rising from 2.75% in 2015 to 3.36% in 2024.5, and increases accounts receivable turnover from 4.38% in 2015 to 7.4% in 2024.5, surpassing alternative credit evaluation methodologies. This research highlights the novelty of combining predictive analytics and reinforcement learning to revolutionize credit assessment, providing a scalable and reliable solution for manufacturers and importers. The findings establish the PRL model as a transformative approach for creating risk-tolerant and efficient financial environments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multiple criteria analysis approach for assessing regional and territorial progress toward achieving the Sustainable Development Goals in Italy","authors":"Idiano D’Adamo ,&nbsp;Massimo Gastaldi ,&nbsp;Antonio Felice Uricchio","doi":"10.1016/j.dajour.2025.100559","DOIUrl":"10.1016/j.dajour.2025.100559","url":null,"abstract":"<div><div>Sustainability is a pressing global challenge demanding an integrated approach balancing economic, environmental, and social perspectives. Numerous indicators have been proposed in the literature to assess progress toward the Sustainable Development Goals (SDGs), including equitable and sustainable well-being (BES). To effectively manage and monitor these indicators, robust analytical models are essential. The present study proposed an integrated analytical framework combining the 0–1 (min–max) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The analysis examined 61 indicators from the 2024 Equitable and Sustainable Well-being of the Territories, a dataset developed by the Italian National Institute of Statistics (ISTAT) to assess regional social, economic, and environmental sustainability across Italy. The results confirmed a persistent north–south divide, with average scores of 3.9 in the north and 1.4 in the south on a 1–5 scale, and central Italy demonstrating an intermediate performance of 3.1. At the regional level, Trentino-Alto Adige, Lombardia, and Valle d’Aosta emerged as top performers, while at the territorial level, Milano, Bologna, and Trieste stood out. These insights highlight the need for stronger synergies between territories to enhance competitiveness and elevate “Made in Italy” on the global stage. Effective regional collaboration may optimize resource allocation, harmonize territorial disparities, and accelerate progress toward the SDGs by leveraging local strengths and fostering sustainable development.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"15 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}