Data Science and Management最新文献_第4页

Prediction of retail commodity hot-spots: a machine learning approach 零售商品热点预测：一种机器学习方法

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-02-18 DOI: 10.1016/j.dsm.2025.02.003

Chao Deng , Xipeng Liu , Jinyu Zhang , Yuhua Mo , Paiyu Li , Xuexia Liang , Na Li

{"title":"Prediction of retail commodity hot-spots: a machine learning approach","authors":"Chao Deng , Xipeng Liu , Jinyu Zhang , Yuhua Mo , Paiyu Li , Xuexia Liang , Na Li","doi":"10.1016/j.dsm.2025.02.003","DOIUrl":"10.1016/j.dsm.2025.02.003","url":null,"abstract":"<div><div>The accurate prediction of hot-spot commodities in the retail market is crucial for inventory management and market strategy formulation. Traditional methods focus on analyzing the sales trends of listed commodities to mine hot-spot commodities; however, these methods are incapable of dealing with new commodities that lack historical sales data. Although existing studies have used commodity features to predict hot-spot commodities, commodity features are complex and diverse, and it is difficult for a single prediction model to fully capture and accurately analyze the relationship between commodity features and sales hot-spots. This study proposes a commodity hot-spot classification prediction method based on an ensemble learning model that combines multiple machine learning methods, namely the gradient boosting decision tree, eXtreme gradient boosting, and light gradient-boosting machine, to analyze the relationship between commodity features and retail market sales trends. In addition, a voting strategy is designed to fuse the model classification results. The experimental results show that the accuracy of the ensemble model is as high as 0.91, outperforming a single model and other approaches. This method can effectively help enterprises to identify hot-spot commodities and provide strong support for new commodity designs and sales strategy adjustments.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 414-422"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842237","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}

引用次数: 0

Climate physical risks: catalyst or constraint for the convergence of the digital and low-carbon economies? 气候物理风险：数字经济与低碳经济融合的催化剂还是制约因素？

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-01-23 DOI: 10.1016/j.dsm.2025.01.004

Ya Cui, Bo Yang

{"title":"Climate physical risks: catalyst or constraint for the convergence of the digital and low-carbon economies?","authors":"Ya Cui, Bo Yang","doi":"10.1016/j.dsm.2025.01.004","DOIUrl":"10.1016/j.dsm.2025.01.004","url":null,"abstract":"<div><div>The digital economy and the low-carbon economy are crucial catalysts of global economic change and sustainable development. This research focuses on China and thoroughly examines the coupling and coordination mechanisms between the digital and low-carbon economies. This study employs the coupling coordination degree model to assess the development level of the coupling and coordination between the two economies. It also investigates the influence of climate-related physical threats on their coupled and coordinated development and presents the following findings. First, the integration and synchronized advancement of China's digital and low-carbon economies exhibit a general upward trend, though the average development level remains relatively low, characterized by considerable regional variations. Moreover, the dynamic distribution of coupling and coordination development levels among locations reveals notable disparities. Second, the coupling coordination degree between China's digital and low-carbon economies exhibits distinct spatial correlation, following a “high-high, low-low” distribution pattern, with transfer channels exhibiting spatial dependence. Third, Tobit model analysis reveals that climate-related physical dangers substantially impede the integration and synchronized advancement of digital and low-carbon economies. The underlying mechanism is the inhibition of green low-carbon technological innovation, with extremely low temperatures exerting the most significant suppressive influence. This suppression varies based on geographic location, data development, and resource endowment.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 500-518"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885611","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}

引用次数: 0

AI-driven innovation in emerging markets: extending the technology acceptance model–technology-organization-environment framework in small- and medium-sized enterprises 新兴市场人工智能驱动创新：中小企业技术接受模型-技术-组织-环境框架的扩展

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-04-28 DOI: 10.1016/j.dsm.2025.04.002

Faizan ul Haq , Norazah Mohd Suki

{"title":"AI-driven innovation in emerging markets: extending the technology acceptance model–technology-organization-environment framework in small- and medium-sized enterprises","authors":"Faizan ul Haq , Norazah Mohd Suki","doi":"10.1016/j.dsm.2025.04.002","DOIUrl":"10.1016/j.dsm.2025.04.002","url":null,"abstract":"<div><div>This study examined the impact of artificial intelligence (AI) adoption on innovation outcomes in Pakistani manufacturing small- and medium-sized enterprises (SMEs), addressing a critical gap in the understanding of how AI drives product and process innovation in resource-constrained settings. While AI adoption (AIA) has been well-studied in larger enterprises and developed markets, its implications for SMEs in emerging economies remain poorly explored. This study investigates the moderating role of leadership support in AIA and AI-driven marketing (AIM) in amplifying innovation outcomes. A quantitative survey of 500 senior and middle management executives from manufacturing SMEs was conducted, and data were analyzed using partial least squares structural equation modeling. Their findings show that technological readiness, organizational preparedness, and strong leadership significantly enhance AIA, resulting in notable improvements in both product and process innovation. Additionally, AIM amplifies the impact of AIA on innovation, thereby maximizing SMEs’ innovative capacity. This study’s theoretical contribution extends the technology acceptance model-technology-organization-environment framework by incorporating leadership support as a moderator for AIA and AIM for innovation outcomes, providing insights into the role of AI in emerging markets. From a managerial standpoint, this study highlights the need for robust leadership, technological infrastructure, and AIM strategies to fully leverage the potential of AI. Policymakers are urged to create supportive environments with strong technological and leadership infrastructures to enable SMEs to achieve sustained growth through AI-driven innovation.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 485-499"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885610","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}

引用次数: 0

Counterfactual synthetic minority oversampling technique: solving healthcare's imbalanced learning challenge 反事实合成少数派过采样技术：解决医疗保健不平衡学习挑战

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-02-03 DOI: 10.1016/j.dsm.2025.01.006

Goncalo Almeida, Fernando Bacao

{"title":"Counterfactual synthetic minority oversampling technique: solving healthcare's imbalanced learning challenge","authors":"Goncalo Almeida, Fernando Bacao","doi":"10.1016/j.dsm.2025.01.006","DOIUrl":"10.1016/j.dsm.2025.01.006","url":null,"abstract":"<div><div>The application of machine learning in the healthcare domain has groundbreaking potential across a wide range of scenarios. However, this potential is often stalled by data-related challenges, such as the imbalanced nature of the domain's data, where critical outcomes tend to be inherently rare. To address this challenge, we propose a novel oversampling approach, the counterfactual synthetic minority oversampling technique (Counterfactual SMOTE), which combines SMOTE with a counterfactual generation framework. Our method intrinsically performs an oversampling process near the decision boundary within a safe region of space, allowing for the generation of informative but non-noisy minority samples. To validate the proposed framework, a rigorous experimental procedure was conducted across a set of highly imbalanced binary classification challenges in healthcare. The results demonstrate the superiority of the proposed method over several of the most commonly used oversampling alternatives presented in the literature. Notably, Counterfactual SMOTE was the only method to present a convincingly superior performance when compared with the original SMOTE. Although the proposed method was specifically validated in the healthcare domain, owing to its relevance and frequently imbalanced nature, we expect the findings of this study to be generalizable to any imbalanced scenario.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 436-446"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842240","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}

引用次数: 0

Automatic method for identification of cycles in COVID-19 time-series data COVID-19时间序列数据周期的自动识别方法

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-02-17 DOI: 10.1016/j.dsm.2025.02.002

Miaotian Li , Ciprian Doru Giurcăneanu , Jiamou Liu

引用次数: 0

DeepSeek: implications for data science and management in the AI era DeepSeek：人工智能时代对数据科学和管理的影响

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.dsm.2025.09.001

Zongben Xu , Shan Liu , Wei Huang , Junmin Shi , Fengmin Xu , Xin Tang , Haibing Lu

引用次数: 0

Unlocking the power of machine learning in big data: a scoping survey 解锁大数据中机器学习的力量：一项范围调查

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-02-15 DOI: 10.1016/j.dsm.2025.02.004

Fadil Mohammed Surur , Abiy Abinet Mamo , Bealu Girma Gebresilassie , Kidus Abebe Mekonen , Abenezer Golda , Rajat Kumar Behera , Kumod Kumar

{"title":"Unlocking the power of machine learning in big data: a scoping survey","authors":"Fadil Mohammed Surur , Abiy Abinet Mamo , Bealu Girma Gebresilassie , Kidus Abebe Mekonen , Abenezer Golda , Rajat Kumar Behera , Kumod Kumar","doi":"10.1016/j.dsm.2025.02.004","DOIUrl":"10.1016/j.dsm.2025.02.004","url":null,"abstract":"<div><div>Machine learning (ML) plays a crucial role in big data (BD) by serving as the cornerstone of efficient data processing and analysis. In particular, ML provides BD with the ability to extract valuable insights from the large data sets. Therefore, this study conducted a scoping survey to define the role of ML in BD by exploring its history and evolution. Subsequently, a framework of ML in BD is proposed, emphasizing its practical applications in addressing the challenges presented by the volume, velocity, variety, and veracity of data. Moreover, BD analytics is described, showcasing how ML paradigms contribute to decision-making. This is followed by an illustration of real-world applications across diverse industries regarding the transformative impact of ML on BD. The survey findings highlight the integration of ML in BD considering volume, variety, velocity, and reliability. Scalable storage solutions, advanced computational architectures, and distributed ML are the avenues for shaping the ML landscape. Similarly, embedding intelligence in preprocessing and interoperable models is an avenue considering veracity. Real-time processing frameworks, temporally aware ML, and edge-computing integration are avenues considering velocity. Automated data quality assurance, explainable artificial intelligence and transparency, and blockchain technology for data provenance are avenues in veracity.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 519-535"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885612","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}

引用次数: 0

ESG performance and executive compensation levels: an empirical study ESG绩效与高管薪酬水平的实证研究

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-07-01 DOI: 10.1016/j.dsm.2025.06.002

Cunbo Yang , Jindong Wang , Haoxiang Huang , Ying Liu

{"title":"ESG performance and executive compensation levels: an empirical study","authors":"Cunbo Yang , Jindong Wang , Haoxiang Huang , Ying Liu","doi":"10.1016/j.dsm.2025.06.002","DOIUrl":"10.1016/j.dsm.2025.06.002","url":null,"abstract":"<div><div>As global environmental concerns grow, corporate environmental, social, and governance (ESG) performance has become an essential indicator for measuring organizational value and sustainability. Considering the inherent complexity of ESG measurement, this study focuses on ESG ratings. Leveraging a decade-long dataset (2012–2021) from the A-share listed firms in China, this study empirically examines the impact of ESG on executive compensation (EC) and the mechanism by which firms’ financial performance plays a role between the two. By employing robust empirical methodologies, we elucidate how financial performance mediates this relationship and offer novel insights into the strategic alignment of sustainability initiatives with organizational reward systems. We find that ESG performance significantly affects EC levels. Specifically, ESG performance can improve EC levels by improving corporate financial performance. Heterogeneity analysis shows that executive salary levels are more affected by ESG performance in businesses with high pollution levels and those where the general manager and board chairperson do not hold concurrent positions. The empirical results demonstrate that integrating sustainability criteria into executive incentive structures and prioritizing robust ESG practices can generate measurable improvements in financial outcomes. Critically, organizational strategies must account for sector-specific environmental impact thresholds and intrinsic variations in corporate governance architecture. This study broadens ESG performance research, provides valuable guidance for establishing corporate ESG compensation, and motivates executives to actively improve corporate ESG performance and thereby achieve sustainable growth.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 403-413"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792218","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}

引用次数: 0

Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection 粒子群优化增强机器学习和深度学习技术在物联网入侵检测中的应用

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-02-28 DOI: 10.1016/j.dsm.2025.02.005

Mourad Benmalek , Abdessamed Seddiki

{"title":"Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection","authors":"Mourad Benmalek , Abdessamed Seddiki","doi":"10.1016/j.dsm.2025.02.005","DOIUrl":"10.1016/j.dsm.2025.02.005","url":null,"abstract":"<div><div>The exponential escalation of cyber threats and attacks targeting Internet of Things (IoT) devices in this decade necessitates the development of effective intrusion detection methods. This paper presents an innovative anomaly-based intrusion detection system that leverages machine learning (ML) and deep learning (DL) models to secure IoT networks. This study utilizes the RT_IoT2022 dataset, a novel dataset that captures complex IoT attack scenarios. The study implements particle swarm optimization (PSO), a bio-inspired metaheuristic, for feature selection and optimization, successfully reducing computational overhead while enhancing model performance. Several models, including the support vector machine, k-nearest neighbors, categorical boosting (CatBoost), naïve Bayes, convolutional neural network, and long short-term memory, have been evaluated for their ability to classify normal and malicious attacks. Our findings underscore the crucial roles of ML and DL in safeguarding IoT networks and the importance of continuous model evaluation using real-world data. Experiments demonstrate that CatBoost combined with PSO outperforms state-of-the-art methods in the literature on the same dataset across all metrics.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 423-435"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842238","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}

引用次数: 0

Enhancing imbalanced text classification: an overlap-based refinement approach 增强不平衡文本分类：基于重叠的细化方法

Data Science and Management Pub Date : 2025-12-01 Epub Date: 2025-03-15 DOI: 10.1016/j.dsm.2025.03.001

Sihem Nouas, Lamia Oukid, Fatima Boumahdi

{"title":"Enhancing imbalanced text classification: an overlap-based refinement approach","authors":"Sihem Nouas, Lamia Oukid, Fatima Boumahdi","doi":"10.1016/j.dsm.2025.03.001","DOIUrl":"10.1016/j.dsm.2025.03.001","url":null,"abstract":"<div><div>The inherent class imbalance within textual data poses a significant challenge for machine learning-based techniques, as the available data often fails to adequately represent all classes. This scarcity of instances can make it even more challenging when there are overlapping regions within different classes. To address these limitations, this study introduces a refinement model for textual data classification with imbalanced datasets. The proposed approach, refined classification using overlap data with bagging and genetic algorithms (ReCO-BGA), aims to refine the classification predictions by creating a two-tier classification process. First, a bagging model is employed, incorporating three distinct classes: majority, minority, and an additional extracted class specifically for overlapping instances. Second, we propose to rectify the predicted overlap instances using a genetic-based oversampling technique. To evaluate the performance of ReCO-BGA, we conducted several experiments, focusing on two practical use cases: hate speech detection and sentiment analysis. The results demonstrated the effectiveness of the proposed method and showed that it outperforms state-of-the-art methods.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 4","pages":"Pages 474-484"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885609","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}

引用次数: 0