Data Science and Management最新文献_第2页

Corrigendum regarding previously published articles 关于以前发表的文章的更正

Data Science and Management Pub Date : 2025-03-01 DOI: 10.1016/j.dsm.2024.12.006

引用次数: 0

Identifying accounting control issues from online employee reviews 从在线员工评论中识别会计控制问题

Data Science and Management Pub Date : 2025-02-12 DOI: 10.1016/j.dsm.2025.02.001

Lukui Huang , Alan Abrahams , Juthamon Sithipolvanichgul , Richard Gruss , Peter Ractham

{"title":"Identifying accounting control issues from online employee reviews","authors":"Lukui Huang , Alan Abrahams , Juthamon Sithipolvanichgul , Richard Gruss , Peter Ractham","doi":"10.1016/j.dsm.2025.02.001","DOIUrl":"10.1016/j.dsm.2025.02.001","url":null,"abstract":"<div><div>This paper presents and describes an approach to generate innovative labeled datasets that enable automated text classifiers to automatically detect online employee reviews referring to accounting control deficiencies, facilitating supplementary monitoring for auditors and management. Employees, who are on the front lines executing policies and procedures, play a critical role in a firm's control environment. Their feedback provides insights into how controls are functioning. Textual data were collected and manually coded using a structured coding scheme mapped to COSO internal control framework (2013) principles. The dataset is unique in that it provides a new source of data that has not been previously used in internal control research, offering new opportunities for exploring the relationship between employee feedback and control weaknesses, and shedding light on potential improvements in internal control practices. Downstream stakeholders (such as researchers, management, investors, and auditors) can benefit by having rapid, automated means for filtering and prioritizing employee reviews for further investigation, with respect to accounting control issue mentions.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 248-256"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757256","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

The impact of mHealth apps’ affordance on consumers’ novel food purchasing decisions 移动健康应用程序对消费者新颖食品购买决策的影响

Data Science and Management Pub Date : 2025-01-22 DOI: 10.1016/j.dsm.2025.01.002

Yunfan Lu , Yaobin Lu , Sumeet Gupta , Ke An

引用次数: 0

The effect of green mergers and acquisitions on the green transformation of heavily polluting enterprises: empirical evidence from China 绿色并购对重污染企业绿色转型的影响：来自中国的经验证据

Data Science and Management Pub Date : 2025-01-21 DOI: 10.1016/j.dsm.2025.01.001

Jingmei Ma, Zhiqing Li, Zile Guo

{"title":"The effect of green mergers and acquisitions on the green transformation of heavily polluting enterprises: empirical evidence from China","authors":"Jingmei Ma, Zhiqing Li, Zile Guo","doi":"10.1016/j.dsm.2025.01.001","DOIUrl":"10.1016/j.dsm.2025.01.001","url":null,"abstract":"<div><div>Against the backdrop of increasingly prominent global environmental issues, heavily polluting enterprises (HPPs) urgently need to find a path to green transformation that achieves sustainable development and overcomes efficiency challenges. Based on data on mergers and acquisitions of HPPs from 2010 to 2023, this article explores the direct impact and mechanisms of green mergers and acquisitions (GMAs) on enterprises green transformation. Research findings are as follows: (1) GMAs significantly promote the green transformation of HPPs, a conclusion that is robust across various tests. (2) Internal control and green innovation quality serve as partial and chain mediators, respectively, in the relationship between GMAs and the green transformation of HPPs. (3) Media pressure negatively affects the impact of GMAs on internal control. (4) The heterogeneity analysis shows that the GMAs of enterprises in the eastern region, non-state-owned enterprises, large enterprises, and enterprises in the electricity, heat, gas, and water production and supply industries have a more obvious impact on green transformation. These findings elucidate the mechanisms through which GMAs drive the green transformation of HPPs and offer empirical insights into supporting the sustainable development of such enterprises in China.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 310-322"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888934","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

Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation 了解用户在社交媒体上的可识别性：一个有监督的机器学习和自我报告调查

Data Science and Management Pub Date : 2024-12-28 DOI: 10.1016/j.dsm.2024.12.005

Xi Chen , Hao Ding , Jian Mou , Yuping Zhao

{"title":"Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation","authors":"Xi Chen , Hao Ding , Jian Mou , Yuping Zhao","doi":"10.1016/j.dsm.2024.12.005","DOIUrl":"10.1016/j.dsm.2024.12.005","url":null,"abstract":"<div><div>The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues. Identifiability can be divided into two: subjective identifiability, which is based on psychological perceptions (i.e., mental space), and objective identifiability, which is based on social media data (i.e., information space). This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique. The findings, based on data from Weibo, a Chinese social media platform, indicate that the top seven features and values for predicting social media identifiability include blog pictures (0.21), blog location (0.14), birthdate (0.12), location (0.10), blog interaction (0.10), school (0.08), and interests and hobbies (0.07). The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants. Based on the degree of deviation between the two, users can be divided into four categories—normal, conservative, active, and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure. This study provides insights into the development of privacy protection strategies based on social media data classification.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 270-283"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886669","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

Exploration of salience theory to deep learning: evidence from Chinese new energy market high-frequency trading 突出理论对深度学习的探索：来自中国新能源市场高频交易的证据

Data Science and Management Pub Date : 2024-12-09 DOI: 10.1016/j.dsm.2024.12.001

Qing Zhu , Jinhong Du , Yuze Li

引用次数: 0

Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms 公共数据开放与股价崩盘风险：来自政府数据平台准自然实验的证据

Data Science and Management Pub Date : 2024-11-29 DOI: 10.1016/j.dsm.2024.11.002

Yongkang Lin , Linlin Zheng , Qiming Zhong

{"title":"Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms","authors":"Yongkang Lin , Linlin Zheng , Qiming Zhong","doi":"10.1016/j.dsm.2024.11.002","DOIUrl":"10.1016/j.dsm.2024.11.002","url":null,"abstract":"<div><div>Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention in both practice and theory. We leverage a quasi-natural experiment from China’s local public data openness platforms. Employing data for A-share listed firms from 2009 to 2021, we use a time-varying difference-in-differences model to systematically examine how public data openness affects corporate stock price crash risk. The results demonstrate that public data openness significantly reduces the accumulation of corporate stock price crash risk. This effect is primarily attributed to lower production of inappropriate information and enhanced information disclosure quality. Further analysis indicates that a supportive institutional environment amplifies the risk-reducing effect of public data openness. This effect is particularly pronounced in firms with strained government-market relationships, non-state ownership, and minimal agency conflicts. These insights highlight the potential that public data openness has for improving information efficiency and facilitating a transition toward digital governance.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 284-295"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886668","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

L2R-MLP: a multilabel classification scheme for the detection of DNS tunneling L2R-MLP：用于检测DNS隧道的多标签分类方案

Data Science and Management Pub Date : 2024-11-13 DOI: 10.1016/j.dsm.2024.10.005

Emmanuel Oluwatobi Asani , Mojiire Oluwaseun Ayoola , Emmanuel Tunbosun Aderemi , Victoria Oluwaseyi Adedayo-Ajayi , Joyce A. Ayoola , Oluwatobi Noah Akande , Jide Kehinde Adeniyi , Oluwambo Tolulope Olowe

{"title":"L2R-MLP: a multilabel classification scheme for the detection of DNS tunneling","authors":"Emmanuel Oluwatobi Asani , Mojiire Oluwaseun Ayoola , Emmanuel Tunbosun Aderemi , Victoria Oluwaseyi Adedayo-Ajayi , Joyce A. Ayoola , Oluwatobi Noah Akande , Jide Kehinde Adeniyi , Oluwambo Tolulope Olowe","doi":"10.1016/j.dsm.2024.10.005","DOIUrl":"10.1016/j.dsm.2024.10.005","url":null,"abstract":"<div><div>Domain name system (DNS) tunneling attacks can bypass firewalls, which typically “trust” DNS transmissions by concealing malicious traffic in the packets trusted to convey legitimate ones, thereby making detection using conventional security techniques challenging. To address this issue, we propose a Lebesgue-2 regularized multilayer perceptron (L2R-MLP) algorithm for detecting DNS tunneling attacks. The DNS dataset was carefully curated from a publicly available repository, and relevant features, such as packet size and count, were selected using the recusive feature elimination technique. L2 regularization in the MLP classifier's hidden layers enhances pattern recognition during training, effectively countering the risk of overfitting. When evaluated against a benchmark MLP model, L2R-MLP demonstrated superior performance with 99.46% accuracy, 97.00% precision, 97.00% F1-score, 99.95% recall, and an AUC of 89.00%. In comparison, the benchmark MLP achieved 92.53% accuracy, 96.00% precision, 97.00% F1-score, 99.95% recall, and an AUC of 87.00%. This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 323-331"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893250","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

Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction 基于VMD-BiLSTM-GRU网络的混合深度学习模型短期交通流预测

Data Science and Management Pub Date : 2024-11-08 DOI: 10.1016/j.dsm.2024.10.004

Changxi Ma , Yanming Hu , Xuecai Xu

{"title":"Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction","authors":"Changxi Ma , Yanming Hu , Xuecai Xu","doi":"10.1016/j.dsm.2024.10.004","DOIUrl":"10.1016/j.dsm.2024.10.004","url":null,"abstract":"<div><div>Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational mode decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 257-269"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866430","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

Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss 含弹球损失的变分模分解门控循环单元模型的石油价格概率预测

Data Science and Management Pub Date : 2024-11-01 DOI: 10.1016/j.dsm.2024.10.003

Zhesen Cui , Tian Li , Zhe Ding , Xi'an Li , Jinran Wu

{"title":"Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss","authors":"Zhesen Cui , Tian Li , Zhe Ding , Xi'an Li , Jinran Wu","doi":"10.1016/j.dsm.2024.10.003","DOIUrl":"10.1016/j.dsm.2024.10.003","url":null,"abstract":"<div><div>Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise prediction intervals that can offer valuable insights. Thus far, we introduced a novel hybrid model to forecast future crude oil prices. Our approach leverages the variational mode decomposition (VMD) to simplify the complexity of the original time series, yielding a set of subseries. These subseries are then modeled using a deep neural network architecture called a gated recurrent unit (GRU). To address the prediction uncertainty, we employed the pinball loss function rather than the mean square error to guide the proposed VMD-GRU. This adaptation extends the traditional GRU-based point forecasting to probabilistic forecasting by estimating quantiles. We evaluated our proposed model on a well-established crude oil price series by conducting both single- and multi-step-ahead forecasting analyses. Our findings underscore the efficacy of the combined model, demonstrating its superior predictive performance compared to benchmark models.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 3","pages":"Pages 237-247"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749080","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