Data Science and Management最新文献

{"title":"The relationship between attribute performance and customer satisfaction: An interpretable machine learning approach","authors":"Jie Wang ,&nbsp;Jing Wu ,&nbsp;Shaolong Sun ,&nbsp;Shouyang Wang","doi":"10.1016/j.dsm.2024.01.003","DOIUrl":"10.1016/j.dsm.2024.01.003","url":null,"abstract":"<div><p>Understanding the relationship between attribute performance (AP) and customer satisfaction (CS) is crucial for the hospitality industry. However, accurately modeling this relationship remains challenging. To address this issue, we propose an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) approach that leverages interpretable machine learning (IML) to improve traditional relationship analysis methods. The IML-DAA employs extreme gradient boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to construct relationships and explain the significance of each attribute. Following this, an improved version of penalty-reward contrast analysis (PRCA) is used to classify attributes, whereas asymmetric impact-performance analysis (AIPA) is employed to determine the attribute improvement priority order. A total of 29,724 user ratings in New York City collected from TripAdvisor were investigated. The results suggest that IML-DAA can effectively capture non-linear relationships and that there is a dynamic asymmetric effect between AP and CS, as identified by the dynamic AIPA (DAIPA) model. This study enhances our understanding of the relationship between AP and CS and contributes to the literature on the hotel service industry.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 164-180"},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000031/pdfft?md5=f340fae10be77a7b1b0ac97f65b1003c&pid=1-s2.0-S2666764924000031-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540135","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":"Metaverse application, flow experience, and Gen-Zers’ participation intention of intangible cultural heritage communication","authors":"Yuhua Cao ,&nbsp;Xiaoli Qu ,&nbsp;Xiangfen Chen","doi":"10.1016/j.dsm.2023.12.004","DOIUrl":"10.1016/j.dsm.2023.12.004","url":null,"abstract":"<div><p>With outstanding advantages in virtuality, immersion, connectivity, and openness, the application of the Metaverse in the development of intangible cultural heritage has demonstrated great potential to enhance Gen-Zers’ participation intention, but the effect and its mechanism remain unclear. This study constructs a theoretical model based on the stimuli-organism-response (SOR) theory, the DeLone and McLean model of information system (IS) success (D&amp;M) model, and flow theory, and conducts an empirical study using a structural equation model and regression analysis based on questionnaire survey data in China to uncover whether and how Metaverse application exerts its impact. Results show that Metaverse application can enhance Gen-Zers’ participation intention in the communication of intangible cultural heritage, and their mechanism follows a chain path of “stimulus-state-response” under the joint action of “technology-individual-environment” in which Metaverse application is the key stimulus factor, flow experience is the mediator, and self-efficacy and subjective norm are moderators. The findings can offer new insights for research on Metaverse application from the perspectives of consequences and effects and can also provide practical implications for Metaverse application as well as the development and communication of intangible cultural heritage.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 144-153"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000619/pdfft?md5=a4682285b334aaec5904fc1c4ff07288&pid=1-s2.0-S2666764923000619-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139126072","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":"Topic prevalence and trends of metaverse in healthcare: A bibliometric analysis","authors":"Pei Wu ,&nbsp;Donghua Chen ,&nbsp;Runtong Zhang","doi":"10.1016/j.dsm.2023.12.003","DOIUrl":"10.1016/j.dsm.2023.12.003","url":null,"abstract":"<div><p>Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare; in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 129-143"},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000607/pdfft?md5=4a86705d550b96068560f4c1c4c8bbee&pid=1-s2.0-S2666764923000607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195946","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":"Secure approach to sharing digitized medical data in a cloud environment","authors":"Kukatlapalli Pradeep Kumar ,&nbsp;Boppuru Rudra Prathap ,&nbsp;Michael Moses Thiruthuvanathan ,&nbsp;Hari Murthy ,&nbsp;Vinay Jha Pillai","doi":"10.1016/j.dsm.2023.12.001","DOIUrl":"10.1016/j.dsm.2023.12.001","url":null,"abstract":"<div><p>Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 108-118"},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000589/pdfft?md5=7be31440af8c8ec0561c9c1630c7620b&pid=1-s2.0-S2666764923000589-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139018223","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":"Divide and recombine approach for warranty database: Estimating the reliability of an automobile component","authors":"Md Rezaul Karim","doi":"10.1016/j.dsm.2023.12.002","DOIUrl":"10.1016/j.dsm.2023.12.002","url":null,"abstract":"<div><p>The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&amp;R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&amp;R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&amp;R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 119-128"},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000590/pdfft?md5=b2090e0cb2adf29a7859934cfe00ea3e&pid=1-s2.0-S2666764923000590-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013440","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":"Data- and management-driven metaverse research","authors":"Zhigeng Pan ,&nbsp;Jiaqi Yan ,&nbsp;Hirotoshi Takeda ,&nbsp;Haibing Lu ,&nbsp;Shan Liu ,&nbsp;Wei Huang ,&nbsp;Jian Mou ,&nbsp;James Christopher Westland","doi":"10.1016/j.dsm.2023.11.003","DOIUrl":"10.1016/j.dsm.2023.11.003","url":null,"abstract":"<div><p>The metaverse has become a very important phenomenon in society because of the emergence of new technologies. The widespread adoption of the metaverse has generated significant discussions about the challenges and opportunities it presents. We invited three panelists to present their personal viewpoints on the metaverse in the 2022 AIS-SIG-ISAP Workshop on Information Systems in Asia-Pacific (ISAP). The discussion indicated that metaverse research is being conducted. Furthermore, it highlighted new research directions and offered research topics related to the advantages or disadvantages of the metaverse. The proposed research topics will offer new insights to academics and practitioners.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 75-78"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000528/pdfft?md5=6125369c75ab32b2ac7de63a7009367c&pid=1-s2.0-S2666764923000528-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139299444","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":"Hybrid scientific article recommendation system with COOT optimization","authors":"R. Sivasankari,&nbsp;J. Dhilipan","doi":"10.1016/j.dsm.2023.11.002","DOIUrl":"10.1016/j.dsm.2023.11.002","url":null,"abstract":"<div><p>Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and Mean Reciprocal Rank (MRR) by 5.7%.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 99-107"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000516/pdfft?md5=a8f578b0b252fb9a7cc519cb31df8416&pid=1-s2.0-S2666764923000516-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614110","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":"Using multi-criteria decision-making and machine learning for football player selection and performance prediction: A systematic review","authors":"Abdessatar Ati ,&nbsp;Patrick Bouchet ,&nbsp;Roukaya Ben Jeddou","doi":"10.1016/j.dsm.2023.11.001","DOIUrl":"10.1016/j.dsm.2023.11.001","url":null,"abstract":"<div><p>Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 79-88"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000504/pdfft?md5=4dfe252f3db079e14d75e256bf48da67&pid=1-s2.0-S2666764923000504-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614979","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":"Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks","authors":"Wasim Khan ,&nbsp;Shafiqul Abidin ,&nbsp;Mohammad Arif ,&nbsp;Mohammad Ishrat ,&nbsp;Mohd Haleem ,&nbsp;Anwar Ahamed Shaikh ,&nbsp;Nafees Akhtar Farooqui ,&nbsp;Syed Mohd Faisal","doi":"10.1016/j.dsm.2023.10.005","DOIUrl":"10.1016/j.dsm.2023.10.005","url":null,"abstract":"<div><p>Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss it has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than the other models; we attribute this to the dataset's low dimensionality as the most probable explanation.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 89-98"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000486/pdfft?md5=e26fa7989cfa05fc83b6e2a56b647889&pid=1-s2.0-S2666764923000486-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372122","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":"Virtual manufacturing in Industry 4.0: A review","authors":"Mohsen Soori ,&nbsp;Behrooz Arezoo ,&nbsp;Roza Dastres","doi":"10.1016/j.dsm.2023.10.006","DOIUrl":"10.1016/j.dsm.2023.10.006","url":null,"abstract":"<div><p>Virtual manufacturing is one of the key components of Industry 4.0, the fourth industrial revolution, in improving manufacturing processes. Virtual manufacturing enables manufacturers to optimize their production processes using real-time data from sensors and other connected devices in Industry 4.0. Web-based virtual manufacturing platforms are a critical component of Industry 4.0, enabling manufacturers to design, test, and optimize their processes collaboratively and efficiently. In Industry 4.0, radio frequency identification (RFID) technology is used to provide real-time visibility and control of the supply chain as well as to enable the automation of various manufacturing processes. Big data analytics can be used in conjunction with virtual manufacturing to provide valuable insights and optimize production processes in Industry 4.0. Artificial intelligence (AI) and virtual manufacturing have the potential to enhance the effectiveness, consistency, and adaptability of manufacturing processes, resulting in faster production cycles, better-quality products, and lower prices. Recent developments in the application of virtual manufacturing systems to digital manufacturing platforms from different perspectives, such as the Internet of things, big data analytics, additive manufacturing, autonomous robots, cybersecurity, and RFID technology in Industry 4.0, are discussed in this study to analyze and develop the part manufacturing process in Industry 4.0. The limitations and advantages of virtual manufacturing systems in Industry 4.0 are discussed, and future research projects are also proposed. Thus, productivity in the part manufacturing process can be enhanced by reviewing and analyzing the applications of virtual manufacturing in Industry 4.0.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 1","pages":"Pages 47-63"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000498/pdfft?md5=52edcee468e2181649498edc52be3bd1&pid=1-s2.0-S2666764923000498-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135220924","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}