Data Science and Management最新文献

{"title":"User-centric avatar design: A cognitive walkthrough approach for metaverse in virtual education","authors":"","doi":"10.1016/j.dsm.2024.05.001","DOIUrl":"10.1016/j.dsm.2024.05.001","url":null,"abstract":"<div><div>Metaverse, once a concept confined to science fiction, has emerged as a transformative reality during the digital era. As this immersive virtual world gains prominence, the role of avatars and the digital representations of users in shaping educational experiences within a metaverse becomes increasingly crucial. This study examined the design of avatars and their effectiveness in virtual education. Using the cognitive walkthrough (CW) method, a well-established user-centric assessment approach, this study examined how users engage in avatar design in virtual educational settings, highlighting their cognitive processes. Through the CW approach, this study identified usability issues and learning challenges, providing valuable insights into the user experience. The investigation focuses on the VRoid platform for avatar design, selected for its user-friendly interface and robust customization features. Participants (n ​= ​12; six men and six women) from diverse educational backgrounds were carefully selected to offer a comprehensive range of perspectives. They performed avatar design tasks that closely mirrored the learning environment of the metaverse, followed by in-depth post-exercise interviews. The findings of this study highlight the essential role of avatar design in enhancing user engagement and improving the learning outcomes of virtual education. Furthermore, we provide practical recommendations for educators and designers to effectively leverage avatar innovation. In a continuously evolving era of metaverse, this research contributes significantly to the ongoing discourse on user-centered design in educational settings, thereby influencing the future trajectory of virtual education.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 4","pages":"Pages 267-282"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141140192","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":"Will China’s carbon-trading policy foster coordinated innovation in green technologies?","authors":"","doi":"10.1016/j.dsm.2024.04.004","DOIUrl":"10.1016/j.dsm.2024.04.004","url":null,"abstract":"<div><div>Accelerating green innovation is crucial for achieving high-quality development in China. Despite this importance, empirical evidence on the harmonization techniques in the context of carbon-trading policies has been remarkably thin. To address this gap, we employed the difference-in-difference (DID) and spatial difference-in-difference (S-DID) models using panel data from 2007 to 2017 for 30 Chinese provinces. Our findings reveal that the carbon-trading policy contributes significantly to the coordinated advancement of green technologies across Chinese provinces and exhibits a local siphoning effect. Specifically, the pilot areas of the policy have attracted talent from neighboring regions, which has fostered local cooperation and promoted coordinated innovation in green technologies within the region.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 4","pages":"Pages 293-303"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054066","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":"Trust framework for self-sovereign identity in metaverse healthcare applications","authors":"Alan Ling ,&nbsp;Sergey Butakov","doi":"10.1016/j.dsm.2024.04.003","DOIUrl":"10.1016/j.dsm.2024.04.003","url":null,"abstract":"<div><div>The research community and digital sector are currently engaged in the development of diverse services within the metaverse ecosystems. Conversely, as concerns about safeguarding personal privacy mount, individuals anticipate exercising greater authority over the way third parties make use of sensitive information, such as medical data. This study aims to develop tools that can facilitate the provision of healthcare services within the metaverse ecosystem in a safe and trustworthy way. This project proposes a trust framework for digital healthcare applications to run in a metaverse environment. The framework is based on the use of self-sovereign identity (SSI) and has been tested on the design principles outlined for the SSI-based architecture. This study outlines two use-case scenarios that can serve as foundations for implementing a trust framework.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 4","pages":"Pages 304-313"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552879","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":"Trust and involvement of Cameroonian software developers in open-source projects","authors":"","doi":"10.1016/j.dsm.2024.04.005","DOIUrl":"10.1016/j.dsm.2024.04.005","url":null,"abstract":"<div><div>Although recent studies have examined collaboration within open-source software projects, the focus has primarily been on their motivations and governance. This study explores the complex dynamics of trust and involvement among Cameroonian software developers in open-source projects. In the context of a rapidly evolving software development landscape, these projects have emerged as a transformative force, redefining global collaboration standards. The qualitative methodological approach involved a survey of 22 participants in open-source software projects, including Cameroonian software developers, project governance actors, and open-source community members. Analyses revealed that the trust given to African software developers, including their effective integration into projects and consideration of their specificities and contributions, has a positive impact on their involvement in and ability to appropriate information technologies. By exploring the interaction between cultural, social, and technological factors, this study enhances our understanding of trust mechanisms within open-source communities, especially those involving remote developers.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 4","pages":"Pages 332-339"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141035693","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":"When cryptography stops data science: Strategies for resolving the conflicts between data scientists and cryptographers","authors":"","doi":"10.1016/j.dsm.2024.03.001","DOIUrl":"10.1016/j.dsm.2024.03.001","url":null,"abstract":"<div><p>The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 238-255"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000134/pdfft?md5=74cffc92910a646ae465235dd70aec61&pid=1-s2.0-S2666764924000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140269093","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":"Assessing the impact of artificial intelligence on customer performance: A quantitative study using partial least squares methodology","authors":"Taqwa Hariguna ,&nbsp;Athapol Ruangkanjanases","doi":"10.1016/j.dsm.2024.01.001","DOIUrl":"10.1016/j.dsm.2024.01.001","url":null,"abstract":"<div><p>The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 155-163"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000018/pdfft?md5=ff997f9e6eeea260084310750d46c9aa&pid=1-s2.0-S2666764924000018-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634853","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":"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":"2024-09-01","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":"Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning","authors":"Abhishek Bajpai ,&nbsp;Harshita Verma ,&nbsp;Anita Yadav","doi":"10.1016/j.dsm.2024.02.001","DOIUrl":"10.1016/j.dsm.2024.02.001","url":null,"abstract":"<div><p>The Internet of things (IoT) is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring, surveillance, and healthcare. To address the limitations imposed by inadequate resources, energy, and network scalability, this type of network relies heavily on data aggregation and clustering algorithms. Although various conventional studies have aimed to enhance the lifespan of a network through robust systems, they do not always provide optimal efficiency for real-time applications. This paper presents an approach based on state-of-the-art machine-learning methods. In this study, we employed a novel approach that combines an extended version of principal component analysis (PCA) and a reinforcement learning algorithm to achieve efficient clustering and data reduction. The primary objectives of this study are to enhance the service life of a network, reduce energy usage, and improve data aggregation efficiency. We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring. Our proposed approach (PQL) was compared to previous studies that utilized adaptive Q-learning (AQL) and regional energy-aware clustering (REAC). Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 189-196"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000110/pdfft?md5=e7e054f24c3ef64041af32bb112d9eb3&pid=1-s2.0-S2666764924000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139881740","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":"Determinants of continuous usage intention of branded apps in omni-channel retail environment: Comparison between experience-oriented and transaction-oriented apps","authors":"Lixia Jiang,&nbsp;Shenglan Yang,&nbsp;Qing Tang,&nbsp;Zhengjie Zhang","doi":"10.1016/j.dsm.2024.01.004","DOIUrl":"10.1016/j.dsm.2024.01.004","url":null,"abstract":"<div><p>Branded applications (apps) have become core touchpoints for improving consumer shopping experiences in omni-channel retailing, and many firms have developed different types of branded apps to provide additional value. Moreover, continuous usage intention is the key to improving enterprises’ gain efficiency and consumers’ brand loyalty. This study aims to reveal how branded apps achieve continuance intention from the perspective of consumer perceptions by combining the technology acceptance model and investigating the impact of differences in channel features on usage behavior between the two types of branded apps. An experiment was designed comparing transaction- and experience-oriented branded apps. A structural equation modeling technique was employed to validate the model based on the survey data of respondents from the experimental groups. The results show that the supportive role of omni-channel has a unique experience mechanism that promotes continuous usage intention. However, there are two discrepant results regarding the effect of perceived complementarity on perceived usefulness in transaction- and experience-oriented branded apps. The mediating role of perceived usefulness between perceived consistency, complementarity, ease of use and consumer satisfaction was supported in the experience-oriented apps but rejected in the transaction-oriented apps.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 197-205"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000092/pdfft?md5=a7bb4247cfcf6752ca93c863b7a071ac&pid=1-s2.0-S2666764924000092-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139966302","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":"Survival strategies for family-run homestays: Analyzing user reviews through text mining","authors":"Jay Krishnan ,&nbsp;Biplab Bhattacharjee ,&nbsp;Maheshwar Pratap ,&nbsp;Janardan Krishna Yadav ,&nbsp;Moinak Maiti","doi":"10.1016/j.dsm.2024.03.003","DOIUrl":"10.1016/j.dsm.2024.03.003","url":null,"abstract":"<div><p>Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located in four thematic areas of Kerala. Accordingly, various techniques have been deployed, such as sentiment and emotional analyses, topic modeling, and clustering methods. Key themes revealed from topic modeling were breakfast, facilities provided, ambience, bathroom, cleanliness, hospitality exhibited, and satisfaction with the host. A lasso logistic regression-based predictive binary text classification model (with 97.6% accuracy) for homestay recommendations was developed. Our findings and predictive model have implications for managers and homestay owners in devising appropriate marketing strategies and improving their overall guest experience.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 228-237"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000158/pdfft?md5=89c56a3dcbb307fb2011d2afb14b790b&pid=1-s2.0-S2666764924000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990805","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}