International Journal of Information Management Data Insights最新文献

{"title":"Transforming business management practices through metaverse technologies: A Machine Learning approach","authors":"Raghu Raman ,&nbsp;Santanu Mandal ,&nbsp;Angappa Gunasekaran ,&nbsp;Thanos Papadopoulos ,&nbsp;Prema Nedungadi","doi":"10.1016/j.jjimei.2025.100335","DOIUrl":"10.1016/j.jjimei.2025.100335","url":null,"abstract":"<div><div>This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100335"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739090","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":"Exploring the impact of social relationships and system experiences on consumers’ acceptance of social commerce in the fashion sector: an empirical study","authors":"Sahil Singh Jasrotia ,&nbsp;Alex Pak Ki Kwok ,&nbsp;Surabhi Koul","doi":"10.1016/j.jjimei.2025.100333","DOIUrl":"10.1016/j.jjimei.2025.100333","url":null,"abstract":"<div><div>This study investigated the influence of social relationships and system experiences on consumers’ acceptance of social commerce (s-commerce) in the fashion sector. Perceived social connectedness, critical mass, ease of use, system capability, enjoyment, and usefulness were examined as factors impacting consumers’ intention to use s-commerce. Data from 558 valid responses were analysed using structural equation modelling. The results revealed that perceived social connectedness indirectly influences perceived usefulness and intention to use s-commerce through system experience-related factors. However, no significant effect was found between perceived system capability and perceived usefulness. The proposed model explains 64% of the variance in perceived usefulness and 62% in intention to use. This study fills a research gap by providing insights into the impact of social relationships and system experiences on consumer acceptance of s-commerce in fashion retailing. It offers guidance for practitioners to improve their s-commerce platforms and marketing strategies.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100333"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684784","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":"Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials","authors":"Himanshu Joshi","doi":"10.1016/j.jjimei.2025.100332","DOIUrl":"10.1016/j.jjimei.2025.100332","url":null,"abstract":"<div><div>This paper examines the key determinants of behavioral intention, user satisfaction, and chatbot adoption among urban, college-educated student populations within Generation Z and Millennials in India. While Millennials grew up with the Internet, Gen Z was born into the era dominated by social media and smartphones, making them inherently tech-savvy and drawn to chatbots for information access. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating technological elements with trust and satisfaction to propose a conceptual model. Using a mixed-method approach, data were collected through a cross-sectional online survey of 487 chatbot users from urban educational institutions in India. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test 11 hypothesized direct relationships. The results suggest that users' willingness to adopt chatbots is significantly influenced by performance expectancy, social influence, trust, and satisfaction. Regarding user satisfaction, both facilitating conditions and trust played substantial roles. Additionally, this study found meaningful associations between facilitating conditions, satisfaction, intention, and adoption. Multi-group analyses revealed notable differences in chatbot adoption factors between Gen Z and Millennials within the study's sampled population. Given the importance of trust in chatbot adoption, the paper highlights that reducing perceived risks can strengthen trust, enhance user satisfaction, and drive chatbot intention and adoption. The above findings offer context-specific insights for chatbot providers in devising strategies to improve user trust, satisfaction, and adoption within similar demographics.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100332"},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591449","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 industry 5.0 readiness—Prototype of a holistic digital index to evaluate sustainability, resilience and human-centered factors","authors":"Anja Brückner ,&nbsp;Mandy Wölke ,&nbsp;Franziska Hein-Pensel ,&nbsp;Edgar Schero ,&nbsp;Heiner Winkler ,&nbsp;Iren Jabs","doi":"10.1016/j.jjimei.2025.100329","DOIUrl":"10.1016/j.jjimei.2025.100329","url":null,"abstract":"<div><div>The European Commission introduced Industry 5.0, marking a paradigm shift in its strategic vision that differs from its predecessor in emphasizing social and sustainable factors. Consequently, a comprehensive reassessment of the social role of industry is inevitable. The European Commission has recognized the conceptual gap in the implementation of Industry 5.0. It recommends the development of technology roadmaps and new tools, including assessments, to guide organizations through this paradigm shift. The aim of this paper is threefold. First, characterizations of the new components of Industry 5.0 are provided to establish a baseline understanding. Second, an approach to measuring the maturity of Industry 5.0 is developed, considering the complexity of Industry 5.0. Third, the prototypical development of an innovative assessment, called the <em>Digital Index</em>, is presented. The assessment tool will offer an approach for companies to examine the requirements for Industry 5.0 and realize them with the use of practical recommendations.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100329"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519011","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":"Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction","authors":"Payam Boozary ,&nbsp;Sogand Sheykhan ,&nbsp;Hamed GhorbanTanhaei ,&nbsp;Cosimo Magazzino","doi":"10.1016/j.jjimei.2025.100331","DOIUrl":"10.1016/j.jjimei.2025.100331","url":null,"abstract":"<div><div>This paper investigates the use of machine learning models for customer churn prediction, focusing on the comparative effectiveness of ensemble approaches such as XGBoost and Random Forest with classical classifiers. The study evaluates the benefits and shortcomings of each strategy in dealing with complicated datasets by analyzing confusion matrices and Receiver Operating Characteristic (ROC) curves in detail. Ensemble models outperformed on key criteria such as accuracy, precision, recall, and F1 scores, yielding excellent results. These results demonstrate the effectiveness of ensemble approaches in producing accurate and trustworthy forecasts, making them suitable for client retention efforts. The report offers practical insights for firms looking to use sophisticated machine learning approaches to make better strategic decisions and retain more customers.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100331"},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510162","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":"CovKG: A Covid-19 Knowledge Graph for enabling multidimensional analytics on Covid-19 epidemiological data considering spatiotemporal, environmental, health, and socioeconomic aspects","authors":"Rudra Pratap Deb Nath ,&nbsp;S.M. Shafkat Raihan ,&nbsp;Tonmoy Chandro Das ,&nbsp;Torben Bach Pedersen ,&nbsp;Debasish Ghose","doi":"10.1016/j.jjimei.2025.100325","DOIUrl":"10.1016/j.jjimei.2025.100325","url":null,"abstract":"<div><div>The Covid-19 pandemic is influenced by many environmental, health, and socioeconomic aspects such as air pollution, comorbidity, occupation, etc. To better manage future pandemics, decision-makers need comprehensive data on Covid-19 mortality and morbidity. Most Covid-19 data sources focus on spatiotemporal aspects, and existing research often overlook the combined impact of multiple interconnected factors. This study introduces a Covid-19 Knowledge Graph (CovKG) derived from 20 data sources, enabling multidimensional analysis of epidemiological data, including time, location, temperature, comorbidity, occupation, and others. CovKG is modeled using RDF, connected to 10,951 external resources, and semantically enriched with Data Cube (QB) and QB for OLAP (QB4OLAP) vocabularies to adhere to the FAIR principles and ensure OLAP compatibility. Finally, we perform a qualitative and comparative evaluation and extract statistical insights across multiple dimensions of Covid-19 epidemiology. When assessed, CovKG answers 100% of competency queries, outperforming other data stores that only answer 39%. CovKG and its analytical interface are available at <span><span>https://bike-csecu.com/datasets/CovKG/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100325"},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510161","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":"Customization of health insurance premiums using machine learning and explainable AI","authors":"Manohar Kapse ,&nbsp;Vinod Sharma ,&nbsp;Rutuj Vidhale ,&nbsp;Varun Vellanki","doi":"10.1016/j.jjimei.2025.100328","DOIUrl":"10.1016/j.jjimei.2025.100328","url":null,"abstract":"<div><div>This study presents an analysis of health insurance premiums across various customer segments. Specifically, it aims to identify the factors influencing the pricing of health insurance premiums, vis a vis their impact on different customer segments. Using a dataset from consumer surveys, coupled with multiple Machine Learning models, the study analyzed and predicted features of importance for premiums paid across various age groups, gender, health conditions, policy duration, and the number of members included in the policy. Finally, the explainable AI was used to predict the weightage of each variable in determining the price of the insurance policy for the individuals. The findings provide crucial insights into the factors such as demographic factors and lifestyle that effectively influence the pricing of health insurance premiums vis a vis their impact on various customer segments. The results of this study will assist prospective buyers and decision-makers in choosing the best health insurance plans.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100328"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143226889","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":"Learning transactions representations for information management in banks: Mastering local, global, and external knowledge","authors":"Alexandra Bazarova ,&nbsp;Maria Kovaleva ,&nbsp;Ilya Kuleshov ,&nbsp;Evgenia Romanenkova ,&nbsp;Alexander Stepikin ,&nbsp;Aleksandr Yugay ,&nbsp;Dzhambulat Mollaev ,&nbsp;Ivan Kireev ,&nbsp;Andrey Savchenko ,&nbsp;Alexey Zaytsev","doi":"10.1016/j.jjimei.2025.100323","DOIUrl":"10.1016/j.jjimei.2025.100323","url":null,"abstract":"<div><div>In today’s world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categorized into two groups: (1) local ones, which focus on a client’s current state, such as transaction forecasting, and (2) global ones, which consider the general customer behaviour, e.g., predicting successful loan repayment. Unfortunately, maintaining separate models for each task is costly. Therefore, to better facilitate information management, we compared eight state-of-the-art unsupervised methods on 11 tasks in search for a one-size-fits-all solution. Contrastive self-supervised learning methods were demonstrated to excel at global problems, while generative techniques were superior at local tasks. We also introduced a novel approach, which enriches the client’s representation by incorporating external information gathered from other clients. Our method outperforms classical models, boosting accuracy by up to 20%.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143226888","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":"Opening a career door!: The role of ChatGPT adoption in digital entrepreneurial opportunity recognition and exploitation","authors":"Cong Doanh Duong,&nbsp;Thi Thanh Hoa Phan,&nbsp;Bich Ngoc Nguyen,&nbsp;Thanh Van Pham,&nbsp;Ngoc Diep Do,&nbsp;Anh Trong Vu","doi":"10.1016/j.jjimei.2025.100326","DOIUrl":"10.1016/j.jjimei.2025.100326","url":null,"abstract":"<div><div>This study aims to extend the Stimulus-Organism-Responses (SOR) model by investigating the influence of AI-related stimulus, particularly ChatGPT adoption in entrepreneurship, on individuals’ cognitive organism (e.g., digital entrepreneurial identity aspiration) and subsequent behavioral responses (e.g., digital entrepreneurial opportunity exploration and exploitation) as well as testing the negative moderation of stressor (i.e., technology anxiety). Using a sample of 1326 MBA students in Vietnam with a stratified sampling approach, structural equation modeling (SEM) is employed to rigorously examine the proposed relationships and test the moderation effect. The findings of the study unravel the complex dynamics within the extended SOR model, showcasing positive relationships between ChatGPT adoption in entrepreneurship and digital entrepreneurial identity aspiration, digital entrepreneurial opportunity exploration, and exploitation. Additionally, the research sheds light on the mediating role of digital entrepreneurial identity aspiration and the moderating effect of technology anxiety, providing nuanced insights into how these factors interact in the context of digital entrepreneurship. This research advances the academic discourse by applying the SOR framework to the context of digital entrepreneurship, investigating how AI-driven stimuli influence cognitive and behavioral dimensions. The inclusion of technology anxiety as a stressor adds a novel dimension, addressing the often-overlooked psychological barriers associated with AI adoption in entrepreneurial contexts. Practitioners can draw practical insights from this research to strategically leverage AI technologies in fostering digital entrepreneurial identity, navigating digital opportunities, and overcoming challenges posed by technology anxiety. While this study offers valuable contributions, its reliance on self-reported data and a cross-sectional design may limit causal inferences and generalizability. Future research should consider longitudinal designs and diverse samples, such as established entrepreneurs or non-Asian populations, to explore ChatGPT adoption in broader contexts.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100326"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096383","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":"Enhancing DataOps practices through innovative collaborative models: A systematic review","authors":"Aymen Fannouch,&nbsp;Jihane Gharib,&nbsp;Youssef Gahi","doi":"10.1016/j.jjimei.2025.100321","DOIUrl":"10.1016/j.jjimei.2025.100321","url":null,"abstract":"<div><div>The rapidly evolving field of Data Operations (DataOps) is essential for enhancing data management within large-scale enterprises. However, persistent challenges, such as inefficiencies in data integration, delivery, and governance, limit its potential impact. These obstacles hamper the seamless implementation of DataOps strategies, slowing down operational processes and affecting organizational performance in data-driven environments. To address these issues, this research employs a systematic literature review, analyzing contributions from 2004 to 2024, to identify relevant solutions and innovations. The study highlights the value of frameworks, methodologies, and advanced technologies—such as automation, cloud platforms, and continuous delivery pipelines—that have reshaped the DataOps landscape. These contributions guide enterprises toward best practices in data strategy and foster improved collaboration across business and IT teams. Building on this analysis, our research also proposes a personal framework designed to offer a comprehensive approach to DataOps strategy. This framework integrates key insights from existing research and provides practical recommendations and best practices to streamline workflows, enhance data governance, and align IT operations with business goals. The enhanced DataOps practices derived from our framework demonstrate significant potential to boost operational efficiency, accelerate decision-making processes, and unlock new growth opportunities. Furthermore, the implementation of such practices sets the foundation for future innovations in data management and offers a path forward for organizations seeking sustainable, long-term value.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096381","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}