International Journal of Information Management Data Insights最新文献

{"title":"Financial textual sentiment connectedness: Evidence from alternative data","authors":"Yudhvir Seetharam,&nbsp;Kingstone Nyakurukwa","doi":"10.1016/j.jjimei.2025.100337","DOIUrl":"10.1016/j.jjimei.2025.100337","url":null,"abstract":"<div><div>This study investigates the connectedness of various firm-level investor sentiment proxies—news, social media, ESG positive (ESGpos), and ESG negative (ESGneg) sentiment using aggregate connectedness measures and a sample of DJIA stocks between 2015 and 2024. Our findings reveal that each sentiment proxy maintains strong internal consistency, predominantly shaped by its own sources. Specifically, news and social media exhibit high self-connection scores, indicating that these proxies are primarily influenced by their respective content. ESG sentiment proxies show minimal cross-influence from news and social media, indicating their distinct and independent nature. Network analysis further highlights that news and social media transmit sentiment shocks, while ESG-based proxies are predominantly receivers. The most significant flow of sentiment shocks is from social media to ESG negative sentiment. This reflects the central role of social media in shaping sentiment within the system, in contrast to the more isolated influence of news. During significant global event periods, ESGpos and ESGneg shift roles, with ESGpos becoming a transmitter and ESGneg a receiver of sentiment shocks. Sector-specific analysis shows that the Financials (Technology) sector is a net transmitter (receiver) of sentiment shocks. The practical implications of the findings are discussed. The paper contributes to the literature, which has treated different sentiment proxies as distinct phenomena despite their interconnectedness. Additionally, we find that the aggregate connectedness measures used in this study exhibit stronger connectedness compared to the traditional Diebold-Yilmaz framework.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100337"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791105","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":"Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets","authors":"Yusuf Aliyu ,&nbsp;Aliza Sarlan ,&nbsp;Kamaluddeen Usman Danyaro ,&nbsp;Abdullahi Sani abd Rahman ,&nbsp;Aminu Aminu Muazu ,&nbsp;Mustapha Yusuf Abubakar","doi":"10.1016/j.jjimei.2025.100330","DOIUrl":"10.1016/j.jjimei.2025.100330","url":null,"abstract":"<div><div>Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100330"},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776600","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 mobile wallet usage among Gen Z: Extending the UTAUT2 model with moderating effects of personal innovativeness and gender","authors":"Fadi Herzallah ,&nbsp;Amer J. Abosamaha ,&nbsp;Yousef Abu-Siam ,&nbsp;Mohammed Amer ,&nbsp;Uzair Sajjad ,&nbsp;Khalid Hamid","doi":"10.1016/j.jjimei.2025.100336","DOIUrl":"10.1016/j.jjimei.2025.100336","url":null,"abstract":"<div><div>This study investigates Generation Z's behavioral intentions toward mobile wallet (m-wallet) usage in Jordan by extending UTAUT2 with personal innovativeness in a dual role—as both a direct predictor and moderator—alongside gender as an additional moderator. Data were collected from 389 Gen Z users across Jordan using an online survey and analyzed using partial least squares structural equation modelling (PLS-SEM). Results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, and personal innovativeness significantly influence behavioral intentions, while hedonic motivation shows no significant effect. Personal innovativeness demonstrated significant moderating effects on the relationship between the three determinants (performance expectancy, facilitating conditions, and hedonic motivation) and behavioral intention. Notably, gender showed no significant moderating effects, suggesting diminishing gender disparities in m-wallet use among Gen Z users. The extended model explains 75.1 % of behavioral intentions variance. This study advances understanding of m-wallet usage by: (1) focusing explicitly on Gen Z users, a demographic not previously studied in Jordan's m-wallet context; (2) examining usage patterns across all regions of Jordan and multiple m-wallet platforms, extending beyond previous studies limited to specific cities or platforms; and (3) revealing the dual role of personal innovativeness in shaping behavioral intentions. These findings provide valuable insights for m-wallet providers and policymakers in developing strategies to enhance usage among young consumers in developing countries.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100336"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767679","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":"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}