International Journal of Information Management Data Insights最新文献

{"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}

{"title":"Exploring the drivers of digital technology adoption for enhancing domestic tax mobilization in Ghana","authors":"Alexander Asmah,&nbsp;Kingsley Ofosu Ampong,&nbsp;Dzifa Bibi,&nbsp;Wihlemina Ofori","doi":"10.1016/j.jjimei.2025.100327","DOIUrl":"10.1016/j.jjimei.2025.100327","url":null,"abstract":"<div><h3>Purpose</h3><div>This study investigates the determinants of tax compliance through the lens of performance expectancy, effort expectancy, social influence, facilitating conditions and hedonic motivation.</div></div><div><h3>Design/methodology/approach</h3><div>The study adopted both quantitative and qualitative research methods to gather data on the adoption of tax technologies. Based on the five determinants, a conceptual framework was developed consisting of seven proposed hypotheses tested through a structural equation model. Interviews were conducted to gain further insight into the drivers of the taxpayers’ portal in Ghana.</div></div><div><h3>Findings</h3><div>The study finds that performance expectancy and effort expectancy are the most significant factors predicting tax compliance intentions, indicating that taxpayers consider the portal as a useful tool in managing their taxes and very easy to use. It reduces their exposure to corrupt government officials and lessens their cost of paying taxes. It is also very convenient and serves as a useful way to avoid the long queues they experience at the tax offices. The study recommends that the Ghana Revenue Authority (GRA) and the Ministry of Finance (MoF) should promote more revenue collection technologies and create more awareness among taxpayers to utilise the portal.</div></div><div><h3>Originality/value</h3><div>The taxpayers’ portal in Ghana was recently introduced to enhance revenue mobilisation. No empirical research has been conducted to identify the adoption factors which will aid its smooth implementation. This paper thus provides significant value to both literature and practice.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100327"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095585","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":"Machine learning in banking risk management: Mapping a decade of evolution","authors":"Valentin Lennart Heß,&nbsp;Bruno Damásio","doi":"10.1016/j.jjimei.2025.100324","DOIUrl":"10.1016/j.jjimei.2025.100324","url":null,"abstract":"<div><div>The techniques used in banks' risk management are evolving as opposed to the process of risk management. It is necessary to respond to these market- and technology-driven changes appropriately. Innovative approaches are needed to overcome the limitations of traditional methods. Machine learning (ML) algorithms are suitable for dealing with the various risk types banks face. Academic literature focuses on applying ML in credit risk management. This article addresses market, operational, liquidity, and other risk types, with the objective to examine how ML algorithms predict, assess, and mitigate these risks and identify both their advantages and challenges. This article systematically reviews 46 recent studies and highlights the expanding role of ML in enhancing risk management strategies. The article has revealed that ML is adequately covered in the context of market and operational risk. The learning ability and predictive capabilities of artificial neural networks and other algorithms are promising for risk management. Our findings offer a concise overview of current ML applications for multiple risk types in banking, identifying research gaps, highlighting opportunities and challenges and providing actionable directions for further studies. By providing a focused overview of the expanding role of ML in banking risk management, we underscore the potential to strengthen the robustness of banks’ strategies and practices.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100324"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095582","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":"Product collaborative filtering based recommendation systems for large-scale E-commerce","authors":"Trang Trinh ,&nbsp;Van-Ho Nguyen ,&nbsp;Nghia Nguyen ,&nbsp;Duy-Nghia Nguyen","doi":"10.1016/j.jjimei.2025.100322","DOIUrl":"10.1016/j.jjimei.2025.100322","url":null,"abstract":"<div><div>The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095581","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":"A robust scheme for securing relational data incremental watermarking","authors":"Maikel Lázaro Pérez Gort,&nbsp;Agostino Cortesi","doi":"10.1016/j.jjimei.2025.100320","DOIUrl":"10.1016/j.jjimei.2025.100320","url":null,"abstract":"<div><div>Watermarking techniques aim to protect relational databases by embedding on them a copyright signal known as the watermark without imposing additional restrictions. However, unlike other digital assets, such as multimedia data, relational data are often subject to frequent updates that may dramatically compromise the quality of the embedded watermark. Hence, it is relevant to implement incremental watermarking for this type of data. Although incremental watermarking is defined in theory as the requirement of generating and inserting a mark whenever data is inserted or updated in a watermarked database (if the new value requires marking), its practical deployment is often ignored in the validation of proposed techniques, possibly due to how its deployment affects other requirements, such as the public system and security. In this work, we present different architectural approaches that, rather than conflicting with security and the public system, are built upon and contribute to them. The experimental results validate their applicability in terms of deployment, portability, scalability, and performance. As an architectural proposal, our work can be applied to different watermarking techniques, regardless of their particularities and the protected databases, making the preservation and enhancement of the watermark possible. Thus, we face the silent threats to security posed by opportunistic malicious operations in the absence of incremental watermarking.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095584","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":"Digital payment adoption: A revisit on the theory of planned behavior among the young generation","authors":"Berto Usman ,&nbsp;Heris Rianto ,&nbsp;Somnuk Aujirapongpan","doi":"10.1016/j.jjimei.2025.100319","DOIUrl":"10.1016/j.jjimei.2025.100319","url":null,"abstract":"<div><div>This study examines how individual behavior is influenced by intentions and factors such as financial literacy, subjective norms, and perceived behavioral control. A quantitative survey was conducted with 263 respondents familiar with fintech applications, specifically digital payments, using purposive and snowball sampling technique. Empirical analysis with Smart-PLS reveals significant effects of these factors. Notably, financial literacy and perceived behavioral control significantly influence intention, whereas subjective norms shows no clear effect. Testing for indirect relationships indicates that intention serves as the sole mediator between perceived behavioral control and digital payment usage behavior. However, intention does not mediate the relationships between financial literacy and digital payment behavior or between subjective norms and digital payment behavior. This study's exploration of intention as a mediator provides valuable insights into the dynamics of these relationships, addressing a knowledge gap in management literature and contributing to the revisit of Theory of Planned Behavior in the context of digital payment adoption.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095583","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":"Sentiment works in small-cap stocks: Japanese stock’s sentiment with language models","authors":"Masahiro Suzuki ,&nbsp;Yasushi Ishikawa ,&nbsp;Masayuki Teraguchi ,&nbsp;Hiroki Sakaji","doi":"10.1016/j.jjimei.2024.100318","DOIUrl":"10.1016/j.jjimei.2024.100318","url":null,"abstract":"<div><div>We calculate sentiment from the Japanese Company Handbook, which contains a compact overview of Japanese companies’ business situation and financial data, using multiple methods, including large language models. Language models such as BERT and ChatGPT are advancing the application of natural language processing (NLP) to financial fields. We construct multiple sentiment calculation methods using sentiment dictionaries, models trained on existing sentiment datasets, ChatGPT, and GPT-4. Our analysis shows that stocks with higher sentiment scores tend to have higher excess returns, while those with lower scores tend to have lower excess returns. This feature is enhanced particularly in small-cap stocks. Comparisons between the models showed higher returns at high sentiment for the model trained with the existing sentiment dataset and lower returns at low sentiment for ChatGPT. The DeBERTaV2 model trained on Economy Watchers Survey data performed best in terms of returns at the highest sentiment quantile.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096272","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":"Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare","authors":"Gurdeep Singh","doi":"10.1016/j.jjimei.2024.100291","DOIUrl":"10.1016/j.jjimei.2024.100291","url":null,"abstract":"<div><div>Smart technologies, specifically wearables are cutting edge innovation of design science with an emerging Artificial Intelligence (AI) capability for sustainable healthcare. Wearable IoT (w-IoT) applications, solutions and systems can promote early warning measures for physiological parameter monitoring and other vital health observation while addressing, streamlining and enhancing emergency response procedures in the provision and deliverance of healthcare services. These solutions exhibit real-time responses with underlying machine-learning (ML) methodologies alongside ubiquitous, context-aware, pervasive and advance software features. AI frameworks, for the development and implementation of solutions are well covered in this study adopting design science (DS) principles for new product development (NPD), comprising various healthcare scenarios for distributed numbers and environments. Physiological or health activity-related data produced by embedded optical smartwatch sensors can instigate sustainable and economical health-oriented solutions for continuous monitoring, semantic predictions for constrained, intractable and autonomous environments to address cardiac disorders. This paper addresses, the practical implementation of the w-IoT health technology solution prototype for real-time applicability, covering problem identification and utilizing design science guidelines, evaluation and contribution by emphasizing on the experimental stage in general and with specificity. It covers performance results rendering research science communication on machine learning models for time series analysis, regression and classification to implement defined and adaptive thresholds, adopting standard deviation and moving average, computing mean square error (MSE), root mean square error (RSME) and mean absolute error (MAE) values, utilizing exponential moving average results on multiple features, prominently targeting resting heartrate data. Machine Learning algorithms for classification with higher F-score or performance metrics adopted are Decision Trees (DT), K-Nearest Neighbours (KNN), XGboost, One-class SVM and Logistic Regression. In Binary classification, KNN achieved F-score of 91 %, followed by DT at 81 % which seems an effective algorithm with flexibility on overfitting with high accuracy result. This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs).</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100291"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095580","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}