International Journal of Information Management Data Insights最新文献

{"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}

{"title":"Enhancing recommender systems with provider fairness through preference distribution awareness","authors":"Elizabeth Gómez ,&nbsp;David Contreras ,&nbsp;Ludovico Boratto ,&nbsp;Maria Salamó","doi":"10.1016/j.jjimei.2024.100311","DOIUrl":"10.1016/j.jjimei.2024.100311","url":null,"abstract":"<div><div>Going beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user preferences and demographic representation, which has been identified as a key aspect of fairness in recommender systems. In particular, <em>provider fairness</em> enables the generation of results which are equitable for different (groups of) providers. In this paper, we raise the problem of <em>how recommendations are distributed when enabling provider fairness</em>. Indeed, on the one hand, users have clear preferences with respect to which providers they choose (<em>e.g.</em>, Italian users mostly buy Italian food), so recommendations should reflect these preferences. On the other hand, content providers should be able to reach a diverse audience, and be visible across the different user groups that expressed a preference for them. Specifically, we consider demographic groups based on their continent of origin for both users and providers, and assess how the preferences of the user groups are distributed across the provider groups. We first show that the state-of-the-art models and the existing approaches that enable provider fairness do not reflect the original distribution of the user preferences. To enable this property, we propose a re-ranking approach that, thanks to the use of buckets associating users and items, favors what we call <em>preference distribution-aware provider fairness</em>. Results on two real-world datasets (<em>i.e</em>., the Book-Crossing and COCO) show that our approach can enable provider fairness and tailor the recommendations to the original distribution of the user preferences, with negligible losses in effectiveness. In particular, in the Books dataset, our approach obtains an overall disparity that is around 6%. On the other hand, in the case of the COCO dataset, the disparities are reduced to 2%.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095578","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":"Automating materiality assessment with a data-driven document-based approach","authors":"Matteo Francia,&nbsp;Enrico Gallinucci,&nbsp;Matteo Golfarelli","doi":"10.1016/j.jjimei.2024.100310","DOIUrl":"10.1016/j.jjimei.2024.100310","url":null,"abstract":"<div><div>Materiality assessment is a critical process for companies to understand the interest perceived by its stakeholders towards topics related to environmental, social, and governance issues. Materiality assessment helps companies define their growth and communicative strategies; recently, it has become crucial within sustainability reporting, i.e., the practice of annually declaring the activities conducted to pursue economic growth in a sustainable way for society. In this paper, we propose a data-driven and automated approach to carry out materiality assessment. Stakeholders’ perception of important topics is obtained by analyzing relevant textual documents (e.g., company reports, press releases, social media posts), identifying mentions of potentially interesting topics, and converting them to scores that produce materiality rankings or matrices. An iterative methodology is proposed to incrementally carry out materiality assessment by progressively building the domain knowledge required to automate the process. Efficiency and effectiveness evaluations are carried out in a real-world scenario.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095579","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":"Leveraging VOSviewer approach for mapping, visualisation, and interpretation of crisis data for disaster management and decision-making","authors":"Umar Ali Bukar ,&nbsp;Md Shohel Sayeed ,&nbsp;Oluwatosin Ahmed Amodu ,&nbsp;Siti Fatimah Abdul Razak ,&nbsp;Sumendra Yogarayan ,&nbsp;Mohamed Othman","doi":"10.1016/j.jjimei.2024.100314","DOIUrl":"10.1016/j.jjimei.2024.100314","url":null,"abstract":"<div><div>Analysing social media data is crucial for crisis management organisations to make timely decisions. Researchers in crisis informatics have devised various methods and systems to process and classify large volumes of crisis-related social media data for effective crisis response and recovery. However, the complexity of previous solutions hampers the timely processing of this data, its visualisation, and its interpretation, which is necessary for effective crisis response. Hence, this study addresses this challenge by employing <em>visualisation of similarities</em> to analyse and visualise crisis datasets to aid crisis management and decision-making. The results reveal a \"nine-cluster community” of relevant keywords comprising “Green, Brown, Red, Blue, Pink, Purple, Yellow, Orange, and Cyan” colours, in both binary and full count. Specifically, the findings reveal various keywords such as the needs for food, water, shelter, medicine, and electricity. Thereafter, the study discusses the implications of VOSviewer for analysing crisis data theoretically and practically.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096266","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}