International Journal of Information Management Data Insights最新文献

{"title":"Content, formats and licensing of datasets from autonomous communities: Value generation, sustainability and proposals for improvement","authors":"Ricardo Curto-Rodríguez,&nbsp;Rafael Marcos-Sánchez,&nbsp;Daniel Ferrández","doi":"10.1016/j.jjimei.2025.100369","DOIUrl":"10.1016/j.jjimei.2025.100369","url":null,"abstract":"<div><div>Open government data (OGD) initiatives, established at all levels of public administration and globally, have significant potential for value generation. However, their actual implementation often reveals significant shortcomings that hinder their potential for value creation. This study addresses a critical gap in the literature by evaluating the design of OGD policies in Spain, focusing specifically on the industrial sector at the autonomous community level. The research assesses the available data's content, formats, and licensing through a population-based analysis of all datasets labeled under the “industry” category across the 17 Spanish autonomous communities. The findings reveal a fragmented and inconsistent landscape: of over 46,000 datasets published by autonomous community governments, only 532 were initially labeled as industry-related, and after a rigorous selection process—removing duplicates, outdated records, and mislabeling entries—only 316 were deemed valid. The study highlights a predominance of non-reusable formats such as HTML and a lack of standardisation in the categorization of information. While most datasets use open licenses (mainly Creative Commons BY), the variability in download options and formats limits their automated processing and reuse. These results underscore the need for standardization criteria, improved data quality, and strategic alignment of OGD initiatives with sectoral priorities such as industrial competitiveness and sustainability. The paper concludes with four contributions to enhance coherence, usability, and impact of open industrial data, aiming to support OGD policymaking and foster innovation ecosystems at the autonomous community level.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100369"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of e-RLSQ on repurchase intention in Vietnam’s e-commerce market: The mediating role of customer satisfaction and trust","authors":"Anh Duc Do ,&nbsp;Dieu Linh Ha ,&nbsp;Minh Trang Pham ,&nbsp;Mai Anh Thi Khuat ,&nbsp;Van Anh Thi Le ,&nbsp;Dieu Ngoc Dieu Nguyen ,&nbsp;Toan Quoc La","doi":"10.1016/j.jjimei.2025.100346","DOIUrl":"10.1016/j.jjimei.2025.100346","url":null,"abstract":"<div><div>In the competitive e-commerce landscape, effective product returns management is crucial for customer retention. This study investigate the impact of electronic reverse logistics service quality (e-RLSQ) on repurchase intention, emphasizing the mediating roles of customer satisfaction and trust in Vietnam’s e-commerce market. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), it explores the relationships between e-RLSQ dimensions, customer perceptions, and repurchase behavior. The findings reveal that Information Quality (INF) and Convenience (CON) significantly enhance customer satisfaction, while Remedies (REMD) and INF foster trust. Both satisfaction and trust mediate the relationship between e-RLSQ and repurchase intention, with satisfaction exerting a stronger influence. These insights highlight the importance of optimizing return processes and ensuring transparent communication to foster long-term customer loyalty. This study contributes to the limited literature on e-RLSQ in emerging markets and provide strategic implications for e-commerce platforms to enhance service quality and customer retention.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100346"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the bibliometric impact of artificial intelligence in radiology: An analytical approach","authors":"Bharti Chogtu ,&nbsp;Ritheesh V ,&nbsp;Ashwath K Naik ,&nbsp;Shubhra Dutta ,&nbsp;Santhosh KV","doi":"10.1016/j.jjimei.2025.100376","DOIUrl":"10.1016/j.jjimei.2025.100376","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) is revolutionizing operations worldwide and is particularly transforming radiology. AI has a key role in enhancing diagnostic accuracy, workflow efficiency, and research output in radiology. This article presents a comprehensive bibliometric analysis of the influence of AI on radiology over five years (2018–2022).</div></div><div><h3>Methodology</h3><div>Reports published between 2018 and 2022 were identified through the Scopus database and categorized based on the AI methodologies employed. The study presents the volume and distribution of studies on AI, identifies publication patterns by country, and measures the impact of studies in terms of citation counts and field-weighted citation indices (FWCIs). Field-weighted view impact (FWVI), a field-normalized view metric that estimates the visibility of studies and the accessibility and engagement of AI studies in radiology, is used in this study.</div></div><div><h3>Results</h3><div>Compared with non-AI studies, the United States leads radiology-related AI publications, with AI-based articles having higher citation indices. The findings reveal a strong increasing trend for AI-related studies over the duration of the study. Moreover, open-access AI publications are found to have higher FWVI scores than subscription-based articles with greater visibility and higher readership.</div></div><div><h3>Conclusion</h3><div>This paper highlights the growing dominance of AI in radiology and how it is influencing trends in clinical development and research. Through publication increase, citation impact, and study availability, this paper provides informative insight into how AI radiology studies are developing.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100376"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving sustainability in emerging economies: Leadership, culture, and knowledge management in environmental performance","authors":"Nhatphaphat Juicharoen ,&nbsp;Khahan Na-Nan ,&nbsp;Sureerut Inmor ,&nbsp;Kanakarn Phanniphong ,&nbsp;Xinyu Wang","doi":"10.1016/j.jjimei.2025.100381","DOIUrl":"10.1016/j.jjimei.2025.100381","url":null,"abstract":"<div><div>This study examines the influence of Green Transformational Leadership (GTL) on Environmental Performance Outcomes (EPO) in Thailand's Eastern Economic Corridor (EEC) industries, with particular attention to the mediating roles of Green Organizational Culture Management (GOCM) and Knowledge Management (KM). Drawing on the Natural Resource-Based View (NRBV), a quantitative survey was conducted with 312 industrial firms using a multi-phase data collection process. The analysis indicates that green transformational leadership has a significant direct effect on environmental performance outcomes (β = 0.240, <em>p</em> &lt; 0.001), while both green organizational culture management and knowledge management partially mediate this relationship. Knowledge management demonstrates a stronger indirect effect (β = 0.085, <em>p</em> &lt; 0.001) compared to green organizational culture management (β = 0.038, <em>p</em> = 0.022), suggesting that knowledge-based systems provide more immediate pathways for translating leadership intent into environmental outcomes. The six demographic and positional control variables showed no significant influence on environmental performance outcome, indicating that the main effects are not driven by respondent characteristics. These findings support the applicability of the Natural Resource-Based View in an emerging-economy context and highlight the complementary roles of culture and knowledge in shaping environmental performance. The study advances theoretical understanding by integrating green transformational leadership, green organizational culture management, and knowledge management into a single framework and offers sector-relevant implications for industries seeking to align leadership development, cultural practices, and knowledge systems with sustainability goals.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and integration of human-AI interactions in service applications: Conceptual framework and review","authors":"Nick Tugarin,&nbsp;Christian van Husen","doi":"10.1016/j.jjimei.2025.100357","DOIUrl":"10.1016/j.jjimei.2025.100357","url":null,"abstract":"<div><div>The rapid advancement of AI technologies has created opportunities and challenges across industries, particularly in service sectors where human interaction is crucial. This study systematically reviews the literature on human-AI interaction in service applications, aiming to understand how these interactions can be effectively designed, economically viable, and human-centered. A systematic literature review covering publications from 2015 to 2024 was conducted, following a structured search protocol that resulted in 90 selected articles. The review identifies key dimensions and interconnected elements of human-AI interaction, which are synthesized into a conceptual framework. This framework outlines the fundamental relationships between these dimensions and provides practical implications for the service industry and directions for future research. Furthermore, real-world application examples illustrate how these findings can be translated into practice. They demonstrate adaptability across different service domains and their potential to inspire innovative, scalable, and human-centric AI solutions.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100357"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach","authors":"Kinza Noor ,&nbsp;Mariam Rehman ,&nbsp;Maria Anjum ,&nbsp;Afzaal Hussain ,&nbsp;Rabia Saleem","doi":"10.1016/j.jjimei.2025.100358","DOIUrl":"10.1016/j.jjimei.2025.100358","url":null,"abstract":"<div><div>Depression is one of the most common mental health issues that seriously affect people's quality of life. The World Health Organization reported that depression overwhelms about 300 million people across the globe. Due to the widespread prevalence of this disorder in society, novel and efficient methods must be developed for effective detection and treatment. In the modern era of social media, individuals often reveal their emotional states by providing daily posts on platforms like X (previously Twitter) and Facebook. The information can be utilized as an essential input for determining whether a person has depression based on their writing content. The disclosure of transformer-based deep learning models provides an opportunity to use pre-trained models to successfully capture complex patterns and nuances in the textual data. This study proposes a novel depression detection method through sentiment analysis by developing a Stacking ENSemble-based Deep learning (SENSDeep) model. The proposed model integrates the capabilities of six pre-trained cutting-edge models, including BERT, RoBERTa, AlBERT, DistilBERT, XLNet, and BART, through stacking ensemble to enhance the predicted performance of the proposed model. The SENSDeep model is evaluated by precision, recall, F1-score, and accuracy. In contrast to other models, the SENSDeep model excels with 96.93 % precision, 97.50 % recall, 97.22 % F1-Score, and 97.21 % accuracy. To our knowledge, SENSDeep is the first deep-learning ensemble model that leverages the capabilities of cutting-edge pre-trained transformer models via stacking, specifically for detecting depression from the textual data.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100358"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal framework for enhancing E-commerce information management using vision transformers and large language models","authors":"Anitha Balachandran ,&nbsp;Mohammad Masum","doi":"10.1016/j.jjimei.2025.100355","DOIUrl":"10.1016/j.jjimei.2025.100355","url":null,"abstract":"<div><div>In the rapidly advancing field of visual search technology, traditional methods that rely only on visual features often struggle with accuracy and relevance. This challenge is particularly evident in e-commerce, where precise product recommendations are critical, and is further complicated by keyword stuffing in product descriptions. To address these limitations, this study introduces BiLens, a multimodal recommendation framework that integrates both visual and textual information. BiLens leverages large language models (LLMs) to generate descriptive captions from image queries, which are transformed into word embeddings, and extracts visual features using Vision Transformers (ViT). The visual and textual representations are integrated using an early fusion strategy and compared using cosine similarity, enabling deeper contextual understanding and enhancing the accuracy and relevance of product recommendations in capturing customer intent. A comprehensive evaluation was conducted using Amazon product data across five categories, testing various image captioning models and embedding methods—including BLIP-2, ViT-GPT2, BLIP-Image-Captioning-Large, Florence-2-large, GIT (microsoft/git-base-coco), Word2Vec, GloVe, BERT, and ELMo. The combination of Florence-2-large and BERT emerged as the most effective, achieving a <span><math><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span> of <span><math><mrow><mn>0.81</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0.14</mn></mrow></math></span> and <span><math><mrow><mi>F</mi><mn>1</mn></mrow></math></span> score of <span><math><mrow><mn>0.49</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0.16</mn></mrow></math></span>. This setup was further validated on the Myntra dataset, showing generalizability with <span><math><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span> of <span><math><mrow><mn>0.59</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0.27</mn></mrow></math></span>, <span><math><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> of <span><math><mrow><mn>0.47</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0.25</mn></mrow></math></span>, and <span><math><mrow><mi>F</mi><mn>1</mn></mrow></math></span> score of <span><math><mrow><mn>0.52</mn><mspace></mspace><mo>±</mo><mspace></mspace><mn>0.24</mn></mrow></math></span>. Comparisons with image-only and text-only baselines confirmed the superiority of the fusion-based approach, with statistically significant improvements in F1 scores, underscoring BiLens’s ability to deliver more accurate, context-aware product recommendations.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100355"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening the UK regulatory framework: Enhancing cybersecurity in supply chains","authors":"Betul Gokkaya ,&nbsp;Konstantina Spanaki ,&nbsp;Erisa Karafili","doi":"10.1016/j.jjimei.2025.100370","DOIUrl":"10.1016/j.jjimei.2025.100370","url":null,"abstract":"<div><div>The increasing risks associated with cybersecurity in global supply chains present a significant problem, threatening the operational integrity and security of organisations on a global scale. The UK’s Network and Information Systems (NIS) Framework, although fundamental in cybersecurity regulation, has significant gaps in effectively addressing the complexities of contemporary global supply chain architectures entangled with quickly advancing cyber threats. In this work, we analyse the UK NIS framework, identify key gaps, and propose solutions drawn from other existing frameworks, e.g., US NIST, EU NIS2. We base this analysis on a comparative evaluation using defined criteria related to supply chain coverage, adaptability, and risk management specificity. We enhanced the cybersecurity in supply chains by proposing novel security requirements plans for each risk profile. Furthermore, we examined various solutions for risk assessments and self-risk assessments for supply chain security. We analysed practical risk assessment approaches, including self-assessment strategies, particularly suited for SMEs. Moreover, we investigated the contracting between supply chains in the context of data and information sharing.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decomposition and CNN-based feature extraction","authors":"Komei Hiruta ,&nbsp;Junsuke Senoguchi","doi":"10.1016/j.jjimei.2025.100360","DOIUrl":"10.1016/j.jjimei.2025.100360","url":null,"abstract":"<div><div>The global economy relies heavily on stock markets, making accurate stock price predictions essential for academic research and practical applications. The task of predicting stock prices presents significant challenges due to the non-linear relationships between historical and future values and the multitude of factors influencing price fluctuations. To address these challenges, we propose an approach that combines wavelet transformation and a convolutional neural network (CNN), both of which are specialized for long-term stock price prediction, to efficiently and automatically extract the features of stock prices at various temporal resolutions. Specifically, we first acquire components with different temporal resolutions using wavelet transform, then convert the wavelet-transformed data into images, and finally perform CNN processing to automatically extract useful temporal features for prediction. Experimental results demonstrate that our method achieves a higher prediction accuracy than conventional machine learning methods, especially in long-term predictions.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100360"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interplay of virtualisation, work design, and business process management: A mixed-methods study","authors":"Luke Bartlett ,&nbsp;Muhammad Ashad Kabir ,&nbsp;Jun Han","doi":"10.1016/j.jjimei.2025.100345","DOIUrl":"10.1016/j.jjimei.2025.100345","url":null,"abstract":"<div><div>Business Process Management (BPM) has emerged as a fundamental aspect of modern business, revolutionising task execution and operational efficiency. This study explored the relationship between virtualisation and work design as well as their impact on BPM system design. An experiment was established where two simulated BPM systems were given to users to operate, one taking advantage of virtualisation and work design, the other not. A mixed method approach involving both quantitative and qualitative approaches was applied to system and usage data collected from these systems as well as a user survey. The analysis illustrates the potential significance of integrating virtualisation and work design in BPM systems. We provide a reflective discussion linking theoretical understanding with empirical evidence. We found that both components not only enhanced the performance and effectiveness of BPM systems, but also improved flexibility, scalability, and user experience. The data supports that a relationship between virtualised resources and work design exists when incorporated into BPM system design. Further, it provides valuable insights into how these elements interact and impact each other. The findings in this study contribute to building a case that establishes the effect of virtualisation and work design on the usability of BPM systems. These findings have very real practical applications that can be applied to existing BPM systems as well as the architecture of future systems. A number of the recommendations made as a result of this research have been applied to commercially available BPM systems.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100345"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}