International Journal of Information Management Data Insights最新文献

{"title":"Green digital leadership and algorithmic management for sustainable supply chains: A serial mediation model","authors":"Mahmoud Abdulhadi Alabdali ,&nbsp;Muhammad Zafar Yaqub ,&nbsp;Josef Windsperger","doi":"10.1016/j.jjimei.2025.100343","DOIUrl":"10.1016/j.jjimei.2025.100343","url":null,"abstract":"<div><div>Supply chains face resilience and sustainability challenges from disruptions and digital advancements, with limited insight into leadership-driven digital solutions. This study investigates how green digital transformational leadership (G-DTL) influences green digital supply chain transformation (G-DSCT) and resilience (G-DSCR). We examine the serial mediation roles of algorithmic management (ALGM) and green digital absorptive capacity (G-DAC) in these relationships. Utilizing transformational leadership and absorptive capacity theories within the stimulus–organism–response framework, we collected and analyzed survey data from 324 supply chain professionals in Saudi Arabia using partial least squares structural equation modeling. The results confirm that G-DTL significantly and positively impacts G-DSCT and G-DSCR, with ALGM and G-DAC sequentially mediating the G-DTL–G-DSCR relationship. Practically, leaders should proactively invest in developing green digital leadership competencies and adopt ALGM tools to enhance their absorptive capacity. This strategic combination enables organizations to tackle disruptions, ensuring sustainable transformation and supply chain resilience.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894992","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":"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":"2025-06-01","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":"Fraud detection skills of Thai Gen Z accountants: The roles of digital competency, data science literacy and diagnostic skills","authors":"Narinthon Imjai ,&nbsp;Watcharawat Promma ,&nbsp;Nimnual Visedsun ,&nbsp;Berto Usman ,&nbsp;Somnuk Aujirapongpan","doi":"10.1016/j.jjimei.2024.100308","DOIUrl":"10.1016/j.jjimei.2024.100308","url":null,"abstract":"<div><div>The issue of accounting fraud presents a significant challenge within the business sector, prompting an increase in scholarly investigations across various contexts. Despite this growing interest, research specifically addressing the Thai context has remained scarce. Thus, this quantitative study aimed to bridge this gap by assessing the proficiency of Thai Gen Z accountants in detecting accounting fraud, with a particular emphasis on their digital, data science, and diagnostic skills. The study collected data from 150 participants using a structured survey questionnaire distributed to licensed accountants affiliated with the Thailand accounting program. It adopted a theoretical framework inspired by social learning theory and information processing theory to examine both direct and mediated relationships among the key variables under investigation. The results were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine these relationships. The results showed that digital competency have significant direct effects on the fraud detection skills, with diagnostic skills playing a key role in the process. The study revealed that digital competency not only furnishes accountants with necessary technological expertise but also bolsters their analytical skills, which are vital for identifying fraudulent activities. Likewise, data science literacy—encompassing skills in predictive analytics, big data management, and data insight communication—significantly enhances accountants' capacity to identify and understand fraudulent patterns. The emergent role of diagnostic skills as a key intermediary emphasizes the importance of comprehensive training programs that foster both technical prowess and critical analytical thinking.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721938","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":"2025-06-01","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":"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":"2025-06-01","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":"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":"2025-06-01","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":"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":"2025-06-01","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":"WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting","authors":"Theodoros Theodoropoulos ,&nbsp;Angelos-Christos Maroudis ,&nbsp;Uwe Zdun ,&nbsp;Antonios Makris ,&nbsp;Konstantinos Tserpes","doi":"10.1016/j.jjimei.2025.100338","DOIUrl":"10.1016/j.jjimei.2025.100338","url":null,"abstract":"<div><div>Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims to expand upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions and the populations that traverse them to establish a more efficient prediction model. The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution significantly outperforms its competitors in an experimental evaluation of 19 forecasting models across several datasets. Finally, an additional ablation study determined that each component of the proposed solution enhances its overall performance.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100338"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878934","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":"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":"2025-06-01","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 affective and cognitive dimensions of customer stickiness in deepfake platforms through the theoretical lens of attachment","authors":"Kanchan Patil ,&nbsp;Dhanya Pramod ,&nbsp;Vijayakumar Bharathi S ,&nbsp;Dhoha AlSaleh","doi":"10.1016/j.jjimei.2025.100344","DOIUrl":"10.1016/j.jjimei.2025.100344","url":null,"abstract":"<div><div>This study aims to understand how deepfakes affect customer stickiness, which characterizes the degree of customer retention on an online retail platform like Metaverse. Metaverse retail platforms can offer deepfake marketing to give customers an innovative buying experience. The study, built on the attachment theory and socio-technological approach, empirically evaluated affective and cognitive responses from 278 metaverse platform users using structural equation modelling. The results show that the technology factors, namely <em>synchronicity, vicarious expression, and security and privacy</em>, impacted platform attachment. The social factor, <em>deepfakes interaction</em>, impacted emotional attachment to deepfakes. The other social factors, deepfakes' familiarity and reputation, did not affect emotional attachment to Deepfakes' content. This study advances the literature on attachment theory and offers practical recommendations for retailers intending to explore deepfake usage on metaverse platforms. Our study proposes strategies for enhancing customers' attachment to retail brands through deepfakes and emphasizes the critical factors influencing customer retention in the context of metaverse retail.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100344"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932178","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}