International Journal of Information Management Data Insights最新文献_第6页

Determinants of artificial intelligence adoption in the financial services industry: Understanding employees’ perspectives 金融服务业采用人工智能的决定因素：了解员工的观点

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.jjimei.2025.100371

Ahyar Yuniawan , Hersugondo Hersugondo , Fuad Mas'ud , Hengky Latan , Douglas W.S. Renwick

{"title":"Determinants of artificial intelligence adoption in the financial services industry: Understanding employees’ perspectives","authors":"Ahyar Yuniawan , Hersugondo Hersugondo , Fuad Mas'ud , Hengky Latan , Douglas W.S. Renwick","doi":"10.1016/j.jjimei.2025.100371","DOIUrl":"10.1016/j.jjimei.2025.100371","url":null,"abstract":"<div><div>This study examines the factors influencing AI adoption in Indonesia’s financial services sector, focusing on knowledge and awareness levels, perceived risks and benefits, self-confidence, and the moderating role of managerial support. Grounded in innovation diffusion theory (IDT), protection motivation theory (PMT), and self-determination theory (SDT), the study analyzes data from 489 employees using structural equation modeling with SmartPLS 4 software to test the hypotheses. The findings reveal that higher levels of knowledge and awareness, along with self-confidence, positively influence AI adoption intentions, while perceived risks and benefits exert a negative effect. Furthermore, managerial support moderates these relationships by enhancing the positive effects of knowledge and awareness levels and self-confidence, while mitigating the negative impact of perceived risks. These results emphasize the critical role of managerial support in promoting AI adoption and highlight the necessity of cultivating a supportive organizational culture and leadership to ensure successful AI integration.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100371"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931755","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}

引用次数: 0

Examining the moderating effect of perceived government support on e-business adoption among Omani SMEs 考察感知到的政府支持对阿曼中小企业采用电子商务的调节作用

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.jjimei.2025.100378

Mahfooz Ahmed , Salim Nasser Al-Riyami , Wan Rohaida Binti Wan Husain , Nurita Binti Juhdi , Fatima Al-Shuwaikh , Ali Haji

{"title":"Examining the moderating effect of perceived government support on e-business adoption among Omani SMEs","authors":"Mahfooz Ahmed , Salim Nasser Al-Riyami , Wan Rohaida Binti Wan Husain , Nurita Binti Juhdi , Fatima Al-Shuwaikh , Ali Haji","doi":"10.1016/j.jjimei.2025.100378","DOIUrl":"10.1016/j.jjimei.2025.100378","url":null,"abstract":"<div><div>This study examines the moderating effect of Perceived Government Support (PGS) on the adoption of e-business among Small and Medium Enterprises (SMEs) in Oman, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). While prior research has examined the direct influence of UTAUT constructs, fewer studies have considered institutional support as a contextual moderator in developing economies. To address this gap, the present study integrates PGS into the UTAUT model to explore its role in shaping e-business adoption in Oman. A quantitative, cross-sectional survey was conducted with 641 SME owners, managers, and decision-makers who had benefited from government support programs. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to test the hypothesised relationships. The results show that Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions significantly influence e-business adoption. More importantly, PGS was found to positively moderate the relationship between Facilitating Conditions and e-business adoption, underscoring the critical role of perceived institutional support in amplifying the enabling environment for digital transformation. The study’s novelty lies in extending UTAUT by incorporating a government-related contextual moderator that reflects the realities of emerging economies. Practically, the findings provide policymakers and SME development agencies with evidence-based insights to design targeted interventions such as infrastructure investment, training, and financial incentives that are especially vital for micro and small enterprises operating under resource constraints.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100378"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324289","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}

引用次数: 0

The role of technology readiness motivators, positive and negative impact toward smart farming technology adoption: Insight from Thai farmers 技术准备激励因素的作用，对智能农业技术采用的积极和消极影响：来自泰国农民的见解

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-10-17 DOI: 10.1016/j.jjimei.2025.100380

Saowakhon Nookhao , Supaporn Kiattisin , Vipa Thananant

{"title":"The role of technology readiness motivators, positive and negative impact toward smart farming technology adoption: Insight from Thai farmers","authors":"Saowakhon Nookhao , Supaporn Kiattisin , Vipa Thananant","doi":"10.1016/j.jjimei.2025.100380","DOIUrl":"10.1016/j.jjimei.2025.100380","url":null,"abstract":"<div><div>Smart farming technology (SFT) supports agriculture 4.0, driving the achievement of sustainable development goals to ensure food security while combating climate change. This study examines the role of motivators and its direct and indirect impacts on SFT acceptance in Thailand. Data were collected from 400 Thai farmers using a questionnaire and analyzes by Structural Equation Model. The study found that farmers with positive attitudes and an innovative mindset are more likely to accept SFT, with an indirect influence through perceived ease of use and perceived usefulness, which act as mediating variables. In terms of direct influence, perceived usefulness has the highest positive direct influence, while perceived cost has the highest negative direct influence on the adoption of SFT by Thai farmers. The results will be beneficial for government agencies and relevant organizations involved in the development of sustainable smart agriculture, particularly for developing countries with agricultural contexts similar to Thailand.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100380"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324290","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}

引用次数: 0

A structural topic modeling of communication research: insights from over a century of journals' abstracts 传播学研究的结构性主题模型：来自一个多世纪期刊摘要的见解

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1016/j.jjimei.2025.100364

Mohamed M. Mostafa , Mohammad Alhur , Ahmed M. Moustafa

{"title":"A structural topic modeling of communication research: insights from over a century of journals' abstracts","authors":"Mohamed M. Mostafa , Mohammad Alhur , Ahmed M. Moustafa","doi":"10.1016/j.jjimei.2025.100364","DOIUrl":"10.1016/j.jjimei.2025.100364","url":null,"abstract":"<div><div>Communication research is a broad and interdisciplinary field that is strongly influenced by several other disciplines, including behavioral and human sciences. This study uses structural topic modeling (STM) to analyze and trace the intellectual structure of the field over the past century based on a twenty-nine communication journals’ corpus encompassing 24,983 abstracts, totaling more than two million words. Results show a wide range of important research themes in the field, including communication theory, media analysis, health communication, rhetorical theory, interpersonal interactions, small group communications, political debate, and speech education. Diachronically, our results reveal that some topics, such as “speech education” and “political debate” have waned over time, whereas other topics, such as “narrative/discourse analysis” and “global policy change” have gained recently more attention from communication scholars. These findings underscore not only the intellectual breadth and historical evolution of communication research but also highlight key paradigm shifts in the field. The study demonstrates how computational text analysis can inform meta-theoretical understanding and strategic planning within academic disciplines.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865416","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}

引用次数: 0

A lightweight transfer learning based ensemble approach for diabetic retinopathy detection 基于轻量级迁移学习的集成方法在糖尿病视网膜病变检测中的应用

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-08-26 DOI: 10.1016/j.jjimei.2025.100372

S JAHANGEER SIDIQ, T BENIL

{"title":"A lightweight transfer learning based ensemble approach for diabetic retinopathy detection","authors":"S JAHANGEER SIDIQ, T BENIL","doi":"10.1016/j.jjimei.2025.100372","DOIUrl":"10.1016/j.jjimei.2025.100372","url":null,"abstract":"<div><div>Diabetic retinopathy (DR) is a fatal and irreversible eye disease that affects millions of people worldwide. It occurs due to high blood sugar level in the body of a diabetic patient, so it requires immediate attention which goes beyond the clinical solutions. With the advancements in deep learning and computer vision there are maximum possibilities of predicting this disease at early stages. Based on the severity of disease, different labels have been assigned to different classes of this disease as follows: 4 for proliferative DR, 3 for severe DR, 2 for moderate DR, 1 for mild DR and 0 for No DR. In this paper we proposed a deep learning-based ensemble approach using pre-trained and customized bi-class (CNN) base-learners like MobileNet, InceptionV3and DenseNet121 which were identified during initial investigation. These deep learning models were used as the base learner because of their promising performance in ensembles compared to the other deep learning base learners. All the work in the literature has studied this as a single complex multi-class problem or a bi-class problem where earlier stages are grouped together (0 to 3) and treated as one class and 4 as separate another class. Our work breaks this multi-class problem into multiple simpler two class problems using OVO(One-Versus-One) approach. Several benchmark data sets such as APTOS 2019, IDRiD, Messidor-2 and DDR which are multi-class data sets were used for training and testing our models. Data augmentation techniques were also utilized. Performance metrics such as precision, recall, f1-score, and accuracy were used for evaluation. Our ensemble models showed a remarkable performance with precision, recall, f1-score, and accuracy for most of the datasets used in this study. In addition to this our ensemble models have minimum number of trainable parameters which makes them an ultimate choice.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100372"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902471","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}

引用次数: 0

Securing the metaverse: Machine learning–based perspectives on risk, trust, and governance 保护元环境：基于机器学习的风险、信任和治理视角

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-07-21 DOI: 10.1016/j.jjimei.2025.100356

Krishnashree Achuthan , Sasangan Ramanathan , Raghu Raman

{"title":"Securing the metaverse: Machine learning–based perspectives on risk, trust, and governance","authors":"Krishnashree Achuthan , Sasangan Ramanathan , Raghu Raman","doi":"10.1016/j.jjimei.2025.100356","DOIUrl":"10.1016/j.jjimei.2025.100356","url":null,"abstract":"<div><div>The rapid expansion of the metaverse presents significant cybersecurity and privacy challenges, requiring structured, data-driven analysis. This study applies the ADO-TCM framework and BERTopic modeling to examine drivers of cybersecurity risk, theoretical responses, and interdisciplinary research gaps. Using PRISMA guidelines, 86 peer-reviewed studies were analyzed to identify key antecedents—technological vulnerabilities, user behavior, regulatory fragmentation, economic incentives, and cultural factors—shaping decisions in compliance, deployment, and education. These, in turn, influence outcomes like trust, threat mitigation, and scalability. The review identifies five latent themes: secure identity, privacy, trust, governance, and AI’s role in shaping risk. The study maps diverse theoretical lenses—cognitive, behavioral, strategic, and technological—used to interpret immersive threats and decision-making in metaverse contexts. Contributing a novel, empirically grounded synthesis, this research advances the information management literature and proposes a forward-looking agenda focused on adaptive security, ethical AI, interoperability, regulatory convergence, and intelligent, user-centric architecture for immersive ecosystems.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100356"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679947","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}

引用次数: 0

Multi-variate LSTM with attention mechanism for the Indian stock market 具有关注机制的印度股市多变量LSTM

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-06-21 DOI: 10.1016/j.jjimei.2025.100350

Ashy Sebastian, Dr. Veerta Tantia

{"title":"Multi-variate LSTM with attention mechanism for the Indian stock market","authors":"Ashy Sebastian, Dr. Veerta Tantia","doi":"10.1016/j.jjimei.2025.100350","DOIUrl":"10.1016/j.jjimei.2025.100350","url":null,"abstract":"<div><div>The advent of attention mechanism has surpassed numerous benchmarks and enabled widespread progress in the realm of natural language processing (NLP). Nevertheless, they have not been adequately leveraged in a time-series context. Accordingly, this paper aims to address this issue by proposing a hybrid, deep-learning model that integrates attention mechanisms and multi-variate long short-term memory (LSTM) for financial forecasting in the Indian stock market. Our model yields superior results as compared to baseline and state-of-the-art models evaluated using MAE and RMSE. Moreover, we employed a modern evaluation criterion based on the methodology advocated by Diebold–Mariano, known as the Diebold–Mariano test (DM test), as a new criterion for evaluation based on statistical hypothesis tests. DM test has been applied in this study to distinguish the significant differences in forecasting accuracy between LSTM with attention and other models. From the results and according to DM-test it is observed that the differences between the forecasting performances of models are significant and that attention mechanism could enhance the accuracy in predicting stock prices by allowing the model to prioritize and concentrate on the most important features and patterns in the data while avoiding overfitting and noise.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330579","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}

引用次数: 0

A systematic review and future agenda on continuance intentions in mobile apps 移动应用程序中持续性意图的系统回顾和未来议程

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-07-03 DOI: 10.1016/j.jjimei.2025.100352

Suryati Veronika , Michael S.W. Lee , Bodo Lang , Pragea Putra

{"title":"A systematic review and future agenda on continuance intentions in mobile apps","authors":"Suryati Veronika , Michael S.W. Lee , Bodo Lang , Pragea Putra","doi":"10.1016/j.jjimei.2025.100352","DOIUrl":"10.1016/j.jjimei.2025.100352","url":null,"abstract":"<div><div>Technology changes at ever increasing speeds. Therefore, it is crucial for practitioners and academics to understand why users’ intend to continue or discontinue their usage. This paper presents a current and comprehensive systematic literature review on continuance intentions for mobile applications. The review analyzes 119 studies from the Scopus database (January 2019–December 2023) using the PRISMA, SPAR, and TCCM frameworks. It identifies key theoretical models, determinants of mobile app continuance intention, research methods, existing gaps, and future research directions. Findings reveal that several well-recognised theoretical models are frequently applied in the literature on continuance intention. Consequently, the variables derived from these models are among the most commonly measured by researchers. Additionally, the majority of studies in this area employ quantitative methods, with structural equation modelling being most widely used. This review categorises the literature based on mobile application classifications and six distinct sets of factors influencing continuance intention: psychological, technical, social, behavioural, contextual, and barriers. Furthermore, it explores the outcomes associated with continuance intention. The paper identifies two primary areas for future research: the development of a conceptual framework and research design. It also highlights research opportunities related to emerging technologies and the gap between intentions and actual behaviours.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100352"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534885","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}

引用次数: 0

Conceptualization and validation of an intelligent digital twin design framework for supply chain risk management 供应链风险管理智能数字孪生设计框架的概念化与验证

International Journal of Information Management Data Insights Pub Date : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1016/j.jjimei.2025.100365

Matteo Gabellini, Alberto Regattieri, Marco Bortolini, Michele Ronchi

引用次数: 0

Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets 码交换豪萨-英语推文情感分析的深度学习技术

International Journal of Information Management Data Insights Pub Date : 2025-06-01 Epub Date: 2025-04-05 DOI: 10.1016/j.jjimei.2025.100330

Yusuf Aliyu , Aliza Sarlan , Kamaluddeen Usman Danyaro , Abdullahi Sani abd Rahman , Aminu Aminu Muazu , Mustapha Yusuf Abubakar

{"title":"Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets","authors":"Yusuf Aliyu , Aliza Sarlan , Kamaluddeen Usman Danyaro , Abdullahi Sani abd Rahman , Aminu Aminu Muazu , Mustapha Yusuf Abubakar","doi":"10.1016/j.jjimei.2025.100330","DOIUrl":"10.1016/j.jjimei.2025.100330","url":null,"abstract":"<div><div>Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100330"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0