International Journal of Information Management Data Insights最新文献_第2页

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 : 2025-08-18 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":"2025-08-18","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

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 : 2025-08-18 DOI: 10.1016/j.jjimei.2025.100365

Matteo Gabellini, Alberto Regattieri, Marco Bortolini, Michele Ronchi

引用次数: 0

Mitigating uncertainty in travel agency selection in Jordan: A signaling theory approach 减轻约旦旅行社选择的不确定性：一个信号理论方法

International Journal of Information Management Data Insights Pub Date : 2025-08-02 DOI: 10.1016/j.jjimei.2025.100362

Fadi Herzallah , Bashar Alhaj Mohammad , Mahmoud Alhayek , Syed Md. Faisal Ali Khan

{"title":"Mitigating uncertainty in travel agency selection in Jordan: A signaling theory approach","authors":"Fadi Herzallah , Bashar Alhaj Mohammad , Mahmoud Alhayek , Syed Md. Faisal Ali Khan","doi":"10.1016/j.jjimei.2025.100362","DOIUrl":"10.1016/j.jjimei.2025.100362","url":null,"abstract":"<div><div>Uncertainty plays a critical role in tourism purchase decisions, particularly in developing markets. This study investigates how Jordanian tourists reduce uncertainty when buying package tours by applying signaling theory. A questionnaire was distributed to 450 tourists with prior travel agency experience, yielding 376 valid responses analyzed using PLS-SEM and IPMA. Results show that information quality, popularity, positive comments, and reputation significantly reduce uncertainty about travel agencies, while return policy has no significant effect. Moreover, uncertainty negatively influences purchase decisions. IPMA findings highlight that reputation is the most important factor in reducing uncertainty, followed by popularity and positive comments. Although return policy scores highest in performance, it has the least impact on uncertainty. This study contributes to tourism literature by identifying specific customer-seller signals that influence perceived uncertainty and by clarifying the link between uncertainty reduction and purchase behavior in the context of travel agencies in developing countries.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100362"},"PeriodicalIF":0.0,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764043","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

Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decomposition and CNN-based feature extraction 时间数据的小波- cnn：通过多分辨率小波分解和基于cnn的特征提取增强长期股票价格预测

International Journal of Information Management Data Insights Pub Date : 2025-07-26 DOI: 10.1016/j.jjimei.2025.100360

Komei Hiruta , Junsuke Senoguchi

{"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 , 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":"2025-07-26","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}

引用次数: 0

Blockchain technology to improve traceability in the coffee supply chain: A systematic literature review 区块链技术提高咖啡供应链的可追溯性：系统的文献综述

International Journal of Information Management Data Insights Pub Date : 2025-07-24 DOI: 10.1016/j.jjimei.2025.100359

Christian Gómez, Benoit Garbinato

{"title":"Blockchain technology to improve traceability in the coffee supply chain: A systematic literature review","authors":"Christian Gómez, Benoit Garbinato","doi":"10.1016/j.jjimei.2025.100359","DOIUrl":"10.1016/j.jjimei.2025.100359","url":null,"abstract":"<div><div>Coffee is consumed worldwide, with its supply chain starting with coffee growers, who benefit least from it. Across its production, distribution, and commercialization processes, there are risks and issues that could damage the safety and authenticity of this product. Therefore, the coffee industry is looking for innovative technologies that allow traceability in the coffee supply chain. In this context, blockchain technology offers a promising solution as it supports traceability via a decentralized system that allows immutable records and transparent access; it also promotes collaborative work and removes intermediaries by generating trust between participants. This systematic literature review describes the state-of-the-art in research and development about the use of blockchain technology to improve traceability in the coffee supply chain. We also outline the open challenges that remain to be addressed in this field. We use the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to achieve this goal. Our findings suggest that the developments are mainly conceptual designs and prototypes, focusing on tracing products and verifying their authenticity using the Ethereum or Hyperledger blockchains. Also, our results show various challenges on the technology side, like efficiency improvements, integration with other technologies, infrastructure, and a lack of standards. There are also challenges at the management level, like the necessity of agreements for traceability processes, data governance, willingness to invest and pay, education, and support to deploy the technology on farms. After overcoming these open challenges, blockchain technology can improve traceability and increase value for stakeholders in the coffee supply chain.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100359"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695251","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

Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach 抑郁检测的情感分析：基于叠加集成的深度学习方法

International Journal of Information Management Data Insights Pub Date : 2025-07-21 DOI: 10.1016/j.jjimei.2025.100358

Kinza Noor , Mariam Rehman , Maria Anjum , Afzaal Hussain , Rabia Saleem

{"title":"Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach","authors":"Kinza Noor , Mariam Rehman , Maria Anjum , Afzaal Hussain , 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":"2025-07-21","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}

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 : 2025-07-21 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":"2025-07-21","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

A Novel Named Entity Recognition approach of Indonesian fake news using part of speech and BERT model on presidential election 基于词性和BERT模型的印尼总统选举假新闻命名实体识别方法

International Journal of Information Management Data Insights Pub Date : 2025-07-14 DOI: 10.1016/j.jjimei.2025.100354

Puji Winar Cahyo , Ulfi Saidata Aesyi , Widodo Agus Setianto , Tatang Sulaiman

{"title":"A Novel Named Entity Recognition approach of Indonesian fake news using part of speech and BERT model on presidential election","authors":"Puji Winar Cahyo , Ulfi Saidata Aesyi , Widodo Agus Setianto , Tatang Sulaiman","doi":"10.1016/j.jjimei.2025.100354","DOIUrl":"10.1016/j.jjimei.2025.100354","url":null,"abstract":"<div><div>Fake news often spreads rapidly and can mislead readers, which makes it important to approach such information with caution. In text-based information, content extraction can be used to determine the meaning and intent of the message. Therefore, this research aims to develop a novel approach for entity detection in Indonesian-language fake news texts by applying BiLSTM-CRF, BiGRU, and BERT models. The novelty of this study lies in the integration of Part-of-Speech (PoS) tagging before processing words for entity detection. Words tagged as Noun (NN) and Proper Noun (NNP) are transformed into entity labels such as ORG for organizations, PER for people, and LOC for locations. Meanwhile, words labeled as Verb (VB) are converted into the ACT entity to represent actions. Evaluations were conducted by integrating PoS tagging with entity detection using the BiLSTM-CRF model, which achieved an F1-Score of 81.26%. The BiGRU-based model achieved an F1-Score of 79.46%, while the BERT-based model achieved the highest F1-Score of 87.38%. These results demonstrate that the BERT model, when combined with PoS tagging, provides the best performance and can effectively be used to detect entities in fake news. The entity detection process was further applied to identify fake news during the 2024 Indonesian presidential and vice-presidential election period. By counting the number of mentions of each candidate and their running mate labeled as PER entities, it has result the Prabowo Subianto–Gibran Rakabuming Raka pair appeared in 49 fake news articles. This was followed by the Ganjar Pranowo–Mahfud MD pair with 14 fake news articles, and the Anies Baswedan–Muhaimin Iskandar pair with 13 articles. All identified data have been filtered to retain only unique entries.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100354"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631524","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

ABERT: Adapting BERT model for efficient detection of human and AI-generated fake news BERT：采用BERT模型有效检测人工和人工智能生成的假新闻

International Journal of Information Management Data Insights Pub Date : 2025-07-14 DOI: 10.1016/j.jjimei.2025.100353

Jawaher Alghamdi , Yuqing Lin , Suhuai Luo

{"title":"ABERT: Adapting BERT model for efficient detection of human and AI-generated fake news","authors":"Jawaher Alghamdi , Yuqing Lin , Suhuai Luo","doi":"10.1016/j.jjimei.2025.100353","DOIUrl":"10.1016/j.jjimei.2025.100353","url":null,"abstract":"<div><div>The proliferation of fake news in digital media poses a significant challenge to the dissemination of accurate information. Transfer learning, particularly with pre-trained language models (PLMs) like BERT, has demonstrated exceptional performance in natural language processing (NLP) tasks. However, the computational expense of fine-tuning the entire model for domain-specific tasks remains a limitation. In this study, we propose a novel approach, Adapt-BERT (ABERT), for the detection of both human and artificial intelligence (AI)-generated fake news. ABERT includes parameter-efficient adapter that enables efficient detection. By freezing the pre-trained BERT network and incorporating lightweight adapter, ABERT achieves comparable performance to fully fine-tuned BERT while reducing the number of trainable parameters by approximately 67.7%. ABERT strikes a balance between performance and computational efficiency, offering a scalable solution to combat the dissemination of fake news in digital media. Experimental evaluations on diverse datasets showcase the effectiveness of the proposed parameter-efficient approach in achieving comparable performance to state-of-the-art (SOTA) methods in the task of fake news detection (FND).</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 2","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614277","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

Development and integration of human-AI interactions in service applications: Conceptual framework and review 服务应用中人机交互的开发与集成：概念框架与回顾

International Journal of Information Management Data Insights Pub Date : 2025-07-14 DOI: 10.1016/j.jjimei.2025.100357

Nick Tugarin, Christian van Husen

{"title":"Development and integration of human-AI interactions in service applications: Conceptual framework and review","authors":"Nick Tugarin, 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":"2025-07-14","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}

引用次数: 0