International Journal of Information Management Data Insights最新文献

{"title":"Mapping technology diffusion with AI: A web-based approach for tracking additive manufacturing adoption","authors":"Julian Schwierzy ,&nbsp;Robert Dehghan ,&nbsp;Sebastian Schmidt ,&nbsp;Nils Grashof ,&nbsp;Hanna Hottenrott ,&nbsp;Michael Woywode","doi":"10.1016/j.jjimei.2025.100387","DOIUrl":"10.1016/j.jjimei.2025.100387","url":null,"abstract":"<div><div>Understanding the diffusion of emerging technologies is essential for capturing the benefits of innovation. Yet, traditional science, technology, and innovation (ST&amp;I) indicators are often limited in measuring technology adoption. This study investigates the potential of analyzing corporate websites through web mining and machine learning to measure the adoption of additive manufacturing (AM) technologies. Furthermore, it examines how regional ST&amp;I indicators — specifically patents and publications — shape AM adoption patterns. Despite still being niche, AM adoption in Germany doubled from 0.37% (2022) to 0.74% (2023) of firms. Regional web-based adoption hot spots largely align with patent and publication activity. In addition, our regression analyses reveal a positive and statistically significant relationship between these indicators and AM diffusion based on our AI-based web indicator. These results underline the potential of WebAI methods to complement traditional ST&amp;I indicators.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100387"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925286","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":"Characterization of the artificial intelligence labor market in Chile: An approach based on unstructured data and the ESCO taxonomy","authors":"Willmer Guevara-Ramirez ,&nbsp;Itziar Martinez-de-Alegria ,&nbsp;Carlos-Gustavo Caballero-Zepeda ,&nbsp;Cristian-Israel Salinas-Astudillo","doi":"10.1016/j.jjimei.2026.100401","DOIUrl":"10.1016/j.jjimei.2026.100401","url":null,"abstract":"<div><div>This study characterizes the labor demand related to artificial intelligence (AI) in Chile to identify the skills and requirements sought by employers. Using unstructured data from online job postings collected from major employment platforms, AI-related vacancies are identified and analyzed through a skills-based approach aligned with the European Skills, Competences, Qualifications, and Occupations (ESCO) taxonomy. The results show that demand is primarily oriented towards technical skills, while soft skills play a complementary and transversal role, with teamwork being the most recurrent. AI-related job demand is concentrated in high-skilled occupations and specialized professional profiles, while patterns observed in the data are consistent with skill-biased and routine-biased technological change hypotheses. The identified skill structure highlights the relevance of advanced digital competencies for emerging AI occupations and provides a basis for updating training programs. Overall, the findings offer empirical evidence on the early diffusion of AI-related jobs demand in Chile and contribute to the discussion on skills development and labor market adaptation in emerging economies.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100401"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173214","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":"Financial literacy and trust in mobile money services on mobile money savings adoption among informal sector workers in Ghana: the mediating role of perceived ease of use","authors":"Abigail Padi ,&nbsp;Daniel Odei Okyere ,&nbsp;Alhassan Musah ,&nbsp;Marshall Wellington Blay","doi":"10.1016/j.jjimei.2026.100392","DOIUrl":"10.1016/j.jjimei.2026.100392","url":null,"abstract":"<div><div>In developing economies like Ghana, mobile money services are rapidly transforming financial inclusion, yet the consistent use of such platforms for savings remains limited among informal sector workers. This study investigates the roles of financial literacy, trust in mobile money services, and perceived ease of use in shaping mobile money savings adoption (MSAD), with a focus on the mediating influence of perceived ease of use (PEU). Drawing on the Technology Acceptance Model and trust theory, data were collected from 3424 informal sector participants and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that financial literacy, trust in mobile money services, and perceived ease of use all have statistically significant and positive effects on mobile money savings adoption. Furthermore, PEU significantly mediates the effects of trust and financial literacy on MSAD. Among control variables, gender was found to have a significant positive influence, whereas education and income were not statistically significant predictors. This study is among the first to holistically examine how trust and financial literacy interact through ease of use to influence mobile savings behaviour in an informal economy. The insights offer novel theoretical contributions to digital financial behaviour literature and inform the design of user-centric savings solutions for financially underserved populations in Sub-Saharan Africa.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100392"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173216","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":"Digital market adoption by underserved MSMEs in developing countries: Mediation and moderation by self-efficacy and trust","authors":"Ratna Juita ,&nbsp;Dedi I. Inan ,&nbsp;Budi Santoso","doi":"10.1016/j.jjimei.2025.100384","DOIUrl":"10.1016/j.jjimei.2025.100384","url":null,"abstract":"<div><div>This study addresses the critical yet fragmented understanding of digital market adoption among underserved MSMEs in developing countries. Despite increasing technology access, these vulnerable communities face significant adoption barriers beyond infrastructure availability. Extending the Technology-Organization-Environment (TOE) framework with individual-level factors, this research investigates the key drivers of digital market adoption intention in this context. Analysing data from 151 underserved MSMEs through partial least squares structural equation modelling (PLS-SEM), the study reveals three key contributions. First, self-efficacy exhibits dual mediating roles: it fully mediates the relationship between facilitating conditions and adoption intention and competitively mediates the relationship between technology anxiety and adoption intention, suggesting that while confidence mitigates anxiety’s negative influence, anxiety can paradoxically drive “desperation adoption” under survival pressures. Second, trust acts as both an enabler and a moderator, influencing the effects of technology anxiety, organizational size, and competitive pressure on adoption. Third, these mediation and moderation mechanisms collectively enhance the model’s explanatory power (R² = 0.76), demonstrating that psychological readiness determines how MSMEs interpret and act upon external supports. The findings underscore that digital adoption in underserved contexts requires interventions that build self-efficacy, foster trust, and align infrastructural investment with psychological empowerment to achieve inclusive digital transformation for underserved MSMEs.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100384"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618354","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 realist synthesis of the implementation of digital health strategies for enhanced health outcomes in Sub-Saharan Africa","authors":"Mourine S. Achieng","doi":"10.1016/j.jjimei.2026.100391","DOIUrl":"10.1016/j.jjimei.2026.100391","url":null,"abstract":"<div><div>Many sub-Saharan African (SSA) countries have developed national Digital Health (DH) strategies. However, there is significant variability in how these strategies are implemented across different regions, especially in resource-constrained settings. This paper conducts a realist synthesis to explain <em>how, for whom</em>, and <em>under what conditions</em> the implementation of DH strategies facilitates sustainable and equitable health outcomes in SSA. A realist review of 30 articles (2014-2025) from Scopus, Google Scholar, and PubMed was conducted. Using Context-Mechanism-Outcome configurations, the analysis identified the underlying causal mechanisms necessary for effective implementation. The synthesis refined seven interconnected programme theories, clustered into three functional categories. 1) Foundational enabling conditions encompassing coordinated governance, strategic institutionalization, and a legal trust framework to establish legitimacy and safety. 2) Technical and human enablers encompassing infrastructure reliability and socio-technical capability-building with peer validation to create operational capacity. 3) Adaptive mechanisms encompassing participatory co-design and evidence-informed adaptation to ensure contextual fit and sustainable evolution. This review suggests that effective, sustainable, and equitable integration of DH is not achieved through isolated interventions, but through a synergistic ecosystem of seven interconnected mechanisms. By reframing the challenge for policymakers and implementers from asking what technology should be deployed to which foundational, enabling, and adaptive mechanisms must be activated for success, the review offers a critical shift in perspective that advances both policy and practice.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100391"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173215","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":"Mapping the landscape of algorithmic management in gig economy: A bibliometric analysis","authors":"Vo Kim Nhan ,&nbsp;Tran Thi Thu","doi":"10.1016/j.jjimei.2026.100402","DOIUrl":"10.1016/j.jjimei.2026.100402","url":null,"abstract":"<div><div>Algorithmic management has become a prominent feature of contemporary organizations, particularly in platform-mediated and data-intensive work contexts. While research on algorithmic management has expanded rapidly across management, information systems, and labor studies, existing reviews remain largely narrative in nature and provide limited insight into the field’s underlying intellectual structure. This study addresses this gap by adopting a bibliometric and network-analytic approach to systematically map the evolution and organization of algorithmic management research. Using bibliographic records retrieved from the Web of Science Core Collection, the study applies co-citation analysis, keyword co-occurrence mapping, and citation burst detection to identify dominant research clusters, influential contributions, and emerging themes. All network-based analyses are conducted using the Web of Science Core Collection. The results reveal a rapidly growing yet structurally fragmented research landscape, characterized by a central cluster focused on platform work and algorithmic control alongside multiple loosely connected technical and domain-specific streams. Temporal analyses further indicate a shift from early technical applications toward increasing attention to organizational, labor, and governance-related issues.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100402"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384797","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 comparative empirical evaluation of semantic clustering algorithms on static word embeddings","authors":"Asefeh Asemi ,&nbsp;Rajab Kiani Shahvandy ,&nbsp;Mahdi Houshangi","doi":"10.1016/j.jjimei.2026.100396","DOIUrl":"10.1016/j.jjimei.2026.100396","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objective&lt;/h3&gt;&lt;div&gt;This study conducts a comprehensive empirical evaluation of semantic clustering algorithms to identify the most effective approach for automatically organizing and extracting meaning from textual data. By systematically comparing the performance of K-means, K-medoids, and DBSCAN on word embeddings from GloVe and Wiki models, it provides data-driven insights for optimizing Natural Language Processing (NLP) pipelines in information management systems. The research suggests a practical framework for selecting clustering algorithms and embedding models based on specific operational objectives, such as document clustering, knowledge base construction, and content-based recommendation.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design/Methodology/Approach&lt;/h3&gt;&lt;div&gt;The investigation employed a two-phase methodology. Initially, predefined word lists were transformed into numerical vectors using pre-trained GloVe and Wiki models. K-means, K-medoids, and DBSCAN algorithms were applied, with performance evaluated via Silhouette Score and Davies-Bouldin Index, complemented by Principal Component Analysis (PCA) for visualization. Results were benchmarked against manually curated semantic groupings. Subsequently, the findings were validated on a large-scale corpus of 303 research articles to assess scalability and real-world applicability.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results/Discussion&lt;/h3&gt;&lt;div&gt;Analysis indicates that, under the evaluated configurations, K-means combined with GloVe embeddings produced comparatively higher semantic coherence and more interpretable cluster structures than the alternative methods considered. K-medoids demonstrated robustness against outliers but yielded less compact groupings. While DBSCAN indicated effective for outlier identification, it consistently underperformed in forming semantically meaningful clusters. The GloVe model significantly outperformed Wiki embeddings in generating precise and interpretable clusters, whereas Wiki produced broader, less distinct groupings. Large-scale validation confirmed these results, with K-means successfully identifying dominant research themes, including digital library adoption (43.2%), reference services (15.2%), and research data management (8.9%)—in a corpus of academic literature. Under the evaluated corpus characteristics and parameter settings, DBSCAN classified most documents as outliers, indicating limited suitability for this specific balanced document collection.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;K-means and K-medoids emerge as comparatively effective algorithms under the evaluated conditions. The study underscores the critical influence of vector representation models, with GloVe embeddings providing superior semantic distinction compared to Wiki. These findings offer clear, actionable guidance for selecting clustering methods in NLP applications, highlighting the necessity of aligning algorithmic choice with specific dataset characteristics and information management goals.&lt;","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100396"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384799","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":"Academic certificate fraud detection by Web-based intelligent access control system: An effective role of Blockchain technology","authors":"Karuturi Soumya ,&nbsp;Karuturi Sai Sharat ,&nbsp;Nune Sreenivas","doi":"10.1016/j.jjimei.2025.100386","DOIUrl":"10.1016/j.jjimei.2025.100386","url":null,"abstract":"<div><div>The need for timely monitoring and management of the authenticity of academic certificates is increasing owing to the easy availability of computational facilities and network connectivity. This study is aimed to develop a means to verify the authenticity of an academic certificate data, such as; sender, receiver and digital signatures, using a blockchain ecosystem. Globally, the number of tertiary education students and graduates in various universities are constantly increasing every year; therefore the need for easy Web – based verification of degree certificates is also generating new business opportunities worldwide. Proof of certification of academic documents by easy means at low cost is not only necessary for the students but is equally required by the employers for a quick and trustworthy verification of the academic documents of the applicants during the recruitment process globally. Thus, a two-server-based framework is designed to support the multiple devices to train a local model using local data, and the gradients of the local model can be later sent to a central server that aggregates them to create a global model. Extensive experimental results confirm that BP can achieve efficient collusion resistance and verifiability of academic certificates results with a straightforward solution that demands the exploration of plausible business models.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100386"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976557","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":"Beyond tickets and texts: Addressing challenges in AI-driven customer service","authors":"Szabina Fodor ,&nbsp;Csaba Csáki ,&nbsp;Andrea Kő","doi":"10.1016/j.jjimei.2026.100395","DOIUrl":"10.1016/j.jjimei.2026.100395","url":null,"abstract":"<div><div>Adapting AI-driven solutions in customer services (CS) is becoming increasingly important to counter the combined impact of increased workload and management expectations. Addressing copious text volumes, text analytics (TA) assumes an important supporting role within CSs. However, the absence of TA and NLP application guidelines slows progress. Utilization of AI advancements in linguistically intricate languages and less digitally developed regions presents challenges.</div><div>Using a systematic in-depth review of the literature and expert feedback, this study presents the TA/NLP landscape in the context of CS. The paper discusses challenges, open issues, and active research areas in TA/NLP, mainly in customer service, and validates its findings with a country example and industry experts. In addition to the theoretical and practical value of the AI-driven customer service landscape, a unique methodological approach, which includes AI-assisted content mining with BERTopic, systematic literature review, domain expert validation, and the example contributes to methodology development.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100395"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173213","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":"BPGV: Behavioral provenance graph views to enhance anomaly detection","authors":"Michael Zipperle ,&nbsp;Yu Zhang ,&nbsp;Min Wang ,&nbsp;Elizabeth Chang ,&nbsp;Tharam Dillon","doi":"10.1016/j.jjimei.2026.100397","DOIUrl":"10.1016/j.jjimei.2026.100397","url":null,"abstract":"<div><div>Provenance-based Intrusion Detection Systems (PIDS) have shown potential in mitigating cyber threats in dynamic real-world environments. PIDS construct provenance graphs from audit logs to detect anomalous nodes, edges, or subgraph patterns. However, as provenance graphs grow in complexity, timely and accurately detecting anomalies becomes increasingly challenging, often resulting in higher false alarm rates. A key limitation of existing graph summarization techniques is their inadequate consideration of graph nodes’ context, leading to limited generalization abilities to capture unseen benign variants. Moreover, there is a lack of subgraph extraction techniques considering contextual information to extract subgraphs for various graph views, leading to reduced robustness and optimization due to the single-model anomaly detection approach. To address these shortcomings, we first present a taxonomy to systematically categorize and evaluate provenance graphs from the perspective of graph summarization, subgraph extraction, and graph representation. Second, we propose a Behavioral Provenance Graph View Anomaly Detection (BPGVAD) framework to detect behavioral anomalies, enabled by two key components: Behavioral Provenance Graph Summarization (BPGS) and Behavioral Provenance Graph Extraction (BPGE). The BPGS generalizes and summarizes nodes based on their context to capture unseen benign node variants. The BPGE extracts subgraphs from different graph views derived from BPGS to enable an optimized multi-model approach for anomaly detection. We evaluated the effectiveness of the BPGVAD framework using the DARAP OpTC dataset, and the results demonstrated improved anomaly detection performance with an accuracy of 99.332%, recall of 1, and a significantly low false alarm rate of 0.669%.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"6 1","pages":"Article 100397"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384796","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}