{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:48:32Z","timestamp":1776091712377,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Ministry of Research, Innovation and Digitization, CNCS\/CCCDI\u2014UEFISCDI","doi-asserted-by":"publisher","award":["COFUND-CETP-SMART-LEM-1"],"award-info":[{"award-number":["COFUND-CETP-SMART-LEM-1"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Academic publications from the Web of Science Core Collection on \u201ce-business\u201d and \u201cartificial intelligence\u201d (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as \u201cbusiness transformation\u201d, \u201cbusiness value\u201d and \u201ce-business strategy\u201d are prominent, contributing significantly to areas like \u201cOperations Research &amp; Management Science\u201d. Additionally, the keyword \u201ce-agribusiness\u201d recently appears connected to \u201cEnvironmental Sciences &amp; Ecology\u201d, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies.<\/jats:p>","DOI":"10.3390\/jtaer20010016","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T05:50:53Z","timestamp":1737525053000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9005-5181","authenticated-orcid":false,"given":"Simona-Vasilica","family":"Oprea","sequence":"first","affiliation":[{"name":"Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Pia\u0163a Roman\u0103, 010374 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-352X","authenticated-orcid":false,"given":"Adela","family":"B\u00e2ra","sequence":"additional","affiliation":[{"name":"Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Pia\u0163a Roman\u0103, 010374 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"ref_1","first-page":"166","article-title":"Quality management of e-business: A key node analysis of ecological network in digital economy by using artificial intelligence","volume":"22","author":"Huo","year":"2019","journal-title":"Rom. 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