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Comprehensive examination of the bright and dark sides of generative AI services: A mixed-methods approach

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dc.contributor.authorYoon, Sang-Hyeak-
dc.contributor.authorYang, Sung-Byung-
dc.contributor.authorLee, So-Hyun-
dc.date.accessioned2025-03-12T04:30:16Z-
dc.date.available2025-03-12T04:30:16Z-
dc.date.issued2025-03-
dc.identifier.issn1567-4223-
dc.identifier.issn1873-7846-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/57920-
dc.description.abstractRecent advancements in artificial intelligence (AI), particularly in generative AI (GAI), have significantly influenced society, prompting extensive discussions about their societal impact. While previous research has acknowledged both the benefits and challenges of AI, the rapid development of GAI has often proceeded without sufficient focus on actionable strategies to address potential risks and unintended consequences. Understanding both the positive and negative aspects of GAI is essential to ensure that technological progress is balanced and responsibly managed to mitigate potential risks and societal harm. This study identifies the positive and negative aspects of GAI from both public and expert viewpoints by applying a valence framework. Using a mixed-methods approach that integrates joint sentiment topic (JST) modeling with the combined use of ChatGPT and expert interviews, we investigated the key positive and negative factors associated with GAI. By integrating the insights gained from these different perspectives, the study proposes strategies for the effective and responsible use of GAI. The study contributes to the existing body of knowledge on GAI by offering a comprehensive understanding of its implications and providing guidance for its ethical and appropriate applications. © 2025 Elsevier B.V.-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier B.V.-
dc.titleComprehensive examination of the bright and dark sides of generative AI services: A mixed-methods approach-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.elerap.2025.101491-
dc.identifier.scopusid2-s2.0-85218411207-
dc.identifier.wosid001444228600001-
dc.identifier.bibliographicCitationElectronic Commerce Research and Applications, v.70, pp 1 - 13-
dc.citation.titleElectronic Commerce Research and Applications-
dc.citation.volume70-
dc.citation.startPage1-
dc.citation.endPage13-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBusiness & Economics-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryBusiness-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.subject.keywordAuthorChatGPT-
dc.subject.keywordAuthorExpert interview-
dc.subject.keywordAuthorGenerative AI-
dc.subject.keywordAuthorJoint sentiment topic modeling-
dc.subject.keywordAuthorMixed-methods approach-
dc.subject.keywordAuthorValence framework-
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