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dc.contributor.authorHan, Shuihua
dc.contributor.authorLiu, Zhenyuan
dc.contributor.authorDeng, Ziyue
dc.contributor.authorGupta, Shivam
dc.contributor.authorMikalef, Patrik
dc.date.accessioned2024-01-05T13:18:45Z
dc.date.available2024-01-05T13:18:45Z
dc.date.created2023-08-30T11:06:55Z
dc.date.issued2023
dc.identifier.citationDecision Support Systems. 2023, .en_US
dc.identifier.issn0167-9236
dc.identifier.urihttps://hdl.handle.net/11250/3110156
dc.description.abstractThis study proposes a novel research framework to examine the effect of digital CSR communications on financial performance while incorporating deep learning techniques to identify firms' CSR communications on social media. Particularly, this research aims to quantify firms' efforts in digital CSR communications by employing cutting-edge deep learning-based natural language processing (NLP) models to detect CSR-related tweets on social media. Utilizing a unique dataset of 65 Chinese public companies in the manufacturing sector between 2015 and 2019, we detected 64,769 long-form tweets posted on WeChat to acquire both digital CSR communications and stakeholder engagement data. Combining financial and secondary data of sample firms, this research reveals the positive but time-lagged influence of digital CSR communications on firms' financial performance, primarily through the lens of agenda-setting theory. We also find that stakeholder engagement plays an essential bridging role in the relationship above, while CSR ratings surprisingly hamper such a positive effect.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleExploring the effect of digital CSR communication on firm performance: A deep learning approachen_US
dc.title.alternativeExploring the effect of digital CSR communication on firm performance: A deep learning approachen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber0en_US
dc.source.journalDecision Support Systemsen_US
dc.identifier.doi10.1016/j.dss.2023.114047
dc.identifier.cristin2170827
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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