Aspect-Based Sentiment Analysis on Social Media Comments (Twitter) : The Attributes of Service Robots in the Hotel and Restaurant Industry
Erişim
info:eu-repo/semantics/openAccessTarih
2024Erişim
info:eu-repo/semantics/openAccessÜst veri
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Scopus EXPORT DATE: 30 September 2024 @ARTICLE{Acar2024, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201003812&doi=10.1080%2f1528008X.2024.2386590&partnerID=40&md5=1be136d9730acfc471716d9c368227e5}, affiliations = {Department of Recreation Management, Karabuk University, Karabuk, Turkey; Department of Radio, Television and Cinema, Karabuk University, Karabuk, Turkey; Department of Public Relations, Gümüşhane University, Gümüşhane, Turkey}, correspondence_address = {A. Acar; Department of Recreation Management, Karabuk University, Safranbolu, Karabuk, Turkey; email: aysegulacar@karabuk.edu.tr}, publisher = {Routledge}, issn = {1528008X}, language = {English}, abbrev_source_title = {J. Qual. Assur. Hosp. Tour.} }Özet
Service robots are becoming more prevalent in the service industry, yet the impact they have on customer experiences is not well-documented. This study identified consumer emotions toward service robots in hotels and restaurants, focusing on how specific attributes of these robots affect consumer emotions. Furthermore, it explored which countries generate the most comments on these service robot attributes and the nature of consumer emotions within these regions. Utilizing sentiment analysis, the study examined 52,513 English-language tweets about hotel and restaurant service robots. Additionally, logistic regression analysis helped quantify the impact of robot attributes–categorized as physical, functional, and relational–on different classes of emotions. The findings reveal that customers of restaurants and hotels were most influenced by the physical and relational attributes of service robots, respectively, with functional attributes having the least impact. The logistic regression analysis offered significant insights into how service robot attributes influence consumer experiences in hotels and restaurants. © 2024 Taylor & Francis Group, LLC.
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https://www.scopus.com/record/display.uri?eid=2-s2.0-85201003812&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=5cb5e4aba52731540225b01cefcf467ahttps://www.tandfonline.com/doi/epdf/10.1080/1528008X.2024.2386590?src=getftr
https://hdl.handle.net/20.500.12440/6329