Systematic Contextual-based Affinity Analytics Research on Association of Manager Response and Customer Reviews

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dc.contributor.authorBabu, Xavier
dc.contributor.authorZhang, Juheng
dc.date.accessioned2023-12-26T18:38:46Z
dc.date.available2023-12-26T18:38:46Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.310
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherf0c03864-8346-4a11-8351-bd9daa023bf6
dc.identifier.urihttps://hdl.handle.net/10125/106692
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining, and Machine Learning for Social Media
dc.subjectaffinity analytics
dc.subjectcontextual embedding.
dc.subjectpanel vector autoregression
dc.subjectreview convergence
dc.subjectsemantic text analytics
dc.titleSystematic Contextual-based Affinity Analytics Research on Association of Manager Response and Customer Reviews
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractWe study the similarity between managers' responses and customer reviews and explore its influence on review convergence, customer ratings, and prices. While previous research has explored the influence of product reviews on price and reputation, little attention has been given to the effectiveness of managers' responses and their impact on product price and rating. This study fills this gap by examining managers' responses and their relationship with product review convergence/divergence. Additionally, we investigate whether managers exhibit similar responses to their peers and whether their responses are tailored to specific product review issues or broadly resemble their past responses. We develop a deep learning framework to understand semantic textual information in managers' responses and analyze the semantic affinity score with reviews. We investigate the dynamic relationships among managers' responses, product reviews, review convergence, product reputation and price with the Panel Vector Autoregression model with a travel dataset from TripAdvisor.com.
dcterms.extent10 pages
prism.startingpage2563

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