Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
We 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.
Recommended Citation
Babu, Xavier and Zhang, Juheng, "Systematic Contextual-based Affinity Analytics Research on Association of Manager Response and Customer Reviews" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 13.
https://aisel.aisnet.org/hicss-57/dsm/data_analytics/13
Systematic Contextual-based Affinity Analytics Research on Association of Manager Response and Customer Reviews
Hilton Hawaiian Village, Honolulu, Hawaii
We 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.
https://aisel.aisnet.org/hicss-57/dsm/data_analytics/13