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A Comparative Study of Business Hotels in Busan and Seoul through Big Data Analysis: Evaluating Online Customer Review

김세은|왕각|Sonam Sherpa|Huh, In-Young
산업개발연구소 (kyungsung university)|2024.09|산업혁신연구 (Journal of Industrial Innovation)|제40권 제3호|pp. 340-354|DOI 10.22793/indinn.2024.40.3.030

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키워드

BusanSeoulcustomer satisfactionbusiness hotelbig data analytics

초록

This research aims to investigate factors influencing customer satisfaction in business hotels across two major South Korean cities, Busan and Seoul, using a combination of frequency analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) clustering, exploratory factor analysis, and linear regression. Analyzing 60 frequently mentioned words from customer reviews, key differences between the two cities were identified. In Busan, words such as “station,” “beach,” and “view” were prominent, indicating a preference for sightseeing and beach-related activities. Conversely, in Seoul, terms like “airport” and “business” were more frequent, suggesting a stronger focus on business travel. t-SNE clustering revealed four significant clusters for each city: Room, Location, Tour, and Service for Busan, and Accommodation, Proximity, Shopping, and Value for Seoul. Exploratory factor analysis identified four key factors: Experience, Proximity, Scenery, and Room for Busan, and Location, Mobility, Room, and Experience for Seoul. Linear regression analysis showed that Experience and Proximity positively influenced customer satisfaction in both cities, while Room quality had a negative impact. The findings underscore the importance of improving room conditions and service quality to enhance overall customer experience. These insights provide valuable guidance for hotel management to tailor their strategies according to the distinct needs of customers in Busan and Seoul.

Abstract

This research aims to investigate factors influencing customer satisfaction in business hotels across two major South Korean cities, Busan and Seoul, using a combination of frequency analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) clustering, exploratory factor analysis, and linear regression. Analyzing 60 frequently mentioned words from customer reviews, key differences between the two cities were identified. In Busan, words such as “station,” “beach,” and “view” were prominent, indicating a preference for sightseeing and beach-related activities. Conversely, in Seoul, terms like “airport” and “business” were more frequent, suggesting a stronger focus on business travel. t-SNE clustering revealed four significant clusters for each city: Room, Location, Tour, and Service for Busan, and Accommodation, Proximity, Shopping, and Value for Seoul. Exploratory factor analysis identified four key factors: Experience, Proximity, Scenery, and Room for Busan, and Location, Mobility, Room, and Experience for Seoul. Linear regression analysis showed that Experience and Proximity positively influenced customer satisfaction in both cities, while Room quality had a negative impact. The findings underscore the importance of improving room conditions and service quality to enhance overall customer experience. These insights provide valuable guidance for hotel management to tailor their strategies according to the distinct needs of customers in Busan and Seoul.

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