Identification of Key Service Features for Evaluating the Quality of Metaverse Services: A Text Mining Approach
- 주제(키워드) 도움말 Metaverse , service quality , text mining , sentiment analysis , topic modeling
- 주제(기타) Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
- 설명문(일반) [Kim, Minjun] Kumoh Natl Inst Technol, Sch Ind Engn, Gumi 39177, South Korea; [Yoo, Ha-Yeon] Ewha Womans Univ, Coll Art & Design, Seoul 03760, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2024
- 세부유형 Article
- 총서유형 Journal
- 실제URL http://www.dcollection.net/handler/ewha/000000213866
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2024.3352008
초록/요약 도움말
Recent advances in the metaverse have revolutionized the way services are experienced, creating a virtual world that seamlessly blends real-life and digital experiences. While research on metaverse services has traditionally focused on technological advancements, recent efforts emphasize the need for a customer-oriented approach to evaluating service quality. However, few studies have explored this customer-oriented approach. To address this gap, this paper identifies and prioritizes nine service features that significantly influence customer satisfaction in metaverse services from a customer-oriented perspective. In particular, this study analyzed 437,527 online customer reviews of Roblox, Bitmoji, and VRchat by employing text mining and machine learning algorithms, such as topic modeling, sentiment analysis, and logistic regression. As a result, the 'co-experience' feature emerges as a crucial factor, closely aligned with user objectives when engaging with metaverse services. These findings provide valuable insights for service managers to enhance their offerings effectively, positioning them favorably in the evolving metaverse landscape.
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