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Korean Academic Society of Business Administration
[ Article ]
korean management review - Vol. 54, No. 5, pp.1437-1455
ISSN: 1226-1874 (Print)
Print publication date 31 Oct 2025
Received 15 Jul 2025 Revised 28 Aug 2025 Accepted 25 Sep 2025
DOI: https://doi.org/10.17287/kmr.2025.54.5.1437

레스토랑 예약 앱 사용자 리뷰의 시계열적 감성 및 주제 반응 탐색: 정교화 가능성 모델 관점에서의 실증 분석

이선녕 ; 윤상혁
(주저자) 동국대학교 경영대학 경영학과 강의전담 교수
(교신저자) 동국대학교 경영대학 경영정보학과 조교수
Temporal Analysis of Sentiment and Topic Responses in Restaurant Reservation App User Reviews: An Empirical Study from the Perspective of the Elaboration Likelihood Model
Sunnyoung Lee ; Sang-Hyeak Yoon
(First Author) Department of Business Administration, College of Business, Dongguk University enhasu19@dongguk.edu
(Corresponding Author) Department of Management Information System, College of Business, Dongguk University yoonsh@dgu.ac.kr


Copyright 2025 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

초록

디지털 플랫폼의 확산에 따라 소비자 리뷰는 사용자의 경험, 감정, 기능 안정성 등을 포괄적으로 반영하며, 서비스 품질 및 브랜드 신뢰 형성에 핵심적인 역할을 수행하고 있다. 특히 레스토랑 예약 앱은 온라인상에서의 리뷰가 오프라인 행동으로 직접 연결된다는 점에서 높은 학술적·실무적 가치를 지닌다. 그러나 기존 연구는 별점이나 리뷰 길이 등 구조적 정보 또는 특정 시점에 한정된 분석에 치우쳐, 리뷰 메시지의 시간적 변화와 감성 흐름을 충분히 설명하지 못하는 한계가 있다. 이에 본 연구는 정교화 가능성 모델(Elaboration Likelihood Model; ELM)에 기반하여, 기능 중심 메시지를 중심 경로, 감성적 표현을 주변 경로로 정의하고 이들의 시계열적 구조 변화를 동태적으로 분석하였다. 2009년부터 2023년까지 수집된 OpenTable 앱 리뷰 46,392건을 대상으로 LDA 토픽모델링, 감성 분석, 공출현 네트워크 분석을 통합적으로 적용하였으며, 감성 점수, 리뷰 길이, 빈도 간 상관관계를 분석하고, 초기-중기-후기 시기로 구분하여 변화 양상을 비교하였다. 분석 결과, 중심 경로는 기능 및 정보 평가 위주로, 주변 경로는 긍정 감성 중심으로 구성되는 경향이 뚜렷하게 나타났다. 그러나 후기기에는 기능적 불만이 주변 경로를 통해 부정 감정으로 확산되는 양상도 나타나, 기존 이론에서 간과된 감성 중심 경로의 새로운 역할 가능성을 시사하였다. 본 연구는 시계열 기반의 리뷰 분석을 통해 설득 메시지의 구조적 진화 과정을 실증적으로 규명하였으며, 앱 전략 수립 및 실무적 의사결정에 유의미한 인사이트를 제공한다.

Abstract

With the proliferation of digital platforms, consumer reviews have come to play a pivotal role in shaping service quality and brand trust, reflecting users’ experiences, emotions, and perceptions of functional stability. In particular, restaurant reservation apps are of significant academic and practical value, as online reviews are directly linked to offline consumer behaviors. However, previous studies have predominantly focused on structural information such as star ratings or review length, or have been limited to analyses at specific points in time, thus failing to sufficiently explain the temporal evolution and affective dynamics of review messages. To address this gap, the present study adopts the Elaboration Likelihood Model (ELM) to define functionality-oriented messages as central route and affective expressions as peripheral route, and dynamically analyzes their temporal structural changes. Integrating LDA topic modeling, sentiment analysis, and co-occurrence network analysis, we examine 46,392 OpenTable app reviews collected from 2009 to 2023. We analyze the correlations among sentiment scores, review length, and review frequency, and compare changes across three periods: early, middle, and late stages. The results show that central route reviews are primarily composed of functional and informational evaluations, whereas peripheral route reviews are characterized by positive affective content. Notably, in the late period, functional dissatisfaction tends to spread as negative sentiment through the peripheral route, suggesting a new potential role for affective pathways that was overlooked in prior theories. By conducting a time-series analysis of user reviews, this study empirically demonstrates the structural evolution of persuasive messages and offers meaningful insights for application strategy development and practical decision-making.

Keywords:

Text mining, Topic modeling, Sentiment analysis, Network analysis, Mobile app reviews, Restaurant reservation app, Elaboration Likelihood Model

키워드:

텍스트 마이닝, 토픽 모델링, 감성 분석, 네트워크 분석, 모바일 앱 리뷰, 레스토랑 예약 앱, 정교화 가능성 모델

References

  • 김남은, 노미진, 김양석 (2023). “모바일 앱 업데이트에 따른 사용자 반응에 관한 연구: 모바일거래시스템 리뷰 분석을 중심으로,” 글로벌경영학회지, 제20권 6호, pp.62-81.
    Kim, N. E., Roh, M. J., and Kim, Y. S. (2023), “A Study on User Reactions to Mobile App Updates: Focusing on Mobile Trading System Review Analysis,” Journal of Global Business Management, 20(6), pp.62-81. [ https://doi.org/10.38115/asgba.2023.20.6.62 ]
  • 김문기 (2023). “모바일 서비스 산업의 이용자 만족·불만 요인 탐색에 관한 연구: 모바일 앱 리뷰 분석을 중심으로,” 문화산업연구, 제23권 1호, pp.47-54.
    Kim, M. G. (2023), “Exploring Factors of User Satisfaction and Dissatisfaction in the Mobile Service Industry: Focusing on Mobile App Review Analysis,” Journal of Cultural Industry Studies, 23(1), pp.47-54. [ https://doi.org/10.35174/JKCI.2023.03.23.1.47 ]
  • 김성준, 김보영 (2022). “삼성 이병철 회장의 경영철학: 구조적 토픽모델을 활용한 탐색적 연구,” 경영학연구, 제51권 2호, pp.331-368.
    Kim, S. J. and Kim, B. Y. (2022), “Business Philosophy of Samsung Chairman Lee Byung-Chul: An Exploratory Study Using Structural Topic Modeling,” Korea Business Review, 51(2), pp.331-368. [ https://doi.org/10.17287/kmr.2022.51.2.331 ]
  • 김인규, 차성수 (2020). “온라인 구전과 마케팅 성과의 다이나믹스 연구: 모바일 게임 앱 리뷰를 중심으로,” 한국콘텐츠학회논문지, 제20권 12호, pp.36-48.
    Kim, I. K. and Cha, S. S. (2020), “Dynamics of Online Word-of-Mouth and Marketing Performance: Focusing on Mobile Game App Reviews,” Journal of the Korea Contents Association, 20(12), pp.36-48.
  • 김지영, 후완서, 간형식 (2020). “온라인 리뷰의 힘: 온라인 리뷰 유형에 따른 소비자 평가,” 상품학연구, 제38권 4호, pp.21-30.
    Kim, J. Y., Hoo, W. S., and Kan, H. S. (2020), “The Power of Online Reviews: Consumer Evaluations by Review Type,” Journal of Product Research, 38(4), pp.21-30.
  • 김지현, 이흠철, 장동수, 김재경 (2024). “온라인 리뷰의 다차원 감정을 활용한 딥러닝 기반 추천 시스템 연구,” 한국경영과학회지, 제49권 1호, pp.1-17.
    Kim, J. H., Lee, H. C., Jang, D. S., and Kim, J. K. (2024), “Deep-Learning-Based Recommendation System Using Multidimensional Emotions of Online Reviews,” Journal of the Korean Operations Research and Management Science Society, 49(1), pp.1-17. [ https://doi.org/10.7737/JKORMS.2024.49.1.001 ]
  • 노한남, 양홍석 (2025). “Strategies to Enhance Market Competitiveness of Korea’s Mobile Virtual Network Operators,” 경영학연구, 제54권 2호, pp.255-279.
    Roh, H. N. and Yang, H. S. (2025), “Strategies to Enhance Market Competitiveness of Korea’s Mobile Virtual Network Operators,” Korea Business Review, 54(2), pp.255-279. [ https://doi.org/10.17287/kmr.2025.54.2.255 ]
  • 박상준, 왕서경, 이영란 (2019). “온라인 리뷰 평점의 방향성과 분포의 모드수와 범위가 구매의도에 미치는 영향,” 경영과학, 제36권 2호, pp.39-51.
    Park, S. J., Wang, S. K., and Lee, Y. R. (2019), “Effects of the Direction, Mode Number, and Range of Online Review Ratings on Purchase Intention,” Korean Management Science Review, 36(2), pp.39-51. [ https://doi.org/10.7737/KMSR.2019.36.2.039 ]
  • 박지영, 홍태호 (2021). “레스토랑의 온라인 리뷰를 통해 감성과 감정이 리뷰 유용성에 미치는 영향에 관한 연구,” 지식경영연구, 제22권 1호, pp.243-267.
    Park, J. Y. and Hong, T. H. (2021), “The Impact of Sentiment and Emotion in Restaurant Online Reviews on Review Usefulness,” Knowledge Management Research, 22(1), pp.243-267.
  • 이승필, 박은일, 류두진 (2025). “설명가능한 기계학습을 이용한 베스트셀러 예측과 영향요인 분석,” 경영학연구, 제54권 1호, pp.81-108.
    Lee, S. P., Park, E. I., and Ryu, D. J. (2025), “Bestseller Prediction and Factor Analysis Using Explainable Machine Learning,” Korea Business Review, 54(1), pp.81-108. [ https://doi.org/10.17287/kmr.2025.54.1.81 ]
  • 이유재 (2016). “서비스품질, 고객만족, 고객충성도, 로열티프로그램 연구에 대한 종합적 고찰과 향후 연구방향,” 경영학연구, 제45권 6호, pp.1763-1787.
    Lee, Y. J. (2016), “A Comprehensive Review of Research on Service Quality, Customer Satisfaction, Customer Loyalty, and Loyalty Programs: Future Research Directions,” Korea Business Review, 45(6), pp.1763-1787. [ https://doi.org/10.17287/kmr.2016.45.6.1763 ]
  • Al-Natour, S., and Turetken, O. (2020). “A comparative assessment of sentiment analysis and star ratings for consumer reviews,” International Journal of Information Management, 54, 102132. [https://doi.org/10.1016/j.ijinfomgt.2020.102132]
  • Alzate, M., Arce-Urriza, M., and Cebollada, J. (2022). “Mining the text of online consumer reviews to analyze brand image and brand positioning,” Journal of Retailing and Consumer Services, 67, 102989. [https://doi.org/10.1016/j.jretconser.2022.102989]
  • Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., and Schweidel, D. A. (2020). “Uniting the tribes: Using text for marketing insight,” Journal of Marketing, 84(1), pp.1-25. [https://doi.org/10.1177/0022242919873106]
  • Bird, S., Klein, E., and Loper, E. (2009). “Natural language processing with Python: analyzing text with the natural language toolkit,” O'Reilly Media.
  • Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). “Latent dirichlet allocation,” Journal of Machine Learning Research, 3, pp.993-1022.
  • Businessresearchinsight (2025). “Online Restaurant Reservation System Market Size, Share, Growth, and Industry Analysis,” https://www.businessresearchinsights.com/market-reports/online-restaurant-reservation-system-market-117586, , retrieved July 2025.
  • Chaiken, S., and Trope, Y. (Eds.). (1999). “Dual-process theories in social psychology,” Guilford Press.
  • Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B., and Thomas, C. D. (2011). “Rapid range shifts of species associated with high levels of climate warming,” Science, 333(6045), pp.1024-1026. [https://doi.org/10.1126/science.1206432]
  • Cheung, C. M., Lee, M. K., and Rabjohn, N. (2008). “The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities,” Internet Research, 18(3), pp.229-247. [https://doi.org/10.1108/10662240810883290]
  • Chevalier, J. A., and Mayzlin, D. (2006). “The effect of word of mouth on sales: Online book reviews,” Journal of Marketing Research, 43(3), pp.345-354. [https://doi.org/10.1509/jmkr.43.3.345]
  • Cui, G., Lui, H.-K., and Guo, X. (2012). “The effect of online consumer reviews on new product sales,” International Journal of Electronic Commerce, 17(1), pp.39-58. [https://doi.org/10.2753/JEC1086-4415170102]
  • Filieri, R. (2016). “What makes an online consumer review trustworthy?,” Annals of Tourism Research, 58, pp.46-64. [https://doi.org/10.1016/j.annals.2015.12.019]
  • Flanagin, A. J., and Metzger, M. J. (2013). “Trusting expert-versus user-generated ratings online: The role of information volume, valence, and consumer characteristics,” Computers in Human Behavior, 29(4), pp.1626-1634. [https://doi.org/10.1016/j.chb.2013.02.001]
  • Guo, X., Barnes, S. J., and Jia, Q. (2017). “Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent Dirichlet allocation,” Tourism Management, 59, pp.467-483. [https://doi.org/10.1016/j.tourman.2016.09.009]
  • Hartmann, J., Heitmann, M., Siebert, C., and Schamp, C. (2023). “More than a feeling: Accuracy and application of sentiment analysis,” International Journal of Research in Marketing, 40(1), pp.75-87. [https://doi.org/10.1016/j.ijresmar.2022.05.005]
  • Hartmann, J., Huppertz, J., Schamp, C., and Heitmann, M. (2023). “Comparing automated text classification methods,” International Journal of Research in Marketing, 40(1), pp.87-104.
  • Hu, M., and Liu, B. (2004). “Mining and summarizing customer reviews,” Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.168-177. [https://doi.org/10.1145/1014052.1014073]
  • Hu, N., Pavlou, P. A., and Zhang, J. (2012). “Overcoming the J-shaped distribution of product reviews,” Communications of the ACM, 55(10), pp.144-152. [https://doi.org/10.1145/1562764.1562800]
  • Huang, J., Zeng, D. D., and Chen, H. (2014). “Analyzing consumer-product graphs: Empirical findings and applications in recommender systems,” Management Science, 60(7), pp.1433-1451.
  • Humphreys, A., and Wang, R. J. H. (2018). “Automated text analysis for consumer research,” Journal of Consumer Research, 44(6), pp.1274-1306. [https://doi.org/10.1093/jcr/ucx104]
  • Kumar, V., Rajan, B., Venkatesan, R., and Lecinski, J. (2021). “Understanding the role of artificial intelligence in personalized engagement marketing,” Journal of Marketing, 85(1), pp.137-155.
  • Leoni, V., and Boto-García, D. (2023), “Apparent and actual hotel scores under Booking. com new reviewing system,” International Journal of Hospitality Management, 111, 103493. [https://doi.org/10.1016/j.ijhm.2023.103493]
  • Liu, B., Hu, M., and Cheng, J. (2021). “Opinion Observer: Analyzing and comparing opinions on the Web,” Proceedings of the 14th International Conference on World Wide Web, pp.342-351. [https://doi.org/10.1145/1060745.1060797]
  • Luo, Y., and Xu, X. (2021). “Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic,” International Journal of Hospitality Management, 94, 102849. [https://doi.org/10.1016/j.ijhm.2020.102849]
  • Mariani, M. M., and Borghi, M. (2018), “Effects of the Booking. com rating system: Bringing hotel class into the picture,” Tourism Management, 66, 47-52. [https://doi.org/10.1016/j.tourman.2017.11.006]
  • Medhat, W., Hassan, A., and Korashy, H. (2014). “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, 5(4), pp.1093-1113. [https://doi.org/10.1016/j.asej.2014.04.011]
  • Mudambi, S. M., and Schuff, D. (2010). “Research note: What makes a helpful online review? A study of customer reviews on Amazon. com,” MIS Quarterly, 34(1), pp.185-200. [https://doi.org/10.2307/20721420]
  • Mudambi, S. M., and Schuff, D. (2010). “What makes a helpful online review? A study of customer reviews on Amazon.com,” MIS Quarterly, 34(1), pp.185-200. [https://doi.org/10.2307/20721420]
  • Mustak, M., Salminen, J., Plé, L., and Wirtz, J. (2021). “Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda,” Journal of Business Research, 124, pp.389-404. [https://doi.org/10.1016/j.jbusres.2020.10.044]
  • OpenTable (2025). “Our story,” https://www.opentable.com/about/, , retrieved July 2025.
  • Park, D. H., and Kim, S. (2008). “The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews,” Electronic Commerce Research and Applications, 7(4), pp.399-410. [https://doi.org/10.1016/j.elerap.2007.12.001]
  • Petty, R. E., and Brinol, P. (2014). “The elaboration likelihood and metacognitive models of attitudes.” Dual-process theories of the social mind, 172-187.
  • Petty, R. E., and Cacioppo, J. T. (1986). “The elaboration likelihood model of persuasion,” Advances in Experimental Social Psychology, 19, pp.123-205. [https://doi.org/10.1016/S0065-2601(08)60214-2]
  • Petty, R. E., and Wegener, D. T. (1998). “Attitude change: Multiple roles for persuasion variables,” In D. T. Gilbert, S. T. Fiske, and G. Lindzey (Eds.), The handbook of social psychology (4th ed.), pp.323-390, McGraw-Hill.
  • Sen, S., and Lerman, D. (2007). “Why are you telling me this? An examination into negative consumer reviews on the Web,” Journal of Interactive Marketing, 21(4), pp.76-94. [https://doi.org/10.1002/dir.20090]
  • Shorbaji, M. F., Alalwan, A. A., and Algharabat, R. (2025), “AI-Enabled Mobile Food-Ordering Apps and Customer Experience: A Systematic Review and Future Research Agenda,” Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 156. [https://doi.org/10.3390/jtaer20030156]
  • Tirunillai, S., and Tellis, G. J. (2014). “Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation,” Journal of Marketing Research, 51(4), pp.463-479. [https://doi.org/10.1509/jmr.12.0106]
  • Tseng, S. M., and Hori, N. (2025), “What You See Isn’t Always What You Get: Investigating the Impact of the Information Disclosure Gap in Online Travel Agencies,” Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 167. [https://doi.org/10.3390/jtaer20030167]
  • Van Eck, N. J., and Waltman, L. (2017). “Citation-based clustering of publications using Cit NetExplorer and VOSviewer,” Scientometrics, 111, pp.1053-1070. [https://doi.org/10.1007/s11192-017-2300-7]
  • Waltman, L., and Van Eck, N. J. (2013). “A smart local moving algorithm for large-scale modularity-based community detection,” The European Physical Journal, 86, pp.1-14. [https://doi.org/10.1140/epjb/e2013-40829-0]
  • Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y., and Freiheit, T. (2021). “Smart manufacturing and intelligent manufacturing: A comparative review,” Engineering, 7(6), pp.738-757. [https://doi.org/10.1016/j.eng.2020.07.017]
  • Zhu, F., and Zhang, X. (2010). “Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics,” Journal of Marketing, 74(2), pp.133-148. [https://doi.org/10.1509/jm.74.2.133]

∙저자 이선녕은 동국대학교 경영대학 경영학과 강의전담 교수로, 뉴욕시립대학교 방문·연구교수(2014-2015)와 성균관대학교 초빙교수(2018-2023)를 역임하였다. 주요 연구는 AI마케팅, 소비자 행동 분석, 마케팅 전략, 설득 커뮤니케이션으로, 텍스트 마이닝을 중심으로 데이터 기반 마케팅 전략을 탐구한다.

∙저자 윤상혁은 연세대학교 정보대학원에서 박사 학위를 취득한 후, 한국기술교육대학교 산업경영학부를 거쳐, 현재 동국대학교 경영대학 경영정보학과 산업경영학부 조교수로 재직 중이다. 50편 이상의 논문과 다수의 저서를 발표하였으며, 주요 연구분야는 생성형 인공지능, 디지털 마케팅, 비즈니스애널리틱스 등이다.