Information Systems Review (경영정보학연구)

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Developing a Web Care Model for Audiobook Platforms Using Machine Learning

머신러닝을 이용한 오디오북 플랫폼 기반의 웹케어 모형 구축에 관한 연구

  • Dahoon Jeong (Department of Economics, Pusan National University) ;
  • Minhyuk Lee (School of Business, Pusan National University) ;
  • Taewon Lee (BK21 Digital finance Education and Research Center, Pusan National University)
  • 정다훈 (부산대학교 경제학부) ;
  • 이민혁 (부산대학교 경영학과) ;
  • 이태원 (부산대학교 경영학과 BK21 교육연구단)
  • Received : 2023.11.27
  • Accepted : 2024.02.12
  • Published : 2024.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

본 연구는 소비자 리뷰와 관리자 답변 간의 관계를 조사하여 소비자 리뷰를 효율적으로 관리하기 위한 웹케어의 필요성을 탐색하는데 목적이 있다. 효과적인 웹케어를 위한 방법론을 제안하고 오디오북 플랫폼 기반의 머신러닝을 이용한 웹케어 모형을 구축하고자 한다. 본 연구에서는 오디오북 플랫폼 4개를 선정하여 소비자 리뷰와 관리자 답변에 대한 데이터 수집 및 전처리 과정을 거쳐 토픽모델링, 주제불일치성, DBSCAN을 활용하고, 다양한 머신러닝 기법을 적용하여 분석을 시행하였다. 실험 결과 관리자 답변의 군집화 및 소비자 리뷰에 대한 답변 예측에서 유의미한 결과를 도출하였으며, 자원의 제한과 비용을 고려한 효율적인 방법론을 제안하였다. 본 연구는 머신러닝을 통해 웹케어 모형을 구축했다는 점에서 학술적인 시사점을 제공하며, 기업의 제한된 비용과 인력을 고려하여 웹케어 모형이라는 효율적인 방법론을 제시함으로써 실무적인 시사점을 지닌다. 본 연구에서 제안된 웹케어 모델은 개별화된 답변과 표준화된 관리자 답변을 제공하여 소비자 참여 및 유용한 정보 제공을 위한 전략적인 기초 자료로 활용될 수 있을 것이다.

Keywords

References

  1. 김기륭, 전자카탈로그 자동 분류기에 대한 연구 (석사학위논문), 서울대학교, 2003.
  2. 성행남, 이태원, "오디오북 플랫폼 유료 사용자의 이용행태에 대한 연구: 지속이용의도 및 시장동향 분석을 중심으로", 인터넷전자상거래연구, 제22권, 제3호, 2022, pp. 115-135. https://doi.org/10.37272/JIECR.2022.06.22.3.115
  3. 우세웅, 오디오북 국내 사례와 출판시장의 인식 연구 (석사학위논문), 서울미디어대학원대학교, 2019.
  4. 이성직, 김한준, "Keyword Extraction from News Corpus using Modified TF-IDF", 한국전자거래학회지, 제14권, 제4호, 2009, pp. 59-73.
  5. 이윤아, 국내 오디오북 이용의 특성에 관한 연구: 이용행태, 동기, 만족도 및 재이용의사를 중심으로 (석사학위논문), 건국대학교, 2022.
  6. 이중원, 박철, "웹케어(Webcare)는 온라인 리뷰 유용성에 영향을 미치는가? 주제 일치성과 정보원 신뢰의 효과", 經營學硏究, 제51권, 제2호, 2022, pp. 437-460. https://doi.org/10.17287/kmr.2022.51.2.437
  7. 황단, 소비자의 부정적인 댓글에 대한 웹케어가 브랜드 평가와 참여 의도에 미치는 영향(석사학위논문), 중앙대학교, 2018.
  8. Breiman, L., "Random forests", Machine learning, Vol.45, 2001, pp. 5-32. https://doi.org/10.1023/A:1010933404324
  9. Chen, T. and C. Guestrin, "Xgboost: A scalable tree boosting system", In Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Data Mining, August 2016, pp. 785-794.
  10. Chen, W., B. Gu, Q. Ye, and K. X. Zhu, "Measuring and managing the externality of managerial responses to online customer reviews", Information Systems Research, Vol.30, No.1, 2019, pp. 81-96. https://doi.org/10.1287/isre.2018.0781
  11. Chen, Y. and J. Xie, "Online consumer review: Word-of-mouth as a new element of marketing communication mix", Management Science, Vol.54, No.3, 2008, pp. 477-491. https://doi.org/10.1287/mnsc.1070.0810
  12. Cortes, C. and V. Vapnik, "Support-vector networks", Machine Learning, Vol.20, 1995, pp. 273-297. https://doi.org/10.1007/BF00994018
  13. Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise", In kdd, Vol.96, No.34, August 1996, pp. 226-231.
  14. Fauzi, M. Z. and A. Abdullah, "Clustering of public opinion on natural disasters in Indonesia using DBSCAN and K-Medoids algorithms", In Journal of Physics: Conference Series, Vol.1783, No.1, 2021, p. 012016.
  15. Hagenauer, J. and M. Helbich, "A comparative study of machine learning classifiers for modeling travel mode choice", Expert Systems with Applications, Vol.78, 2017, pp. 273-282. https://doi.org/10.1016/j.eswa.2017.01.057
  16. Herhausen, D., S. Ludwig, D. Grewal, J. Wulf, and M. Schoegel, "Detecting, preventing, and mitigating online firestorms in brand communities", Journal of Marketing, Vol.83, No.3, 2019, pp. 1-21. https://doi.org/10.1177/0022242918822300
  17. Hong, J.-Y. and W.-N. Lee, "Consumer complaint behavior in the online environment", Web Systems Design and Online Consumer Behavior, New Jersey, 2005, pp. 90-105.
  18. Hu, Y.-H. and K. Chen, "Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings", International Journal of Information Management, Vol.36, No.6, 2016, pp. 929-944. https://doi.org/10.1016/j.ijinfomgt.2016.06.003
  19. Indah, R. N. G., R. Novita, O. B. Kharisma, R. Vebrianto, S. Sanjaya, T. Andriani, ... and R. Rahim, "DBSCAN algorithm: twitter text clustering of trend topic pilkada pekanbaru", In Journal of Physics: Conference Series, Vol.1363, No.1 November 2019, p. 012001.
  20. Jin, W., Y. Chen, S. Yang, S. Zhou, H. Jiang, and J. Wei, "Personalized managerial response and negative inconsistent review helpfulness: The mediating effect of perceived response helpfulness", Journal of Retailing and Consumer Services, Vol.74, No.103398, 2023.
  21. Kumar, N., L. Qiu, and S. Kumar, "Exit, voice, and response on digital platforms: An empirical investigation of online management response strategies", Information Systems Research, Vol.29, No.4, 2018, pp. 849-870. https://doi.org/10.1287/isre.2017.0749
  22. Lee, J., S. B. Park, and S. Lee, "Are negative online consumer reviews always bad? A two-sided message perspective", Asia Pacific Journal of Information Systems, Vol.25, No.4, 2015, pp. 784-804. https://doi.org/10.14329/apjis.2015.25.4.784
  23. Li, C., G. Cui, and L. Peng, "Tailoring management response to negative reviews: The effectiveness of accommodative versus defensive responses", Computers in Human Behavior, Vol 84, 2018, pp. 272-284. https://doi.org/10.1016/j.chb.2018.03.009
  24. Mohammed, S. M., K. Jacksi, and S. R. Zeebaree, "Glove word embedding and DBSCAN algorithms for semantic document clustering", In 2020 International Conference on Advanced Science and Engineering (ICOASE), December 2020, pp. 1-6.
  25. Newman, D., J. H. Lau, K. Grieser, and T. Baldwin, "Automatic evaluation of topic coherence", In Human language technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, June 2010, pp. 100-108.
  26. Proserpio, D. and G. Zervas, "Online reputation management: Estimating the impact of management responses on consumer reviews", Marketing Science, Vol.36, No.5, 2017, pp. 645-665. https://doi.org/10.1287/mksc.2017.1043
  27. Sparks, B. A., K. K. F. So, and G. L. Bradley, "Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern", Tourism Management, Vol.53, 2016, pp. 74-85. https://doi.org/10.1016/j.tourman.2015.09.011
  28. Van Noort, G. and L. M. Willemsen, "Online damage control: The effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms", Journal of Interactive Marketing, Vol.26, No.3, 2012, pp. 131-140. https://doi.org/10.1016/j.intmar.2011.07.001
  29. Wang, L., X. Ren, H. Wan, and J. Yan, "Managerial responses to online reviews under budget constraints: Whom to target and how", Information & Management, Vol.57, No.8, 2020, p. 103382.
  30. Wang, Y. and A. Chaudhry, "When and how managers' responses to online reviews affect subsequent reviews", Journal of Marketing Research, Vol.55, No.2, 2018, pp. 163-177. https://doi.org/10.1509/jmr.15.0511
  31. Wei, W., L. Miao, and Z. J. Huang, "Customer engagement behaviors and hotel responses", International Journal of Hospitality Management, Vol 33, 2013, pp. 316-330. https://doi.org/10.1016/j.ijhm.2012.10.002
  32. Xie, K. L., K. K. F. So, and W. Wang, "Joint effects of management responses and online reviews on hotel financial performance: A data-analytics approach", International Journal of Hospitality Management, Vol.62, 2017, pp. 101-110. https://doi.org/10.1016/j.ijhm.2016.12.004
  33. Zhang, X., S. Qiao, Y. Yang, and Z. Zhang, "Exploring the impact of personalized management responses on tourists' satisfaction: A topic matching perspective", Tourism Management, Vol 76, 2020, p. 103953.
  34. Zhang, Y. and C. Vasquez, "Hotels' responses to online reviews: Managing consumer dis- satisfaction", Discourse, Context & Media, Vol.6, 2014, pp. 54-64. https://doi.org/10.1016/j.dcm.2014.08.004
  35. Zhang, Z., H. Li, F. Meng, and Y. Li, "The effect of management response similarity on online hotel booking: Field evidence from Expedia", International Journal of Contemporary Hospitality Management, Vol.31, No.7, 2019, pp. 2739-2758. https://doi.org/10.1108/IJCHM-09-2018-0740