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Customer Attitude to Artificial Intelligence Features: Exploratory Study on Customer Reviews of AI Speakers

인공지능 속성에 대한 고객 태도 변화: AI 스피커 고객 리뷰 분석을 통한 탐색적 연구

  • 이홍주 (가톨릭대학교 경영학부)
  • Received : 2019.01.22
  • Accepted : 2019.05.15
  • Published : 2019.06.30

Abstract

AI speakers which are wireless speakers with smart features have released from many manufacturers and adopted by many customers. Though smart features including voice recognition, controlling connected devices and providing information are embedded in many mobile phones, AI speakers are sitting in home and has a role of the central en-tertainment and information provider. Many surveys have investigated the important factors to adopt AI speakers and influ-encing factors on satisfaction. Though most surveys on AI speakers are cross sectional, we can track customer attitude toward AI speakers longitudinally by analyzing customer reviews on AI speakers. However, there is not much research on the change of customer attitude toward AI speaker. Therefore, in this study, we try to grasp how the attitude of AI speaker changes with time by applying text mining-based analysis. We collected the customer reviews on Amazon Echo which has the highest share of AI speakers in the global market from Amazon.com. Since Amazon Echo already have two generations, we can analyze the characteristics of reviews and compare the attitude ac-cording to the adoption time. We identified all sub topics of customer reviews and specified the topics for smart features. And we analyzed how the share of topics varied with time and analyzed diverse meta data for comparisons. The proportions of the topics for general satisfaction and satisfaction on music were increasing while the proportions of the topics for music quality, speakers and wireless speakers were decreasing over time. Though the proportions of topics for smart fea-tures were similar according to time, the share of the topics in positive reviews and importance metrics were reduced in the 2nd generation of Amazon Echo. Even though smart features were mentioned similarly in the reviews, the influential effect on satisfac-tion were reduced over time and especially in the 2nd generation of Amazon Echo.

Keywords

References

  1. 김정훈, 송영은, 진윤선, 권오병 2015. "텍스트마이닝을 통한 댓글의 공감도 및 비공감도에 영향 을 미치는 댓글의 특성 연구," 한국IT서비스학회지 (14:2), pp. 159-176. https://doi.org/10.9716/KITS.2015.14.2.159
  2. 박명석, 권영진, 이상용 2018. "댓글이 음원 판매량에 미치는 차별적 영향에 관한 텍스트마이닝분석," 지식경영연구 (19:2), pp. 91-108. https://doi.org/10.15813/kmr.2018.19.2.005
  3. 연합뉴스 2018. "한국 A I스피커 시장 급성장…올해 세계 톱 5 전망", 2018/07/12, http://www.yonhapnews.co.kr/bulletin/2018/07/11/0200000000AKR20180711151200017.HTML?sns=copy
  4. 이홍주 2018a. "A Ghost in t he Shell? 고객 리뷰를 통한 스마트 스피커의 인공지능 속성이 평 가에 미치는 영향 연구," 한국IT서비스학회지 (17:2), pp. 191-205. https://doi.org/10.9716/KITS.2018.17.2.191
  5. 이홍주 2018b. "헬스케어 서비스 리뷰를 활용한 서비스 품질 차원 별 중요 단어 파악 방안," 지식경영연구 (19:4), pp. 171-185. https://doi.org/10.15813/kmr.2018.19.4.010
  6. 컨슈머인사이트 2018. "뜨거운 AI스피커 시장, 차가운 소비자 평가", 이동통신리포트, 2018/07/09,https://www.consumerinsight.co.kr/voc_view.aspx?no=2924&id=ins02_list&PageNo=1&schFlag=0
  7. Archak, N., Ghose, A., and Ipeirotis, P. G. 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science (57:8), pp. 1485-1509. https://doi.org/10.1287/mnsc.1110.1370
  8. Arun, R., Suresh, V., Veni Madhavan, C. E., and Narasimha Murthy, M. N. 2010. "On finding the natural number of topics with latent dirichlet allocation: Some observations," In Advances in knowledge discovery and data mining, Mohammed J . Zaki, Jeffrey Xu Yu, Balaraman Ravindran and Vikram Pudi (eds.). Springer Berlin Heidelberg, 391-402. http://doi.org/10.1007/978-3-642-13657-3_43
  9. Blei, D. M. 2012. "Probabilistic Topic Models," Communications of the ACM ( 55:4), pp. 77-84. https://doi.org/10.1145/2133806.2133826
  10. Buschken, J., and Allenby, G. M. 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science (35:6), pp. 953-975. https://doi.org/10.1287/mksc.2016.0993
  11. Cao, Q., Duan, W., and Gan, Q. 2011. "Exploring determinants o f voting for t he 'helpfulness' of online user reviews: A text mining approach," Decision Support Systems (50:2), pp. 511-521. https://doi.org/10.1016/j.dss.2010.11.009
  12. Chen, K., Kou, G., Shang, J., and Chen, Y. 2015. "Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches," Electronic Commerce Research and Applications (14:1), pp. 58-74. https://doi.org/10.1016/j.elerap.2014.11.004
  13. Deveaud, R., Juan, E. S., and Bellot, P. 2014. "Accurate and effective latent concept modeling for adhoc information retrieval," Document numerique (17:1), pp. 61-84. http://doi.org/10.3166/dn.17.1.61-84
  14. Ghose, A., Ipeirotis, P. G., and Li, B. 2012. "Design Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science (31:3), pp. 493-520. https://doi.org/10.1287/mksc.1110.0700
  15. Griffiths, T. L., and Steyvers. M. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences (101: suppl 1), pp. 5228-5235. http://doi.org/10.1073/pnas.0307752101
  16. Hu, N., Pavlou, P. A., and Zhang, J. 2009. "Overcoming the J-shaped Distribution of Product Reviews," Communications of the ACM (52:10), pp. 144-147. https://doi.org/10.1145/1562764.1562800
  17. Humphreys, A., and Wang, R. J. 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research (44: 6), pp. 1274-1306. https://doi.org/10.1093/jcr/ucx104
  18. Juan, C., Tian, X., Jintao, L., Yongdong, Z . , and Sheng, T. 2008. "A density based method for adaptive lDA model selection." Neurocomputing -16th European Symposium on Artificial Neural Networks (72:7-9), pp. 1775-1781. http://doi.org/10.1016/j.neucom.2008.06.011
  19. Kabir, K. 2018. "Smart speakers - everything you need to know," What Hi-Fi?, Available at https://www.whathifi.com/advice/smartspeakers-everything-you-need-to-know (Accessed February 14, 2018)
  20. Lee, H. J., Lee, M., and Lee, H. 2017. "Compilation of Tweets Sentiment into SERVQUAL for Tracking Social Perception on Public Service," The 19th International Conference on Electronic Commerce, 2017, Pangyo, Korea.
  21. Lee, T. Y., and Brad low, E. T., 2011. "Automated marketing research using online customer reviews," Journal of Marketing Research (48:5), pp. 881-894. https://doi.org/10.1509/jmkr.48.5.881
  22. 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
  23. Murzintcev, N. 2016. "ldatuning," R package. https://cran.r-project.org/web/packages/ldatuning/ldatuning.pdf
  24. Netzer, O., Feldman, R., Goldenberg, J., and Fresko, M. 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science (31:3), pp. 521-543. https://doi.org/10.1287/mksc.1120.0713
  25. Palese, B., and Usai, A. 2018. "The relative importance of service quality dimensions of E-commerce experiences," International Journal of Information Management (40), pp. 132-140.
  26. Ramage, D., Rosen, E., Chuang, J., Manning, C. D., and McFarland, D. A. 2009. Topic Modeling for the Social Sciences. Presented at the Neural Information Processing Systems (NIPS) Workshop on Applications for Topic Models: Text and Beyond, Whistler, Canada.
  27. Scott, M., and Bondi, M. 2010. Keyness in Texts. Amsterdam, Philadelphia: John Benjamins, pp. 21-42.

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