지식경영연구 (Knowledge Management Research)

한국지식경영학회 (The Knowledge Management Society of Korea)
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Airbnb 숙소 유형에 따른 호스트의 자기소개 텍스트가 공유성과에 미치는 영향

Impact of Self-Presentation Text of Airbnb Hosts on Listing Performance by Facility Type

  • 투고 : 2020.10.24
  • 심사 : 2020.11.29
  • 발행 : 2020.12.31
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초록

최근 빠르게 성장하고 있는 숙박 공유경제 시장에서 품질에 대한 불확실성은 사용자의 만족도에 영향을 미치는 위험요소지만, 이는 시설 제공자가 공개하는 정보를 통해 완화될 수 있다. 그 중 시설 제공자의 본인에 대한 자기소개는 사용자와의 정서적 교류를 통해 심리적 거리를 제거함으로써 공유 성과에 긍정적 영향을 미친다. 본 연구는 대표적인 숙박공유경제 플랫폼인 Airbnb에서 호스트의 자기소개가 포함하는 정보의 종류에 따라 공유성과에 미치는 영향을 분석하고, Airbnb의 숙소 유형에 따라 차이를 분석하였다. 이를 위해 호스트가 공개하는 자기소개 텍스트를 문장별로 분리하고 비지도 학습기반의 딥러닝 방법인 Attention-Based Aspect Extraction 방법을 활용하여 각 문장이 포함하는 의미를 추출하였다. 추출된 의미를 토대로 자기소개 텍스트가 포함하는 의미가 공유성과에 미치는 영향과 숙소 유형에 따른 교호작용 효과를 분석하였다. 연구결과, 숙소 유형별로 호스트의 특정 성향이 공유성과에 긍정적인 영향을 미치는 것을 확인하였고, 이를 통해 숙소 유형에 따라 공유성과를 극대화하기 위한 마케팅 전략에 대한 실증적인 함의를 제공한다.

In accommodation sharing economy, customers take a risk of uncertainty about product quality, which is an important factor affecting users' satisfaction. This risk can be lowered by the information disclosed by the facility provider. Self-presentation of the hosts can make a positive effect on listing performance by eliminating psychological distance through emotional interaction with users. This paper analyzed the self-presentation text provided by Airbnb hosts and found key aspects in the text. In order to extract the aspects from the text, host descriptions were separated into sentences and applied the Attention-Based Aspect Extraction method, an unsupervised neural attention model. Then, we investigated the relationship between aspects in the host description and the listing performance via linear regression models. In order to compare their impact between the three facility types(Entire home/apt, Private rooms, and Shared rooms), the interaction effects between the facility types and the aspect summaries were included in the model. We found that specific aspects had positive effects on the performance for each facility type, and provided implication on the marketing strategy to maximize the performance of the shared economy.

키워드

과제정보

이 논문은 한국연구재단 연구비(NRF-2016R1C1B1010940)와 산림청(한국임업진흥원) 산림과학기술 연구개발사업(2019150B10-1923-0301)에 의해 수행되었다

참고문헌

  1. 김연미, 한진수 (2011). 호텔 웹 광고의 광고속성이 광고태도와 브랜드태도, 구매의도에 미치는 영향. 지식경영연구, 12(1), 1-16. https://doi.org/10.15813/KMR.2011.12.1.001
  2. 이경민, 배채윤, 정남호 (2018). 4차 산업혁명 시대의 공유경제생태계 정책 제안: 우버 (Uber) 사례를 중심으로. 지식경영연구, 19(1), 175-202. https://doi.org/10.15813/kmr.2018.19.1.010
  3. 임현아, 최재원, 이홍주 (2019). 텍스트 분석을 통한 제품 분류체계 수립방안: 관광분야 App 을 중심으로. 지식경영연구, 20(3), 139-154. https://doi.org/10.15813/kmr.2019.20.3.009
  4. Abrate, G., & Viglia, G. (2019). Personal or product reputation? Optimizing revenues in the sharing economy. Journal of Travel Research, 58(1), 136-148. https://doi.org/10.1177/0047287517741998
  5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning research, 3(5), 993-1022.
  6. Botsman, R., & Rogers, R. (2010). What's mine is yours-How collaborative consumption is changing the way we live. London: Collins.
  7. Chang, K. T. T., Chen, W., & Tan, B. C. Y. (2012). Advertising effectiveness in social networking sites: Social ties, expertise, and product type. IEEE Transactions on Engineering Management, 59(4), 634-643. https://doi.org/10.1109/TEM.2011.2177665
  8. Chang, T., Mills, G., & Wildt, A. R. (1996). Impact of product information on the use of price as a quality cue. Psychology and Marketing, 13(1), 55-75. https://doi.org/10.1002/(SICI)1520-6793(199601)13:1<55::AID-MAR4>3.0.CO;2-O
  9. Cheng, M., & Foley, C. (2018). The sharing economy and digital discrimination: The case of Airbnb. International Journal of Hospitality Management, 70, 95-98. https://doi.org/10.1016/j.ijhm.2017.11.002
  10. Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58-70. https://doi.org/10.1016/j.ijhm.2018.04.004
  11. Ellison, N. B., Hancock, J. T., & Toma, C. L. (2012). Profile as promise: A framework for conceptualizing veracity in online dating self-presentations. New Media and Society, 14(1), 45-62. https://doi.org/10.1177/1461444811410395
  12. Ellison, N., Heino, R., & Gibbs, J. (2006). Managing impressions online: Self-presentation processes in the online dating environment. Journal of Computer-Mediated Communication, 11(2), 415-441.
  13. Garcia, M. N., Munoz-Gallego, P. A., Viglia, G., & Gonzalez-Benito, O. (2019). Be social! The impact of self-presentation on peer-to-peer accommodation revenue. Journal of Travel Research, 59, 1268-1281.
  14. Giuffrida, M., Mangiaracina, R., Perego, A., & Tumino, A. (2017). Cross-border B2C e-commerce to Greater China and the role of logistics: A literature review. International Journal of Physical Distribution and Logistics Management, 47(9), 772-795. https://doi.org/10.1108/IJPDLM-08-2016-0241
  15. Goffman, E. (1978). The presentation of self in everyday life. London: Harmondsworth.
  16. Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  17. Guttentag, D., Smith, S., Potwarka, L., & Havitz, M. (2018). Why tourists choose Airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3), 342-359. https://doi.org/10.1177/0047287517696980
  18. Hassanein, K., & Head, M. (2005). The impact of infusing social presence in the web interface: An investigation across product types. International Journal of Electronic Commerce, 10(2), 31-55. https://doi.org/10.2753/JEC1086-4415100202
  19. He, R., Lee, W. S., Ng, H. T., & Dahlmeier, D. (2017). An unsupervised neural attention model for aspect extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1, 388-397.
  20. Jin, W., Ho, H. H., & Srihari, R. K. (2009). A novel lexicalized HMM-based learning framework for web opinion mining. In Proceedings of the 26th Annual International Conference on Machine Learning, 465-472.
  21. Lutz, C., & Newlands, G. (2018). Consumer segmentation within the sharing economy: The case of Airbnb. Journal of Business Research, 88, 187-196. https://doi.org/10.1016/j.jbusres.2018.03.019
  22. Ma, X., Hancock, J. T., Mingjie, K. L., & Naaman, M. (2017). Self-disclosure and perceived trustworthiness of Airbnb host profiles. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 2397-2409.
  23. Mikolov, T., Yih, W. T., & Zweig, G. (2013). Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 746-751.
  24. Mimno, D., & McCallum, A. (2008). Topic models conditioned on arbitrary features with Dirichlet-Multinomial regression. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI, 411-418.
  25. Mimno, D., Wallach, H., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 262-272.
  26. Mou, J., Zhu, W., & Benyoucef, M. (2019). Impact of product description and involvement on purchase intention in cross-border e-commerce. Industrial Management and Data Systems, 120(3), 567-586. https://doi.org/10.1108/IMDS-05-2019-0280
  27. Ordenes, F. V., Theodoulidis, B., Burton, J., Gruber, T., & Zaki, M. (2014). Analyzing customer experience feedback using text mining: A linguistics-based approach. Journal of Service Research, 17(3), 278-295. https://doi.org/10.1177/1094670514524625
  28. Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108, 42-49. https://doi.org/10.1016/j.knosys.2016.06.009
  29. Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A model of text for experimentation in the social sciences. Journal of the American Statistical Association, 111(515), 988-1003. https://doi.org/10.1080/01621459.2016.1141684
  30. Roder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 15, 399-408.
  31. Schau, H. J., & Gilly, M. C. (2003). We are what we post? Self-presentation in personal web space. Journal of Consumer Research, 30(3), 385-404. https://doi.org/10.1086/378616
  32. Wagner, W. (2010). Steven Bird, Ewan Klein and Edward Loper: Natural language processing with Python, analyzing text with the natural language toolkit. Language Resources and Evaluation, 44(4), 421-424. https://doi.org/10.1007/s10579-010-9124-x
  33. Wang, L., Liu, K., Cao, Z., Zhao, J., & De Melo, G. (2015). Sentiment-aspect extraction based on restricted boltzmann machines. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, 616-625.
  34. Wang, W., Pan, S. J., Dahlmeier, D., & Xiao, X. (2016). Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv: 1603.06679.
  35. Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634-639. https://doi.org/10.1016/j.chb.2010.04.014
  36. Younis, E. M. (2015). Sentiment analysis and text mining for social media microblogs using open source tools: An empirical study. International Journal of Computer Applications, 112(5), 44-48.
  37. Zhang, J. (2019a). Listening to the consumer: Exploring review topics on Airbnb and their impact on listing performance. Journal of Marketing Theory and Practice, 27(4), 371-389. https://doi.org/10.1080/10696679.2019.1644953
  38. Zhang, J. (2019b). What's yours is mine: Exploring customer voice on Airbnb using text-mining approaches. Journal of Consumer Marketing, 36, 655-665. https://doi.org/10.1108/JCM-02-2018-2581
  39. Zhang, L., Yan, Q., & Zhang, L. (2020). A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb. Decision Support Systems, 133, 113288. https://doi.org/10.1016/j.dss.2020.113288