DOI QR코드

DOI QR Code

Exploring Simultaneous Presentation in Online Restaurant Reviews: An Analysis of Textual and Visual Content

  • Lin Li (School of Management, Kyung Hee University) ;
  • Gang Ren (College of Business Administration, Kookmin University) ;
  • Taeho Hong (College of Business Administration, Pusan National University) ;
  • Sung-Byung Yang (School of Management, Kyung Hee University)
  • 투고 : 2019.02.18
  • 심사 : 2019.04.11
  • 발행 : 2019.06.30

초록

The purpose of this study is to explore the effect of different types of simultaneous presentation (i.e., reviewer information, textual and visual content, and similarity between textual-visual contents) on review usefulness and review enjoyment in online restaurant reviews (ORRs), as they are interrelated yet have rarely been examined together in previous research. By using Latent Dirichlet Allocation (LDA) topic modeling and state-of-the-art machine learning (ML) methodologies, we found that review readability in textual content and salient objects in images in visual content have a significant impact on both review usefulness and review enjoyment. Moreover, similarity between textual-visual contents was found to be a major factor in determining review usefulness but not review enjoyment. As for reviewer information, reputation, expertise, and location of residence, these were found to be significantly related to review enjoyment. This study contributes to the body of knowledge on ORRs and provides valuable implications for general users and managers in the hospitality and tourism industries.

키워드

과제정보

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2925146).

참고문헌

  1. Bagheri, A., Saraee, M., and De Jong, F. (2014). ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences. Journal of Information Science, 40(5), 621-636. https://doi.org/10.1177/0165551514538744
  2. Banerjee, S., Bhattacharyya, S., and Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17-26. https://doi.org/10.1016/j.dss.2017.01.006
  3. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
  4. Chen, Y. S., Chen, L. H., and Takama, Y. (2015). Proposal of LDA-based sentiment visualization of hotel reviews. IEEE International Conference on Data Mining Workshop (ICDMW) Proceeding, 687-693.
  5. Cheng, Y. H., and Ho, H. Y. (2015). Social influence's impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883-887. https://doi.org/10.1016/j.jbusres.2014.11.046
  6. Chevalier, J. A., and Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354. https://doi.org/10.1509/jmkr.43.3.345
  7. Cho, S. Y., Choi, J. E., Lee, K. H., and Kim, H. W. (2015). An online review mining approach to a recommendation system. Information Systems Review, 17(3), 95-111. https://doi.org/10.14329/isr.2015.17.3.095
  8. Cho, S. Y., Kim, H. K., Kim, B. S., and Kim, H. W. (2014). Predicting movie revenue by online review mining: Using the opening week online review. Information Systems Review, 16(3), 111-132. https://doi.org/10.14329/isr.2014.16.3.113
  9. Cialdini, R. B. (2001). Harnessing the science of persuasion. Harvard Business Review, 79(9), 72-81.
  10. Cloud Vision API (2019). Cloud vision. [1] Retrieved from https://cloud.google.com/vision/Accessed 2018.1.20.
  11. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.
  12. De Pelsmacker, P., Dens, N., and Kolomiiets, A. (2018). The impact of text valence, star rating and rated usefulness in online reviews. International Journal of Advertising, 37(3), 340-359. https://doi.org/10.1080/02650487.2018.1424792
  13. Fang, B., Ye, Q., Kucukusta, D., and Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498-506. https://doi.org/10.1016/j.tourman.2015.07.018
  14. Filieri, R. (2016). What makes an online consumer review trustworthy? Annals of Tourism Research, 58, 46-64. https://doi.org/10.1016/j.annals.2015.12.019
  15. Filieri, R., McLeay, F., Tsui, B., and Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956-970.
  16. Forman, C., Ghose, A., and Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313. https://doi.org/10.1287/isre.1080.0193
  17. Fu, X., Guo, L., Yanyan, G., and Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet Lexicon. Knowledge-Based Systems, 37, 186-195. https://doi.org/10.1016/j.knosys.2012.08.003
  18. Gan, Q., Ferns, B. H., Yu, Y., and Jin, L. (2017). A text mining and multidimensional sentiment analysis of online restaurant reviews. Journal of Quality Assurance in Hospitality & Tourism, 18(4), 465-492. https://doi.org/10.1080/1528008X.2016.1250243
  19. Ghose, A., and Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498-1512. https://doi.org/10.1109/TKDE.2010.188
  20. Gretzel, U., Sigala, M., Xiang, Z., and Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25(3), 179-188. https://doi.org/10.1007/s12525-015-0196-8
  21. Griffiths, T. L., and Steyvers, M. (2004). Finding scientific topics. The National Academy of Sciences of the United States of America Proceedings, 101(suppl 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  22. Gu, B., and Ye, Q. (2014). First step in social media: Measuring the influence of online management responses on customer satisfaction. Production and Operations Management, 23(4), 570-582. https://doi.org/10.1111/poms.12043
  23. Guo, Y., Barnes, S. J., and Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using Latent Dirichlet allocation. Tourism Management, 59, 467-483. https://doi.org/10.1016/j.tourman.2016.09.009
  24. Hsu, W., Lee, M. L., and Zhang, J. (2002). Image mining: Trends and developments. Journal of Intelligent Information Systems, 19(1), 7-23. https://doi.org/10.1023/A:1015508302797
  25. Hu, N., Bose, I., Koh, N. S., and Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52(3), 674-684. https://doi.org/10.1016/j.dss.2011.11.002
  26. Hu, N., Zhang, T., Gao, B., and Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417-426. https://doi.org/10.1016/j.tourman.2019.01.002
  27. Huang, A. H., Chen, K., Yen, D. C., and Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17-27. https://doi.org/10.1016/j.chb.2015.01.010
  28. Hwang, Y., Choi, S., and Mattila, A. S. (2018). The role of dialecticism and reviewer expertise in consumer responses to mixed reviews. International Journal of Hospitality Management, 69, 49-55. https://doi.org/10.1016/j.ijhm.2017.10.009
  29. Hyam, R. (2017). Automated image sampling and classification can be used to explore perceived naturalness of urban spaces. PloS One, 12(1), e0169357.
  30. Jabr, W., Qi, Z., Lohtia, R., and Guillory, M. D. (2018). The influence of information display and availability on reviewer usefulness status. The proceedings of Americas Conference on Information Systems (AMCIS), 14-24.
  31. Janis, I., and Hovland, C. (1959). Personality and persuasibility. New Haven: Yale University Press, CT.
  32. Jeong, E., and Jang, S. S. (2011). Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations. International Journal of Hospitality Management, 30(2), 356-366. https://doi.org/10.1016/j.ijhm.2010.08.005
  33. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., and Li, S. (2013). Salient object detection: A discriminative regional feature integration approach. In Conference On Computer Vision and Pattern Recognition, IEEE, 2083-2090.
  34. Karimi, S., and Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39-48. https://doi.org/10.1016/j.dss.2017.02.001
  35. Kim, M., and Lennon, S. (2008). The effects of visual and verbal information on attitudes and purchase intentions in internet shopping. Psychology & Marketing, 25(2), 146-178. https://doi.org/10.1002/mar.20204
  36. Korfiatis, N., GarciA-Bariocanal, E., and SaNchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205-217. https://doi.org/10.1016/j.elerap.2011.10.003
  37. Leary, B. (2018). Yelp's top 100 places to eat for 2018. Retrieved from https://www.yelpblog.com/2018/02/yelps-top-100-places-to-eat-for-2018/Accessed 2018.1.25.
  38. Lee, D., Gopal, A., and Lee, D. (2017). Micro-Giving: On the use of mobile devices and monetary subsidies in charitable giving. Available at SSRN 3280553.
  39. Lee, J., and Lee, H. J. (2016). Your expectation matters when you read online consumer reviews: The review extremity and the escalated confirmation effect. Asia Pacific Journal of Information Systems, 26(3), 449-476. https://doi.org/10.14329/apjis.2016.26.3.449
  40. Li, H., Wang, C. R., Meng, F., and Zhang, Z. (2018a). Making restaurant reviews useful and/or enjoyable? The impacts of temporal, explanatory, and sensory cues. International Journal of Hospitality Management. Online Publication.
  41. Li, L., Lee, K. Y., and Yang, S.-B. (2018b). Exploring the effect of heuristic factors on the popularity of user-curated 'Best places to visit' recommendations in an online travel community. Information Processing & Management. Online Publication.
  42. Li, X., Wu, C., and Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56(2), 172-184. https://doi.org/10.1016/j.im.2018.04.007
  43. Lin, T. M., Lu, K. Y., and Wu, J. J. (2012). The effects of visual information in eWOM communication. Journal of Research in Interactive Marketing, 6(1), 7-26. https://doi.org/10.1108/17505931211241341
  44. Lin, Y. S., and Huang, J. Y. (2006). Internet blogs as a tourism marketing medium: A case study. Journal of Business Research, 59(10-11), 1201-1205. https://doi.org/10.1016/j.jbusres.2005.11.005
  45. Liu, N., and Han, J. (2016). Dhsnet: Deep hierarchical saliency network for salient object detection. In Conference on Computer Vision and Pattern Recognition, IEEE, 678-686.
  46. Liu, Z., and Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140-151. https://doi.org/10.1016/j.tourman.2014.09.020
  47. Louvigne, S., Uto, M., Kato, Y., and Ishii, T. (2018). Social constructivist approach of motivation: social media messages recommendation system. Behaviormetrika, 45(1), 133-155.
  48. Luca, M. (2016). Reviews, reputation, and revenue: The case of Yelp.com.
  49. Mawhinney, J. (2019). 45 Visual content marketing statistics you should know in 2019. Retrieved from https://blog.hubspot.com/marketing/visual-content-marketingstrategy#sm.0001sdcqnoj0qf67xfy1llpmev7v2/Accessed 2019.3.19.
  50. Mou, J., Ren, G., Qin, C., and Kurcz, K. (2019). Understanding the topics of export cross-border e-commerce consumers feedback: an LDA approach. Electronic Commerce Research, 1-29.
  51. 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), 185-200. https://doi.org/10.2307/20721420
  52. Nazlan, N. H., Tanford, S., and Montgomery, R. (2018). The effect of availability heuristics in online consumer reviews. Journal of Consumer Behaviour, 17(5), 449-460. https://doi.org/10.1002/cb.1731
  53. Nikolenko, S. I., Koltcov, S. and Koltsova, O. (2017). Topic modelling for qualitative studies. Journal of Information Science, 43(1), 88-102. https://doi.org/10.1177/0165551515617393
  54. Park, E., Chae, B., and Kwon, J. (2018). The structural topic model for online review analysis: Comparison between green and non-green restaurants. Journal of Hospitality and Tourism Technology. Online publication.
  55. Park, S., and Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67-83. https://doi.org/10.1016/j.annals.2014.10.007
  56. Racherla, P., and Friske, W. (2012). Perceived 'usefulness' of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548-559. https://doi.org/10.1016/j.elerap.2012.06.003
  57. Reiley, L. (2015). Yelp heavy hitters talk about company's growing clout and struggles. Retrieved from https://www.tampabay.com/things-to-do/consumer/yelp-heavy-hitters-talk-about-companys-growing-clout-and-struggles/2242766/ Accessed 2018.1.15.
  58. Ren, G., and Hong, T. (2017). Investigating online destination images using a topic-based sentiment analysis approach. Sustainability, 9(10), 1765.
  59. Ryan, R. M., and Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
  60. Schroeder, M. A., Lander, J., and Levine-Silverman, S. (1990). Diagnosing and dealing with multicollinearity. Western Journal of Nursing Research, 12(2), 175-187. https://doi.org/10.1177/019394599001200204
  61. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., and Lee, K. C. (2016). Content complexity, similarity, and consistency in social media: A deep learning approach. SSRN Electronic Journal.
  62. Singh, P. V., Sahoo, N., and Mukhopadhyay, T. (2014). How to attract and retain readers in enterprise blogging? Information Systems Research, 25(1), 35-52. https://doi.org/10.1287/isre.2013.0509
  63. Singh, R., and Woo, J. (2019). Applications of machine learning models on Yelp data. Asia Pacific Journal of Information Systems, 29(1), 117-143. https://doi.org/10.14329/apjis.2019.29.1.117
  64. Tabachnick, B. G., and Fidell, L. S. (2007). Multivariate analysis of variance and covariance. Using Multivariate Statistics, 3, 402-407.
  65. 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), 463-479. https://doi.org/10.1509/jmr.12.0106
  66. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://doi.org/10.1287/isre.11.4.342.11872
  67. Vossen, P., Caselli, T., and Cybulska, A. (2018). How concrete do we get telling stories? Topics in Cognitive Science, 10(3), 621-640. https://doi.org/10.1111/tops.12366
  68. Vu, H. Q., Li, G., Law, R., and Zhang, Y. (2019). Exploring tourist dining preferences based on restaurant reviews. Journal of Travel Research, 58(1), 149-167. https://doi.org/10.1177/0047287517744672
  69. Wang, Y. S., Lin, H. H., and Liao, Y. W. (2012). Investigating the individual difference antecedents of perceived enjoyment in students' use of blogging. British Journal of Educational Technology, 43(1), 139-152. https://doi.org/10.1111/j.1467-8535.2010.01151.x
  70. Weiss, A. M., Lurie, N. H., and MacInnis, D. J. (2008). Listening to strangers: whose responses are valuable, how valuable are they, and why? Journal of Marketing Research, 45(4), 425-436. https://doi.org/10.1509/jmkr.45.4.425
  71. Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001
  72. Xu, P., Chen, L., and Santhanam, R. (2015). Will video be the next generation of e-commerce product reviews? Presentation format and the role of product type. Decision Support Systems, 73, 85-96. https://doi.org/10.1016/j.dss.2015.03.001
  73. Yang, S.-B., Hlee, S., Lee, J., and Koo, C. (2017a). An empirical examination of online restaurant reviews on Yelp. com: A dual coding theory perspective. International Journal of Contemporary Hospitality Management, 29(2), 817-839. https://doi.org/10.1108/IJCHM-11-2015-0643
  74. Yang, S.-B., Shin, S., Joun, Y., and Koo, C. (2017b). Exploring the comparative importance of online hotel reviews' heuristic attributes in review helpfulness: A conjoint analysis approach. Journal of Travel & Tourism Marketing, 34(7), 963-985. https://doi.org/10.1080/10548408.2016.1251872
  75. Yelp (2018). An Introduction to Yelp Metrics as of September 30, 2018. Retrieved from https://www.yelp.com/factsheet/ Accessed 2018.1.27.
  76. Yoo, K. H., and Gretzel, U. (2008). What motivates consumers to write online travel reviews? Information Technology & Tourism, 10(4), 283-295. https://doi.org/10.3727/109830508788403114
  77. Zhang, Y., and Lin, Z. (2018). Predicting the helpfulness of online product reviews: A multilingual approach. Electronic Commerce Research and Applications, 27, 1-10. https://doi.org/10.1016/j.elerap.2017.10.008