Travellers Segmentation and Choice Prediction through Online Reviews: The Case of Wellingtons Hotels in New Zealand
Customer choice and segmentation through online reviews can help hotels to improve their marketing strategy development. Nevertheless, old-style approaches are unproductive in analysing online data generated by customers because of size, dissimilar proportions and structures of online review data. Therefore, this research aims to develop a method for 5-star hotels segmentation and travellers choice forecast through online reviews analysis using machine learning methods. Assessment of method was directed through the gathering of data from travellers ratings of Wellingtons 5-star hotels on different features in TripAdvisor. Results confirm that the projected hybrid machine learning approaches can be applied as a progressive recommender mediator for 5-star hotel segmentation by applying big data obtained from online social media settings.
Ahani, A., Nilashi, M., & Ahmadi, H. (2016). Evaluating the barriers of hospital information system implementation using analytic network processes (ANP) method. Journal of Soft Computing and Decision Support Systems, 3(4), 30-38.
Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in spa hotels through tripadvisors online reviews. International Journal of Hospitality Management, 80, 52-77.
Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A.R., Knox, K., Samad, S. and Ibrahim, O., (2019). Revealing customers satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331-343.
Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017a). Firm performance through social customer relationship management: Evidence from small and medium enterprises. Paper presented at the 2017 International Conference on Research and Innovation in Information Systems (ICRIIS).
Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017b). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75, 560-578.
Balli, F., & Tsui, W. H. K. (2016). Tourism demand spillovers between Australia and New Zealand: evidence from the partner countries. Journal of Travel Research, 55(6), 804-812.
Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.
Chen, C.-F., & Tsai, D. (2007). How destination image and evaluative factors affect behavioral intentions? Tourism Management, 28(4), 1115-1122.
Cronin Jr, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218.
Davies, D., & Bouldin, D. (1979). A cluster separation measure, IEEE transactions on patter analysis and machine intelligence. vol. In: PAMI-1.
De Pelsmacker, P., Van Tilburg, S., & Holthof, C. (2018). Digital marketing strategies, online reviews and hotel performance. International Journal of Hospitality Management, 72, 47-55.
Ernst, D., & Dolnicar, S. (2018). How to avoid random market segmentation solutions. Journal of Travel Research, 57(1), 69-82.
Gao, B., Li, X., Liu, S., & Fang, D. (2018). How power distance affects online hotel ratings: the positive moderating roles of hotel chain and reviewers travel experience. Tourism Management, 65, 176-186.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483.
Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. International journal of service industry management, 7(4), 27-42.
Hsu, T.-K., Tsai, Y.-F., & Wu, H.-H. (2009). The preference analysis for tourist choice of destination: A case study of Taiwan. Tourism Management, 30(2), 288-297.
Jafari-Moghadam, S., Zali, M., & Sanaeepour, H. (2017). Tourism entrepreneurship policy: a hybrid MCDM model combining DEMATEL and ANP (DANP). Decision Science Letters, 6(3), 233-250.
Lu, C., Berchoux, C., Marek, M. W., & Chen, B. (2015). Service quality and customer satisfaction: qualitative research implications for luxury hotels. International Journal of Culture, Tourism and Hospitality Research, 9(2), 168-182.
Mohsin, A., & Lockyer, T. (2010). Customer perceptions of service quality in luxury hotels in New Delhi, India: an exploratory study. International Journal of Contemporary Hospitality Management, 22(2), 160-173.
Morteza, Z., Reza, F. M., Seddiq, M. M., Sharareh, P., & Jamal, G. (2016). Selection of the optimal tourism site using the ANP and fuzzy TOPSIS in the framework of Integrated Coastal Zone Management: A case of Qeshm Island. Ocean & coastal management, 130, 179-187.
Neirotti, P., Raguseo, E., & Paolucci, E. (2016). Are customers reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. International Journal of Information Management, 36(6), 1133-1143.
Nilashi, M., Ahani, A., Esfahani, M.D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., Sharef, N.M. and Akbari, E., (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, 767-783.
Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R., & bin Ibrahim, O. (2016). Determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technological Forecasting and Social Change, 111, 244-264.
Nilashi, M., Bin Ibrahim, O., Mardani, A., Ahani, A., & Jusoh, A. (2018a). A soft computing approach for diabetes disease classification. Health Informatics Journal, 24(4), 379-393.
Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCAANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.
Nilashi, M., Ibrahim, O., & Ahani, A. (2016a). Accuracy improvement for predicting Parkinsons disease progression. Scientific reports, 6, 34181.
Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017a). A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics, 34(4), 133-144.
Nilashi, M., Bagherifard, K., Rahmani, M., & Rafe, V. (2017b). A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers & industrial engineering, 109, 357-368.
Nilashi, M., Salahshour, M., Ibrahim, O., Mardani, A., Esfahani, M. D., & Zakuan, N. (2016b). A new method for collaborative filtering recommender systems: the case of yahoo! movies and tripadvisor datasets. Journal of Soft Computing and Decision Support Systems, 3(5), 44-46.
Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., . . . Hassan, O. (2019). Factors Influencing Medical Tourism Adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS Approach. Computers & Industrial Engineering, 106005.
Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., & Sanzogni, L. (2019). Analysis of Travellers Online Reviews in Social Networking Sites Using Fuzzy Logic Approach. International Journal of Fuzzy Systems, 21(5), 1367-1378.
Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., & Alizadeh, A. (2018b). Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science, 28, 168-179.
Oliver, R. L. (2014). Satisfaction: A behavioral perspective on the consumer: A behavioral perspective on the consumer: Routledge.
Pahari, S., Ghosh, D., & Pal, A. (2018). An Online Review-Based Hotel Selection Process Using Intuitionistic Fuzzy TOPSIS Method. In Progress in Computing, Analytics and Networking (pp. 203-214): Springer.
Peng, K.-H., & Tzeng, G.-H. (2019). Exploring heritage tourism performance improvement for making sustainable development strategies using the hybrid-modified MADM model. Current Issues in Tourism, 22(8), 921-947.
Ryan, C. (2002). Tourism and cultural proximity: Examples from New Zealand. Annals of Tourism Research, 29(4), 952-971.
Tsui, K. W. H. (2017). Does a low-cost carrier lead the domestic tourism demand and growth of New Zealand? Tourism Management, 60, 390-403.
Wu, C.-S., Lin, C.-T., & Lee, C. (2010). Optimal marketing strategy: A decision-making with ANP and TOPSIS. International Journal of Production Economics, 127(1), 190-196.
Xia, H., Vu, H. Q., Lan, Q., Law, R., & Li, G. (2019). Identifying hotel competitiveness based on hotel feature ratings. Journal of Hospitality Marketing & Management, 28(1), 81-100.
Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137, 199-210.
Yeoman, I., & McMahon?Beattie, U. (2015). New Zealand's future: the potential for knitting tourism. Journal of Tourism Futures, 1(2), 152-155.
Zavadskas, E. K., Turskis, Z., & Kildien?, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179.
Zhang, H., Gu, C.-l., Gu, L.-w., & Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS & information entropyA case in the Yangtze River Delta of China. Tourism Management, 32(2), 443-451.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.