A Bayesian statistics approach to hospitality research

References

• Adhikary, M., & Adhikari, A. (2019). Micro-modelling of individual tourist’s information-seeking behaviour: A heterogeneity-specific study. Current Issues in Tourism, 22(6), 705–728. https://doi.org/https://doi.org/10.1080/13683500.2017.1307816
   Web of Science ®Google Scholar
• Ampountolas, A. (2019). Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics, 25(5), 734–756. https://doi.org/https://doi.org/10.1177/1354816618801741
   Web of Science ®Google Scholar
• Arbelo, A., Arbelo-Pérez, M., & Pérez-Gómez, P. (2018). Estimation of profit efficiency in the hotel industry using a Bayesian stochastic frontier model. Cornell Hospitality Quarterly, 59(4), 364–375. https://doi.org/https://doi.org/10.1177/1938965518762841
   Web of Science ®Google Scholar
• Assaf, A. (2009). Are the U.S. airlines really in crisis? Tourism Management, 30(6), 916–921. https://doi.org/https://doi.org/10.1016/j.tourman.2008.11.006
   Web of Science ®Google Scholar
• Assaf, A. (2011). Accounting for technological differences in modelling the performance of airports: A Bayesian approach. Applied Economics, 43(18), 2267–2275. https://doi.org/https://doi.org/10.1080/00036840903101779
   Web of Science ®Google Scholar
• Assaf, A. G., & Barros, C. P. (2011). Bayesian cost efficiency of Luanda, Angola hotels. The Service Industries Journal, 31(9), 1549–1559. https://doi.org/https://doi.org/10.1080/02642060903576068
   Web of Science ®Google Scholar
• Assaf, A. G., Li, G., & Song, H. (2019). Modeling and forecasting regional tourism demand. Journal of Travel Research, 58(3), 383–397. https://doi.org/https://doi.org/10.1177/0047287518759226
   Web of Science ®Google Scholar
• Assaf, A., & Magnini, V. (2012). Accounting for customer satisfaction in measuring hotel efficiency: Evidence from the US hotel industry. International Journal of Hospitality Management, 31(3), 642–647.
   Web of Science ®Google Scholar
• Assaf, A. G., Oh, H., & Tsionas, M. (2017). Bayesian approach for the measurement of tourism performance: A case of stochastic frontier models. Journal of Travel Research, 56(2), 172–186. https://doi.org/https://doi.org/10.1177/0047287516633528
   Web of Science ®Google Scholar
• Assaf, A. G., & Tionas, E. G. (2015). Incorporating destination quality into the measurement of tourism performance: A Bayesian approach. Tourism Management, 49, 58–71. https://doi.org/https://doi.org/10.1016/j.tourman.2015.02.003
   Web of Science ®Google Scholar
• Assaf, A. G., Tsionas, M., Kock, F., & Josiassen, A. (2021). A Bayesian non-parametric stochastic frontier model. Annals of Tourism Research, 87, Article 103116. https://doi.org/https://doi.org/10.1016/j.annals.2020.103116
   Google Scholar
• Assaf, A. G., Tsionas, M., & Oh, H. (2018). The time has come: Toward Bayesian SEM estimation in tourism research. Tourism Management, 64, 98–109. https://doi.org/https://doi.org/10.1016/j.tourman.2017.07.018
   Web of Science ®Google Scholar
• Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2015). Understanding satisfied and dissatisfied hotel customers: Text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1–24. https://doi.org/https://doi.org/10.1080/19368623.2015.983631
   Web of Science ®Google Scholar
• Canova, F. (2011). Methods for applied macroeconomic research. Princeton University Press.
   Google Scholar
• Carlin, B., & Chib, S. (1995). Bayesian model choice via Markov Chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B (Methodological), 57(3), 473–484. https://doi.org/https://doi.org/10.1111/j.2517-6161.1995.tb02042.x
   Google Scholar
• Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100. https://doi.org/https://doi.org/10.1080/07474938408800053
   Google Scholar
• Donovan, T. M., & Mickey, R. M. (2019). Bayesian statistics for beginners. A step-by-step approach. Oxford Press.
   Google Scholar
• Giovagnoli, A. (2008). Bayesian statistics [class handout].
   Google Scholar
• Johnson, M., Griffiths, T. L., & Goldwater, S. (2007). Bayesian inference for PCFGS via Markov Chain Monte Carlo. In NAACL HLT 2007 – Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference (pp. 139–146). Association for Computational Linguistics (ACL).
   Google Scholar
• Kulshrestha, A., Krishnaswamy, V., & Sharma, M. (2020). Bayesian BILSTM approach for tourism demand forecasting. Annals of Tourism Research, 83, 102925. doi:10.1016/j.annals.2020.102925
   Web of Science ®Google Scholar
• Lam, C., & McKercher, B. (2013). The tourism data gap: The utility of official tourism information for the hospitality and tourism industry. Tourism Management Perspectives, 6, 82–94. https://doi.org/https://doi.org/10.1016/j.tmp.2012.12.003
   Google Scholar
• Mariani, M. M., & Borghi, M. (2018). Effects of the booking.com rating system: Bringing hotel class into the picture. Tourism Management, 66, 47–52. https://doi.org/https://doi.org/10.1016/j.tourman.2017.11.006
   Web of Science ®Google Scholar
• Pan, B., MacLaurin, T., & Crotts, J. C. (2007). Travel blogs and the implications for destination marketing. Journal of Travel Research, 46(1), 35–45. https://doi.org/https://doi.org/10.1177/0047287507302378
   Google Scholar
• Papastathopoulos, A., Ahmad, S. Z., Al Sabri, N., & Kaminakis, K. (2020). Demographic analysis of residents’ support for tourism development in the UAE: A Bayesian structural equation modeling multigroup approach. Journal of Travel Research, 59(6), 1119–1139. https://doi.org/https://doi.org/10.1177/0047287519874131
   Web of Science ®Google Scholar
• Perles-Ribes, J. F., Ramón-Rodríguez, A. B., Moreno-Izquierdo, L., & Torregrosa Martí, M. T. (2016). Winners and losers in the Arab uprisings: A Mediterranean tourism perspective. Current Issues in Tourism, 21(16), 1810–1829. https://doi.org/https://doi.org/10.1080/13683500.2016.1225697
   Web of Science ®Google Scholar
• Perles-Ribes, J. F., Ramón-Rodríguez, A. B., & Ortuño, P. A. (2019). Brexit announcement: Immediate impact on British tourism in Spain. Cornell Hospitality Quarterly, 60(2), 97–103. https://doi.org/https://doi.org/10.1177/1938965518777699
   Web of Science ®Google Scholar
• Perles-Ribes, J. F., Ramón-Rodríguez, A. B., Such-Devesa, M. J., & Moreno-Izquierdo, L. (2019). Effects of political instability in consolidated destinations: The case of Catalonia (Spain). Tourism Management, 70, 134–139. https://doi.org/https://doi.org/10.1016/j.tourman.2018.08.001
   Web of Science ®Google Scholar
• Rigollet, P. (2016). Lecture 17: Bayesian statistics [Video file]. https://ocw.mit.edu/courses/mathematics/18-650-statistics-for-applications-fall-2016/lecture-videos/lecture-17-video/
   Google Scholar
• Rossi, P. E., & Allenby, G. M. (2003). ) Bayesian statistics and marketing. Marketing Science, 22(3), 304–328. https://doi.org/https://doi.org/10.1287/mksc.22.3.304.17739
   Web of Science ®Google Scholar
• Taraldsen, G., & Lindqvist, B. H. (2010). Improper priors are not improper. The American Statistician, 64(2), 154–158. https://doi.org/https://doi.org/10.1198/tast.2010.09116
   Web of Science ®Google Scholar
• Van Dongen, S. (2006). Prior specification in Bayesian statistics: Three cautionary tales. Journal of Theoretical Biology, 242(1), 90–100. https://doi.org/https://doi.org/10.1016/j.jtbi.2006.02.002
   PubMed Web of Science ®Google Scholar
• Wasserman, L. (1996). The conflict between improper priors and robustness. Journal of Statistical Planning and Inference, 52(1), 1–15. https://doi.org/https://doi.org/10.1016/0378-3758(95)00109-3
   Web of Science ®Google Scholar
• Wong, K. K. F., Song, H., & Chon, K. S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), 773–780. https://doi.org/https://doi.org/10.1016/j.tourman.2005.05.017
   Web of Science ®Google Scholar