본문 바로가기
KERD
논문KCI 등재

A Review of Forecasting Studies for the Hotel Industry: Focusing on results, contributions and limitations

최정길|누루라인|Nigora Baymuminova|Shi-Nyu Xu|Yi-Wei Zhang
사람과세계경영학회 (People&Global Business Association)|2022.10|Global Business and Finance Review|제27권 제5호|pp. 65-82|DOI Https://doi.org/10.17549/gbfr.2022.27.5.65

0

조회

0

다운로드

1

인용

0

참고문헌

키워드

ReviewHotel industryForecasting studies

초록

Purpose: The purpose of this study is to conduct a thorough review of the results, contributions, and limitations of forecasting studies in the hotel industry to uncover how those studies contribute to the body of knowledge and what limitations of those studies should be filled by future studies. Design/methodology/approach: To uncover the overall trends and evolution of hotel forecasting findings, this study used a systematic literature review approach to ensure the strictness and transparency of the review process. To select a paper to be reviewed, two main review processes including keyword searching and citation counts of each paper were conducted. To assure the rigor of our first two procedures, this study took further steps including cross-checking the results of those studies and calculating the average citation per year of the selected studies. The selected papers were analyzed based upon their forecasting areas, the contribution of each paper by using citation score, frequency of using methodology or techniques, demographic characteristics of authors, and major trends of the forecasting studies. Findings: This research has compiled a list of the most cited papers, as well as the average number of citations per year, the top list of the most used nations and subject areas which manifested the contribution to the body of literature in this study. Research limitations/implications: Future research should consider using other platforms such as Web of Science (WoS) or Scopus to emphasize more the impact and value of the literature study. The outcome of this study will help other researchers to have a better understanding of the used models in forecasting the industry as well as to decipher the most and the least studied research areas in the forecasting hotel industry. Originality/value: This study explored the overall development, contributions, and limitations of forecasting studies in the hospitality industry in an updated, thorough literature analysis. The themes and types of research that have already been studied can be seen by the researchers, as well as those that have not yet been explored.

Abstract

Purpose: The purpose of this study is to conduct a thorough review of the results, contributions, and limitations of forecasting studies in the hotel industry to uncover how those studies contribute to the body of knowledge and what limitations of those studies should be filled by future studies. Design/methodology/approach: To uncover the overall trends and evolution of hotel forecasting findings, this study used a systematic literature review approach to ensure the strictness and transparency of the review process. To select a paper to be reviewed, two main review processes including keyword searching and citation counts of each paper were conducted. To assure the rigor of our first two procedures, this study took further steps including cross-checking the results of those studies and calculating the average citation per year of the selected studies. The selected papers were analyzed based upon their forecasting areas, the contribution of each paper by using citation score, frequency of using methodology or techniques, demographic characteristics of authors, and major trends of the forecasting studies. Findings: This research has compiled a list of the most cited papers, as well as the average number of citations per year, the top list of the most used nations and subject areas which manifested the contribution to the body of literature in this study. Research limitations/implications: Future research should consider using other platforms such as Web of Science (WoS) or Scopus to emphasize more the impact and value of the literature study. The outcome of this study will help other researchers to have a better understanding of the used models in forecasting the industry as well as to decipher the most and the least studied research areas in the forecasting hotel industry. Originality/value: This study explored the overall development, contributions, and limitations of forecasting studies in the hospitality industry in an updated, thorough literature analysis. The themes and types of research that have already been studied can be seen by the researchers, as well as those that have not yet been explored.

참고문헌 (56)

  1. Aliyev, R., "Development of fuzzy time series model for hotel occupancy forecasting", Sustainability(Switzerland), 11(3), pp.1~13 (2019)[DOI]
  2. Ampountolas, A., "Forecasting hotel demand uncertainty using time series Bayesian VAR models", Tourism Economics, 25(5), pp.734~756 (2019)[DOI]
  3. Andrew, W. P., "Forecasting hotel occupancy rates with time series models: An empirical analysis", Journal of Hospitality & Tourism Research, 14(2), pp.173~182 (1990)[DOI]
  4. Assaf, A. G., "Forecasting occupancy rate with Bayesian compression methods", Annals of Tourism Research, 75, pp.439~449 (2019)[DOI]
  5. Beel, J., "Google scholar’s ranking algorithm:The impact of citation counts (an empirical study)", Proceedings of the 2009 3rd International Conference on Research Challenges in Information Science, RCIS 2009 (2009)
  6. Brannas, K., "A new approach to modelling and forecasting monthly guest nights in hotels", International Journal of Forecasting, 18(1), pp.19~30 (2002)[DOI]
  7. Caicedo-Torres, W., "A Machine Learning Model for Occupancy Rates and Demand Forecasting in the Hospitality Industry", ~ (2016)
  8. Chen, C., "Forecasting and optimisation for hotel revenue management", Journal of Revenue and Pricing Management, 6(3), pp.163~174 (2007)[DOI]
  9. Chen, J., "Predicting citation counts of papers", Proceedings of 2015 IEEE 14th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2015, pp.434~440 (2015)
  10. Choi, J. G., "Developing an economic indicator system (a forecasting technique) for the hotel industry", International Journal of Hospitality Management, 22(2), pp.147~159 (2003)[DOI]
  11. Choi, J. G., "Forecasting industry turning points: The US hotel industry cycle model", International Journal of Hospitality Management, 18(2), pp.159~170 (1999)[DOI]
  12. 최정길, "A Review of Forecasting Studies for the Restaurant Industry: Focusing on results, contributions and limitations", Global Business and Finance Review, 27(2), pp.61~77 (2022), 사람과세계경영학회
  13. Fiori, A. M., "Prediction accuracy for reservation-based forecasting methods applied in Revenue Management", International Journal of Hospitality Management, 84, pp.102332~ (2020)[DOI]
  14. Ghalehkhondabi, I., "A review of demand forecasting models and methodological developments within tourism and passenger transportation industry", Journal of Tourism Futures, 5(1), pp.75~93 (2019)[DOI]
  15. Ghalia, M. B., "Computational Intelligence in Economics and Finance (Vol. 8, Issue 4)", pp.59~92 (2004), Springer
  16. Goh, C., "The methodological progress of tourism demand forecasting: A review of related literature", Journal of Travel and Tourism Marketing, 28(3), pp.296~317 (2011)[DOI]
  17. Jiao, E. X., "Tourism forecasting: A review of methodological developments over the last decade", Tourism Economics, 25(3), pp.469~492 (2019)[DOI]
  18. Kim, C. S., "Review of reviews: A systematic analysis of review papers in the hospitality and tourism literature", International Journal of Hospitality Management, 70, pp.49~58 (2018)[DOI]
  19. Klimberg, R. K., "Advances in Business and Management Forecasting (Vol. 7)" (2010), Elsevier
  20. Konarasinghe, K. M. U. B., "Forecasting Foreign Guest Nights in Ancient Cities of Sri Lanka", Proceedings of International Forestry and Environment Symposium, 22, pp.1~16 (2018)
  21. Law, R., "Room occupancy rate forecasting: A neural network approach", International Journal of Contemporary Hospitality Management, 10(6), pp.234~239 (1998)[DOI]
  22. Law, R., "Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique", Journal of Travel & Tourism Marketing, 16(2-3), pp.71~77 (2004)[DOI]
  23. Lee, M., "Modeling and forecasting hotel room demand based on advance booking information", Tourism Management, 66, pp.62~71 (2018)[DOI]
  24. Lee, M., "A Machine Learning Approach To Improving Forecasting Accuracy Of Hotel Demand: A Comparative Analysis Of Neural Networks And Traditional Models", Issues In Information Systems, 21(1), pp.12~21 (2020)[DOI]
  25. Li, G., "Tourism forecasting research: A perspective article", Tourism Review, 75(1), pp.263~266 (2020)[DOI]
  26. Li, G., "Introduction to the special issue:Tourism forecasting - New trends and issues", Tourism Economics, 25(3), pp.305~308 (2019)[DOI]
  27. Lim, C., "Forecasting h(m)otel guest nights in New Zealand", International Journal of Hospitality Management, 28(2), pp.228~235 (2009)[DOI]
  28. Lin, V. S., "A review of Delphi forecasting research in tourism", Current Issues in Tourism, 18(12), pp.1099~1131 (2015)[DOI]
  29. Morales, D. R., "Forecasting cancellation rates for services booking revenue management using data mining", European Journal of Operational Research, 202(2), pp.554~562 (2010)[DOI]
  30. Otudi, I., "The Impact of Forecasting Methods on Demand Projection in Fast Food Restaurant", PalArch’s Journal of Archaeology of Egypt, 2021, pp.18(15), 334~343
  31. Pan, B., "Forecasting hotel room demand using search engine data", Journal of Hospitality and Tourism Technology, 3(3), pp.196~210 (2012)[DOI]
  32. Pan, B., "Forecasting Destination Weekly Hotel Occupancy with Big Data", Journal of Travel Research, 56(7), pp.957~970 (2017)[DOI]
  33. Pereira, L. N., "An introduction to helpful forecasting methods for hotel revenue management", International Journal of Hospitality Management, 58, pp.13~23 (2016)[DOI]
  34. Phumchusri, N., "Hotel daily demand forecasting for high-frequency and complex seasonality data: A case study in Thailand", Journal of Revenue and Pricing Management, 19(1), pp.8~25 (2020)[DOI]
  35. Prilistya, S. K., "Tourism Demand Time Series Forecasting: A Systematic Literature Review", ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering, pp.156~161 (2020)
  36. Rajopadhye, M., "Forecasting uncertain hotel room demand", Information Sciences, 132(1-4), pp.1~11 (2001)[DOI]
  37. Rice, W. L., "Forecasting campground demand in US national parks", Annals of Tourism Research, 75, pp.424~438 (2019)[DOI]
  38. Sánchez-Medina, A. J., "Using machine learning and big data for efficient forecasting of hotel booking cancellations", International Journal of Hospitality Management, 89, pp.102546~ (2020)[DOI]
  39. Schwartz, Z., "Hotel revenue-management forecasting: Evidence of expert-judgment bias", Cornell Hotel and Restaurant Administration Quarterly, 45(1), pp.85~98 (2004)[DOI]
  40. Schwartz, Z., "Improving the accuracy of hotel reservations forecasting: Curves similarity approach", Journal of Travel Research, 36(1), pp.3~14 (1997)[DOI]
  41. Schwartz, Z., "Hotel daily occupancy forecasting with competitive sets: A recursive algorithm", International Journal of Contemporary Hospitality Management, 28(2), pp.267~285 (2016)[DOI]
  42. Schwartz, Z., "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizons", Tourism Economics, 27(2), pp.273~291 (2021)[DOI]
  43. Sierag, D., "A call for exploratory data analysis in revenue management forecasting: A case study of a small and independent hotel in The Netherlands", International Journal of Revenue Management, 10(1), pp.28~51 (2017)[DOI]
  44. Song, H., "A review of research on tourism demand forecasting", Annals of Tourism Research, 75, pp.338~362 (2019)[DOI]
  45. Tian, M., "How does culture influence innovation? A systematic literature review", Management Decision, 56(5), pp.1088~1107 (2018)[DOI]
  46. Wang, T., "A k-means group division and lstm based method for hotel demand forecasting", Tehnicki Vjesnik, 28(4), pp.1345~1352 (2021)[DOI]
  47. Weatherford, L. R., "A comparison of forecasting methods for hotel revenue management", International Journal of Forecasting, 19(3), pp.401~415 (2003)[DOI]
  48. Wu, D. C., "New developments in tourism and hotel demand modeling and forecasting", International Journal of Contemporary Hospitality Management, 29(1), pp.507~529 (2017)[DOI]
  49. Wu, D. C., "Are customer reviews just reviews? Hotel forecasting using sentiment analysis", Tourism Economics, 28(3), pp.795~816 (2021)[DOI]
  50. Wu, W. Y., "Forecasting and Evaluating the Tourist Hotel Industry Performance in Taiwan Based on Grey Theory", Tourism and Hospitality Research, 8(2), pp.137~152 (2008)[DOI]
  51. Yang, Y., "Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data", Journal of Travel Research, 53(4), pp.433~447 (2014)[DOI]
  52. Yüksel, S, "An integrated forecasting approach to hotel demand", Mathematical and Computer Modelling, 46(7-8), pp.1063~1070 (2007)[DOI]
  53. Zakhary, A., "Forecasting hotel arrivals and occupancy using Monte Carlo simulation", Journal of Revenue and Pricing Management, 10(4), pp.344~366 (2011)[DOI]
  54. Zhang, B., "Forecasting hotel accommodation demand based on LSTM model incorporating internet search index", Sustainability(Switzerland), 11(17), pp.1~14 (2019)[DOI]
  55. Zhang, G., "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model", Tourism Economics, 23(7), pp.1496~1514 (2017)[DOI]
  56. Zhang, M., "Weekly Hotel Occupancy Forecasting of a Tourism Destination", Sustainability, 10(12), pp.4351~ (2018)[DOI]

관련 논문AI추천

A Review of Forecasting Studies for the Restaurant Industry: Focusing on results, contributions and limitations장예위, 최정길, 누루라인사람과세계경영학회 · Global Business and Finance Review2022 · 제27권 제2호Predicting Hotel Economic Trends Using Machine Learning and Time Series Analysis Models이규태, 장호성한국비교정부학회 · 한국비교정부학보2025 · 제29권 제2호A Study on the Development of a Hotel Economic Trend Predictive Model Using Machine Learning Models이규태, 홍경옥한국비교정부학회 · 한국비교정부학보2024 · 제28권 제1호A Keyword Network Analysis of Tourism Literature from 2008 to 2019정의범, 조혜진관광경영학회 · 관광경영연구2020 · 제24권 제5호Conceptualizing Global Investment Evaluation Model by Understanding the Environmental Key Risk Variables and their Impacts on the Market Size of the Hotel Industry정젤나, 최정길사람과세계경영학회 · Global Business and Finance Review2018 · 제23권 제3호호텔 수요 예측을 위한 전역/지역 모델을 선택적으로 활용하는 시계열 예측 모델박기현, 안현철, 정경호사)한국빅데이터학회 · 한국빅데이터학회 학회지2024 · 제9권 제1호

이 간행물의 다른 논문 (9건)

Integrating Digital Transformation and Servitization into Digital Servitization: A Case Study on Domino'sMarasini Durga Prasad, DonHee Lee, Seong No Yoonpp. 1-16Ichimoku Cloud Forecasting Returns in the U.S.Matthew Lutey, Dave Rayomepp. 17-26Employee Effects on Organizational Outcomes: Korean Travel IndustryHeon Seopp. 27-41What Determines Saudi Arabia's Development Finance? An Empirical ApproachElham Bokhari, 오진환pp. 42-54Safety Stock Optimization under Lead Time AmbiguityJoon Yeop Kwon, 김종근, 김판수pp. 55-64
원문 보기