Revenue Management and Pricing Optimization in Hospitality and Tourism Consulting: A Systematic Literature Review
DOI:
https://doi.org/10.53819/81018102t3139Abstract
Revenue management (RM), the collection of strategies that firms use to scientifically manage demand for their products and services, is considered as one of the most successful application areas of operations research. This systematic literature review (SLR) explores revenue management techniques in the hospitality and tourism. Such techniques include dynamic pricing algorithms, forecasting demand with urbane statistical models, overbooking strategies, ancillary revenue optimization, and the impact of behavioral economics on pricing decisions. The study conducted an SLR using the PRISMA model and identified 365 Scopus indexed documents. Descriptive analyses, citation analysis, co-citation analysis, and keyword co-occurrence analysis were used to investigate the intellectual structure of the revenue management literature. Findings showed that the concept of RM has been globally examined since 1989 with greater interest over time. According to co-citation analysis, three schools of thought are identified, including customer orientation, operational performance, and revenue management technique. Most highly influential documents are conceptual papers. Results also reveal that 6 dominant topics in the research field of RM such as dynamic pricing, tourism, hotel, hospitality, machine learning, and consumer behavior, have recently been examined in the literature. This review concludes that RM techniques can significantly improve price optimization and foster data-driven decision making in the management of businesses in the hospitality sector. Limitations of RM strategies such as the potential lack of understanding of specific hospitality contexts, reliance on outdated data, and challenges in accurately forecasting demand and managing distribution channels are also discussed. Similarly, there can be disconnect between theoretical models and practical application, especially regarding human resources and the impact of external factors like economic conditions. It also suggests future research directions for further enhancing RM techniques and pricing optimization in the hospitality and tourism consulting.
Keywords: Revenue Management, Pricing Optimization, Hospitality, Tourism Consulting, Systematic Literature Review
References
Ara, M., Foysal, M., Huda, M. A., & Bairagi, S. (2022). A model to develop hotel management system to optimize revenue. Khulna university Studies, 167-188. https://doi.org/10.53808/KUS.2022.19.02.2116-se
Bale, E. Z., & Emmanuel, A. (2024). Investigating the Impact of Dynamic Pricing Strategies on Revenue Optimization in the Hospitality Industry. International Journal of Financial Research and Business Development, 05(7), 141 – 147.
Binesh, F., Belarmino, A., & Raab, C. (2021). A meta-analysis of hotel revenue management. Journal of Revenue and Pricing Management, 20(5), 546-558. https://doi.org/10.1057/s41272-020-00268-w
Brotherton, B. (1999). Towards a definitive view of the nature of hospitality and hospitality management. International journal of Contemporary Hospitality Management, 11(4), 165- 173. https://doi.org/10.1108/09596119910263568
Carrera-Rivera, A., Ochoa, W., Larrinaga, F., & Lasa, G. (2022). How to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 9, 101895. https://doi.org/10.1016/j.mex.2022.101895
Davis, J. M., Gallego, G., & Topaloglu, H. (2014). Assortment optimization under variants of the nested logit model. Operations Research, 62(2), 250-273. https://doi.org/10.1287/opre.2014.1256
Denizci-Guillet, B. (2020). An evolutionary analysis of revenue management research in hospitality and tourism. Is there a paradigm shift?. International Journal of Contemporary Hospitality Management, 32(2), 560-587. https://doi.org/10.1108/IJCHM-06-2019-0515
Denizci-Guillet, B., & Mohammed, I. (2015). Revenue management research in hospitality and tourism. A critical review of current literature and suggestions for future research. International Journal of Contemporary Hospitality Management, 27(4), 526-560. https://doi.org/10.1108/IJCHM-06-2014-0295
Erdem, M., & Jiang, L. (2016). An overview of hotel revenue management research and emerging key patterns in the third millennium. Journal of Hospitality and Tourism Technology, 7(3), 300-312. https://doi.org/10.1108/JHTT-10-2014-0058
Feng, Y., & Xiao, B. (1999). Maximizing revenues of perishable assets with a risk factor. Operations Research, 47(2), 337-341. https://doi.org/10.1287/opre.47.2.337
Gallego, G., & Van Ryzin, G. (1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management science, 40(8), 999-1020. https://doi.org/10.1287/mnsc.40.8.999
Gao, J. (2025). Optimizing hotel revenue management through dynamic pricing algorithms and data analysis. Journal of Computational Methods in Sciences and Engineering, 25(2), 1200-1209. https://doi.org/10.1177/14727978241298467
González-Serrano, L., & Talón-Ballestero, P. (2020). Revenue management and E-tourism: The past, present and future. In Handbook of e-Tourism (pp. 1-28). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-05324-6_76-1
Hausberg, J. P., & Korreck, S. (2021). Business incubators and accelerators: a co-citation analysis-based, systematic literature review (pp. 39-63). Edward Elgar Publishing. https://doi.org/10.4337/9781788974783.00009
Hayes, D. K., Hayes, J. D., & Hayes, P. A. (2021). Revenue management for the hospitality industry. John Wiley & Sons.
Kimes, S. E. (1989). The basics of yield management. Cornell Hotel and Restaurant Administration Quarterly, 30(3), 14-19. https://doi.org/10.1177/001088048903000309
Kimes, S. E. (2000). Revenue management on the links: applying yield management to the golf-course industry. Cornell Hotel and Restaurant Administration Quarterly, 41(1), 120-127. https://doi.org/10.1177/001088040004100129
Kimes, S. E. (2002). Perceived fairness of yield management. Cornell hotel and restaurant. Administration Quarterly, 43(1), 21-30. https://doi.org/10.1177/0010880403260107
Kimes, S. E. (2004). Restaurant revenue management: implementation at Chevys Arrowhead. Cornell Hotel and Restaurant Administration Quarterly, 45(1), 52-67.
Kimes, S. E., & Wirtz, J. (2003). Has revenue management become acceptable? Findings from an international study on the perceived fairness of rate fences. Journal of service research, 6(2), 125-135. https://doi.org/10.1177/1094670503257038
Koseoglu, M. A., Tetteh, I. L., & King, B. (2019). Decision tools: A systematic literature review, co-citation analysis and future research directions. Nankai Business Review International, 10(4), 591-617. https://doi.org/10.1108/NBRI-07-2018-0045
McGill, J. I., & Van Ryzin, G. J. (1999). Revenue management: Research overview and prospects. Transportation science, 33(2), 233-256. https://doi.org/10.1287/trsc.33.2.233
Meatchi, S., Camus, S., & Lecointre-Erickson, D. (2021). Perceived unfairness of revenue management pricing: developing a measurement scale in the context of hospitality. International Journal of Contemporary Hospitality Management, 33(10), 3157-3176. https://doi.org/10.1108/IJCHM-11-2020-1344
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj, 339. https://doi.org/10.1136/bmj.b2535
Narong, D. K., & Hallinger, P. (2023). A keyword co-occurrence analysis of research on service learning: Conceptual foci and emerging research trends. Education Sciences, 13(4), 339. https://doi.org/10.3390/educsci13040339
Nechval, N., Berzins, G., & Nechval, K. O. N. S. T. A. N. T. I. N. (2023). Optimal statistical estimation and dynamic adaptive control of airline seat protection levels for several nested fare classes under parametric uncertainty of customer demand models. WSEAS Trans. Math., 22, 395-408. https://doi.org/10.37394/23206.2023.22.47
Ozmec-Ban, M., Škurla Babić, R., Vidović, A., & Bračić, M. (2022). A review of ancillary services implementation in the revenue management systems. Promet-Traffic & Transportation, 34(4), 581-594. https://doi.org/10.7307/ptt.v34i4.4065
Raza, S. A., Ashrafi, R., & Akgunduz, A. (2020). A bibliometric analysis of revenue management in the airline industry. Journal of Revenue and Pricing Management, 19(6), 436-465. https://doi.org/10.1057/s41272-020-00247-1
Sauer, P. C., & Seuring, S. (2023). How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions. Review of Managerial Science, 17(5), 1899-1933. https://doi.org/10.1007/s11846-023-00668-3
Subying, C., & Yoopetch, C. (2023). A bibliometric review of revenue management in the tourism and hospitality industry, 1989–2021. Sustainability, 15(20), 15089. https://doi.org/10.3390/su152015089
Szymański, B., Belobaba, P. P., & Papen, A. (2021). Continuous pricing algorithms for airline RM: revenue gains and competitive impacts. Journal of Revenue and Pricing Management, 20(6), 669-688. https://doi.org/10.1057/s41272-021-00350-x
Talón-Ballestero, P., González-Serrano, L., Flecha-Barrio, M. D., & Orea-Giner, A. (2023). A longitudinal analysis of revenue management strategies and measures implemented in the hospitality industry during the COVID-19 crisis. International Marketing Review, 40(5), 1134-1157. https://doi.org/10.1108/IMR-12-2021-0387
United Nations World Tourism Organisation (UNWTO, 2010). International Recommendations for Tourism Statistics 2008; Statistical Papers (Ser. M); Department of Economic and Social Affairs: New York, NY, USA, 2010.
Vulcano, G., Van Ryzin, G., & Maglaras, C. (2002). Optimal dynamic auctions for revenue management. Management Science, 48(11), 1388-1407. https://doi.org/10.1287/mnsc.48.11.1388.269