The Role of Artificial Intelligence–Based Decision Support Systems in Managerial Decision-Making in the Hospitality Industry: A Systematic Literature Review
DOI:
https://doi.org/10.37385/ceej.v7i3.10497Keywords:
Artificial Intelligence, Decision Support System, Hospitality IndustryAbstract
The advancement of Artificial Intelligence (AI) has accelerated the adoption of Decision Support Systems (DSS) to assist managerial decision-making in the increasingly complex and dynamic hospitality industry. This study aims to systematically examine how AI-based DSS are utilized to support managerial decision-making in the hospitality sector, with a particular focus on the types of decisions supported, the AI techniques employed, the benefits obtained, and the challenges of implementation. This research adopts a Systematic Literature Review (SLR) approach guided by the PRISMA framework. A comprehensive literature search was conducted using the Scopus database, resulting in 32 peer-reviewed journal articles that met the inclusion criteria within the publication period of 2017–2026. The findings indicate that AI-based DSS are predominantly used to support operational and tactical decisions, particularly in demand and occupancy forecasting, dynamic pricing and revenue management, workforce scheduling, and service quality management. Machine learning and predictive analytics emerge as the most widely applied AI techniques, while rule-based systems are used to a more limited extent. The literature also highlights key benefits, including improved decision accuracy, enhanced operational efficiency, and better service quality. However, these benefits are accompanied by challenges related to data quality, system transparency, and organizational readiness. This study provides a structured synthesis of the role of AI-based DSS in managerial decision-making within the hospitality industry and offers a foundation for future research and managerial practice.
References
Alrumi, A. R. (2023). HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE TO IMPROVE MANAGEMENT INFORMATION SYSTEMS. International Journal for Quality Research, 18(1), 115–128. https://doi.org/DOI – 10.24874/IJQR18.01-08
Antonio, N., De Almeida, A., & Nunes, L. (2019). An automated machine learning based decision support system to predict hotel booking cancellations. Data Science Journal, 18(1), 1–20. https://doi.org/10.5334/dsj-2019-032
Baidya, A., Saha, A. K., & Roy, A. (2025). Application of deep learning, machine learning and multi-criteria decision analysis for ecotourism potentiality assessment: a case study of the Sundarban Biosphere Reserve, India. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00496-2
Borrero, J. D., Mariscal, J., & Vargas-Sánchez, A. (2022). A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors. Stats, 5(4), 1145–1158. https://doi.org/10.3390/stats5040068
Buhalis, D., & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, 71(November 2017), 41–50. https://doi.org/10.1016/j.ijhm.2017.11.011
Cali, S., & Balaman, S. Y. (2019). Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Computers and Industrial Engineering, 129(December 2018), 315-332. https://doi.org/10.1016/j.cie.2019.01.051
Chebrolu, S. K. (2025). Ai-Powered Business Intelligence: a Systematic Literature Review on the Future of Decision-Making in Enterprises. American Journal of Scholarly Research and Innovation, 4(01), 33–62. https://doi.org/10.63125/gq69nv41
Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62(March), 101257. https://doi.org/10.1016/j.techsoc.2020.101257
Curlin, T., Jakovic, B., and Wittine, Z. (2024). The perspectives of decision support in hotel industry: Systematic review and bibliometric analysis. Journal of Decision Systems, 33(4), 629-644. https://doi.org/10.1080/12460125.2023.2263677
Doborjeh, Z., Hemmington, N., Doborjeh, M., & Kasabov, N. (2022). Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154–1176. https://doi.org/10.1108/IJCHM-06-2021-0767
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(August 2019), 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Gao, W., Zhang, Q., Lu, Z., Wu, D., & Du, X. (2018). Modelling and application of fuzzy adaptive minimum spanning tree in tourism agglomeration area division. Knowledge-Based Systems, 143, 317–326. https://doi.org/10.1016/j.knosys.2017.06.007
Guillet, B. D., & Chu, A. M. C. (2021). Managing Hotel Revenue amid the COVID-19 Crisis. International Journal of Contemporary Hospitality Management, 33(2), 604–627. https://doi.org/https://doi.org/10.1108/IJCHM-06-2020-0623
Haenlein, M., & Kaplan, A. (2021). Artificial intelligence and robotics: Shaking up the business world and society at large. Journal of Business Research, 124(October 2020), 405–407. https://doi.org/10.1016/j.jbusres.2020.10.042
Huang, L., Chen, S., Shen, D., Pan, Y., Yang, J., & Hu, Y. (2023). A hybrid meta-heuristic algorithm with fuzzy clustering method for IoT smart electronic applications. International Journal of Embedded Systems, 16(1), 57–66. https://doi.org/10.1504/IJES.2023.134120
Ismael, G. T., Mana, A., Bischoff, J., Kasra, G., & Singh, A. (2025). A systematic review of machine learning applications in hotel occupancy forecasting. European Journal of Tourism, Hospitality and Recreation, 15(2), 311–327. https://doi.org/10.1038/s41746-024-01312-4
Ivanov, S., & Webster, C. (2017). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies: A cost-benefit analysis. International Scientific Conference Contemporary Tourism Traditions and Innovations, 168-177. https://ssrn.com/abstract=3007577
Jeong, J. S., and Ramirez-Gomez, A. (2018). Development of a web graphic model with fuzzy decision-making trial and evaluation laboratory/multi-criteria spatial decision support system (F-DEMATEL/MC-SDSS) for sustainable planning and construction of rural housings. Journal of Cleaner Production, 199, 584-592. https://doi.org/10.1016/j.jclepro.2018.07.227
Jiang, J., & Wang, B. (2026). Data Mining-Driven Personalized Recommendation in the Smart Tourism Systems. Journal of Circuits, Systems & Computers, 35(4). https://doi.org/https://doi.org/10.1142/S0218126625504274
Kim, R. Y. (2021). When does online review matter to consumers? The effect of product quality information cues. Electronic Commerce Research, 21(4), 1011–1030. https://doi.org/10.1007/s10660-020-09398-0
Kwok, P. K., & Lau, H. Y. K. (2019). Hotel selection using a modified TOPSIS-based decision support algorithm. Decision Support Systems, 120(March), 95–105. https://doi.org/10.1016/j.dss.2019.02.004
Li, P., Xiao, X., & Seekamp, E. (2022). Climate adaptation planning for cultural heritages in coastal tourism destinations: A multi-objective optimization approach. Tourism Management, 88, 104380. https://doi.org/10.1016/j.tourman.2021.104380
Liu, P., Dang, R., Wang, P., Xu, Y., & Zhang, Y. (2025). Online–offline combined adaptive hotel recommendation system considering attribute importance and group consensus. Decision Support Systems, 196(May 2024), 114503. https://doi.org/10.1016/j.dss.2025.114503
Lu, X., & Zhu, J. (2024). Design and Development of Middle-Aged and Elderly Tourism Products Based on Fuzzy Decision Support System. International Journal of Computational Intelligence Systems, 17(1). https://doi.org/10.1007/s44196-024-00509-5
Ma, B., Teo, C. C., Wong, Y. D., & Sun, S. (2025). Delivery time reliability in on-demand food delivery: Heterogeneity from attribution effects. Transportation Research Part E: Logistics and Transportation Review, 202(June), 104335. https://doi.org/10.1016/j.tre.2025.104335
Mariani, M. (2020). Big Data and analytics in tourism and hospitality: a perspective article. Tourism Review, 75(1), 299–303. https://doi.org/10.1108/TR-06-2019-0259
Marinakou, E., Giousmpasoglou, C., & Papavasileiou, E. F. (2025). The Use of Artificial Intelligence (AI) in Talent Acquisition: The Case of Greek Luxury Hotels. Strategic Change, 34(4), 533–543. https://doi.org/10.1002/jsc.2632
Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017). A Big Data Analytics Method for Tourist Behaviour Analysis. Information and Management, 54(6), 771–785. https://doi.org/10.1016/j.im.2016.11.011
Mohammed, I., & Denizci Guillet, B. (2025). Heuristics and biases in human–algorithm interaction and hotel revenue management override decision-making. International Journal of Contemporary Hospitality Management, 37(2), 358–379. https://doi.org/10.1108/IJCHM-02-2024-0288
Mousavian, S., Miah, S. J., & Zhong, Y. (2023). A design concept of big data analytics model for managers in hospitality industries. Personal and Ubiquitous Computing, 27(4), 1509–1519. https://doi.org/10.1007/s00779-023-01714-3
Nnanna, P., Amujo, O., Ezenkwu, C. P., & Ibeke, E. (2025). Leveraging LLMs for User Rating Prediction from Textual Reviews: A Hospitality Data Annotation Case Study. Information (Switzerland), 16(12), 1–14. https://doi.org/10.3390/info16121059
Page, M. J., Joanne, & Pattrict. (2022). A declaração PRISMA 2020: Diretriz atualizada para relatar revisões sistemáticas. PAN American Journal of Public Health, 31(2), 1–12. https://doi.org/10.5123/S1679-49742022000200033
Penna, P., Baldrighi, E., Betti, M., Bolognini, L., Campanelli, A., Capellacci, S., Casabianca, S., Ferrarin, C., Giuliani, G., Grilli, F., Intoccia, M., Manini, E., Moro, F., Penna, A., Ricci, F., & Marini, M. (2021). Water quality integrated system: A strategic approach to improve bathing water management. Journal of Environmental Management, 295(June), 113099. https://doi.org/10.1016/j.jenvman.2021.113099
Sharda, R., Delen, D., & Turban, E. (2023). Decision support and business analytics: Systems, models, and methods (11th ed.). Pearson Education.
Song, M. (2025). Research on intelligent decision support platform for tourism enterprises based on multi-source heterogeneous data fusion. Scientific Reports, 15(1), 1–18. https://doi.org/10.1038/s41598-025-23486-x
Souha, A., Ouaddi, C., Benaddi, L., Abdelmalek, H., Bouziane, E. M., & Jakimi, A. (2025). A DSL-Driven Solution and Code Generation for Accelerated Tourism Decision Support System Development. IEEE Access, 13(April), 101558–101574. https://doi.org/10.1109/ACCESS.2025.3578440
Tan, W. J., Yang, C. F., Château, P. A., Lee, M. T., & Chang, Y. C. (2018). Integrated coastal-zone management for sustainable tourism using a decision support system based on system dynamics: A case study of Cijin, Kaohsiung, Taiwan. Ocean and Coastal Management, 153(November 2017), 131–139. https://doi.org/10.1016/j.ocecoaman.2017.12.012
Varra, L., & Rossi, C. (2019). Role and Activities of Revenue Manager between Individual and Team Competencies: Empirical Evidence in a Tourist Destination. Journal of Service Science and Management, 12(07), 832–858. https://doi.org/10.4236/jssm.2019.127057
Wang, X., Zhao, Y., Zhang, W., Li, Y., Shi, X., Xia, R., Su, Y., Li, X., & Xu, X. (2025). Artificial Intelligence-Based Architectural Design (AIAD): An Influence Mechanism Analysis for the New Technology Using the Hybrid Multi-Criteria Decision-Making Framework. Buildings, 15(21). https://doi.org/10.3390/buildings15213898
Wang, Y., Li, J., Su, D., & Zhou, H. (2023). Spatial-temporal heterogeneity and built environment nonlinearity in inconsiderate parking of dockless bike-sharing. Transportation Research Part A: Policy and Practice, 175(July), 103789. https://doi.org/10.1016/j.tra.2023.103789
Wu, J., Liu, Q., Chen, L., Zhao, W., & Liu, H. (2026). Dynamic hypergraph neural networks for consumer purchase path prediction: Integrating promotions, experiences, and store heterogeneity. Decision Support Systems, 202(December 2025), 114614. https://doi.org/10.1016/j.dss.2026.114614
Yang, D., & Miao, X. (2024). Predicting hotel booking cancellations using tree-based neural network. PeerJ Computer Science, 10, 1–16. https://doi.org/10.7717/peerj-cs.2473
Yao, Y., Chen, J., & Chen, Y. (2025). Chinese senior travelers encounter with smart tourism: why are they falling into the travel digital divide? Journal of Hospitality and Tourism Management, 65(38), 101336. https://doi.org/10.1016/j.jhtm.2025.101336
Zahrani, A. M., Ziaeian, M., & Bamakan, S. M. H. (2025). The metaverse and sustainable tourism: Opportunities, challenges and future trends. Sustainable Futures, 10(September 2024), 101006. https://doi.org/10.1016/j.sftr.2025.101006
Zajia, J. B., Díaz, I. A., & Cachipuendo, J. (2025). Artificial Intelligence Applications in Tourism: Innovations, Challenges and Opportunities. World Conference on Information Systems and Technologies, 32(3), 327–347. https://doi.org/https://doi.org/10.1007/978-3-032-01234-0_27
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