款待
旅游
独创性
酒店管理学
酒店业
营销
计算机科学
数据科学
服务(商务)
知识管理
人工智能
管理科学
业务
社会学
工程类
定性研究
社会科学
政治学
法学
作者
Zohreh Doborjeh,Nigel Hemmington,Maryam Doborjeh,Nikola Kasabov
出处
期刊:International Journal of Contemporary Hospitality Management
[Emerald (MCB UP)]
日期:2021-12-23
卷期号:34 (3): 1154-1176
被引量:131
标识
DOI:10.1108/ijchm-06-2021-0767
摘要
Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.
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