Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115127
DC FieldValueLanguage
dc.contributorSchool of Hotel and Tourism Managementen_US
dc.creatorQiu, Xen_US
dc.creatorHao, Fen_US
dc.creatorKong, Hen_US
dc.creatorWang, Ken_US
dc.creatoriu, Sen_US
dc.creatorLiu, Jen_US
dc.date.accessioned2025-09-09T07:41:59Z-
dc.date.available2025-09-09T07:41:59Z-
dc.identifier.issn1938-8160en_US
dc.identifier.urihttp://hdl.handle.net/10397/115127-
dc.language.isoenen_US
dc.publisherRoutledgeen_US
dc.subjectContent analysisen_US
dc.subjectCueutilization theoryen_US
dc.subjectCustomer Experience Quality (CEQ)en_US
dc.subjectfsQCAen_US
dc.subjectSmart hotelsen_US
dc.titleExploring the drivers of customer experience quality in smart hotels : a content and fsqca approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1080/19388160.2025.2542173en_US
dcterms.abstractThe rise of smart hotels represents a key innovation transforming customer experience in the digital era. The study aims to explore the critical issue of customer experience quality (CEQ) in smart hotels by systematically analyzing the factors influencing CEQ and proposing strategic optimization configurations. Based on cue utilization theory and complexity theory, 5,852 online reviews from six representative smart hotels are analyzed by content analysis and fuzzy-set qualitative comparative analysis (fsQCA). Eight critical factors are identified: staff performance, price and value, location, smart service, safety and security, learning, servicescape, and food and beverage. The configurational analysis reveals four distinct pathways to high CEQ: (1) staff performance-servicescape-oriented type, (2) smart service-servicescape-oriented type, (3) staff performance-smart service-safety and security-oriented type, and (4) staff performance-smart service-servicescape-oriented type. These findings advance theoretical understanding of the human – technology interplay in smart hotels and offer practical guidance for enhancing CEQ and competitive advantage through strategic configurations.en_US
dcterms.abstract智能酒店的兴起正在重塑数字时代的旅游消费体验。本研究系统探讨智能酒店顾客体验质量(CEQ)的影响因素,并提出优化的组态配置路径。基于线索利用理论和复杂性理论,采用内容分析与模糊集定性比较分析(fsQCA)对六家代表性智能酒店的5,852条在线评论进行研究,确定了人工服务、价格价值、地理位置、智能服务、安全保障、学习机会、服务景观和餐饮品质八大关键因素。组态分析揭示了实现高CEQ的四种典型路径: 人工服务-服务景观导向型、智能服务-服务景观导向型、人工服务-智能服务-安全保障导向型,以及人工服务-智能服务-服务景观导向型。研究深化了智能酒店中人-技术要素协同的理论认识,并为酒店管理者通过战略组态优化提升顾客体验质量和竞争优势提供了实践参考。en_US
dcterms.alternative探索智能酒店客户体验质量的驱动因素 : 内容分析和fsQCAen_US
dcterms.bibliographicCitationJournal of China tourism research (中國旅游硏究), Published online: 06 Aug 2025, Latest Articles, https://doi.org/10.1080/19388160.2025.2542173en_US
dcterms.isPartOfJournal of China tourism research (中國旅游硏究)en_US
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105012839706-
dc.identifier.eissn1938-8179en_US
dc.description.validate202509 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera4009b-
dc.identifier.SubFormID51914-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis project received funding from The Hong Kong Polytechnic University (UGC) [P0045695];the Research Grants Council under the RGC Early Career Scheme [P0047204, 25504823]; theInnovation and Technology Commission [P0043294, ITS/028/22FP]; and the Research GrantsCouncil under the RGC General Research Fund [15505324].en_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
dc.description.oaCategoryGreen (AAM)en_US
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