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By focusing on Sustainable Development Goal 12 (SDG 12) and tourism automation, this perspective paper aims to investigate how tourism and automation will work to create a world in which tourism has more sustainable production and consumption patterns.

This perspective paper reviews the past developments of automation in tourism in the context of sustainable production and consumption patterns, the lessons learned from the COVID-19 pandemic and looks at the future of tourism and how automation will help it be more sustainable in terms of consumption and production patterns.

The insights from this analysis suggest that automation technologies will play a major role in both the supply and demand sides of the tourism and hospitality industry, encouraging increased tourism sustainability. While automation technologies will have the greatest impact on the supply side in the near future, as such technologies will be used to minimise waste and energy usage, creating large gains for environmental protection, the technologies will also benefit responsible consumption. Big data and analytical technologies will work in ways to ensure that consumers are nudged into consumer practices that are increasingly sustainable.

This perspective paper synthesises the literature on the subjects, namely, automation and SDG 12 in tourism, and points to important new future research agenda. This is one of the first papers in tourism to blend automation and SDG 12 literature to shed light on the use of automation in sustainable consumption and production in tourism.

通过聚焦于可持续发展目标12和旅游自动化, 本前瞻性文章旨在探讨旅游业和自动化如何共同创造一个让旅游产业拥有更可持续的生产和消费模式的世界。

本文回顾了旅游自动化在可持续生产和消费模式背景下的发展, 从COVID-19大流行中学到的教训, 并展望旅游业的未来以及自动化如何帮助其在消费和生产模式方面变得更加可持续。

根据分析, 自动化技术将在旅游和酒店业的供求两侧发挥重要作用, 促进旅游业的可持续性发展。虽然自动化技术在近期内将对供应侧产生最大影响, 因为这些技术将被用来最小化废物和能源使用, 为环境保护创造巨大收益, 但这些技术也将惠及负责任消费。大数据和分析技术将以确保消费者被引导向越来越可持续的消费实践。

本前瞻性论文综合了关于旅游中的自动化和可持续发展目标12的文献, 并指出了重要的新的未来研究议程。这是旅游业中第一批结合自动化和可持终发展目标12文献以阐明旅游中可持续消费和生产的自动化使用的论文之一。

Al centrarse en el ODS12 y la automatización del turismo, este artículo de perspectiva pretende investigar cómo el turismo y la automatización trabajarán para crear un mundo en el que el turismo tenga unos patrones de producción y consumo más sostenibles.

Este artículo de perspectiva revisa los desarrollos pasados de la automatización en el turismo en el contexto de los patrones de producción y consumo sostenibles, las lecciones aprendidas de la pandemia COVID-19, y examina el futuro del turismo y cómo la automatización le ayudará a ser más sostenible en términos de patrones de consumo y producción.

Las conclusiones de este análisis sugieren que las tecnologías de automatización desempeñarán un papel importante tanto en la oferta como en la demanda de la industria del turismo y la hotelería, fomentando una mayor sostenibilidad del turismo. Mientras que las tecnologías de automatización tendrán el mayor impacto en el lado de la oferta en un futuro próximo, ya que dichas tecnologías se utilizarán para minimizar los residuos y el uso de energía, creando grandes ganancias para la protección del medio ambiente, las tecnologías también beneficiarán al consumo responsable. Los macrodatos y las tecnologías analíticas funcionarán de manera que se incite a los consumidores a adoptar prácticas de consumo cada vez más sostenibles.

Este documento de perspectiva sintetiza la bibliografía sobre los temas, a saber, la automatización y el ODS12 en el turismo, y apunta a una nueva e importante agenda de investigación futura. Se trata de uno de los primeros trabajos sobre turismo que combina la literatura sobre automatización y ODS12 para arrojar luz sobre el uso de la automatización en el consumo y la producción sostenibles en el turismo.

This study aims to develop a framework to identify and prioritize the key factors in automation and artificial intelligence (AI) implementation in the hospitality and tourism industry.

This paper used the analytic hierarchy process, a multi-criteria decision-making method, to prioritize the factors influencing automation and AI implementation. This paper developed a model with five criteria (human knowledge, services, robotics applications, internal environment and institutional environment) and 23 sub-criteria obtained from previous studies. This paper designed a questionnaire in the form of pairwise comparisons based on the proposed hierarchical structure. This paper used a nine-point ranking scale to show the relative significance of each variable in the hierarchy and tested the model among staff from 35 five-star hotels and top-rated tourism agencies in the United Arab Emirates.

Human knowledge, services and robotics applications were the most significant factors influencing automation and AI implementation. Practitioners and researchers in the hospitality and tourism industry could apply the proposed framework to develop sustainable strategies for implementing and managing automation and AI. The proposed framework may also be useful in future studies examining AI implementation in the hospitality and tourism industry.

This paper developed a framework for policymakers that identifies and could help to overcome some of the challenges in implementing automation and AI in the hospitality and tourism sector around the world. The results provide an agenda for future research in this area.

酒店业和旅游业中的自动化和人工智能

摘要

本研究旨在开发一个框架, 以确定和优先考虑酒店业和旅游业自动化和人工智能（AI）实施的关键因素。

我们使用层次分析法, 一种多准则决策方法, 对影响自动化和人工智能实施的因素进行优先排序。我们建立了一个包含五个标准（人类知识、服务、机器人技术应用、内部环境和制度环境）的模型, 并从先前的研究中获得了23个子标准。基于所提出的层次结构, 我们设计了一份以成对比较的形式进行的问卷调查。我们使用九点评分量表来说明每个标准或子标准在层级中的相对重要性, 并在阿联酋35家五星级酒店和顶级旅游机构的员工中测试了该模型。

人类知识、服务和机器人应用是影响自动化和人工智能实施的最重要因素。酒店业和旅游业的从业人员和研究人员可以应用拟议的框架来制定实施和管理自动化和人工智能的可持续战略。拟议的框架也可能有助于未来研究人工智能在酒店业和旅游业的实施。

我们为决策者开发了一个框架, 用来识别并帮助其克服全球酒店业和旅游业实施自动化和人工智能方面的一些挑战。研究结果为今后该领域的研究提供了一个议程。

Automatización e inteligencia artificial en hostelería y turismo

Resumen

Este estudio tuvo como objetivo desarrollar un marco para identificar y priorizar los factores clave en la implementación de la automatización y la inteligencia artificial (IA) en la industria hotelera y turística.

utilizamos el proceso de jerarquía analítica, un método de toma de decisiones de varios criterios, para priorizar los factores que influyen en la automatización y la implementación de la IA. Desarrollamos un modelo con cinco criterios (conocimiento humano, servicios, aplicaciones robóticas, entorno interno y entorno institucional) y 23 subcriterios obtenidos de estudios previos. Diseñamos un cuestionario en forma de comparaciones por pares basados en la estructura jerárquica propuesta. Usamos una escala de clasificación de nueve puntos para mostrar la importancia relativa de cada variable en la jerarquía y probamos el modelo entre el personal de 35 hoteles de cinco estrellas y las agencias de turismo mejor calificadas en los Emiratos Árabes Unidos.

el conocimiento humano, los servicios y las aplicaciones robóticas fueron los factores más importantes que influyeron en la automatización y la implementación de la inteligencia artificial. Los profesionales e investigadores de la industria hotelera y turística podrían aplicar el marco propuesto para desarrollar estrategias sostenibles para implementar y gestionar la automatización y la IA. El marco propuesto también puede ser útil en estudios futuros que examinen la implementación de la IA en la industria hotelera y turística.

desarrollamos un marco para los formuladores de políticas que identifica y podría ayudar a superar algunos de los desafíos en la implementación de la automatización y la inteligencia artificial en el sector de la hospitalidad y el turismo en todo el mundo. Los resultados proporcionan una agenda para futuras investigaciones en esta área.

The purpose of this paper is to systematically analyze existing studies related to the adoption of smart technologies in cruise tourism services, particularly robots, artificial intelligence, service automation and virtual reality. More specifically, the authors intend to highlight the current state of research on this topic, present the findings within a conceptual framework and propose a research agenda.

The relevant literature was extracted using two major electronic databases, web of science (WoS) and Scopus. The authors identified 31 articles from high-quality journals and used a systematic review and the VOSviewer software to analyze them.

Since 2014, there has been an increase in the number of studies related to smart technologies in cruise tourism services. At first, researchers focused on Royal Caribbean’s robotic bartender arm, whereas other technologies such as digital signage, self-service options, facial recognition and virtual culinary experiences received less attention. However, the interest in exploring these last smart technologies has grown significantly since 2019. The adoption of RAISA in the cruise tourism service (ASCT) framework was proposed, identifying five major domains: cruise robotic technology, technology innovation, cruise passengers’ engagement behavior, cruise passengers’ technology readiness and privacy perception and knowledge expertise. These domains provide valuable guidance for future research in this field.

To the best of the authors’ knowledge, this is the first study to systematically analyze literature on the adoption of new technologies in cruise tourism services, specifically focusing on the major technologies available on cruise ships.

本文的主要目标是系统地分析与邮轮旅游服务中智能技术采用（RAISA）相关的现有研究, 特别是关于机器人、人工智能、服务自动化和虚拟现实等方面。具体而言, 我们意图突出此主题的当前研究现状, 将研究结果呈现在一个概念框架中, 并提出一个研究议程。

我们使用了两个主要的电子数据库 WoS 和 Scopus 提取相关文献。我们从高质量期刊中找到了 31 篇文章, 并采用系统综述和 VOSviewer 软件对它们进行了分析。

自 2014 年以来, 与邮轮旅游服务中智能技术相关的研究数量不断增加。最初, 研究人员着重关注皇家加勒比邮轮公司的机器人调酒师臂, 而其他技术, 如数字标识、自助选项、面部识别和虚拟烹饪体验等, 得到的关注较少。然而, 自 2019 年以来, 探索这些智能技术的兴趣显著增长。提出了 ASCT 框架, 识别了五个主要领域：邮轮机器人技术; 技术创新; 邮轮旅客参与行为; 邮轮旅客技术准备度; 以及隐私感知和专业知识。这些领域为该领域的未来研究提供了有价值的指导。

这是第一篇系统分析邮轮旅游服务中新技术采用文献的研究, 特别聚焦于邮轮上的主要技术。

Artificial intelligence (AI) and big data analysis may further enhance the automated and smart features of tourism and hospitality services. However, it also poses new challenges to human resource management. This study aims to explore the direct and indirect effects of employees’ AI perception on career resilience and informal learning as well as the mediating effect of career resilience.

This paper proposed a theoretical model of AI perception, career resilience and informal learning with perceived AI as the antecedent variable, career resilience as the mediate variable and informal learning as the endogenous variable. Targeting the employees working with AI, a total of 472 valid data were collected. Data were analyzed using structural equation modeling with AMOS software.

Findings indicated that employees’ perception of AI positively contributes to career resilience and informal learning. Apart from the direct effect on informal learning, career resilience also mediates the relationship between AI perception and informal learning.

Research findings provide both theoretical and practical implications by revealing the impact of AI perception on employees’ career development, leaning activities, explaining how AI transforms the nature of work and career development and shedding lights on human resource management in the tourism and hospitality field.

本文提出了人工智能感知为前因变量、职业弹性为中介变量、非正式学习为内生变量的理论模型。以旅游业AI工作环境中的员工为研究对象, 本课题共收集了472份来自中国的有效数据, 并通过结构方程建模（SEM）来进行相关模型检验。

人工智能和大数据分析可能会使旅游和酒店服务更加自动化和智能化, 但这也对人力资源管理提出了新的挑战。本研究旨在探讨员工对人工智能（AI）的感知对职业弹性和非正式学习的直接和间接影响, 以及职业弹性的中介作用。

研究结果显示, 员工对人工智能的感知对职业弹性和非正式学习有积极影响。除了对非正式学习的直接影响外, 职业弹性在人工智能 (A I) 感知和非正式学习之间起中介作用。

本研究在以下几个方面具有重要的理论和实践意义：解释了人工智能感知对员工职业发展和学习行为的影响, 以及它是如何改变工作性质和员工职业发展的; 研究发现对旅游和酒店行业的人力资源管理具有实践指导意义。

La IA y el análisis de big data pueden potenciar aún más las características automatizadas e inteligentes de los servicios de turismo y hostelería. Sin embargo, también plantea nuevos retos a la gestión de los recursos humanos. Este estudio pretende explorar los efectos directos e indirectos de la percepción de la IA por parte de los empleados sobre la resiliencia profesional y el aprendizaje informal, así como el efecto mediador de la resiliencia profesional.

En este trabajo se propone un modelo teórico de percepción de la IA, resiliencia profesional y aprendizaje informal con la IA percibida como variable antecedente, la resiliencia profesional como variable mediadora y el aprendizaje informal como variable endógena. Dirigidos a los empleados que trabajan con IA, se recogieron un total de 472 datos válidos. Los datos se analizaron mediante un modelo de ecuaciones estructurales (SEM) con el software AMOS.

Los Resultados indicaron que la percepción de la IA por parte de los empleados contribuye positivamente a la resiliencia profesional y al aprendizaje informal. Aparte del efecto directo sobre el aprendizaje informal, la resiliencia profesional también media en la relación entre la percepción de la IA y el aprendizaje informal.

Los Resultados de la investigación proporcionan implicaciones tanto teóricas como prácticas al revelar el impacto de la percepción de la IA en el desarrollo profesional de los empleados, las actividades de aprendizaje, explicar cómo la IA transforma la naturaleza del trabajo y el desarrollo profesional, y arrojar luz sobre la gestión de los recursos humanos en el ámbito del turismo y la hostelería.

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

本研究使用集成的四层神经网络构建面向方面的情感分类模型：BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

This paper aims to explore and discuss the impact of digital innovations from a business eco-systemic perspective. Key smart technology application themes in the cruise industry are extracted and synthesised in a “Smart Cruise Ecosystem” (SCE) framework.

Information communication technologies (ICTs) advancements and smart tools revolutionise interactions and affect all transactions, transforming the cruise experience. Gradually a Smart Cruise Ecosystem emerges by incorporating all technologies available and involving cruise passengers, who as smart actors interact dynamically with stakeholders, creating value before, during and after the cruising experience. The COVID-19 pandemic outbreak stressed the need for touchless and digital interactions as well as real-time information, fast-tracking the deployment of smart technologies. The diffusion of ICTs in the cruise industry is multi-faceted and dynamic, resulting in a number of smart-technology use-cases.

Smart technology constitutes a comprehensive smart ecosystem to manage all actors, controls, devises and systems to optimise ship operations and management, while co-creating value for guests and crew in an effective way. The multiplex SCE proposed is enabled by digital technologies collecting, storing, accessing and processing big data dynamically, including: object detection, Internet of Things, Internet of Everything, satellite communications, Big Data, automation, robotics, Artificial Intelligence and Machine Learnin, Cloud Computing, Augmented, Virtual and Mixed Reality. A range of interoperable and interconnected supporting systems form the basis of the smart ecosystem.

The proposed framework offers a holistic perspective of the smart-cruising domain, highlighting innovations, interfaces, dependencies, along with the corresponding key limitations and challenges. The synthesis and conceptual structure provided serves as a topology for guiding and connecting further research in smart cruising.

本文从商业生态系统的角度探索和讨论了数字创新的影响。在“智能邮轮生态系统”框架中提取和综合邮轮行业的关键智能技术应用主题

信息通信技术 (ICT) 的进步和智能工具彻底改变了交互并影响所有交易, 从而改变了邮轮体验。通过整合所有可用技术并让邮轮乘客参与其中, 智能邮轮生态系统逐渐出现, 他们作为聪明的参与者与利益相关者动态互动, 在巡航体验之前、期间和之后创造价值。 COVID19 大流行的爆发强调了对非接触式和数字化交互以及实时信息的需求, 以快速跟踪智能技术的部署。信息通信技术在邮轮行业的传播是多方面的和动态的, 产生了许多智能技术用例

智能技术构成了一个全面的智能生态系统, 用于管理所有参与者、控制、设计和系统, 以优化船舶运营和管理, 同时以有效的方式与用户和船员共同创造价值。提出的多元智能巡航生态系统（SCE）由数字技术实现, 包括物体检测、物联网、卫星通信、大数据、自动化、机器人、人工智能、云计算、AR、VR, 动态收集、访问和处理大数据。一系列可互操作和互连的支持系统构成了智能生态系统的基础。

本研究提议的理论框架提供了智能巡航领域的整体视角, 突出了创新、接口和依赖关系, 以及相应的关键限制和挑战。所提供的综合和概念结构用于指导和连接智能领域的进一步研究提供划分类型。

The purpose of this study, a current systematic literature review, is to synthesize the extant literature on consumers’ adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. This study also outlines insights for academia, practitioners, AI marketers, developers, designers and policymakers.

This study used a content analysis approach to conduct a systematic literature review for the period of 10 years (2011–2020) of the various published studies themed around consumer’s adoption of AIR in HATS.

The synthesis draws upon various factors affecting the adoption of AIR, such as individual factors, service factors, technical and performance factors, social and cultural factors and infrastructural factors. Additionally, the authors identified four major barriers, namely, psychological, social, financial, technical and functional that hinder the consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.

To the best of the author’s/authors’ knowledge, this study is a first attempt to synthesize the factors that drive consumers’ adoption of artificial intelligence and robots in the hospitality and tourism industry. The present work also advances the tourism and consumer behavior literature by offering an integrated antecedent-outcome framework.

Figure 2 The objective of the current systematic literature review is to synthesize the extant literature on consumer’s adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. For that purpose, authors conducted content analysis of extant literature on consumer’s adoption of AIR in HATS from 2011 to 2020. Authors presented an integrated antecedent outcome framework of the factors that drive consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.

这篇系统性文献综述的目的是综合现有关于消费者在酒店和旅游部门（HATS）中采用人工智能和机器人（AIR）的文献, 以便全面了解它。这项研究还概述了学术界、从业者、人工智能营销人员、开发人员、设计师和决策者的见解。

本研究使用内容分析方法对 10 年（2011–2020 年）期间的各种已发表研究进行系统的文献回顾, 主题围绕消费者在 HATS 中采用 AIR。

本研究揭示了四大服务：自动化、定制、信息传播、旅游移动性和导航服务。 此外, 作者确定了阻碍消费者在酒店和旅游业采用人工智能和机器人的四大障碍, 即心理、社会、财务、技术和功能

本研究首次尝试综合推动消费者在酒店和旅游业中采用人工智能和机器人的因素。本文还通过提供一个综合的前因结果框架, 推进了旅游和消费者行为文献。

El objetivo de la actual revisión sistemática literaria es sintetizar la literatura existente sobre la adopción de la inteligencia artificial y la robótica (IAR) por parte de los consumidores en el contexto del sector hotelero y turístico (SHT) para ganar un entendimiento comprensivo del mismo. Este estudio también traza visiones para los académicos, profesionales, comercializadores de AI, desarrolladores, diseñadores, y los elaboradores de las políticas a seguir.

El presente estudio siguió un enfoque de análisis de contenido para realizar una revisión sistemática de la literatura durante el período de 10 años (2011–2020) de los diversos estudios publicados y basados en la adopción de IAR en SHT, por parte de los consumidores.

Este estudio desvela cuatro grandes servicios: automatización, personalización, difusión de información, movilidad turística y servicios de navegación. Adicionalmente, los autores identificaron cuatro barreras principales, a saber; psicológicas, sociales, financieras, técnicas y funcionales, que impiden la adopción de la inteligenica artificial y la robótica por parte del consumidor, en la industria de la hospitalidad y el turismo.

Este estudio es un primer intento de sintetizar los factores que impulsan la adopción de la inteligencia artificial y la robótica por parte de los consumidores en la industria hotelera y turística. El presente trabajo también fomenta la literatura sobre el turismo y el comportamiento del consumidor, ofreciendo un marco integrado de resultados precedentes.

Many hotels allocate guests to specific rooms immediately after reservation. This happens because individual rooms are sold (and there is no concept of room type) or because the assignment is done by hand at reservation or because of a connection with a channel manager, which is immediately fixing the room number after a reservation request. This early allocation is suboptimal, and it causes the unnecessary rejection of some reservations when the hotel has a high occupancy level. The purpose of this paper is to investigate different room allocation algorithms, including an optimal one (called RoomTetris), aiming at higher occupancy levels and profitability.

The methodology is based on theoretical results and experimentation. The optimality or the proposed RoomTetris algorithm is demonstrated. Experiments are executed in different contexts, including realistic ones, through the adoption of a hotel simulator, to measure the improvements in the occupancy rate of the optimal and heuristic strategies with respect to random or sub-optimal assignments of rooms.

The main results are that smart allocation algorithms can greatly reduce the rejection rate (reservation requests which cannot be fit into the hotel room plan) and improve the occupancy level, the percentage of available rooms or beds sold for the various periods.

This analysis can be extended by considering cancellations and overbookings. A second possibility to add flexibility in room allocation for hotels having more than one type of rooms is that the hotel can upgrade and offer a high-price room to the customer, which given an even large flexibility to fix rooms by shifting customers to other compatible types. In addition, more complex integrations with revenue management can also be considered, for cases in which the cost of a room depends on the number of guests.

Given that the difference in occupancy rate of the optimal algorithm is particularly large in high season and high-request periods, periods which are usually associated to higher rates and higher volumes, the proposed algorithm will improve the main financial performance indicators such as revenue per available room by an even bigger multiplier, depending on the hotel pricing policy. Because the room allocation process can be completely automated, the adoption of appropriate smart allocation algorithms represents a low-hanging fruit to be picked by efficient hotel managers.

To the best of the knowledge this is the first proposal of an optimal algorithm (with proof of optimality) for the considered problem.

很多酒店, 特别是私人、家庭经营型、或者精品酒店, 在客人预定后立刻分派指定的房间给客人。这往往是因为独立房间售卖（没有特殊房型概念）或者因为客人在预定时, 工作人员手动指派房间, 亦或者是因为预订系统与渠道管理系统链接, 直接在预定后指派房间号。这种早期的分派程序是不优化的, 往往在酒店住房率高的时候, 会造成一些不必要的房间预定失败, 继而带来的利润损失。本论文旨在研究不同房间指派参数配置, 包括最优系统（RoomTetris）, 使得酒店达到更高住房率的同时产生高利润。

本论文采用理论讨论和实验等研究方法, 并展示了提出的RoomTetris参数的最优性。本论文还将其参数放在不同的情景中做实验, 以显示其提高酒店针对随机或者次优化分派的最佳启发式策略中的住房率。

研究结果表明智能型分派参数能够大大降低预定失败率（预定需求不能符合酒店房型供给）, 并且提高住房率和利润。住房时间并不是必须的参数, 极具个性化服务, 比如让客人选房间号, 可能导致利润损失（因为最优房间分派无法实现）, 房型的设计也应该参与到最优房间分派的效果中来。

预定取消和超额预定的情况也应该加入到分析中来。第二种对于拥有不止一种房型的酒店来说, 可能增加房间分派的情况在于为客人升级房型, 这样可以将客人转到其他适合房型以解决房间分派问题。此外, 更复杂系统兼容财务管理系统应该被考量, 有的时候, 房间的成本取决于客人的数量。

由于最优算法的住房率区别在于旺季和高预定时段, 也就是高房间价格和高预定量, 本论文提出的最佳算法将提高主要财务指标, 比如RevPAR（平均客房收益）。由于房间分配系统可以完全实现自动化, 那么采用智能分派系统无疑是有效酒店管理中的优质选择。

据作者所知, 此文章是首篇关于此类话题的研究优质算法（且被证实其最佳）。

Voice assistants (VAs) empower human–computer interactions by recognising human speech and implementing commands pronounced by users. This paper aims to investigate VA-enabled interactions between hotels and guests in the hospitality context. The research positions VAs within the artificial intelligence (AI)-enabled Internet of Things (IoT) context, disrupting old practices and processes. Smart hospitality uses VAs to support effortless value cocreation for guests cost-effectively. The research examines consumer perceptions and expectations of hospitality VAs and explores VA capabilities through expert technology providers.

This empirical paper investigates the current use and future implications of VAs for hotel environments. It uses qualitative, semi-structured in-depth interviews with 7 expert hospitality VA technology providers and 21 hotel guests who have VA experience. The research adopts a demand and supply approach, addressing the VAs in hospitality holistically.

The findings illustrate the requirements from both end-users’ sides, hotels and guests, exploring VA advantages and challenges. The analysis demonstrates that VAs increasingly become digital assistants. VA technology helps hotels to improve customer service, expand operational capability and reduce costs. Although in its infancy, VA technology has made progress towards optimising hotel operations and upgrading customer service. The study proposes a speech-enabled interactions model.

This research stimulates the transformation of hospitality services by using VAs and the development of smart hospitality and tourism ecosystems. The study can benefit from further research with hotel managers, to reflect hoteliers’ points of view and investigate their perception of VAs. Further research can also explore different aspects of consumer–VA interaction in different contexts.

The paper makes a significant contribution to hospitality management and human–computer interaction best practices. It supports technology providers to reconsider how to develop suitable technology solutions towards improving their strategic competitiveness. It also explains how to use VAs cost-effectively and profitably while adding value to travellers’ experience.

VA studies are often focussed on the technology in private households, rather than in commercial or hotel spaces. This paper contributes to the emerging literature on AI and IoT in smart hospitality and explores the acceptance and operationalisation of VAs. The research contributes to the conceptualisation of VA-enabled hotel services and explores positive and negative features, as well as future prospects.

语音（数码, 虚拟或者人工智能）(VAs) 助理能够识别真人声音和系统指令声音从而为人机互动提供助力。 本研究基于酒店业已有的案例探索了酒店和客人语言支持的互动的本质。本研究探索了关于客房服务的表现和实用性的顾客认知和顾客期望。

本研究借用了定性半结构化深入访谈来收集一手数据, 调查了酒店环境中的语音助理目前的运用情况和未来影响。本研究访谈了7位酒店科技服务提供者和21位有使用个人智能扬声器经验的酒店客人。本研究以供需模型来全方位解决此项问题。

研究结论强调了从双方使用者（酒店和客人）角度考量, 并且考虑到语音仪器带来的所有的优势和挑战的重要性。基于顾客之前对这些仪器的使用经验, 本论文深入阐述了对公共空间这些语音助理的使用功能。本分析证明了由AI技术支持下, VAs 可以灵活的识别语音。VAs理解词汇内容并且也懂得词汇作为数据助手来支持智能家庭的自动化以及履行商业职责背后的意义来。酒店从业人员可以尝试运用此科技来提高用户体验, 扩展运营空间和减少成本。从访谈的数据可以证明, 尽管还在初级阶段, VAs 科技已经对酒店运营优化和晋级客户服务做出了显著的贡献和成就。本研究提出了一项介于酒店和客人之间的VA 语言支持的互动模型。

之前的研究主要针对私人使用, 而不是商业或者酒店空间。考虑到敌对环境和客人和酒店的文化差异, 旅游和酒店成为了尤其有挑战性的领域。本研究通过语音识别革新了酒店服务。本研究针对VAs接受程度和运用进行详细研究从而为酒店领域的AI和IoT提供了专业文献。本文为酒店服务中的VAs使用做出了开拓性的调查并且探讨了正面以及负面的特征和未来展望。

本研究为酒店和旅游管理补充了文献, 尤其是在酒店业人机互动领域。本研究通过语音识别技术对酒店服务的转型革新做出贡献。此项研究可以受益于以酒店管理者为视角的进一步研究。相比于收集科技设备供应方的见解, 对酒店管理者进行访谈可以提供更直接的信息来了解酒店员工对数码语音助理和对语音科技的总体接受程度。

酒店经常寻求途径来提高和客户的互动。本论文对酒店管理和人机互动领域提供了应用典例。本研究提供了酒店客人针对语音设备功能性的总体评价作为有价值的信息, 进而帮助科技提供者来重新审视针对顾客需要从而量身定制服务的策略和方式。最终, 本研究在提高行业战略竞争力层面分享了关于发展和采用此项强大有力科技的见解。

The rapid growth of artificial intelligence is disrupting various industries, including the tourism sector. This paper aims to outline the applications, benefits and risks of ChatGPT and large language models in general on tourism. It also aims to establish a research agenda for investigating the implications of these models in tourism.

Drawing on the available literature on ChatGPT, large language models and artificial intelligence, the paper identifies areas of application of ChatGPT for several tourism stakeholders. Potential benefits and risks are then considered.

ChatGPT and other similar models are likely to have a profound impact on several tourism processes. They will contribute to further streamline customer service in front-of-house operations and increase productivity and efficiency in back-of-house operations. Although negative consequences for human resources are expected, this technology mostly enhances tourism employees.

To the best of the authors’ knowledge, this is one of the first studies that explore the potential implications of ChatGPT in tourism and hospitality.

人工智能的快速发展正在颠覆包括旅游业在内的各个行业。 本文旨在概述ChatGPT和大型语言模型在旅游业中的应用、好处和风险。同时, 旨在建立一个研究议程, 以调查这些模型在旅游业中的影响。

本文借鉴了关于ChatGPT、大型语言模型和人工智能的现有文献, 确定了ChatGPT在几个旅游利益相关者中的应用范围, 然后考虑了潜在的好处和风险。

ChatGPT和其他类似的模型可能会对一些旅游过程产生深远的影响。它们将有助于进一步简化前台业务的客户服务, 并提高后台业务的生产力和效率。虽然对人力资源的负面影响是可以预见的, 但这项技术主要是增强旅游业的员工能力。

这是首批探索ChatGPT在旅游业和酒店业潜在影响的研究之一。

A partir de la bibliografía disponible sobre ChatGPT, grandes modelos lingüísticos e inteligencia artificial, este artículo identifica las posibles áreas de aplicación de ChatGPT y actores que se pueden beneficiar. De igual forma, se examinan los posibles beneficios y riesgos.

El rápido crecimiento de la inteligencia artificial está afectando diversas industrias, incluyendo la del turismo. Este artículo pretende esbozar las aplicaciones, ventajas y riesgos de ChatGPT, así como los grandes modelos lingüísticos, en turismo. También pretende establecer una agenda de investigación para estudiar las implicaciones de estos modelos en el turismo.

Es probable que ChatGPT y otros modelos similares tengan un profundo impacto en varios procesos turísticos, contribuyendo a racionalizar, aún más, el servicio al cliente en las operaciones de front-of-the-house y aumentando la productividad y eficiencia en el back-of-the-house. Aunque se prevén consecuencias negativas para los recursos humanos, esta tecnología servirá sobre todo para potenciarlos.

Éste es uno de los primeros estudios que exploran las implicaciones potenciales de ChatGPT en el turismo y la hostelería.