Search results

A summary statement of the meaning of tourism intelligence is built in this chapter by considering multiple sources. Tourism intelligence is then cast as the sum of the resources available to a decisionmaker coupled with their interpretive ability to use it. Academic researchers can contribute to this resource base but need to deal with the likely use of other inputs by decisionmakers. Tourism intelligence can be a bridge between academic inputs and broader influences provided that concerns about credibility, trustworthiness, and accessibility of the scholarly work are well managed. The tourism intelligence concept has value for all stakeholders and the chapters in this volume follow a structure to assist the transition from analysis to action.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups.

A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior.

Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics.

This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.

对不同类型太空旅游所识别的不同类别太空游客行为的研究有限。 为了解决这一缺陷, 这项工作研究了哪些因素使消费者参与轨道和/或亚轨道太空旅游, 以及动机、约束和人工智能三个维度。 为了实现研究目标, 在比较轨道和亚轨道太空旅游群体时, 开发了一个综合研究模型, 包括内在和外在动机、内在和人际约束以及对人工智能的认识和信任三个维度。

对希望在未来参与轨道 (n = 332) 和亚轨道 (n = 332) 太空旅游的受访者进行了问卷调查。 利用偏最小二乘法 (PLS)-结构方程模型 (SEM)、模糊集定性比较分析 (fsQCA)、多组分析和深度学习来了解潜在的太空游客行为。

外在动机对行为意图的积极影响最大, 其次是对人工智能的认识和信任, 而内在约束对行为意图有强烈的负面影响。 令人惊讶的是, 人际约束对于 PLS-SEM 来说是微不足道的, 但对于 fsQCA 来说仍然是充分的因果配置之一。 有趣的是, 这两类太空旅游具有非常鲜明的特点。

这项工作创建了一个全面的综合研究模型, 具有动机、约束和人工智能三个维度, 以及潜在的轨道和亚轨道太空旅游群体, 以确定未来的消费者行为。 重要的是, 这项研究采用了多种分析方法, 使用四种不同的方法来更好地揭示潜在的轨道和亚轨道太空游客。

existe una investigación limitada sobre el comportamiento de las diferentes categorías de turistas espaciales identificados por diferentes tipos de turismo espacial. Para abordar esta deficiencia, este trabajo examina qué factores hacen que los consumidores participen en el turismo espacial orbital y/o suborbital, junto con tres dimensiones de motivación, restricción e inteligencia artificial. Para lograr los objetivos del estudio, se desarrolló un modelo de investigación integral que incluía tres dimensiones de motivación intrínseca y extrínseca, restricción intrapersonal e interpersonal, y conocimiento y confianza en la inteligencia artificial, al comparar grupos de turismo espacial orbital y suborbital.

se realizó un cuestionario con los encuestados que querían participar en el turismo espacial orbital (n = 332) y suborbital (n = 332) en el futuro. Se utilizaron modelos de ecuaciones estructurales (SEM) de mínimos cuadrados parciales (PLS), análisis comparativo cualitativo de conjuntos borrosos (fsQCA), análisis multigrupo y aprendizaje profundo para comprender el comportamiento potencial del turista espacial.

la motivación extrínseca tiene el mayor impacto positivo en la intención de comportamiento, seguida de la conciencia y la confianza en la inteligencia artificial, mientras que la restricción intrapersonal afecta negativamente la intención de comportamiento. Sorprendentemente, la restricción interpersonal es insignificante por PLS-SEM, pero sigue siendo una de las configuraciones causales suficientes por fsQCA. Curiosamente, los dos tipos de turismo espacial tienen características muy distintas.

este trabajo creó un modelo de investigación integral integral con tres dimensiones de motivación, restricción e inteligencia artificial, junto con posibles grupos de turistas espaciales orbitales y suborbitales para identificar el comportamiento futuro del consumidor. Es importante destacar que este estudio empleó métodos de análisis múltiple utilizando cuatro enfoques diferentes para arrojar mejor luz sobre los posibles turistas espaciales orbitales y suborbitales.

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

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.

This study explores the influence of cultural heritage tourism perception on China's tourism image. It analyzes the role of the spiritual bond established between overseas Chinese youth and the motherland during their visit to the cultural heritage sites in China. This study constructs a theoretical model with 350 overseas Chinese youth as samples based on the identity theory, Stimulus-Organism-Response (S-O-R) theory, and Howard-Sheth model. The results show that cultural heritage tourism perception directly and positively promotes cultural identity among overseas Chinese youth. It is also indirectly and positively associated with their cultural identity through enhancing the tourism image. Cultural intelligence plays a positive moderating role between cultural heritage tourism perception and cultural identity. The results provide significant implications for developing cultural heritage tourism and cultural communication.

Dynamic, volatile, and time-sensitive industries, such as tourism, travel and hospitality require agility and market intelligence to create value and achieve competitive advantage. The aim of the current study is to examine the influence of big data (BD) on the performance of service organizations and to probe for a deeper understanding of implementing BD, based on available technologies.

An ethnographic study was conducted following an abductive approach. A primary qualitative research scheme was used with 35 information technology and database professionals participating in five online focus groups of seven participants each. Analytical themes were developed simultaneously with the literature being revisited throughout the study to ultimately create sets of common themes and dimensions.

BD can help organizations build agility, especially within dynamic industries, to better predict customer behavioral patterns and make tailor-made propositions from the BD. An integrated BD-specific framework is proposed to address value according to the dimensions of need, value, time and utility.

Little research exists on the key drivers of BD use for dynamic, real-time and agile businesses. This research adds to the developing literature on BD applications to support organizational decision-making and business performance in the tourism industry.

This study responds to scholars’ recent calls for more empirical research with contextual understanding of the use of BD to add value in marketing intelligence within business ecosystems. It delineates factors contributing to BD value creation and explores the impacts on the respective service encounters.

The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand.

This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods.

AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip.

To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.

This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm’s competitors. Empirically, it investigates the application of the CIA model on online reviews. This proposed model clarifies the confusion between terms such as competitive intelligence, competitor intelligence and competitor analysis and provides a more efficient process for managers.

The approach of the model integrates text mining techniques as a big data method with network analysis to form a competitor analysis. This study has considered two centrality metrics – degree centrality and betweenness centrality – to identify the functional associations among the resources elaborated by the customers of the hotels.

Findings show online reviews may be used as a solid source of intelligence. The intelligence maps visualized through the text-net technique is an efficient representation of tourist satisfaction and dissatisfaction with a tourism company and its competitors.

The proposed approach can be used in the hotel industry along with many others. The implications for scholars and managers and the possible directions for future research are also discussed in the study.

This study develops a new approach for competitive intelligence practices in the hotel industry and tests a new method for competitor analysis as a part of the competitive intelligence and analysis approach developed in this study.

本研究提出了一个竞争对手情报与分析(CIA)模型, 可用于分析企业的竞争对手。对CIA模型在网络评论中的进行了实证研究。该模型厘清了竞争情报、竞争对手情报和竞争对手分析等术语之间的概念混淆, 为管理者提供了一个更有效的流程。

该模型将文本挖掘技术作为大数据方法与网络分析相结合, 来进行竞争对手分析。本研究考虑了两个中心性指标——度中心性和中介中心性——来识别酒店客户精心设计的资源之间的功能关联。

结果表明, 在线评论可被用作可靠的情报来源。情报地图通过文本网络技术可视化有效地展示了游客对旅游公司及其竞争对手的满意度和不满意度。

本文所提出的方法可用于酒店行业及许多其他行业。同时, 本文也探讨了本研究对学者与管理者的启示, 以及未来可能的研究方向。

本文提出了一种新的酒店行业竞争情报的实践方法, 并测试了一种新的竞争对手分析法, 作为竞争情报和分析方法的一部分。

关键词 竞争情报, 竞争对手情报, 文本挖掘, 网络分析, 在线评论, 酒店

Este estudio propone un modelo de análisis de la inteligencia competitiva (CIA) que puede utilizarse para el análisis de los competidores de la empresa. Empíricamente, investiga la aplicación del modelo de la CIA a las reseñas on line. El modelo propuesto aclara la confusión entre términos como inteligencia competitiva, inteligencia de la competencia y análisis de la competencia y proporciona un procedimiento más eficiente para los gerentes.

El enfoque del modelo integra las técnicas de minería de textos, como método de Big Data, con el análisis de redes para realizar el análisis de la competencia. En este estudio se han considerado dos métricas de centralidad -centralidad de grado e intermediación- para identificar las asociaciones funcionales entre los recursos elaborados por los clientes de los hoteles.

Los hallazgos muestran que las reseñas on line pueden ser utilizadas como una fuente sólida de inteligencia. Los mapas de inteligencia visualizados mediante la técnica de redes de texto son una representación eficiente de la satisfacción e insatisfacción de los turistas con la empresa turística y sus competidores.

El enfoque propuesto puede utilizarse en la industria hotelera junto con otros muchos. En el estudio también se analizan las implicaciones para los investigadores y los gerentes y las posibles directrices para investigaciones futuras.

Este artículo desarrolla un nuevo enfoque para la aplicación de la inteligencia competitiva en la industria hotelera y prueba un método nuevo para el análisis de la competencia como parte del enfoque del Análisis de la Inteligencia Competitiva desarrollado en este estudio.

Palabras clave Inteligencia competitiva, Inteligencia del competidor, Minería de textos, Análisis de redes, Reseñas on line, hoteles

Technology revolutionises the tourism industry and determines the strategy and competitiveness of tourism organisations and destinations. This paper aims to explore the transformational and disruptive nature of technology for tourism.

This paper is based on systematic research.

Technology innovations bring the entire range of stakeholders together in tourism service ecosystems. Technology-empowered tourism experiences increasingly support travellers to co-create value throughout all stages of travel. Ambient Intelligence (AmI) Tourism (2020-future) is driven by a range of disruptive technologies. Inevitably smart environments transform industry structures, processes and practices, having disruptive impacts for service innovation, strategy, management, marketing and competitiveness of everybody involved.

The paper synthesises developments in technology for tourism and proposes a future perspective.