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How does the tourism and hospitality industry use artificial intelligence? A review of empirical studies and future research agenda
Abstract
In response to the increased interest in artificial intelligence (AI) and service robotics within tourism and hospitality (T&H) research, the current study examined 123 articles thematically to determine how AI is defined and which themes are most associated with the phenomena. Several journal articles on AI were extracted and analyzed using bibliometrics (citation and co-citation) and content analysis to uncover the salient themes. We first reviewed the emergent definition of AI and its relevant themes. Then, we presented the methodology and reported quantitative bibliometric and qualitative thematic analysis findings. Subsequently, we revealed recent trends, context, theory, and nomological network in the domain, followed by the main themes and clusters, such as anthropomorphism, theory-based works, methodological foundations, service robots, and employee and customer views on service robots, and their relations with AI in T&H. The research also offers aresearch agenda that outlines opportunities for future AI studies.
... Copious literature has shown consumers' perception of the performance efficacy of AI is critical and could affect the acceptance or otherwise of consumers (e.g. Lin et al., 2020;Chi et al., 2022;Gursoy et al., 2019;Saydam et al., 2022). In this regard, Saydam et al. (2022) observed that performance efficacy is one of the key motivational drivers of consumer acceptance of AI in hospitality and tourism praxis. ...
... Lin et al., 2020;Chi et al., 2022;Gursoy et al., 2019;Saydam et al., 2022). In this regard, Saydam et al. (2022) observed that performance efficacy is one of the key motivational drivers of consumer acceptance of AI in hospitality and tourism praxis. Other factors include perceived usefulness, hedonic motivation, and intrinsic motivation. ...
... Hedonic motivation. Existing studies have shown that hedonic motivation is an important antecedent to technology adoption Thusi and Maduku, 2020;Saydam et al., 2022). Hedonic motivation, in relation to technology, is defined as the derivation of pleasure from technology usage . ...
Purpose
This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.
Design/methodology/approach
An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.
Findings
This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).
Practical implications
Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.
Originality/value
This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.
... With such a rapid pace of change, the question remains as to how AI will further transform organizations. It is possible to find studies about the historical development of AI in general (Lv et al., 2022;Mariani et al., 2022;Saydam et al., 2022;Tussyadiah, 2020), and how it might affect the travel and tourism industry as a whole. These studies are high-level, conceptual and speculative (Kong et al., 2022). ...
... These studies are high-level, conceptual and speculative (Kong et al., 2022). With the exception of Grundner and Neuhofer (2021) and Saydam et al. (2022), who examined tourism destinations and hospitality respectively, few studies explain how AI may impact specific sectors or business functions. To derive a deeper understanding of the probable effects of AI and its potential impact on organizations, this three-part study examines the potential impact of AI on the marketing function of hotels; answering Samala et al.'s (2020) call for further research on the concept of AI and its application to the tourism sector. ...
... Hospitality marketing includes a vast array of activities, including segmentation, value proposition, product, and experience design, distribution, pricing, customer relationship management, and reputation management; many of which offer great potential for the application of AI. Existing research on the application of AI to both the hotel sector in general and hotel marketing in particular, tends to be either descriptive or methodological speculative (Saydam et al., 2022). It largely highlights the origins and potential of AI but fails to examine how these developments impact the sector or its future operations (Knani et al., 2022). ...
Understanding how Artificial Intelligence (AI) impacts organizational functions supports stakeholders to prepare accordingly and profit from these developments. Adopting a grounded theory approach, this study uses three interlinked stages (in-depth interviews, focus groups and a questionnaire-based survey) to explore the impact of AI on the marketing function of hotels. The results identify ten trends related to AI’s contribution to hotel marketing, clustered in four themes. AI reengineers internal processes and procedures by enabling data and content as catalysts of competitiveness; empowering the augmented worker and performing mass personalization and customization. AI also impacts relationships with stakeholders by determining return on investment; improving sustainability; and governing legal aspects and ethics regarding data use. AI supports networks to which the organizations belong by concentrating and integrating organizations and transforming distribution models. AI transforms customer processes and services by engaging smart and predictive customer care and by employing predictive and augmented product and service design. The study illustrates the changes that AI will likely bring to hospitality and tourism marketing, developing a research agenda and raising discussion points for academic and industry practitioners respectively.
... Thus, the tourism and hospitality management concerns associated with controlling client reactions to these technologically mediated surroundings merit consideration (Froehle & Roth, 2004). It remains to be seen how AI-driven technology will enter the service industries, but there are numerous studies to suggest that the influence will be profound and limited only by customer and staff acceptance of technology's involvement in customer service interactions (Saydam et al., 2022). The comprehensive customer-technology nexus is still in its infancy, however, especially in the tourism and hospitality industry. ...
... The Henn-na Hotel, in Japan, was the first hotel to be staffed primarily by robots when it opened in 2015 (Ivanov et al., 2019). Since then, tourism and hospitality organizations have had a tendency to use robotics in their organizations to gain a competitive advantage (Saydam et al., 2022). Presently, hotels use robots for duties such as welcoming guests, carrying baggage into guests' rooms, and cleaning public spaces. ...
... Several industries, particularly the hospitality industry, have been impacted by the emergence of novel pandemics and technological advancements. This development influenced consumer psychology and altered customer behavior (Saydam et al., 2022). Due to changes in customer behavior, service providers in hospitality are increasingly adopting technologically-friendly services. ...
Growing scientific attention to technology has led to new guidelines for comprehending consumers’ experiences with the technology. Understanding the relationship between technology and consumers is crucial for advancing thought as well as practice in this subject. This study aims to look at the origins, significant subjects, scientific advances, and future advancements in customer reactions to technological research. To accomplish this aim, we ran analysis in R with the visualization tools VOSviewer and Biblioshiny in order to conduct a bibliometric study. Employing the Boolean strategy, journal articles were obtained from the Scopus database up to August 17, 2022. This research looked at customer reactions to technology literature from various angles, including citations, journals, keywords, and geographies. Then, bibliographic coupling, co-citation, and co-occurrence analysis were carried out. The analysis showed how customer reactions to technology literature have changed over the past 2 decades. This study provided insight into the role of technology adoption and COVID-19 in customer reactions to technology, and identified potential and constraints in this area.
... Yet, while there is ample research on its application and use (e.g. Doborjeh et al., 2022;Saydam et al., 2022;Kong et al., 2023;Dwivedi et al., 2024), little attention is given to the dark side (Grundner & Neuhofer, to Van der Rest et al. (2020, p. 113) scholarly attention should be given to the ethical implications of legal forms of indirect price discrimination, "through which consumers will be allowed to 'freely' sort themselves into different microsegments, especially when the 'self-selection' is enticed by deceptive personalized applications of psychological pricing and neuromarketing". ...
As pricing in hospitality and tourism evolves due to the opportunities presented by artificial intelligence (AI), so do the concerns that come with technological advancements. There is a fine line between what is possible, what is profitable, and what is ethical, sustainable and responsible in the use of AI for pricing. This viewpoint article draws attention to the dark side of pricing, and presents a framework towards sustainable pricing, against the backdrop of the current European Union legal framework. The framework includes ethical guidelines, self-regulation, self-protection and technological regulation, which should be considered as a whole.
... They can also perform complex tasks such as ordering services, making reservations or recommending new destinations through online applications. This has led to increasing adoption and interest in hospitality and tourism by consumers and tourists as well as relevant authorities (Saydam et al., 2022). These examples show that AI reduces the need for human labor and poses a threat to many business lines in the future. ...
Purpose
This study aims to conduct research by making use of studies investigating the negative effects of artificial intelligence on the future careers and work motivation of tourism employees.
Design/methodology/approach
In this research, a literature review, which is one of the qualitative research methods, was used. The study was completed by using a total of 13 articles and two book chapters investigating the negative aspects of artificial intelligence in the research data Science Direct and Web of Science databases as the main references.
Findings
In the articles examined as a result of the research, it was predicted that the entry of artificial intelligence into the tourism sector poses a threat to the future careers of many tourism employees, and this will cause tourism employees to lose their focus and motivation at work. Another conclusion reached as a result of the research is that many tourism workers will be unemployed in the future due to artificial intelligence-supported information systems and robots.
Originality/value
When the literature was reviewed, there was no research that directly examined the negative effects of artificial intelligence on tourism sector employees. Therefore, this research is unique and important in this respect.
... The scope of our investigation was limited to English-language documents, with a particular emphasis on scholarly journal articles. Hence, the exclusion of books, book chapters, and conference papers as sources of prior literature has been implemented, following the recommendation of Saydam et al. (2022). The authors contend that these sources are not considered the most current repositories of knowledge production within a specific field. ...
... For instance, it is crucial to enhance the speech and facial recognition systems of AI to make the associated services more responsive and to personalize immersive encounters based on historical consumption data to attract more clients and provide favorable results (Li et al., 2022). Due to the growing interest in AI and service robotics within tourism and hospitality, Saydam et al. (2022) research study analyzed 123 articles thematically to identify how AI is defined and which themes are most closely related to the phenomenon. Theoretical and conceptual works on AI and service robotics from the perspective of employees and customers (cluster 1), conceptual understanding and a systematic review of the literature on AI and service robotics in the T&H industry (cluster 2), conceptual understanding and a systematic review of the literature on AI and service robotics in the T&H sector (cluster 3) and anthropomorphism in AI literature (in cluster 4) are the focus of this research. ...
Tourism is one of the biggest industries in the world and its contribution to the global economy has continued to grow. Due to the rapid development of technology, tourism has seen some critical changes in how people interact with the industry. By applying artificial intelligence (AI) to different aspects of the tourism business, it is possible to increase efficiency by using resources more effectively. This paper aims to provide insights into how AI technologies can be applied to different aspects of tourism operations and services to improve the customer experience both online and offline and at service providers such as hotels. A literature review is conducted based on the PRISMA methodology by running searches on databases Scopus and Web of Science. This research contributes to providing an overview of how current AI technologies are used in the tourism industry and how they may be used in the fu- ture to enhance customers’ experiences when interacting with different aspects of tourism. It also examines various concerns that need further investigation before adoption can occur. The review shows that the application of AI technologies can improve numerous facets of tourism operations and services, resulting in numerous advantages.
... Although there are relatively recent studies on the application of AI in hospitality, some authors, such as Saydam et al. [40], who explore the use of AI and robotics in hospitality, point out a significant lack of research regarding the impact of AI on improving business and sustainability in hospitality. The same authors argue that out of all 123 studies they encountered dealing with AI applications in hospitality, they primarily focus on theoretical foundations, even when it comes to the impact of AI on business sustainability. ...
This study explores the impact of artificial intelligence (AI) on customer perceptions and behavior in restaurants, airline companies, and hotel sectors within the hospitality industry of Iran. The primary objective is to analyze how AI affects customer trust, brand engagement, electronic word-of-mouth (eWOM), and tourists' readiness to use AI technologies. Using a comparative analysis approach and surveys, this research tests hypotheses about the effects of artificial intelligence on various dimensions of customer interaction. The findings highlight significant relationships between the quality of artificial intelligence and customer engagement metrics, such as trust and brand loyalty, which are crucial for understanding and predicting customer behavior in response to technological advancements. This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry.
... STDs make use of technologies such as the Internet of Things (IoT) [10], big data [11], artificial intelligence, and digital platforms allowing them to better understand the needs and behaviors of tourists. This facilitates the personalization of services and improved resource management [9,12,13]. ...
The data sharing strategy involves understanding the challenges and problems that can be solved through the collaboration of different entities sharing their data. The implementation of a data space in Mallorca is based on understanding the available data and identifying the problems that can be solved using them. The use of data through data spaces will contribute to the transformation of destinations into smart tourism destinations. Smart tourism destinations are considered as smart cities in which the tourism industry offers a new layer of complexity in which technologies, digitalization, and intelligence are powered by data. This study analyzes four scenarios in which geo-dashboards are developed: flood exposure of tourist accommodation, land-cover changes, human pressure, and tourist uses in urban areas. The results of applying the geo-dashboards to these different scenarios provide tourists and destination managers with valuable information for decision-making, highlighting the utility of this type of tool, and laying the foundations for a future tourism data space in Mallorca.
... AI chatbots can provide travelers highly tailored, efficient support (Skavronskaya et al., 2023). To successfully deploy and implement such technologies, it is essential to grasp the elements that influence their acceptability and adoption by users (Saydam et al., 2022). A common theoretical framework used to explore the adoption of novel technologies is the technology acceptance model (i.e. ...
Purpose-The present study's aims are twofold: 1) to contribute to theory development by accounting for both personality and trust in the conceptualization of technology acceptance using the technology acceptance model (TAM) as the theoretical framework; and 2) to explore the influence of ChatGPT-integrated chatbots on tourism behavior. Design/methodology/approach-The target population for this study was travelers who previously used technology (website/ app) to plan their holiday abroad. An online survey questionnaire created with Google Forms was distributed via a panel company (iPanel). A screening question was included to filter out respondents who have not previously used technological means to plan their holiday abroad. A panel company (iPanel) was hired to collect data from a convenience sample of 305 Israeli tourists who met the above criterion between August 22 and 27, 2023, and were at least 18. Findings-A significant and positive relationship was observed between trust in ChaptGPT and perceived usefulness. Furthermore, a significant and positive association was observed between perceived ease of use and intentions to use ChatGPT-integrated chatbots to plan future holidays. Post hoc analyses suggest that perceived ease of use mediates the relationship between extraversion and trust, trust mediates the relationship between perceived ease of use and perceived usefulness and age moderates the relationship between perceived ease of use and behavioral intentions. Research limitations/implications-Data was collected from a convenience sample of Israeli travelers. Hence, generalizations to other countries, nationalities and cultures should be treated carefully; the study is cross-sectional and thus represents respondents' beliefs and behavioral intentions at a particular time; and the study is based on one of several theoretical frameworks that can be used to conceptualize behaviors associated with using AI by tourists. Practical implications-The findings of the present study point to the importance of accounting for tourists' personal factors, such as personality and age, in developing AI products in the tourism industry. chief executive officers and relevant shareholders would benefit from conducting market research to obtain insights into the factors that may enhance or hamper tourists' adoption of AI-based technology for planning their holidays abroad. Originality/value-Previous work falls short of accounting for personality traits and trust in a single model using the TAM framework. To the best of the authors' knowledge, this is the first study empirically investigating tourism behavior related to ChatGPT based chatbots as a tool to plan future holidays abroad. Furthermore, the possible role of age as a moderating variable was overlooked in past research. An alisis de la influencia de ChatGPT en el comportamiento turístico utilizando el Modelo de Aceptaci on de la Tecnología Resumen Objetivo: Los objetivos del presente ossib son dos: 1) contribuir al ossibleo de la teoría incorporando la personalidad y confianza en la conceptualizaci on de la aceptaci on de la tecnología empleando el Modelo de Aceptaci on de la Tecnología como marco te orico; 2) analizar la influencia de los chatbots integrados en ChatGPT en el comportamiento turístico. Diseño/metodología/enfoque: La poblaci on objetivo de este ossib fueron los viajeros que anteriormente utilizaban tecnología (p agina web/aplicaci on) para planificar sus vacaciones en el extranjero. Se distribuy o un cuestionario online creado con Google Forms a trav es de una empresa de paneles (iPanel). Se incluy o una pregunta de selecci on para filtrar a los encuestados que no habían utilizado previamente medios tecnol ogicos para planificar sus vacaciones en el extranjero. Se contrat o a una empresa de paneles (iPanel) para recopilar datos de una muestra de conveniencia de 305 turistas israelíes que cumplieron el criterio anterior entre el 22 y el 27 de ossib de 2023, y con una edad ossib de 18 años. Resultados: Se identific o una relaci on ossibleon y ossible entre la confianza en ChaptGPT y la utilidad percibida. Adem as, se evidenci o una asociaci on ossibleon y ossible entre la facilidad de uso percibida y la intenci on de ossible chatbots integrados en ChatGPT para planificar futuras vacaciones. Los an alisis post-hoc sugieren que la facilidad de uso percibida media la relaci on entre la ossibleon y la confianza; la confianza media la relaci on entre la facilidad de uso percibida y la utilidad percibida, y la edad modera la relaci on entre la facilidad de uso percibida y las intenciones de comportamiento. Limitaciones/implicaciones de la investigaci on: 1) los datos se recopilaron de una muestra de conveniencia de viajeros israelíes. Por tanto, las generalizaciones a otros países, nacionalidades y culturas deben tratarse con cuidado; 2) el ossib es transversal y, por tanto, representa las creencias y las intenciones de comportamiento de los encuestados en un momento determinado; 3) el ossib se basa en uno de los diversos marcos te oricos que pueden emplearse para conceptualizar la intenci on de los turistas de ossible chatbots integrados en ChatGPT para planificar futuras vacaciones en el extranjero. Implicaciones ossible: Los resultados del presente ossib señalan la importancia de tener en cuenta los factores personales de los turistas, como la personalidad y la edad, en el ossibleo de chatbots basados en ChatGPT para su uso en la industria turística. Los directores de tecnología y los stakeholders relevantes se beneficiarían de investigaci on de mercado para obtener informaci on sobre los factores que pueden mejorar o dificultar la adopci on de chatbots basados en ChatGPT por parte de los turistas para planificar futuras vacaciones en el extranjero. Originalidad: Los trabajos anteriores no tienen en cuenta los rasgos de personalidad y la confianza en un unico modelo utilizando ossible TAM. Este es el primer ossib que investiga empíricamente el comportamiento turístico relacionado con los chatbots basados en ChatGPT como herramienta para planificar las futuras vacaciones en el extranjero. Adem as, en investigaciones anteriores no se ossible el ossible papel de la edad como variable moderadora. Palabras clave Comportamiento turístico, Modelo de aceptaci on de la tecnología, Rasgos de personalidad, Intenciones de comportamiento, Confianza Tipo de papel Trabajo de investigaci on j TOURISM REVIEW j
... It is well known that tourism stakeholders enhance customer experiences and gain competitive advantages by integrating artificial intelligence into their business strategies (Samara et al., 2020). Furthermore, the broad-ranging contributions of artificial intelligence applications to the tourism sector, along with future strategies and the potential for sustainable growth, are crucial (Saydam et al., 2022). ...
This article explores the impact of artificial intelligence (AI) on the aviation and hospitality industries, both of which are rapidly evolving due to technological advancements. It aims to understand the increasing importance of artificial intelligence by examining the various ways in which it is used in these sectors through qualitative research. The research included an analysis of online sources such as airport and hotel websites, booking platforms, and social media accounts of travel-related businesses. This comprehensive data collection provides insight into the various applications of artificial intelligence in tourism. Thematic analysis was then used to categorise the data according to similar uses, providing a detailed understanding of the role of AI in these areas. It compares and examines artificial intelligence applications adopted by aviation and hospitality organisations, evaluating their effectiveness and differences. The study reveals the various ways in which AI is being integrated into these industries and highlights its significant contributions across various dimensions. It also highlights how AI can deliver competitive advantage, improve customer experiences, and introduce innovative paradigms to the aviation and hospitality industries. One important aspect of the research is its ability to provide a deeper understanding of emerging AI trends in these sectors and lay a strong foundation for future research. Ultimately, this study provides valuable insight to stakeholders in aviation and hospitality, equipping them with an informed perspective on leveraging AI for growth and long-term sustainability in their respective industries.
... Second, our analysis focused only on journal and conference papers, excluding "books" and "dissertations," and the search was limited to English-language publications. Future research could also revisit SRID with more diverse data types [129]. Finally, more recent trends are not necessarily detected by the various co-cited networks since most recent publications are not sufficiently cited [130] Author Contributions: methodology, Jianmin Wang.; software, Yongkang Chen.; validation, Fusheng Jia, and Yongkang Chen.; data curation, Jianmin Wang; writing-original draft preparation, Yongkang Chen.; writingreview and editing, Fang You. ...
(1) Background: The social robot interaction design is critical in determining acceptance and user experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: based on 2416 papers related to social robot interaction design collected in the Web of Science (WOS) database, a comprehensive review was conducted by using bibliometrics to draw a knowledge map using VOSviewer and CiteSpace in an integrated manner. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1the study of human-robot relationships in social robot, #2Research on the Emotional Design of Social Robot, #3research on social robot for children's psychotherapy, #4research on companion robot for elderly rehabilitation, and #5research on educational social robot. The reference co-citation identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.
The paper aims to identify the differences in attitudes of particular generational segments of gastronomy consumers toward AI-generated food marketing images. DALL-E engine was used to generate 56 images of dishes, which were initially ranked by professional chefs and reduced to a final set of 18 images used for four photo-elicitation focus groups. Qualitative evaluation of the data showed three topics to be crucial during the discussion of agents: the ability to recognize the meal or ingredient (M/I-R), preference toward presented food, and ability to recognize pictures as artificial (AR). There were significant differences in the meaning construction of such topics based on particular generational segments, i.e., Baby Boomers (BB), Generation X (GX), Generation Y (GY), or Generation Z (GZ). Findings show that GZ manifested the highest M/I-R scores but the lowest AR scores. GZ also preferred dishes for which M/I-R was manifested over those with missing M/I-R. GY and GX showed lower M/I-R scores as well as low AR scores. However, GY and GX showed a will to experiment by choosing dishes independently on M/I-R manifestation. BB segment showed the lowest M/I-R scores, but it was the only segment able to achieve high AR scores. As for the GZ, the BB segment also preferred dishes with M/I-R manifestation over the unidentified dishes. The results present marketing implications, especially since the BB segment is the most (and only) suspicious group toward AI-generated food images as it tends to elaborate deeper analysis of the presented images, leading to increased artificiality recognition.
Purpose
Following the remarkable debut of ChatGPT and its rapid adoption by a global audience since its launch in November 2022, this study delves into educators' perceptions of ChatGPT within the specialized domains of tourism and hospitality education. While acknowledging ChatGPT’s swift rise and advanced capabilities, this research aims to comprehensively explore educators' perspectives, advantages and concerns regarding its integration into academic settings.
Design/methodology/approach
A qualitative approach was utilized to reveal dominant themes from in-depth, semi-structured face-to-face interviews with twenty lecturers in tourism faculties in North Cyprus. Collected responses from respondents were subjected to analysis using Leximancer software.
Findings
Our analysis unearthed seven significant themes encapsulating educators' perceptions of ChatGPT: 1 – “reliability and accuracy concerns”; 2 – “dependency”; 3 – “student engagement”; 4 – “ethical considerations”; 5 – “time efficiency and productivity”; 6 – “teacher-student interaction” and 7 – “learning flexibility”. Noteworthy positive perceptions emerged regarding “student engagement,” “time efficiency and productivity,” and “learning flexibility.”
Originality/value
This study contributes to the originality of research by addressing the underexplored aspect of educators' perceptions of ChatGPT within the domains of tourism and hospitality education, shedding light on its potential implications, advantages and drawbacks in a specialized educational context. Furthermore, it aims to offer insights into educators' recommendations for the effective incorporation of ChatGPT technologies into this specific educational setting, filling a crucial gap in understanding the integration of artificial intelligence (AI) in specialized fields of study.
Purpose
This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.
Design/methodology/approach
This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.
Findings
Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.
Practical implications
Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.
Originality/value
This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.
Emerging technologies are driving the Fourth Industrial Revolution, heralding a transformative paradigm that significantly reshapes people’s daily lives and social interactions. Within the tourism industry, the integration of artificial intelligence (AI) technology and virtual reality (VR) presents promising opportunities to enhance tourist experiences. This conceptual article explores the potential of combining ChatGPT, an AI-powered chatbot, with 360 VR videos to revolutionize the way tourists engage with destinations and make travel decisions. Through this seamless fusion of personalized conversational interactions and immersive virtual experiences, the integration aims to evoke emotions, establish emotional connections, and ultimately enhance overall user satisfaction in the realm of tourism. By effectively implementing ChatGPT and VR technologies, the tourism industry can offer personalized, interactive, and gratifying travel experiences for fostering tourists’ positive psychological engagement with destinations and facilitating more informed travel decisions.
Future trends in Information, Communication and
Computing Technology
Emergence of Data Science in The Hospitality
Industry to Provide Unique Customer Service
Quality – A Case Study
Chatbots have become widely used in hotels, restaurants, and related industries. This study presents a systematic literature review (SLR) of research papers that investigate the use of chatbots in restaurants, hotels, and related services. The Scopus and Web of Science databases are utilized to ascertain the pertinent research publications concerning the utilization of chatbots in the domains of hotels, restaurants, and tourism (N = 48) from 2019 to 2023. The review has adhered to the TCM framework and using the SPAR-4-SLR protocol. The work has identified multiple theme areas and theoretical influences, and has also outlined potential avenues for future research fronts and propositions. This review aims to provide guidance for future studies focusing on the research questions and theoretical influences related to service bots in the hospitality industry. The existing research on the utilization of chatbots in the context of restaurants and hotels is very scarce. Therefore, this systematic literature review aims to offer a thorough and all-encompassing overview of this particular domain. This study will enhance the existing knowledge base and facilitate future research on the application of chatbots in various service areas.
This study explores whether AI is perceived as a threat or an opportunity and examines whether the perception of AI varies according to demographic factors, religious commitment, trust in science, beliefs in conspiracy theories, and previous experiences with AI utilizing data collected from 1443 participants. While most respondents believe that AI will simplify life (63%), increase efficiency (62%), and therefore, the development of AI should be encouraged (51%), a significant portion of the respondents is concerned that AI will increase unemployment (52%) and lead to social inequalities (47%). Around 21% of the respondents believe that AI may destroy humanity eventually. Respondents’ age, gender, occupation, religious commitment, beliefs in conspiracy theories, and previous experiences with AI (familiarity) significantly influence respondents’ perception of AI both as an opportunity and threat. Findings suggest that AI is paradoxically seen as a double-edged sword, perceived both as an opportunity and a threat, which indicates a pronounced confusion about AI among respondents.
Keywords: Artificial intelligencesocial impactsopportunitiesthreats
https://www.tandfonline.com/doi/full/10.1080/10447318.2023.2297114
One of the most signicant recent developments has been the development of articial intelligence (AI) and machine learning, as well as the prospective applications of these technologies in the tourism and hospitality sector. One of the most recent hot topics is ChatGPT, an OpenAI AI language model. In November 2022, a brand-new AI Chabot named ChatGPT put into the market and quickly garnered one million users. Since then, its applications have swept the globe. The tourism and hospitality industry, one of the largest and most important in the world, is always evolving to meet customers' needs. Unfortunately, there has not been much scholarly research on ChatGPT and its relationship to tourism and hospitality yet. This study aims to investigate the ChatGPT potentials and limitations in the tourism and hospitality sector. This paper conducts a literature review and collects secondary data sources to attain the study's objectives.
Building upon the communication privacy management theory, the research reveals the effect of self-disclosure on the identified mechanisms of perceived emotional value, performance expectancy, and privacy concerns, which in turn, influence customers' intention to compliment and complain via AI-enabled platforms. Findings from two quasi-experiments with 439 valid responses from U.S. customers suggest that customers are more likely to express their feelings when low self-disclosure AI technology is presented. The results suggest a prominent role of privacy concerns in mediating the effect of self-disclosure on customers’ intention to compliment and complain. The effects of self-disclosure also channel through perceived emotional value and performance expectancy when customers want to leave a compliment. The moderating effect of reward timing was examined. Similarities and differences between customers’ intentions to compliment or complain using AI-enabled platforms are discussed to provide theoretical and practical implications.
Purpose
The purpose of this study is to provide insights and guidance for practitioners in terms of ensuring rigorous ethical and moral conduct in artificial intelligence (AI) hiring and implementation.
Design/methodology/approach
The research employed two experimental designs and one pilot study to investigate the ethical and moral implications of different levels of AI implementation in the hospitality industry, the intersection of self-congruency and ethical considerations when AI replaces human service providers and the impact of psychological distance associated with AI on individuals' ethical and moral considerations. These research methods included surveys and experimental manipulations to gather and analyze relevant data.
Findings
Findings provide valuable insights into the ethical and moral dimensions of AI implementation, the influence of self-congruency on ethical considerations and the role of psychological distance in individuals’ ethical evaluations. They contribute to the development of guidelines and practices for the responsible and ethical implementation of AI in various industries, including the hospitality sector.
Practical implications
The study highlights the importance of exercising rigorous ethical-moral AI hiring and implementation practices to ensure AI principles and enforcement operations in the restaurant industry. It provides practitioners with useful insights into how AI-robotization can improve ethical and moral standards.
Originality/value
The study contributes to the literature by providing insights into the ethical and moral implications of AI service robots in the hospitality industry. Additionally, the study explores the relationship between psychological distance and acceptance of AI-intervened service, which has not been extensively studied in the literature.
The use of artificial intelligence (AI) technologies in service recovery is transforming the frontline interfaces across the tourism industry, as AI chatbots are now being designed to show empathy. Using a multi-method approach combining survey, experimental, and field data obtained from hotel guests, this study explores the effects of chatbot–employee collaborative empathic responses on customer retention under various service-recovery contexts. It finds that congruence (vs. incongruence) and higher (vs. lower) levels of congruence in chatbot–employee empathic responses more effectively retain customers. Further, the effects of incongruence and congruence on customer retention diminish when the chatbot’s identity is disclosed but are strengthened when employees’ acceptance of a chatbot increases. Only the negative effects of empathic response incongruence correspondingly increase when chatbot efficiency and flexibility (ambidexterity) increase. These findings suggest that tourism practitioners can rely on chatbot–employee collaboration to accomplish service-recovery tasks but should pay attention to chatbot-side and employee-side uncertainties in a service triad, especially chatbot ambidexterity.
Purpose
The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.
Design/methodology/approach
This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management .
Findings
Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.
Research limitations/implications
This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.
Originality/value
This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.
In hospitality and tourism research, cutting-edge technologies (CETs) are receiving growing attention. However, most reviews on CETs focus on specific types of CETs and are devoid of theories focusing on the stakeholders–CETs interaction. Consequently, a comprehensive review and an integrated theoretical framework from an interaction perspective is necessary. To address this gap, we conducted a bibliometric analysis of 554 articles published between 2003 and 2023 to identify three research clusters and key entities of CETs. Moreover, drawing on stimulus-organism-response (SOR) theory and media equation theory, we built a new integrated theoretical framework for understanding the stakeholders’ interaction with the CETs. The focus of this new framework centers on how CETs’ representations and CETs’ types directly and indirectly interact to affect stakeholders’ outcomes. This study represents the first attempt to combine bibliometric and qualitative analysis, contributing to a forward-thinking review and theoretical building that accelerates and enhances research in CETs.
This research exhibits and empirically validates an expansion of the Unified Theory of Acceptance and Use of Technology (UTAUT2) and integrates artificial intelligence (AI) and pandemic threats to explain customers’ utilitarian-versus-emotional behavioral intentions towards AI-adopting hotels amid and post-COVID-19. Utilizing data gathered from 416 customers, the findings confirmed that customers’ perceived importance of AI amid and post-COVID-19 has a direct positive effect on their behavioral intentions towards hotels adopting those technologies, with perceived benefits of technology playing a more significant mediating role than customers’ trust intervening in that correlation. This provides evidence for the utilitarian perception of customers during crises and offers updated insights into the dynamics that constitute and trigger hotel customers’ behavioral intentions toward AI. The results provide hoteliers with a valid understanding and rationalization of how to utilize AI to address customers’ crucial concerns and interests amid and post-COVID-19 and in similar crises.
Purpose
Growing recognition of the metaverse has implied its far-reaching impacts on the tourism and hospitality industry. This paper sets out to detail the status of metaverse-related research in tourism and hospitality, propose intriguing directions for future studies and highlight multiple areas that call for immediate attention from practitioners in navigating the metaverse phenomenon.
Design/methodology/approach
This viewpoint paper referenced the extant academic discussion on the metaverse, based on which timely suggestions for academia and practices are proposed.
Findings
This viewpoint paper presents an account of the metaverse and discusses the status of metaverse-related research in hospitality and tourism. It then proposes intriguing avenues for future research around the topics of marketing, reconceptualizing service quality, attitude and behaviors, electronic customer-to-customer interactions, transformative impacts on the society well-being and research methodology. Multiple areas that call for immediate attention from practitioners in navigating the metaverse phenomenon are also highlighted. Both scholars and industry organizations are called upon to assume some responsibility for mapping out protocols to guide the appropriate development, use and governance of metaverse worlds. Governments and policymakers are further encouraged to consider the ramifications of metaverse development for individuals and society and to devise proactive mitigation strategies.
Practical implications
This viewpoint paper proposes several directions for future business practices in the areas of co-creation, experiential consumption, and emerging critical issues in healthcare, human resources, and social media services. It expects to inspire more discussion about the potential impacts of metaverse on the wider society. Its practical significance will further expand the theoretical foundation of the metaverse research and makes this viewpoint paper an intriguing prospect.
Originality/value
The nascent stage of academic discussion intended to guide the development of metaverse is noteworthy, which forms a notable contrast with the growing recognition of its potential of co-creating transformational experiences in hospitality and tourism. This viewpoint paper joins the current academic conversations acknowledging this phenomenon in hospitality and tourism. Provided the notable topicality and empirical relevance, the expanded scope and rich content the present viewpoint paper provides for metaverse will offer a fruitful ground for future research to tap further into currently underrepresented areas.
We apply a resource-based view to investigate how the adoption of Artificial Intelligence (AI) affects competitive capabilities and performance. Following prior work on using chess as a controlled setting for studying competitive interactions, we compare the same players’ capabilities and performance across conventional, centaur, and engine chess tournaments. Our analysis shows that AI adoption triggers interrelated substitution and complementation dynamics, which make humans’ traditional competitive capabilities obsolete, while creating new sources of persistent heterogeneity when humans interact with chess engines. These novel human-machine capabilities are unrelated, or even negatively related, to traditional capabilities. We contribute an integrated view of substitution and complementation, which identifies AI as the driver of these dynamics and explains how they jointly shift the sources of competitive advantage.
Abstract
Purpose: The purpose of the 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.
Design/Methodology/ Approach: The present 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.
Findings: The synthesis draws upon various factors affecting the adoption of AIR, such as individual factors, service factors, technical & performance factors, social & 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.
Originality: 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.
Keywords: Robotics, Artificial Intelligence, Tourism, Hospitality, Systematic Review, Antecedent-Outcome Framework.
Purpose
This paper aims to investigate potential consumers’ willingness to pay for robot-delivered services in travel, tourism and hospitality, and the factors that shape their willingness to pay.
Design/methodology/approach
An online survey yielded a sample of 1,573 respondents from 99 countries. Independent samples t -test, Analysis of variance (ANOVA), cluster, factor and regression analyses were used.
Findings
Respondents expected to pay less for robot-delivered services than human-delivered services. Two clusters were identified: one cluster willing to pay nearly the same price for robotic services as for human-delivered services, whilst the other expected deep discounts for robotic services. The willingness-to-pay was positively associated with the attitudes towards robots in tourism, robotic service experience expectations, men and household size. It was negatively associated to travel frequency, age and education.
Research limitations/implications
The paper’s main limitation is its exploratory nature and the use of a hypothetical scenario in measuring respondents’ willingness to pay. The data were gathered prior to the COVID-19 pandemic and do not reflect the potential changes in perceptions of robots due to the pandemic.
Practical implications
Practitioners need to focus on improving the attitudes towards robots in tourism because they are strongly and positively related to the willingness to pay. The marketing messages need to form positive expectations about robotic services.
Originality/value
This is one of the first papers to investigate consumers’ willingness to pay for robot-delivered services in travel, tourism and hospitality and factors that shape their willingness to pay.
With the increased use of robots in the event industry, most of the literature centers on factors that influence robot adoption, with less attention being paid to the understanding of how customers interact and connect with anthropomorphic robots. With the growing research in this field, questions remain about the event attendees' perceptions and evaluations of humanoid service robots (HSRs) and the interactive effects that shape customers' thinking and behavior towards the use of this technology. This study proposes a conceptual model to examine the role of social presence, while explaining the moderating roles of eeriness and identity threat through two experimental studies, using photo scenarios as stimuli in an online survey, with closed-ended questions. Study 1 was a single-factor between-subject experiment that manipulated a conference registration task in an event setting. Results revealed that event attendees were more satisfied with service employees (SE) than with HSR; and social presence mediated the relationship between type of service provider (HSR vs. SE) and satisfaction. Study 2 examined eeriness and identity threat as moderators and results indicated that eeriness and identity threat significantly moderate the indirect effect of the type of service provider (HSR vs. SE) on satisfaction through social presence. The study provides valuable insight about the evaluation of humanoid service robots by event attendees by deepening our understanding of how event attendees engage with service robots to form stronger connections with such a technology. Taken together, the findings of this study offer managerial implications by balancing event industry employees’ affinity for and the acceptance of the service robots.
Purpose
This study aims to explore the factors affecting customers’ attitudes to the adoption of robots in hotels and travel agencies.
Design/methodology/approach
Structural equation modelling was used to test the extended technology acceptance model based on data collected from 570 customers of hotels and travel agencies.
Findings
The findings revealed that hotel customers have more positive attitudes to service robots than their peers in travel agencies.
Originality/value
This research contributes to the literature on robots in tourism and responds to the call to investigate customers’ attitudes to the adoption of robots in developing countries.
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Purpose
This study aims to investigate the impact of both physical and personality-related anthropomorphic features of an artificial intelligence service robot on the cognitive and affective appraisals and acceptance of consumers during service delivery.
Design/methodology/approach
The proposed hypotheses that investigate the effects of service robots’ physical appearance on the emphasis consumers place on each evaluation criteria they use in determining their willingness to accept the use of service robots in service delivery and the moderating role of sense of humor are tested by conducting two studies using scenario-based experiments.
Findings
The results show that humanlike appearance leads to higher performance expectancy, mascot-like appearance generates higher positive emotions and machine-like appearance results in higher effort expectancy. The effects of humanlike and mascot-like appearances on consumer acceptance are moderated by the sense of humor of service robots. However, the sense of humor effect is attenuated with a machine-like appearance owing to the lack of anthropomorphism.
Practical implications
This study provides crucial insights for hospitality managers who plan to use service robots in service delivery. The findings highlight the key roles of appearance type and sense of humor of service robots in influencing the appraisals and acceptance of consumers regarding the use of service robots in service delivery.
Originality/value
This study focuses on comparing the effects of traditional and mascot-like appearances of service robots on consumer appraisals and identifies sense of humor as a cute anthropomorphized personality trait of service robots.
In reaction to the growing attention paid to big data and artificial intelligence in hospitality and tourism research, we systematically reviewed 270 relevant studies to identify topical themes and trends. We first briefly reviewed the emergence definition of big data. Next, we introduced the methodology of literature collection and presented results of bibliometric analysis. Then, we identified types of big data used and the application of artificial intelligence in big data usage in hospitality and tourism research, followed by unveiling major themes of big data and artificial intelligence research in extant literature such as forecasting, industry development, marketing, performance analysis, consumer behaviors, attitudes, and so on. In addition, implications and challenges of applying big data and artificial intelligence in hospitality and tourism research and new directions for future research are identified. Finally, we discuss limitations of our review, proposing future research directions for scholars.
The COVID-19 pandemic has severely affected the restaurant industry due to enforced closures and limitations on social gatherings, prompting restaurateurs to innovate and adapt in order to ensure the viability of their businesses. Pandemic has also induced changes in our perceptions of safety in public spaces, necessitating the adoption of social distancing and more widespread use of online platforms for purchasing and communication. While the pandemic might be a catalyst for the adoption of contactless technologies, some restaurateurs remain hesitant to invest in service robots because they are not convinced of the return on investment and the potential value service robots can deliver to their customers. Therefore, this study aims to explore customer value perceptions of service robots and their impact on customers’ attitudes and behaviors toward robotic restaurants. Findings yielded by a survey of 445 potential diners in Taiwan shows that customers’ willingness to use and to pay more for robotic restaurants are determined by their attitudes toward robots, which are influenced by functional, conditional, epistemic, emotional, co-creation, and social values. Our survey results also reveal that the importance of conditional value is amplified by crisis-specific antecedents, namely the need for physical distancing and mysophobia. These findings have implications for restaurant pricing policies and can be considered by restaurant managers when formulating strategies aimed at sustaining their business in these challenging times.
Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.
Drawing on the dual process theory, this study investigates the impacts of systematic and heuristic cues on travelers’ cognitive trust, emotional trust, and adoption intention toward artificial intelligence (AI)–based recommendation systems in travel planning. The moderating effect of perceived risk is also examined. Two studies with both scenario-based surveys and lab experiment approaches are conducted. Findings suggest that while travelers utilize both systematic and heuristic cues, effects of systematic cues on adoption as a decision aid is stronger than the effects of heuristic cues. Emotional trust has a stronger impact on intention to adopt as a delegated agent than cognitive trust. Perceived risk moderates the relationships between systematic and heuristic cues, trust, and adoption intentions. When travelers perceive high risk, they rely more on systematic cues through building cognitive trust. However, when the level of perceived risk is low, travelers depend more on heuristic cues through establishing emotional trust.
Service providers in tourism and hospitality are beginning to welcome robots as a customer service option. Given this trend, it is important to explore the factors driving tourists’ willingness to adopt such new technology. This study focuses on the role of crowding, an environmental factor widely observed in destinations susceptible to over-tourism, in shaping tourists’ willingness to adopt service robots. Based on one survey and two experiments, the present research demonstrates that a destination which is more (vs. less) crowded generally motivates tourists to favor robot-provided services rather than those from human staff. Furthermore, findings reveal that this pattern manifests because more (vs. less) social crowding reduces tourists’ motivation to interact with others, as evidenced by social withdrawal tendency.
This study examines the essential characteristics of Virtual Reality (VR) that influence individual visit intention towards a touristic product. Despite the extensive research about VR, however, only little has examined the impact of factors that alter customers’ attitudes and trigger purchasing intention. This study applied Information Systems Success Model and conducted a survey using the convenience sampling method with international tourists who visited North Cyprus. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, and findings demonstrated that VR has great potential to influence visitors’ final destination by promoting tourism products and services. This paper revealed that VR—as a marketing medium—creates positive impacts and stimulates individuals’ intentions to visit a destination. The study provides implications for tourism sector actors such as tourism planners, policymakers, travel agencies, and hotel managers as well as prosocial guest experience to improve their marketing strategies.
Drawing upon affordance theory, this study positions artificial intelligence (AI) as a commercial service in examining its influence on customer engagement in the hotel context. In particular, we seek to understand linkages between customer perceptions of AI service quality, AI customer satisfaction and engagement. Given the multiplicity of services offered by service organisations, customers’ preference for AI service is modelled as a moderator of customer perceptions and attitudes towards AI. Data was collected from a sample of hotel customers in Australia who had previously used AI tools or services. Our results reveal a significant chain effect between AI service indicators, service quality perceptions, AI satisfaction and customer engagement. AI preference has a significant moderation effect on information quality and satisfaction. These findings provide new insights into the consumer services literature and have important implications for marketing practitioners.
In view of the prevalence of artificial intelligence (AI) – powered applications in service organizations, this paper draws on service profit chain theory and proposes these applications as a service product for employees (referred to as internal customers) and customers. The outcome of which is to examine the influence of AI service quality on internal and external customer loyalty. Customer satisfaction and engagement are modeled as mediators, whereas emotional intelligence (EI) is a moderator in AI – customer loyalty relationships. Two studies were undertaken with employees and customers in Australian-based hotels to examine these relationships. The results show that AI service quality is significantly related to internal and external customer satisfaction and engagement. Both internal and external customer engagement have significant effects on customer loyalty and play a significant mediation role in the service quality – customer loyalty relationship. EI has only a significant moderation effect on the relationship between AI service quality and customer engagement for internal and external customers. Discussion and implications of the research findings are provided for researchers and practitioners.
Despite the pervasiveness of service robots in hospitality industry, it is unclear how highly human-like service robots elicit aversive effect on consumers' use intention in addition to discomfort and when the aversive effect can be mitigated. Three experimental studies were conducted, showing that highly human-like service robots elicit greater consumer discomfort and decrease task attraction toward robots, in turn weakening consumers' use interaction. Moreover, this research identified that emotional-social tasks (vs. mechanical tasks) mitigated the aversive effects of highly human-like service robots on consumers' responses. The research extends the uncanny valley and mind perception theories and offers some guidelines for employing service robots with different degree of anthropomorphism.
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.
The automation of services taking advantage of the significant opportunities offered by artificial intelligence and other Industry 4.0 technologies is receiving increasing attention both from academics and practitioners. Interest in the subject has been boosted significantly by the healthcare crisis generated by COVID-19 and the need to maintain social distancing while continuing to provide efficient services. The purpose of this brief paper is threefold: (i) to introduce and summarize the current state of automated forms of interaction in services; (ii) to provide an overview of the six papers published in this special issue; and (iii) to describe the possibilities for future research that emerged at the AIRSI2019 (Artificial Intelligence and Robotics in Service Interactions) Conference. The AIRSI2019 conference was the precursor to this special issue and provided an excellent opportunity to explore with leading international researchers the extraordinary development possibilities presented in this research context.
In the context of the health risks of the COVID-19 pandemic, tourists’ choices have shifted to reflect a subconscious psychological mechanism – the behavioral immune system – that facilitates human organisms to better identify plausible threats to ones’ health through environment cues. This research draws upon this theoretical lens to assess tourists’ pre-trip hotel evaluation in two 2 × 2 between-subject experiments. Experiment 1 (robot vs. human) tested the service provider’s effect on hotel selection evaluation through the mediation of sense of control and the moderation of pandemic risk. Experiment 2 examined this chain of relationship through the moderation of hotel type. This research contributes to the literature by underscoring the pathogen-avoidance mechanism in tourist evaluation and the peril of robotization.
Purpose
Drawing on chaos theory as an overarching approach, as well as guidelines from effectuation and transformative learning theories, this study aims to evaluate the changing marketing channels in the hospitality industry in the wake of the COVID-19 pandemic. It also aims to develop a conceptual framework that demonstrates the transformation of the marketing structure; in particular, the transformation of hospitality organizations, employees and customers.
Design/methodology/approach
The study uses the hermeneutic method and conceptually evaluates the existing actors of the services marketing structure. It also discusses how to transform this structure into the new normal in the wake of the COVID-19 pandemic.
Findings
The findings of the study demonstrated that COVID-19 has resulted in changing marketing channels in the hospitality industry. These include external, internal, interactive and substitutional marketing channels. In response to these changes, the hospitality industry needs to adopt a more transformative marketing structure that requires the transformation of hospitality companies, employees and customers.
Research limitations/implications
The conceptualized transformation of the services marketing structure could help hospitality practitioners, employees and customers to understand the new normal and acquire new abilities, meanings, awareness and learning accordingly.
Originality/value
This study uses chaos, effectuation and transformative learning theories to reconceptualize the hospitality services marketing structure. The contribution of this paper lies in the conceptual pathways it suggests for transforming hospitality firms, employees and customers and for demonstrating their transformed roles and positions in the wake of the pandemic.
This paper aims to examine willingness to accept artificial intelligence (AI) devices, focusing on the so-called Gen Z population. This study presumes that specific knowledge of a business process is important for AI adoption in hospitality services. A research model, grounded in the artificially intelligent device use acceptance (AIDUA) framework, used data collected from 786 respondents. The model was tested using PLS-SEM methodology. The modified framework was supported by Gen Z, with hedonic motivation having the greatest effect on Gen Z members’ emotions and their willingness to use AI devices in hospitality. The frequency of smartphone usage played a significant moderating role between the perceived effort of AI usage and emotions. This study helps AI designers and business managers when designing and implementing AI devices in a hospitality environment. Based on this study’s findings, policymakers and educational institutions can try to advance their curricula, emphasizing the importance of new technologies.
Purpose
The hospitality industry experienced an unanticipated challenge from the COVID-19 pandemic. However, research in this area is scarce. Accordingly, this study aims to unfold a three-angled research agenda to intensify the knowledge advancement in the hospitality sector. It proposes a theoretical framework by extending the protection motivation theory (PMT) to explain the guest’s intent to adopt artificial intelligence (AI) and robotics as a protective measure in reaction to COVID-19.
Design/methodology/approach
The research is centered on outlining the pertinent literature on hospitality management practices and the guest’s transformed behavior during the current crisis. This study intends to identify a research agenda based on investigating hospitality service trends in today’s changing times.
Findings
The study sets out a research agenda that includes three dimensions as follows: AI and robotics, cleanliness and sanitation and health care and wellness. This study’s findings suggest that AI and robotics may bring out definite research directions at the connection of health crisis and hospitality management, taking into account the COVID-19 crisis.
Practical implications
The suggested research areas are anticipated to propel the knowledge base and help the hospitality industry retrieve the COVID-19 crisis through digital transformation. AI and robotics are at the cusp of invaluable advancement that can revive the hotels while re-establish guests’ confidence in safe hotel practices. The proposed research areas are likely to impart pragmatic lessons to the hospitality industry to fight against disruptive situations.
Originality/value
This study stands out to be pioneer research that incorporated AI and robotics to expand the PMT and highlights how behavioral choices during emergencies can bring technological revolution.
This study investigates the antecedents and consequences of brand satisfaction with the moderating role of type of barista. For this, data were collected from customers who used a coffee shop operated by robot baristas and customers who used a coffee shop operated by human baristas. The data analysis results showed that the four types of brand experience, such as sensory, affective, behavioral, and intellectual brand experiences, help to enhance brand satisfaction, which positively affects brand attitude, brand attachment, and brand loyalty. Finally, the type of barista plays a moderating role in the relationship between (1) sensory brand experience and brand satisfaction and (2) intellectual brand experience and brand satisfaction.
Purpose
This paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations.
Design/methodology/approach
First, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers.
Findings
The presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings.
Research limitations/implications
Hospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.”
Originality/value
The current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.
Purpose
COVID-19 is expected to enhance hospitality robotization because frontline robots facilitate social distancing, lowering contagion risk. Investing in frontline robots emerges as a solution to recover customer trust and encourage demand. However, we ignore how customers perceive these initiatives and, therefore, their efficacy. Focusing on robot employment at hotels and on Generation Z customers, this study aims to analyze guests’ perceptions about robots’ COVID-19 prevention efficacy and their impact on booking intentions.
Design/methodology/approach
This study tests its hypotheses combining an experimental design methodology with partial least squares. Survey data from 711 Generation Z individuals in Spain were collected in 2 periods of time.
Findings
Generation Z customers consider that robots reduce contagion risk at hotels. Robot anthropomorphism increases perceived COVID-19 prevention efficacy, regardless of the context where the robots are used. Robots’ COVID-19 prevention efficacy provokes better attitudes and higher booking intentions.
Research limitations/implications
The sampling method used in this research impedes this study’s results generalization. Further research could replicate this study using random sampling methods to ensure representativeness, even for other generational cohorts.
Practical implications
Employing robots as a COVID-19 prevention measure can enhance demand, especially if robots are human-like. Hoteliers need to communicate that robots can reduce contagion risk, particularly in markets more affected by COVID-19. Robots must be employed in low social presence contexts. Governments could encourage robotization by financially supporting hotels and publicly acknowledging its benefits regarding COVID-19 prevention.
Originality/value
This study combines preventive health, robotics and hospitality literature to study robot implementation during the COVID-19 pandemic, focusing on Generation Z guests – potential facilitators of robot diffusion.
Purpose-Drawing on the self-determination theory, the assemblage theory and customer experience literature, this paper aims to develop a framework to understand motivational customer experiences with chatbots.
Design/methodology/approach-This paper uses a multimethod approach to examine the interaction between individuals and airlines' chatbots. Three components of self-determined interaction with the chatbot (competence, autonomy and relatedness) and five components of the customer-chatbot experience (sensory, intellectual, affective, behavioral and social) are analyzed qualitatively and quantitatively.
Findings-The findings confirm the direct influence of self-determined interaction on customer experience and the direct effects of these two constructs on participants' attitudes toward and satisfaction with the chatbot. The model also supports the mediating roles of customer experience and attitude toward the chatbot.
Practical implications-This paper offers managers a broad understanding of individuals' interactions with chatbots through three elements: motivation to use chatbots, experiential responses and individuals' valuation of whether the interactions have amplified (or limited) the outcomes obtained from the experience.
Originality/value-This paper contributes to the hospitality and tourism literature with a hybrid approach that reflects on current theoretical developments regarding human-and interaction-centric interpretations of customer experience with chatbots.
Purpose: This study aims to (1) examine the effect of restaurant employees’ challenge-hindrance appraisals toward smart technology, artificial intelligence, robotics, and algorithms (STARA) awareness on individual competitive productivity (ICP), and (2) explore the mediating roles of employees’ work engagement and organizational commitment on the relationship between challenge-hindrance appraisals and ICP.
Design/methodology/approach: Data were collected through an online survey. One hundred and ninety employees who worked at full-time and non-management positions in the U.S. quick-service restaurants participated. Partial least squares structural equation modeling was used for the data analysis.
Findings: The study identified that restaurant employees’ challenge appraisals toward STARA awareness positively influenced ICP. This relationship is positively mediated by employees’ work engagement.
Practical implications: This study makes practical contributions to human resource practices in restaurants. Employees’ challenge appraisals toward STARA awareness transmit the job insecurity stressor to a higher level of ICP. Restaurant managers should provide employees with adequate resources and support for non-management employees’ professional competency growth. Quick-service restaurants can enjoy a competitive advantage in the market by enhancing employees’ competitive productivity (CP).
Originality/value: This study enriches the literature on the CP model, cognitive appraisal theory, and person-environment fit theory. The study investigated employees’ challenge and hindrance appraisals toward emerging STARA awareness and emphasized their distinct characteristics to drive ICP in the quick-service restaurant sector.
Previous studies provide inconsistent evidence regarding the effect of anthropomorphism on customers’ willingness to use AI service agents. This paper explains the reason by introducing service context. Two situational experiments are used to demonstrate that under the context of high perceived control, customers expect AI service agents with more anthropomorphic designs to perform better and prefer highly human-like AI service agents. However, under the context of low perceived control, customers perceive stronger threat in facing AI service agents with more anthropomorphic designs and prefer less human-like AI service agents. Moreover, we find that this effect is significant only in social scenarios. These findings provide new insights into previous inconsistent evidence regarding anthropomorphic design’s influence on customers’ willingness to use AI service agents. Our findings also have important implications for AI service agents design in different service contexts and advance the literature on human–robot interaction and marketing.
Across two studies, this research presents a novel extension to the service coproduction literature, demonstrating when and why consumers with low- versus high-innovativeness tendencies are willing to pay more to coproduce hospitality and tourism services. Findings suggest that, in in-person coproduction settings, low-innovativeness consumers are willing to pay more to coproduce (vs. not) with human employees, while high-innovativeness consumers are willing to pay more to coproduce (vs. not) with robots. Such effects were attenuated in tech-enabled remote coproduction settings, where only high-innovativeness consumers were willing to pay more to coproduce. PROCESS analyses further revealed that self-competence mediated the conditional effect of coproduction involvement on willingness to pay more. In support of our theoretical framework, we demonstrated that lowering the challenging level of the coproduction task increased (decreased) low- (high-) innovativeness consumers’ willingness to pay more for coproduction involvement. These findings offer notable theoretical and managerial implications.
Purpose
This study aims to explore the antecedents of perceived value and the moderating effect of trust and the relationship between these antecedents and perceived value in the context of the service sector.
Design/methodology/approach
The multivariate statistical analysis technique of structural equation modeling was used to test the proposed theoretical model.
Findings
The results indicate that self-efficacy, motivation, social influence, facilitating conditions and emotions have a significant and direct relationship with customers’ perceived value and that trust can enhance the effect of these antecedents on perceived value. These findings have several significant implications for service robot implementation within the service sector.
Originality/value
With the advancement in artificial intelligence and sensor technology, various industries have launched the practice of deploying intelligent robots to build competitive advantages. The use of intelligent robots to assist with the customer service process and improve consumers’ experience within the service sector is becoming more commonplace.
Artificial intelligence (AI) contactless services thrived during the COVID-19 pandemic, while their consequences remained unclear. Based on media equation theory and means-end chain theory, this study proposed a model explaining the effect of AI contactless services on customers’ psychological safety, perceived control, hedonic value, and service quality. Data were collected from hotel customers with an online panel survey and a site survey in Wuhan, China. Chi-square statistics indicated that there were no significant differences between the two datasets. A structural equation modeling analysis of the combined data (n=316) suggested that two dimensions of AI contactless services led to the examined customers’ psychological safety, which in turn positively influenced their hedonic value and service quality but negatively affected their perceived control. Additionally, customer psychological safety was found to mediate certain relationships between contactless service attributes and perceived value. The research findings contribute to AI service applications and influence theoretically and practically.
Purpose - This study aimed to develop a framework to identify and prioritize the key factors in automation and artificial intelligence (AI) implementation in the hospitality and tourism industry.
Design/methodology/approach – We used the analytic hierarchy process, a multi-criteria decision-making method, to prioritize the factors influencing automation and AI implementation. We developed a model with five criteria (human knowledge, services, robotics applications, internal environment, and institutional environment) and 23 sub-criteria obtained from previous studies. We designed a questionnaire in the form of pair-wise comparisons based on the proposed hierarchical structure. We 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.
Findings – 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.
Originality – We developed a framework for policy-makers 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.
The hospitality and tourism industry faces serious challenges during public health emergencies such as COVID-19. Managers are concerned not only about how to maintain business and provide humanized services but also about social distancing. This study presented artificial intelligence (AI) technology-based service encounters as a possible solution and examined the antecedents and consequences of the encounter triad including customers, employees, and AI. Based on a systematic literature review, the study identified 4 modes of AI technology-based service encounters: AI-supplemented, AI-generated, AI-mediated, and AI-facilitated encounters. In addition, the study developed an integrated model to specify the factors that influence AI technology-infused service encounters in general and the customer service outcomes that result from the encounters. The findings contribute to service management and AI application theoretically and practically.
Service robots (SR) are increasingly valued and embraced; they are here to stay. Research on collaborative intelligence to better understand robotic-human partnerships is scarce. To bridge that gap this study aimed to examine the value of SR from the guest’s perspective, thus gain a deeper understanding of the co-value creation process in the context of full-service hotels. A mixed-method design was used to capture the depth and breadth of perceived value of SR. Study 1 is a qualitative study probing consumers’ sense making regarding SR. Study 2 used structural equation modeling to test the hypotheses derived from Study 1. Results indicate that perceived privacy, functional benefits of SR, and robot appearance positively influence consumers’ attitude towards adoption of SR. Functional benefits and novelty had an impact on the individuals’ anticipated overall experience. Attitude and anticipated overall experience, in turn, enhanced consumers’ acceptance of SR. Implications, limitations, and future research are discussed.
The accelerated deployment of humanoid robots in hospitality services precipitates the need to understand related consumer reactions. Four scenario-based experiments, building on social presence and social cognition theories, examine how humanoid robots (vs. self-service machines) shape consumer service perceptions and intentions vis-à-vis concurrent presence/absence of human staff. The influence of consumers’ need for human interaction and technology readiness is also examined. We find that anthropomorphizing service robots positively affects expected service quality, first-visit intention, and willingness to pay, as well as increasing warmth/competence inferences. These effects, however, are contingent on the absence of human frontline staff, which can be understood by viewing anthropomorphism as a relative concept. Humanoid robots also increase psychological risk, but this poses no threat to expected service quality when consumers’ need for human interaction is controlled for. Hence, humanoid robots can be a differentiating factor if higher service quality expectations are satisfied. Additionally, we show that a humanoid robot’s effect on expected service quality is positive for all but low levels of technology readiness. Further implications for theory/practice are discussed.
Self-service technology (SST) and its applications are changing the way the hospitality and tourism industry provides products and services to their customers. Although the use of SST is a remarkable change for service providers because its use can meet customers’ pursuit of an efficient life, research provides a sufficient overview of how the usage of SST influences customers’ service experience in the tourism and hospitality settings remains lacking. Thus, this study aims to provide an understanding of previous SST research and a basis for exploring the influential factors regarding SST adoption. To achieve this purpose, SST papers in leading hospitality and tourism academic journals were reviewed. Although the number of articles is limited, content analysis allows the authors to understand a phenomenon and identify research topics and methods in SST studies. Also, this study contributes to existing SST literature by providing future research guidelines.
Purpose
The prevalence of artificial intelligence (AI) has considerably affected management and society. This paper aims to explore its potential impact on hospitality industry employees, bringing enlightenment to both employees and managers.
Design/methodology/approach
Data were collected from a survey of 432 employees who worked in full-service hotels in China. Structural equation modeling (SEM) was used to analyze the data.
Findings
Results presented a positive relationship between AI awareness and job burnout. No significant direct relationship was found between AI awareness and career competencies. Organizational commitment mediated the relationship between AI awareness and career competencies, as well as the relationship between AI awareness and job burnout.
Research limitations/implications
This study contributes to human resource management in the hospitality industry to theoretical and practical aspects. Theoretically, it enriched both career theory and fit theory. Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.
Practical implications
Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.
Originality/value
The study aims to analyze the impact of AI from a career perspective. It provided theoretical support and evidence for hotel managers for the effects of AI awareness on hotel employees. The study conveys a potential topic of concern that the hospitality industry may face in the future.
Despite the rise of human-robot interaction research, the mixed findings of human-likeness in consumer evaluation exist. Focusing on the restaurant sector, this research investigates how service robots’ varying levels of human-likeness of attributes (i.e., visual, vocal and verbal) influence consumption outcomes (e.g., service encounter evaluation, revisit intentions and positive word of mouth intentions) and the underlying mechanisms through cognition (i.e., perceived credibility) and positive emotion per Appraisal Theory. Drawing on a consumer experiment involving a total of 587 participants, results suggest that humanlike voice emerges as a dominant attribute affecting all three consumption outcomes. Humanlike language style positively affects service encounter evaluation but barely affects the other two outcomes. The significant effect of humanlike voice on three consumption outcomes is only explained by positive emotion whereas the effect of humanlike language style on service encounter evaluation is explained by both cognition (i.e., perceived credibility) and emotion.
As artificial intelligent technologies have been increasingly applied in tourism and hospitality industry, the service failure caused by artificial intelligence assistant and how to recover them are worth empirical studying. Laboratory experiments were employed to test the impact of cuteness in service failure, with effective manipulation of cute appearance, cute voice and cute language style of artificial intelligence assistant. By utilizing three studies with seven experiments, this research demonstrated the positive effect of cuteness design of artificial intelligence assistant on customer tolerance of service failure and further revealed the two mediating paths (tenderness and performance expectancy) as well as the boundary (failure severity and time pressure) of the cuteness effect. These findings contribute to the knowledge on artificial intelligent assistant service and provide insight for cute design using in tourism and hospitality industry.
Purpose
This paper aims to explore theme park visitors’ attitudes toward interacting with robots and investigated the qualities and functions of robotic servers and their influence on customers’ loyalty. A structural equation modeling approach was used to identify the complex relationships among variables in the entire network.
Design/methodology/approach
An online survey randomly assigned respondents to four different robotic server scenarios with robots that look like humans, animals, cartoon characters and anime features. The influence of robot types was investigated by manipulating robot type with four different pictures; however, the data were analyzed with a structural equation modeling model to identify the complex relationships rather than one-way analysis of variance to identify influences of robot types on different variables in separate analyzes.
Findings
The data collected from the 385 experienced theme park visitors revealed that perception of robots with human orientation and safety qualities had the strongest effect on the perceived robotic functionality, while emotions and co-creation qualities hardly had any effect on the perceived functionality, which included utilitarian rather than experiential functions such as excitement. Human orientation qualities, regardless of the specific robotic design, had a significant impact on perceived robotic functionality. The study also revealed a strong positive influence of perceived robotic functionality on customer loyalty.
Originality/value
The debate of whether or not to introduce and blend the growing robotic technology into the theme park experience is in its infancy. The study contributes to the theory of how robotics qualities and functions can augment customer loyalty.
Artificial intelligence is another advance in technology for the hotel industry and its role is undetermined at this time. The overarching purpose of this treatise was to examine hotel employees’ perception of AI and its impact by identifying the critical role of job insecurity, job engagement, and turnover intention through a pragmatic approach. An explanatory sequential mixed-methods design was used by conducting a quantitative study with an empirical survey method followed by a qualitative study with a case study method. The results from the quantitative study demonstrated that perceived job insecurity significantly affected perceived job engagement and perceived job insecurity indirectly affected turnover intention through intermediary variable of perceived job engagement. There were no statistical differences between non-managerial positions and managerial posi tions. These results were fully supported by the qualitative study. The implications from these findings were provided to articulate the influence of AI on hotel employees.
•During Covid-19, people are more willing to visit a hotel/restaurant with robots.•People think that robots in hotels/restaurants can lower interpersonal interaction.•Reducing interpersonal interaction can lower perceived viral transmission.•Chinese are more likely to visit hotels/restaurants with robots than Americans.
The purpose of this study was to explore customers’ perceptions and behaviors when using chatbots in restaurant takeout orders. Built on the social presence theory, this study conducted a lab experiment to examine and compare three ordering methods in quick-service and full-service restaurants. Results revealed that phone ordering and online ordering were both better than chatbot ordering in terms of satisfaction and behavioral outcomes. The phone ordering method elicited best social presence and cognitive attitudes, while the online ordering method generated highest order amounts. Chatbot ordering is better suited for use in quick-service restaurants due to their simpler menus. In terms of order items, chatbot method was used for simple menu items and core products, phone method for specials and more complicated items, while online method for more expensive items and add-ons. The findings offer new insight for restaurant practitioners into designing and adopting chatbots.
Services are changing at an impressive pace boosted by the technological advances felt in Robotics, Big Data, and Artificial Intelligence (AI) that have uncovered new research opportunities. Our objective is to contribute to the literature by exploring the pros and cons of the use of service robots in the hospitality industry and to practice, by presenting the architectural and technological characteristics of a fully automated plant based on a relevant case. To achieve such goal, this article uses a systematic literature review to assess the state-of-the-art, characterize the unit of analysis, and find new avenues for further research. The results indicate that, in high customer contact settings, service robots tend to outperform humans when performing standardized tasks, because of their mechanical and analytical nature. Evidence also shows that, in some cases, service robots have not yet achieved the desired technological maturity to proficiently replace humans. In other words, the technology is not quite there yet, but this does not contradict the fact that new robot technologies, enabled by AI, will be able to replace the employees’ empathetic intelligence. In practical terms, organizations are facing challenges where they have to decide whether service robots are capable of completely replacing human labor or if they should rather invest in balanced options, such as human-robot systems, that seem to be a much more rational choice today.
This study introduces MAK approach to investigate intellectual structure of fields which
combines text-net analysis (TNA), latent dirichlet allocation (LDA), and co-citation analysis. Researchers have previously deployed co-citation analysis to reveal the intellectual structure of fields. However, in these applications, the research has two technical limitations—small representativeness in datasets analyzed and the primary consideration for dated documents—towards the co-citation analysis. These limitations impede the formation of a larger picture in the structure. The present study seeks to eliminate these limitations by utilizing TNA and LDA methods as topic modeling approaches for 38,368 journal articles as references with 125,154 appearances in 2680 articles published between 1980 and 2019 in the Strategic Management Journal (SMJ). We suggest researchers should embrace MAK approach as complementary approach to research, with its focus on the intellectual structures of the field. We provide a workfow to show potential research applications and address advantages and limitations associated with the two new methods.
This paper investigates the literary corpus on the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs). It provides a quantitative overview of the academic literature that constitutes the field. The paper discusses the relationships between AI and rapid developments in machine learning and sustainable development (SD). Specifically, the aim is to understand whether this branch of computer science can influence production and consumption patterns to achieve sustainable resource management according to Sustainable Development Goals (SDGs) outlined in the UN 2030 Agenda. Moreover, the paper aims to highlight the role of Knowledge Management Systems (KMS) in the cultural drift toward the spread of AI for SBMs. Despite the importance of the topic, there is no comprehensive review of the AI and SBM literature in light of SDGs. Based on a database containing 73 publications in English with publication dates from 1990 to 2019, a bib-liometric analysis is conducted. The findings show that the innovation challenge involves ethical, social, economic , and legal aspects. Thus, considering that the development potential of AI is linked to the UN 2030 Agenda for SD, especially to SDG#12, our results also outline the framework of the existing literature on AI and SDGs, especially SDG#12, including AI's association with the cultural drift (CD) in the SBMs. The paper highlights the key contributions, which are: i) a comprehensive review of the key underlying relationship between AI and SBMs, offering a holistic view as needed, ii) identifying a research gap regarding KMS through AI, and iii) the implications of AI concerning SDG#12. Academic and managerial implications are also discussed regarding KMS in the SBMs, where the AI can represent the vehicle to meet the SDGs allowing for the identification of the cultural change required by enterprises to achieve sustainable goals. Thus, business companies, academic research practitioners, and state policy should focus on the further development of the use of AI in SBMs.
This study examines the interaction effect of past experience and education level on the perceived usefulness of hotel technology. This study also investigates the influence of the perceived usefulness of hotel technologies, such as artificial intelligence, robotics, and service automation, on the behavioral intention of customers through their past experience. A total of 301 international tourists participated in this study. Results show that the past experiences and education level of tourists exhibit an interaction effect on the perceived usefulness of hotel technologies in terms of understanding and answering questions, providing accurate information, and communicating through various languages. The perception of technology adoption tends to show the commonality for the inexperienced tourists regardless of education level. However, such a perception fluctuates among experienced tourists with increased educational level, particularly among the graduate group. In addition, the effect of the perceived usefulness of hotel technologies on behavioral intention is stronger for experienced hotel customers than for inexperienced ones. Academic and managerial implications are also discussed for future research development.
Purpose
This paper aims to research, identify and discuss the benefits and overall role of big data and artificial intelligence (BDAI) in the tourism sector, as this is depicted in recent literature.
Design/methodology/approach
A systematic literature review was conducted under the McKinsey’s Global Institute (Talwar and Koury, 2017) methodological perspective that identifies the four ways (i.e. project, produce, promote and provide) in which BDAI creates value. The authors enhanced this analysis methodology by depicting relevant challenges as well.
Findings
The findings imply that BDAI create value for the tourism sector through appropriately identified disseminations. The benefits of adopting BDAI strategies include increased efficiency, productivity and profitability for tourism suppliers combined with an extremely rich and personalized experience for travellers. The authors conclude that challenges can be bypassed by adopting a BDAI strategy. Such an adoption will stand critical for the competitiveness and resilience of existing established and new players in the tourism sector.
Originality/value
Besides identifying the benefits that BDAI brings in the tourism sector, the research proposes a guidebook to overcome challenges when introducing such new technologies. The exploration of the BDAI literature brings important implication for managers, academicians and consumers. This is the first systematic review in an area and contributes to the broader e-commerce marketing, retailing and e-tourism research.
This paper develops a methodology for the early detection of reactivation of tourist markets to help mitigate the effects of the COVID-19 crisis, using Skyscanner data on air passenger searches (>5,000 million) and picks (>600 million), for flights between November 2018 and December 2020, through ForwardKeys. For future travel during the May to September 2020 period, the desire to travel (based on the number of flight searches) has dropped by about 30% in Europe and the Americas, and by about 50% in Asia, while intention to travel (the number of flight picks, the final selections amongst flight searches) has dropped a further 10–20%. Most source markets remain optimistic about air travel during the last quarter of 2020, suggesting a U shape recovery. However, optimism has dwindled as time passes, suggesting a flatline L shape. A traffic light dashboard for domestic and inbound air travel demand to Spain shows how destination managers might use Big Data relating to the early recovery of key source markets to develop targeted marketing strategies. We show how Big Data provides timely granular data essential in highly volatile situations, and we argue that destination management organisations must improve their Big Data analytical and evidence-based, decision-making skills.