tourism and hospitality 2

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Chapter 1. History and Overview

1.1 What is Tourism?

Before engaging in a study of tourism , let’s have a closer look at what this term means.

Definition of Tourism

There are a number of ways tourism can be defined, and for this reason, the United Nations World Tourism Organization (UNWTO) embarked on a project from 2005 to 2007 to create a common glossary of terms for tourism. It defines tourism as follows:

Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes. These people are called visitors (which may be either tourists or excursionists; residents or non-residents) and tourism has to do with their activities, some of which imply tourism expenditure (United Nations World Tourism Organization, 2008).

Using this definition, we can see that tourism is not just the movement of people for a number of purposes (whether business or pleasure), but the overall agglomeration of activities, services, and involved sectors that make up the unique tourist experience.

Tourism, Travel, and Hospitality: What are the Differences?

It is common to confuse the terms tourism , travel , and hospitality or to define them as the same thing. While tourism is the all-encompassing umbrella term for the activities and industry that create the tourist experience, the UNWTO (2020) defines travel as the activity of moving between different locations often for any purpose but more so for leisure and recreation (Hall & Page, 2006). On the other hand, hospitality can be defined as “the business of helping people to feel welcome and relaxed and to enjoy themselves” (Discover Hospitality, 2015, p. 3). Simply put, the hospitality industry is the combination of the accommodation and food and beverage groupings, collectively making up the largest segment of the industry (Go2HR, 2020). You’ll learn more about accommodations and F & B in Chapter 3 and Chapter 4 , respectively.

Definition of Tourist and Excursionist

Building on the definition of tourism, a commonly accepted description of a tourist is “someone who travels at least 80 km from his or her home for at least 24 hours, for business or leisure or other reasons” (LinkBC, 2008, p.8). The United Nations World Tourism Organization (1995) helps us break down this definition further by stating tourists can be:

• Domestic (residents of a given country travelling only within that country)
• Inbound (non-residents travelling in a given country)
• Outbound (residents of one country travelling in another country)

Excursionists  on the other hand are considered same-day visitors (UNWTO, 2020). Sometimes referred to as “day trippers.” Understandably, not every visitor stays in a destination overnight. It is common for travellers to spend a few hours or less to do sightseeing, visit attractions, dine at a local restaurant, then leave at the end of the day.

The scope of tourism, therefore, is broad and encompasses a number of activities and sectors.

Spotlight On: United Nations World Tourism Organization (UNWTO)

UNWTO is the United Nations agency responsible “for the promotion of responsible, sustainable and universally accessible tourism” (UNWTO, 2014b). Its membership includes 159 countries and over 500 affiliates such as private companies, research and educational institutions, and non-governmental organizations. It promotes tourism as a way of developing communities while encouraging ethical behaviour to mitigate negative impacts. For more information, visit the UNWTO website .

NAICS: The North American Industry Classification System

Given the sheer size of the tourism industry, it can be helpful to break it down into broad industry groups using a common classification system. The North American Industry Classification System (NAICS) was jointly created by the Canadian, US, and Mexican governments to ensure common analysis across all three countries (British Columbia Ministry of Jobs, Tourism and Skills Training, 2013a). The tourism-related groupings created using NAICS are (in alphabetical order):

• Accommodation
• Food and beverage services (commonly known as “F & B”)
• Recreation and entertainment
• Transportation
• Travel services

These industry groups (also commonly known as sectors) are based on the similarity of the “labour processes and inputs” used for each (Government of Canada, 2013). For instance, the types of employees and resources required to run an accommodation business whether it be a hotel, motel, or even a campground are quite similar. All these businesses need staff to check in guests, provide housekeeping, employ maintenance workers, and provide a place for people to sleep. As such, they can be grouped together under the heading of accommodation. The same is true of the other four groupings, and the rest of this text explores these industry groups, and other aspects of tourism, in more detail.

It is typical for the entire tourist experience to involve more than one sector. The combination of sectors that supply and distribute the needed tourism products, services, and activities within the tourism system is called the Tourism Supply Chain. Often, these chains of sectors and activities are dependent upon each other’s delivery of products and services. Let’s look at a simple example below that describes the involved and sometimes overlapping sectoral chains in the tourism experience:

Before we seek to understand the five tourism sectors in more detail, it’s important to have an overview of the history and impacts of tourism to date.

Long Descriptions

Figure 1.2 long description: Diagram showing the tourism supply chain. This includes the phases of travel and the sectors and activities involved during each phase.

There are three travel phases: pre-departure, during travel, and post-departure.

Pre-departure, tourists use the travel services and transportation sectors.

During travel, tourists use the travel services, accommodations, food and beverage, recreation and entertainment, and transportation sectors.

Post-departure, tourists use the transportation sector.

[Return to Figure 1.2]

Media Attributions

• Front Desk by Staying LEVEL is licensed under a CC BY-NC 4.0 Licence .

Tourism according the the UNWTO is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes.

UN agency responsible for promoting responsible, sustainable, and universally accessible tourism worldwide.

Moving between different locations for leisure and recreation.

The accommodations and food and beverage industry groupings.

someone who travels at least 80 km from his or her home for at least 24 hours, for business or leisure or other reasons

A same-day visitor to a destination. Their trip typically ends on the same day when they leave the destination.

A way to group tourism activities based on similarities in business practices, primarily used for statistical analysis.

Introduction to Tourism and Hospitality in BC - 2nd Edition Copyright © 2015, 2020, 2021 by Morgan Westcott and Wendy Anderson, Eds is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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The magazine of Glion Institute of Higher Education

• What is tourism and hospitality?

Tourism and hospitality are thriving industries encompassing many sectors, including hotels, restaurants, travel, events, and entertainment.

It’s an exciting and dynamic area, constantly evolving and adapting to changing customer demands and trends.

The tourism and hospitality industry offers a diverse range of career opportunities that cater to various interests, skills, and qualifications, with positions available from entry-level to executive management.

The booming tourism  and hospitality industry also offers job security and career growth potential in many hospitality-related occupations.

What is tourism?

Tourism is traveling for leisure, pleasure, or business purposes and visiting various destinations, such as cities, countries, natural attractions, historical sites, and cultural events, to experience new cultures, activities, and environments.

Tourism can take many forms, including domestic, or traveling within your country, and international tourism, or visiting foreign countries.

It can also involve sightseeing, adventure tourism , eco-tourism, cultural tourism, and business tourism, and it’s a huge contributor to the global economy, generating jobs and income in many countries.

It involves many businesses, including airlines, hotels, restaurants, travel agencies, tour operators, and transportation companies.

What is hospitality?

Hospitality includes a range of businesses, such as hotels, restaurants, bars, resorts, cruise ships, theme parks, and other service-oriented businesses that provide accommodations, food, and beverages.

Hospitality is all about creating a welcoming and comfortable environment for guests and meeting their needs.

Quality hospitality means providing excellent customer service, anticipating guests’ needs, and ensuring comfort and satisfaction. The hospitality industry is essential to tourism as both industries often work closely together.

What is the difference between tourism and hospitality?

Hospitality and tourism are both related and separate industries. For instance, airline travel is considered as part of both the tourism and hospitality industries.

Hospitality is a component of the tourism industry, as it provides services and amenities to tourists. However, tourism is a broader industry encompassing various sectors, including transportation, accommodation, and attractions.

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Is tourism and hospitality a good career choice?

So, why work in hospitality and tourism? The tourism and hospitality industry is one of the fastest-growing industries in the world, providing a colossal number of job opportunities.

Between 2021 and 2031, employment in the hospitality and tourism industry is projected to expand faster than any other job sector, creating about 1.3 million new positions .

A tourism and hospitality career  can be a highly rewarding choice for anyone who enjoys working with people, has a strong service-oriented mindset, and is looking for a dynamic and exciting career with growth potential.

Growth and job opportunities in tourism and hospitality

Tourism and hospitality offers significant growth and job opportunities worldwide. The industry’s increasing demand for personnel contributes to economic and employment growth, particularly in developing countries.

The industry employs millions globally, from entry-level to high-level management positions, including hotel managers, chefs, tour operators, travel agents, and executives.

It provides diverse opportunities with great career progression and skill development potential.

Career paths in tourism and hospitality

There are many career opportunities in tourism management and hospitality. With a degree in hospitality management, as well as relevant experience, you can pursue satisfying and fulfilling hospitality and tourism careers in these fields.

Hotel manager

Hotel managers oversee hotel operations. They manage staff, supervise customer service, and ensure the facility runs smoothly.

Tour manager

Tour managers organize and lead group tours. They work for tour companies, travel agencies, or independently. Tour managers coordinate a group’s transportation, accommodations, and activities, ensuring the trip runs to schedule.

Restaurant manager

Restaurant managers supervise the daily operations of a restaurant. They manage staff, ensure the kitchen runs smoothly, and monitor customer service.

Resort manager

Resort managers supervise and manage the operations of a resort. From managing staff to overseeing customer service, they ensure the entire operation delivers excellence.

Entertainment manager

Entertainment managers organize and oversee entertainment at venues like hotels or resorts. They book performers, oversee sound and lighting, and ensure guests have a great experience.

Event planner

Event planners organize and coordinate events, such as weddings, conferences, and trade shows. They work for event planning companies, hotels, or independently.

vent planners coordinate all aspects of the event, from the venue to catering and decor.

Travel consultant

Travel consultants help customers plan and book travel arrangements, such as flights, hotels, and rental cars. They work for travel agencies or independently. Travel consultants must know travel destinations and provide superb customer service.

What skills and qualifications are needed for a career in tourism and hospitality?

Tourism and hospitality are rewarding industries with growing job opportunities. Necessary qualifications include excellent skills in communication, customer service, leadership, problem-solving, and organization along with relevant education and training.

Essential skills for success in tourism and hospitality

A career in the tourism and hospitality industry requires a combination of soft and technical skills and relevant qualifications. Here are some of the essential key skills needed for a successful career.

• Communication skills : Effective communication is necessary for the tourism and hospitality industry in dealing with all kinds of people.
• Customer service : Providing excellent customer service is critical to the success of any tourism or hospitality business . This requires patience, empathy, and the ability to meet customers’ needs.
• Flexibility and adaptability : The industry is constantly changing, and employees must be able to adapt to new situations, be flexible with their work schedules, and handle unexpected events.
• Time management : Time management is crucial to ensure guest satisfaction and smooth operations.
• Cultural awareness : Understanding and respecting cultural differences is essential in the tourism and hospitality industry, as you’ll interact with people from different cultures.
• Teamwork : Working collaboratively with colleagues is essential, as employees must work together to ensure guests have a positive experience.
• Problem-solving : Inevitably, problems will arise, and employees must be able to identify, analyze, and resolve them efficiently.
• Technical skills : With the increasing use of technology, employees must possess the necessary technical skills to operate systems, such as booking software, point-of-sale systems, and social media platforms.

Revenue management : Revenue management skills are crucial in effectively managing pricing, inventory, and data analysis to maximize revenue and profitability

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Education and training opportunities in tourism and hospitality

Education and training are vital for a hospitality and tourism career. You can ensure you are prepared for a career in the industry with a Bachelor’s in hospitality management   and Master’s in hospitality   programs from Glion.

These programs provide a comprehensive understanding of the guest experience, including service delivery and business operations, while developing essential skills such as leadership, communication, and problem-solving. You’ll gain the knowledge and qualifications you need for a successful, dynamic, and rewarding hospitality and tourism career.

Preparing for a career in tourism and hospitality

To prepare for a career in tourism and hospitality management, you should focus on researching the industry and gaining relevant education and training, such as a hospitality degree . For instance, Glion’s programs emphasize guest experience and hospitality management, providing students with an outstanding education that launches them into leading industry roles.

It would help if you also worked on building your communication, customer service, and problem-solving skills while gaining practical experience through internships or part-time jobs in the industry. Meanwhile, attending industry events, job fairs, and conferences, staying up-to-date on industry trends, and networking to establish professional connections will also be extremely valuable.

Finding jobs in tourism and hospitality

To find jobs in tourism and hospitality, candidates can search online job boards, and company career pages, attend career fairs, network with industry professionals, and utilize the services of recruitment agencies. Hospitality and tourism graduates can also leverage valuable alumni networks and industry connections made during internships or industry projects.

Networking and building connections in the industry

Networking and building connections in the hospitality and tourism industry provide opportunities to learn about job openings, meet potential employers, and gain industry insights. It can also help you expand your knowledge and skills, build your personal brand, and establish yourself as a valuable industry professional.

You can start networking by attending industry events, joining professional organizations, connecting with professionals on social media, and through career services at Glion.

Tips for success in tourism and hospitality

Here are tips for career success in the tourism and hospitality industry.

• Gain relevant education and training : Pursue a hospitality or tourism management degree from Glion to gain fundamental knowledge and practical skills.
• Build your network : Attend industry events, connect with colleagues and professionals on LinkedIn, and join relevant associations to build your network and increase your exposure to potential job opportunities.
• Gain practical experience : Look for internships, part-time jobs, or volunteering opportunities to gain practical experience and develop relevant skills.
• Develop your soft skills : Work on essential interpersonal skills like communication, empathy, and problem-solving.
• Stay up-to-date with industry trends : Follow industry news and trends and proactively learn new skills and technologies relevant to tourism and hospitality.
• Be flexible and adaptable : The tourism and hospitality industry constantly evolves, so be open to change and to adapting to new situations and challenges.
• Strive for excellent guest service : Focus on delivering exceptional guest experiences as guest satisfaction is critical for success.

Tourism and hospitality offer many fantastic opportunities to create memorable guest experiences , work in diverse and multicultural environments, and develop transferable skills.

If you’re ready to embark on your career in tourism and hospitality, Glion has world-leading bachelor’s and master’s programs to set you up for success.

Photo credits Main image: Maskot/Maskot via Getty Images

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• Open access
• Published: 25 November 2023

Systematic review and research agenda for the tourism and hospitality sector: co-creation of customer value in the digital age

• T. D. Dang   ORCID: orcid.org/0000-0003-0930-381X 1 , 2 &
• M. T. Nguyen 1  

Future Business Journal volume  9 , Article number:  94 ( 2023 ) Cite this article

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Metrics details

A Correction to this article was published on 07 February 2024

This article has been updated

The tourism and hospitality industries are experiencing transformative shifts driven by the proliferation of digital technologies facilitating real-time customer communication and data collection. This evolution towards customer value co-creation demands a paradigm shift in management attitudes and the adoption of cutting-edge technologies like artificial intelligence (AI) and the Metaverse. A systematic literature review using the PRISMA method investigated the impact of customer value co-creation through the digital age on the tourism and hospitality sector. The primary objective of this review was to examine 27 relevant studies published between 2012 and 2022. Findings reveal that digital technologies, especially AI, Metaverse, and related innovations, significantly enhance value co-creation by allowing for more personalized, immersive, and efficient tourist experiences. Academic insights show the exploration of technology’s role in enhancing travel experiences and ethical concerns, while from a managerial perspective, AI and digital tools can drive industry success through improved customer interactions. As a groundwork for progressive research, the study pinpoints three pivotal focal areas for upcoming inquiries: technological, academic, and managerial. These avenues offer exciting prospects for advancing knowledge and practices, paving the way for transformative changes in the tourism and hospitality sectors.

Introduction

The tourism and hospitality industry is constantly evolving, and the digital age has brought about numerous changes in how businesses operate and interact with their customers [ 1 ]. One such change is the concept of value co-creation, which refers to the collaborative process by which value is created and shared between a business and its customers [ 2 , 3 ]. In order to facilitate the value co-creation process in tourism and hospitality, it is necessary to have adequate technologies in place to enable the participation of all stakeholders, including businesses, consumers, and others [ 4 , 5 ]. Thus, technology serves as a crucial enabler for value co-creation. In the tourism and hospitality industry, leading-edge technology can be crucial in co-creation value processes because it can facilitate the creation and exchange of value among customers and businesses [ 6 , 7 ]. For example, the development of cloud computing and virtual reality technologies has enabled new forms of collaboration and co-creation that were not possible before [ 8 , 9 , 10 ]. Recent technologies like AI, Metaverse, and robots have revolutionized tourism and hospitality [ 11 , 12 , 13 ]. These technologies are used in various ways to enhance the customer experience and drive business success. AI can personalize the customer experience using customer data and personalized recommendations [ 14 ]. It can also optimize operations by automating tasks and improving decision-making. The metaverse, or virtual reality (VR) and augmented reality (AR) technologies, are being used to offer immersive and interactive experiences to customers [ 10 , 11 ]. For example, VR and AR can create virtual tours of hotels and destinations or offer interactive experiences such as virtual cooking classes or wine tastings [ 15 ]. Robots are being used to aid and interact with customers in various settings, including hotels, restaurants, and tourist attractions. For example, robots can provide information, answer questions, and even deliver room services [ 12 , 16 ]. The COVID-19 pandemic has underscored the crucial interplay between public health, sustainable development, and digital innovations [ 17 ]. Globally, the surge in blockchain applications, particularly in the business, marketing and finance sectors, signifies the technological advancements reshaping various industries [ 18 ]. These developments, coupled with integrating digital solutions during the pandemic, highlight the pervasive role of technology across diverse sectors [ 19 , 20 , 21 ]. These insights provide a broader context for our study of the digital transformation in the tourism and hospitality sectors. Adopting new technologies such as AI, the Metaverse, blockchain and robots is helping the tourism and hospitality industry deliver customers a more personalized, convenient, and immersive experience [ 22 ]. As these technologies continue to evolve and become more prevalent, businesses in the industry need to stay up-to-date and consider how they can leverage these technologies to drive success [ 23 , 24 ].

Despite the growing body of literature on customer value co-creation in the tourism and hospitality sector, it remains scattered and fragmented [ 2 , 25 , 26 ]. To consolidate this research and provide a comprehensive summary of the current understanding of the subject, we conducted a systematic literature review using the PRISMA 2020 (“ Preferred Reporting Items for Systematic Reviews and Meta-Analyses ”) approach [ 27 , 28 ]. This systematic review aims to explore three primary areas of inquiry related to the utilization of AI and new technologies in the tourism and hospitality industry: (i) From a technology perspective, what are the main types of AI and latest technologies that have been used to enhance co-creation values in tourism and hospitality?; (ii) From an academic viewpoint—What are the future research directions in this sector?; (iii) From a managerial standpoint—How can these technologies be leveraged to enhance customer experiences and drive business success?. In essence, this study contributes valuable insights into the dynamic realm of customer value co-creation in the digital age within the tourism and hospitality sector. By addressing the research questions and identifying gaps in the literature, our systematic literature review seeks to provide novel perspectives on leveraging technology to foster industry advancements and enhance customer experiences.

The remaining parts of this article are structured in the following sections: “ Study background ” section outlines pertinent background details for our systematic literature review. In “ Methodology ” section details our research objectives, queries, and the systematic literature review protocol we used in our study design. In “ Results ” section offers the findings based on the analyzed primary research studies. Lastly, we conclude the article, discuss the outstanding work, and examine the limitations to the validity of our study in “ Discussion and implications ” section.

Study background

Amidst the COVID-19 pandemic, the tourism sector is experiencing significant transformations. Despite the substantial impact on the tourism industry, the demand for academic publications about tourism remains unabated. In this recovery phase, AI and novel technologies hold immense potential to assist the tourism and hospitality industry by tackling diverse challenges and enhancing overall efficiency. In this section, the study provides some study background for the review processes.

The relationship between tourism and hospitality

Tourism and hospitality are closely related industries, as the hospitality industry plays a crucial role in the tourism industry [ 29 ]. Academics and practitioners often examine tourism and hospitality because they are related industries [ 2 , 30 ]. Hospitality refers to providing travelers and tourists accommodation, food, and other services [ 31 ]. These can include hotels, resorts, restaurants, and other types of establishments that cater to the needs of travelers [ 32 ]. On the other hand, the tourism industry encompasses all the activities and services related to planning, promoting, and facilitating travel [ 31 ]; transportation, tour operators, travel agencies, and other businesses that help facilitate tourist travel experiences [ 33 ]. Both industries rely on each other to thrive, as travelers need places to stay and eat while on vacation, and hospitality businesses rely on tourists for their income [ 32 , 33 , 34 ].

In recent years, the tourism industry has undergone significant changes due to the increasing use of digital technologies, enabling the development of new forms of tourism, such as “smart tourism” [ 8 , 10 ]. Smart tourism refers to using digital technologies to enhance the customer experience and improve the efficiency and effectiveness of the industry [ 1 ]. These technologies, including AI and Metaverse, can be used in various aspects of the tourism industry, such as booking and reservation processes, customer service, and the management of tourist attractions [ 4 , 11 ]. The hospitality industry, which includes hotels and restaurants, is closely linked to the tourism industry and is also adopting intelligent technologies to improve the customer experience and increase efficiency [ 1 , 22 ]. Recent studies have explored the impact of these technologies on the tourism and hospitality sectors and have identified both benefits and challenges for stakeholders [ 10 , 35 , 36 ].

Customer value co-creation in tourism and hospitality

Customer value co-creation in tourism and hospitality refers to the process by which customers and businesses collaborate to create value by exchanging services, information, and experiences [ 2 , 33 ]. This process involves the customer and the business actively creating value rather than simply providing a product or service to the customer [ 37 ]. Studies have found that customer value co-creation in tourism and hospitality can increase customer satisfaction and loyalty [ 2 ]. When customers feel that they can contribute to the value of their experience, they are more likely to feel a sense of ownership and involvement, which can lead to a more positive overall evaluation of the experience [ 5 , 38 ]. In the tourism industry, customer value co-creation can increase satisfaction with the destination, trips, accommodation, services, and overall experiences [ 4 ]. These can be achieved by allowing customers to choose their room amenities or providing opportunities to interact with staff and other guests [ 5 , 39 ]. Customer value co-creation in tourism and hospitality can be a powerful solution for businesses to increase customer satisfaction and loyalty. By actively involving customers in creating value, businesses can create a more personalized and engaging experience for their customers.

AI, Metaverse, and new technologies in tourism and hospitality

The impact of AI, the Metaverse, and new technologies on the tourism and hospitality industries is an area of active research and debate [ 2 , 4 , 29 , 40 ]. First, using AI and new technology in tourism and hospitality can improve the customer experience, increase efficiency, and reduce costs [ 13 , 41 , 42 , 43 ]. For instance, chatbots and virtual assistants facilitate tasks like room bookings or restaurant reservations for customers. Concurrently, machine learning (ML) algorithms offer optimized pricing and marketing strategies and insights into customer perceptions within the tourism and hospitality sectors [ 44 , 45 , 46 , 47 ]. However, there are also concerns about the potential negative impact of AI on employment in the industry [ 48 ]. Second, The emergence of the Metaverse, a virtual shared space where people can interact in real time, can potentially revolutionize the tourism and hospitality industries [ 10 ]. For example, VR and AR experiences could allow travelers to visit and explore destinations without leaving their homes [ 15 , 49 ], while online events and social gatherings could provide new business opportunities to connect with customers [ 11 ]. However, it is unclear how the Metaverse will evolve and its long-term impact on the tourism and hospitality industries [ 4 , 10 , 11 ]. Last, other emerging technologies, such as blockchain, AI-Robotics, and the Internet of Things (IoT), can potentially transform the tourism and hospitality industries [ 18 , 45 , 48 ]. For example, blockchain could be used to secure and track the movement of travel documents [ 18 ], while IoT-enabled devices could improve the efficiency and personalization of the customer experience [ 50 ]. As with AI and the Metaverse, it is difficult to predict the exact impact of these technologies on the industry, but they are likely to play a significant role in shaping its future [ 18 , 40 ]. In the aftermath of the pandemic, the healthcare landscape within the tourism and hospitality sector is undergoing significant transformations driven by the integration of cutting-edge AI and advanced technologies [ 38 , 51 , 52 ]. These technological advancements have paved the way for personalized and seamless experiences for travelers, with AI-powered chatbots playing a pivotal role in addressing medical inquiries and innovative telemedicine solutions ensuring the well-being of tourists [ 52 , 53 ].

This study background provides essential context for the subsequent systematic literature review, as it contextualizes the field’s key concepts, frameworks, and emerging technologies. By examining these aspects, the study aims to contribute valuable insights into the post-pandemic recovery of the tourism and hospitality industry, paving the way for future research opportunities and advancements in the field.

Methodology

This study meticulously adopted a systematic literature review process grounded in a pre-defined review protocol to provide a thorough and objective appraisal [ 54 ]. This approach was geared to eliminate potential bias and uphold the integrity of study findings. The formulation of the review protocol was a collaborative effort facilitated by two researchers. This foundational document encompasses (i) Clear delineation of the study objectives, ensuring alignment with the research aim; (ii) A thorough description of the methods used for data collection and assessment, which underscores the replicability of our process; (iii) A systematic approach for synthesizing and analyzing the selected studies, promoting consistency and transparency.

Guiding the current review process was the PRISMA methodology, a renowned and universally esteemed framework that has set a gold standard for conducting systematic reviews in various scientific disciplines [ 27 , 28 ]. The commendable efficacy of PRISMA in service research substantiates its methodological robustness and reliability [ 55 ]. It is not only the rigorous nature of PRISMA but also its widespread acceptance in service research that accentuates its fittingness for this research. Given tourism and hospitality studies’ intricate and evolving nature, PRISMA is a robust compass to guide our SLR, ensuring methodological transparency and thoroughness [ 56 , 57 ]. In essence, the PRISMA approach does not merely dictate the procedural intricacies of the review but emphasizes clarity, precision, and transparency at every phase. The PRISMA methodology presents the research journey holistically, from its inception to its conclusions, providing readers with a clear and comprehensive understanding of the approach and findings [ 58 ].

Utilizing the goal-question-metrics approach [ 59 ], our study aims to analyze current scientific literature from the perspectives of technicians, researchers, and practitioners to comprehend customer value co-creation through the digital age within the Tourism and Hospitality sector. In order to accomplish this goal, we formulated the following research questions:

What are the main types of AI and new technologies used to enhance value co-creation in the tourism and hospitality industries?

What are the future research directions in customer value co-creation through AI and new technologies in the tourism and hospitality sector?

How do managers in the tourism and hospitality sector apply AI and new technologies to enhance customer co-creation value and drive business success?

The subsequent subsections will provide further details regarding our search and analysis strategies.

Search strategy and selection criteria

We collected our data by searching for papers in the Scopus and Web of Science databases, adhering to rigorous scientific standards. We included only international peer-reviewed academic journal articles, excluding publications like books, book chapters, and conference proceedings [ 60 , 61 , 62 ]. The research process covered the period from 2009 to 2022, as this timeframe aligns with the publication of the first studies on value co-creation in the tourism industry in 2009 and the first two studies on value co-creation in general in 2004 [ 63 , 64 ]. The selection of sources was based on criteria such as timelines, availability, quality, and versatility, as discussed by Dieste et al. [ 2 ]. We employed relevant keywords, synonyms, and truncations for three main concepts: tourism and hospitality, customer value co-creation, and AI and new technologies in smart tourism and hospitality. To ensure transparency and comprehensiveness, we followed the PRISMA inclusion criteria, detailed in Table 1 , and utilized topic and Boolean/phrase search modes to retrieve papers published from 2009 to 2022. The final search string underwent validation by experts to ensure accuracy and comprehensiveness:

A PRISMA diagram was produced to understand better this study’s search strategy and record selection.

Study selection and analysis procedure

The current study utilized the PRISMA framework to document our review process. One hundred two papers were retrieved during the initial search across the databases. Table 1 outlines the criteria for selecting the studies based on scope and quality. The study adhered to the PRISMA procedure (as shown in Fig.  1 ) and applied the following filters:

We identified and removed 17 duplicate records during the ‘identification’ step.

We excluded 27 publications in the ‘Screening’ step based on the title and abstract.

We excluded 31 publications based on the entire text in the eligibility step.

PRISMA flow diagram

As a result, we were left with a final collection of 27 journal articles for downloading and analysis. Two trained research assistants conducted title and abstract screenings separately, and any disagreements about inclusion were resolved by discussing them with the research coordinator until an agreement was reached. Papers not in English, papers from meetings, books, editorials, news, reports, and patents were excluded, as well as unrelated or incomplete papers and studies that did not focus on the tourism and hospitality domain. A manual search of the reference lists of each paper was conducted to identify relevant papers that were not found in the database searches. After this process, 27 papers were left for a full-text review.

This study used the Mixed Methods Appraisal Tool (MMAT) to evaluate the quality of qualitative, quantitative, and mixed methods research studies included [ 65 , 66 ]. According to the findings, the quality of the study met the standards of a systematic review. Additional information can be obtained from Additional file 1 : Appendix 1.

In this section, we will report the results of our data analysis for each research question. We will begin by describing the characteristics of the studies included in the systematic literature review, such as (1) publication authors, titles, years and journals, topics, methods, and tools used in existing studies. Then each facet was elaborated by the following questions: (i) What are the main types of AI and new technologies used to enhance value co-creation in the tourism and hospitality industries? (ii) What are the future research directions in customer value co-creation through AI and new technologies in the tourism and hospitality sector? (iii) How do managers in the tourism and hospitality sector apply AI and new technologies to enhance customer co-creation value and drive business success?

Studies demographics

Figure  2 shows the yearly publication of articles on customer co-creation of value in tourism and hospitality through AI and new technologies. The chart’s data suggests two main findings. Firstly, the research on customer value co-creation in tourism and hospitality through AI and new technologies is still in its early stages (1 paper in 2012). However, the annual number of published articles from 2017 to the present appears to be generally increasing. This trend implies that the application of value co-creation in this field is gaining academic attention and is becoming an emerging research area. Based on this trend, we anticipate seeing more studies on this topic published in the following years.

Publication Years with research methods

Regarding research type, 14 papers (52%) conducted quantitative research, employing statistical analysis, structural equation modeling, and data mining methods. Meanwhile, 11 papers (41%) conducted qualitative research using interviews, thematic analysis, and descriptive analysis. Only two papers (7%) used mixed research (combining quantitative and qualitative methods). The survey and interview methods (both individual and group) were found to be more common than other research methods. This suggests that interviews provide greater insight into participant attitudes and motivations, enhancing accuracy in quantitative and qualitative studies. Additionally, certain studies employed content analysis, big data analysis using UGC, and data from online platforms, social media, and big data.

Regarding the publishing journals, we found that 27 papers were published in 22 journals (refer to Table 2 ), where three journals had more than one paper on co-creation value through AI and new technologies in tourism and hospitality, indicating their keen interest in this topic. Most publications were in the Journal of Business Research, with four studies on co-creation value through AI and new technologies in tourism and hospitality. Two related studies were published in the Tourism Management Perspectives and Journal of Destination Marketing & Management. This distribution indicates that most current research on co-creation value through AI and new technologies in tourism and hospitality was published in journals in the tourism and hospitality management field. However, some journals in the computer and AI field have also published papers on co-creation value through AI and new technologies in tourism and hospitality, including Computers in Industry, Computers in Human Behavior, Computational Intelligence, and Neuroscience.

Regarding data analytics tools, SmartPLS, AMOS, NVivo and PROCESS tools are the 5 most popular software graphic tools used in studies, while Python and R are the two main types of programming languages used. In total, 27 studies, 14 refer to using AI applications and data analytics in this research flow. Metaverse and relative technologies such as AR and VR were included in 8 studies. Three studies used service robots to discover the value co-creation process. There are include two studies that have used chatbots and virtual assistants.

Publication years and journals

In recent systematic literature reviews focusing on general services, tourism, and hospitality, there has been a notable emphasis on traditional factors shaping customer experience [ 26 , 67 , 68 ]. However, this study uniquely positions itself by emphasizing the digital age’s profound impact on value co-creation within this sector. The subsequent part digs more into the specifics of this study, building on these parallels. The detailed findings offer nuanced insights into how value co-creation in tourism and hospitality has evolved, providing a more extensive understanding than previous works.

Result 1—technology viewpoints: What are the main types of AI and new technologies used to enhance value co-creation in the tourism and hospitality industries?

Several types of AI and new technologies have been used to enhance co-creation values in the tourism and hospitality industry. Nowadays, AI, ML, and deep learning can all be used to enhance customer value co-creation in the tourism and hospitality industry [ 42 , 69 , 70 ]. There are some AI applications identified through the review process:

First, personalization and customized recommendations: AI and ML can be used to analyze customer data, such as their past bookings, preferences, and reviews, to personalize recommendations and experiences for them [ 7 , 69 , 71 , 72 ]. Cuomo et al. examine how data analytics techniques, including AI and ML, can improve traveler experience in transportation services. Applying AI and ML can help customers discover new experiences and activities they may not have considered otherwise [ 13 ]. Relating to data mining applications, Ngamsirijit examines how data mining can be used to create value in creative tourism. Moreover, the study also discusses the need for co-creation to create a successful customer experience in creative tourism and ways data mining can enhance the customer experience [ 73 ].

Second, user-generated content and sentiment analysis: ML and Natural Language Processing (NLP) can be used to analyze user-generated content such as reviews and social media posts to understand customer needs and preferences [ 12 , 37 ]. This can help businesses identify opportunities to create customer value [ 74 ]. NLP can analyze customer reviews and feedback to understand the overall sentiment toward a hotel or destination [ 75 ]. This can help businesses identify areas for improvement and create a better customer experience [ 70 ]. In the study using NLP to analyze data from Twitter, Liu et al. examine the impact of luxury brands’ social media marketing on customer engagement. The authors discuss how big data analytics and NLP can be used to analyze customer conversations and extract valuable insights about customer preferences and behaviors [ 74 ].

Third, recent deep learning has developed novel models that create business value by forecasting some parameters and promoting better offerings to tourists [ 71 ]. Deep learning can analyze large amounts of data and make more accurate predictions or decisions [ 39 , 41 ]. For example, a deep learning model could predict the likelihood of a customer returning to a hotel based on their past bookings and interactions with the hotel [ 72 ].

Some applications of the latest technologies that have been used to enhance co-creation values in tourism and hospitality include

Firstly, Chatbots and virtual assistants can enhance customer value co-creation in the tourism and hospitality industry in several ways: (i) Improved customer service: Chatbots and virtual assistants can be used to answer customer questions, provide information, and assist with tasks such as booking a room or making a reservation [ 45 ]. These tools can save customers and staff time and improve customer experience [ 76 ]; (ii) Increased convenience: Chatbots and virtual assistants can be accessed 24/7, meaning customers can get help or assistance anytime [ 50 ]. These tools can be handy for traveling customers with questions or who need assistance outside regular business hours [ 44 ]; (iii) Personalization: Chatbots and virtual assistants can use natural language processing (NLP) to understand and respond to customer inquiries in a more personalized way [ 45 , 70 ]. This can help improve the customer experience and create a more favorable impression of the business. Moreover, this can save costs and improve customers [ 16 ].

Secondly, metaverse technologies can enhance customer value co-creation in the tourism and hospitality industry in several ways: (i) Virtual tours and experiences: Metaverse technologies can offer virtual tours and experiences to customers, allowing them to visit and explore destinations remotely [ 77 ]. This technology can be beneficial for customers who are unable to travel due to pandemics or who want to preview a destination before deciding to visit in person [ 49 ]; (ii) Virtual events: Metaverse technologies can be used to host virtual events, such as conferences, workshops, or trade shows, which can be attended by customers from anywhere in the world [ 9 ]. This can save time and money for businesses and customers and increase the reach and impact of events; (iii) Virtual customer service: Metaverse technologies can offer virtual customer service, allowing customers to interact with businesses in a virtual setting [ 25 ]. This can be especially useful for customers who prefer to communicate online or in remote areas; (iv) Virtual training and education : Metaverse technologies can offer virtual training and education to employees and customers [ 41 ]. Metaverse can be an effective and convenient way to deliver training and can save time and money for both businesses and customers [ 7 ]; (v) Virtual reality (VR) experiences: Metaverse technologies can be used to offer VR experiences to customers, allowing them to immerse themselves in virtual environments and participate in activities that would be difficult or impossible to do in the real world [ 77 ]. This can enhance the customer experience and create new business opportunities to offer unique and memorable experiences [ 71 ].

Thirdly, IoT and robots can enhance customer value co-creation in the tourism and hospitality sector in several ways: (i) One way is by providing personalized and convenient customer experiences [ 12 ]. For example, hotels can use IoT-enabled devices to allow guests to control the temperature and lighting in their rooms, as well as access hotel amenities such as room service and concierge services [ 50 ]; (ii) In addition, robots can be used to provide assistance and enhance the customer experience in various ways [ 16 , 40 ]. For example, robots can be used to deliver items to guest rooms, assist with check-in and check-out processes, and provide information and directions to guests [ 12 ]; (iii) Both IoT and robots can be used to gather customer feedback and data in real-time, which can help to improve the quality and effectiveness of tourism and hospitality services [ 76 ]. For example, hotels can use IoT-enabled devices to gather data on guest preferences and needs, which can be used to tailor services and experiences to individual customers. This can help to improve customer satisfaction and loyalty [ 76 ]. Overall, using IoT and robots in the tourism and hospitality sector can help improve the industry’s efficiency and effectiveness and enhance the customer experience.

Result 2—academic viewpoints: What are the future research directions in customer value co-creation through AI and new technologies in the tourism and hospitality sector?

From an academic perspective, there are several potential future research directions in customer value co-creation through the digital age in the tourism and hospitality sector. Some possibilities include: (1) Understanding how different technologies and platforms facilitate co-creation: Researchers could investigate how different technologies and platforms, such as social media, mobile apps, or virtual reality, enable or inhibit co-creation in the tourism and hospitality industry; (2) Investigating the impact of co-creation on business performance: Researchers could examine the relationship between co-creation and business performance in the tourism and hospitality sector and identify the factors that drive success in co-creation initiatives; (3) Investigating the impact of AI and automation on co-creation: As AI and automation technologies become more prevalent in the industry, research could focus on the impact these technologies have on co-creation and value creation, including the potential for AI to facilitate or hinder co-creation; (4) Investigating the impact of the Metaverse on customer behaviour: Research could focus on understanding how the Metaverse affects customer behaviour and decision-making, and how companies can use this information to facilitate co-creation and value creation [ 9 ]; (5) Analysing the use of social media and other digital platforms for co-creation: Researchers could study how companies in the tourism and hospitality sector use social media and other digital platforms to facilitate co-creation with customers, and the impact that these platforms have on value creation [ 7 , 45 , 78 ]. Researchers could investigate how social interactions and communities in the Metaverse enable or inhibit co-creation in the tourism and hospitality industry and the impact on customer satisfaction and loyalty; (6) Examining the ethical implications of the Metaverse and AI: Researchers could explore the ethical considerations surrounding the use of the Metaverse and AI in the tourism and hospitality sector, such as issues related to privacy and data security, and the potential for these technologies to perpetuate or exacerbate societal inequalities [ 48 , 75 , 77 ].

Result 3—Management viewpoints: How do managers in the tourism and hospitality sector apply AI and new technologies to enhance customer co-creation value and drive business success?

There are several ways managers in the tourism and hospitality industry can apply AI and new technologies to enhance customer experiences and drive business success. We suggest four main possibilities: (1) Implementing chatbots or virtual assistants to encourage customer co-creation: Managers can use chatbots or virtual assistants to provide quick and convenient customer service, helping businesses respond to customer inquiries and resolve issues more efficiently [ 76 ]. Then, encourage customer co-creation by inviting customers to participate in the creation of new experiences and products by gathering feedback and ideas through online forums and focus groups [ 45 ]. This can help build a sense of community and engagement and can also lead to the development of new, innovative products and experiences that will attract more customers [ 50 , 79 ]; (2) Leveraging personalization technologies and using predictive analytics: Managers can use AI-powered personalization technologies to analyze customer data and preferences and offer personalized recommendations and experiences [ 42 , 72 , 80 ]. This can help businesses better understand and anticipate customer needs and create more tailored and satisfying experiences that drive co-creation value. Managers can leverage AI-powered predictive analytics technologies to analyze data and predict future customer behavior or trends [ 75 ]. This can help businesses anticipate customer needs and make informed decisions about resource allocation and planning, enhancing co-creation value. Managers can use personalization technologies and predictive analytics to analyze customer feedback and identify areas for improvement [ 37 ]. These can help businesses better understand customer needs and preferences and create more satisfying and valuable experiences that drive co-creation value [ 7 , 36 , 41 ]; (3) Using the Metaverse to facilitate co-creation: Managers can leverage the Metaverse to allow customers to design and customize their own experiences, which can help create value in collaboration with customers [ 25 , 71 , 77 ]. Managers can use VR and AR technologies to create immersive and interactive customer experiences in the Metaverse [ 81 ]. This can help businesses differentiate themselves and stand out in a competitive market. Managers can use data analysis tools to understand how customers behave in the Metaverse and use this information to create more personalized and satisfying experiences [ 9 ]. Managers can leverage the Metaverse to facilitate co-creation with customers, for example, by enabling customers to design and customize their own experiences [ 49 , 81 ]. This can help businesses create value in collaboration with customers; (4) Integrating AI-robotics into operations to support value co-creation: Analyse your business processes to identify tasks that can be automated using AI-powered robotics, such as check-in and check-out, room service, or concierge services [ 12 , 82 ]. Managers can consider using AI-powered robots for tasks such as check-in and check-out or for delivering amenities to guests. Use AI and the latest technologies to streamline the booking and check-in process, making it faster and more convenient for customers [ 16 ]. This can include using virtual assistants to handle booking inquiries or facial recognition technology to allow customers to check in at their hotel simply by showing their faces. These can help businesses reduce labor costs and improve efficiency, enhancing co-creation value [ 16 ]. We summarize three viewpoints in Fig.  3 below.

Summary of value co-creation through the Digital Age in Tourism and Hospitality

Combining these three viewpoints as a research agenda for tourism and hospitality in the AI and digital age holds immense potential. It addresses critical aspects such as customer experience enhancement, leveraging customer-generated content, and exploring cutting-edge technologies to create value co-creation opportunities. Researching these areas allows the industry to stay at the forefront of the digital revolution and deliver exceptional customer experiences that drive business success in the next few years.

Discussion and implications

This study aimed to develop a systematic literature review of customer value co-creation in the hospitality and tourism industry using the PRISMA protocol [ 27 ]. The study findings highlighted that tourism and hospitality should take advantage of AI and new technologies, as it brings significant advantages. Value co-creation in the tourism and hospitality sector refers to creating value through the collaboration and participation of multiple stakeholders, including tourists, employees, and the industry [ 2 ]. AI, Metaverse, and other new technologies can significantly enhance value co-creation in this sector by enabling more personalized, immersive, and efficient tourist experiences [ 40 , 80 , 81 ].

From a technology viewpoint, the study reveals that manifestations of customer value co-creation through the digital age are related to AI and the latest technologies such as Metaverse, robots, IoT, chatbots, intelligence systems, and others that shape co-creation [ 42 ]. AI applications and new technologies can help shape customer value co-creation in this sector. AI can follow the rules, think like an expert, learn from data, and even create virtual and augmented reality experiences [ 4 , 10 ]. Chatbots, personalization, predictive analytics, and robotics are examples of how AI and technology can create unique and fun travel experiences [ 16 , 40 , 74 , 83 ].

From an academic viewpoint, researchers look at ways technology can help people enjoy their travels and stay in hotels by boosting the value co-creation process [ 2 ]. They are looking at how different technologies, like social media, can help people create value for themselves and others [ 45 , 84 ]. They are also looking at how AI and the virtual world can change people’s decisions and how companies can use this information to help people [ 77 , 80 ]. Finally, researchers are looking into the ethical issues of using technology in tourism and hospitality [ 48 , 75 , 77 ].

From the manager’s viewpoint, managers in the tourism and hospitality industry can use AI and new technologies to create better customer experiences and drive success [ 70 , 80 ]. These can include using chatbots or virtual assistants to help customers and get their feedback [ 50 , 76 ], using personalization technologies to understand customer needs [ 69 ], using the Metaverse to have customers design their own experiences [ 10 ], and using AI-robotics to automate tasks [ 16 , 82 ].

In light of the findings from this systematic literature review, policymakers in the tourism and hospitality sectors must revisit and revitalize current strategies. Embracing digital age technologies, especially AI and metaverse tools, can significantly enhance customer value co-creation. This necessitates targeted investments in technology upgradation, capacity-building, and skilling initiatives. While the initial resource allocation may appear substantial, the long-term returns regarding elevated customer satisfaction, increased tourism inflow, and industry-wide growth are undeniable. Policymakers must ensure a collaborative approach, engaging stakeholders across the value chain for streamlined adoption and implementation of these advancements.

Overall, the use of AI, Metaverse, and other new technologies can significantly enhance co-creation value in the tourism and hospitality sector by enabling more personalized, immersive, and efficient experiences for tourists and improving the efficiency and effectiveness of the industry as a whole [ 15 ].

Theoretical implications

The systematic literature review using the PRISMA method on customer value co-creation through the digital age in the tourism and hospitality sector has several theoretical implications.

First, this research paper addresses earlier suggestions that emphasize the significance of further exploring investigations on customer value co-creation in the hospitality and tourism sector [ 2 , 85 ].

Second, the review highlights the importance of adopting a customer-centric approach in the tourism and hospitality industry, in which customers’ needs and preferences are central to the design and delivery of services [ 35 , 86 ]. This shift towards customer value co-creation is driven by the increasing use of digital technologies, such as the IoT, AI, and ML, which enable real-time communication and data gathering from customers [ 1 , 40 ].

Third, the review highlights the role of digital technologies in enabling personalized and convenient customer experiences, which can help improve satisfaction and loyalty [ 87 ]. Using AI-powered chatbots and personalized recommendations based on customer data can enhance the customer experience, while using IoT-enabled devices can allow guests to control and access hotel amenities conveniently [ 12 ].

Fourth, the review suggests that adopting digital technologies in the tourism and hospitality sector can increase the industry’s efficiency and effectiveness [ 88 ]. Businesses use ML algorithms to automate tasks and analyze customer data, which can help streamline processes and identify areas for improvement [ 39 , 80 ].

Overall, the systematic literature review using the PRISMA method sheds light on adopting a customer-centric approach and leveraging digital technologies for customer value co-creation in tourism and hospitality. Over the next five years, researchers should focus on exploring the potential of emerging technologies, developing conceptual frameworks, and conducting applied research to drive meaningful transformations in the industry. By aligning strategies with these implications, organizations can thrive in the dynamic digital landscape and deliver exceptional customer experiences, ultimately contributing to their success and competitiveness in the market [ 2 , 4 , 15 , 29 , 33 , 89 ].

Practical implications

The systematic literature review using the PRISMA method on customer value co-creation through the digital age in the tourism and hospitality sector has several management implications for organizations in this industry.

First, the review suggests that adopting a customer-centric approach, in which customers’ needs and preferences are central to the design and delivery of services, is crucial for success in the digital age [ 40 , 86 ]. Therefore, managers should focus on understanding and meeting the needs and preferences of their customers and consider how digital technologies can be leveraged to enable real-time communication and data gathering from customers [ 15 , 80 ].

Second, the review highlights the importance of using digital technologies like the IoT, AI, and ML to enable personalized and convenient customer experiences [ 40 , 50 ]. Managers should consider how these technologies can enhance the customer experience and improve satisfaction and loyalty [ 36 , 39 ].

Third, the review suggests that adopting digital technologies in the tourism and hospitality sector can lead to increased efficiency and effectiveness in the industry [ 7 , 16 ]. Therefore, managers should consider how these technologies can streamline processes and identify areas for improvement [ 42 ]. Further, regarding privacy concerns, managers must spend enough resources to secure their customers’ data to help boost the customer value co-creation process [ 48 , 77 ].

Fourth, policymakers can foster an environment conducive to value co-creation by incorporating customer-centric strategies and leveraging digital technologies. Effective policies can enhance customer experiences, promote sustainable growth, and drive economic development, ensuring a thriving and competitive industry in the digital age.

The practical implications of applying AI and new technology for managerial decision-making in the tourism and hospitality industry are vast and promising [ 90 ]. Managers can navigate the dynamic digital landscape and drive meaningful co-creation with customers by embracing a customer-centric approach, leveraging personalized technologies, addressing efficiency and data security considerations, and strategically adopting AI-powered tools. By staying abreast of technological advancements and harnessing their potential, businesses can thrive in the next five years and beyond, delivering exceptional customer experiences and enhancing value co-creation in the industry.

Limitations and future research

The research, anchored in the PRISMA methodology, significantly enhances the comprehension of customer value co-creation within the digital ambit of the tourism and hospitality sectors. However, it is essential to underscore certain inherent limitations. Firstly, there might be publication and language biases, given that the criteria could inadvertently favor studies in specific languages, potentially sidelining seminal insights from non-English or lesser-known publications [ 91 ]. Secondly, the adopted search strategy, governed by the choice of keywords, databases, and inclusion/exclusion guidelines, might have omitted pertinent literature, impacting the review’s comprehensiveness [ 57 ]. Furthermore, the heterogeneous nature of the studies can challenge the synthesized results’ generalizability. Finally, the swiftly evolving domain of this research underscores the ephemeral nature of the findings.

In light of these limitations, several recommendations can guide subsequent research endeavors. Scholars are encouraged to employ a more expansive and diverse sampling of studies to curtail potential biases. With the digital technology landscape in constant flux, it becomes imperative to delve into a broader spectrum of innovations to discern their prospective roles in customer value co-creation [ 18 ]. Additionally, varied search strategies encompassing multiple databases can lend a more holistic and inclusive character to systematic reviews [ 27 ]. Moreover, future research could investigate the interplay between political dynamics and the integration of novel technologies, enriching the understanding of value co-creation in a broader socio-political context. Lastly, integrating sensitivity analyses can ascertain the findings’ robustness, ensuring the conclusions remain consistent across diverse search paradigms, thereby refining the review’s overall rigor.

In conclusion, this review highlights the pivotal role of digital technologies in customer value co-creation within the tourism and hospitality sectors. New AI, blockchain and IoT technology applications enable real-time communication and personalized experiences, enhancing customer satisfaction and loyalty. Metaverse technologies offer exciting opportunities for immersive interactions and virtual events. However, privacy and data security challenges must be addressed. This study proposed a comprehensive research agenda addressing theoretical, practical, and technological implications. Future studies should aim to bridge research gaps, investigate the impact of co-creation on various stakeholders, and explore a more comprehensive array of digital technologies in the tourism and hospitality sectors. This study’s findings provide valuable insights for fostering innovation and sustainable growth in the industry’s digital age. Despite the valuable insights gained, we acknowledge certain limitations, including potential biases in the search strategy, which underscore the need for more inclusive and diverse samples in future research.

Availability of data and materials

The review included a total of 27 studies published between 2012 and 2022.

Change history

07 february 2024.

A Correction to this paper has been published: https://doi.org/10.1186/s43093-023-00293-2

Abbreviations

• Artificial intelligence

Augmented reality

Internet of Things

Machine learning

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

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Dang, T.D., Nguyen, M.T. Systematic review and research agenda for the tourism and hospitality sector: co-creation of customer value in the digital age. Futur Bus J 9 , 94 (2023). https://doi.org/10.1186/s43093-023-00274-5

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The impact of COVID-19 on the tourism and hospitality Industry: Evidence from international stock markets

a School of Management, University of Science and Technology of China, Hefei, Anhui, China

c Department of Information Systems, City University of Hong Kong, Hong Kong, China

b Business School, Sichuan University, Chengdu, Sichuan, China

Stephen Shaoyi Liao

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COVID-19 seriously affects the tourism and hospitality industry. In this study, we investigate the behavior of 40 tourism and hospitality stock market indices worldwide from two perspectives. First, empowered by the Granger causality test and network analysis, we test the spillover effects among these stock markets and find that the dynamics of interconnectedness network structures differ significantly in the pre-pandemic and in-pandemic periods. Second, we employ econometric models to explore how the influence of COVID-19 on these stock markets varies by considering the interconnectedness structure, the government response stringency index, and other country-level characteristics. We find that the interconnectedness structure significantly and robustly affects stock returns in the tourism and hospitality markets. Our investigation provides a better understanding of the impact of COVID-19 on tourism and hospitality industry.

1. Introduction

On March 11, 2020, the WHO publicly declared that “COVID-19 can be characterized as a pandemic.” The COVID-19 pandemic has had severe consequences for public health, economics, politics, and society ( Gössling et al., 2020 ). As of August 2, 2022, there were 575,887,049 confirmed cases of COVID-19 and 6,398,412 deaths worldwide ( WHO, 2022 ). In addition, COVID-19 caused immediate and long-term damage to a majority of industries ( Yarovaya et al., 2021 ). It is clear that the tourism and hospitality industry, which plays a critical role in a nation’s, or even the global economy and community, is among the most negatively impacted economic sectors during the COVID-19 pandemic ( Lin & Falk, 2021 ). The impact on the tourism and hospitality industry can be explained as a side effect of many government policy implementations, such as the enforcement of social distancing, public event cancellations, travel controls, stay-at-home requirements, and limitations on gathering size. These policies are implemented to contain the spread of COVID-19 and flatten the death and infection curves; however, they dramatically affect the tourism and hospitality industry ( Chen et al., 2020 ). The COVID-19 pandemic has resulted in difficult times for the global tourism and hospitality industry ( Clark et al., 2021 ). Based on the study of ( Mazur et al., 2021 ), it can be seen that by March 2020, the hospitality and entertainment industry had lost more than 70 % of its market capitalization in United States, as indicated by the S&P1500 stock indices.

The tourism and hospitality industry highly vulnerable to environmental, political, and socioeconomic factors, have been widely studied in the past because of various political crises, wars, natural disasters, and pandemics ( Barbhuiya & Chatterjee, 2020 ). However, as many studies have indicated, compared to previous crises, the economic crisis caused by COVID-19 is quite different in its scope, duration, and severity ( Ding et al., 2021 ). Therefore, it is necessary to investigate the impact of COVID-19 on the tourism and hospitality industry.

There are several studies focusing on the influence of COVID-19 on the tourism and hospitality industry by using stock market data, those previous studies include the impact of COVID-19 on the changing distributions of travel and leisure industry returns using quantile regression models and daily stock data ( Lee & Chen, 2022 ); the influence of government interventions on U.S. travel and leisure companies’ returns ( Chen et al., 2020 ); using daily stock data to investigate the influence of COVID-19 on travel or leisure industry in Spain ( Gil-Alana & Poza, 2020 ), USA ( Carter et al., 2022 , Song et al., 2021 ), India ( Pandey & Kumar, 2022 ), and Taiwan and Mainland China ( Wang et al., 2022 ); investigating the performance of the stock market and volatility in the travel and leisure industry for three Nordic countries using daily data ( Lin & Falk, 2021 ); the impact of government interventions on nine countries’ travel and leisure industry return and volatility using panel quantile regression models ( Wang et al., 2021 ).

These studies mainly concentrate on COVID-19′s influence on tourism and the hospitality market from either firm, one industry, or regional perspective. Few studies have focused on investigations from a global perspective. In addition, almost no study has considered the spillover effect among tourism and hospitality markets in different countries worldwide. The spillover effect exists in the global tourism and hospitality markets for the following reasons. First, the tourism and hospitality industry has become more interdependent ( Mitra et al., 2019 ). Tourism and hospitality firms not only compete or cooperate in their home country but also compete and cooperate with other countries in the form of hotel chains, combo offers, and so on ( Balli & Tsui, 2016 ). COVID-19 affects the global tourism and hospitality industry supply chain ( Sigala, 2020 ), and thus leads to spillover effects. Second, tourism demand reveals an interdependent structure at the global level ( Cao et al., 2017 ). However, many government response policies to COVID-19 have restricted people’s movement. One country’s tourism stock market distress caused by the panic of COVID-19 can quickly spread to other countries, resulting in a global spillover effect among tourism and hospitality markets in many countries, presenting a co-movement phenomenon. Thus, it is necessary to investigate the spillover effect in international tourism and hospitality markets.

In this study, we investigated the impact of COVID-19 on tourism and hospitality markets from two perspectives. First, by using a panel dataset consisting of tourism and hospitality stock market indices for 40 countries or regions, we test the spillover effect among the tourism and hospitality markets using the Granger causality test and network analysis. Specifically, we use the Granger causality test to estimate statistically significant spillover effects among these market indices and show interconnectedness between any paired markets if a spillover effect exists. We then construct a series of interconnectedness networks using the rolling window technique and explore the dynamics of the network structure according to three global interconnectedness measures (i.e., degree of centralization, transitivity, and density).

We find that the dynamic structure of interconnectedness differs significantly. Before the pandemic, the level of interconnectedness between different stock markets was low, and it increased after the pandemic, providing evidence for the existence of a higher spillover effect during the COVID-19 outbreak. To the best of our knowledge, there has been no research on the spillover effect in international tourism and hospitality stock markets. Second, we construct econometric models to investigate how the effect of COVID-19 on the tourism and hospitality stock market varies by considering the interconnectedness structure of the stock market, which is measured by the degree, closeness, and average nearest neighbor degree (ANND), government response stringency index (GRSI), and other country-level characteristics. We find that network interconnectedness significantly and robustly affects the stock returns of the tourism and hospitality markets.

This study contributes to the literature in three ways. First, we conducted a worldwide study to investigate the impact of the COVID-19 pandemic on the tourism and hospitality stock market in 40 countries, as the existing literature mainly focuses on regional or national analysis. Second, we explore the spillover effect and co-movement phenomenon among these stock markets through network analysis and demonstrate the statistically significant differences in the behavior of pre-pandemic and pandemic stock returns by investigating the dynamics of the interconnectedness network structure. Third, we present a better mapping and understanding of the possible determinants of tourism and hospitality stock market returns by incorporating local interconnectedness measures (i.e., the local network features of each stock market) into the econometric model. We empirically find that network interconnectedness can significantly explain the variations in tourism and hospitality market returns.

The remainder of this paper is organized as follows. Section two is a review of relevant literature; section three investigates the interconnectedness structure between different tourism and hospitality markets; section four presents the regression analysis and section five concludes the research findings.

2. Literature review

The rapid spread of COVID-19 has proven to have a global spillover effect between countries, causing unprecedented economic and financial distress. We believe that the unexpected health crisis differs from any previous disaster and is much worse than the global financial crisis ( Baker et al., 2020 ). It has triggered an unparalleled response from the scientific community and spawned a growing volume of academic research that focuses on its economic and financial influence. For example, the interplay between COVID-19 and stock market returns ( Dong et al., 2021 , Li et al., 2021 , Rehman et al., 2021 ), investment ( Giofré, 2021 ), volatility ( Li, 2021 , Tissaoui et al., 2021 ); the impact of COVID-19 on commodities ( Bakas and Triantafyllou, 2020 , Corbet et al., 2021 ), exchange rates ( Feng et al., 2021 , Njindan Iyke, 2020 ), cryptocurrencies ( Hsu et al., 2021 , Wüstenfeld and Geldner, 2021 ), real estate ( Ling et al., 2020 , Tanrıvermiş, 2020 ) and bonds ( Naeem et al., 2021 , O'Hara and Zhou, 2021 ).

In addition to the aforementioned financial influence investigations, there is plenty of research exploring the impact of COVID-19 on the tourism and hospitality industry ( Arbulú et al., 2021 , Duro et al., 2021 , Huang et al., 2021 , Uğur and Akbıyık, 2020 ). As suggested by ( Bai et al., 2020 ), the tourism industry is one of the most affected by the outbreak because it affects both the supply and demand for travel. Furthermore, Sigala (2020) pointed out that COVID-19′s impact on tourism is uneven in space and time, and is large and international. Many countries have implemented a series of policy responses to prevent the spread of COVID-19. These policies, such as international, regional, and domestic restrictions, directly affect the tourism and hospitality industry’s value chain. Some of these policies include social distancing, national and international travel restrictions, and stay-at-home requirements ( Chen et al., 2020 ).

COVID-19′s impact on the tourism and hospitality industry, presented in existing studies, is mainly national or regional. Using a strong dependence model based on fractional integration, Gil-Alana and Poza (2020) provided evidence that COVID-19 had a permanent effect on the Spanish tourism sector. Lin and Falk (2021) used a “Markov regime-switching model” to investigate stock market volatility in three Nordic countries, and their results suggested that the COVID-19 period was full of idiosyncratic risk. Lee and Chen (2022) studied the international impact of COVID-19 in 65 countries and investigated the COVID-19 variables (i.e., deaths, confined cases, recovered cases) and government response stringency (GRSI) on travel and leisure industry stock returns via quantile regression.

In addition to the COVID-19 literature, we also review the literature on the impact of previous economic shock events, such as SARS ( Zeng et al., 2005 ) and the global financial crisis (GFC) of 2008 ( Solarin, 2016 ). ( Chen et al., 2005 ) used regression analysis to explore the relationship between macroeconomic and shock events (e.g., the 921 earthquakes, the 2003 Iraqi war, the SARS outbreak) and hotel stock returns. Chen et al. (2013) studied the impact of the SARS outbreak in Taiwan using an event study approach and found that hotel stock returns declined significantly over the month following the SARS outbreak. ( Zopiatis et al., 2019 ) explored the relationship between the performance of tourism industry stock (i.e., returns and volatility) and the outbreak of unexpected non-macro incidents across five different regions, revealing that unexpected non-macro incidents, such as acts of terrorism, natural catastrophes, and war conflicts, could have a very short-term effect on the selected stock indices with a significant drop. Furthermore, ( Wang et al., 2013 ) investigated the influence of enterovirus 71, dengue fever, SARS, and H1N1 on the stock market and found a significant abnormal return on company shares.

Although these disaster tourism impact studies use either a regression model or an event-study approach to explain the average conditions and focus on the determinants of the impact, no study has focused on the global spillover effect between countries as COVID-19 spreads. Obviously, COVID-19 affects the global supply chain of the tourism and hospitality industry, and distress in the tourism and hospitality stock market triggered by the panic of the disaster in one country can quickly spread to other countries, and thus may lead to the phenomenon of co-movement in the market ( Mishra et al., 2020 ). Therefore, this study focuses on the spillover effect and investigates the interconnectedness structure among international tourism and hospitality stock markets.

3. Interconnectedness among world tourism and hospitality markets

3.1. tourism and hospitality stock market data.

The empirical dataset includes the daily closing prices of tourism and hospitality stock market indices of 40 countries or regions, including Australia, Spain, China, the United Kingdom, India, Italy, Mexico, Sweden, Korea, and the United States. 1 We downloaded the daily closing price series from Investing.com . The sampling period was from January 2019 to February 2021, which covers the announcement date of the COVID-19 pandemic. We calculate the return series by dividing the natural logarithm of the closing prices by the hysteretic closing prices.

Table 1 depicts the results of the descriptive analysis of stock returns for the eight selected countries because of limited space. 2 The average stock returns are close to zero over the entire sample interval, except for China and Sweden. Unconditional volatility characterized by standard deviation is the largest for the United States, followed by the tourism and hospitality stock market in Italy; the United States and Italy are among the hardest hit countries by COVID-19. The skewness for most of the tourism and hospitality stock markets is negative, except for Sweden. In addition, kurtosis values for most markets exceed three, except for China, which reveals that most countries have a leptokurtic distribution for their tourism and hospitality stock returns and points out the presence of outlier events. The distributional properties of most return series are not normal. This is further verified by the results of the Jarque–Bera (JB) test, which rejects the normality of the return distributions at a significance level of 1 %. We also perform ADF, PP, and KPSS tests to quantitatively examine the stationarity of these return series, and the results reveal that the market returns are all stationary series. We also conclude that all return series exhibit significant ARCH behavior, based on the ARCH test. Finally, the break dates obtained by testing the structural changes in the time-series regression model were mostly in March 2020. They were close to March 11, 2020, when the WHO announced that COVID-19 was a global pandemic. And summary statistics for the return series for all countries and regions can be seen in Table A1 .

Summary Statistics for the Return Series for Selected 8 Countries.

Note : JB is the empirical statistics of the Jarque-Bera test for normality. The ADF, PP, and KPSS tests are used to check the stationarity of the return’s series. ARCH (20) denotes Engle’s test to check the presence of ARCH effects up to 20 lags. ∗, ∗∗, and ∗∗∗ refer to significance levels of 10 %, 5 %, and 1 % for testing. Break Date is obtained by testing the structural changes in time series regression models.

We further explored whether the stock markets react differently to the outbreak of the COVID-19 pandemic by comparing the return and volatility of tourism and hospitality stock markets between two different sub-periods: the pre-pandemic period, which was before March 11, 2020, and the in-pandemic period, which was after March 11, 2020.

Table 2 presents the summary statistics of stock returns and volatility for the two subperiods. The return of the tourism and hospitality stock market has a lower mean value during the in-pandemic period, but the mean difference is not significant. In addition, volatility increased by 100 % compared with the pre-pandemic period, and this mean difference is statistically significant, highlighting that COVID-19 significantly affected the volatility of the tourism and hospitality stock markets in all countries. These notable distinctions in the behavior of the tourism and hospitality stock markets between the two sub-periods constitute a major motivation for conducting further empirical analyses.

Summary Statistics of the Stock Returns (×100) and Volatility for the Different Sub-periods.

Note: ∗, ∗∗, and ∗∗∗ refer to significance levels of 10 %, 5 %, and 1 % for testing the mean difference between In-Pandemic period and Pre- Pandemic period.

3.2. Interconnectedness analysis from global perspective

3.2.1. measure of interconnectedness.

We find that the volatility in the hospitality stock market generally increases sharply after the announcement of the COVID-19 pandemic, revealing that there is strong interconnectedness across markets in different countries due to the spillover effect ( Diebold and Yilmaz, 2012 ). Thus, the analysis of the likely spillover effect across markets since the emergence of COVID-19 serves as an ‘‘early warning’’ regarding the severity of crisis’ consequences ( Salisu et al., 2020 ). To measure the interconnectedness among these stock market indices, we use the pairwise Granger Causality Test to capture the interconnectedness of statistically significant causal relationships among these stock market indices ( Billio et al., 2012 ). To investigate the dynamics of interconnectedness, it is more reasonable to measure not only the degree of interconnectedness between market indices but also the directionality of such relationships. The Granger causality test is an ideal technique to match such an objective, as it can estimate the interdependence of pairwise market indices and identify the direction of interdependence based on the forecast power of two time series for the corresponding market indices.

For any pairwise market index, we can obtain two corresponding time series X and Y based on the daily price. Time series X is said to be “Granger-cause” Y if the past value of X contains information that helps predict Y above and beyond the information contained in the past values of Y alone. The mathematical formulation of this test is based on the linear regressions of X on Y and X on Y.

Where ∊ t X and ∊ t Y are two uncorrelated white noise processes, m is the maximum lag considered, and a i , b i , c i , and d i are coefficients of the models. Granger causality implies that Y t causes X t when b i is different from zero. Similarly, X t causes Y t when d i differs from zero. When both b i and d i are different from zero, a feedback relationship exists between the time series X t and Y t . The Bayesian information criterion (BIC) was considered as the model selection criterion for the number of lags in the test. Causality is based on the F-test of the null hypothesis that coefficients b i or d i are equal to zero according to the direction of Granger causality.

In this study, we used the return of the tourism and hospitality market index to conduct Granger causality tests. However, we need to capture returns specifically pertaining to the tourism and hospitality industry, instead of general movements in the entire stock market. To remove the effect of general market movements, we use the following initially estimated regression model:

where r j , τ represents the return of tourism and hospitality market j on day τ and r m , τ denotes the return value based on the CRSP value-weighted index on day τ . Here we follow Dimson (1979) and employ both the lagged and led terms of tourism and hospitality market returns, considering nonsynchronous trading. The industry-specific return W j , τ is calculated using the equation W j , τ = l n ( 1 + ε j , τ ^ ) .

3.2.2. Interconnectedness structure in world tourism and hospitality markets

To describe the interconnectedness between these tourism and hospitality stock markets, we use individual market returns to build directed Granger causality linkages ( Billio et al., 2012 ) between tourism and hospitality stock markets. Granger causality 3 in market returns can be viewed as a proxy for return spillover effects across markets ( Danielsson et al., 2011 ). In particular, if the return of the tourism and hospitality stock market in country A Granger-causes the return of the market in country B, we draw a direct link from A to B, because the Granger-causality test captures the lagged propagation of return spillovers across markets. Granger causality tests were performed using daily data with 125-day rolling windows. Thus, we can construct a network that depicts the interconnectedness structure of tourism and hospitality stock markets among the 40 countries in each rolling window.

Fig. 1 , Fig. 2 show two interconnectedness structures of the international tourism and hospitality stock market for two rolling-window sub-periods: July 1st, 2019, to January 1st, 2020, and March 1st, 2020, to September 1st, 2020. These are representative time periods encompassing both pre-pandemic and in-pandemic periods. The interconnectedness structure is depicted by the network diagram of Granger causality relationships, which are statistically significant at the 5 % level among the tourism and hospitality stock market returns of these 40 countries. We can see that the degree of interconnectedness among these markets dramatically increases from the sub-period March 1st, 2020, to September 1st, 2020.

The network diagram of interconnectedness structure in the rolling-window from July 1st, 2019 to January 1st, 2020.

The network diagram of interconnectedness structure in the rolling-window from March 1st, 2020 to September 1st, 2020.

To comprehensively investigate the interconnectedness structure of the tourism and hospitality stock markets, we introduce the following global interconnectedness measures 4 based on the entire network topology.

First, we present the degree of centralization, defined as the number of linkages, as the proportion of all possible linkages in these markets. The degree of centralization measures the fraction of statistically significant Granger causality relationships in the tourism and hospitality markets. Second, we used a transitivity measure. Transitivity, known as the clustering coefficient in the network, is defined as the frequency at which triangular connections occur in the network. Transitivity measures the probability that neighbors of one tourism and hospitality stock market have a statistically significant Granger causality relationship. Third, we introduce density, which measures the magnitude in a network’s density. All three measures are normalized by the number of stock markets in the network so that proper benchmarking can be performed between these networks.

Fig. 3 shows the changes in the three interconnectedness measures for the daily network of the tourism and hospitality stock markets from June 2019 to February 2021. We can see that all three measures have similar evolutionary trends. They vary slightly during the pre-pandemic period, but fluctuate intensely during the in-pandemic period. Taking transitivity, for example, during the pre-pandemic period, transitivity varies between 0.2 and 0.4, but shows a significant increase at the beginning of the In-Pandemic period, exceeding 0.8. Thus, we can conclude that the interconnectedness structure is highly dynamic among these tourism and hospitality markets; these dynamics are different between the pre-pandemic and in-pandemic periods. Therefore, there is a significant spillover effect in tourism and hospitality markets between countries, which is supported by the interconnectedness analysis.

The dynamic of the three interconnectedness measures for daily network for the tourism and hospitality stock markets from June 2019 to February 2021. The vertical line indicates the time of the COVID-19 Pandemic Announcement by WHO, which indicates two subsamples: Pre-Pandemic period and In-Pandemic period.

To verify the conclusion numerically, we further conducted summary statistics of the three network measures. Table 3 presents the numerical results, where the values of the three measures in the in-pandemic period all doubled compared with the corresponding values in the pre-pandemic period at a significance level of 1 %. The variation in these measures was also high between the two sub-periods. COVID-19 has had a significantly negative impact on the world’s tourism and hospitality stock markets, leading to a high spillover effect between countries.

Summary Statistics of the Interconnectedness Measures in the Different Sub-periods.

Note: ∗, ∗∗, and ∗∗∗ refer to significance levels of 10 %, 5 %, and 1 % for testing the mean difference between In-pandemic period and the pre- pandemic period.

4. Regression analysis

As mentioned above, the interconnectedness structure is significantly different between the pre-pandemic and in-pandemic periods, and there is a high spillover effect in the tourism and hospitality markets between countries after the announcement of the COVID-19 pandemic. Further, we investigate whether the local interconnectedness structure (the local network feature of each stock market), as well as the COVID-19 variables and country-level characteristics, can explain the change in stock market returns.

4.1. The determinants of tourism and hospitality stock market return

To estimate the impact of COVID-19 on the stock returns of the global tourism and hospitality industry, we consider a range of variables that may drive the performance of tourism and hospitality stock markets. First, we needed to quantify the spread of COVID-19. We used the change in the number of cases as our primary proxy for the spread of COVID-19. In particular, we closely followed Ding et al., who computed the growth rate of the cumulative number of confirmed cases in each country. The growth rate, GRC i , t was calculated using GRC i , t = l n 1 + CCC i , t - l n ( 1 + CCC i , t - 1 ) . where i and t are the index country and day, respectively. CCC i , t represents the cumulative number of confirmed cases in country i on day t. Notably, to strengthen our findings, similar to Erdem (2020) and Iyke (2020), we corroborate our findings with the cumulative number of confirmed deaths ( CCD i , t ). The growth in the confirmed death rate, GRD i , t , is calculated using GRD i , t = l n 1 + CCD i , t - l n ( 1 + CCD i , t - 1 ) .

Second, we consider three variables to measure the local interconnectedness structure: degree, closeness, and ANND. The three variables are local network features of each stock market used to measure the local interconnectedness structure. In particular, degree is defined as the total number of connections linked to the node, which measures the sum of the number of stock markets that significantly Granger-cause the focal market and the number of stock markets that are significantly Granger-caused by the focal market. Closeness is calculated as the reciprocal of the sum of the shortest path lengths between the markets in one country and the other country, which measures the number of steps between two markets on average. ANND is defined as the average degree of the nearest neighbor for each market, and measures the neighbor’s interconnectedness structure for the focal market.

Third, we employ the Chicago Board Options Exchange Volatility Index (VIX) and crude oil prices (Oil). VIX is the global stock market uncertainty index and can measure the level of market risk and investors’ sentiments such as fear and stress. The VIX has been verified to have a significant influence on travel and leisure industry returns ( Grechi et al., 2017 ). In a study by Mohanty et al. (2014 ), crude oil price had a negative influence on the tourism and hospitality stock markets.

Finally, we utilize several control variables to capture country-specific heterogeneity: government response stringency index (GRSI), GDP per capita (GDPC), and human development index (HDI). The GRSI measures the strictness of government responses and has a score from 0 to 100, with higher scores describing countries with stricter government responses. GDPC measures a country’s economic growth and development. HDI is an indicator provided by the United Nations Development Programme that considers life expectancy, education, and per capita income ( Haug et al., 2020 ). The HDI provides a basic overview of a country’s human development involving three key dimensions and reflects how well people can enjoy a long and healthy life.

4.2. Panel data regression models

Our empirical model for tourism and hospitality stock returns is based on the daily closing price series. The method we used to estimate the regression model of panel data is based on the one-way fixed effects model, which was employed to address the individual-specific effect. The form of the fixed effects model is written as:

where all subscript notation i , t refer to the value taken from country i at time t . Specifically, Return i , t is the stock index return and Return i , t - 1 is the first lag of Return i , t . GRC i , t is the proxy for COVID-19, representing the growth rate of COVID-19 confirmed cases. Interconnectedness i , t is the local interconnectedness structure for tourism and hospitality stock markets in each country, which can be measured by the three network measures: degree, closeness (CC), and ANND; Oil t is the WTI crude oil price return. VIX t is the Chicago Board Options Exchange (CBOE) Volatility Index. GRSI i , t is the government response stringency index proposed by the Oxford University. GDPC i , t is the GDP per capita, which measures the country’s economic development level. HDI i , t is the human development index. α i is individual-specific effect. Here, we do not consider the time-fixed effects as some of our data are time-invariant variables that are correlated with individual-specific fixed effects. We also do not consider the country-fixed effect because the country-fixed effects estimation approach requires estimating country-specific intercepts, which can significantly reduce the number of degrees of freedom ( Zaremba et al., 2020 ). Thus, we turn to include region-fixed effects to control the variations that vary across regions but are constant over time. Furthermore, we also consider another high-level individual effect— income-fixed effects. In detail, we check the region-fixed effects by grouping the countries into seven different regions (East Asia & Pacific, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, North America, South Asia, Sub-Saharan Africa), and the income-fixed effects are checked by grouping the countries into three different income levels (High income, Lower middle income, Upper middle income). Fixed effects model can reduce the selection bias and we consider it is more appropriate to include region and income specific fixed effects models rather than country-fixed effects models.

For comparison study, we consider another three regression models. The first model considered the interaction effect between COVID-19 and the control variables based on the baseline regression in Equation (4); the second model is pooled OLS regression model which use standard ordinary least squares (OLS) regression without any cross-sectional or time effects; the third model is pooled OLS regression model with interactions. The three models are expressed as follow:

Before presenting the regression results, we provide the descriptive statistics for all variables in Table 4 .

Summary Statistics.

In particular, Table 4 shows the minimum, maximum, mean, standard deviation, median values of lag variable: Return i , t - 1 ; main variable: GRC i , t ; interconnectedness variables: D e g r e e , CC, ANND; and five control variables: Oil t , VIX t , GRSI i , t , GDPC i , t , HDI i , t . The total number of observations is 9328.

In addition to the descriptive analysis, we also conduct Pearson correlation analysis to test the correlation relationships between all variables. Pearson correlation coefficient is used to indicate the degree to which two variables are linear correlated. According to the Pearson correlation results in Table 5 , we can see that interconnectedness variables of D e g r e e , CC and ANND, ANND and Oil t , Oil t and VIX i , t , GDPC i , t and HDI i , t are correlated.

Pearson Correlation Results.

4.3. Baseline empirical results

In this subsection, we first concentrate on the investigation of individual variables’ influence on tourism and hospitality stock market returns based on regression model (4) and (6). Subsequently, we explored the interaction effect among these variables based on Equation (5) , (7) . Table 6 shows the results of the fixed effects models and pooled OLS regression model estimating the panel data’s regression model without interaction terms. We need to mention that the all model results’ tables hereinafter don’t show the estimates of the intercept terms. It is because for pooled regression model, the intercept term is identical for all units. But for each fixed effects model, it has the distinctive intercept for each unit so the intercept term is non-fixed. For simplicity, we remove the estimating results of all models’ intercepts so that we can combine the results in one table. In addition, we introduced three variables (Degree, CC, ANND) to measure the local interconnectedness structure from different perspectives, but only one interconnectedness measure is employed at a time. In detail, in Table 6 , the model 1, model 2 and model 3 present the results of pooled OLS regression model, the region-fixed effect model, and income-fixed effects model, respectively, by considering the degree as the interconnectedness measure. Similarity, model 4–6 presents the results by considering the CC as the interconnectedness measure, and model 7–9 presents the results by considering the ANND as the interconnectedness measure.

Baseline models’ results without interactions.

Note : *** p < 0.01, ** p < 0.05, *p < 0.1.

First, we can see that the local interconnectedness structure positively and significantly influences tourism and hospitality stock market returns, as the corresponding three variables are positive values at significance level of 1 %. Indeed, higher value of local interconnectedness denotes that there are more linkages between the corresponding country with other countries, such as more tourism and hospitality industry supply chain linkages among these countries ( Mitra et al., 2019 ), and tourism demand interdependence structure ( Cao et al., 2017 ). Thus, if the tourism and hospitality industry is hit by an adverse shock, then having higher interconnectedness provides more channels to diversify away the effect. Thus, the interconnectedness can increase the supply chain stability, facilitate risk sharing and diversify the risk during the pandemic, which indicates that the local interconnectedness structure positively and significantly influences tourism and hospitality stock market returns.

Second, we observe that the tourism and hospitality stock return is positively correlated with serial lagged return, which indicates positive serial correlation. A positive serial correlation does not violate market efficiency and shows the power of partial adjustment of the market ( Rosenberg & Rudd, 1982 ). The coefficient of the lagged variable indicates the lagged response to the market. We find that the magnitude of the return lagged coefficient is neither large nor small, indicating that the non-synchronous response is medium.

Third, GRC, which measures the growth rate of the cumulative number of confirmed cases, has noteworthy negative impacts on stock returns, which is also supported by the work of Gil-Alana and Poza (2020) and ( Lee & Chen, 2022 ). The increasing number of COVID-19 confirmed cases signifies that the virus transmission is still continuing, and there is an underlying risk endangering the healthcare system. People get infected and seek medical support, which brings about sudden rising healthcare demands. With the rapid growth of infected people, hospitals lose capacity and doctors are overloaded. Many medical resources, such as intensive care units (ICUs), beds, protective clothing, and masks, are occupied and consumed quickly. This result demonstrates the concerns regarding unmet healthcare demands and healthcare system instability.

Fourth, Table 6 shows that overall tourism and hospitality stock returns react positively to government intervention. This result indicates that restriction policies strengthen market confidence and provide more advantages than disruptions. Effective stringent policies can prevent the spread of COVID-19, prevent or subdue investor panic, protect public safety, and maintain a stable economic environment. Even though some lockdown and quarantining policies have generated a certain degree of short-term negative influence on people’s travel and economic activities, our results prove that the stock market response to government interventions is overall positive, which is also supported by the work of Lee and Chen, 2022 , Wang et al., 2021 .

Fifth, the VIX has a negative impact on the change in tourism and hospitality stock market returns. This is because the VIX contains information on short-term market volatility in the next month. The VIX is often used as a proxy for global stock market uncertainty and investor fear sentiment. The upward variation of the VIX indicates that the market becomes pessimistic, and the overall market situation becomes more unstable; thus, stock returns are adversely affected. Our VIX results are similar to those of ( Grechi et al., 2017 ), ( Ersan et al., 2019 ), ( Lee & Chen, 2022 ), and ( Wang et al., 2021 ).

Sixth, we investigate the effect of crude oil prices on stock returns, and the result is consistent with what the literature has documented ( Lee and Chen, 2022 , Mohanty et al., 2014 , Qin et al., 2021 ): it negatively interacts with the stock returns. Crude oil is important for global economic stability and development because it is one of the most important natural resources worldwide. If the oil price increases sharply, it has a huge impact on the budgets of families, companies, and governments with rising costs. Thus, crude oil price shocks will increase financial market uncertainties, reduce cash flow, and depress stock returns.

Seventh, GDPC is observed to respond positively to stock returns only in the income-fixed effects model, while the pooled OLS regression model and region-fixed effects model do not show any significance. This is because GDPC captures a country’s economic development level. The fundamental level of return is somewhat correlated with the GDPC. Meanwhile, since GDPC indicates the development level of the country, the income level of the nation is strongly associated with GDPC, so a significant GDPC effect is only seen in the income-fixed effects models. Finally, stock returns react to HDI in a negative manner, but the effect is only observed in Models 3, 6, and 9. The reason that the effect is negative is that the overall society is fighting this unprecedented pandemic, and many social resources are consumed to decrease the increasing number of confirmed cases and provide infected patients with sufficient healthcare services. Although people can enjoy a long and healthy life, the economic development is ignored and depressed since many economic activities have stopped, so the efficiency of the whole society is affected. Investors hold pessimistic attitudes towards economic development, even though the HDI goes up in the short term.

Table 7 shows the results of the panel data regression with interactions, following Eqs. (5) , (7) . The interaction model was employed to explore the interaction between GRC and three country-level variables (i.e., GRSI, GDPC, and HDI).

Baseline models results’ with interactions.

Note: *** p < 0.01, ** p < 0.05, *p < 0.1.

According to Table 7 , we can see that the magnitude, polarity, and significance level of the three interconnectedness factors (degree, CC, ANND), return lagged, GRC, GRSI, VIX, oil, and GDPC effects show almost no change, as shown in Table 6 . In addition, the main GDPC effect in income-fixed Models 3, 6, and 9 is still significant, but the significance level is reduced by 5 %. The HDI also loses its significance. This implies that neither GDPC nor HDI are strong indicators that contribute to the variation in tourism and hospitality stock returns. As for the interaction terms, we can see that only the interaction term of GRC × G R S I is highly significant, with a positive relationship. This result implies that the negative effect of the rapid growth of confirmed cases diminishes when countries carry out more stringent government interventions and also confirms the effectiveness of government interventions such as travel bans, lockdowns, quarantine, and social distancing policies in reducing the number of COVID-19 confirmed cases. Overall, a government’s stringency response helps encourage investors and maintain stock prices when the number of COVID-19 confirmed cases in the country increases.

4.4. Robustness checks

We performed the robustness checks for the results in three ways. Firstly, we re-estimate the baseline regression models using weekly data. Secondly, we use GRD, which is defined as the growth rate of confirmed deaths, as a proxy for the COVID-19 variable. Thirdly, we employ a two-stage panel data regression model to account for endogeneity concerns ( Kremer & Nautz, 2013 ).

4.4.1. Using weekly level data

The first way to check the models’ robustness is to re-estimate the baseline regression models expressed in equation (4) and (6) with other frequency data.

In Section 4.3, daily stock return data is used to estimate the all regression models and here we plan to use weekly level data to confirm whether the observed effects are robust. Thus, we first convert the daily level data into weekly level by summing up the daily level value of each variable. Then, the prepared weekly data is used to re-estimate the models in Eqs. (4) and (6). The estimation results are shown in Table 8 . According to Table 8 , firstly, we can confirm that the effects of three interconnectedness variables and GRC are robust since the P-values are significant even though their significance level has reduced. Secondly, the effect of return lag variable is also robust and the significance level has increased. Thirdly, the effects of GRSI, VIX, and Oil are also robust and their results are consistent with what is shown in Section 4.3. GDPC effect is not robust since the coefficient is not significant in any models including the income-fixed effects model, which is different from section 4.3′s result. HDI maintains its significant effect, which is consistent with section 4.3′s results.

Baseline models’ results using weekly data without interactions.

4.4.2. Using growth rate of confirmed deaths

We use the growth rate of confirmed deaths as an alternative variable to measure COVID-19 to avoid arbitrariness in the selection of a proxy for the pandemic. In particular, we use the growth rate in the cumulative number of confirmed deaths (GRD) instead of GRC and build the following models:

where models of Eqs. (8) and (10) have no interactions and models of Eqs. (9) , (11) explore the interaction effect.

Table 9 , Table 10 present the regression results using the GRD. From them, we can confirm the robustness of our earlier findings in Table 6 , Table 7 . Notably, GRD negatively and significantly affected tourism and hospitality stock market returns. Unlike GRC, which indicates the transmission rate of the virus, GRD indicates the human fatality rate of the virus. Aging countries with a large percentage of elderly are especially severe because this group is more vulnerable to COVID-19; they could die from their basic diseases, infected with a higher severe case rate than young and middle-aged patients ( Daoust, 2020 , Liu et al., 2020 ). However, the population is aging rapidly in many countries such as Japan and Germany. According to WHO statistics, the number of people aged over 60 is 1 billion, and this number is estimated to increase to 1.4 billion by 2030 and 2.1 billion by 2050. 5 Thus, this result indicates the vulnerability of an aging society and public fear of death. In addition, we find that the other four variables (interconnectedness, VIX, oil, and GRSI) in Table 6 , Table 7 have the same significant effect on returns, as shown in Table 6 , Table 7 . This implies that these four independent variables are strong indicators, and their effects on returns are robust, even though we use GRD as a proxy for COVID-19. Meanwhile, we observed a significant interaction term of GRD × G R S I . This accords with what we observed in Table 6 : governments’ effective reactions undermine the negative impact of GRD because a series of government policies can reduce the possibility of social interactions, control the growth rate of confirmed cases, and protect the public from death. Finally, we notice that the adjusted R 2 , which indicates the percentage of variation in which the dependent variable can be explained by the useful independent variables, is smaller than that in Table 6 , Table 7 . This means that GRC is a better proxy than GRD, but overall, we confirm the COVID-19 effect on stock returns.

Regression results using growth rate of confirmed deaths without interactions.

Regression results using growth rate of confirmed deaths with interactions.

4.4.3. Two-stage panel data regression

A critical issue in identifying the determinants of tourism and hospitality stock market returns may be endogeneity. To further verify the results against possible endogeneity concerns, we used two-stage panel data regression. Endogeneity can result from simultaneity, reverse causality, or omitted variable biases.

In the first stage, we regress “GRC” on exogenous variables, the instrument should satisfy-two requirements: firstly, it should correlate with GRC i , t but uncorrelated with Return i , t ; secondly, it should be conceptually valid. We select four instruments that satisfy the exogeneity conditions. First, we employ GRC i , t - 1 , the first lag of GRC i , t , which assumes the process to be autoregressive of order 1 and shows that today’s values of GRC are determined by its past values ( Kizys et al., 2021 , Zaremba et al., 2020 ). Second, we used PD, which denotes population density. It is a fundamental factor affecting the transmission of COVID-19 coronavirus ( Zaremba et al., 2021 ). Third, we employed the A g e 65 which represents the number of people over 65. Finally, we include HB, which denotes the hospital beds per thousand, as a proxy for hospital equipment. Indeed, the elderly are the most vulnerable to the COVID-19 coronavirus and the capacity of hospital equipment is essential for infected patients to receive treatment. The first-stage regression was as follows:

In the second stage, we use the fitted values from the first stage as covariates. The validity of the instruments is tested using the Hansen-J test to identify any restrictions. To explore the effects, we build the following models in the second stage:

where models of Eqs. (13) and (15) have no interactions and models of equation (14) , (16) explore the interaction effect.

Table 11 summarizes the coefficient estimates for the two-stage panel data regression model without interactions. In this time, the fitted value of variable “DCT” shows a mild significant effect in most models, the return lag only shows a significance level at 10 %, and the other four variables (interconnectedness, VIX, Oil, GRSI) keep the same effects as revealed in the baseline model. The GDPC effect is significant only in the income fixed effects model. The HDI effect was not significant in any of the models. The adjusted R 2 was relatively small, indicating that additional useless independent variables were used. From this result, we can confirm the robustness of interconnectedness, VIX, Oil, GRSI effects in all models, and the GDPC effect under the income-fixed effects model.

Two-stage Panel Data Regression Results without Interactions.

Table 12 presents the results of the two-stage regression analysis with the interaction effect. These regression results strongly support most of our previous findings. Specifically, the effects of interconnectedness (degree, CC, ANND), COVID-19 (i.e., the fitted value of variable “DCT”), GRSI, VIX, oil, and GRC × G R S I ^ on stock returns are robust, while GDPC and HDI effects are insignificant and temporary.

Two-stage Panel Data Regression with Interactions.

5. Conclusions and limitations

The COVID-19 pandemic has severely impacted the global economy. The tourism and hospitality industry bore the brunt because of various policies, such as visa restrictions, flight restrictions, border closures, and social quarantines. In this study, we investigate this influence on the tourism and hospitality markets from a new perspective. Regarding the existence of the spillover effect, we first employ the Granger-causality test to identify the statistically significant spillover effect and construct a network of interconnectedness to depict the co-movement phenomenon among the tourism and hospitality markets using country-level panel data. Second, we further explore whether the interconnectedness structure can explain the variation in market returns by incorporating it with other determinants (the spread of COVID-19, the VIX, crude oil price, etc.). We find that the interconnectedness structure among these tourism and hospitality markets, measured by three global network indicators (degree of centralization, transitivity, and density), is significantly different between the pre-pandemic and in-pandemic periods. The interconnectedness of the network is denser during the pandemic period owing to the influence of COVID-19. In addition, we find that the local interconnectedness structure, measured by three local network features (degree, ANND, and CC), can significantly and positively influence market returns. We also confirm that GRC and GDC, which are proxies for the spread of COVID-19 and have a negative influence on market returns, and GRSI, which measures the government’s response stringency, and has a significantly positive impact on stock returns.

This study provides a better understanding of the influence of the COVID-19 pandemic and provide profound insights and implications for stakeholders like government policy makers, stock market investors, and managers of the tourism sectors. Specifically, the results of this study can help governments and policymakers better manage the risks of unexpected disasters similar to the novel coronavirus pandemic in the future. The role of government interventions in the tourism and hospitality markets is advocated and favored because the government policies are demonstrated to have achieved good results. As for the other stakeholders in the tourism and hospitality stock markets, it is better to monitor the real-time spillover effect of the dynamic environment, be aware of the unforeseen contagion risk, keep a close eye on the government policies, optimize the investment decisions, and diversify the investment risk. As for the managers of tourism sectors, they have to realize that COVID-19 has a great negative impact on this industry and they need to take immediate actions in response to the pandemic, including shutting down sometimes, reducing the operating cost, and taking new managerial strategies accordingly.

However, our study has several limitations. First, the investigation period was relatively short owing to the nature of the abrupt pandemic. A longer study period may have allowed the evaluation and verification of our findings. Second, we only have a limited dataset because of data availability constraints (e.g., some countries may have no tourism and hospitality market index); there are possibly some other variables that may influence market returns, but we were unable to fully capture them. These issues should be discussed in future studies. Third, it is interesting to investigate the differences between the general and sectorial market indices but regrettably, the major research work in this paper is to investigate the impact of COVID-19 on the tourism and hospitality industry, we have not extended the discussion with a comparative study to compare the differences between the general market and sectorial specific impacts. Further research work can elaborate more on this topic.

CRediT authorship contribution statement

Yan Liu: Conceptualization, Methodology, Data curation, Project administration, Writing – original draft, Writing – review & editing. Xian Cheng: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Software, Writing – original draft, Writing – review & editing. Stephen Shaoyi Liao: Supervision, Investigation, Funding acquisition, Resources.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study is supported by Research Grants Council of the Hong Kong Special Administrative Region, China (GRF11501520), the National Natural Science Foundation of China (NSFC71871151), Key Program for International S&T Cooperation Projects of China (No. kh2201037).

1 For detail information for all the countries/regions and indices, please refers to Appendix A in the Online Appendix Materials.

2 We present the descriptive analysis for all Tourism and Hospitality Stock indices in Appendix B in the Online Appendix Materials.

3 More information about Granger-causality test, please refers to the Appendix C in the Online Appendix Materials.

4 We investigate the interconnectedness from both global and local perspectives. For the definition of global interconnectedness measures and local interconnectedness measures, please refers to Appendix D in the Online Appendix Materials.

5 https://www.who.int/health-topics/ageing#tab=tab_1 .

Appendix A. Summary statistics for the return series for all Index.

Summary statistics for the return series for all Countries and regions.

Appendix B. Interconnectedness measures

Network analysis starts with constructing a series of networks. After the networks have been constructed, it is time to analyzing the network structure, capturing the difference in the network measures, etc. Each network has a unique structure and the network structure could be presented with several network measures. If we analyze the global measures of the network, instead of analyzing each node, we view the whole network as the smallest analyzing unit. In contrast, if we keep eye on the local network measures, it implies our analysis is node-wise.

B1. Global interconnectedness measures

For the ease of illustration, we draw a six-nodes network as an example to show what are the global interconnectedness measures and local interconnectedness measures respectively.

In this paper, we employ-three measures of global interconnectedness: degree of centralization, transitivity and density.

Firstly, degree of centralization is the simplest centrality measure, defined as the percentage of existing degree of all nodes over all possible degree in the network. The degree of a node is the total count of connections in the network. In this case, the count of existing degree is 16, and the count of all possible degree is 6*5 = 30. Thus, the degree of centralization is 0.53.

Secondly, transitivity means the overall probability that a network have adjacement nodes interconnected. Transitivity reveals that the existence of clusters, subgroups cliques within the network. Transitivity is the ratio calculated as follows:

In this case, the observed number of closed triples is 5, and the maximum possible number of closed triples is 42. Thus, the value of transitivity equals to 0.12.

Thirdly, density measures the health and effectiveness of the network, illustrates that the proportion of actual connections in the potential connections. In this scenario, “potential connection” is the connection between two nodes that potentially exist, which only correlates with the number of the nodes. “Actual connection” refers to the connection that truly exists.

In this case, the observed number of actual connections is 8, the number of potential connections is 15. Thus, the value of density equals to 0.53.

B2. Local interconnectedness measures

Three node-wise interconnectedness measures: degree, closeness, and Average Nearest Neighbor Degree (ANND) are employed as the interconnectedness variables in the estimation model.

Degree is the simplest measure of centrality, which defined as the total count number of connections linked to the node. Degree is normalized to (0,1). For example, as Fig. 1 shows, the degree value of node 1 is 2.

Closeness is measure of centrality for a network, which is calculated as the reciprocal of the sum of the shortest paths’ length between one country and the other country. Closeness measures the average farness between countries. The mathematic expression of closeness is:

where C AB is the length of the shortest path form country A to country B, and we let C AB  = N-1 if country A and country B are separated without any path. Closeness is also normalized to (0,1).

For example, as Fig. 1 shows, the closeness value of node 1 is 1/1 + 1/2 + 1/3 + 1/2 + 1/2 = 2.83.

Average Nearest Neighbor Degree (ANND) is defined as the average degree of the nearest neighbor for each Country. For example, if country i has statistically significant Granger causality relationships with k countries, the mean value of the degree for k countries is the average nearest neighbor degree of country i . ANND is also normalized to (0,1). For example, as Fig. 4 shows, the ANND value of node 1 is (3 + 5)/2 = 4.

A six-nodes network.

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NC tourism leaders tell Charlotte to drop talk of using hospitality taxes for transit

N orth Carolina tourism leaders on Monday criticized “concerning comments” by some Charlotte City Council members about putting tourism tax revenue into infrastructure projects, leading city leaders to backtrack on the idea’s potential.

In a letter emailed to council members, Mayor Vi Lyles, city manager Marcus Jones and news outlets, the NC Restaurant & Lodging Association said such a move could impact businesses and their employees. It also noted that another North Carolina county was taken to court over its “misuse” of occupancy tax money it put towards public safety.

The letter came a week after council member Renee Johnson floated the idea of using dollars from the city’s hospitality fund, which typically go toward projects like stadium renovations, to help pay for transportation needs. Some other council members said it was an idea worth discussing, but not everyone was on board.

Charlotte has struggled for years to find a way to fund its multi-billion dollar transit plan amid pushback from the state legislature over a potential sales tax increase. The city needs General Assembly help to get a tax increase on the ballot, but Republican leaders in Raleigh have called the plan impractical and said it needed to be more focused on road capacity instead of public transit.

“We have resources in another pot,” Johnson suggested at the April meeting of the council’s Transportation, Planning and Development Committee.

The NCRLA said the tourism taxes that go into the hospitality fund are “targeted taxes” that can and should only be used for tourism-related expenses.

“These (are) very real needs, but they are needs of the community at large and should not be paid for on the back of one industry that is already singularly taxed for many expenses that tourists and locals greatly enjoy,” NCRLA President and CEO Lynn Minges said in the letter.

After receiving the letter, city leaders said changes are unlikely to move forward.

What was proposed at City Council meeting?

Currently, Charlotte’s hospitality fund — revenue from hotel occupancy, rental car and prepared food and beverage taxes — can pay only for projects that fall within three buckets:

Convention Center, which includes convention center facilities, marketing, amateur sports facilities and stadiums with more than 60,000 seats.

Tourism, which includes marketing for programs and events that draw visitors and construction as well as maintenance of convention centers, civic centers, auditoriums and museums

The NASCAR Hall of Fame, which includes construction and maintenance of the Hall of Fame, the Crown Ballroom and associated parking facilities.

At last week’s committee meeting, Johnson suggested the city follow Asheville and work to amend state law to allow the fund to also pay for transportation needs. Johnson said it appeared Asheville’s effort to amend the law was supported by the hospitality industry.

But in Monday’s letter, the NCRLA pushed back on that claim.

“The passage of HB 1057 did have local hospitality industry support, but the way the language is now being manipulated and misinterpreted by local leadership in Asheville, is not,” the letter said.

It also noted that the North Carolina Court of Appeals ruled in March that Currituck County misused occupancy tax money when using it “for police and other emergency services.”

‘We urge you to stay the course’

The NCRLA said in its letter the state’s occupancy tax was originally conceived to help municipalities promote tourism. The hospitality industry agreed to it with the understanding it would bring in more business for them, according to the letter.

“No other industry has targeted taxes. Those targeted taxes should not be used to pay for general fund expenses that are primarily used by the community at large and other businesses. If the original need of tourism promotion is no longer needed, and the tax has met its goals, then that tax should be lowered or eliminated,” the letter said.

The group added it was “ready to help you and support you in finding other mechanisms by which to pay for infrastructure needs.”

“We urge you to stay the course in funding your infrastructure needs through the proposed increase in sales tax,” the letter said.

‘Creates more problems’: Lyles, council members respond

In a response to the NCRLA, Mayor Vi Lyles said in an emailed letter the conversation that took place about the use of hospitality funds “does not question or indicate any change in city policy.”

“The City of Charlotte does not intend to change how we utilize funds generated by hospitality taxes or work with our hospitality industry,” she wrote in a Tuesday letter.

Council member Malcolm Graham, who called Johnson’s proposal “a terrible idea” during the committee meeting, told The Charlotte Observer Monday, “in no way do I support” the idea.

“It creates more problems than it fixes,” said Graham, who chairs the council’s economic development committee

Graham emphasized the proposal was not an official agenda item during the transportation committee’s meeting.

“It is not and should not be an agenda item for public discussion,” he said.

Council member Ed Driggs — who chairs the transportation committee and said previously Johnson’s idea was “a conversation worth having” — said Monday he doesn’t “expect the suggestion to gain much traction on Council.”

“Charlotte is very different from Asheville, and there are many reasons why a diversion of hospitality funds would be a bad idea here,” he said in a statement to the Observer.

Johnson did not immediately respond to a request for comment on the letter.

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The Education Division of the Taipei-Moscow Economic and Cultural Coordination Commission (ED TMECCC) Speaks at the Education in Tourism: Theory and Practice Conference at Russian State University for the Humanities

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William Lu, Secretary of the Education Division of TMECCC, was invited to be one of the six speakers at an international conference on Education in Tourism: Theory and Practice, held at the Russian State University for the Humanities (RSUH) on March 13, 2019. The conference was chaired by Alexander Logunov, Chair of the Department of History, Politics and Law, and the attendees from related industries and institutions included: Roman Skoriy, President of the National Tourist Union; Thomas Wilken, Director of Operations at the Moscow Marriott Hotel Novy Arbat; Nelly Kuleshova, Learning & Quality Assurance Manager of Four Seasons Hotel Moscow; and Andrey Yatsenko, Co-president of the National Committee on Russian Ecological Security. An estimated 100 people participated.

The conference covered many key subjects, including education for the tourism industry; tourism as an educational project in the 21 st century; innovative methods of training people for the tourism industry, and national policies regarding developing tourism in the Russian Federation for children and young people.

William Lu began his address by saying that tourism is often referred to as “an industry without pollution”. This makes it an industry sector worthy of serious attention when it comes to economic development, and he urged the tourism-related industries, companies, and institutions to provide more internship opportunities for students.

He then mentioned Taiwan’s allowing visa-free access from 6 September, 2018 till 31 July this year, and the new direct flights available to Taipei from Moscow and Vladivostok, and he encouraged more Russian people to visit Taiwan. The number of Taiwanese tourists visiting Russia has grown rapidly in the past 3 years; it has more than tripled, from 3,522 in 2015 to 10,859 in 2017. A number of Taiwanese hotels, travel agencies and restaurants have become regular participants of the Moscow International Travel & Tourism Exhibition (MITT) because they anticipate booming numbers of Russian tourists will visit Taiwan.

William then focused on tourism and education, expressing his hope for more and closer cooperation between Russia and Taiwan combining Mandarin Chinese learning and tourism and hospitality, given that studying Mandarin Chinese has become a popular trend there. Many Chinese language centers affiliated with universities in Taiwan and the tourism industry have created innovative tours that combine experiencing local culture with some language learning, and overseas visitors can enjoy a trip to Taiwan that’s more than just sightseeing. He also mentioned some related on-going cooperative education projects, including a language exchange program which sends 30–40 Russian students to study Mandarin Chinese in Taiwan each year. ­

The Russian State University for the Humanities is one of Russia’s key universities focusing on humanitarian research, and it is one of the 39 participants in the national Develop Export Potential of Russian Educational System project. It has more than 30,000 students and 1,900 academic staff. It was established in 1991, as the result of the merger between Moscow Public University (which was founded in 1908 through an initiative of Alfons Shanyavsky) and the Moscow State Institute for History and Archives (founded in 1930). The university’s motto is “Centuries-old Tradition, Contemporary Technology”. It has signed over 240 cooperation agreements with leading foreign universities and research institutions, and more than 700 foreign students study or do an internship at RSUH each year. It looks forward to further cooperation with Taiwan.

Photo from left: Dr. Olga Pavlenko, Vice-Rector for Scientific Affairs; Prof. Alexander Logunov, Chair of the Department of History, Politics and Law; Dr. Vera Zabotkina, Vice-Rector for International Cooperation; Associate Prof. Svetlana Gorelova, from the Contemporary Tourism and Hospitality Division; Roman Skoriy, President of the National Tourism Union; and William Lu, Secretary of the Education Division of TMECCC, at the international conference on Education in Tourism: Theory and Practice, at the Russian State University for the Humanities.