Smart Travel Experiences: A Bibliometric Analysis of Knowledge Domains and Research Areas

Abstract

This study aimed to identify the knowledge domains and current research areas of smart travel experience research. A total of 95 articles investigating smart travel experiences published between 2010 and 2021 were derived to conduct a citation analysis, a co-citation analysis, and a bibliographic coupling analysis. Thematic analysis revealed four knowledge domains (experience design, structure equation modeling methodology, role of technology, and destination ecosystem) and five current research areas (conceptualization, technology adoption, destination management, innovation, and co-creation). The constituents of each domain (12 sub-knowledge domains) and research area (16 sub-research areas) are discussed to reveal research gaps and propose future research directions.

Keywords

bibliometric analysis knowledge domain literature review smart tourism smart travel experience

Highlights

Smart travel experience is conceptualized as an advanced experience concept.

This study identifies four knowledge domains in smart travel experience research.

This study identifies five current research areas in smart travel experience research.

Research gaps are identified in each knowledge domain and current research area.

Introduction

People use the adjective “smart” as a prefix to describe all kinds of objects that they subjectively judge to be smart, ranging from smart people (e.g., Albert Einstein) and smart cars (e.g., Tesla) to smart cities (e.g., Barcelona). It is unsurprising that the word “smart” has been applied to the tourism context, because the tourism industry has witnessed an ongoing technological trend to stage travel experiences through before, during, and after trips (Gretzel, 2011). While there has been little consensus about the definition of smart tourism in the literature (Mehraliyev et al., 2020), it has widely been referred to as the proliferation of technologies to generate agile solutions and values in service innovation, strategic management, tourism marketing, and destination competitiveness for all involved in the tourism ecosystem, by integrating physical and digital tourism networks (Buhalis, 2022).

Since Femenia-Serra and colleagues (2019) proposed a new term “smart tourist” to conceptualize smart tourism as a new profiling market segment which is more interested in technological travel elements than mass tourism, many scholars have tended to investigate smart tourism from a niche tourism perspective by highlighting its differences with mass tourism (Gajdošík, 2020; Gajdošík et al., 2020). Hence, many existing review studies on smart tourism have followed the research paths of other forms of niche tourism to focus on “smart tourism” as a whole, and have identified different tourism concepts, such as travel experience, as key research areas (e.g., Bastidas-Manzano, Mehraliyev et al., 2020; Sanchez-Fernandez & Casado-Aranda, 2021). This approach conceptualizes smart travel experience as a subsector of smart tourism, implying that its knowledge domains and theoretical foundations have come from the concept of smart tourism. However, it has overlooked smart travel experience as a distinct research area that emerges from generic experience studies, because smart tourism is far more than a form of niche tourism, for two main reasons.

First, since Buhalis and Amaranggana (2014) theorized the concept of smart tourism and incorporated it with travel experience design, many scholars have revealed that the extensive technological applications in tourism have provided new opportunities to stage new and innovative travel experiences with unique and memorable digital encounters (Roy et al., 2019). Consistent with many scholars who suggested smart tourism as a new paradigm that serves as a holistic tourism development framework for existing tourism concepts (e.g., travel experience; Koo, Mendes-Filho, & Buhalis, 2019), Kabadayi and colleagues (2019) asserted that a smart experience is an advanced concept of a traditional experience and a digital experience, emphasizing that there are high levels of interactivity and interoperability in staging new personalized and seamless travel experiences. Hence, smartness is more than a form of niche concept; it is a comprehensive experience framework to re-conceptualize travel experiences (Xiang et al., 2021), and transform all kinds of conventional travel experiences into smart travel experiences within a tourism destination (Femenia-Serra & Ivars-Baidal, 2021; Roy et al., 2019).

Second, along with rapid technological penetration, many statistics have echoed Cott’s (1985) statement that “we have become utterly dependent upon technology” (p. 1135). Smartness has become a necessity more than a fantasy in our daily lives (Coskun et al., 2018). This phenomenon has challenged the core tenet of niche tourism in which the niche aspects—such as bird or ski activities—directly induce niche tourists’ visitation and create peak travel experiences for them, because it is rare that tourists visit a place because of its smart technologies. In this sense, similar to a food consumption experience (Quan & Wang, 2004), a smart travel experience could merely be the extension or intensification of an individual’s technological usage habits formed at home, serving as a supportive element of all travel experience. Rachao and colleagues (2019) believed that it is important to extract travel experience as a distinct research area from niche tourism (i.e., food tourism), when the niche aspects (i.e., food) do not represent a peak travel experience but a supporting travel experience. Since Adhikari and Bhattacharya (2016) discovered that many existing studies on customer experience have overlooked the supportive role of smart systems in experience consumption, the theoretical connections between smartness and travel experience should be explored by identifying the inner knowledge structures and current research areas of smart travel experiences.

Unlike other review studies that have identified smart travel experience as a subsector of smart tourism (Bastidas-Manzano et al., 2021; Mehraliyev et al., 2020), this study followed Xiang and colleagues (2021) to conceptualize it as an advanced experience concept that emphasizes the use of smart technologies as a catalyst to connect tourism service providers and tourists in the co-creation process of travel experience. As highlighted by Tussyadiah (2014), a smart travel experience does not represent a unique proposition describing technological applications, but a process in which tourism service providers redesign and enrich existing travel experiences in “smarter” ways. Hence, it would be hard to say if mobile payment at restaurants is a smart experience unless the payment process has been redesigned (e.g., checkout counters or billing staff are not required), because mobile payment only represents a different payment method: it does not really involve redesigning or enriching existing consumer experiences. Since there could be distinct experiences arising from the concept of smartness that are highly relevant for advancing travel experience research, this study aims to address two main research questions: (1) What are the knowledge domains on which smart travel experience literature is built? (2) What are the current research areas in smart travel experience literature?

Literature Review

Since the concept of smart tourism was coined in 2010, this topic has unsurprisingly become an area of interest to academic scholars. While many review studies have considered smart tourism as a subsector of tourism to separate the concept of smartness from the mainstream tourism literature (e.g., Mehraliyev et al., 2019; 2020; Ye et al., 2020), Gretzel and colleagues (2015a) suggested smart tourism as a development framework that represents the integration of smartness into three different layers of a tourism system: smart destination, smart business ecosystem, and smart experience. This framework has encouraged scholars to investigate smart tourism from a broader perspective in order to understand the concept of smartness in tourism.

The first research area is rooted in the smart destination layer and describes smart tourism as a destination in which technological applications, such as free wi-fi systems, USB charging ports, and sensors, are extensively installed and integrated with physical infrastructure. Through focusing on the identification of the characteristics of a smart tourism destination from the perspective of destination developers (Gretzel et al., 2015a), this research area started with investigations into which forms of hard smartness (i.e., technological infrastructure) are vital for the development of smart tourism destinations (e.g., Zhang et al., 2012) and has recently moved to a more comprehensive discussion on the interaction between hard smartness and soft smartness (i.e., people and institutional factors; Koo et al., 2016). As a pioneering conceptualization of smart tourism in the literature, several review studies were recently conducted in this area to focus on the knowledge domains of technological development, destination competitiveness, and urban planning (e.g., Bastidas-Manzano et al., 2021; Gelter et al., 2021; Koo et al., 2016).

The second research area has been developed in the second layer (the smart business ecosystem) and describes smart tourism as a complex business ecosystem (Gretzel et al., 2015a). It has highlighted the compositional differences between traditional tourism and smart tourism. Unlike the traditional tourism distribution chain that was defined as a hierarchical food chain in order to clearly distinguish consumers, producers, and intermediaries, this research area conceptualizes smart tourism as an ecosystem in which all tourism stakeholders are dynamically interconnected and can simultaneously act as consumers, producers, and intermediaries to co-create values through digitalization (Gretzel et al., 2015b). Considering smart tourism as a catalyst of co-creation in tourism, many review studies have been conducted from a system management perspective to understand the knowledge domains and existing research areas of how smartness facilities co-create in tourism (e.g., Mohammadi et al., 2020).

The last research area is grounded on the third layer (smart experience) and focuses on the convergence between information technologies and travel experiences from the perspective of tourists. Tourism literature has been no stranger to smart experience since the increasing proliferation of technological applications in tourism because smart experience is the main distinction between smart tourism and smart city, which distinguishes tourism studies from other study fields, such as urban planning and technology (Gretzel et al., 2015a). Since Spohrer and colleagues (2007) defined an experience system as a “value-co-production configuration of people, technology, other internal and external service systems, and shared information” (p. 2), there has been an ongoing trend to recognize smart tourism as an experience design framework that provides a new path to enhance all kinds of travel experience by emphasizing the core role of technologies in experience design (Buhalis & Amaranggana, 2015). Tussyadiah (2014) proposed a theoretical foundation for experience design in tourism and consulted a case study of mobile-mediated tourism experiences in Pennsylvania to illustrate how the concept of smartness could be utilized to enhance the experiential values of travel activities. Eide and colleagues (2017) also asserted that the concept of smartness is the main driver of experience innovations in tourism.

While the comprehensive experience framework of smartness in the tourism literature was also observed by Hosany and colleagues (2022), many review studies have simply recognized smart experience as a subsector of smart tourism (e.g., Chen et al., 2021; Johnson & Samakovlis, 2019; Mehraliyev et al., 2019, 2020), overlooking it as a research area emerging from the travel experience literature. Instead of focusing on the holistic concept of smartness in travel experience, many review studies have remained focused on smaller and occasionally technology-specified smart experiences, such as immersive technology-mediated travel experience (Fan et al., 2022) and robotic travel experience (Tung & Law, 2017). Given the theoretical differences between smartness and technological application (Buhalis & Amaranggana, 2015), Gretzel (2021) criticized that many tourism studies are self-claimed to investigate smart tourism, because smartness does not really mean technological applications. Hence, the concept of smartness demands more attention from the perspective of travel experience, calling for a systematic review to capture the knowledge evolvement of smart travel experience research by assessing its research domains and current research areas.

Methodology

Bibliometric Analysis in Tourism

Bibliometric analysis is a popular method used to determine the knowledge structure of various tourism concepts (Köseoglu et al., 2016) such as travel experience (Kim & So, 2022). As a method of analysis that helps detect hidden knowledge structure to better understand the “foundations and architecture” of tourism knowledge (Benckendorff & Zehrer, 2013, p. 125), bibliometric analysis is commonly used for the crisis phase of scientific revolutions in which new paradigms are needed to refine existing theories or concepts for new realities (Kuhn, 1970). Smartness has widely been recognized as a new normal that requires new research paradigms in various study fields such as urban planning (Kunzmann, 2014) and tourism design (Koo et al., 2019). Specifically, Stienmetz and Fesenmaier (2013) suggested that the concept of smartness challenge traditional tourism knowledge in four ways: physical acts to virtual acts, product-focused to capability-focused, static and fixed to evolving and shapeable, and linear and sequential to matrixed and simultaneous. Since Xiang and colleagues (2021) asserted that the concept of smartness in tourism demands new research paradigms, new theoretical frameworks, and new methodological approaches, a bibliometric analysis that helps identify knowledge domains and existing research areas is important to guide knowledge development in smart tourism.

Despite its widely proven ability to better understand the creation of tourism knowledge (Benckendorff & Zehrer, 2013), bibliometric analysis has faced two main criticisms. First, it relies purely on number of citations to measure the influence of publications (Garfield, 1979) and overlooks possible biases caused by the inaccessibility of publications and negative citations. Specifically, Jha and colleagues (2017) estimated that negative citations may account for up to 14% of total citations. Furthermore, as a type of review technique that reveals the characteristics of the existing studies based exclusively on the citation network, bibliometric analysis lacks deeper critical analysis to understand the knowledge structure (Giaccone et al., 2016). Thus, a mixed-method approach—a combination of various review techniques—is highly encouraged when performing an extensive literature review in a selected study area because the methods are complementary in nature and provide more valuable insights (Zupic & Čater, 2015). Hence, this study adopted four different review techniques (i.e., citation analysis, co-citation analysis, bibliographic coupling analysis, and thematic analysis) to better understand (1) the knowledge domains and (2) the current research areas in smart travel experience research.

Second, despite the diversity of bibliometric analysis methods, many tourism scholars have relied purely on co-citation analysis to identify knowledge domains and current research areas (Köseoglu et al., 2015). Specifically, co-citation analysis is a relational bibliometric analysis method to explore relationships within research, based on the assumption that documents are likely to share similar ideas with documents which frequently appear together in reference lists (i.e., are co-cited) (Benckendorff & Zehrer, 2013). Since co-citation analysis focuses mainly on how old publications are cited by recent articles, Zupic and Čater (2015) argued that it is suitable for identifying the knowledge domains of a particular study field. However, the minimum citation threshold (i.e., the minimum frequency for two articles to be co-cited) in co-citation analysis has limited its ability to identify recent study areas, because the citation number depends on the year of publication and authors’ reputations. In other words, co-citation analysis is likely to overlook many recent articles that represent current research areas but contain a few or no citations.

Given the limitation of co-citation analysis, Galvagno and Giaccone (2019) encouraged more applications of bibliographic coupling analysis in newly developed tourism research areas. Specifically, while bibliographic coupling analysis is a relational bibliometric analysis method similar to co-citation analysis, it focuses on the number of references two documents share to determine their similarity instead of on how they are co-cited. Since this approach does not contain a minimum citation threshold to include documents, recent articles with a few or no citations can be analyzed to better identify current research areas (Galvagno & Giaccone, 2019). Given their different focuses on the relationships within research, Most et al. (2018) believed that applying both mitigates the limitations and retains the advantages of each method, because co-citation analysis is suitable for identifying knowledge domains and bibliographic coupling analysis is suitable for identifying current research areas.

Application of Bibliometric Analysis

This study followed Zupic and Čater’s (2015) four-step approach—(1) research design, (2) compilation of bibliometric databases, (3) analysis, and (4) visualization—to answer the two research questions.

Step 1: Research design

The first step was to determine the appropriate bibliometric techniques for addressing the research objectives. Since this study aimed to identify the knowledge domains and current research areas in smart travel experience research, a mixed-method approach was utilized. First, citation analysis was used to provide an overview of smart travel experience research by counting the frequency of citations, authors, and source titles. Second, as an advanced form of citation analysis, co-citation analysis considers the number of times two articles are jointly cited in other publications to detect the similarity and dissimilarity in the literature, because it is assumed that the number of joint citations depends on the similarity between two articles in terms of their research topic, theoretical framework, or methodology (Benckendorff & Zehrer, 2013; Köseoglu et al., 2015). Third, unlike co-citation analysis, which is useful for identifying the most influential schools of thought in a specific study field, bibliographic coupling helps to identify the most recent research frontiers because it serves as a mirror image of co-citation analysis by considering the number of references two articles share (Zupic & Čater, 2015). Galvagno and Ciaccone (2019) believed that the joint use of co-citation analysis and bibliographic coupling is useful to provide accurate indications of the knowledge structure of a specific field. Lastly, following the three citation-based review techniques, an inductive approach using the six-step thematic analysis approach proposed by Braun and Clarke (2006) was adopted to interpret the knowledge domains and current research areas.

Step 2: Compilation of bibliometric databases

The second step was to develop a bibliometric database that included relevant smart travel experience research. As a concept originating from smart cities, smart tourism has been investigated in various study fields. Since the role of travel experience may be de-emphasized in other study fields, only articles in the tourism and hospitality field were used to construct our sample. This study selected the Web of Science (WoS) Core Collection and Scopus databases to initially build the bibliometric database, because they represent broad tourism and hospitality works (Benckendorff & Zehrer, 2013) and thus their combination helps increase a bibliometric database’s reliability and comprehensiveness (Johnson & Samakovlis, 2019). Furthermore, in addition to the articles from the WoS and Scopus databases, articles published in the Journal of Smart Tourism were also included in the bibliometric database. Unlike many other journals dedicated to the knowledge contribution of smart tourism that publish articles in multiple languages (e.g., the Journal of Tourism Intelligence and Smartness), the Journal of Smart Tourism was a recently launched English journal dedicated to the knowledge contribution of smart travel experience studies (Koo & Chung, 2021). Since Li and colleagues (2017) discovered that there is a large gap between different academic research communities in terms of conceptualizing smartness, the exclusion of non-English primary journals and articles helps maintain a consistent focus on the conceptualization of the smart travel experience.

The scope of the data was determined on the basis of three factors. First, only studies written in the English language were included. Second, since the buzzword “smart tourism” was coined in 2010, this study only included studies published between 2010 and 2021. Third, except for the conference articles published in Information and Communication Technologies in Tourism (ENTER), only full-length research papers were included to ensure the consistency of the research results. Compared to other conference papers, there are three main reasons to include articles published in ENTER. First, the initial scan of the research showed that many frequently cited smart tourism articles were published in ENTER. Hence, many of them were thought of as pioneers establishing the research foundation of the smart travel experience (Mehraliyev et al., 2020). Second, smart tourism has consistently appeared as a key research topic in ENTER since 2015, symbolizing the conference’s continuous interests on the concept of smartness in tourism. Third, as articles published in ENTER are subject to a thorough peer-review process in which many popular smart tourism scholars serve as reviewers, they were seen as credible and pertinent as journal articles and thus were retained as part of our sample.

This study adopted the building block method to conduct a literature search. Specifically, this method was praised by Manthiou and Klaus (2022) in the study context of technology-mediated travel experience, because it helps recognize the role of diversified tourism service providers or smart technologies in forming overall travel experience across the entire journey. In practical terms, the building block method suggests dividing travel experience into different service touchpoints or contexts, identifying potential terms for each element, and combining the terms for each element using Boolean functions (i.e., AND and OR; De Keyser et al., 2020). Since this study conceptualized smart travel experience as the combination between the concepts of smartness and travel experience, the search terms were initially divided into two constituent elements: smartness and travel experience, of which potential and synonymous terms/contexts were identified after checking the existing review studies on “smartness” (Mukti et al., 2022) and “travel experience” (Adhikari & Bhattacharya, 2016).

Six contexts (i.e., tourism, travel, technology, destination, attraction, and hotel) were identified for the “smartness” element, whereas six synonymous terms (i.e., experience, travel experience, tourist experience, customer experience, guest experience, and service experience) were identified for the “travel experience” element. It is to be noted that generic terms (e.g., customer experience) of “travel experience” were also identified to include tourism articles whose authors tended to use these generic terms to expand their study scopes to a broader context, because the exclusive focus on the tourism and hospitality field helps cross-validate these articles’ relevance to the “travel experience” element. As a result, a search rule: “smart tourism” OR “smart travel” OR “smart technology” OR “smart destination” OR “smart attraction” OR “smart hotel” AND “experience” OR “travel experience” OR “tourist experience” OR “customer experience” OR “guest experience” OR “service experience” was applied to identify 51 articles in WoS database, 72 articles in Scopus database, 101 articles published in ENTER on SpringerLink (https://link-springer-com-443.web.bisu.edu.cn/), along with five articles that were manually extracted from the Journal of Smart Tourism. A Microsoft Excel spreadsheet containing the information on 229 articles was constructed for data screening.

The two authors read the titles, abstracts, and keywords of all articles in the spreadsheet independently to identify relevant articles and discussed any discrepancies within identifications. Since this study exclusively focused on the “smart travel experience” layer proposed by Gretzel and colleagues (2015a), smart travel experience had to represent the primary focus of the articles. Since this study conceptualized smart travel experience as a concept that must involve redesigning or enriching existing travel experience and is different to technological applications in tourism, the two authors independently reviewed the full text of articles focusing on the travel experience created by a specific smart technology and determined their relevance to this study’s scope. For example, Chuang’s (2020) study, which focuses on tourists’ use of mobile app travel guides in a travel experience, was removed from the database because it simply conceptualized it as a new information distribution channel but did not distinguish it from existing travel experience. Specifically, only minor inconsistencies in classification were found and were mitigated by the research team during meetings. Overall, 134 articles were removed for three different reasons: (1) duplication between databases (n = 41), (2) literature review articles (n = 5), and (3) irrelevant research focus (n = 88). After checking the reference list of each extracted article to ensure that all relevant articles were covered, the final sample consisted of 95 articles with 5,289 citations (Supplement Table 1).

Step 3: Analysis

The data analysis consisted of three stages. First, a frequency analysis was conducted to detect any spelling and formatting errors in the information of the 95 articles. Second, after correcting all errors, three citation-based review techniques, namely citation analysis, co-citation analysis, and bibliographic coupling analysis, were utilized to visualize the bibliographic relationships between smart travel experience articles. Third, on top of the citation-based findings, the six-step thematic analysis approach suggested by Braun and Clarke (2006) was utilized to identify relevant knowledge domains and current research areas for each network cluster. After reviewing the title and abstract of each article several times to confirm their relationships with each other, the two authors focused on the research objective, research perspective, and research methodology to identify common themes within the formed clusters by reading the full text independently. Then, the two authors compared the resulting themes for internal homogeneity and external heterogeneity and discussed any discrepancies within the results. For those articles with multiple research aspects to be included in more than one resulting theme, the two authors focused on their research objectives and research implications to determine their main research focus and categorize them into a more representative theme. For example, A86 (Wozniak et al., 2018), as an article investigating tourists’ psychological drivers and barriers in a smart travel experience, was sub-categorized as “technology adoption in destination” instead of “drawbacks of technology adoption,” because its main study aim was to understand tourists’ experiences using mobile devices, but privacy concerns (i.e., drawbacks) were only treated as a minor factor. During meetings, the two authors discussed inconsistencies in coding and confirmed the themes until an agreement was reached.

Step 4: Visualization

Visualization of Similarities Viewer (VOSviewer), a computer program for bibliometric mapping, was utilized to visualize the bibliometric networks and clusters using a smart local moving algorithm proposed by Waltman and Van Eck (2013). VOSviewer has been a popular software for various review studies in the tourism and hospitality study field (e.g., Mehraliyev et al., 2020) because it effectively provides an in-depth analysis of findings in small- and medium-sized networks (Waltman & Van Eck, 2013).

Results

Citation Analysis

In all, 5,289 citations from 95 tourism and hospitality articles related to smart travel experiences were extracted. Figure 1 shows an increasing trend in terms of publication number, indicating that smart travel experience is a hot topic in the tourism literature. Specifically, the number of smart travel experience publications remained steady after several papers focusing on smart travel experiences created by mobile technologies were published in 2013, because many scholars were found to have a more specific technological focus, such as social media platforms, virtual reality technology, or platform technology, when studying travel experiences. This changed after 2019 when the concept of the Internet-of-Things began to become popular in the literature, emphasizing the cooperation amongst technologies in the formation of smart travel experiences. Supplement Table 2 reports the top 10 most cited articles. Specifically, six of these 10 articles were published in ENTER, indicating this annual conference’s strong knowledge contribution to the smart travel experience.

Figure 1.

Smart Travel Experience Publications Between 2010 and 2021.

Co-Citation Analysis

Following Mehraliyev and colleagues’ (2020) suggestion that it is difficult to interpret co-citation results if all cited references are included in the analysis, this study adopted a threshold of three citations to select the influential works. As a result, the final sample for the co-citation analysis included 63 references, whose research titles were coded from R1 to R63. Supplement Table 3 provides information on the references selected for the co-citation analysis.

The upper part of Figure 2a presents the co-citation network identifying four main knowledge domains: (1) experience design (red cluster: 22 references), (2) structural equation modeling (SEM) methodology (green cluster: 21 references), (3) role of technologies (blue cluster: 13 references), and (4) destination ecosystem (yellow cluster: seven references). The size of each node indicates the degree of centrality of a reference, with a large node representing that the reference has a high centrality and considerable influence on the formation of the domains. The bottom part of Figure 2b, on the other hand, presents the density visualization of the network, in which warm colors and bold fonts indicate central and influential references in a particular knowledge domain. Based on the results of the co-citation analysis, a thematic analysis was performed for the four formed clusters to identify 12 sub-domains (Table 1).

Figure 2.

A Co-Citation Network of Smart Travel Experience Research.

Table 1.

Thematic Analysis of Knowledge Domains.

Key Knowledge Domains	Sub-Knowledge Domains	N	Referencing Articles (Supplement Table 3)
Experience design	Conceptualization	10	R2, R13, R20, R39, R43, R44, R51, R53, R54, R55
	Extension	7	R18, R23, R37, R49, R58, R59, R60
	Management	5	R12, R15, R25, R41, R42
SEM methodology	Conceptualization	11	R5, R8, R9, R16, R21, R22, R24, R29, R34, R35, R48
	Antecedents and consequences	7	R10, R26, R28, R32, R33, R40, R50
	Research design	3	R1, R4, R17
Role of technologies	Destination management	5	R3, R7, R45, R46, R61
	Conceptualization	5	R6, R31, R38, R62, R63
	Tourist attitude	3	R47, R52, R57
Destination ecosystem	Destination management	3	R11, R14, R30
	Tourist behavior	2	R36, R56
	Social phenomenon	2	R19, R27

The first knowledge domain, located in the bottom left corner of Figure 2a, consists of 22 references. A six-step thematic analysis approach suggested that those references focus on three different aspects of experience design (conceptualization, extension, and management). In addition to some highly cited review studies on customer experience (e.g., R2: Binkhorst & Den Dekker, 2009), pioneering conceptual studies of experience such as R13 (Cohen, 1979) and R43 (Pine & Gilmore, 1998) were found to construct the red cluster. As indicated in Figure 2b, this knowledge domain was found to be concentrated in two different areas: (1) around R12 (Cabiddu et al., 2013) and R39 (Neuhofer et al., 2012), which propose management frameworks for designing and staging travel experiences, and (2) around R58 (Wang et al., 2014), which considers smart travel experience as an extension of daily technological use to outline the nature of tourism in experience design.

The second knowledge domain is represented by 21 green nodes on the right side of Figure 2a. This knowledge domain consists of three sub-domains (conceptualization, antecedents and consequences, and research design) relevant to the SEM methodology. Specifically, this knowledge domain was found to be concentrated around R22 (Gretzel et al., 2015a) and R24 (Gretzel et al., 2015b; Figure 2b), which are two prominent studies proposing “smartness” as a catalyst of value-added customer experiences. This conceptualization serves as a strong theoretical background for scholars to examine the causal relationship between the antecedents and outcomes of a smart travel experience using the SEM methodological research design as discussed by R1 (Bagozzi & Yi, 1988) and R17 (Fornell & Larcker, 1981). For example, R26 (Huang et al., 2017) investigated the mediating roles of exploration and exploitation in the relationship between a smart travel experience and travel satisfaction.

The third knowledge domain is focused on the role of technologies in a smart travel experience and is represented by 13 blue nodes at the top of Figure 2a. Three sub-domains were identified (destination management, conceptualization, and tourist attitude). Given the importance of technologies in a smart travel experience, as highlighted by R38 (Neuhofer et al., 2014), this knowledge domain emerged from the concepts of destination management and tourist attitude to provide empirical findings on how a smart travel experience should be managed and designed using different technologies, such as wearable devices (R47; Tussyadiah, 2014), mobile technologies (R57; Wang et al., 2012), and gamification (R62; Xu et al., 2017). Figure 2b shows that this knowledge domain was concentrated around R57 (Wang et al., 2012), whose research focus was on the role of smartphones in a smart travel experience and whose findings supported Gretzel (2011), who argued that mobile technology is the key element fostering the development of smart tourism.

The fourth knowledge domain is represented by seven yellow nodes scattered around the center of Figure 2a and conceptualizes a smart travel experience as a product of a destination ecosystem. This knowledge domain discusses smart travel experiences from three different perspectives (destination management, tourist behavior, and social phenomenon) of an ecosystem. For example, from a social phenomenological perspective, R19 (Gretzel, 2011) criticized the technology-oriented conceptualization of smart travel experiences in the literature, arguing that an intelligent ecosystem should be built to function as a true conversational partner to tourists within a socio-technical system. While this knowledge domain consists of nodes with a relatively low degree of centrality, studies located near the blue cluster, such as R36 (Minazzi & Mauri, 2015) and R56 (Wang & Fesenmaier, 2013), focused more on how tourists behave to co-create smart travel experiences with service providers, whereas studies located near the red cluster, such as R11 (Buonincontri et al., 2017), stressed their focus on the process of co-creation in a smart tourism ecosystem.

Bibliographic Coupling

Ninety-two articles were included in the bibliographic coupling analysis because three articles (A21, A38, and A21) with a total strength of 0 (not connected to each other) were excluded from the visualizations. As shown in Figure 3, each node represents an article, with the larger nodes indicating those with greater importance in terms of their citation number. The upper part of Figure 3a presents the network visualization, with the VOSviewer algorithm identifying five intellectual structures of the smart travel experience domain: (1) conceptualization (red cluster: 34 articles), (2) technology adoption (green cluster: 23 articles), (3) destination management (blue clusters: 17 articles), (4) innovation (yellow cluster: nine articles), and (5) co-creation (purple cluster: nine articles). The bottom part of Figure 3b presents the overlay visualization, in which cold colors indicate early works and warm colors indicate latest works to visualize the research trend. Based on the results of the bibliographic coupling analysis, a thematic analysis was performed for the five formed clusters to identify 16 sub-research areas (Table 2).

Figure 3

A Bibliographic Coupling Analysis Network of Smart Travel Experience Research.

Table 2.

Thematic Analysis of Current Research Areas.

Key Research Areas	Sub-Research Areas	N	Referencing Articles (Supplement Table 2)
Conceptualization	Design	16	A9, A11, A15, A29, A48, A50, A52, A54, A58, A60, A61, A70, A74, A81, A85, A90
	Tourism future	6	A7, A23, A25, A65, A69, A72
	Formation	5	A32, A33, A40, A46, A53
	Niche tourism	4	A5, A26, A27, A79
	Management	3	A55, A94, A95
Technology adoption	Destination	9	A1, A19, A22, A35, A42, A44, A75, A76, A86
	Hotel	5	A17, A39, A45, A87, A92
	Attraction	3	A2, A88, A91
	Transportation	3	A37, A47, A71
	Drawback	3	A24, A59, A68
Destination management	Destination development	12	A3, A18, A30, A31, A36, A49, A51, A62, A63, A73, A89
Destination management	Destination promotion	5	A4, A10, A20, A41, A83
Innovation	Technology	4	A12, A16, A56, A57
	Service	3	A6, A34, A80
	Management	2	A14, A78
Co-creation		9	A8, A13, A28, A66, A67, A77, A82, A84, A93

The first cluster, located in the bottom right corner of Figures 3a and 3b, consists of 34 articles focusing on the conceptualization of a smart travel experience within smart tourism. As the largest research area, five sub-research areas were identified (design, tourism future, formation, niche tourism, and management). Many articles in the cluster adopted the concepts of experience economy to conceptualize smart travel experiences as something that can be designed by external factors (e.g., immersive technologies, A58: Neuburger & Egger, 2017), formed by tourists’ psychological feelings (e.g., happiness, A46: Lee et al., 2017), and managed through systems (e.g., a transport system, A95: Zheng et al., 2020). While some articles conceptualized the smart travel experience as a niche tourism activity that only attracts some market segments, and focused on how tourists behave differently in a smart travel experience, other scholars conceptualized it as the future of tourism development to highlight the fundamental concepts of smartness in tourism. For example, A7 (Buhalis, 2020) coined a new term “ambient intelligence tourism” to highlight how a travel experience is becoming smarter in the tourism industry.

The second cluster, located in the bottom left corner of Figures 3a and 3b, consists of 23 articles focused on tourists’ technology adoption behaviors to understand smart travel experience. This cluster triangulates the identification of the SEM methodology as a key knowledge domain in the co-citation analysis because most articles in the cluster adopted behavioral theories, such as the technology acceptance model (A92: Yang et al., 2021) and the theory of planned behavior (A35: Jeong & Shin, 2020), to examine the mechanism through which smart tourism technologies trigger tourists’ positive psychological and behavioral responses using a SEM methodological approach in different tourism contexts, including destination, hotel, attraction, and transportation. The thematic analysis also discovered that some articles also adopted SEM to understand the drawbacks of a smart travel experience by examining the negative psychological impacts, such as privacy concern (A24: Femenia-Serra et al., 2019) and co-destruction (A59: Neuhofer, 2016), on tourists’ responses. While this cluster was constructed recently, with many of the articles being published in 2020 and 2021 (Figure 3b), it was largely separated from the research area of conceptualization, possibly echoing Mehraliyev and colleagues’ (2020) criticism that scholars simply treat smart tourism as a new study venue for reaffirming the well-established effects of behavioral theories and overlook the uniqueness of a smart travel experience.

The third cluster, located in the middle part of Figures 3a and 3b, consists of 17 articles focusing on destination management in staging a smart travel experience. Unlike the articles in the first and second clusters, which investigated smart travel experiences from the perspectives of academia and tourists, respectively, the articles in this cluster mainly focused on the management and promotion roles of tourism suppliers, including destination management organizations (A31: Gretzel & Koo, 2021; A64: Qi, 2021) and tourism service providers (A36: Johnson et al., 2021; A63: Perles-Ribes & Ramón-Rodríguez, 2018). Since most articles in this cluster built on the theoretical base of destination ecosystem to emphasize the ontological difference between a “smart” travel experience (as an ecosystem in which all stakeholders are interconnected) and an “unsmart” travel experience (as a hierarchical food chain in which each stakeholder has their assigned role; Gretzel et al., 2015b), they connected the first cluster (conceptualization) with the second cluster (technology adoption) to treat the smart travel experience as a holistic concept of destination management beyond tourists’ adoption of technology (A31: Gretzel & Koo, 2021).

The fourth cluster, located in the middle of Figures 3a and 3b, consists of nine articles whose research focus was on innovation. This is a newly developed research area that largely overlaps with the second and third clusters because most of the articles in this cluster were published between 2020 and 2021 (Figure 3b). While this research area has a mixed research focus, the articles mainly focus on innovations in three different aspects of a smart travel experience (technology, service, and management) to enrich and diversify tourism offerings. The articles that overlapped with the second cluster (i.e., green nodes) were based on the perspective of tourists to focus on technology innovations (e.g., A56: Minor et al., 2021; A57: Neuburger et al., 2018), whereas those that overlapped with the third cluster (i.e., blue nodes) were based on the perspectives of tourism service suppliers to investigate how a smart travel experience should be part of service and management innovation to foster the recovery of tourism and enhance the well-being of tourists (e.g., A12: Buonincontri & Marasco, 2017; A78: Uysal et al., 2020).

The fifth cluster, located at the top of Figures 3a and 3b, consists of nine articles focusing on the co-creation activities in a smart travel experience. As a research area in which no sub-area was identified, it largely overlaps with the third cluster (destination management), revealing a strong link between these two research areas because they were both developed from the knowledge domain of destination ecosystem. The main difference between them is the theoretical foundation upon which they were built. Specifically, articles in the third cluster were based on the destination management literature, conceptualizing a smart travel experience as a product managed by service providers, while articles in the fourth cluster were based on the service-dominant logic and suggested that a smart travel experience is co-created by various stakeholders. Since A82 (Wang et al., 2013) proposed the service-dominant logic as a theoretical foundation to understand the concept of the smart travel experience, it has encouraged various studies to investigate the concept of co-creation in the formation of a smart travel experience (e.g., A13: Buonincontri & Micera, 2016; A93: Ye et al., 2021).

Discussion and Conclusion

Gaps in the Knowledge Domains

The co-citation analysis revealed four knowledge domains of smart travel experience research (experience design, SEM methodology, role of technologies, and destination ecosystem) and 12 sub-knowledge domains. The results showed that studies have mainly drawn on three (psychology, management, and ecology) main knowledge domains to investigate smart travel experiences, overlooking the other five common domains: (1) sociological, (2) economic, (3) geographic, (4) political, and (5) education to understand the multidisciplinary nature of tourism knowledge (Wu et al., 2012). Hence, a series of knowledge domains can be introduced to strengthen the theoretical foundation of smart travel experience research.

First, as triangulated by the large size of the SEM methodology knowledge domain in the co-citation analysis, many smart travel experience studies drew on psychological and behavioral knowledge domains to investigate the antecedents and outcomes of a smart travel experience using traditional technology adoption theories. While only a few of them provided an in-depth discussion on their research paradigm, they relied on a reductionist methodological approach to assume that a psychological or behavioral science phenomenon (e.g., tourists’ attitudes toward a smart travel experience) is linearly formed by several independent components (e.g., perceived usability, perceived attractiveness, and perceived intuitiveness). This argument was also reflected by several conceptual articles (i.e., the formation research area) focusing on the identification of smart travel experiential domains. However, this assumption contradicts the sociological perspective that highlights the multifaceted and complex natures of an experience (Selstad, 2007). New methodological grounds, such as the complexity theory approach (Baggio & Sainaghi, 2011), should be utilized to better understand a smart travel experience as a “value-co-creation” configuration of people, technologies, and information in a smart travel experience (Gretzel et al., 2015b).

Second, as indicated by the co-citation analysis results, many studies drew on the study fields of management (e.g., experience management, technology management, destination ecosystem management) to investigate the feasibility of designing and staging smart travel experiences. However, in contrast to Tseng and colleagues (2010), who suggested economy as a well-established knowledge domain in tourism studies, the views of tourism economists and finance experts on smart travel experience design are largely missing in the existing smart travel experience research. Their perspective is important not only because Gretzel and colleagues (2016) described smart tourism as a new “economy with new resources, new players, and new exchange models,” but also because it remains doubtful whether the investments involved in staging a smart travel experience outweigh the benefits in terms of increased satisfaction and profitability. Leung and colleagues (2021) discovered that consumers tend to spend more when interacting with tourism service personnel than with smart technologies, even though smart technologies help to stage a better consumption experience. Hence, empirical investigations on the profitability of smart travel experiences are needed to ensure the worthiness and feasibility of smart tourism development.

Third, geographic and political studies remain scarce in smart travel experience research. While Jeong and Shin (2020) identified 23 forms of smart travel experience, what they identified were exclusively technological applications. What are the travel activities involved in a smart travel experience? How have tourist movements been changed by smart travel experiences? The extensive technological applications in smart travel experiences have also informed some political issues related to tourist movements. Specifically, despite some recent works investigating privacy in a smart travel experience (e.g., Femenia-Serra et al., 2021; Jeong & Shin, 2021), most existing studies have drawn on psychology to investigate how privacy functions as an obstacle to tourists enjoying a smart travel experience but have failed to expand the theoretical foundation from a political perspective. Foucault proposed the concept of panopticism to theorize privacy through surveillance, and this has served as an insightful concept to investigate privacy in smart travel experiences (Hall & Ram, 2019), which is supported by extensive data collection, sharing, and exchange activities (Gretzel et al., 2015a). While tourism scholars have been slow in considering surveillance and its conceptual implications in tourism spaces (Morgan & Pritchard, 2005), the relation of surveillance to privacy in a smart travel experience should be better investigated to understand the political power mechanisms which regulate people’s travel experience.

Fourth, inspired by the social norm that “smarter means better,” tourism scholars have generally assumed that it is a rational human action for tourists to actively engage in a smart travel experiences (Gretzel, 2011). However, to challenge the romanticism of smart tourism development, Weaver and Moyle (2019) proposed the concept of "tourist stupidity” in smart tourism to argue that tourists may be stupid in four different ways (i.e., abstention, sabotage, non-use error, and misuse error) to obstruct smart tourism development. While this paper emphasized the importance of educating tourists in smart travel experiences, it was only a conceptual article. In other words, more empirical studies investigating when, how, and which strategies should be designed to educate tourists in a smart travel experience are needed from an educational perspective. Doing so may possibly answer questions related to whether smart tourism serves as a niche tourism market that is only preferred by certain market segments.

Lastly, given that smart tourism is a technology-oriented concept, it was surprising to see that many studies relied on human psychology to understand smart travel experiences, overlooking a computer/technology perspective. Since experience is primarily described as a subjective construct representing events that “[exist] only in the mind of an individual” (Pine & Gilmore, 1998, p. 99), most tourism scholars have adopted a human-centric approach to assume that all external stimuli are passive entities that tourists invest with meaning. A human-centric approach was a useful perspective for understanding travel experiences until various technologies were utilized to create smart travel experiences. These technologies, such as artificial intelligence, service robots, and chatbots, demonstrate their own connectivity, semantic depth, computation, and memory capabilities to support the stages of a smart travel experience by breaking through the ontological borders originally assigned to them (Hoffman & Novak, 2018). Hence, a nonhuman/computer-centric approach would be an essential extension of the current literature to evaluate how technologies play an expressive role in “co-creating” a smart travel experience with tourists.

Gaps in the Research Areas

The bibliographic coupling analysis revealed five existing research areas in the smart travel experience research (conceptualization, technology adoption, destination management, innovation, and co-creation) and 16 sub-research areas. Despite the exponential growth of investigations on smart travel experiences, the aforementioned research areas on smart travel experience are far from well established.

In the research area of conceptualization, there appears to be little consensus about the definition of a smart travel experience because tourism scholars tend to adopt various frameworks and perspectives to define it (Li et al., 2017). It was surprising to see that some recent studies still conceptualized smart travel experience as a niche tourism activity, because such a practice has deemphasized the universal application of smartness. While several recent studies included the unique features of smartness to conceptualize a smart travel experience (e.g., Azis et al., 2020; Jeong & Shin, 2020), new frameworks and perspectives are still needed to advance the current technology-oriented conceptualization of a smart travel experience. Since many consultancy and government reports were found to focus exclusively on technological investments rather than on smartness development, it is important to understand what makes a travel experience smart or unsmart. Otherwise, the buzzword “smart” will be problematically linked with technologies to destroy the practical values of staging a smart travel experience without a well-established boundary that distinguishes a smart travel experience from an unsmart travel experience.

The second research area consists of articles focused on identifying the factors that influence tourists’ technology adoption behaviors. While it is possible to recognize tourists’ technology adoption behaviors as an indicator of their preference for smart travel experiences, the rationale that connects these two elements must be better informed; otherwise, the smart travel experience will only be a framework for a technology-enabled travel experience, de-emphasizing the key role of “smartness” in smart tourism development (Gretzel, 2021). Furthermore, this research area has mainly adopted traditional behavioral theories (e.g., the technology acceptance model and the theory of planned behavior, both of which place a strong emphasis on the utilitarian elements of a technological experience) as their theoretical underpinning to understand tourists’ technology adoption behaviors, overlooking many other aspects that cultivate a smart travel experience. Although recent attempts have started considering the hedonic elements, this egoistic approach fails to capture the co-created nature of a smart travel experience when tourists are encouraged to help other stakeholders by sharing their own travel experience and personal information (Femenia-Serra & Ivars-Baidal, 2021; Gretzel & Yoo, 2008; Gretzel et al., 2015b). Thus, how altruism shapes tourists’ smart travel experience can be the agenda for future research.

Third, in the research area of destination management, while more recent articles investigating how destinations manage a smart travel experience were identified, the perspective of tourism service providers only represents a relatively small amount of the smart travel experience research and remains at a conceptual level. Supplier-focused empirical studies should be conducted to understand suppliers’ motivations and challenges in staging a smart travel experience. Unlike the second research area (technology adoption) in which scholars have expanded the investigations into much narrower contexts (e.g., hotel, attraction, and transportation), destination-level analysis and destination-specific case studies still dominate the destination management studies. Thus, the generalizability of these results to other specific tourism and hospitality contexts remains doubtful. Leung’s (2019) study on hotel service providers’ attitudes towards a smart hotel experience acts as a good example to encourage other researchers to adopt a much narrower focus in terms of their study context, because travel experience design is highly context dependent. It would be interesting to understand how a smart travel experience should be designed and promoted in different hedonic (e.g., theme parks and museums) and utilitarian settings (e.g., airports and business centers).

The fourth research area focuses on innovations in a smart travel experience. While there has been an ongoing trend for tourism scholars to shift their technological focuses from social medias and mobile technologies to virtual/augmented reality technologies and artificial intelligences, the roles of many other smart technologies, such as service robots, chatbots, blockchain, and beacon technologies, in a smart travel experience are still under explored. Furthermore, since many studies on smart travel experiences have been inspired by technology adoption studies to focus on a particular technology, such as virtual reality technology (Neuburger et al., 2018) and artificial intelligences (Minor et al., 2021), it is argued that technology innovation studies should focus not only on technological advancement but also on technological collaborations with service and management innovation. Buonincontri and Marasco’s (2017) study is a good starting point to investigate how different tourism components are integrated to stage innovative travel experiences, because tourists are concerned with the ways in which smart technologies are integrated with service and management aspects to generate additional values in their travel experiences rather than on how advanced or diversified they are (Mehraliyev et al., 2020).

Unlike the third research area (destination management), which mainly focuses on the roles of destinations, the fifth research area takes a broader perspective to consider co-creation activities amongst tourism stakeholders. However, most of the articles in this research area only focused on co-creation activities between tourists, tourism service providers, and destination management organizations. Different from staging a traditional travel experience, staging a smart travel experience involves more than simply tourists, tourism service providers, and destination management organizations, because many other stakeholders, such as urban planners (who are responsible for installing technology infrastructures), technological developers (who are responsible for designing smart tourism technologies), data companies (who are responsible for collecting, storing, and sharing touristic data), and residents, are also involved. Thus, their perspectives should be considered when investigating how a smart travel experience is co-created. Apart from understanding the concept of co-creation in a smart travel experience, the concept of co-destruction should also be understood. In the same way that an experience is collaboratively co-created, it can also be collaboratively co-destroyed during the process of cooperation (Echeverri & Skålén, 2011). Hence, a better understanding of the antecedents of co-destruction in a smart travel experience is required to complement the overoptimistic discussion on co-creation in the literature.

Theoretical and Methodological Contribution

In addition to identifying various research gaps in the existing smart travel experience literature, this study also makes three key contributions to the literature. First, unlike other smart tourism review studies that have taken a broader perspective to investigate the research trends in the smart tourism literature and treated smart travel experience as a sub-sector (e.g., Bastidas-Manzano et al., 2021; Mehraliyev et al., 2020), this study focused exclusively on reviewing articles relevant to smart travel experiences. This perspective is important not only because this research area has been a core outcome of the smart tourism development that has received the most academic attention (Bastidas-Manzano et al., 2021; Femenia-Serra & Ivars-Baidal, 2021), but also because existing review studies have overlooked smart tourism as a comprehensive experience design framework instead of a form of niche tourism (Boes et al., 2016). Hence, a specific focus on the experience concepts is needed to understand the knowledge development in this particular research area of the smart tourism literature (Kim & So, 2022).

Second, this study adopted three bibliographic analysis methods (i.e., citation analysis, co-citation analysis, and bibliographic coupling) to identify the knowledge domains and the current research areas in the smart travel experience research. While bibliographic studies on smart tourism have been conducted along with the rapidly increasing scholarship in this area, they mainly relied on co-citation analysis to identify current research areas. However, Zupic and Čater (2015) argue that co-citation analysis is more suitable for a set of old publications and less appropriate for recent research frontiers. The over-reliance on co-citation analysis may result in biases when capturing the current research areas (Galvagno & Giaccone, 2019). Hence, this study demonstrated that the integration of different bibliographic analysis methods may help discover the inner knowledge structure of the smart travel experience research, which contains many recent articles with a few or no citations, by triangulating the findings of co-citation analysis and bibliographic coupling analysis to draw conclusions (Most et al., 2018).

Lastly, since most review studies relied on the WoS and Scopus databases to select relevant smart tourism articles, the scope of their studies was mainly limited to top-ranked tourism journals. However, as an emerging field of study, smart tourism has generated extensive scholarly discussions in conference proceedings and newly developed journals that are usually low ranked. In addition to reviewing relevant articles from the WoS and Scopus databases, this study expanded the scope to the proceedings of a famous annual conference on smart tourism (i.e., ENTER) and a smart tourism-specific journal (i.e., Journal of Smart Tourism). As the results revealed, six of the 10 most cited articles were published in ENTER and three of the 23 articles published in 2021 were from the Journal of Smart Tourism. From a methodological standpoint, their strong contributions highlight the potential for other review studies to be misleading as they did not include these highly relevant articles in their bibliographic analysis.

Practical Implications

This study can serve as a reference for tourism and hospitality practitioners to comprehensively understand the theoretical development of a smart travel experience. Since many recent articles conceptualized a smart travel experience as the future of tourism development, smartness no longer has a value-added element in a travel experience. It is important for tourism and hospitality practitioners to recognize the importance of a smart travel experience, especially as tourists were found to value a low-touch, or even no-touch, travel experience more in the post-pandemic world (Lew et al., 2020). Besides, as shown in the bibliographic coupling analysis, supplier-focused studies remain scarce in the smart travel experience research. Hence, tourism and hospitality practitioners are encouraged to cooperate with academic researchers to better understand the practical and economic values of a smart travel experience.

Limitations and Future Research Directions

This study has three limitations that should be acknowledged. First, similar to other bibliographic reviews, the co-citation analysis and bibliographic coupling analysis may not truly reflect the influence of articles because self-citation and negative citation may create bias. Second, this study only reviewed articles written in the English language. Hence, the findings are largely limited to the English academic research community; however, Li and colleagues (2017) discovered that there is a gap between different academic research communities in terms of the conceptualization of smart tourism. Lastly, only articles published in tourism and hospitality journals were considered, given their high relevance to smart travel experiences. However, the smart travel experience is also a hot topic in other study fields, such as urban planning and computer science. Future studies should extend the coverage and compare results to identify the differences between study fields.

Supplemental Material

sj-docx-1-jht-10.1177_10963480221130998 – Supplemental material for Smart Travel Experiences: A Bibliometric Analysis of Knowledge Domains and Research Areas

Supplemental material, sj-docx-1-jht-10.1177_10963480221130998 for Smart Travel Experiences: A Bibliometric Analysis of Knowledge Domains and Research Areas by Wai Ching Wilson AU and Nelson K. F. TSANG in Journal of Hospitality & Tourism Research

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Wai Ching Wilson AU

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Wai Ching Wilson AU (corresponding author: wilsonwc.au@connect.polyu.hk) is a full-time PhD candidate at the School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong.

Nelson K. F. TSANG (nelson.tsang@polyu.edu.hk) is an associate professor at the School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong.

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