Abstract
In the fourth industrial revolution, the application or adoption of technology in the tourism and hospitality industry became mainstream and received significant attention from research scholars. But application of Systematic Literature Review (SLR) to elucidate the dynamics of technology in the tourism and hospitality industry has hardly been experimented in any prior research. Therefore, the literature gap pertaining to the nexus of technology and tourism industry is addressed in this chapter. Although the literature in tourism has extensively discussed technology application or adoption in tourism and hospitality industry, the research structure in this field remained scattered and fragmented. The current chapter is intended to bridge the gap and is attempted to achieve three goals. First, to understand the corpus of research on “Application and Adoption of technology in tourism and hospitality”, SLR is conducted only based on research articles extracted from Web of Science and Scopus on the specified topic using some specific search strings to unfold the corpus of research on use and adoption of technology in tourism and hospitality industry. To conduct the SLR, the study adopts four bibliometric analysis process named-co-citation, network visualization through co-occurrence data, multi-dimensional scaling, and hierarchical cluster analysis that shed light on the intellectual structure on technology application in tourism industry. Second, the chapter illuminates on how the technology shape tourism in a new form. Third, the chapter delineates the models and theories that were addressed in the previous studies to describe how technology is applied and adopted in the tourism industry. The findings of the study help the researchers and academicians to understand the research trends in this area and how knowledge structure has been evolved over the years.
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Keywords
- Technology
- Tourism
- Technology adoption
- Smart tourism
- Artificial Intelligence
- Augmented Reality (AR)
- Virtual Reality (VR)
- Robochat
Introduction
The fourth industrial revolution focuses on the exponential growth of emergingtechnologies which triggers customer-centric revolution in modern tech-based tourism business. Technologies such as Robotics, Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), Internet of Things (IoT), blockchain technology, fully automated vehicles, nano technology, quantum computing, fifth generation wireless technology and many other advanced technologies have affected the marketers’ strategies and tourists’ experience in tourism and hospitalityindustry (Car et al., 2019; Pencarelli, 2020). The rapid growth of information and communication technology, e-business platforms, digital marketing, social media marketing has effectively revolutionized the dynamics of tourism and hospitality industry (Herrero et al., 2018; Law et al., 2014; Moreno & Tejada, 2019). These technological advancement have brought unprecedented challenges and opportunities in operation, strategy setting, process innovation and process re-engineering throughout the industry (Law et al., 2014).The tourism and hospitality industry has been heavily revolutionized with the digital transformation and adoption of smart technologies (Law et al., 2014; Mariani, 2019). Companies operating under tourism industry are reaping benefits of technologies through improved customer experience by focusing on customers’ preferences (Pappas et al., 2021). Technology can facilitate the tourism industries to improve the efficiency, productivity, maximum reach, visibility, traceability, value co-creation and co-innovation (Pappas et al., 2021; Pencarelli, 2020). Conversely, such technological solution can boost the customer experiences by combining tourism product and services (Stankov & Gretzel, 2020), allowing access for information and inter modular transportation or by shaping the physical environment around the customers (Chung et al., 2017; Pappas et al., 2021).
The revolutionary impact of technology has been foresighted significantly in most of the industries and business (Leung, 2020; Osei et al., 2020; Sigala & Gretzel, 2017). Unlike other industries, technology has transformed the tourism and hospitality landscape altering previous operational and managerial techniques with a new sophisticated business model (Ivanov et al., 2020). Extant literature on application/adoption of technology in tourism industry ushers that researchers have emphasized on different aspects of technology usage in tourism and hospitality industry. Scholars focused on use of robotics or automation in tourism industry (Çakar & Aykol, 2020; Christou et al., 2020; de Kervenoael et al., 2020; Fusté-Forné, 2021; Hou et al., 2021; Ivkov et al., 2020; Kelly et al., 2019; Kim et al., 2021; Nam et al., 2020; Park, 2020; Zeng et al., 2020a, b; Zhong et al., 2020), Artificial Intelligence (Fusté-Forné, 2021; Kelly et al., 2019; Kim et al., 2021; Mingotto et al., 2020; Nam et al., 2020; Tuomi et al., 2020; Tussyadiah, 2020), smart tourism (Ivanov, 2019; Jeong & Shin, 2020; Lacka, 2020; Lerario & Varasano, 2020; Leung, 2019; Pai et al., 2020; Santos-Júnior et al., 2020; Shen et al., 2020a, b), Augmented Reality (AR) or Virtual Reality (Aluri, 2017; Bogicevic et al., 2019; Cranmer et al., 2020; Flavián et al., 2021; Han et al., 2019; Liang & Elliot, 2021; Jung et al., 2020; Kang, 2020; Kim et al., 2020; Kim & Hall, 2019; Lee et al., 2020; Lee & Kim, 2021; Lin et al., 2020; Lo & Cheng, 2020; Loureiro et al., 2020; Sun et al., 2021; Van et al., 2020; van Nuenen & Scarles, 2021; Walmsley & Kersten, 2020; Wei, 2019; Yuce et al., 2020; Yung et al., 2021; Yung & Khoo-Lattimore, 2019), use of digital platform or social media in tourism (Cheunkamon et al., 2020; Foroudi et al., 2020; Inversini & Masiero, 2014; Kovács et al., 2021; Kowalczyk-Anioł & Nowacki, 2020; Li et al., 2018; Li & Law, 2020; Lin et al., 2020; Mosweunyane et al., 2019; Munar & Jacobsen, 2014; Pateli et al., 2020; Sharmin et al., 2021; Shen et al., 2020a, b; Sigala, 2018; Singh & Srivastava, 2019; Sultan et al., 2021; Zhou et al., 2020), application of Blockchain in tourism (Bodkhe et al., 2020; Filimonau & Naumova, 2020; Kizildag et al., 2019; Nuryyev et al., 2020; Rashideh, 2020; Thees et al., 2020; Tyan et al., 2020), use of (ICT) Information and communication technology (Alabau-Montoya & Ruiz-Molina, 2020; Law et al., 2019; Xiang, 2018), mixed reality technology (Bae et al., 2020), mobile application (Briciu et al., 2020), application of Big Data in tourism and hospitality industry (Kubo et al., 2020; Li & Law, 2020; Rashideh, 2020; Stylos et al., 2021; Valls & Roca, 2021; Yallop & Seraphin, 2020; Zeng et al., 2020a, b; Zhang et al., 2020).
Previous studies on the application of technologies in tourism and hospitality usher that researches on this topic are diversified such as scholars addressed their research from tourists’ perspective or service provider’s perspective. It is evident that technology transformed the tourism and hospitality industry into a new paradigm. Although some of the studies have conducted review to the tourist’s perception on adoption of technology or firms challenges to adopt new business model but very few studies have addressed the technological impact in tourism industry from particular aspects only such as innovation research in hospitality and tourism (Lelo de Larrea et al., 2021); mobile technology (Law et al., 2018); technology adoption in fourth industrial revolution (Osei et al., 2020); tourism forecasting with internet data (Li et al., 2021); automation in tourism (Tussyadiah, 2020); use of ICT in restaurant (Moreno & Tejada, 2019), use of Blockchain (Bodkhe et al., 2020); adoption of user generated content (Ukpabi & Karjaluoto, 2018) and use of AR or VR in tourism industry (Loureiro et al., 2020; Wei, 2019).
Numerous studies have been investigated focusing exclusively on tourism industry practices or embracement of any particular technology in TourismIndustry. But, till the date, the field of Tourism research lacks to delineate a complete picture, where all the fourth revolution technologies will be enlightened in a single frame for their impact in tourism and hospitality industry through SLR. Furthermore, there is a dearth of knowledge that how technology alter the tourists’ travel experience and tourism service providers’ business strategy with new business model. Therefore, the present chapter is intended for systematic and quantitative evaluation of the past and present technology application research in tourism and hospitality industry for assessing the potential contributions of this flourishing research domain. This chapter answers two questions: first, what is the knowledge structure of technology adoption in tourism and hospitality industry?; second, how does technology impact tourism and hospitality industry?
By adopting SLR approach, the chapter contributes to the existing literature in some ways. First, the chapter provides a comprehensive research overview of the domain of technology adoption in tourism and hospitality industry over the last thirty years. To fulfill this research objectives, the study is carried out by extensive literature survey using different renowned database like Scopus, Science Direct, and web of Science. The systematic reviews “summarize in an explicit way what is known and not known about a specific practice related question” (Briner & Denyer, 2009, p. 19). The value of SLR can provide a clear understanding of the current state of the research regarding the topic being investigated in a single platform. Considering the knowledge expansion in this domain over the years, the target of this chapter is to present a comprehensive overview of the research conducted in the domain of technology adoption in tourism and hospitality industry and its reflection in literature. The chapter attempted to explore the historical basis of technologyadoption in tourism and hospitality domain. In general, as past and present studies influenced future research, this chapter helps the academician and practitioner to understand the trends of technological impact on tourism and hospitality industry in details.
Section 1.2 of this chapter presents the methodology used for SLR including data collection mechanisms from the publication database, the screening process and the data selection process. Section 1.3 and section 4 delineates the descriptive and thematic analysis respectively. The rest of the part is dedicated to conclusion and managerial and theoretical implication of this study.
Methods of Systematic Literature Review (SLR)
The study adopts SLR as it ensures the most efficient and high quality approach for exploring and evaluating extensive literature (Tranfield et al., 2003). SLR is used to evaluate and interpret the all-available research in a specific research area or phenomenon of interest where literature review can strengthen the foundation of research in a field of interest. SLR is a more authentic and verifiable source which incorporates a more comprehensive and unbiased search. According to Liliani et al. (2020), SLR establishes a defined procedure, and follows a strict review protocol to reduce researcher bias, and maintains the research process’s independence, while permitting exploration and discovery that aids in the development of knowledge. The basic difference between traditional narrative review and SLR exists is comprehensive and unbiased search (Tranfield et al., 2003). Extensive literature survey ushers that articles based on SLR can be in different forms such as Structured review articles with discussion on methods, theories discussed constructs that were used by the previous researchers and contexts and conditions of the study (Paul & Singh, 2017; Canabal & White, 2008); Conceptual framework based papers with further research directions (Paul & Criado, 2020), theoretical review paper (Paul & Rosad-Serrano, 2019), meta-analysis (Knoll & Matthes, 2017), bibliometric review (Randhawa et al., 2016). The study deploys SLR process consisting of data collection, data analysis and theme exploration using co-word analysis following the guidelines of Tranfield et al. (2003) and some extensively cited review articles (Rosad-Serrano et al., 2018; Keupp & Gassmann, 2009; Canabal & White, 2008).
Data Collection for SLR
The data collection for this study was carried out following the protocol as management review protocol is widely used to explore, discover and develop any research area by providing flexibility to the researchers to modify through the course of study, whilst it also ensure that reviews are less open to researcher’s bias (Tranfield et al., 2003). The data collection protocol used in this study includes a rigorous search strategy with some specific criterion for inclusion and exclusion of the articles to fulfill the objective of Systematic literature review. The search strategy incorporates some relevant steps such as identification and selection of relevant keywords and search terms, selection of database for searching the articles, further modification of keywords and applying search strategy (Liliani et al., 2020; Tranfield et al., 2003).
Data Searching Strategy
The study used Scopus and Web of Science for extracting dataset to get relevant research papers on “Adoption or Application of Technology in Tourism and Hospitality industry”. We used both Scopus and Web of Science as these are the reliable and widely accepted database and includes a wide range of peer reviewed journals in Business, Economics and management (Anees-Ur-Rehman et al., 2016; Foroudi et al., 2021; Fouroudi et al., 2020; Paul & Feliciano-Cestero, 2020). The search strategy deploys Boolean operators which includes some simple words (AND, OR, NOT or AND NOT) widely used to narrow or expand the search combining the keywords and search terms (Galvan & Galvan, 2017). Using Boolean operators in search produces a more usable search result, yet to saves time and efforts (Galvan & Galvan, 2017). The study employed some additional criterion in searching strategy to limit the results to peer reviewed academic articles within the selected databases. The reviewers have narrowed the datasets following some restrictions criterion. First, the search was limited to the publications which were peer reviewed articles. Second, the search was done only within the domain of application of technology in Tourism and HospitalityManagement and with the selected search strings such as Technology) OR (Augmented Reality) OR Virtual Reality OR Artificial Intelligence OR Big data OR ICT OR Social Media” AND “Tourism OR Hospitality OR Tourist Experience”. Third, the reviewers attempted to extract only articles which were published in scholarly journals in the selected domains. Fourth, the search was limited to English articles in all databases. Five, we screened all the abstracts of papers to decide on the contents of the articles that ultimacy helped to exclude some irrelevant articles. Finally, the data set was extracted from the database considering the relevance and context of the study and their connection with the research objectives. Initially search in different debases presented different amount publications such as Scopus 4441, web of science 323. After applying all selection criterion, we could finally select 989 from Scopus and 123 from web of science (Table 1.1).
Selection of Articles
Study selection or article selection from selected database refers to identification and selection of primary studies those are directly connected with research questions and objectives (Kitchenham & Charters, 2007). To reduce the bias and human error (Tranfield et al., 2003), the study adopted a systematic protocol that includes deciding on selection criteria, extraction methods, sorting duplicate articles among the databases, and selection of study based on title, abstract and keywords (Liliani et al., 2020; Tranfield et al., 2003). In this stage, the reviewers have gone through a rigorous process to select the most relevant articles to meet the objective of the study. After collection data from different data bases, the reviewers have gone through the full articles before inclusion and exclusion decision. In these circumstances, the writers debated and agreed on the decision to include or exclude. After deciding on conflicting and confusing articles, the finally selected articles were moved to a spreadsheet to filter the duplication of articles among the databases. After final section of articles from different databases, all the articles were moved to the spreadsheet of Scopus which directly imported from Scopus for further analysis through the VOSviewer. Two reviewers sincerely cheeked the title, keywords, and abstracts of all selected papers to ensure relevance of the papers with the objectives of the study. In selection through the title, the reviewers have focused on those papers that addressed the Technology and Tourism or Hospitality in the title of the papers. Next, keywords were reviewed ensure unification and consistency (i.e., singular/plural). In the full-text selection, all of the articles were read carefully focusing on Application of technology in Tourism Industry.
The application of data collection protocol, procedures of selection, and assessment criterion are depicted in Fig. 1.1.
The data collection and selection protocol. (Source: developed by the authors, 2021)
Merging Scopus and Web of Science Data and Removing Duplicates
After extracting the data from Scopus and Web of science, the data were merged to perform bibliometric analysis. We adopted PRISMA method for inclusion and exclusion of articles for this study. Using search strings, articles related to application of technology in tourism and hospitality were extracted from Scopus and Web of Science in bib text format. Extracted data set were compiled using text maker software, and two separate datasets were constructed for the analysis using R studio. Initially we converted both files into bibliographic framework. Then, data set for web of science was merged into Scopus data set and duplicates were removed through R programming.
Data Analysis Tools
The data analysis using SLR is conducted through descriptive analysis and thematic analysis after retrieving the data through different appraisal mechanism (Liliani et al., 2020). In the first part of the data analysis, the descriptive analysis is presented. According to (Tranfield et al., 2003) the descriptive analysis is used in SLR to depict the profile of the selected articles using some categories and classification scheme. The classification scheme is done based on the published articles and it categorizes the total articles into different segments like circulation of published articles in different journals, contexts, time periods, and countries; reports numerous policies and strategies for crisis period suggested by earlier studies; presents a set of theoretical standpoints within which those studies are explored. The second part of the analysis ushers some relevant theme. To explore the theme, the study uses Co-word analysis which is similar to the content analysis (Muñoz-Leiva et al., 2012) using the final data set. Co-word analysis has been applied as text mining to the articles’ titles, abstracts, and keywords (van Eck & Waltman, 2010). Using the VOSviewer software, the data set is analyzed to find the relevant themes. Multiple keywords that appear together in the same article are identified by co-word connections. The number of articles in which the keywords appear together determines the link between them (van Eck & Waltman, 2010). The interpretative approach was utilised to extract data from the collection and discover consensus or emerging topics in the thematic analysis (Tranfield et al., 2003). Plainly, this uses two analysis software like R programming and VOSviewer to analyze the extracted data.
Analysis Phase
The data set to carry out the bibliometric analysis has been prepared by merging the extracted data from Scopus and Web of Science (WoS) databases through R. Scopus and Web of science are more reliable, prestigious and widely accepted data base for the bibliometric studies (Aria & Cuccurullo, 2017; Foroudi et al., 2021; Fouroudi et al., 2020; Rey-Martí et al., 2016). Table 1.2 indicates the data information that are used in the bibliometric analysis. Figure 1.1 reveals years wise distribution of journal from 1991 to 2021 within the 30 years. The inclusion or research paper in 1991 reveals that application of technology in tourism is a mature topic in the tourism literature as scholars have focused the necessity of technology in tourism in 1991. Figure 1.2 also indicates that interest of scholars in research of application of technology in tourism and hospitality is increasing dramatically from last 5 years. The exponential growth of researchers’ interest on this topic has resulted highest number of articles such as 102,99,197 and 217 in the year of 2017, 2018, 2019, 2020, respectively.
Evolution of research in application of technology in tourism. (Source: developed by the authors using R Studio, 2021)
Bibliographic Coupling
In bibliographical coupling, two publications that refer to the same document are matched because they have a lot of shared references. It implies that the main publications have a lot of intellectual capital in common (Woerner & Wixom, 2015; Khanra et al., 2021; Shin & Perdue, 2019). Table 1.2 shows the most contributing authors, journals, organizations and countries in the sample of current based on the bibliographic coupling on the selected database. Table 1.2 reveals most 20 contributing authors, journals, countries and authors’ affiliation in terms of producing articles over the years.
Citation Analysis
After merging the data using R programming, we have analyzed the dataset to identify widely cited paper in technology and tourism area. We focused on the citation analysis because an in-depth analysis of authors’ citation provides a basic idea regarding the knowledge structure (Foroudi et al., 2021; Paul & Feliciano-Cestero, 2020) in technology and tourism research. We considered the total number of citations to select widely cited paper in this area. Table 1.3 and Fig. 1.3 indicated the highly cited paper. The analysis shows that the mostly cited paper focused on the progress of information technology in tourism sector (Table 1.4).
Citation analysis of the author. (Source: developed by the authors using VOSviewer, 2021)
Prestige Analysis
The influence and popularity of contributors to a publication as reported by bibliographic coupling and citation analyses, respectively, may not reflect its prestige ((Khanra et al., 2020). An enhanced PageRank algorithm can be used to investigate the prestige of publications (Khanra et al., 2020). Using an enhanced version of the PageRank algorithm, this technique identifies articles that are important in shaping a research domain (Khanra et al., 2020). This algorithm gives high priority to publications that have been cited by well-known publications (Khanra et al., 2021). Table 1.5 shows the top prestigious articles from a sample of 1181 articles that were identified and ranked according to their PageRank Score.
Co-authorship Analysis
Co-authorship analysis provides valuable insight into the collaborative networks that form the intellectual capital of a research subject (Khanra et al., 2020). The propensity of a group of coauthors to cite specific publications may have an effect on the literature on a particular topic (Caviggioli & Ughetto, 2019; Khanra et al., 2021).
Co-citation Analysis
It is known that citing or refereeing a pair of publication by two articles is considered as co-cited (Xu et al., 2018). Research papers which are often cited by other articles mostly discuss on similar topic (Hjørland, 2013). According to Hjørland (2013: p. 1314), in bibliometric analysis we use citation and co-citation indexes based on the assumption that “there are (normally) subject relations and semantic relations between citing and cited documents”. Co citation is considered as a method of exploratory data analysis (EDA) through which we can explore the data structure (Xu et al., 2018). Using the bibliometric package in R programming co-citation analysis was performed. Figures 1.4, 1.5, 1.6, and 1.7 present the co-citation map which is randomly generated by the software. The map represents 4 different clusters. Articles belonging to each cluster are connected to each other based on the theme of the articles. After extracting the data from Scopus and web of science, we merged the both data set and converted Web of science data into Scopus data frame using R software. In this previous section, we performed citation analysis using citation frequency to identify the highly cited paper (Samiee et al., 2015). We performed the co citation analysis based on Multi-dimensional scaling (MDS) method because MDS provides opportunity to researchers to screen the intellectual structure of the domain on which they are conducting the research (Foroudi et al., 2021; Samiee et al., 2015). In accordance with suggestions of researchers (Foroudi et al., 2021; Samiee et al., 2015), we conducted co-citation analysis to configure intellectual structure of research in technology and tourism research.The resulting co-citation map fragmented the total data set into four clusters. The clusters produced by the R programming indicates that papers within one cluster related to same theme. Extensive survey on these clusters ushers that clusters are all related to technology and tourism and focused on some specific domain (Table 1.6).
Network of authors from co-authorship analysis*. * minimum publications = 3; minimum citations = 10. (Source: developed by the authors using VOSviewer, 2021)
Network of authors from coauthorship analysis*. * minimum publications = 3; minimum citations = 10. (Source: developed by the authors using VOSviewer, 2021)
Network of authors from coauthorship analysis*. * minimum publications = 3; minimum citations = 10. Network of authors from co-authorship analysis*. * minimum publications = 3; minimum citations = 10 (source: developed by the authors using VOSviewer, 2021)
Co-citation Network based on author and MDS. (Source: developed by the authors using VOSviewer, 2021)
Thematic Synthesis of Technology and Tourism Research through Co-word Analysis
Co-word Analysis
A co-word analysis is a kind of content analysis (Kassarjian, 1977; Muñoz-Leiva et al., 2012). Co-word analysis converts a large number of descriptors (or keywords) to a collection of network graphs that effectively depict the strongest relationships between them. To detect the theme, the study adopts the guidelines of (He, 1999). The exploration of themes through the co-word analysis goes through some consecutive steps such as (1) raw data collection (2) selection of item types to analyze, (3) relevant information’s extraction from the raw data, (4) similarity calculation between items on the basis extracted information, and (5) a clustering algorithm use for detecting the themes. The study analyses the authors’ keywords and indexed keywords. Although (Courtial et al., 1993) initially proposed the co-word analysis method from detecting and representing the relationship between concepts from the selected research articles but later, many other authors like (He, 1999) used the co word analysis for theme detection from textual data. Co-word analysis is used to uncover themes within a specific field of study, as well as relationships between them, their relevance to a larger region, and the degree to which they are internally structured (Muñoz-Leiva et al., 2012). Conceptualization of research topic can be derived from the analysis of co-occurrences of keywords (Fahimnia et al., 2015). Co-word in the current study is performed using authors keywords and indexed keywords.
This section explores the themes and research domain on technologyapplication in tourism and hospitality industry. Along with quantitative approach to classify the literature review, we focus on the exploration of themes on that topic using authors’ keyword and keywords plus and visualizing those themes with the help of VOSviewer software (see www.vosviewer.com) (van Eck & Waltman, 2016; Foroudi et al., 2021). According to van Eck and Waltman (2016), and Foroudi et al. (2021), network visualization is a powerful and cutting edge method for representing the graphical representation of bibliometric analyses, enabling researchers to better understand and visualize the study domain. Using the text mining capabilities of VOSviewer software, we built a term map based on co-occurrence data in the most widely cited publication on technology in tourism and hospitality (see Fig. 1.8). Researchers can use the co-occurrence map to interpret study topics within a research domain, with the size of the circles indicating how many citations each keyword has received. Since closely related keywords in the same color are more likely to be related, they display a stronger correlation. According to Fig. 1.8, the most highly used keywords in this sample of this study are technology (82) times, Smart Tourism (66), ICT (54), Internet (51), Innovation (42), Social Media (46), Virtual Reality (34), Augmented Reality (30), E-tourism (46). Figure 1.8 and Table 1.7 usher that the keywords are segregated into different clusters. Keywords are that close and related are having stronger association among those keywords which have been visualized in the same color in the graph. Cindering the density of the keywords the followings’ themes have been explored.
Theme exploration using keywords. (Source: developed by the authors using VOSviewer, 2021)
Smart Tourism
Smart tourism is a novel approach dealing with new realities in tourism brought about by the effect of innovative information and communication technologies (ICTs) on destinations, travelers, and businesses (Tyan et al., 2020). According to Pencarelli (2020), the term “Smart Tourism“ refers to the efficient and effective use of technological, human, and social resources for pursuing sustainability principles to improve people’s quality of life in smart tourist locations while also increasing customers’ tourist experiences. The Smart Tourism destinations are places that use available technology to co-create value, pleasure, and experiences for visitors while also providing benefits and income to tourism organizations and destinations. Furthermore, the literature on Smart Tourism Destinations defines them as places that collect and analyze data in order to better understand tourists’ needs and behaviors and, as a result, can offer better services and experiences in a more context-aware and real-time manner (Xiang & Fesenmaier, 2017). In accordance with the study of (Huang et al., 2012; Tyan et al., 2020), it can be said that the true sense of Smart Tourism Destinations is to use ICTs to concentrate on and cater to tourists’ needs in order to improve tourism service efficiency and management.Smart Tourism has turned the tourism and hospitality industry into a new paradigm. It has brought changes in the tourist experience to service providers’ strtegies and ultimalty leading to a competitive smart industry gradually. This paradigm shift of toursim towards smart tourism is modifiying the concepts and languages what we are experiencing in the tourism and hospitality industry. According to (Pencarelli, 2020), everything is becoming “Smart” in tourism, and thus, we talk about smart travelers, smart cities, smart destinations, smart eyewear, smart cards, smart cars, and smart ecosystems, among other things. Its recommeded that smart tourism should be adopted considering the concept of smart citites so that it can improve the quality of life, better tourist experience and finally ensure sutanability.
Virtual Reality
“Virtual reality (VR) tourism, using the three-dimensional (3D) world of an innovative technology, is formed via a combination of visual, kinetic, and audio elements so that users can actually experience a real object from a tourist perspective” (Kim et al., 2020: p. 69). With the advancement of technology, VR tourism ensures a holistic picture of hotels and toursm destinations by reducing consumers’ risk as it familarize consumers with unfamilarses destinations (Kim et al., 2020). Planning and management, heritage protection, marketing, accessibility, education, and entertainment are the six key tourism-related fields where VR has been used extensivly (Guttentag, 2010; Jude & Ukekwe, 2020; Yuce et al., 2020). VR appraoch has also been used to bridge information gap between tourist and sevice provider (Caron-Fasan et al., 2020). Thus, VR has reshapesd the tourism industry by providing new customer experience.
Social Media in Tourism Industry
Social Media anoher significant blessings of technology in this era. According to (Kim & Law, 2015), promoting tourism acitivities through the social media refers to connecting, partnering and sharing of visual and conetent material with social media among the users of online communities. Social media provides different types of benefits to both tourists and tourism service providers by faciltaing interaction among the customer or between customers and service providers. It offers entertainment and trendimess through different types of comments. Considerning the necessity of adopting social media, tourism and hospitality also embraced this blessings into this industry.Social media has dramatically changed the way of creating and sharing information (Buhalis & Foerste, 2015). Both customers and service providers can share ideas direclty with their target segemnt. According to Hays et al. (2013), social media refers to the practises, activities, and behaviours of online communities of individuals who use conversational media to share knowledge, information, and ideas. Conversational media are web-based apps that allow users to quickly produce and share material in the form of images, words, videos, and audios.
Application of Internet of Things (IoT) in Tourism
According to Sethi and Sarangi (2017), the IoT is a paradigm in which devices with sensors, actuators, and processors connect with one another in order to accomplish a meaningful goal. Tourism and hospitaility industry has already adopted the IoT in different arena of tourism such as, Geo-location technologies, Medical (wellness) tourism, Ecotourism, Retail tourism services, Virtual concierge, airport management (Car et al., 2019), through the geolocation technologies tourist an have a good travel experience as it provides to tract different location from route planning to accommodation arrangement and journey planning. IoT has a profound impact on medical tourism as using IoT technologies, patients’ health can be monitored from a remote palce (Car et al., 2019). Besides this, IoT has brought a dramatic change in the tourist travel experiences by introucing different types of apps.
Theoretical Background in Technology-Tourism Research
Systematic literature reviews on the topic of technologyapplication in tourism and hospitality industry revels that various theories have been utilized to study the topic of this study. Theories like Technology Adoption Model (TAM), Technology Organization and Environment Model (TOE), Stimuli Organism and Response Theory (SOR), Innovation diffusion model (DIT), Task technology fit (TTF) theory and applied the unified theory of acceptance and use of technology (UTAUT) have been widely used theories in technology and tourism research. The table summarizes the widely used theories along with corresponding studies in brief (Table 1.8).
Conclusion
The study uses four bibliometric analysis processes to conduct the SLR: co-citation, network visualisation through co-occurrence data, multi-dimensional scaling, and hierarchical cluster analysis, which illuminates the intellectual structure of technology application in the tourism industry. Second, the chapter discusses how technology is influencing tourism in a new way. Third, the chapter describes how technology is implemented and embraced in the tourism business, based on the models and ideas discussed in prior research. The study’s findings show that the Fourth industrial revolution technologies and ubiquitous usage have been rapidly infiltrating every aspect of the tourism industry. At present era, technologies are not merely considered instrumental in mitigating communication gap, rather technologies act as a transformative driver which influence industry operation and also tailor functions and expectations of stakeholders (Sigala, 2018). Rapid technological advancements introduce new terms in the same domain such as VR, Smart Tourism, IoT in tourism and Social media in tourism causing fundamental disruptions through empowering tourism actors to form new market, management practices, competitive advantages and offerings. As a result, technology is transforming tourism management and marketing from a static and utilitarian perspective (in which managers and tourists use technology as tools) to a transformative perspective (in which tourism markets and actors (tourism providers, stakeholders, intermediaries, and tourists) shape and are shaped by technology). The wide array of technological innovations are also expected to alter future outllook of the tourism sector. Therefore, it has become unequivocal stating that success of future tourism business will require a proper blend of smarter customer experience and technologyapplications that are unobtrusive and which pose no ethical, moral or privacy concerns. Developed countries are technologically sufficient to facilitate the tourism industry, and developing countries follow the same footprints as their developed counterparts. Moreover, developing countries promote their tourism attractions to the developed countries. Consequently, developing countries are positioning themselves in top list in global market share in world tourism leaving behind America and Europe. Government can play pivotal role through initiatives such as adequate infrastructure, financial support, leadership that will open up strategic window for sustainable tourism in the long run. Furthermore,repositioning of the tourist destinations, exploiting emerging opportunities and making the best use of local products and service may be few strategies to attract travellers. Institutional support is also required to anticipate and manage the negative cultural, social, environmental and economic problems arising from tourism and promote smart tourism with best customer experience towards the globe.
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Hossain, M.R., Akhter, F., Sharma, A., Hassan, A. (2022). Thirty Years of Research on Application of Technology in Tourism and Hospitality Industry: A Systematic Literature Review. In: Hassan, A. (eds) Technology Application in Tourism in Asia. Springer, Singapore. https://doi.org/10.1007/978-981-16-5461-9_1
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