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• Review Article
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• Published: 07 October 2023

A ten-year review analysis of the impact of digitization on tourism development (2012–2022)

• Chunyu Jiang   ORCID: orcid.org/0000-0002-6072-8365 1 &
• Seuk Wai Phoong   ORCID: orcid.org/0000-0002-9925-0901 1  

Humanities and Social Sciences Communications volume  10 , Article number:  665 ( 2023 ) Cite this article

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• Development studies
• Science, technology and society

Many tourism-related activities have been suspended due to the nationally enforced lockdown to combat the Coronavirus pandemic. The tourism industry suffered immensely from the lockdown, and as a result of this, digital tourism began gaining traction and attracted public attention. This study analyses the impact of digitalization on the social and economic sustainability of the tourism industry via systematic literature network analysis. The findings indicated that digitalization impacts economic sustainability, encompassing economic benefits in tourism product development, tourism consumption, and industrial development. Moreover, digitalization fosters social development, cultural awareness, and tourism participation in digital technology and cultural heritage. This study identified publication trends and research hotspots using bibliometric analysis, and it was confirmed that Sustainability was the top journal in published digital and tourism sustainability-related articles, followed by the International Journal of Tourism Research, Tourism Management , and Current Issues in Tourism . This study resulted in two implications: identifying the knowledge gap and evidence-based decision-making based on the (previous) literature. Recommendation for future research is also discussed in this study, which is helpful to policymakers, tourism planners, and researchers to develop strategies grounded in research.

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Introduction.

From 2019 through 2022, the Coronavirus disease 2019 (COVID-19) wreaked havoc on the world’s tourism business (Navarro-Drazich and Lorenzo, 2021 ). Tourism contributes to many nations’ gross domestic product (GDP) as it is intertwined with various industries (Gössling et al., 2017 ). Examples of tourism products include lodging options such as hotels and Airbnb. Food and drink, theme parks, museum visits, and fashion items such as clothes and bags are additional examples of tourism products that boost the economic health of the individual and the nation.

Tourism is regarded as a complex service-driven industry, one of the characteristics of which is that if external influences disrupt the tourism sector, other industries linked to it will also be directly affected. Tourism development refers to creating and maintaining the tourism industry in a particular location and is closely linked to economic and social progress (Telfer and Sharpley, 2015 ). Over the past four decades, global tourism development has reported intense growth performance and research on tourism development (Capocchi et al., 2019 ). Kreishan ( 2010 ) posited that the impact of tourism development on destination development is a commonly discussed issue, particularly in terms of tourism development improving economic efficiency and local competitiveness. The growth of tourism currently is significant not only from an economic perspective but also from a social perspective, as evidenced by the optimization of the local social structure (Yang et al., 2021 ), increased community participation (W. Li, 2006 ), participation of women (Ferguson, 2011 ), and increased cultural awareness (Carbone, 2017 ). Also, the development of the tourism industry benefits the environment by increasing environmental protection awareness and providing greater funding for initiatives to conserve resources and the environment (Zhao and Li, 2018 ).

However, unmanaged over-tourism can cause serious harm, according to Berselli et al. ( 2022 ). From an economic standpoint, excessive tourism can result in higher prices and imbalanced industrial structure development, which lowers industries’ overall resilience. Social issues arising from over-tourism include the commercialization of culture (Wang et al., 2019 ), the shift in locals’ attitudes from friendliness to hostility towards tourists (Kim and Kang, 2020 ), and the emergence of on-stage authenticity (Taylor, 2001 ). In terms of the environment, issues such as excessive carbon emissions causing global warming (Liu et al., 2022 ), damage to water and soil resources, destruction to flora and fauna (Gössling and Hall, 2006 ), and even harm to cultural heritage (Zhang et al., 2015 ) are some of the effects of over-tourism. Since the development of the tourism industry combines economic, social, and cultural phenomena, as well as the past COVID-19 disruptions, the industry’s suspension for several years presents a significant opportunity for all stakeholders to reposition tourism for sustainable development.

Some studies suggest the tourism industry will recover after COVID-19 (Zhong et al., 2021 ). However, given the abovementioned problems caused by over-tourism, what needs to be considered is the sustainability of the tourism industry post-COVID-19. Researchers and tourism stakeholders are becoming more aware of the importance of the concept of sustainable development (Miceli et al., 2021 ), especially since COVID-19, as the tourism or hospitality industry remains one of the least developed sectors in terms of sustainable tourism practices (Kim and Park, 2017 ). Korstanje and George ( 2020 ) noted that over-tourism is a chronic disease that mere temporary changes cannot treat; it can be minimized via education and training to raise awareness. The tourism industry needs to rethink how to develop in a sustainable and healthy direction (Higgins-Desbiolles et al., 2019 ), not only in terms of ecotourism or green tourism but also in terms of putting the concept of sustainability into practice at a deeper level as it faces multiple pressures and challenges of an overarching environment, economy, and society.

Sustainability is often cited as one of the reasons for improved competitiveness among different tourism destinations (Han et al., 2019 ). The United Nations 2030 (UN, 2030 ) Agenda for Sustainable Development has developed a Sustainable Development Goals (SDGs) plan, defined as a set of global goals for fair and sustainable health at every level, from the planetary biosphere to the local community. The aim is to end poverty, protect the planet, and ensure that everyone enjoys peace and prosperity now and in the future. The basic concept is that productivity can be preserved for future generations. Due to the general emphasis of the United Nations World Tourism Organization (UNWTO) on sustainable tourism and the industry’s economic importance, the SDGs and its associated millennium development goals (MDGs) have become critical elements for research into tourism’s contribution to sustainable development and overall sustainability (Saarinen et al., 2011 ; Saarinen and Rogerson, 2014 ). Winter et al. ( 2020 ) indicated that as sustainable tourism development needs to take complete account of the combined social, economic, and environmental impacts, stakeholders are expected to integrate scientific management and practice for future sustainability using updated and innovative technologies that can provide more tourism opportunities for groups unable to travel directly while enhancing environmentally-friendly behavior. Bramwell and Lane ( 2011 ) suggested that effective policy support is also crucial to implementing sustainable tourism development, as the path to sustainable development is guided and monitored by excellent and progressive policies. From a postmodernist perspective, social media and place brand authenticity in smart tourism are essential to place trust, place identity, and place brand image, while the development of this brand authenticity is one of the critical indicators of the visitor experience (Handayani and Korstanje, 2017 ). As a result, Korstanje et al. ( 2022 ) contended that new paradigms and strategies must be created to confront risks to tourism in the 21st century and satisfy the SDGs by 2030.

Several studies are underway to determine the impact of various programs and strategies on the environmental component of sustainability practices (Goralski and Tan, 2020 ). Yalina and Rozas ( 2020 ) suggested that a digital workplace can promote environmental sustainability. Although there have been studies on the digitalization of tourism and environmental sustainability, such as Loureiro and Nascimento ( 2021 ), who reviewed digital technology on the sustainability of tourism using bibliometric methods, there is a need for a thorough examination of the impact of digital transformation on sustainable tourism growth, particularly in terms of economic and social dimensions (Feroz et al., 2021 ). Therefore, the objective of this study is to review the impact of tourism digital technology development on the economic and social sustainability of tourism development to offer future research guidance.

With the growing literature and the emergence of cross-disciplinary research related to sustainability and digitalization in tourism development, it is critical to analyze the changes in its research, summarize the focus of previous research content, and predict future research prospects. As a result, this study will address the above research gaps by answering the following three questions.

RQ1: What are the prominent documents, authors, sources, organizations, and keywords in digitalization for the economic and social sustainability of tourism development?

RQ2: What are the linkages based on bibliographic coupling, co-authorship, co-occurrence, and citation in digitalization for the economic and social sustainability of tourism development?

RQ3: What is the future research agenda based on the results of this study?

Literature review

Several review papers on tourism research are now available and relevant to this study. Ülker et al. ( 2023 ) assumed that there are currently 136 bibliometric studies in the tourism and hospitality industry, of which the literature review studies on overall trends in the tourism and hospitality industry are continuously being updated (Chang and Katrichis, 2016 ; Wang et al., 2023 ). Also, economic development in the tourism industry (Comerio and Strozzi, 2019 ), tourism marketing (Mwinuka, 2017 ), tourism and education (Goh and King, 2020 ), hospitality (Manoharan and Singal, 2017 ), Airbnb (Andreu et al., 2020 ), and even COVID-19 review articles related to tourism development are available (Bhatia et al., 2022 ).

With the emergence of cross-disciplinary digital-related technologies, the link between tourism and digitalization has become one of the hot topics of research, and as a result, several literature review articles on digitalization and tourism have been published, such as on robotics (Buhalis and Cheng, 2020 ; Ivanov et al., 2019 ; Pizam et al., 2022 ), ICT (Buhalis and Law, 2008 ; Law et al., 2014 ), big data (Li et al., 2018 ; Stylos et al., 2021 ), smart tourism (Buhalis, 2020 ; Mehraliyev et al., 2020 ), social media (Buhalis and Inversini, 2014 ; Mirzaalian and Halpenny, 2019 ), eye-tracking (Muñoz-Leiva et al., 2019 ; Scott et al., 2019 ), AI (Buhalis and Moldavska, 2022 ; Doborjeh et al., 2022 ; Dwivedi et al., 2023 ), VR (Koohang et al., 2023 ; Wei, 2019 ), AR (Jingen Liang and Elliot, 2021 ; Tscheu and Buhalis, 2016 ; Yovcheva et al., 2012 ), MR (Buhalis and Karatay, 2022 ), and the Metaverse (Ahuja et al., 2023 ; Buhalis et al., 2022 , 2023 ; Go and Kang, 2023 ).

Due to the rise of sustainability research, the literature review on sustainability research in tourism has seen a stark increase (León-Gómez et al., 2021 ; Ruhanen et al., 2018 ; Streimikiene et al., 2021 ). The proliferation of studies related to digitalization and sustainable tourism development has led to a considerable number of review articles (Elkhwesky et al., 2022 ; Gössling, 2017 ; Loureiro and Nascimento, 2021 ; Nascimento and Loureiro, 2022 ; Rahmadian et al., 2022 ). Feroz et al. ( 2021 ) conducted a literature study on the environmental aspects of tourism sustainability and digitalization; however, there is a distinct lack of studies on the economic and social dimensions.

Therefore, the study’s unique value is that it presents the first literature review in the field of digitalization and social and tourism economic sustainability development using a novel method of systematic literature network analysis (SLNA), filling a gap in the literature review landscape and addressing the need for more comprehensive, detailed, and up-to-date research endeavor.

Methodology

Colicchia and Strozzi ( 2012 ) proposed a systematic literature review analysis (SLNA) to identify past research trends more sophisticatedly, integrated, and scientifically. This method is currently used in reviews of sustainable development research (Afeltra et al., 2021 ) but is rarely used in reviews of tourism sustainability; therefore, SLNA is used in this study.

Systematic literature review (SLR) and bibliographic network analysis (BNA) are the two phases of SLNA. These actions comprise the first phase of SLR, which includes choosing the study’s final selection, conducting a dialectical examination of the most pertinent articles, and evaluating the results. Next, citation analysis and bibliographic coupling of BNA are also included in this paper to investigate the relationship between the previous literature, assess the research trends, and aid in uncovering future research innovation opportunities. Bibliographic coupling is a scientific mapping technique regarding two articles with a common citation contentedly comparable. This technique permits the segmentation of publications into thematic clusters utilizing published references to understand the most recent developments in current research issues (Donthu et al., 2021 ). Citation analysis reveals which papers are influential and their authors and journals and aids in comprehending what past literature has contributed (Pilkington and Meredith, 2009 ).

First phase: systematic literature review (SLR)

Introduction of slr.

The most widely used and reputable databases are the Web of Science (WOS) and Scopus (Garrigos-Simon et al., 2018 ); thus, both were used in this study to eliminate data search omissions, broaden the search scope, and improve the accuracy of data outputs.

Figure 1 shows the flow diagram for systematic bibliometric analysis. Firstly, this paper takes “virtual reality or augmented reality or artificial intelligence or big data or mobile technology or internet of technology or social platform technology) and (sustainable tourism development or sustainability of tourism or green tourism or ecotourism” as keywords. The search process began by searching topics (including article titles, abstracts, and keywords). The language of the articles was set to English and had to be published between 2012 and 2022. The search process resulted in 91 articles. The data were extracted on February 15, 2022, per Fig. 2 .

This figure shows the overall process of this study from database selection until suggestions for future research. Source: Own elaboration.

The criteria and steps used to identify the selected target literature are explained in this diagram. Source: Own elaboration.

A review article with scholarly worth and contribution is required to describe the literature’s links and contents and examine and critique it precisely (Hart, 2018 ). As seen in Fig. 3 , the following research topics are divided into two categories: economic sustainability (which includes topics such as economic benefit, industry development, and tourist consumption) and social sustainability (which includes topics such as tourist behavior, social development, cultural awareness, and participation).

The research topics are divided into two categories: economic sustainability (which includes topics such as economic benefit, industry development, and tourist consumption) and social sustainability (which includes topics such as tourist behavior, social development, cultural awareness, and participation. Source: Own elaboration.

The SLRs are used to locate, appraise, and synthesize existing, completed, and documented work (Cocchia, 2014 ), facilitating classification and summarization, particularly for micro-profiling within macro-level fields of study.

Digitalization’s impact on economic sustainability

Digitalization’s impact on economic benefits.

Adequately improving the economic development of tourism is also one of the sustainable needs for developing tourism. At a time when tourism has been devastated by COVID-19, the tourism industry has almost ceased to exist. Therefore, one of the most popular research topics is maintaining substantial economic benefits while allowing the tourism industry to flourish sustainably.

Digital technology has piqued researchers’ interest due to its potential benefit to the tourism industry. Technologies that directly improve the economic situation are classed as economic benefits, and per many studies, digitization positively impacts local economic development and may bring objective revenue to tourism (Tables 1 – 7 ).

Digital technology promotes economic development. The growth of information communication technologies (ICT) positively impacts China’s tourism industry while promoting economic growth (Shehzad et al., 2019 ). As a rapidly evolving digital technology, mobile technology has significantly minimized asymmetric information, enhanced local GDP growth, and increased citizens’ financial capital through tourism (Kim and Kim, 2017 ; Phoong et al., 2022 ). Technologies such as 3D virtual, mixed reality (MR), virtual reality (VR), or augmented reality (AR) applied in heritage tourism can effectively increase local economic income and the added value of tourism (Manglis et al., 2021 ; Martinez-Grana et al., 2019 ). Furthermore, marketing tools such as small programs and network technologies confer several advantages to tourism stakeholders, such as the ability to help local communities contribute value and support the tourism economy (Caciora et al., 2021 ; Lin et al., 2020a , b ). Also, smart heritage city tourism technology tools can drive the tourism economy to inaccessible areas (Gomez-Oliva et al., 2019 ).

The increase in income is proportional to increased economic benefits. ICT is often used in the tourism industry, which has an essential impact on the tourism service industry, one of which is the improvement of tourism income (Gomez-Oliva et al., 2019 ; Koukopoulos and Koukopoulos, 2019 ). Virtual tourism technologies, such as AR and VR, are digital tools that can help overcome cultural heritage tourism challenges, such as reviving the tourism industry and resolving funding shortages (Lu et al., 2022 ). Mobile money, such as electronic traveler’s checks and credit cards, can assist low-income people in taking advantage of their marginal savings and encourage implementing a cashless economy for tourism sustainability (Singh, 2017 ).

Second, digital marketing technologies are frequently utilized by hotels to improve hotel performance, which increases profit (Theocharidis et al., 2020 ; Vitezic et al., 2015 ). Another example is Muslim-friendly apps promoting the international trade of products during the tourism process (Cuesta-Valiño et al., 2020 ),

Digitalization’s impact on tourism industrial development

Technological development has driven the tourism industry in local tourist cities, organizations, businesses, and governments. From the perspective of industrial market development, ICT, extensive data network marketing, and other virtual tourism technologies can create market development potential and improve market positioning for companies (Ammirato et al., 2021 ; Filipiak et al., 2020 ; Ma et al., 2021 ).

Adopting and applying information in the tourism industry are commonly regarded as a source of corporate innovation. The implementation of ICT can increase the profitability of tourism enterprises while also increasing organizational productivity (Croitoru and Manoliu, 2016 ; De Lucia et al., 2021 ; Duy et al., 2020 ; Obonyo et al., 2018 ). VR, AR, 3D digital technology, and mobile technology can all be used to improve a company’s performance and competitiveness in the tourism industry (Cranmer et al., 2021 ; Koukopoulos and Koukopoulos, 2018 ; Pavlidis et al., 2022 ; Yuce et al., 2020 ), and these technologies have made significant economic contribution to economic sustainability.

The application and implementation of ICT play an essential role in developing the tourism industry (Adeola and Evans, 2020 ; Tan et al., 2019 ; Zhou and Sotiriadis, 2021 ). Also, digital advanced technologies, such as MR technology adopted by museums, AR technology adopted by destinations, and smart tourism products and tourism ecological reservation systems have made significant contributions in the front-end development stage, providing opportunities to monitor the future development of tourism, as well as being beneficial to the formulation and implementation of tourism industry strategies at later stages (Graziano and Privitera, 2020 ; Tsai et al., 2018 ). The abovementioned electronic environment is an excellent lubricant for tourism’s active and healthy development (Maiorescu et al., 2016 ). Moreover, apps can help customers understand legacy cities more from the standpoint of heritage preservation and help cities promote tourist city development (Briciu et al., 2020 ).

From the perspective of products sold and variations in product types, online services in Muslim-friendly apps can be helpful for market segmentation and promotion of product positioning and sales (Cuesta-Valiño et al., 2020 ). Furthermore, virtual multi-sensory technologies can improve the company’s potential, increase public awareness, and sell products (Martins et al., 2017 ). Undeniably, the development of digitalization enriches the cultural service products of museums in developing heritage tourism (Palumbo, 2021 ), and AR technology also increases the diversification of products in water tourism (Kaźmierczak et al., 2021 ).

Digitalization’s impact on tourism consumption

Tourists’ spending power can reflect the overall economic development of the tourism industry as one of the contributing variables, and the number of tourists and the value of tourist flow are two measurement criteria of tourism consumer spending. Tourism apps, for example, can make traveling more convenient for tourists, increasing tourism consumption (Lin et al., 2020a , b ). Virtual tourism products or augmented reality technology allow tourists to spend more leisure time, increasing consumption (da Silva, 2021 ; Pehlivanides et al., 2020 ).

The application of virtual tourism technology is also helpful in improving the attractiveness of tourists (Cai et al., 2021 ; Manglis et al., 2021 ; Martins et al., 2017 ). Meanwhile, big data analytic tools, e-marketing (WOM), and mobile applications positively influence customers’ intention to travel and contribute to improving tourism sustainability (Gajdosik, 2019 ; Kim and Chang, 2020 ; Pica et al., 2018 ). With the application and construction of ICT, the demand for tourism has increased, and the number of tourists has also increased (Adeola and Evans, 2020 ; Kabassi, 2017 ; Kumar and Kumar, 2020 ). In addition to enhancing tourists’ imagination, virtual tourism technology and 3D digital technology can also be used as practical tools to further develop tourism and increase the number and flow of tourists (Bae et al., 2020 ; Graziano and Privitera, 2020 ; Pavlidis et al., 2022 ). Word-of-mouth marketing has increased the number of tourists (Fernandez-Lores et al., 2022 ; Wang et al., 2020 ).

Digitalization’s impact on social sustainability

Digitalization’s impact on tourist behavior.

Virtual tourism technology is gradually being implemented in the tourism industry, focusing on increasing the satisfaction of the elderly and disabled (Lu et al., 2022 ). Artificial intelligence and virtual reality are integrated into human-computer interaction system equipment, boosting service quality and increasing tourist satisfaction (Van et al., 2020 ). The mixed experience helps enrich tourists’ feelings about the surroundings, thereby boosting tourists’ contentment (Bae et al., 2020 ), and the succinct information and dependable system offered by VR can promote tourists’ satisfaction (Yuce et al., 2020 ). 3D digital technology to build innovative and appealing tourism items can help boost consumer satisfaction and positive feedback (Pavlidis et al., 2022 ).

Tourism stakeholders’ use of tourism apps is critical to increasing tourist satisfaction (Lin et al., 2020a , b ). For example, tourism management in Ho Chi Minh City’s use of Web 4.0 can increase customer satisfaction and loyalty in the long run (Duy et al., 2020 ). The mobile usability and ease of use of social media as a suitable medium directly impact satisfaction (Sharmin et al., 2021 ). It can also serve as a platform for tourists to communicate and contribute to increased satisfaction (Jamshidi et al., 2021 ). Simultaneously, tourism safety is an essential factor that influences tourist satisfaction, and the use of closed-circuit television (CCTV) and unmanned aerial vehicles (UAVs) can help to improve tourism safety (Ko and Song, 2021 ). The use of mobile technologies and payment mechanisms in the tourism process is also a fascinating study. Through electronic technology, two-dimensional code payment techniques improve tourists’ pleasure (Lou et al., 2017 ). Furthermore, incorporating digital innovation into hotel management structures increases hotel performance and client satisfaction (Vitezic et al., 2015 ).

Tourism satisfaction is directly related to tourism experience, and tourism experience is one of the most important criteria to measure in the tourism process. The findings suggest that using virtual immersion technologies such as AR, VR, and MR in the tourism process can significantly improve the tourist experience (Bae et al., 2020 ; Fernandez-Lores et al., 2022 ; Franco and Mota, 2021 ; Lee and Kim, 2021 ; Yin et al., 2021 ).

Additionally, the intention and motivation of tourism drive tourism behavior from the psychological aspect. Digital innovative technology can boost tourists’ interest in tourism products and locations, enrich their understanding of tourism culture, attract more tourists, enhance tourists’ preferences, and strengthen their desire to visit (Caciora et al., 2021 ; Cranmer et al., 2021 ; Gajdosik, 2019 ; Kang, 2020 ; Kaźmierczak et al., 2021 ; Manglis et al., 2021 ; Monterroso-Checa et al., 2020 ;). Digital marketing tools can ramp up customers’ desires and habits (Theocharidis et al., 2020 ), and digital mobile programs can increase tourists’ attention, influencing their overall view of the tourism experience (Wang et al., 2020 ). Big data can also be utilized to foresee client wants and expectations, allowing for a better understanding of customer needs (Del Vecchio et al., 2018 ). For example, Internet of Things technology can scientifically guide and divert tourists to alleviate the problem of local saturation and overload in scenic sites, thus improving the tourist experience (Xie and Zhang, 2021 ). It can also provide various cultural tourism content to enhance and support the experience of active tourists (Ammirato et al., 2021 ).

Digitalization’s impact on social development

Tourism planners and governments can use the analytic hierarchy process (AHP) and geographic information system-remote sensing (GIS-RS) technology to accurately select sites, develop eco-tourism activities, relieve the burden of tourism in the region, and thus help the locals create new employment opportunities (Chaudhary et al., n.d. ). Virtual tourism technology, such as AR, can also aid in analyzing tourist flow and conditions, improve safety, and expand job chances (Franco and Mota, 2021 ). Advances and innovations in tourism ICT can benefit enterprises enough to increase job prospects (De Lucia et al., 2021 ). Virtual tourism, ICT, mobile technology, smart heritage tourism technology, and innovative marketing methods improve stakeholders’ quality of life, increasing the tourism system and community awareness (Lemmi and Deri, 2020 ).

Digitalization’s impact on cultural awareness

Virtual technologies, such as AR, VR, and mobile augmented reality (MAR), are now widely used in cultural heritage tourism, with the potential to protect cultural heritages and enhance the potential of heritage management, thereby contributing to cultural communication (Bec et al., 2019 ; Caciora et al., 2021 ; Graziano and Privitera, 2020 ). Some studies indicate that online engagement platforms, mobile application technologies, and smart tourism models can all support the socially sustainable growth of culture (Bonacini et al., 2018 ; Pica et al., 2018 ; Zubiaga et al., 2019 ).

AR, VR, and other techniques can promote tourists’ behavior in underwater cultural tourism and raise public awareness of natural heritage protection among tourists (Manglis et al., 2021 ). Research on low-carbon travel modes is frequently concerned with tourism sustainability, and big data marketing technology can supply tourists with more low-carbon transport schemes, thus increasing tourists’ environmental consciousness (Ma et al., 2021 ). As a common medium for cultural communication, social media can raise tourists’ awareness of environmental protection (Haque et al., 2021 ).

Digitalization’s impact on participation

Tourists’ active participation in cultural heritage can be enhanced by digital technology, as can people’s feeling of belonging and responsibility to society (Koukopoulos and Koukopoulos, 2019 ; Permatasari et al., 2020 ). Virtual technology can also encourage public participation in preserving and promoting cultural heritages (Caciora et al., 2021 ), while digital media can help tourism businesses improve public relations and social participation (Camilleri, 2018 ; Haque et al., 2021 ). Increased smart tourism destinations optimize the potential for these communities to involve the destinations’ residents and impact their lives due to the improved urban tourism experience.

Stakeholders are closely linked to the sustainable development of tourism. Innovative applications of digital technology can better manage destination stakeholders, strengthening their linkages (Camilleri, 2018 ), help promote their participation in the development of tourist destinations (del Vecchio et al., n.d. ; Gajdosik, 2019 ), and create a democratic and sustainable system when promoting cultural heritage, which balances the opinions of different stakeholders.

The interactive network platform empowers local communities and encourages local inhabitants and tourists to communicate, which promotes the healthy growth of resident-tourism relationships (Dionisio et al., 2019 ). Also, ICT tourism apps influence the ultimate perception of older tourists’ travel experiences, stimulate tourists’ interest in world cultural heritage sites (WCHS), and increase contact and understanding between tourists and destinations (Ramos-Soler et al., 2019 ). Social media can help tourists increase their knowledge of environmental protection, which increases the participation of tourists and citizens and helps formulate sustainable goals (Haque et al., 2021 ).

Second phase: bibliographic network analysis (BNA)

The VOSviewer is the analysis tool used in this work to visualize the impact of digital technology on sustainable tourism development in economic and social aspects. VOSviewer employs the visualization of similarities (VOS) mapping approach to create a map (Moya‐Anegón et al., 2007 ).

Bibliographic coupling network of sources

Bibliographic coupling analysis mainly measures the similarity of documents by the number of identical references cited by documents. Although co-citation refers to the appearance of two documents in the same reference list, bibliographic coupling refers to the number of references that a group of papers share; for example, paper A and paper B are coupled if they both cite document C (Garrigos-Simon et al., 2018 ). In other words, bibliographic coupling happens when two documents quote the same document (Phoong et al., 2022 ; Mulet-Forteza et al., 2018 ), demonstrating the power of one publication in comparison to a group of others (Cavalcante et al., 2021 ). It should be pointed out that the size of the sphere represents the number of similar citations. This paper analyzes the bibliographic coupling network of sources, and the findings are summarized in Fig. 4 . Per Fig. 4 , there are 9 clusters, and the journal source with the highest number of similar citations is Sustainability . It can, therefore, be concluded that this journal has the most citations and published articles on this subject.

This figure refers to the number of references shared by a group of papers. Source: Own elaboration.

Citation network of documents

Citations are formed when two documents cite the same document and are used to illustrate the relation between documents and study fields. Figure 5 shows four clusters, each representing the degree of connection and the extent of influence in size. This study has the highest influence, according to the largest green group. It offers insight into the impact of reality and virtual reality on heritage tourism, stating that these technologies favorably impact tourists’ experiences (Bec et al., 2019 ).

Cluster size indicates the degree of connection and influence of the literature and research area. There are four groups, with the blue (Encalada et al., 2017 ) and green (Bec et al., 2019 ) groups representing the two articles that are relatively most influential. Source: Own elaboration.

This Blue Group study is also prominent, proposing that the widespread use of information and communication technologies, such as cloud computing, the Internet of Things, and data mining with high processing performance, are the key to tourism’s sustainability (Encalada et al., 2017 ).

The number of citations between documents is used in co-citation analysis to determine their relevance. Figure 6 shows which publications are cited most frequently, and it is clear that tourism management and sustainability are the two commanding the most attention. Generally, the closer two journals are located to each other, the stronger their relatedness. For example, according to an article published in Tourism Management , virtual reality has significantly increased tourism intention and consumption (Tussyadiah et al., 2018 ). Simultaneously, this article presents the finding, which illustrates that combining history with cutting-edge technology in immersive spaces can preserve and manage legacy and enrich the visitor experience and, as a result, engagement with history (Bec et al., 2019 ).

This figure represents the citation strength of publications. The circle distance represents relevance. Source: Own elaboration.

Co-occurrence network of Keywords and titles

The significance of keyword co-occurrence analysis in bibliometrics resides in an intuitive understanding of hot subjects in the study field through the frequency and relevance of terms (Phoong et al., 2022 ). Before that, the following considerations must be made.

To begin, each node in the network map indicates a keyword, and the size of the ball represents the number of keywords that appear. The larger the ball, for example, indicates the higher frequency of keywords occurring. Second, the larger the co-occurrence rate between terms, the thicker the curve between the second keywords. In the third, on the network map, different color groups reflect different theme collections, while the same color represents similar subjects (Loureiro and Nascimento, 2021 ).

Figures 7 and 8 illustrate overlay visualization (Fig. 8 ) and network visualization (Fig. 7 ). From Fig. 7 , the keywords of high frequency include tourism (37 occurrences), technology (35 occurrences), tourist (32 occurrences), experience (31 occurrences), information (25 occurrences), application (23 occurrences), data (22 occurrences), analysis (21 occurrences), impact (21 occurrences), sustainability (14 occurrences) and sustainable development (9 occurrences). Some of Red Network Group’s primary keywords are tourism, information, impact, communication technology, virtual reality, new technology, and cultural tourism. The study’s content focuses on the impact of the relationship between information technology and tourism. Yellow Network Group’s primary keywords are destination, tourism destination, environment, and AR, mainly concentrated on destination environment and AR application research. The green network group comprises tourists, analysis, process, big data, management, stakeholders, case studies, innovation, and other topics. This group has conducted more studies on the effect of digital technology on enterprise management from stakeholders’ perspectives. The Blue Network Group focuses on technology, experience, data, service, research, relationships, social media, sustainable development, tourist satisfaction, intention, and other related topics, and this group study is particularly interested in the influence of technology on tourist experience and satisfaction.

The same color indicates a close relationship between the keywords. The red network group focuses on tourism and information technology, the yellow network group concentrates on destinations and the environment, and the blue group emphasizes tourists and technology, the green group concerns tourists and analyses. Source: Own elaboration.

Darker colors indicate older keywords such as tourists, information, data, research, etc. and lighter colors show the recent hot keywords such as big data, AR, VR, sustainable development, etc. Source: Own elaboration.

After conducting a literature review on digital technology’s economic and social implications on sustainable tourism development over the last ten years and creating a density visualization network map, it can be concluded that tourist experience, information technology, augmented reality, and data are research hotspots. As a result, most studies on tourism sustainability in social and economic dimensions focus on the impact of digital technology on the tourist experience.

Even though they are all co-occurrence analyses of keywords in literature, the emphasis in each network map is different. Generally, overlay visualization and network visualization are comparable to a certain extent; however, the color differs in overlay visualization (Fig. 8 ). In the lower right corner, there is also a quantification table. Purple indicates that the keywords are older, while yellow indicates that they are more modern. For example, keywords such as big data, augmented reality, sustainable development, creation, and intention are yellow, indicating a recent research hotspot, but keywords such as communication technology, information, environment, and service are purple, indicating that these themes were formerly popular.

Results and discussion

The data were collected from 2012 until February 2022. Analysis of the published articles shows a significant increase in publications on digitalization and tourism sustainability development. In 2017, seven articles were published, 10 in 2018, 16 in 2019, and 23 in 2020 and 2021. Furthermore, there are 6 published in the first two months of 2022. These findings illustrate a rise in data availability for digitalization and sustainable tourism development research and suggest that researchers are considering this topic more seriously, demonstrating its value to academic research.

According to the findings, Sustainability was the top journal in published digital and tourism sustainability-related articles. This is followed by the International Journal of Tourism Research , Tourism Management , and Current Issues in Tourism . The number of publications on the relevant subject has increased steadily, particularly in recent years, indicating that this form of research is increasingly gaining attention. Research over the last decade has shown the existence of a certain number of empirical studies on the relationship between digitalization and tourism social and economic sustainability, and from the bibliometric analysis, it emerges that the current research direction on tourism social and economic sustainability has shifted from exploring ICT to AR and VR. Moreover, Tourism Management and Sustainability have the highest citation.

In summary, this study answers RQ1 using the bibliometric literature analysis, while a systematic literature review used to answer RQ2 and RQ3 is discussed in the conclusion and further recommendation sections.

The content of relevant articles published in WOS and Scopus in this research area over the last decade was visually analyzed through bibliometric and systematic literature analysis, and a total of 91 articles meeting the research criteria were selected to provide information on the status of the impact of digitalization on the social and economic aspects of sustainable tourism development, as well as to identify specific research fields and research topics. It can be concluded that the digitalization of the social dimension of tourism sustainability is more richly studied and explored from a more diverse perspective, considering not only the tourists’ but also the residents’ perspectives. There are two implications in the present study. The first is that this study pinpointed the knowledge gaps. Systematic literature review analysis is used in this study to identify the gaps in the existing body of research in tourism development. By reviewing the previous literature and synthesizing the findings, researchers can identify the areas receiving limited or much attention. This insight is valuable for policymakers, tourism planners, and researchers when dealing with specific areas where future research is warranted. Furthermore, the publication trend and popular research themes were also discussed in this study. This enables the policymaker and tourism planner to understand tourism development and the potential for improved policies and practices. The second implication is enabling evidence-based decision-making in tourism development. Researchers can identify patterns, trends, and best practices by synthesizing the findings from multiple studies. This evidence-based approach helps policymakers, destination managers, and tourism stakeholders make informed decisions and develop strategies grounded in research. However, there is a lack of a more comprehensive perspective to explore in an integrated manner. For example, social and economic sustainability development sometimes does not increase simultaneously, and perhaps there is a particular imbalance between the two when using certain digital technologies. Therefore, it can be observed from this study that there is a lack of research in the past ten years that has explored both the economic and social sustainability of tourism comprehensively and that future research could emphasize the integration of social and economic sustainability, even a synthesis study of three dimensions: environmental social, and economic.

Therefore, when considering future developments, several challenges were raised.

Lack of integration study of social and economic dimensions.

Lack of cooperative research among other disciplines.

Lack of suitable theory and conceptual model for sustainable development research in the tourism area.

Lack of universality in different regions based on proposed digital technology.

Lack of research from the perspective of subject education or particular population as the research object.

Based on this literature study, relatively few research topics about this research area are suggested, and the following research scope and questions can be referred to as a priority in the future research process so that research trends can be accurately grasped more quickly and efficiently.

What is the impact of digital technologies on the economic and social sustainability of destinations?

How do digital technologies used in cultural heritage tourism impact tourism sustainability?

What is the impact of digital technology on education?

How can tourism companies improve employee satisfaction, loyalty, and sustainable performance through digital technology?

How can we create a globally accessible and digital system for tourism destinations for sustainable development goals?

How does digitalization impact sustainable development from stakeholders’ perspectives?

The above suggestions and research direction recommendations can provide new research inspiration to researchers in the same field for future research, and this study is expected to help other researchers understand the current research trends related to the digitalization of sustainable tourism development.

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Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability

Proceedings of the TURITEC 2023 Conference, October 19–20, 2023, Málaga, Spain

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• Antonio J. Guevara Plaza   ORCID: https://orcid.org/0000-0002-2400-0668 0 ,
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Computer science, University of Malaga, Malaga, Spain

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Campus de Teatinos, Universidad de Málaga, Fac de Turismo, Málaga, Spain

Department of geography, university of málaga, málaga, spain.

• Includes papers with empirical case studies on the application of IT to tourism
• Advances the current knowledge base of ICT and tourism
• This book is open access, which means that you have free and unlimited access

Part of the book series: Springer Proceedings in Business and Economics (SPBE)

Included in the following conference series:

• TURITEC: International Conference on Tourism and Information and Communication Technologies

Conference proceedings info: TURITEC 2023.

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About this book

This open-access book presents the best research papers from the XIV International Congress on Tourism and Information and Communications Technologies (TURITEC2023), held in Málaga, Spain from 19 to 20 October 2023. The book explores the profound impact of COVID-19 on the tourism industry and the increasing importance of digitalization and Information and Communication Technologies (ICTs) as key drivers for the industry's recovery, alongside sustainability. This curated collection of research papers offers conceptualizations, methodologies, analyses, and empirical case studies that illuminate the path to a resilient and sustainable future for tourism.

• Artificial intelligence applied to tourism
• Smart destinations
• User generated content in tourism
• Platform economy and tourism
• Social media in tourism
• Blockchain and tourism
• Text mining
• Content analysis
• Digitalization
• Mobility and tourism distribution
• Platform economies

Table of contents (25 papers)

Front matter, digitalization, mobility and user-generated content (ugc), best practices in technology usage for promotion of music festivals in spain.

• Diego R. Toubes, Noelia Araújo-Vila, José Antonio Fraiz-Brea

Tracking Tourist Flows Through Wi-Fi Sensor Technology in Seville

• Irene N. Franco, Concepción Foronda-Robles, Federico Rollán, Pino Canales

Lexical Competence in New Digital Environments: The Metaverse and Its Application to Tourism Science

• Isabel Serra-Pfennig

Optimizing Tourism Data Extraction and Analysis: A Comprehensive Methodology

• José Javier Galán-Hernández, Ramón Alberto Carrasco-González, Gabriel Marín-Díaz

Assessing Tourists’ Perception of ‘Smartness’ in a Destination: A Case Study of Tenerife Island

• Miquel Armand Mesegue-Basallo, Julia Marti-Ochoa, Berta Ferrer-Rosell, Eva Martin-Fuentes

Analysis of the Trend in the Number of Followers on Social Networks in Spanish World Heritage Cities: A Comparative Study of Facebook, Instagram and Twitter

• Marcelino Sánchez-Rivero, María Cristina Rodríguez-Rangel, Juan Carlos Díez-Apolo, Luis Murillo-González

Assessment of Functional and Emotional Factors in the Hotel Experience Through UGC

• Elena Sánchez-Vargas, José Manuel Hernández-Mogollón, Sergio López-Salas, Bárbara Sofía Pasaco-González, Ana Moreno-Lobato

Analysis of the Opinions of Users of the Tripadvisor Web Platform on the Cultural Tourism Resources of Málaga

• Marco Antonio Soto-Rumiche, José Luis Caro

Rise and Fall of TripAdvisor: The Lack of Participation and Its Causes

• José Luis Ximénez de Sandoval

Artificial Intelligence and Tourism

Data platform for a data-driven tourism organization. a conceptual architecture.

• Juan Vidal-Gil, Ramón Alberto Carrasco-González, María Francisca Blasco-López

Innovation and AI: An Opportunity for Spanish Tourism in the Post COVID-19 Era

• Aimée Torres-Penalva, Luis Moreno-Izquierdo

Tourism Intelligence, Key to Reviving the Sector

• Carlos Hernández-White, Beatriz Rodríguez-Díaz, Alfonso Expósito-García

Big Data in Real Time for the Management of Tourist Destinations: The TOURETHOS Platform Technological Model

• José Juan Hernández-Cabrera, Ana María Plácido-Castro, Jacques Bulchand-Gidumal

Big Data and Business Intelligence in Cruise Destinations

• Josep Maria Espinet, Carles Mulet-Forteza, Berta Ferrer-Rosell

Big Data in Restaurant Management: Unsupervised Modelling of Ticket Data and Environmental Variables for Sales Forecasting

• Ismael Gómez-Talal, Lydia González-Serrano, Pilar Talón-Ballestero, José Luis Rojo-Álvarez

Predictors of the Success of Yacht Charter in Andalusia from a Leading P2P Platform Using Machine Learning

• Amor Jiménez-Jiménez, Pilar Sancha, Juan Manuel Martín-Álvarez, Ana Gessa

Last Tendencies in Acquiring Text Competence in the Field of Tourism. The Case of Chatbots and AI

• María Dolores Fernández de la Torre Madueño

Other volumes

Editors and affiliations.

Antonio J. Guevara Plaza

Alfonso Cerezo Medina

Enrique Navarro Jurado

About the editors

Antonio Guevara Plaza is Dean of the Faculty of Tourism at the University of Málaga, Spain, and Professor at the Computer Sciences Department. He is President of the Spanish Conference of Deans of Tourism (CEDTUR), involving 45 Spanish universities with undergraduate, postgraduate, and Ph.D. studies in Tourism. His research interests focus on Information Technology and Communication (ICT) applied to tourism. He is the principal researcher of the SICUMA research group (Cooperative Information Systems, University of Málaga). He has directed several projects for R & D related to the implementation of ICT in tourism and published research works at relevant journal publications.

Enrique Navarro-Jurado holds a PhD in Geography and is Director of the Andalusian Institute for Tourism Research and Innovation (Málaga, Spain). His research focuses on tourism destinations, planning and management, limits to growth and tourist carrying capacity, sustainability indicators and technologies applied to sustainability, tourism and climate change. He is principal investigator of several R+D+i projects and has participated in more than 30 research projects worldwide and in technology transfer contracts. With over 40 publications in high-impact international journals, he serves on various boards of trustees, scientific associations, and as a journal editorial member. Additionally, he contributes as an advisor to the Blue Plan (UNEP-UN) and various regional and local plans.

Bibliographic Information

Book Title : Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability

Book Subtitle : Proceedings of the TURITEC 2023 Conference, October 19–20, 2023, Málaga, Spain

Editors : Antonio J. Guevara Plaza, Alfonso Cerezo Medina, Enrique Navarro Jurado

Series Title : Springer Proceedings in Business and Economics

DOI : https://doi.org/10.1007/978-3-031-52607-7

Publisher : Springer Cham

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s) 2024

Softcover ISBN : 978-3-031-52606-0 Published: 25 June 2024

eBook ISBN : 978-3-031-52607-7 Published: 24 June 2024

Series ISSN : 2198-7246

Series E-ISSN : 2198-7254

Edition Number : 1

Number of Pages : XVIII, 281

Number of Illustrations : 9 b/w illustrations, 37 illustrations in colour

Topics : Tourism Management , Artificial Intelligence , IT in Business

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Digital Transformation

Technologies are emerging, disrupting and affecting our lives in ways that indicate we are at the beginning of a Fourth Industrial Revolution, a new era in which digitalization builds and impacts societies in new and often unanticipated ways. It is worthwhile considering exactly what kind of shifts we are experiencing and how we can ensure, collectively and individually, that this revolution creates benefits for all.

Tourism was one of the first sectors to digitalize business processes on a global scale, bringing flight and hotel booking online to become a digital pioneer. As information and communications technology (ICT) became a global phenomenon, tourism was a consistent early adopter of new technologies and platforms.

A digitalized tourism sector must innovate and generate new business opportunities to ensure the continued competitiveness, growth and sustainable development of the sector. The ultimate goal is to make a solid contribution to achieving the Sustainable Development Goals of the United Nations and the global development community.

With the aim of realizing UNWTO Secretary-General Zurab Pololikashvili’s new Visions and Priorities, specifically the priorities of “Innovation & digital transformation” and “Investment & entrepreneurship”, the Strategy on Innovation, Investment and Digital Transformation has been developed.

Digital Transformation & SDGs

Digitalization is expected to continue propelling the travel experience on its trajectory towards becoming more seamless, frictionless, and high quality and in a way that contributes to the Sustainable Development Goals (SDGs) of the United Nations..

The use of technologies including the ‘Internet of Things’, location-based services, artificial intelligence, augmented and virtual reality, and blockchain technology (see definitions below) has resulted in a tourism offer that is more attractive, efficient, inclusive, and economically, socially and environmentally sustainable than its predecessor. It has also facilitated innovation and rethinking of processes, with a view to tackling challenges such as seasonality and overcrowding and developing smarter destinations.

Digitalization has a positive environmental impact and can yet have a greater one, with innovations in manufacturing, smart assets and efficient use of resources contributing to a more sustainable industry footprint.

Some major impacts on the sector as a whole are the development of smart travel facilitation , smart destinations , and a new wave of job profiles .

Smart travel facilitation

In the same way the smartphone has transformed telecommunications and media, a comprehensive smart travel model – one that includes smart visas, borders, security processes and infrastructure – will revolutionize tourism. With consolidation of these tools, passengers can book their flights and check in online, have their boarding passes on their smartphones, go through automated clearance gates and even validate their boarding passes electronically to board planes. These measures improve both travel facilitation and security.

Smart destinations

A smart destination is one with a strategy for technology, innovation, sustainability, accessibility and inclusivity along the entire tourism cycle: before, during and after the trip. A smart destination is also one with residents as well as tourists in mind, factoring multilingualism, cultural idiosyncrasies and seasonality into tourism planning.

This is why smart destinations are key to the transformation of the tourism sector. By continuously and accurately measuring, integrating and analyzing data for efficient decision-making, prioritization and anticipation of challenges, they create a seamless and exciting experience for tourists while managing local resources efficiently.

Smart destinations can make tourism governance more inclusive through inclusive entities, such as boards, trusts or foundations, which represent all public/private stakeholders in the destination. They can help ensure maximum accessibility in sites, products and services, eliminating barriers to mobility. And they allow us to analyse sustainable tourism management through different lenses.

Consider, for example, the challenge of seasonality, where population change from variation in tourist flows makes tourism’s impact difficult to measure. Technology-based data tools, such as remote sensors and big data management systems, can help destination managers capture and process large volumes of data for a greater understanding of the impact of seasonality on tourism and on sustainability. It can therefore help them to predict and manage tourist flows in order to more efficiently and effectively manage destinations.

Job creation

The future of travel is technology-based, so tourism jobs will require both technical and advanced soft skills used to effectively implement and manage smart initiatives. The greatest societal impact of digital transformation in tourism may be the effect on the sector’s workforce, which directly and indirectly represents 1 in every 10 jobs worldwide. Like in other economic sectors, intelligent automation will change the nature of some travel jobs and eradicate others altogether. However, digitally-enabled growth will also generate new employment opportunities that could outpace the automation of existing roles, especially as strong growth is forecast for the sector.

Startups and micro, small and medium enterprises (MSMEs) – drivers of technology-based innovation and entrepreneurship in tourism – will take on an increasingly important role in developing the skills needed for the jobs of the future. The sector should therefore prioritize strengthening startups and MSMEs so they can become integral parts of the tourism value chain, and boosting technology- and skills-based education, training and policies that stimulate innovation and decent employment.

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Technology: Disruptive Innovation in the Tourism Industry

Digital transformation is reshaping jobs and customer relations in the tourism industry. Technologies such as machine learning, which can predict how many hotel reservations will be canceled, and Wi-Fi analytics, which monitors user behavior, are revolutionizing our understanding of business management. In addition, apps and bots have emerged as new tools for increasing customer loyalty. In this mobile environment, disruptive companies like Airbnb or Uber have created communities of users where customers double as owners and managers. New companies must adopt a resilient attitude and expand their scope, cultivating connections and empowering users in order to create exponential growth.

Not all innovation is technological. Although digital transformation has reached the tourism industry in earnest and is gradually changing jobs and customer relations, it is important to understand that technology is not an end, but a means, and that tourism is an individual experience, often shared on- and offline.

Not all innovations are specifically sought; sometimes they arise in response to a problem or need. Sixty years ago, the manager of a busy hotel realized it was time for breakfast and none of the staff responsible for serving it had come to work. He decided to place a long table in the hall and lay out everything the guests needed to serve themselves; hence, the breakfast buffet was born. It may not have been a technological solution, but it was certainly an innovation.

In short, to be innovative, an action must mark a milestone, such as:

• Opening a new market.
• Creating a new product.
• Modifying an existing product.
• Improving a production method.
• Finding a new source of supply.
• Creating a new form of organization.

Technology: Transforming Jobs and Relations

We live in a time of transformation, a time of change in which many questions that once had simple answers have become difficult, even as other questions are arising for the first time. One question that is no longer simple is that of occupation. Myriad new positions have emerged in response to new jobs and ways of working: CMO, CEO, COO, CFO, etc. Soon we will have space debris managers, virtual habitat designers, biohackers, and even designers of human parts for 3D printing.

At a time when robotics and artificial intelligence are posing new challenges, it is also worth asking how many of the jobs today considered normal and necessary are likely to be automated or replaced by new technologies .

Not all innovations are specifically sought; sometimes they arise in response to a problem or need.

In the world of tourism, one issue that has been simplified is travelling itself. The sharing economy has given rise to platforms such as BlaBlaCar, which has undergone exponential growth in services, profits, and number of users. In this context, we need to consider how humans are evolving in the context of a society deeply impacted by technology and, at the same time, how we, the business world, and, consequently, markets all work.

One key to understanding this whole change is that technology permeates the reasoning and experiential part of the brain much more powerfully and markedly in the new generations. In the current era of digital evolution, generations understand their environment and the tourism experience differently: some people live in the moment, interacting with all five senses at an individual level, others share it with their close friends and family, and still others do all that and also share it on social media in real time.

This reality is increasingly present. Our thinking may be linear, but technology and the incorporation of these habits by younger generations are exponential. In a linear approach, if you take one step, you advance one yard; if you take two, you advance two yards; and so on successively until you have taken fifty steps and advanced fifty yards. Technology, on the other hand, presents us with a different reality: like Moore’s Law, it works exponentially. Thus, if you take one step, you advance one yard; if you take two steps, you advance two yards; but if you take a third step, you will travel twice the distance as in the previous step, that is, four yards. And so it goes: four steps, eight yards, etc. This exponential progress is clearly reflected in the time it has taken users to adopt different technologies (see Chart 1).

Machine Learning

Machine learning offers a new way of understanding business management. The huge data and information repository we have today, i.e., big data, allows us to intelligently predict what will happen tomorrow and to access that information. In other words, in addition to information based on the forecasts of our hotel or business’s computer system, we can access reliable information on what will happen in the future, the number of reservations that will be cancelled, the number of no shows we can expect, and other variables that will help us adequately meet our human resource needs, streamline management, and deliver a better service. It is a technology -based work methodology that uses past information to predict future events.

For the tourism industry, big data can be very interesting from a macro perspective, but it is less so at the micro level. While the macro approach can provide massive amounts of information with regard to aspects such as tourist flows, ultimately, we are talking about individual companies, which need functional information to help them make business decisions. Machine learning takes the massive information available today, polishes it, and uses it to predict the future and meet the company’s objectives. In the case of hotels, data are extracted from three sources: the property management system or PMS (hotel management software including a record of the last years of business), big data, and manual inputs, i.e., the data the receptionist, manager, or sales associate directly includes as key.

Machine learning takes the massive information available today, polishes it, and uses it to predict the future and meet the company’s objectives.

Wi-Fi Analytics and Geolocation Technology

Another factor that will undoubtedly have one of the largest impacts on the tourism experience is the rise of the Internet of Things and the digitalization of places.

The driving force behind this reality is currently smartphones. We have reached a point where, before leaving home, we check to make sure we have three things: our wallet, our house keys, and our phone. This shift in habits has opened up an interesting field of work in technology and innovation , giving rise to solutions such as positioning beacons and beacons that can be used to identify mobile phones and, thus, their owners with a certain ease. The next step is to use mobile apps to contact users at given points, thereby generating an interaction with a place, hotel, leisure area, city, museum, etc.

Thus, it is possible to determine how much time a customer will have to wait to be served or how many have left because of the wait. This information can then be used to reorganize and streamline management, increasing the return and improving the service. It will likewise be possible to monitor production processes in order to organize and distribute tasks more efficiently based on need. For instance, it will be possible to monitor a hotel cleaning process, so that it can be analyzed with the same precision as a factory production line, determining the actual cleaning times, flows, paths, and waste of the process, as you would with the Lean Management method.

Apps vs. Bots

In the current mobile environment, virtually all businesses seek to have their own app. Consequently, users often download apps only to delete them one or two days later. In light of this reality, it is important to consider why users should download a hotel’s app if they are only going to stay for a few nights. To make it worth their while, hotels should focus on value, seeking additional services that encourage guests to download their app and use it with some frequency, so as to establish a bond with the guest and generate a new way to increase guest loyalty.

However, in today’s fast-paced world, game changers can happen overnight, as when Facebook opened its Messenger platform to chatbots. Because such a large percentage of people have the Facebook app on their phone, the social network’s decision prompted many companies to develop intelligent information systems to deliver information to customers through these bots, allowing them to cover some of the services provided by many standalone apps developed especially for customers with considerably more agility. These chatbots, which imitate human behavior , are also more likely to be used because they work with the Facebook app, which customers already check daily.

Of course, whether to use a system based on an app or a bot depends on the type of business, the service being provided, and the type of customer a company is seeking to serve. Above all, it depends on the type of people who will be overseeing the activity of these types of applications. This, in turn, gives rise to new professions, new tasks and activities that will give rise to new jobs (see Chart 2).

As a result of these realities, not only are products changing and improving, but markets and organizations are changing too. The mobile start-up generation is seeing exponential growth in users and revenue. The next step is the Internet of Things, which will surely usher in a disruptive change in how companies grow and develop. The Pentagrowth Report, by Ideas for Change, identified five laws of growth for these new companies:

• Connect: with people, situations, and things.
• Collect: the smaller the effort an organization has to make to build its available inventory, the bigger its growth potential.
• Empower: integrate users into the company’s business processes.
• Enable: develop tools that others can use in the company’s environment to create overall value.
• Share: to create a community loyal to the resource created by the company.

The next step is the Internet of Things, which will surely usher in a disruptive change in how companies grow and develop.

Companies grow in relation to these laws based on what might be called the generative principles of exponential growth : Reach, Interaction, and Resilience.

The Reach principle correlates the Share and Connect levers with user growth rates. The organization’s potential reach depends on the number of people, situations, and things connected through the networks on which it operates and the number of value units that users can freely share with others.

The Interaction principle is the result of the correlation between the Collect and Empower levers, i.e., the number of value units included in the inventory and the number of roles that users can play.

Finally, an organization’s Resilience depends on the number of business partners that have developed their commercial offerings and lifestyles based on its standards and market and the number of people who feel a sense of shared ownership of its open resources.

When we analyze these levers and principles in relation to some of the most disruptive companies in the tourism industry, such as Airbnb or Uber, we find that they owe their initial edge to their community, their exponential growth in terms of connected users. These users establish different roles by incorporating their own assets, such as their apartment or car, into the company’s centralized and intermediated offering. Thus, in addition to being users, they become sales associates, service managers, and influencers, all based on an enormous network undergoing exponential growth driven by user feedback.

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Acceptance and use of ICT in tourism: the modified UTAUT model

Journal of Tourism Futures

ISSN : 2055-5911

Article publication date: 12 September 2022

Issue publication date: 6 June 2024

This study aims to investigate the technology usage behaviour of the tourists in line with the modified unified theory of acceptance and use of technology (UTAUT) model.

Design/methodology/approach

Data were collected from a survey of 265 tourists using the random sampling technique. Partial least squares-based structural equation modelling (PLS-SEM) technique was used to analyze the data.

The findings revealed that performance expectancy, hedonic motivation and habit significantly influence the behavioural intention of tourists to use information and communication technology (ICT), while effort expectancy, social influence, and facilitating conditions do not have a significant influence. However, actual ICT usage behaviour largely depends on the behavioural intention of the tourists, and their habits, while the facilitating conditions do not have any influence in this case.

Practical implications

The findings uncover the core factors influencing tourists' actual ICT use behaviour that can assist the concerned stakeholders in designing tourism planning and sales. The study results also offer pathways for the world's tourism industry for a healthy recovery from the COVID-19 pandemic.

Originality/value

The findings have made robust contributions by extending the existing UTAUT-based literature by adding two new moderators in the relationship between behavioural intention and actual ICT usage behaviour.

• Behavioural intention
• ICT usage behaviour
• Modified UTAUT

Ali, M.B. , Tuhin, R. , Alim, M.A. , Rokonuzzaman, M. , Rahman, S.M. and Nuruzzaman, M. (2024), "Acceptance and use of ICT in tourism: the modified UTAUT model", Journal of Tourism Futures , Vol. 10 No. 2, pp. 334-349. https://doi.org/10.1108/JTF-06-2021-0137

Emerald Publishing Limited

Copyright © 2022, Md Borak Ali, Rahat Tuhin, Md Abdul Alim, Md Rokonuzzaman, Sheikh Matiur Rahman and Md Nuruzzaman

Published in Journal of Tourism Futures . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Information and communication technology (ICT) provides unique opportunities for tourists and tourism businesses ( Lu et al. , 2015 ) that promote the globalization of the industry. ICT tools can help the organization acquire, process, analyse, store, retrieve, disseminate and apply its key information ( Jadhav and Mundhe, 2011 ). The application of ICT has transformed tourism businesses from local ethnocentric thinking to global orientation ( Pencarelli, 2020 ). This new understanding of tourism business creates better prospects for the firms to survive and increase their competitive position ( Law et al. , 2009 ). Therefore, the application of ICT in the tourism sector has become inevitable for the development, transformation and upgradation of this industry ( Wu, 2017 ). The development and adoption of ICT change the behavioural pattern of the economic agents as well as enhance their collaboration with the social and economic conditions ( Gossling, 2021 ) that are particularly true in the tourism industry during the COVID-19 pandemic. Travel consumers receive significant savings for exploring the amenities with the internet, smartphone and other technologies that help their holiday planning. Therefore, applying ICT for marketing tourism products is a viable alternative for tourism business success ( Mahajan et al. , 2011 ).

The technology-bound transformation in the tourism industry reflects the online-based customer reservation system to global distribution systems ( Carlisle et al ., 2021 ). Internet-based technologies, social networking devices and mobile technologies have allowed tourism businesses and consumers to engage, interact and yield experiences to an unprecedented scale. Particularly, instituted by the new collaborative dimensions of technologies, the marketplace has shifted towards the increased power and control of consumers ( Alt and Klein, 2011 ). The application of technology is well discussed in the unified theory of acceptance and use of technology (UTAUT) model of Venkatesh et al. (2003) . Later, Venkatesh et al. , (2012) extended the UTAUT model known as UTAUT2, which incorporates significant improvements in consumer technology adoption literature ( Satama, 2014 ). Earlier studies reviewed the adoption of technologies in various sectors, that is, mobile commerce ( Rahman and Sloan, 2017 ), e-mail systems ( Alraja, 2015 ), online shopping ( Alsharif, 2013 ) and wireless LAN technology ( Anderson and Schwager, 2004 ). This important innovation has received very little attention among the tourism stakeholders, particularly the tourists, for purchasing tourism products. As such, the extant literature loosely explores ICT usage behaviour among tourists. This situation also impedes the growth of the tourism industry due to the limited use of ICT, particularly in developing countries.

The digitalization process has accelerated the development of most industrial sectors across the globe. Still the ICT-based tourism platform has not been properly developed in most developing countries ( Rahman, 2021 ). In their study, Jeon et al . (2020) explored the UTAUT theory to identify and validate the antecedents influencing customers' intention to adopt self-service technology. Cang et al. (2022) described blockchain-based UTAUT and examined the determinant factors affecting the acceptance of new technology in Korea, where tourism is the dominant business. Kamboj and Joshi (2021) studied the UTAUT model perspective on smartphone apps at a tourism destination. Sharma et al. (2020) explored consumer behaviour in purchasing travel online in Fiji and Solomon Islands using UTAUT framework. Considering the extension of the UTAUT model, Phaosathianphan and Leelasantitham (2019) investigated the antecedent factors that influence user acceptance of the intelligent travel assistant (ITA) for eco-tourism. Therefore, most of the existing literature studied technology adoption processes in different contexts in different geographic locations and examined the role of various influencing factors, highlighting the UTAUT model. However, there is still a paucity in the literature that exclusively studied the influence of the antecedent factors of ICT usage intention and actual usage behaviour in tourism and hospitality research.

Besides, the existing ICT-based model UTAUT, particularly explained the predictors [i.e. performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC)] of behavioural intention (BI) and actual usage behaviour. Later on, UTAUT2 included three additional factors [i.e. hedonic motivation (HM), price value (PV), and habit (Hbt)] as the predictors for the same endogenous variables. There is, however, a dearth of studies that exclusively addressed the above-mentioned aspects and their interplaying effects for tourism booking and reservation, as well as its marketing and sales. To date, literature is also sparse that examined the interaction effect for any of the predictor variables used in earlier UTAUT models. These gaps in the literature uncover new research agenda, which has given the scope of conducting the current study. To fill the much-needed gaps in the literature, we proposed the modified UTAUT model (see, Figure 1 ) with the combination of the antecedents and new moderators of ICT usage behaviour in tourism. Thus, we were interested in examining the interaction effects of an observed variable (i.e. price value) with a demographic variable (i.e. residency). Although price value is extensively related to many facets, its influence on tourists' behavioural intention and actual usage behaviour is hardly discussed in the existing literature. Residents of different locations have different views on ICT usage ( Galliano et al. , 2011 ). Thus, the residents from all eight divisional areas of Bangladesh have been taken for the current investigation. These divisions are substantially different in terms of income, education, and profession, impacting tourists' ICT usage behaviour ( Akhter and Sumi, 2014 ). Hence, the geographic location of the tourists (i.e. residency) can also influence the rate of ICT usage that will be examined in this study. Our understanding of the new recipes in the modified UTAUT model can better explain the ICT usage behaviour in the tourism industry that the empirical evidence will essentially explore.

The current study is particularly interested in examining the effects of technology-based factors on users' intention and usage behaviour of ICT in tourism purchase among Bangladeshi tourists. Bangladesh is considered for the field study as it has a wide variety of tourism attractions ( Akter et al ., 2020 ), but the ICT usage rate among the tourists is low ( Roy and Ahmed, 2019 ). Hence, the current study finds substantial merit in conducting this research in Bangladesh as a field of study. The following section discusses the background literature and hypotheses. Then, the methodology is presented, followed by the analysis of data. Discussion and implications are shown in the later part of the paper concluding with the future research directions.

2. Background literature and hypotheses

Before the development of the UTAUT model, the study of ICT usage behaviour was based on eight models, such as the technology acceptance model (TAM) of Davis (1989) , the theory of planned behaviour (TPB) of Ajzen (1985) , a model combining TAM and TPB (C-TAM-TPB) of Taylor and Todd (1995) , the motivational model (MM) of Davis et al. (1992) , the model of PC utilization (MPCU) of Thompson et al. (1991) , the social cognitive theory (SCT) of Bandura (1986) and the diffusion of innovation (DOI) theory of Rogers (1995) . Most of these theoretical models were established for the diffusion and adoption of innovation in ICT. For this research, a framework was developed based on the theoretical foundation of UTAUT and UTAUT2, together with the TPB model, to fill the much-needed gaps in the literature. This framework has proposed the antecedents (i.e. PE, EE, SI, FC, HM and Hbt) of ICT usage behaviour. The proposed framework also accommodates the price value and residency as the moderators of the relationship between BI and ICT usage behaviour.

2.1 Performance expectancy (PE)

Performance expectancy has a significant positive influence on the behavioural intention of tourists in responding to ICT usage.

2.2 Effort expectancy (EE)

Effort expectancy has a significant positive influence on the behavioural intention of tourists in responding to ICT usage.

2.3 Social influence (SI)

Social influence has a significant positive impact on the behavioural intention of tourists in responding to ICT usage.

2.4 Facilitating conditions (FC)

Facilitating conditions have a significant positive influence on the behavioural intention of tourists to ICT usage.

Facilitating conditions have a significant positive influence on the actual ICT usage behaviour of tourists.

2.5 Hedonic motivation (HM)

Hedonic motivation has a significant positive influence on the behavioural intention of tourists to ICT usage.

2.6 Habit (Hbt)

Habit has a direct significant positive influence on the behavioural intention of tourism stakeholders in responding to ICT in tourism.

Habit has a direct significant positive influence on the actual ICT usage behaviour of tourism stakeholders in responding to ICT in tourism.

2.7 Behavioural intention (BI) and ICT usage behaviour (ICTUB)

Behavioural intention has a direct significant positive influence on actual ICT usage behaviour in responding to ICT in tourism.

2.8 Moderators

The effect of behavioural intention on actual ICT usage behaviour is significantly moderated by the price value, as higher the price value of ICT, the higher the effect on ICT usage.

The effect of behavioural intention on actual ICT usage behaviour will significantly be moderated by the residency of tourists, and as such, the effect will be stronger for tourists living in the capital city than those living outside the capital city.

3. Methodology

In the realm of the positivist paradigm, this study examines the antecedents of ICT usage intention and actual ICT usage behaviour through quantitative investigation. The target population was the students of different universities of Bangladesh, particularly those tourists who use ICT devices for purchasing tourism products.

The study holds 29 reflective type indicators under eight constructs for estimating the proposed research model. The measures for each of the constructs were adopted from existing UTAUT model-based literature (e.g. Alsharif, 2013 ; Ibukun et al ., 2016 ; Gupta and Dogra, 2017 ; Rahman and Sloan, 2017 ; Davis, 1989 ; Bagozzi and Lee, 2002 ; Mutlu and Der, 2017 ; Venkatesh et al ., 2012 ). The details of the indicators under each construct can be found in Table 2 . The survey instrument was designed from the selected indicators under each construct modelled in this research. All responses were measured with a 6-point Likert type scale, where 6 represents strongly agree, and 1 means strongly disagree. The questionnaire also contained demographic profiles of the participants. Before the final survey, the questionnaire was pretested for its accuracy and ease of understandability to the respondents. In the pretesting process, required amendments and corrections were taken place. Once, the questionnaire was completed, a pilot study of 18 samples was conducted to check the data trends.

This study employed both online and face-to-face survey. University students from Bangladesh (who were studying either bachelor's or master's courses) were selected as the sample population of the study. University students were chosen for the survey because they tend to use technology more than other groups in tourism purchases ( Bhuiyan et al ., 2020 ). For the final survey, respondents were randomly selected from three groups: Facebook, tourist club and university residential halls. The structured questionnaire was uploaded to the individual Facebook accounts of the randomly selected students from the University of Dhaka and University of Khulna, Bangladesh. Other respondents were also randomly selected from the members of the tourist club of the University of Rajshahi and the university residential halls of the Islamic University, Bangladesh. These respondents were approached face-to-face for the survey. A total of 300 questionnaires were distributed to the respondents both online and in person. Among 300 distributed questionnaires, 265 primary samples were collected from the respondents, with a response rate of 88.33%. However, 27 responses were discarded due to outlier and incomplete answers. Finally, the study analysed 238 data using SPSS 26 and SmartPLS 3.2.9 software.

4. Data analysis

4.1 sample profile.

The socio-demographic profiles of the samples were estimated through frequency distribution analysis in SPSS software (see Table 1 ). It was found that the majority of the respondents were between 21 and 30 years old (82.8%), and 10.5% were between 18 and 20 years. Regarding education, the majority of the tourists (74.8%) were at the graduate level and 21.9% were at the postgraduation level. It also found that 22.3% of the respondents' families earned less than BDT 10,000 a month, 26.1% were between BDT 10,001 and BDT 20,000 income level and the remaining 31.9% of respondents' family incomes were between BDT 20,001 and BDT 30,000. Results also showed that male (77.3%) tourists have more travel tendency than female (22.7%) tourists. Regarding marital status, 88.2% of the total respondents fall into single compared to married respondents (10.9%). Regarding the profession, the majority (85.7%) of respondents were students, and the remaining 14.3% were service holders and self-employed. Finally, 14% of the respondents lived in the capital city, while the majority (86%) were from other parts of the country.

4.2 Measurement model

Before analysing the measurement model, we assessed the mean and standard deviation (SD) scores of the observed variables. The results suggested that mean and SD scores are between 5.28 and 2.38, and 0.87 and 1.43, respectively. Further, all the items under a single factor were evaluated with the dimension reduction technique, confirming that all the items represent their corresponding factor. Then, this study conducted PLS-SEM analysis in two steps: (1) testing the reliability and validity of the measurement model and (2) the assessment of the structural model.

Reliability and validity : The statistical programme evaluates the psychometric attributes of the measurement model and estimates the parameters of the structural model. The indicator reliability of the measurement model is assessed by looking at the item loadings. According to the guiding principle of reliability of measures, the loading of each item is required to be ≥ 0.70; however, in exploratory studies, loading of 0.40 is acceptable ( Hair et al ., 2013 ). The accepted loadings score in this study is ≥ 0.60 (see Table 2 ), which suggests that indicators are reliable. The composite reliability (CR) of each of the constructs exceeds the threshold of 0.7 ( Hair et al ., 2013 ); thus, the measurement model has satisfactory internal consistency reliability. Table 2 also shows that the CR for the constructs ranges between 0.73 and 0.87, which is above the recommended threshold of 0.7 ( Hair et al. , 2013 ).

Table 2 shows that all the constructs have substantial AVE scores within the thumb rule of ≥0.50 ( Hair et al ., 2013 ). The study assessed discriminant validity at the item level, examining the cross-loadings matrix. All items demonstrated substantial discriminant validity as the loading of the individual items within a construct was greater than their cross-loadings ( Barclay et al ., 1995 ). Further, the findings of the intercorrelation matrix suggest adequate discriminant validity of the measurement model as the square root of AVE of a specific construct exceeds the intercorrelations score of its corresponding construct(s) ( Fornell and Larcker, 1981 ) (see, Table 3 ). In addition, we found the maximum heterotrait-monotrait (HTMT) ratio of correlation value of 0.74, which is below the most conservative critical HTMT value of 0.85. The study results suggested substantial discriminant validity, both at the item and construct levels of the model. Thus, our findings confirmed the validity and reliability of the measurement model. Finally, we found a satisfactory model fit with a substantial score for standardized root mean square residual (SRMR) and normed fit index (NFI) that are 0.07 and 0.59, respectively.

4.3 Regression model

A PLS bootstrapping procedure was assessed with 5,000 iterations to determine the path relationships in the structural model. The results of hypotheses testing suggested that PE ( β  = 0.22, t  = 2.98), HM ( β  = 0.24, t  = 2.82) and Hbt ( β  = 0.27, t  = 3.69) had statistically significant influence on BI, and BI ( β  = 0.14, t  = 1.94) and Hbt ( β  = 0.21, t  = 2.97) had also statistically significant influence on actual ICT usage behaviour. Thus, H1 , H5 , H6 , H7 and H9 were supported. However, EE, SI, FC on BI, and FC on actual ICT usage behaviour do not explain significant influence. Thus, the empirical data did not support the hypotheses H2 , H3 , H4 and H8 . The detailed results of the path coefficient and corresponding t -statistics are summarized in Table 4 .

This study estimated the explanatory power of the exogenous latent constructs in the study model. The R 2 value of behavioural intention of ICT usage (38%) and actual ICT usage behaviour (10.3%) suggest the satisfactorily predictive ability of the research model. Our results of the f 2 effect size showed weak effects of latent constructs on the dependent constructs (see, Table 4 ). However, we found substantial predictive relevance of the exogenous latent constructs on their corresponding endogenous constructs (i.e. BI = 0.17 and ICTUB = 0.04) through testing the constructs' cross-validation redundancy ( Q 2 ) ( Chin, 2010 ). We also assessed the q 2 value for the model and found mixed results (see, Table 4 ). Our results of the f 2 effect size and q 2 value of predictive relevance of the model were consistent with the hypothesis testing results.

4.4 Interaction effect

We further assessed the moderating effects of price value and residency of the respondents using the process macro of Hayes (2013) in the SPSS. The interaction effect showed that PV has a statistically significant positive effect on the relationships between BI and ICTUB, whereas respondents' residency has a significant negative effect on the same relationship path (see Table 5 ).

4.5 Mediating effect

Existing studies have only looked into the direct relationship of FC to ICTUB and Hbt to ICTUB. In this study, we were further interested in examining the mediating relationship of those paths through BI to ICT usage. We also used the process macro of Hayes (2013) to assess the mediation effect of BI. Results found a significant positive mediating effect of BI on FC to actual ICT usage behaviour as there is no zero value between the lower limit and upper limit of the bootstrapping confidence interval (i.e. 0.0250 and 0 0.0944) (see Table 6 ). From a similar understanding, we were also interested in testing the mediation effect of BI in the relationship between habit and actual ICT usage behaviour. The result reveals no zero value between the lower and upper limits of the bootstrapping confidence interval (i.e. 0.0171 and 0.0969), which further signifies a positive mediating effect of behavioural intention.

5. Discussions and implications

The study was undertaken to meet the growing needs of ICT application in the tourism industry and examine the possibility of sustainable ICT-based tourism infrastructure in developing countries. In this study, we critically examine the technology acceptance and its use by tourists. We have found that most technology users in tourism are young adults between 20 and 30 years old. This segment of tourists has sufficient income for travelling, education and the ability to use ICT-enabled devices. We considered six antecedent latent constructs with two endogenous constructs essentially taken from the UTAUT and UTAUT2 model. The empirical data reflect substantial reliability and validity of the model. The assessment of path relationships reveals mixed findings.

The findings suggest that performance expectancy is a significant predictor that has a direct and positive influence on the behavioural intention of tourists. This result is consistent with the existing studies (e.g. Rahia et al ., 2019 ; Gupta and Dogra, 2017 ; Ibukun et al ., 2016 ). The finding indicates the users have a higher BI to ICT choices if higher PE is associated with using ICT. Thus, performance expectancy is a critical predictor of ICT usage intention. Existing literature asserts that the lower the efforts to understand a technology, the more the intention to adopt the technology ( Kang, 2014 ; Satama, 2014 ). The current study, however, shows an insignificant influence of effort expectancy on BI. This result is consistent with Lu and Su (2009) but contradicts the existing UTAUT ( Venkatesh et al ., 2003 ) and UTAUT2 ( Venkatesh et al ., 2012 ) models.

The statistical analysis related to the impact of social influence nevertheless reveals insignificant finding consistent with the existing studies (e.g. Mutlu and Der, 2017 ; Gupta and Dogra, 2017 ; Baptista and Oliveira, 2015 ). However, this finding is inconsistent with other studies (e.g. Venkatesh et al ., 2003 ). The result shows that SI becomes less important when individuals have limited experience with online services. Therefore, tourism organizations must ensure that users have a continuous positive experience when using ICT because their experience may affect their colleagues and fellow people. The relationship between facilitating conditions and BI to using ICT was also found insignificant. This result indicates that respondents show limited intention towards using ICT in the tourism sector, assuming that they do not have the relevant facilitating conditions that would enable them to do so. This result contradicts existing studies (e.g. Venkatesh et al. , 2012 ) that revealed the potential of facilitating conditions to significantly impact predicting BI. Other findings limit the effect of FC on predicting technology usage behaviour ( Skoumpopoulou et al ., 2018 ). Our study reveals that greater FC can increase the actual ICT usage, but not significantly. This finding is consistent with the existing research (e.g. Martins et al. , 2014 ).

Hedonic motivation has a direct significant positive influence on BI and the actual ICT usage behaviour of tourists. Similar findings were also found in earlier UTAUT-based studies (e.g. Baptista and Oliveira, 2015 ; Venkatesh et al ., 2012 ; Gupta and Dogra, 2017 ). These suggest that tourism practitioners need to establish ICT infrastructure in a way that they feel more entertaining, enjoyable and easy to use in tourism. Furthermore, the finding states that habit has a direct significant positive influence on the BI of tourists in responding to ICT usage in tourism. It is a fact that people who are habituated to ICT usage feel comfortable using it for other endeavours. The current findings support earlier studies on the habit-intention relationship (e.g. Luo et al ., 2010; Venkatesh et al ., 2012 ). Habit is also found as the predictor of ICT usage behaviour, assuming that they have the relevant habit that enables tourists' intention to use ICT, which is consistent with that of Gupta and Dogra (2017) . Thus, tourism providers need to address some features of ICT in the selling process that essentially associate tourists' habits. This means tourism products must comply with other products and services that are largely available on the online platforms.

The statistical analysis reveals that the usage intention of ICT in tourism services is an important predictor of actual ICT usage behaviour. The results are consistent with earlier findings ( Mutlu and Der, 2017 ; Gupta and Dogra, 2017 ). However, we find this relationship marginally significant. Our measurement model reflects below-average loadings for all three indicators of actual ICT usage behaviour. We predict that tourists of Bangladesh have a strong intention of ICT usage; however, ICT infrastructure, particularly in the tourism sector, is still undeveloped. It is for hope that Bangladesh government has considered ICT development as the fast-track agenda for its tourism expansion.

The study findings offer a novel contribution to the existing technology acceptance and usage-based literature, particularly in travel and tourism. Earlier UTAUT-based literature signifies the influence of six predictor variables (i.e. PE, EE, SI, FC, HM and Hbt) on behavioural intention for using ICT. This study has found a significant positive influence of PE, HM and Hbt to predict tourists' behavioural intention and actual ICT usage behaviour but has found no significant influence of EE, SI and FC on the same relationship paths. However, the noticeable contribution of this study refers to the moderation effect of PV and residency of the respondents. Our empirical findings statistically prove that PV can better interplay with BI, rather than as a predictor; it is especially applicable in the ongoing COVID-19 pandemic situation. Since 2020, people around the world have been incrementally using ICT for their necessities, whether for purchasing foods and shopping goods or household supplies. According to study data, people always prefer cost-effective ICT devices along with their virtual platforms. Similarly, current findings indicate that tourists perceive higher PV as they need more technology-driven services to make a travel plan during the pandemic situation that can better influence ICT usage behaviour. Thus, our understanding of the new role of PV offers further insights into technology acceptance and use in tourism. We also tested the moderating effect of residency as a distinct demographic variable, which has not been tested in earlier UTAUT-based studies. We found tourists who live in the capital city (i.e. Dhaka) are more inclined towards ICT usage than those who do not live in the capital city. This finding also suggests that tourists residing away from the capital city have limited ICT facilities. The findings reflect the modified UTAUT model. So policy-makers can use these findings as an important input for future decision-making purposes to expand ICT facilities across the country.

In addition, we are interested in examining the mediation effects of behavioural intention in the relationships of FC → ICTUB and Hbt → ICTUB to expand the theoretical contribution of the study. This research has established the mediating role of behavioural intention in FC → ICTUB and Hbt → ICTUB relationships. This finding affirms that BI significantly mediates the relationship between facilitating conditions and actual ICT usage behaviour. Although the direct effect of FC on actual ICT usage behaviour was not find significant, in the FC→ICTUB relationship through BI, FC can significantly influence the actual ICT usage behaviour of tourists. In line with the other studies, we established a link of the UTAUT model with TPB in tourism study by accommodating behavioural intention and actual usage behaviour, which is considered further theoretical contribution to the ICT-based tourism literature. Thus, the modified UTAUT model has confirmed its wider applicability from the findings of the current research. In particular, the study model largely contributes to the application and adoption of technology in tourism.

Besides the theoretical contributions, the study findings refer to several practical implications. First , the results uncover the core factors influencing tourists' actual ICT usage behaviour. Second , this study suggests that tourists' ICT usage behaviour is the function of various facets (see, Figure 1 ). Thus, the findings will assist tourism businesses in designing technology-oriented tourism planning and sales so that their services and offerings comply with the tourists' needs which resultantly can maximize the socio-economic welfare of the destination communities. Third , the findings will inform the government at both local and national levels about the growing demand for ICT in the tourism sector, which can assist the government in taking the necessary measures and formulating policy for the sound tourism industry in the developing country. As the Bangladesh government has taken several projects to develop ICT-based tourism, the findings benefit both public and private stakeholders for better awareness of the market needs for ICT application. In this way, the dissemination of shared information at different levels of government can assist in taking the necessary measures to formulate balanced policy in the growing demand for the ICT-enabled tourism sector in developing countries, and Bangladesh in particular. Last but not least , researchers and practitioners can also gain novel insights from the current findings for designing future tourism research agendas where technology is a key component.

6. Conclusion and future research directions

This study investigates the factors relating to tourists' ICT usage behaviour. Prior studies conclude to usefulness and ease of use as the pioneer attributes of ICT usage, leading to their adoption. The study findings support the ICT application for purchasing tourism services. The study findings confirmed salient constructs that influence ICT application in tourism to meet the demand of the 21st century.

The study, however, oversees several issues that would widen the scope of tourism research. This research depends only on cross-sectional data, which limits the depth of the findings. The findings can also be more generalized if they can be confirmed with the qualitative investigation. The limitations thus open up for further research scope. This research investigates tourists only; the ICT practices of other tourism stakeholders need to be included in future research, including the tourism supply chain. Future research may focus on the resistance to ICT acceptance and use in tourism. In addition, further studies could extend current research by analysing cross-cultural differences in determining the factors affecting behavioural intention of ICT usage in the tourism sector.

Modified UTAUT model

Socio-demographic characteristics of the sample

Source(s): Field survey

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Buhalis , D. ( 2003 ), eTourism: Information Technology for Strategic Tourism Management , Pearson education (Financial Times/Prentice-Hall) , Harlow .

Fishbein , M. and Ajzen , I. ( 1975 ), Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research , Addison-Wesley Pub. Co , Boston .

Oliveira , T. , Faria , M. , Thomas , M.A. and Popovič , A. ( 2014 ), “ Extending the understanding of mobile banking adoption: when UTAUT meets TTF and ITM ”, International Journal of Information Management , Vol.  34 No.  5 , pp.  689 - 703 .

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• Open access
• Published: 24 October 2018

Integration of ICT and tourism for improved promotion of tourist attractions in Ethiopia

• Mekonnen Wagaw   ORCID: orcid.org/0000-0001-9367-7528 1 &
• Feven Mulugeta 1  

Applied Informatics volume  5 , Article number:  6 ( 2018 ) Cite this article

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Information and communication technology (ICT) is revolutionizing the lives of people and operations of organizations. ICT has become a major driver of touristic sectors to effectively promote tourist attractions and services. As a result, many countries have succeeded in using ICTs and more precisely the internet to develop their tourism industries. However, the use of ICT in promoting tourist attractions in Ethiopia is still low. Hence, this survey research empirically studied the factors affecting the integration of ICT and tourism. The findings show that social influence, perceived usefulness, perceived ease of use, cost effectiveness, competitive advantage, and facilitating conditions such as experience, ICT resources and skill significantly affect behavioral intention to use ICT in the tourism sector of Ethiopia.

Information and communication technology (ICT) is revolutionizing the lives of people and operations of business organizations. Business organizations use ICT to process, store, disseminate, and promote their products and services globally. Beginning from the introduction of the internet, people have been accessing any information at anytime from anywhere. Hence, it is becoming inevitable to live without the aid of ICT.

Being the world’s largest economic endeavor, tourism is enhancing economies of countries. It accounts for 10% of the global gross domestic product (GDP) and 8.7% of the world’s jobs (Meriague 2014 ). Furthermore, due to globalization, strong tourism sector is considered to be a sign of a country’s social development, evolution, and progression (Meriague 2014 ).

Since tourism is one of the major sectors in today’s world, many countries are competing to attract tourists through all means of communication, and such communication has become a major driver of touristic sectors all over the world. The role of communication is to inform prospective tourists and influence their choices regarding touristic destinations and the type of touristic products they purchase.

Many countries have succeeded in using ICTs and more precisely the internet to develop their tourism industries. For example, Malaysia and Australia have been very successful in attracting many tourists through these means (Mohsin 2005 ). On the other hand, countries such as Iran have not been able to increase their number of international visitors, largely due to a lack of ICTs and internet development (Salavati and Hashim 2015 ).

Although Ethiopia possesses numerous natural, religious, historical, and cultural tourist attractions, utilization of tourism as a sector of the country’s economy goes five decades back. Considering the economic contribution of tourism to Ethiopia, the first tourism office was established in 1962 during the imperial regime (Ali 2017 ). During the military regime, the sector’s contribution reduced drastically but beginning from the 1990s, the number of tourists increased. Among Ethiopia’s fascinating tourist attractions, nine of them are UNESCO world heritage sites (Ali 2017 ). However, the tourism sector’s economic contribution and its potential are incomparable. According to research findings, unless a country promotes its tourist attractions to the rest of the world, it is impossible to increase the number of visitors. Hence, the integration of well-crafted ICT solutions is needed, and since we are living in a digitized world, it is necessary for the tourism industry to rely on ICTs and especially the internet as tool of international communication.

Problem statement

Since tourism is one of the major sectors in today’s world, many countries are competing to attract tourists through all means of communication and such communication has become a major driver of touristic sectors all over the world. The role of communication is to inform prospective tourists and influence their choices regarding touristic destinations and the type of touristic products they purchase.

To attract prospective tourists in this digitized world, modern ICT strategies are needed, and it is necessary for the tourism industry to rely on ICTs and especially the internet as tools of international communication.

Ethiopia has been attracting a huge number of foreign tourists visiting a variety of magnificent natural, cultural, historical, and religious heritages found in the country. However, the potential of those tourist attractions and number of visitors are incomparable. Moreover, provision of sufficient information to tourists and promotion using modern ICT services is very low. As a result of this, the sector’s contribution to the nation’s GDP is insignificant. The sector’s contribution to the nation’s GDP was 4.1% in 2015 (WTTC 2016 ).

Hence, this research has the objective of investigating the integration of ICT in the tourism sector for improved promotion of the Ethiopian tourist attractions so as to enhance the sector’s contribution to the country’s GDP. To empirically measure the factors affecting the integration of ICT and tourism, this research work adopted the Unified Technology Acceptance Theory (UTAUT) developed by Venkatesh et al. in 2003 and two additional constructs were included from related literature.

To investigate the integration of ICT in the Ethiopian tourism sector for improved promotion of the Ethiopian tourist attractions so as to enhance the sector’s contribution to the country’s development.

This research work empirically investigated sample tourism organizations to answer the following research questions: (1) what is the current status of the integration of ICT in the Ethiopian tourism sector? (2) What are the factors affecting the integration of ICT and tourism in the Ethiopian context? and (3) To what extent do these factors affect the integration of ICT and tourism in Ethiopia?

Theoretical framework

The fundamental theoretical framework of this research arises from a body of research in integration of ICT and tourism. With the objective of identifying determinants that affect integration of ICT and tourism for improved promotion of tourist attractions in Ethiopia, highly related and relevant literature on the issue are reviewed.

There are many theories on technology acceptance. For instance, Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen ( 1975 ) predicted that subjective norms and attitudes determine our behavioral intentions. Then, in 1989, Davis et al. came up with Technology Acceptance Model (TAM). As stated by this theory, intention to use a technology is determined by individual’s perceived usefulness and perceived ease of use and intention to use determines actual use of a technology. Next, diffusion of innovation (DOI) was created by (Rogers 1995 ). This theory states that “Individuals are seen as possessing different degrees of willingness to adopt innovations and thus it is generally observed that the portion of the population adopting an innovation is approximately normally distributed over time. Breaking this normal distribution into segments leads to the segregation of individuals into the following five categories of individual innovativeness (from earliest to latest adopters): innovators, early adopters, early majority, late majority, and laggards” (Rogers 1995 ). Besides, Task Technology Fit Theory (TTF) was developed by Goodhue and Thompson in 1995 . According to this theory, if information technology is capable to match with the tasks of users, IT is more likely to have positive impact on individual performance. Moreover, the Unified Technology Acceptance Theory (UTAUT) was developed by Venkatesh et al. in 2003. This theory states that users’ technology acceptance and subsequent usage behavior is determined by performance expectancy, effort expectancy, social influence, and facilitating conditions. According to Venkatesh et al., this theory used gender, experience, age and voluntariness of use as moderators for intention of use and behavior.

On the basis of UTAUT, the researchers of this study included two more constructs to increase the study’s scope. Hence, this research hypothesized that integration of ICT and tourism in promoting tourist attractions is affected by perceived usefulness, social influence, perceived ease of use, cost effectiveness, competitive advantage, and facilitating conditions, see Table  1 .

Based on the theoretical propositions of UTAUT and related relevant literature, this study proposed 11 hypotheses with regard to the integration of ICT and tourism for improved promotion of tourist attractions.

This research work is proposed to measure the following 11 hypotheses so as address research questions and achieve the stated objective. Table  2 summarizes these hypotheses.

Study design

In this empirical study, organizations in Ethiopia working at tourism were surveyed in consideration of positivist philosophical assumptions.

Positivist epistemology assumes that knowledge is measurable and it is objectively described (Heyman 2009 ). Thus, this survey research used quantitative approach throughout the data collection, analysis, and interpretation phases.

The primary data collection instrument was standardized questionnaire which constituted structured questions for each of the constructs using a 5-Likert scale ranging from 1 “strongly disagree” to 5 “strongly agree”. From the distributed 453 questionnaires, 429 were filled and returned back, yielding a response rate of 94.6%.

The research participants were selected based on stratified sampling technique. This is because the tourism sector encompasses varied institutes such as national and regional tourism and culture organizations, tour operators, travel agents, and destination marketing organizations. The criteria for stratification were (1) type of tourism enterprise, (2) service type, (3) experience, and (4) location.

The research population included all tour and travel operators in the country, and federal and regional tourism and culture offices were included. From this population, a sample of 429 samples was studies. The data analysis process started immediately after measuring the validity and reliability of the collected data. Since this research work deployed quantitative research, deductive data analysis method was used. The theory-based quantitative data were analyzed using the SPSS statistical software.

The data were collected from research participants after getting informed consents from study participants using an attachment on the research questionnaire. Their privacy and the information they provide were kept confidential. Most of the study participants were male and between the age of 35 and 45 whom account 73% and 33%, respectively (see Table  3 ). About 68% of the study participants were selected from privately owned tourism organizations. Most of these organizations have spent between 6 and 10 years (34%) providing tour and travel services which accounted 32% (Fig.  1 ).

Proposed research model

The analyzed data showed that a higher number of both computer and internet skilled workers were found in private touristic service providers than governmental providers (see Fig.  2 ). Most these workers have basic computer and internet skills than intermediate and advance skills in private and governmental touristic sectors. Study participants in private touristic organizations had better computer and internet skills than their counter parts.

Basic Computer and Internet Skills (n = 429)

Only 16% of the study participants working in touristic organizations reported that they had know-how of advanced computer and internet use skills. This implies that most of the employs do not know the intermediate and advanced services of their computers and the internet.

According to the respondents’ responses, touristic organizations use some promotional mechanisms to promote tourist attractions and touristic services (see Fig.  3 ). They use magazines, newspapers, flyers, websites, social media, and television and radio. However, flyer (31%) and magazine (26%) are the dominant tools. Although websites, social media, and television/radio have higher capability to be accessed by higher number of tourists globally, the percentage of these tools being used by participants low. According to the analysis result, website users are 23%, while social media and television/radio users are 8% and 2%, respectively.

Promotional Mechanisms (n = 429)

Reliability and validity of the model

Cronbach Alpha Coefficient was used for measuring the validity of the study. As per the Psychometric Theory (Nunnally and Bernstein 1978 ), the acceptable Cronbach Alpha value has to be greater than 0.7. The analyzed data of this study revealed that the seven constructs had above 0.7 (see Table  4 ). Similarly, although an acceptable composite reliability has to exceed 0.7, the result of this study showed that the seven constructs scored above 0.7. Furthermore, Average Variance Extracted (AVE) was used to evaluate convergent validity. Assuming that 50 or more of the variance of the indicators ought to be accounted, a study’s AVE result has to exceed 0.5 (Fornell and Larcker 1981 ). All constructs in this study resulted in AVE above 0.5.

The Pearson Product-momentum correlation coefficient ( r ) was used to test the hypotheses of this study. According to Kothari ( 2004 ), in case of measuring association between variables, Karl Pearson’s coefficient of correlation is the widely used measure. In correlation analysis, if the correlation coefficient exceeds 0.5 ( r  > 0.5), it shows significant relationship between variables.

To measure factors affecting ICT integration in the Ethiopian tourism, this study empirically analyzed eleven hypotheses. Subsequently, the analysis result showed that positive relationship between the variables at a significance level of 0.05 (see Table  5 ).

The first hypothesis (H1) states that there would be a significant positive association between social influence (SI) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.80 at p  < 0.05, which shows strong positive correlation. The second hypothesis (H2a) states that there would be a significant positive association between perceived usefulness (PU) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.84 at p  < 0.05, which shows strong positive correlation. The third hypothesis (H2b) states that there would be a significant positive association between perceived usefulness (PU) and social influence (SI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.85 at p  < 0.05, which shows strong positive correlation. The fourth hypothesis (H3) states that there would be a significant positive association between perceived ease of use (PEU) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.79 at p  < 0.05, which shows strong positive correlation. The fifth hypothesis (H4a) states that there would be a significant positive association between cost effectiveness (CE) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.78 at p  < 0.05, which shows strong positive correlation. The sixth hypothesis (H4b) states that there would be a significant positive association between cost effectiveness (CE) and perceived usefulness (PU) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.83 at p  < 0.05, which shows strong positive correlation. The seventh hypothesis (H5a) states that there would be a significant positive association between competitive advantage (CA) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.75 at p  < 0.05, which shows strong positive correlation. The eight hypothesis (H5b) states that there would be a significant positive association between competitive advantage (CA) and cost effectiveness (CE) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.66 at p  < 0.05, which shows moderate positive correlation. The ninth hypothesis (H5c) states that there would be a significant positive association between competitive advantage (CA) and perceived usefulness (PU) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.68 at p  < 0.05, which shows moderate positive correlation. The tenth hypothesis (H5d) states that there would be a significant positive association between competitive advantage (CA) and social influence (SI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.62 at p  < 0.05, which shows moderate positive correlation. The ninth hypothesis (H6) states that there would be a significant positive association between facilitating conditions (FC) and behavioral intention (BI) to use ICT in the tourism sector. The correlation coefficient between these two variables is r  = 0.82 at p  < 0.05, which shows strong positive correlation.

Furthermore, using multiple regression analysis between the dependent and independent variables, the standardized weights of predictors of behavioral intention (BI) were determined (see Eq.  1 ):

where Y  = variable to be predicted or dependent variable (DV), X  = variable that predicts Y , α  = intercept, β  = coefficient of X , ɛ  = regression residual (error).

The regression analysis revealed that, see Table  6 , standardized weights of the independent variables such as social influence (SI), perceived usefulness (PU), perceived ease of use (PEU), cost effectiveness (CE), competitive advantage (CA) and facilitating conditions (FC), and the dependent variable behavioral intention (BI):

According to McKelvey and Zavoina ( 1975 ), the coefficient of determination ( R 2 ) determines the proportion of variance in the dependent variable which is predictable from the independent variable. High coefficient of determination shows greater explanatory power of a regression model. Accordingly, the coefficient of determination of behavioral intention (BI) to use ICT in Ethiopian tourism is 0.844, which means that the regression model explained 84.4% of the variance in BI. Thus, this high value of R 2 depicts that the regression model is very good and the model statistically significant at F  = 381.1, confidence interval 95%, and p  < 0.001. Figure  4 shows the standardized coefficients or determination weights of the independent variables (SI, PU, PEU, CE, CA, and FC) on the dependent variable (BI).

Coefficient of regression analysis

The empirical demonstration of the proposed model enabled to identify predictors that determine intention to use ICT in tourism. Social influence has significant impact on the study participants’ behavioral intention to use ICT in tourism. As social influence to use ICT in tourism increases, the behavioral intention to use it also increases. There is also strong positive association between perceived usefulness and behavioral intention to use ICT in the tourism. This shows that users are more interested to integrate ICT in their tourism activities when they think that such technologies will be helpful. The finding also showed significant positive relationship between perceived ease of use and behavioral intention to use ICT in tourism. That means tourism organizations are motivated to use ICTs when such technologies are easy to use letting them to have more time for other activities. There was significant association between cost effectiveness and behavioral intention to use ICT in tourism. Cost-effective ICT facilities are more preferable by tourism organizations. Moreover, there is significant relationship between competitive advantage and behavioral intention to use ICT in tourism. This suggests that users’ behavioral intention to use ICT in tourism increases when they believe that the technology will improve their competitive advantages over their counter parts. Besides, the research illustrated significant association between facilitating conditions and behavioral intention to use ICT in tourism. Tourism organizations’ behavioral intention to use ICT increases when facilitating conditions such as users’ experience, resources, and background ICT skill increases.

Implications and conclusions

Using an extended Unified Technology Acceptance Theory (UTAUT), this empirical research found out factors affecting integration of ICT in tourism for promoting Ethiopian tourist attractions. Consequently, the study addressed the three research questions raised in the beginning of the research work. The first question was “What is the current status of the integration of ICT in the Ethiopian tourism sector?”. Although ICT plays significant role in promoting tourist attraction, particularly in the developed world and some developing countries, this research result shows that the integration of ICT in the tourism sector of Ethiopia is low. This shows that major improvements in the integration of ICT in promotion of tourist attractions in Ethiopia is required by the tourism stakeholders such as national and regional tourism and culture organizations, tour operators, travel agents, and destination marketing organizations. The second research questions states that “What are the factors affecting the integration of ICT and tourism in the Ethiopian context?”. The result showed that social influence, perceived usefulness, perceived ease of use, competitive advantage, cost effectiveness, and facilitating conditions were significantly associated with integration of ICT in tourism in Ethiopia. This implies that the improved model could be applicable to other developing nations that have similar settings or context with Ethiopia. The last research question was “To what extent do these factors affect the integration of ICT and tourism in Ethiopia?”. According to the analysis result, social influence, perceived usefulness, perceived ease of use, cost effectiveness, competitive advantage and facilitating conditions significant predictors of organizations perceived behavioral intention to use ICT in the tourism sector for promoting Ethiopian tourist attractions. Take a look at the correlation coefficients from Table  5 .

Limitations

This empirical study was carried out using data gathered from tourism organizations in Ethiopia. However, it would be better if data from other countries were included.

Future works

This improved model is a foundation for future research works on acceptance, adoption, or assimilation of ICT in tourism organizations particularly in the settings of developing nations. Future study could also focus on the technology or ICT adoption strategies of tourism organizations. Moreover, to measure the long-term effect of ICT adoption in the tourism sector, longitudinal study could be required.

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Authors’ contributions

The corresponding author, MW, worked from initiation to close out of the research work. The co-author, FM, also included her valuable contribution to this research particularly in reviewing related literature, data gathering and encoding activities.

Acknowledgements

Our first respected gratitude goes to those research participants who devoted their valuable time and effort and contributed their valuable experiences to this study. Second, our gratitude goes to Bahir Dar University for its financial support and ambitious plan and encouragement of researchers to engage in research. Finally, we would like to dedicate this research work to our families for their love, encouragement, patience, and unconditional support.

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We confirm that this research is an original work and there is no any individual or organization having competing interest on this study.

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• ICT and tourism integration
• ICT and tourism

Feed the Future Innovation Lab for Markets, Risk and Resilience

Testing ICT Solutions for Rural Development and Resilience

• by Sarah Sitts
• June 27, 2024

MRR Lab researchers test innovations to increase well-being and resilience of rural households. As rural households are far from population centers and spread out from one another, a common need is overcoming distance for the exchange of information. Information and Communications Technologies (ICT) can offer powerful solutions.

Specifically, multiple MRR projects are using rigorous research methods to test whether and how ICT solutions can be an effective way to: 

• Provide information to dispersed populations 
• Improve market efficiencies for farmers
• Reduce basis risk in index insurance

Below, we examine each of these aims and the type of ICT solution being tested by MRR researchers.  Each section includes a brief description of how the technology is applied in MRR projects, and an overview of its advantages, challenges, and limitations. 

Aim:  Provide information to dispersed populations 

Ict tool:  phone-based messaging .

With long distances and often poor roads, rural areas present challenges around both time and cost in meeting with large numbers of people. As a result, in-person meetings with extension agents or health workers, who have valuable information to pass on, can be rare. 

Phone-based messaging—whether text or audio—offers an inexpensive way to reach large numbers of people, with marginal costs of each additional message to an additional person often only a few cents. Phones can also be a critical way to reach people with limited mobility outside of their home—such as women in some socio-cultural contexts, or during a pandemic. The technologies require only a basic phone, ideal for targeting lower-income, rural households. 

Bulk SMS messaging does not require any pre-recording; messages can be quickly composed and sent. The MRR/ ALL-IN project,  Impact of Agro-Weather and Market Information on Productivity and Resilience in Farming Communities in Kenya is evaluating a Government of Kenya program that sends text messages via SMS to farmers with cultivation advice for their crop, based on the weather. 

Audio messages have the added advantage of effectively reaching people with limited literacy. Interactive Voice Response (IVR) is the phone system technology that allows incoming callers to interact with pre-recorded voice messages (commonly known as a phone tree in customer service contexts). MRR/ALL-IN project,  Digital Innovations to Improve Market Access for Horticultural Produce in Malawi uses such a platform for farmers to call in for extension information. IVR also supports automated, outbound calls. Digital Communication to Reinforce Nutrition and Household Resilience in Northern Ghana  sends audio messages to women with nutrition and hygiene information, and has observed impacts on behavior. 

Phone-based messaging presents some challenges for research teams. Phones are sometimes shared within households, in which case men tend to be the primary user, potentially complicating reach to women (and even knowing whether women have been reached). Some households frequently change their phone number. Further, increased scam calls can make people hesitant to accept a call from a phone number they don’t recognize. Lastly, while phone-based messaging can be impactful, it cannot replace the types of learning and social capital formed in group settings.  

Aim:  Improve market efficiencies for farmers

Ict tool:  digital marketplace  .

The price at which farmers sell their harvest is a critical determinant of their income. This price can be constrained by lack of information about prices in different markets. If farmers have incurred the cost and trouble of transporting their produce to a local output market, they generally accept that market’s price—unaware or unable to take advantage of higher prices elsewhere. However, prices can have significant geographic variation, depending on local levels of supply and demand. Digital marketplace platforms can allow buyers and sellers to much more easily find one another, leading to more consistent pricing among participants. 

Digital marketplace platforms may allow users to engage through a smartphone app or simple text messaging. Sellers (farmer or intermediaries) can post what they have for sale and receive and negotiate purchase offers. Some platforms have staff that help match buyers and sellers. 

The MRR/ALL-IN  Digital Innovations to Improve Market Access for Horticultural Produce in Malawi project is following a best practice by co-creating with their app with intended users. (The  Principles for Digital Development , endorsed by many bilateral and multilateral donors, including USAID, include: “Design with People: Good design starts and ends with people who will manage, use, and ideally benefit from a given digital initiative.”) 

Digital marketplaces that require a smartphone may see reduced uptake, given that smartphone ownership can still be low—though often growing—in rural areas, and digital skills may be limited. The MRR/ALL-IN  Digital Literacy, Output Market Access, and Demand for Rural e-commerce in Nigeria  project is studying methods of building digital skills to increase farmer uptake of digital marketplace platforms. 

A previous project,  Building Market Linkages for Smallholder Farmers in Uganda found that its digital marketplace intervention decreased price variability across markets in the project area. The researchers found significant increases in revenue only among larger-scale farmers, pointing to ongoing challenges of scale for farmers to reach buyers. Even with digital marketplaces, farmers may find continued advantages of aggregating their harvests with others for sale (through cooperatives, etc.). The digital marketplace was found to decrease the profits of one group: market intermediaries, who have earned an income by collecting farm produce from dispersed farmers and remote markets—and who had power to negotiate farm gate prices downward. (Full findings  here .) 

Many digital marketplace platforms that have been established around the world struggle to cover their operating costs after donor funding ends and as a result are discontinued. An impactful digital marketplace that has altered market relationships may need to take steps to minimize the disruption of its exit on market actors. 

Aim: Reduce basis risk in index insurance 

Ict tool:  picture-based insurance .

Index insurance makes it possible for insurance companies to cover smallholder farmers by removing the high time and travel costs of traditional claim assessors to verify individual losses. Instead, insurers use an objective, quantitative index, often based on weather conditions; a payout is triggered when the index values deviate a pre-determined amount over normal levels. A number of technologies are used to measure weather conditions for insurance indices, from satellites to in-ground soil sensors. Despite advances in these technologies, index insurance contracts continue to carry “basis” risk—the possibility that a farmer will experience significant losses due to a cause intended to be covered by the insurance contract (i.e., low rainfall), but the measures employed by the insurance provider do not measure levels sufficient for a payout trigger. 

To reduce basis risk, several MRR projects are testing solutions that use digital photography to enable remote evaluation of crop losses on individual fields. Picture-Based Insurance (PBI) is an insurance solution that triggers payouts when there is damage visible in pictures of insured crops. Over time, farmers or field agents take photos of crops in an app on a smartphone or tablet; the photos are then reviewed by a team of evaluators (usually in the capital city) to determine if a payout should be made. 

In the MRR project,  Crop Insurance Innovations to Build Trust in Financial Services for Agriculture in Ethiopia ,  PBI is introduced as part of a fail-safe mechanism in an index insurance contract. If a farmer claims to have experienced crop loss due to a covered reason that the index measurement failed to pick up, the farmer can submit photos to be assessed. In the MRR/ALL-IN project,  Adapting to Climate Risk with Mutual Weather-Index Crop Insurance in Nigeria ,  PBI is used as a payout mechanism alongside an index. (PBI can also be the sole mechanism for which payout eligibility is determined. In this case, when the payout trigger is only documented by photograph, the product is no longer considered index insurance.) 

Using digital photography is far cheaper than in-person claims visits to fields, though paying evaluators to go through and assess the photos can still be a significant cost. Critically, evaluating photographs to determine losses is more subjective than quantitative indices, and the incentives of photo evaluators should be considered. 

Over the past two decades, the use of ICT for development and agriculture has skyrocketed, and a great many projects around the world now use solutions described under the first two sections. But the use of rigorous, randomized controlled trial research on the solutions’ effectiveness, as conducted by MRR researchers, is less common. As more data is collected over the next year, the MRR Lab anticipates new insights to share around maximizing positive, cost-effective impacts with these technologies.  

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Out of the Centre

Savvino-storozhevsky monastery and museum.

Zvenigorod's most famous sight is the Savvino-Storozhevsky Monastery, which was founded in 1398 by the monk Savva from the Troitse-Sergieva Lavra, at the invitation and with the support of Prince Yury Dmitrievich of Zvenigorod. Savva was later canonised as St Sabbas (Savva) of Storozhev. The monastery late flourished under the reign of Tsar Alexis, who chose the monastery as his family church and often went on pilgrimage there and made lots of donations to it. Most of the monastery’s buildings date from this time. The monastery is heavily fortified with thick walls and six towers, the most impressive of which is the Krasny Tower which also serves as the eastern entrance. The monastery was closed in 1918 and only reopened in 1995. In 1998 Patriarch Alexius II took part in a service to return the relics of St Sabbas to the monastery. Today the monastery has the status of a stauropegic monastery, which is second in status to a lavra. In addition to being a working monastery, it also holds the Zvenigorod Historical, Architectural and Art Museum.

Belfry and Neighbouring Churches

Located near the main entrance is the monastery's belfry which is perhaps the calling card of the monastery due to its uniqueness. It was built in the 1650s and the St Sergius of Radonezh’s Church was opened on the middle tier in the mid-17th century, although it was originally dedicated to the Trinity. The belfry's 35-tonne Great Bladgovestny Bell fell in 1941 and was only restored and returned in 2003. Attached to the belfry is a large refectory and the Transfiguration Church, both of which were built on the orders of Tsar Alexis in the 1650s.  

To the left of the belfry is another, smaller, refectory which is attached to the Trinity Gate-Church, which was also constructed in the 1650s on the orders of Tsar Alexis who made it his own family church. The church is elaborately decorated with colourful trims and underneath the archway is a beautiful 19th century fresco.

Nativity of Virgin Mary Cathedral

The Nativity of Virgin Mary Cathedral is the oldest building in the monastery and among the oldest buildings in the Moscow Region. It was built between 1404 and 1405 during the lifetime of St Sabbas and using the funds of Prince Yury of Zvenigorod. The white-stone cathedral is a standard four-pillar design with a single golden dome. After the death of St Sabbas he was interred in the cathedral and a new altar dedicated to him was added.

Under the reign of Tsar Alexis the cathedral was decorated with frescoes by Stepan Ryazanets, some of which remain today. Tsar Alexis also presented the cathedral with a five-tier iconostasis, the top row of icons have been preserved.

Tsaritsa's Chambers

The Nativity of Virgin Mary Cathedral is located between the Tsaritsa's Chambers of the left and the Palace of Tsar Alexis on the right. The Tsaritsa's Chambers were built in the mid-17th century for the wife of Tsar Alexey - Tsaritsa Maria Ilinichna Miloskavskaya. The design of the building is influenced by the ancient Russian architectural style. Is prettier than the Tsar's chambers opposite, being red in colour with elaborately decorated window frames and entrance.

At present the Tsaritsa's Chambers houses the Zvenigorod Historical, Architectural and Art Museum. Among its displays is an accurate recreation of the interior of a noble lady's chambers including furniture, decorations and a decorated tiled oven, and an exhibition on the history of Zvenigorod and the monastery.

Palace of Tsar Alexis

The Palace of Tsar Alexis was built in the 1650s and is now one of the best surviving examples of non-religious architecture of that era. It was built especially for Tsar Alexis who often visited the monastery on religious pilgrimages. Its most striking feature is its pretty row of nine chimney spouts which resemble towers.

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Creative brand partnerships celebrate innovation in the fashion world

Saturday, 29 Jun 2024

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A passion for the animal world meets the design expertise of specialty Italian brand Poldo in a series of garments and accessories. Photo: Giorgio Armani

Collaborations between brands with celebrities, other artists, non-profit organisations and even other brands are common in the fashion industry.

Here are some of the latest campaigns and products to come out of partnerships in the fashion industry.

K-9 couture

Giorgio Armani recently teamed up with Poldo Dog Couture to create a collection of clothing and accessories for dogs.

The collection’s second drop features products made from contrasting blue and fuchsia ikat cotton, complementing the first drop in soft blue and black velvet.

A passion for the animal world, which has always been extremely important to Giorgio Armani, meets the design expertise of specialty Italian brand Poldo in a series of comfortable garments and matching special items.

Characterised by minimal design, high-quality materials and attention to detail, the selection includes a coat, jumper, bed, travel mat, collar, harness and leash, all available in various fabrics and colours, developed for dogs of all sizes.

Read more: Pack it up, pack it in: Carry all your essentials in a fashionable backpack

Nostalgic glamour

The Barbie x Aldo limited edition collection inspired by the Barbie DreamHouse features footwear and accessories, paying homage to the vibrant trends of the 90s with holographic materials and rhinestone embellishments.

One of the highlights of the range is the Barbie Runway sandal, a leather open-toe design that features a light pink upper and strappy details that wrap around the ankle and calf, adorned with small rhinestone hearts.

The collection also includes the standout Barbie Malibu pump, a hot pink, rhinestone-covered heel with a pointed toe and Barbie’s signature logo on the side.

Collaboration for a cause

As part of its partnership with Unicef, Louis Vuitton presents its new 2024 Silver Lockit collection, created in collaboration with house ambassador, actress Millie Bobby Brown, who is also a Unicef Goodwill Ambassador.

Just like with earlier collections, this range features the emblematic miniature Lockit engraved with the word “LEARN”, a term which holds particular meaning to Brown as it shines a light on her actions to raise awareness of children’s rights and well-being.

For the first time, a full collection including an individual earring, a bracelet, and a pendant has been produced, with the bracelet being released in four colours this year: red, blue, pink and black, and the pendant redesigned with a longer chain.

The brand has raised for than US$22mil (RM103.6mil) for Unicef since 2016 through sales of the Silver Lockit collection, which features new editions every year.

Innovative designs

Loewe launched an exclusive collection of jewellery in collaboration with artist Lynda Benglis, a known name in post-war American sculpture.

The collection features 20 designs, each conceived as a piece of wearable sculpture, created through Benglis’ exploration of motifs and techniques synonymous with her work including knotted, pleated, poured,

and extruded forms.

Original maquettes using foil, mesh, clay, wire and polyurethane have been translated into rings, cuffs, brooches, earrings and pendants in 18ct gold, sterling silver, aluminium, enamel and crystal.

Using materials as diverse as polyurethane, latex, sparkles, paper, plaster, bronze and water, Benglis has provocatively challenged our understanding of what sculpture can be.

Read more: Give leather a break and get carried away with the beauty of woven bags instead

Tribute to tartan

Fendi’s latest collaboration with Prickly Thistle – Cloth of Identity, comes in the form of a new, unique bag, part of the Baguette “hand in hand” initiative that celebrates Scotland’s rich textile heritage.

Prickly Thistle is a contemporary tartan design and manufacturing brand located in the Scottish Highlands, founded by native Claire Campbell, who adopts the philosophy of making less with natural resources, as close to home as possible.

Tartan, an inherently Scottish pattern, is a timeless symbol of pride, honour and identity as it indicates one’s belonging to a clan, company, family or place.

The Fendi Baguette “hand in hand” bag is entirely crafted from the exclusive tartan created for the project, officially registered within the Scottish Register of Tartans and protected by intellectual properties rights.

It is embellished with a plisse motif and small buckles on the sides, emphasising the punk spirit of the bag, with a shoulder strap crafted to resemble a belt.

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Tags / Keywords: fashion , trends , ready-to-wear , accessories , footwear , jewellery , bags

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