COVID-19 and the Unsettled Questions of Digital Governance

A meeting on e-commerce at the World Trade Organisation, source: WTO photos

How will ongoing debates on digital governance shape the future of digital development?

One of the important implications of the COVID-19 pandemic has been the further acceleration of growth in the digital economy and the expansion of cross-border digital flows. Driven by the pandemic, large and small businesses across the world adapted their business models by shifting completely or partially to internet-based models. As a result, digital transactions, within and across countries, increased dramatically over the last couple of years. While measurement of such flows is challenging, some reports estimate that global Internet Protocol (IP) traffic was expected to more than triple between 2017 and 2022 and that domestic and international IP traffic in 2022 will exceed all Internet traffic up to 2016.

This growth has intensified the debates around digital governance. These debates have begun prior to the pandemic as the growth in the digital economy on the one hand and the move by some states to adopt “interventionist” digital policies drove intense discussions on how to govern the digital world and where to draw the line between sovereignty of states on the one hand and the need to adopt international rules and norms to maintain the global nature of the digital world.

The success of some countries, particularly China, in building digital capacities and firms through selective, and often limited, integration in the global digital market have intensified those debates as other countries began to look to the Chinese model as a guidance for their digital strategies. As a result, questions around the appropriate forum to govern digital issues, the limits of state power vis-à-vis international rules and norms, and the applicability of such rules to different economies have dominated digital policy debates for a number of years. Some of those debates have taken place within regional blocs such as the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) while others have taken place within international bodies that are focused on digital governance such as the Internet Governance Forum (IGF).

While these debates continued in different forums, a difficult link between digital governance and the international trading system was established largely as a result of pressure from the advanced economies. Issues such as data flows, source code and algorithms, and cybersecurity, amongst others, became increasingly linked to trade regimes with recent trade agreements adopting digital chapters that include rules on a range of digital issues.

While the link between trade agreements and the digital world is not always clear (while some cross-border flows are trade flows, a huge percentage of these flows are not trade-related, and the two are very difficult to separate), the trade regime offered an established forum with the ability to produce binding and enforceable rules to govern the digital space. Today, negotiations on digital issues continue in a number of multilateral, regional, and bilateral trade forums as states pursue different visions of the digital economy and how to govern digital flows. The advanced economies, in particular the United States, the EU, Japan, and Australia in addition to emerging economies such as India and China are the key drivers of these processes.

The implications of such processes for development issues are profound, and often overlooked. The economic and social value of data, for instance, is not yet fully understood and, as such, it is unclear what adopting binding international rules around data flows will exactly entail. Some argue that developing countries will benefit from global open data policies as it gives them an opportunity to integrate in the digital economy and to achieve technological progress. Others, however, question this position and argue that developing countries should resist any rules that could undermine their policy space to adopt digital policies. As discussions on these issues continue in different forums, more engagement from digital development scholars is needed.

In the context of the dramatic expansion of the digital economy driven by the pandemic, better understanding the implications of digital governance for digital development particularly in lower-income and smaller developing countries is crucial to help shape the processes driving digital governance and to ensure that digital rules do not undermine the objectives of economic and social development that are increasingly tied to digital issues in today’s world.

The Organisational Context for Successful ICT4D Practitioners

How can their organisational context best support those who implement ICT4D projects?

People – designers, builders, operators, champions – are critical to the successful implementation of ICT4D projects.  The digital development organisations that employ these practitioners already know that.  But what they know far less about is how to create a supportive organisational context that will improve ICT4D practitioner performance and, hence, ICT4D project success rates.

I have therefore been undertaking field research in East Africa designed to tease out components of supportive context, based on interviews in five organisations which were a mix of NGOs and social enterprises.  To date, I have identified six “habits of highly-effective digital development organisations”:

1. Reinforcing Mission Congruence

The most-effective contexts were those in which ICT4D practitioners were given a clear sense of how their work fitted with the organisation’s wider mission, which typically related to social impact.  As well as giving practitioners the bigger picture of their contribution, this also helped create a unity of purpose with shared goals of making a difference.

2. Strong Non-Monetary Rewards

Money is tight in most digital development organisations but they can successfully motivate their practitioners with non-monetary rewards.  Flexibility on working hours and opportunities for work-life balance came up repeatedly in this category, alongside recognition from peers of one’s contribution.

3. Involvement in Monetary Reward-Setting

A role for non-monetary rewards does not mean money is unimportant – it is!  But just as important as the amount was the process by which pay was calculated.  Supportive contexts were those where pay was transparently calculated and openly discussed, and hence where ICT4D practitioners felt involved in the process of decision-making.

4. Support for Career Progression

To make their best contribution to ICT4D projects, practitioners needed to feel that they were making progress in their careers.  Though often backed by direct mentoring, organisational support here varied by career stage.  Early-career practitioners had a strong perceived need for skills development: not narrow task-specific skills but a broad and hybrid mix of technical and non-technical capabilities.  This worked best where their organisation offered them a mix of different roles but also ensured access to high-quality digital tools and infrastructure.  Mid-career professionals also wanted growth opportunities but they focused less on technical skills and more on being given the autonomy and responsibility to develop leadership capabilities.

5. Meeting Personal Goals

ICT4D practitioners give their best to their projects and their organisation when they have a perception of reciprocation; particularly in terms of being helped to achieve their personal goals.  Goals of social impact and skills-building for career progression were mentioned already, but supportive contexts could provide other things – networks of stakeholder relations to build social capital for the future, and facilitation of personal development projects.

6. Socio-Emotional Support

ICT4D often has a technical bias but practitioners worked best in cultures attuned to the human side of work, and in which they felt their whole selves were recognised.  These were organisations that were more like “families” than “well-oiled machines”; in which peers and managers cared about wellbeing and would take time to listen and engage with personal problems; and in which socialisation and hence a sense of belonging were actively encouraged.

These findings may themselves have some specificity to East African digital development organisations.  Each organisation may thus need to identify the dimensions of organisational support that will work with its particular ICT4D practitioners.  Nonetheless, these six habits should be a useful starting point for all organisations.

If you would like further details about the six habits, or my ongoing work using these to develop interventions for digital development organisations, then feel free to contact me: epiphania.kimaro[@]manchester.ac.uk

Photo credit: Gladness Mayenga

Global South researchers succeeding against the odds: how are they different?

Understanding the Context

How are some global South researchers able to overcome contextual constraints and become highly cited?

There is a clear research divide between the global South and the global North[1] in terms of research investment and capabilities. The average national expenditure on research and development in Southern countries is 0.38% compared to 1.44% in Northern countries[2]. The number of researchers per million population in 2017 was 713 in the global South and 4,351 in the global North[3]. This had implications on the volume and impact of scientific outputs produced by the global South in comparison to the global North. Excluding China and India, in 2018 global North countries produced an average of more than 35,000 scientific and technical journal articles per country while global South countries produced 4,000 journal articles per country, out of which less than 2% made it to the top 1% most cited articles globally. This can be partially explained by the lower levels of investment and English proficiency, smaller relative populations of researchers, institutional exclusion factors and/or biases against Southern researchers when it comes to accepting their papers in top tier journals or awarding grants.

Despite all of the aforementioned challenges, there are a few Southern researchers who are able to achieve better outcomes than their peers. Such researchers could provide valuable insights and lessons that might help to better understand and even mitigate the current North–South divide in research outputs and citation. This blog post will highlight some of the valuable insights emerging from our recently published study that attempted to uncover publication-level and individual-level factors underlying the outperformance of information systems researchers in Egypt.

The Method

 This study employed the “data-powered positive deviance” (DPPD) methodology that uses digital datasets to identify positive deviants (those performing unexpectedly well in a specific outcome measure that is digitally recorded, mediated or observed) and potentially also to understand the characteristics and practices of those positive deviants (PDs) if digitally recorded.

Three main steps were conducted to identify and characterise PDs, as shown in Figure 1:

  • In the Define step, we defined our study population and the performance indicators that will be used to assign a score for each researcher. The study population comprised 203 information system researchers in Egyptian public universities. Six well-known citation metrics (h-index, g-index, hc-index, hi-index, aw-index and m-quotient) were calculated for each researcher using Publish or Perish and Google Scholar bibliometrics. Several citation metrics were used to avoid putting certain groups at a disadvantage due to factors such as the length of their research career, the size of their research departments, the age of their papers or their publication strategies.
  • The Determine step aims at identifying the PDs based on the scores calculated in the previous step. In this study, PDs or outliers were defined as researchers who significantly outperformed their peers in at least one of the six citation metrics. The interquartile (IQR) method was used to identify those outliers based on their deviation from the median, i.e. lying beyond the 1.5*IQR added to the third quartile in at least one of the six citation metrics.
  • The third step, Discover, consists of three main stages. In Stage 1, primary data was collected through in-depth interviews from a sample of PDs to explore practices, attitudes and attributes that might distinguish them from non-PDs. During Stage 2, the key findings from Stage 1 plus other predictors of research performance drawn from the literature were used to design a survey tool. That survey then targeted the whole population and tested if the proposed differentiators were significantly different between the two groups. Finally, in Stage 3, the Scopus database was used as the basis for analysis of researcher publications; extending and validating some of the findings identified in the previous stages.

Figure 1: Summary of the applied DPPD method

 What we found

 A combination of data sources (interviews, surveys, publications) and analytical techniques (PLS regression, topic modelling) were used to identify significant predictors of positively-deviant information system researchers. One of the key findings was that PDs contributed to the creation of roughly half (48%) of the publications and achieved nearly double (1.7x) the total number of citations of non-PDs despite representing roughly one-eighth (13%) of the study population. While there were significant predictors of outperformance that are structural (e.g. gender, academic rank and role, workplace perceptions), our focus in this post is on highlighting factors that are transferable i.e. practices and strategies that are to some extent within the control of the individual researchers. Table 1 provides a summary of such factors.

Individual-Level Predictors

 

Positive Deviants

Non-Positive Deviants

Travelling abroad to obtain their PhD degree

More PDs got their PhDs from global North countries 

Fewer non-PDs got their PhDs from global North countries

International research collaborations

Frequently part of multi-country research teams 

Seldom part of multi-country research teams

Co-authorship

Published more papers with foreign reputable authors

Published fewer papers with foreign reputable authors

Securing research grants and travel funds 

Secured more grants and travel funds

Secured fewer grants and travel funds

Research approach

Less inclined to do radical research

More inclined to do radical research

Student supervisions

Supervised a larger number of postgraduate students

Supervised a smaller number of postgraduate students

Capacity development  

More PDs took scientific writing and English writing courses

Fewer non-PDs took scientific writing and English writing courses

Publication-Level Predictors

Length of paper

Longer papers

Shorter papers

Length of abstract

Longer abstracts

Shorter abstracts

Length of title

Longer titles

Shorter titles

Number of authors and affiliations

More authors and affiliations

Fewer authors and affiliations

Number of references

More references

Fewer references 

Publication type

More journal articles and fewer conference papers

More conference papers and fewer journal articles

Quality of journals

Higher SJR journals

Lower SJR journals

Publishers

Published more in Elsevier Journals

Published less in Elsevier Journals

Topics

PDs publish fewer papers covering business process management and neural networks and published more papers in wireless sensor networks and hardware systems

Non-PDs publish more papers covering business process management and neural networks and published fewer papers in wireless sensor networks and hardware systems

 Table 1: Significant transferable predictors of outperformance

The analysis also included a visualization of topic prevalence over time for the PD corpus and non-PD corpus as presented in Figure 2. It shows topics, such as Classification Models, where PDs were early movers and then they were followed by NPDs. There is a greater prevalence of Expert Systems and GIS-related topics in the PD corpus in comparison to the NPD corpus. Conversely, there is lower prevalence of Neural Networks and Business Process Management & Process Mining. There are also topics that had very similar proportions over time for both groups, such as Social Network Mining.

Figure 2: Topic proportions of PD corpus (left) and non-PD corpus (right) over time

 Implications for practice and policy

This analysis cannot, of course, guarantee that applying these factors more broadly would lead to the same outcomes achieved by PDs. Nonetheless, there would be value in individual Southern researchers reflecting on the research- and paper-related behaviours that have been shown associated with positively-deviant research profiles. For instance, Southern researchers work in contexts of resource limitation, hence, research grants and travel funds are of outmost importance. Including partners from Northern universities (as PDs do) increases the chances of securing the funds as those partners are more familiar with grant procurement processes and more experienced in writing proposals. Studying abroad also seems to put Southern researchers at an advantage as it does not just equip them with the technical know-how and the degree needed to pursue their academic careers, but also helps them establish channels of collaboration with their supervisors and their PhD granting universities, long after they returned to their home countries. Those long standing relationships provide further access to research grants either directly or via joint grant applications.

In terms of paper-related strategies, Southern researchers could avoid low-visibility local conferences and can select journals instead as they are more likely to deliver citations. Publishing with more authors (domestic and international) could also help pay for journal publication fees, with fees split across more authors or paid from overseas sources. Publishing with foreign authors could also help Southern researchers overcome the institutional biases[4] among editors, reviewers in single-blind or open review systems, and readers. PDs’ preference for working on established research areas rather than on radical research topics may also help in relation to institutional barriers, with research that builds incrementally on existing ideas and literature being more likely to be accepted for publication by referees, and cited by others working in the established area. Hence, Southern researchers seeking more citations could consider contributing to mainstream topics that build on existing work. Along the same lines, having multiple authors and affiliations increases the likelihood of citations, as each author has their own network and bringing those networks together can increase readership. Similarly, publishing papers with a larger number of references increases paper visibility through citation-based search in databases that allow it, such as Google Scholar, and through the “tit-for-tat” hypothesis i.e. authors tend to cite those who cite them.[5]

Higher education institutions and higher education policy makers may also reflect on the findings, and consider strategic implications for training, resource provision, collaborations, etc. For example, English and scientific/formal writing courses were associated with PD performance; such courses could be prerequisites for starting a PhD research. There could be more academic training designed around research grant writing and providing guidance on funding bodies that researchers can apply to. International research collaborations appeared as an important predictor of PDs; so, university senior managers and policy makers can explore ways to reduce barriers and increase opportunities for overseas PhD study, post-PhD return, and ongoing joint research projects with global North universities.

Citation rates are, of course, not the “be all and end all” of research: there are and should be other motivations and indicators of research. However, we hope the findings presented here can provide valuable “food for thought” for global South researchers.

 ________ 

[1] The terms “South” and “Southern” will be used to refer to countries classified as upper-middle income, lower-middle income, and low income. Accordingly, the terms “North” and “Northern” will be used to refer to countries that are members of the OECD (Organisation for Economic Co-operation and Development) or are classified as high-income economies by the World Bank based on estimates of gross national income per capita.

[2] Blicharska, M., Smithers, R. J., Kuchler, M., Agrawal, G. K., Gutiérrez, J. M., Hassanali, A., Huq, S., Koller, S. H., Marjit, S., Mshinda, H. M., & Masjuki, H. (2017). Steps to overcome the North-South divide in research relevant to climate change policy and practice. Nature Climate Change, 7(1), 21–27.

[3] World Bank. (2020). Science & Technology Indicators. World Bank.

[4] Karlsson, S., Srebotnjak, T., & Gonzales, P. (2007). Understanding the North-South knowledge divide and its implications for policy: A quantitative analysis of the generation of scientific knowledge in the environmental sciences. Environmental Science and Policy, 10(7–8), 668–684.; Gibbs, W. W. (1995). Lost science in the third world. Scientific American, 273(2), 92–99.; Leimu, R., & Koricheva, J. (2005). What determines the citation frequency of ecological papers? Trends in Ecology & Evolution, 20(1), 28–32.

[5] Webster, G. D., Jonason, P. K., & Schember, T. O. (2009). Hot topics and popular papers in evolutionary psychology: Analyses of title words and citation counts in evolution and human behavior, 1979–2008. Evolutionary Psychology, 7(3), 147470490900700300.

 

Understanding smart tourism and smart tourism ecosystems

As a fashionable and novel tourism agenda, the theory of smart tourism is still evolving but the literature brings out three perspectives. First, tourists . The primary starting point of smart tourism is to fully satisfy the tourists’ need for scenic spots and create more value for them. In this sense, smart tourism is seen as a new pattern of tourism operation, which regards the tourist as the basic service object [1][2][3]. Second, managers (e.g., in government and tourism enterprises). Smart tourism is about achieving a comprehensive and thorough system which aims to offer accurate, convenient and ubiquitous tourism information applications, as well as a range of travel services [4]. In this case, the managers refer to the local scenic area managers and staff, government officials, and the company offering the technology  [5]. Third, technology. Although the main point of the smart tourism system is the service, the capabilities and foundation of smart tourism is technology[6]. This refers to the highly systematic, detailed interaction between physical tourism and information resources [7], including digital data exchange[8].

Accompanied by the development of information and communication technologies (ICTs), the evolution of tourism goes through three stages: traditional tourism, e-tourism and smart tourism. Traditional tourism refers to people moving to countries outside their usual environment[9]. With evolution of technology, e-tourism emerged to address users’ interactivity and web-based technology was used to enhance the tourism experience and information governance, which is considered an early step of smart tourism[10].Subsequently, e-tourism evolved into smart tourism, building on technology infrastructure and ICTs (e,g. cloud service, big data). Importantly, smart tourism emphasises explicitly the support of variously smart activities and value-addition through the dynamic interaction between different actors[11]. The difference between e-tourism and smart tourism is detailed in the table below.

  e-Tourism Smart Tourism
Sphere digital bridging digital & physical
Core technology websites sensors & smartphones
Travel phase pre-& post-travel during trip
Lifeblood information big data
Paradigm interactivity technology-mediated co-creation
Structure value chain / intermediaries ecosystem
Exchange B2B, B2C, C2C public-private-consumer collaboration
Table 1 Comparison of e-tourism and smart tourism [12]

Smart tourism systems

Based on the development of ICTs, the smart tourism system (STS) is regarded as a complex system based on a digital service platform to support smart tourism, which addresses the innovation service provided to different stakeholders[7] [9]. Specifically, it emphasizes the actors’ intelligent demands of value-added through information service creation, delivery and exchange[13]. Thereby, the STS is characterized by value co-creation through the integration of sources into both micro and macro levels[3][8]. The table below presents the established system in China based on literature and reality [14][15].

STS Sub-system STS Functionalities STS Instances
Forecasting system Passenger flowWeather forecastQueuing-time forecast Wind speed sensorHumiture sensorImage recognition
Panoramic virtual reality system Virtual tourism experienceVirtual community Panoramic photographyVR 
Intelligent management system Smart vehicle and transportReal-time traffic Crowd handling RFID·        Video surveillanceTourist-flow monitoring 
Smart guide system Scenic spot interpreterPersonalized tours route E/Robot tour map Mobile app Electronic map Voice navigation
Smart recommend system Scenic spot recommendation Recommended route QR codeMobile app
Table 2 Smart tourism systems in China

Figure 1 demonstrates the conceptual model of a non-profit smart tourism system, which is summarized from the current literature[15].The objective is that the STS provides service to government, enterprise, tourists, and residents [16]. Importantly, these applications are not isolated and individual but also service the interaction requirements. For example, from the perspective of tourism, the STS faces the tourist (T), the connection between tourists (T2T), and the interaction needs between the tourist and government (T2G).

Figure 1 A conceptual model of structuring STS adapted from [15]

Smart tourism ecosystem (STE)

When linking the ecosystem with the smart tourism system, an STE can be established, which contains the characteristic of both the smart tourism system and wider ecosystem components. An STE consists of two layers.  The information ecology layer emphasizes the interaction between humans, firms, technology and their environment[9].This indicates the importance of the different actors related to information behaviour and information systems. The service system layer emphasizes the interactions through institutions and technologies to provide services to the beneficiaries, to exchange resources, and to co-create value [12].

Gretzel and Werthner defined the STE as “a tourism system that uses smart technology to create, manage, and provide smart tourism services/ experiences”[17].It is characterized by intensive information sharing and value co-creation. Buhalis and Amaranggana indicated that an STE aims to provide sustainable, enhanced/rich, valuable travel service and experience [6].To reach this, digital ecosystems that provide technical resources and facilitate interactions within and between stakeholders form the core of STE[17]. In other words, the generation of tourism experiences always requires extensive coordination and cooperation between different industry stakeholders and government players [18].

As shown in Figure 2, Gretzel & Werthner proposed a schematic representation of an STE [17].Their study describes an STE as an interactive space supported by a digital ecosystem and containing various types of actors, which are distinguished as tourism consumers (TC), residential consumers (RC), tourism suppliers (TS), other industry suppliers (OS), government agencies, destination marketing organizations (DMO) and intermediaries. These actors are not necessarily discrete, as a single player can play multiple roles. Moreover, this model is also adopted by other researchers like Brandt et al, who proposed an social media analytics (SMA)-enabled STE model, which also indicated the RC, TC, TS and government as vital actors [19]. The tourism consumers (TCs) have resources and, because they have access to the digital ecosystem, can be organized among themselves or mixed with closely related residential consumers (RCs) and act as producers. Through smart technology, tourism providers (TS) or other business-focused groups can connect and create new service offerings. In an ecosystem, the main source of ‘food’ for a ‘species’ is data/information, and the effective conversion of this food into rich tourism experiences can lead to a longer life for the ‘species’. Telecom companies and banking/payment support service providers representing other industry providers (OS) are vital ‘predators’ in the ecosystem and provide essential information to the system. Destination marketing organizations (DMOs) perform traditional information brokering, marketing, and quality control functions, while various intermediaries facilitate transactions through innovative data and devices [9]. Government utilization of social media in the STE could enhance the development and the value co-creation of local tourism [12].

Figure 2 Smart tourism ecosystem [17]

To conclude, STE studies regard the STE as a complex information and service ecosystem, which involves multiple actors like government, tourists, platform providers, residents etc. The main characteristics of STE are 1) the dynamic interactions between multiple actors; 2) the value co-creation during the actors’ interactions; 2) sustainable development; 4) the creation and exchange of tourism resources; 5) the innovation service. Thus, STEs could facilitate the interaction between actors, the exchange and creation of tourism resources, and value creation. This could further improve the tourism experiences and enhance the sustainable development of smart tourism. 

References

[1] Yao, G. (2012). Analysis of smart tourism construction framework. Nanjing University of Posts and Telecommunications (The Social Sciences Edition), 14(2), 5e9.

[2] Fu, Y., & Zheng, X. (2013). China smart tourism development status and counter- measures. Development Research, 4, 62e65.

[8] Hunter, W. C., Chung, N., Gretzel, U., & Koo, C. (2015). Constructivist research in smart tourism. Asia Pacific Journal of Information Systems, 25(1), 105–120.

[3] Shafiee, S., Ghatari, A. R., Hasanzadeh, A., & Jahanyan, S. (2019). Developing a model for sustainable smart tourism destinations: A systematic review. Tourism Management Perspectives, 31, 287-300.

[4] Jin, W. (2012). Smart tourism and the construction of tourism public service system. Tourism Tribune, 27(2), 5e6.

[5] Huang, C., Goo, J., Nam, K., & Yoo, C. (2016). Smart tourism technologies in travel planning: The role of exploration and exploitation. Information & Management, 54(6), 757-770. doi: 10.1016/j.im.2016.11.010

[6] Buhalis, D., & Amaranggana, A. (2013). Smart tourism destinations. In Z. Xiang, & L. Tussyadiah (Eds.), Information and communication technologies in tourism 2014 (pp. 553e564). Cham, New York: Springer.

[7] Law, R., Buhalis, D., & Cobanoglu, C. (2014). Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management, 26(5), 727–750.

[9] Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188.

[10] Werthner, H., & Ricci, F. (2004). E-commerce and tourism. Communications of the ACM, 47(12), 101-105.

[11] Lamsfus, C., Martín, D., Alzua-Sorzabal, A., & Torres-Manzanera, E. (2015). Smart tourism destinations: An extended conception of smart cities focusing on human mobility. In information and communication technologies in tourism 2015 (pp. 363-375). Springer, Cham.

[12]Park, J. H., Lee, C., Yoo, C., & Nam, Y. (2016). An analysis of the utilization of Facebook by local Korean governments for tourism development and the network of smart tourism ecosystem. International Journal of Information Management, 36(6), 1320-1327.

[13] Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management. 

[14] Zhu, W., Zhang, L., & Li, N. (2014). Challenges, function changing of government and enterprises in Chinese smart tourism. Information and Communication Technologies in Tourism, 10.

[15] Wang, X., Li, X. R., Zhen, F., & Zhang, J. (2016). How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tourism Management, 54, 309-320. 

[16] Zhang, L., Li, N., & Liu, M. (2012). On the basic concept of smarter tourism and its theoretical system. Tourism Tribune, 27(5), 66–73.

[17]Gretzel, U., Werthner, H., Koo, C., & Lamsfus, C. (2015). Conceptual foundations for understanding smart tourism smart tourism ecosystems. Computers in Human Behavior, 50, 558-563.

[18]Mill, R. C., & Morrison, A. M. (2002). The tourism system. Kendall Hunt

[19]Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smart tourism  ecosystems. Information & Management, 54(6), 703-713