Using social media photos to identify tourism preferences in smart tourism destination

Figueredo, Mickael; Cacho, Nélio; Thome, Antonio; Cacho, Andréa do Nascimento Barbosa; Lopes, Frederico; Araújo, Maria Valéria Pereira de

doi:10.1109/bigdata.2017.8258423

Cited by 15 publications

(10 citation statements)

References 17 publications

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“…A large number of studies have delved into how travelers revert to technologies such as smartphones, social networks, Big Data, IoT, and websites to improve travel satisfaction (Cimbaljević et al, 2019; Huang et al, 2017). A significant challenge facing the tourism sector is how to use these data to detect tourist preferences (Figueredo et al, 2017; Gajdošík, 2019). Online social networks connect millions of tourists while they are traveling and offer them services such as information, tour guides, accommodations, and so on.…”

Section: Citation Mappingmentioning

confidence: 99%

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Bastidas-Manzano

Sánchez‐Fernández²,

Casado‐Aranda

2020

Journal of Hospitality & Tourism Research

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Despite the emergence of the notion of smart tourism destinations in tourism research, few studies have delved deeply into the topic, and the little research focusing on the subject has not offered an overview of its evolution, current scope, and potential domains of interest. This study aims to fill this gap in literature by applying a bibliometric approach. The current research offers a comprehensive review of 258 investigations published between 2013 and 2019 in outlets indexed by the ISI Web of Science database and other relevant publications on information technology related to hospitality and tourism. The findings indicate the domains of tourism, journals, and themes that merit consideration in smart tourism destination future research and serve both professionals and academics as a guide for future lines to explore.

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Section: Citation Mappingmentioning

confidence: 99%

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Bastidas-Manzano

Sánchez‐Fernández²,

Casado‐Aranda

2020

Journal of Hospitality & Tourism Research

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“…As discussed earlier, IoT, big data and machine learning can provide significant impetus to changes in heritage management. IoT facilitates collection of visitor data on preferences, behaviour and characteristics that can be used to leverage improvements in customer satisfaction while reducing overall operating costs (Figueredo et al , 2017; Huang et al , 2017). The corollary is that these advances can be used to modernize and enhance heritage tourism that is engaging, low cost and more sustainable.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Big data can improve decision-making in tourism, improving strategies and performance (Buhalis and Leung, 2018). IoT can help to collect information on tourist behaviour and preferences via the connection of devices that can be used to improve customer satisfaction while reducing costs (Figueredo et al , 2017; Huang et al , 2017). Such advances can be used to streamline and improve heritage tourism to offer a high-quality, low-cost, sustainable digital visitor experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Heritage protection and tourism income: the tourism heritage Kuznets curve

Barnes

2022

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Purpose Protecting a nation’s heritage is an essential element for sustainable development. Heritage attracts tourism and drives tourist income, as well as bringing wider economic, social and environmental benefits. This study aims to improve understanding of the relationship between heritage protection spending in countries and economic development via tourism revenues via economic logic and the Kuznets curve. Design/methodology/approach This study applies economic modelling to test for the presence of a Kuznets curve. This study uses rare available matched data on heritage protection spending from UNESCO and on travel and tourism total contribution to gross domestic product (GDP) from the World Travel and Tourism Council. Findings This paper finds evidence of a negative quadratic relationship between heritage protection spending per capita and tourism GDP per capita, whereby increases in heritage spending initially lead to more tourism income (typically less developed nations), but then tourism spending increases more rapidly for smaller increases heritage spending on top of this initial investment (typically more developed nations). No countries are yet at the stage where they reap high tourism income from much lower levels of heritage spending. Research limitations/implications The findings suggest that the future of heritage tourism will be very different to that of today; future tourism is likely to develop significantly through innovations in management approach and advanced technologies to manage the balance between demand and heritage degradation. Practical implications The predictive model allows us to plot future possibilities for heritage tourism, which in turn enables more informed decision-making regarding the planning and management of future heritage resources and tourism demand. Tourism heritage institutions and their benefactors should further plan and invest in innovations (such as technologies and management practices) to advanced economically and move along the tourism heritage Kuznets curve. Originality/value To the best of the author’s knowledge, this paper formally tests, for the first time, for the presence of a Kuznets curve in the relationship between heritage protection spending and tourism income. It provides original evidence of the value of applying the underlying theory of the Kuznets curve in a tourism context.

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“… Digitization: Generally, tourists perform tasks electronically such as searching, purchasing tickets, making reservations and communicating (Zhu et al , 2014). Moreover, smart tourism destination solutions can improve the automation of activities for tourism organizations so that the competitiveness of destinations is enhanced (Figueredo et al , 2017). Information exchange: This refers to the simultaneous dissemination and exchange of information related to tourism activities (Buhalis and Amaranggana, 2013; Zhu et al , 2014).…”

Section: Research Categories Of Smart Tourism Destinationsmentioning

confidence: 99%

Section: Research Categories Of Smart Tourism Destinationsmentioning

confidence: 99%

Smart tourism destinations: a systematic review

Shafiee

Ghatari

Hasanzadeh

et al. 2021

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Purpose This study aims to present a systematic review of smart tourism articles using a meta-synthesis method. Effective systematic reviews are essential for assisting stakeholders in implementing smart destinations. A systematic comprehension of studies on smart tourism is needed regarding the various components of smart tourism destinations, the metrics to map these components and their expected results. This study creates a framework for understanding how smart tourism destinations are theorized and developed. Design/methodology/approach Based on the meta-synthesis approach, this study collects, analyzes and synthesizes relevant research in smart tourism published in online databases by following a predetermined review protocol. Findings This study contributes to the discourse on smart tourism destinations by increasing the knowledge on the subject of smart destinations in regard to different categories. The selected articles were analyzed according to the proposed research questions and classified into three main categories: components, measurement and outputs. This study presents a new archetype for developing smart destinations and addresses efforts to bridge the gap in this research field. Practical implications This paper is noteworthy for stakeholders because it provides a comprehensive vision into the components that influence the growth of smart destinations. The advantage of the proposed methodology is that it creates a framework for understanding how smart destinations are theorized. Furthermore, it is helpful to use qualitative methodologies that efficiently allow the analysis of related literature and that also offer conceptual insights. Originality/value The findings provide information that can be used to help shape a fully conceptualized understanding of the smart destinations concept and can also prove important in providing a guide for policymakers and stakeholders in the tourism industry who seek to intelligently develop tourism destinations.

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Using social media photos to identify tourism preferences in smart tourism destination

Cited by 15 publications

References 17 publications

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Heritage protection and tourism income: the tourism heritage Kuznets curve

Smart tourism destinations: a systematic review

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