Digital Divide in E-Tourism

Reverté, Francesc González; Luque, Pablo Díaz

doi:10.1007/978-3-030-05324-6_109-1

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Widening the divide between digital haves and have-nots-On the other hand, smart technologies can also negatively impact the international community, increasing the digital divide between developed and developing countries and exacerbating global economic and social inequalities [70] . For example, in some cases, the rapid development of new technologies in wealthy countries can lead to the displacement of workers in developing countries, as jobs are automated or outsourced to areas with lower labour costs.…”

Section: Social and Economic Inequalitiesmentioning

confidence: 99%

Smart technology: Ecosystem, impacts, challenges and the path forward

Katuk,

Wan Abdullah,

Sugiharto

et al. 2023

Infor. Syst. Smart. City

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Smart technologies have become increasingly integral to modern society, bringing many benefits and opportunities. However, they also raise important questions about their impacts on individuals, organisations, and society and how everyone can move forward smoothly in integrating and adopting technology. Therefore, this study analysed and reviewed the literature to address the questions. The study provides an overview of the potential benefits and challenges of smart technologies and applications, including artificial intelligence, machine learning, natural language processing, robotics, and the Internet of Things. The review also explores the key entities involved in a smart technology ecosystem, including governments, businesses, and international communities, and each entity’s unique role in developing and implementing smart technologies. Additionally, the review highlights the importance of ensuring a smooth transition to smart technologies, including the need for effective policies, regulations, and ethical considerations. The key takeaways from this review are the need to balance the benefits and opportunities of smart technologies with the challenges and risks they pose. Individuals, society, businesses and governments must play their respective roles in ensuring that smart technologies are developed, implemented, and used in ways that are responsible, ethical, and sustainable.

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Section: Social and Economic Inequalitiesmentioning

confidence: 99%

Smart technology: Ecosystem, impacts, challenges and the path forward

Katuk,

Wan Abdullah,

Sugiharto

et al. 2023

Infor. Syst. Smart. City

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“…In doing so, the study highlights the need to develop digital literacy and spread awareness in the tourism sector as it embarks on a slow road to recovery following the pandemic. This is significant in view of the considerable digital divide in the tourism industry, which makes it difficult to compete in an information-rich environment (Reverte & Luque, 2020).…”

Section: Implications For Researchmentioning

confidence: 99%

Disinformation ‘gatecrashes’ tourism: An empirical study

Vasist

Krishnan

2023

Annals of Tourism Research

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“…The analysis of consumer behaviour and tourism demand is a relevant topic in e-Tourism research and has been the subject of numerous studies (Coenders et al, 2016;Gardella et al, 2021;Garín-Muñoz & Pérez-Amaral, 2011;Reverte & Luque, 2021). This may be because identifying and interpreting the factors that make travellers more likely to book tourism services online is of great relevance to policymakers and companies.…”

Section: Introductionmentioning

confidence: 99%

Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies

Mendieta-Aragón

Muñoz²

2023

invest. tur

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Digital markets have altered how economic agents interact and have changed the behaviour of tourists. In addition, the COVID-19 pandemic has shown that it is necessary to constantly monitor the evolution of digital consumer behaviour and the factors that influence it, as they are dynamic elements that evolve over time. This paper analyses digital inequalities and validates the main factors influencing tourists to book online tourism services. This research uses a set of microdata with 69,752 and 23,779 observations to analyse the booking mode of accommodation and transportation services, respectively, obtained from the Resident Travel Survey of the National Statistics Institute of Spain during the period 2016-2021. The article confirms variations in the online consumer profile and in the trip's characteristics. One of the most relevant findings is the narrowing of the generational gap in the online contracting of tourist services. However, there are remaining digital inequalities, such as regional inequalities and others based on the education level and income of tourists. It is also highlighted that different types of trips, depending on the destination, the type of accommodation or transport have a different propensity to be booked through digital purchase channels. The accessibility to big data sources and recent advances in machine learning models have also made the methodologies for analysing digital consumer behaviour evolve and must be incorporated into tourism studies. This study compares the predictive performance of different methodologies in the context of e Tourism. In particular, we evaluate the potential predictive power that could be obtained using machine learning techniques to explain consumer behaviour in e-Tourism and use it as a benchmark to compare it with the results obtained using traditional statistical methods. The selected predictive evaluation metrics show that the logistic regression statistical model outperforms the predictive power of the Multilayer Perceptron neural network and presents values very close to the maximum predictive power achieved by the Random Forest algorithm.

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Digital Divide in E-Tourism

Cited by 5 publications

References 27 publications

Smart technology: Ecosystem, impacts, challenges and the path forward

Smart technology: Ecosystem, impacts, challenges and the path forward

Disinformation ‘gatecrashes’ tourism: An empirical study

Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies

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