An analysis of the relationship between ICT infrastructure and international tourism demand in an emerging market

Nguyen, Quyet; Nguyen, Cong Van

doi:10.1108/jhtt-11-2021-0331

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…The impact of ICT is huge for tourism and hospitality sector; their application enters all aspects of business. It is becoming more and more important to adapt certain businesses to digital transformation (Nguyen and Nguyen 2022). A growing trend of identifying work in tourism with digitization is driver of development of hospitality sector.…”

Section: Evolution Of Hypothesismentioning

confidence: 99%

THE READINESS FOR IMPLEMENTING ICTs IN HOTEL INDUSTRY ORGANIZATIONS: EVIDENCE FROM THE REGIONAL LEVEL

Stanković,

Portolan

2023

Tourism in Southern and Eastern Europe

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Purpose – Information and communication technologies (ICTs) usage is one of the key elements in the competitive struggle and achieving a competitive advantage in the hotel industry. The main goal of this paper is to empirically analyse the existence of readiness to implement ICTs in a sight of the potential challenges which hotels face due to the lack of such technology in their business. Methodology – This article, through researching the attitudes of managers at the top, middle and operational management levels, analyses the readiness of the organizations in hotel industry to implement ICTs as well as the main opportunities and advantages of their application. The data were analysed by acting the methods of descriptive and inferential statistics in order to test the significance of the examination attitudes obtained from respondent. Findings – The obtained results indicate the importance of the readiness for companies in the hotel industry to apply ICT and the inclination for digital transformation of business. There is exigency to provide an insight into the state of readiness for the implementation of digital technologies in the hotel industry, as well as a proposal for the values that these actions result. Contribution – The main contributions of this paper are fulfilling the literature gap towards the mentioned research issue for the implementation of ICT in the hotel industry at the regional level and providing insight from the viewpoints of managers rather than from technology experts.

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Section: Evolution Of Hypothesismentioning

confidence: 99%

THE READINESS FOR IMPLEMENTING ICTs IN HOTEL INDUSTRY ORGANIZATIONS: EVIDENCE FROM THE REGIONAL LEVEL

Stanković,

Portolan

2023

Tourism in Southern and Eastern Europe

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How information and communication technologies contribute to rural tourism resilience: evidence from China

Zhong,

Zhang,

Zhang

et al. 2024

Electron Commer Res

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Forecasting international tourist arrivals in South Korea: a deep learning approach

Zhang,

Lin,

Yhang

2024

JHTT

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Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

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An analysis of the relationship between ICT infrastructure and international tourism demand in an emerging market

Cited by 4 publications

References 56 publications

THE READINESS FOR IMPLEMENTING ICTs IN HOTEL INDUSTRY ORGANIZATIONS: EVIDENCE FROM THE REGIONAL LEVEL

THE READINESS FOR IMPLEMENTING ICTs IN HOTEL INDUSTRY ORGANIZATIONS: EVIDENCE FROM THE REGIONAL LEVEL

How information and communication technologies contribute to rural tourism resilience: evidence from China

Forecasting international tourist arrivals in South Korea: a deep learning approach

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