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One of the main factors that can impact the cost of holidays to a particular destination is the exchange rate; exchange rate fluctuations impact the overall price of the holiday and should be expected to effect tourism demand. This paper aims to scrutinize the volatility of the real effective exchange rate between the source market relative to the holiday destination and tourism demand volatility, where the influence of disaggregated data is noted.

The study uses multivariate conditional volatility regressions to simulate the time-varying conditional variances of international visitor demand and exchange rates for the relatively mature Caribbean tourist destination of Barbados. Data on the country’s main source markets, the UK, the USA and Canada is used, where the decision to disaggregate the analysis by market allows the authors to contribute to policymaking, particularly the future of tourism marketing.

The volatility models used in the paper suggests that shocks to total arrivals, as well as the USA and UK markets tend to die out relatively quickly. Asymmetric effects were observed for total arrivals, mainly due to the combination of the different source markets and potential evidence of Butler’s (1980) concept of a tourist area’s cycle of growth. The results also highlight the significance of using disaggregated tourism demand models to simulate volatility, as aggregated models do not adequately capture source market specific shocks, due to the potential model misspecification. Exchange rate volatility is postulated to have resulted in the greater utilization of packaged tours in some markets, while the effects of the market’s online presence moderates the impact of exchange rate volatility on tourist arrivals. Markets should also explore the potential of attracting higher numbers of older tourist, as this group may have higher disposable incomes, thereby mitigating the influence of exchange rate volatility.

Some of the explanatory variables were not available on a high enough frequency and proxies had to be used. However, the approach used was consistent with other papers in the literature.

The results from the paper suggest that the effects of exchange rate volatility in key source markets were offset by non-price factors in some markets and the existence of the exchange rate peg in others. In particular, the online presence of the destination was one of those non-price factors highlighted as being important.

In most theoretical models of tourism demand, disaggregation is not normally considered a significant aspect of the model. This paper contributes to the literature by investigating the impact real effective exchange rate volatility has on tourism demand at a disaggregated source country level. The approach highlights the importance of modeling tourism demand at a disaggregated level and provides important perspective from a mature small island destination.

该研究采用多元条件波动回归来拟合相对成熟的加勒比海旅游目的地巴巴多斯的国际游客需求和汇率的时变条件方差。本研究逐一分析了该国主要客源市场（英国, 美国和加拿大）的数据, 从而为政策制定, 尤其是对今后的旅游营销做出贡献。

汇率是影响到特定目的地度假成本的主要因素之一。汇率波动会影响整体的度假成本, 并会影响旅游需求。基于按客源地分类的数据, 本文详细研究了客源市场相对于度假目的地的实际有效汇率的波动性以及旅游需求的波动性。

本文使用的波动模型表明, 汇率冲击对入境总人数以及美国和英国市场影响短暂。冲击对总入境人数产生的不对称效应, 主要是由于不同的客源市场加总和巴特勒（1980）关于旅游区增长周期概念所致。本文结论还凸显了使用基于客源地数据的旅游需求模型来模拟波动性的重要性, 因为加总数据不能充分捕获具体客源地市场的冲击从而产生模型设定作物。汇率波动会引起某些市场中团体游客的增加, 而目的地的线上热度影响会调节汇率波动对游客人数的影响。市场还应探索吸引更多老年游客的潜力, 因为该群体的可支配收入可能更高, 从而减轻了汇率波动的影响。

由于一些解释变量的数据频率不够高, 本文不得不使用一些替代指标。所使用的方法与文献中的其他论文一致。

该论文的结果表明, 在某些客源地市场, 汇率波动的影响会被某非价格因素所抵消, 而在另一些主要客源地市场, 固定汇率的存在刚好规避了汇率波动产生的影响。目的地的线上热度是重要的非价格因素之一。

在大多数旅游需求理论模型中, 按客源地拆分的数据通常不被视为模型的重要方面。本文的理论贡献则是通过研究实际有效汇率波动对不同客源国的旅游需求的影响强调了旅游需求建模中使用基于客源地数据的重要性, 并以一个成熟的小岛目的地为角度进行了阐述。

Uno de los principales factores que pueden afectar al costo de las vacaciones a un destino en particular es el tipo de cambio; Las fluctuaciones del tipo de cambio afectan a el precio general de las vacaciones y es normal que afecten a la demanda turística. Este documento analiza la volatilidad del tipo de cambio efectivo real entre el mercado de origen en relación con el destino de vacaciones y la volatilidad de la demanda turística, donde se observa la influencia de los datos desagregados.

El estudio emplea regresiones de volatilidad condicional multivariadas para simular las variaciones condicionales variables en el tiempo de la demanda de visitantes internacionales y los tipos de cambio para el destino turístico caribeño relativamente maduro de Barbados. Se emplean datos sobre los principales mercados de origen del país, el Reino Unido, los Estados Unidos de América y Canadá, donde la decisión de desagrerar el análisis por mercado permite a los autores contribuir a la formulación de políticas, en particular al futuro del marketing turístico.

Los modelos de volatilidad utilizados en el documento sugieren que los shocks en las llegadas totales, así como en los mercados de los Estados Unidos y el Reino Unido, tienden a desaparecer con relativa rapidez. Se observaron efectos asimétricos para las llegadas totales, principalmente debido a la combinación de los diferentes mercados de origen y la evidencia potencial del concepto de Butler (1980) del ciclo de crecimiento de un área turística. Los resultados también resaltan la importancia de utilizar modelos desagregados de demanda turística para simular la volatilidad, ya que los modelos agregados no capturan adecuadamente los shocks específicos del mercado de origen, debido a la posible especificación errónea del modelo. Se postula que la volatilidad del tipo de cambio influye en una mayor utilización de los paquetes turísticos en algunos mercados, mientras que los efectos de la presencia del mercado en linea (online) moderan el impacto de la volatilidad del tipo de cambio en las llegadas de turistas. Los mercados también deberían explorar el potencial de atraer un mayor número de turistas mayores, ya que este grupo puede tener mayores ingresos disponibles, mitigando así la influencia de la volatilidad del tipo de cambio.

Algunas de las variables explicativas no estaban disponibles en una frecuencia alta y se tuvieron que utilizar proxies. Sin embargo, el enfoque utilizado fue consistente con otros artículos en la literatura.

Los resultados del documento sugieren que los efectos de la volatilidad del tipo de cambio en los mercados de origen clave fueron compensados por factores no relacionados con los precios en algunos mercados y la existencia de la vinculación del tipo de cambio en otros. En particular, la presencia en línea (online) del destino fue uno de esos factores no relacionados con el precio destacados como importantes.

En la mayoría de los modelos teóricos de la demanda turística, la desagregación normalmente no se considera un aspecto significativo del modelo. Este documento contribuye a la literatura al investigar el impacto que la volatilidad efectiva del tipo de cambio real tiene sobre la demanda turística a nivel de país de origen desagregado. El enfoque resalta la importancia de modelar la demanda turística a un nivel desagregado y proporciona una perspectiva importante desde un destino insular pequeño y maduro.

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.

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.

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.

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.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

This paper aims to examine the impact of the existing information and communication technology (ICT) infrastructure and the development of the destination’s ICT on the tourism demand of international tourists in an emerging economy, Vietnam.

Using time-series data from 1995 to 2019, this study applies vector error correction model to analyse the impact of ICT infrastructure in the short- and long term.

The results of analysis show that although ICT infrastructure does not affect the number of international tourists in the short term, it positively contributes to tourism development in the long term. In addition, the results also show that in the short term, consumer prices have a negative impact on tourist arrivals while having a positive effect in the long run.

This study only considers the impact of ICT infrastructure on the whole without going into each factor reflecting different aspects of the ICT infrastructure. Moreover, this research only stops at the pre-pandemic period, so it has not shown the role of ICT infrastructure in travel and tourism demand during severe pandemic periods.

The research results are an essential basis to support the Vietnamese Government’s strategy to pursue an accelerated investment policy in ICT infrastructure, especially investment in the tourism and hotel industries. On the other hand, the research results also create more motivation and confidence for managers and operators of destinations in Vietnam to invest in ICT infrastructure and apply ICT in management.

This study adds to the literature on tourism–ICT linkages in an emerging tourism market directly between ICT infrastructure and international arrivals with a dynamic time series–based approach that considers the dynamics in the tourist demand identification model. In addition, this study used consumer price index to assess the impact of price on tourist demand instead of using the exchange rate or using the relative prices between the origin and destination countries.

本文探讨了现有 ICT 基础设施和目的地 ICT 发展对新兴经济体越南国际游客旅游需求的影响。

该研究使用 1995 年至 2019 年的时间序列数据, 应用矢量纠错模型 (VECM) 来分析 ICT 基础设施的短期和长期影响。

分析结果表明, 虽然ICT基础设施在短期内不会影响国际游客数量, 但从长远来看对旅游业发展有积极贡献。此外, 研究结果还表明, 在短期内, 消费价格对游客人数产生了负面影响, 而从长期来看, 则产生了积极影响。

研究结果是支持越南政府加快信息通信技术基础设施投资政策战略的重要基础, 特别是对旅游业和酒店业的投资。另一方面, 研究成果也为越南旅游目的地的管理者和经营者投资ICT基础设施和在管理中应用ICT创造了更多动力和信心。

本研究通过考虑旅游需求识别模型中的动态的基于动态时间序列的方法, 增加了有关新兴旅游市场中旅游与 ICT 联系的文献, 该联系直接在 ICT 基础设施和国际入境者之间进行。此外, 该研究使用 CPI 来评估价格对游客需求的影响, 而不是使用汇率或使用来源国和目的地国之间的相对价格。

This study aims to determine the mutual association between the volatility of macroeconomic indicators (MIs) and India’s tourism demand.

Bivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are applied to estimate the volatility spillover effect (VSE) from one market to another. Compared to the other methods, bivariate GARCH has wide acceptance for estimating the VSE. The monthly MIs and tourism demand data (2012–2021) are gathered for empirical analysis.

The evidence of the growth-led tourism (GLT) demand is seen. In the short term, tourism-led growth (TLG) is indicated. However, this TLG does not sustain itself in the long run. There is significant evidence in favour of the VSE from the MIs to the tourism demand ensuring GLT in India.

The main implication of the current study is to ignore the short-term influence of tourism demand on the economy because it does not sustain itself in the long run. However, the long-term influence of macroeconomic indicators on tourism demand should be seen with caution. Hedging, if possible, may be considered to protect the tourism sector’s interests from adverse economic fallouts.

There is a lack of studies on the volatility (especially on the VSE) between MIs and tourism demand. Hence, this study fills the research gap and presents a novel and unique contribution to the extent of the knowledge body on the topic and significantly contributes.

双变量GARCH模型用于估计从一个市场到另一个市场的波动溢出效应(VSE)。与其他方法相比, 双变量GARCH在估计波动溢出效应时得到了广泛的接受。收集2012-2021年的月度管理信息系统和旅游需求数据进行实证分析。

该研究旨在确定宏观经济指标(MIs)的波动与印度旅游需求之间的相互关系。

GLT(增长主导的旅游需求)的证据显而易见。从短期来看, 旅游导向型增长(TLG)可行。然而, 这种旅游导向型增长并不能长期维持下去。有重要的证据支持印度管理信息系统到旅游导向型增长的旅游需求波动溢出效应。

当前研究的主要启示是忽略了旅游需求对经济的短期影响, 因为从长远来看, 它无法自我维持。然而, 宏观经济指标对旅游需求的长期影响应谨慎看待。如有可能, 可考虑对冲, 以保护旅游业的利益不受不利的经济影响。

目前对管理信息需求与旅游需求之间的波动（尤其是波动溢出效应）的研究较少。因此, 本研究填补了这个研究空白, 并对该主题知识体系的内容呈现新颖而独特的促进作用, 有显著的贡献作用。

Los modelos GARCH bivariantes se aplican para estimar el efecto indirecto de la volatilidad (VSE) de un mercado a otro. En comparación con otros métodos, el GARCH bivariante goza de gran aceptación para estimar el VSE. Para el análisis empírico se recopilan los MI mensuales y los datos de demanda turística (2012–2021).

El estudio se centra en medir la relación mutua entre la volatilidad de los indicadores macroeconómicos (MI) y la demanda turística de la India.

Se observan indicios de GLT (demanda turística impulsada por el crecimiento). A corto plazo, se evidencia el TLG (crecimiento impulsado por el turismo). Sin embargo, este TLG no se mantiene a largo plazo. Existen pruebas significativas a favor del VSE de los MI a la demanda turística que garantizan el GLT en India.

La principal implicación del presente estudio es desestimar la influencia a corto plazo de la demanda turística en la economía porque no se sostiene a largo plazo. Sin embargo, la influencia a largo plazo de los indicadores macroeconómicos en la demanda turística debe considerarse con cautela. Por ello, la cobertura de riesgos puede plantearse para proteger los intereses del sector turístico de las repercusiones económicas adversas.

Existe una carencia de estudios sobre la volatilidad (especialmente en el VSE) entre los MI y la demanda turística. En consecuencia, este estudio realiza una aportación investigadora mediante una contribución novedosa y única en la ampliación del conocimiento sobre el tema de análisis.

The purpose of this study is to provide a textual approach to quantify the perception of uncertainty from management side and investigate how firms manage their overseas investment dynamics when perceiving an increase in economic policy uncertainty (EPU).

Using a textual analysis approach, the study evaluates firm-level perception of EPU. Based on the data from China's listed firms between 2007 and 2018, it examines the association between firm-level perception of EPU and overseas investment using probit model and fixed effects regression with robust standard error adjusted for heteroscedasticity and clustered by firm.

The study finds that the level of EPU perceived by individual firms is heterogeneous. Moreover, it finds that firm-level perception of EPU is positively associated with firms' overseas investment. When perceiving an increase in EPU, firms are more likely to invest abroad and their overseas investment is more diverse. Further analysis shows that the positive association between firm-level perception of EPU and overseas investment is weaker in firms with higher financing cost, investment irreversibility and management incentive but stronger in firms with more intensive industry competition. However, it does not find significant difference in the impact of firm-level perception of EPU on overseas investment of state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). The results are robust to using alternative measures of primary variables and to endogeneity concerns.

First, although the data on outward foreign direct investment (OFDI) at the national and provincial levels are comprehensive, the data on OFDI at the firm level are still relatively scarce. As the firm-level OFDI data become available, future study could be extended to OFDI flow. Second, future study could use other information disclosed by firms to evaluate their perception of EPU from host countries and examine the impact of bilateral EPU on overseas investment. Third, by evaluating firm-level perception of uncertainty in terms of a particular type of economic policies, such as fiscal policy, monetary policy, trade policy and foreign investment policy, future study could probe the sources of EPU affecting firms' overseas investment.

First, although uncertainty increases the volatility of firms' investment activities, firms can recognize and seize investment opportunities in an uncertain economic environment and make profits through resource integration. Second, as the association between firm-level perception of EPU and overseas investment depends on firm and industry characteristics, firms with higher financing cost, investment irreversibility and management incentive should be more cautious when making overseas investment decisions during uncertainty times. Third, governments should increase the transparency and the stability of their economic policies to help firms plan their investment policies.

The study extends the literature related to EPU measurement by constructing a firm-level perception index of EPU based on firms' annual reports using a textual analysis approach. Moreover, it sheds some light on the mechanism of how firms modulate their overseas investment activities under uncertainty.