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This study examines the influence of economic policy uncertainty on financial flexibility before and during the coronavirus disease 2019 (COVID-19) pandemic. Few prior studies have examined this association specifically for debt and cash flexibility.

Using quarterly data from 2016 to 2022, 1014 observations were collected from the S&P Capital IQ database for listed tourism companies in India. The pre-pandemic period is defined as 2016 Q1 to 2020 Q1, whereas the pandemic period is from 2020 Q2 to 2022 Q3. The data are analysed using ordinary least squares, probit, logit and difference-in-difference (DID) estimation.

The evidence of this study suggests a negative association of economic policy uncertainty with debt flexibility during the COVID-19 pandemic. The findings also suggest that COVID-19 induced economic policy uncertainty results in high cash flexibility. This meets the expectations for the crisis period, as firms are likely to hold more cash and less debt capacity to manage their operations. The results are robust for various estimation techniques.

This study is limited to one emerging country and is specific to one non-financial sector. Future research could extend to more emerging countries and include other non-financial sector companies.

The findings of this research are useful for tourism sector managers as they can effectively manage their cash and debt flexibility during crisis periods. They will need to prioritise cash flexibility over debt flexibility to manage operations effectively. Policymakers need to provide clear and stable economic policies to help firms manage their debt levels during a crisis.

To the best of the author's knowledge, no existing studies have investigated the influence of economic policy uncertainty on the financial flexibility of tourism companies before and during the COVID-19 pandemic. Furthermore, this study establishes a novel set of critical determinants, such as economic policy uncertainty.

As digital technologies expand access to new forms of legalized gambling, including sports betting and online gaming, it is important to assess the impact of macroeconomic and equity market outcomes on fund flows into gambling. The authors’ findings will be of interest to policymakers and the gambling industry, as various forms of gambling, including day trading, gain broad public acceptance.

The authors examine the impact of macroeconomic forces, business cycles, and financial market wealth on gambling. The authors propose a nonlinear model linking aggregate gambling expenditures to macroeconomic, stock market, and gambling industry variables. The authors estimate the proposed model using nonlinear estimation procedures.

The authors find that price of wagering, incomes, and supply of gambling opportunities are the primary determinants of wagering demand. Aggregate wagering is negatively impacted by realized stock returns and market volatility, but rises during recessions.

To the best of the authors’ knowledge, the questions posed and addressed in this manuscript have not been addressed in prior literature.

This study aims to investigate the knowledge domain and development trends that appear in the scholarly corpus on religious tourism.

The most common themes evolving in the religious tourism research field are figured out by conducting keyword and trend analyses using the bibliographic data collected from 988 research articles published in Social Science Citation-indexed journals listed in the Web of Science database between 1992 and 2022.

It has been found that the number of publications has increased exponentially. European countries are the major contributors to religious tourism research. Research has mainly clustered around the areas of spiritual experience, identity, cultural heritage, pilgrimage, tourist attitude, behavior and satisfaction. Judaism, Hinduism and Buddhism are religions that have received relatively little research attention.

Future research should focus on the sustainability of religious tourism sites, mitigating the adverse impact of the commercialization of religious tourism products and recovering religious tourism activities from the COVID-19 impact.

The findings are useful for corporate practitioners, site managers and entrepreneurs to take advantage of the valuable opportunities this segment offers. These findings are useful for scholars and policymakers in acquiring the latest knowledge of developments in this field.

The insights obtained by using a holistic approach are valuable for religious tourists who want to understand the importance of visiting religious sites.

This study identifies key themes that have evolved in religious tourism. In so doing, it presents an agenda for pushing this research corpus forward.

This article examines dynamic volatility spillovers between stock index returns of four main hospitality sub-sectors in US during the coronavirus disease 2019 (COVID-19) pandemic. These are tourism and travel, hotel and lodging, recreational services and food and beverages. Volatility spillovers are explicitly used as accurate and informative proxies for risk contagion between sectors during turbulent times.

The authors employ dynamic conditional correlation-generalized autoregression heteroskedasticity (DCC-GARCH) and wavelet coherence analysis (WCA) to analyze the phenomenon. The authors’ timeframe is divided into three main sub-periods, namely the pre-pandemic, the first wave and the second wave periods.

This study’s results reveal immense negative shocks in returns of all four sub-sectors on the Black Monday (8th March 2020). Moreover, high volatility persistence was observed during both waves with an exception of tourism and travel which exhibited lower volatility persistence during the second wave. The authors discovered magnified contagion effects between tourism and travel, hotel and lodgment and recreational services during the first wave of the pandemic with tourism and travel being the main volatility transmitter. Lower magnitudes of spillovers were observed between food and beverages and other sub-sectors with a decoupling effect being evident during the second wave.

This study’s findings contribute to the contagion theory by providing evidence of disproportional volatility spillover among hospitality sub-sectors despite being exposed to similar turbulent economic conditions.

Crucial implications can be drawn from this study’s findings to assist in risk management, asset valuation and portfolio management. The importance of close monitoring, safety measures, international diversification and adequacy of liquid assets during health crises cannot be stresses enough for hospitality firms. Retail investors, speculators and asset managers can take advantage of this study’s findings to design trading strategies and hedge against risk.

A body of knowledge pertaining to effects of crises such as COVID-19 on hospitality stocks has been proliferating. Nonetheless, there is still a relative dearth of empirical literature on volatility spillover between hospitality sub-sectors especially during periods of rising economic uncertainties.

In this paper, the authors investigate the inbound tourism demand elasticities of the Middle East and North African (MENA) countries. The authors emphasize the role of external and internal conflicts, world gross domestic product and relative prices over the period 1995–2017.

This study applies the heterogeneous panel data estimators based on the fully modified-OLS (FM-OLS), dynamic-OLS (DOLS) and the recently developed method of moments quantile regression (MMQR).

The empirical results indicate that the effect of external and internal conflicts on inbound tourism demand is negative and inelastic with external conflict having a stronger effect. The effect of both classifications of conflicts diminishes as the market share of the tourist destination increases. In addition, the role of the world GDP on tourism demand is positive and elastic, suggesting that tourism is a luxury good while an increase in relative prices diminishes inbound tourism demand.

The paper, therefore, concludes that if policy measures are not put in place to curtail incidences of conflicts, economic growth in these countries may suffer setbacks. This by implications could affect the attainment of the sustainable development goals (SDGs) targets.

Purpose: This study links SDG goal 9 of industry, innovation strategies and another infrastructural environment to branding relating to destination and interactive marketing. Digital marketing tools with various applications aim to offer hi-tech services to customers in interactive marketing services, namely multiple goods and services, data and innovative techniques in the tourism and travel sector. Exploring the study would add to the existing literature supporting interactive marketing procedures and destination branding. Branding relating to destinations fosters tourists around the globe facilitating economic growth and development and supporting the local economies.

Need for the Study: It is observed from the literature that very few studies have been identified across the globe from various researchers on the interactive marketing and destination branding that ensures brand loyalty and reassesses the intent of the tourists just before the epidemic pandemic in the form of COVID-19. Artificial Intelligence, as part of information technology, offers various interactive marketing services in the form of different social media marketing strategies, attractive websites for tourists and travel providers and image building on destination branding. This study would help fill the marketing gap, which results in branding relating to destination, brand loyalty and reassessing the intent of various tours and travel plans just before the pandemic.

Methodology: The study focused on the literature, demonstrating the stimulus organism methodology and examining the impact of potential marketing strategy, which is interactive focusing on the destination branding, loyalty relating to the brand and also procedures to revert with the intent that would motivate and facilitate the customer’s confidence showering the loyalty relating to the brand in the travel and tourism sector.

Findings and their Practical Implications: The study revealed that the marketing relating to interactive methodologies in the hi-tech digital approaches ought to be carried out to create opportunities for prospective tourists willing to get information about various tourists destinations with the help of various marketing techniques such as different social media applications, easy access of websites for accessing the tourist destinations and relevant information, accessing images pertinent to the tourism destinations with the ease of chat box and providing suitable audios and video sources to the potential customers.

The purpose of this paper is to examine the 2015 Union Cycliste Internationale (UCI) Road World Cycling Championship in Richmond, Virginia.

An array of eclectic methods included in situ witness observations of several of the races, 21 semi-structured random interviews and multiple discussions with elements of UCI, the city of Richmond’s planners, residents and business owners during and after the championship in fall 2015.

This paper has uncovered five findings: First, the material investment was considerably smaller than that of other events (common good criterion CGC i – good governance); second, pre-planning was critical to successfully hosting the event; third, this event included not only two entities as one would expect at first glance, but many (common good criterion CGC ii – good management); fourth, a filière approach to community service and the exploitation of clustered thematic activities was of critical importance to successfully hosting the 2015 UCI Road World Cycling Championship; and fifth, this event enabled the opportunity to market other city and regional assets (common good criterion CGC iii – good outcomes).

Cities hoping to bid for events ought to consider hosting unique events such as road championships. Those cities will benefit from careful event pre-planning, responsible event hosting and post-event legacies in the form of socio-economic and mindscape memories.

Bidding and pre-event planning is increasingly seen as an opportunity to locate, create and develop support for common good urban projects, which will remain valuable after the event is over or which will need to be built in spite of the bid’s result.

This study fills an unresearched gap on the impact of events on a city’s future non-motorized sustainable transportation priorities.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.