nep-tur New Economics Papers
on Tourism Economics
Issue of 2021‒05‒24
three papers chosen by
Laura Vici
Università di Bologna

  1. Responsible Recovery from COVID-19: An Empirical Overview of Tourism Industry By Jong, Meng-Chang; Soh, Ann-Ni
  2. The impact of coronavirus on tourism sector - an analytical study By Bouarar, Ahmed Chemseddine; Mouloudj, Kamel; Mouloudj, Smail
  3. Using social network and semantic analysis to analyze online travel forums and forecast tourism demand By A Fronzetti Colladon; B Guardabascio; R Innarella

  1. By: Jong, Meng-Chang; Soh, Ann-Ni
    Abstract: Over the past few decades, the world has seen a stunning transformation of the tourism industry. The tourism industry is one of the world's largest and fastest growing economic sectors. Thus, it is one of the key economic drivers in most developed and developing countries. Despite the rapid growth of the tourism industry, it is considered a vulnerable industry because it must accommodate the demand changes of tourists, shifts in economic environmental and other unexpected factors such as natural disasters and crises, especially the recent COVID-19 pandemic. Therefore, this paper aims to review the key determinants affecting tourism demand. In general, the tourist arrivals and tourism receipts have been chosen to proxy tourism demand in the existing literatures. In modelling tourism demand model, the independent variables consist of f income level of the tourists, tourism price, exchange rate, transportation cost, word of mouth and other key parameters. Various techniques such as ARDL, Markov-switching, and panel analysis have been utilized in previous studies to investigate the dynamic relationship between the variables in tourism demand model. The recent outbreak, COVID-19 is leaving tremendous impacts to the world economy, especially the tourism industry. In sum, the research on pre-crisis, mid-crisis, and post-crisis are equally important in gathering information for future tourism recovery and development plans.
    Keywords: COVID-19; Tourism demand; Tourism restart; Tourism recovery
    JEL: C10 C32 C33 I18 L83
    Date: 2021–05–17
  2. By: Bouarar, Ahmed Chemseddine; Mouloudj, Kamel; Mouloudj, Smail
    Abstract: The COVID-19 pandemic is still taking its toll on all aspect of human life and on all sectors of the global economy without any exception, perhaps among its most perceptible effects was its impact on the tourism sector as banning flight, and restricting mobility and travel were among the first measures that were meant to curb the propagation of the virus. Therefore the aim of the study is to shed light on the impact that COVID-19 pandemic has inflicted and still inflicting on tourism sector, in a bid to fulfill the purposes of the study we conducted an analytical study, the scale of the analysis was of an international level, with special focus on some of the best worldly recognized touristic destinations, along with alluding to the case of Algeria, the study concluded that the tourism sector in countries featuring high dependency on tourism revenues are affected most by the pandemic, while in Algeria the impact of the pandemic on the tourism sector was relatively weak since Algerian’s revenues from tourism are very low. Finally, the study suggested a set of recommendations for Algerian government to lessen the impact of corona virus on tourism sector.
    Keywords: Coronavirus; Crises; Hospitality; International Tourism
    JEL: I1 L8 L83
    Date: 2020–08–30
  3. By: A Fronzetti Colladon; B Guardabascio; R Innarella
    Abstract: Forecasting tourism demand has important implications for both policy makers and companies operating in the tourism industry. In this research, we applied methods and tools of social network and semantic analysis to study user-generated content retrieved from online communities which interacted on the TripAdvisor travel forum. We analyzed the forums of 7 major European capital cities, over a period of 10 years, collecting more than 2,660,000 posts, written by about 147,000 users. We present a new methodology of analysis of tourism-related big data and a set of variables which could be integrated into traditional forecasting models. We implemented Factor Augmented Autoregressive and Bridge models with social network and semantic variables which often led to a better forecasting performance than univariate models and models based on Google Trend data. Forum language complexity and the centralization of the communication network, i.e. the presence of eminent contributors, were the variables that contributed more to the forecasting of international airport arrivals.
    Date: 2021–05

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