nep-tur New Economics Papers
on Tourism Economics
Issue of 2020‒03‒09
two papers chosen by
Laura Vici
Università di Bologna

  1. Operationalisation of destination management organisations in Romania By OECD
  2. Tourism Demand Forecasting with Tourist Attention: An Ensemble Deep Learning Approach By Shaolong Sun; Yanzhao Li; Shouyang Wang; Ju-e Guo

  1. By: OECD
    Abstract: This report provides an analysis of the state of play for tourism in Romania and examines opportunities and challenges for destination development at the subnational level. In addition, it includes an operating manual providing practical guidance for tourism practitioners, setting out the steps required to establish and operate an effective DMO. It has been produced to help public and private sector stakeholders in Romania to work in partnership to plan, develop, manage and market their destinations. The aim is to strengthen tourism structures at local, regional and national levels, so that Romania is able to compete effectively in international markets, in a way that will bring maximum benefit to the country and its destinations. Examples of international best practices, and recommendations to develop an effective and self-sustaining network of regional DMOs are also presented.
    Keywords: destination management organisation, marketing, regional attractiveness, regional development, Romania, tourism, tourism promotion
    Date: 2020–03–05
  2. By: Shaolong Sun; Yanzhao Li; Shouyang Wang; Ju-e Guo
    Abstract: The large amount of tourism-related data presents a series of challenges for tourism demand forecasting, including data deficiencies, multicollinearity and long calculation time. A Bagging-based multivariate ensemble deep learning model, integrating Stacked Autoencoders and KELM (B-SAKE) is proposed to address these challenges in this study. We forecast tourist arrivals arriving in Beijing from four countries adopting historical data on tourist arrivals arriving in Beijing, economic indicators and tourist online behavior variables. The results from the cases of four origin countries suggest that our proposed B-SAKE model outperforms than benchmark models whether in horizontal accuracy, directional accuracy or statistical significance. Both Bagging and Stacked Autoencoder can improve the forecasting performance of the models. Moreover, the forecasting performance of the models is evaluated with consistent results by means of the multi-step-ahead forecasting scheme.
    Date: 2020–02

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