New developments in tourism and hotel demand modeling and forecasting

Abstract

Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Where Can Tourism-Led Growth and Economy-Driven Tourism Growth Occur?

“…First, tourism-related variables might be measured inconsistently across different national statistical systems. For instance, according to United Nations World Tourism Organization (UNWTO, 2017) and Wu, Song and Shen (2017), visitor arrivals, tourist arrivals (excluding same-day visitors), tourist arrivals at hotels and similar establishments, nights of tourists in hotels and similar establishments are used by different countries to record international tourist arrivals statistics. Second, as argued by del P. , the results of TLG tests are sensitive to the specifications of econometric models; therefore, the substantial diversity that exists among model specifications in past studies impedes further comparisons and synchronization of the empirical results.…”

Critical success factors for tourism‐led growth

“…In the case of beach destinations, Kozak, Uysal, and Birkan () contend that climatic conditions are the main resource for tourist. Wu, Song, and Shen () review literature on tourism demand and confirm that climate has a long term impact on tourism, because it has a clear influence on tourists' preferences for destinations. Goh () concludes that it is therefore evident that climate influences not only a destination's image but also the demand for a destination as well as their spending decisions; therefore, it has a profound impact on the development and success of the tourism industry.…”

A decomposition-ensemble approach for tourism forecasting

“…In the prediction of tourism demand, point forecasts usually do not provide information about the degree of variability or uncertainty related to the forecast, while interval forecasts can provide a range for the estimate with a specific confidence level. As a consequence, the interval provides more useful information for tourism practitioners and policymakers to formulate relevant strategies and policies (Wu, Song, & Shen, 2017). Referring the research in Li et al (2019), this study produces both point and interval forecasts for tourist arrivals.…”