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Employing World Bank data, this article finds that the observed worldwide transition from industrial to service activity has an insignificant long‐term impact on global de‐materialization. Unlike earlier studies that focus on actual material flows for specific sectors, this study considers economy‐wide sectoral composition changes from 1960 to 1998 for 206 countries, and infers some future scenarios based on observed trends. Sensitivity analysis under alternative assumptions about technological change and average industry material intensity indicates that, unless de‐materialization is “weakly” defined, a leveling off of world gross domestic product at some time this century will probably be required to achieve it.

This study aims to apply a new forecasting approach to improve predictions in the hospitality industry. To do so, the authors developed a multivariate setting that allows the incorporation of the cross-correlations in the evolution of tourist arrivals from visitor markets to a specific destination in neural network models.

This multiple-input-multiple-output approach allows the generation of predictions for all visitor markets simultaneously. Official data of tourist arrivals to Catalonia (Spain) from 2001 to 2012 were used to generate forecasts for one, three and six months ahead with three different networks.

The study revealed that multivariate architectures that take into account the connections between different markets may improve the predictive performance of neural networks. Additionally, the authors developed a new forecasting accuracy measure and found that radial basis function networks outperform the rest of the models.

This research contributes to the hospitality literature by developing an innovative framework to improve the forecasting performance of artificial intelligence techniques and by providing a new forecasting accuracy measure.

The proposed forecasting approach may prove very useful for planning purposes, helping managers to anticipate the evolution of variables related to the daily activity of the industry.

A multivariate neural network framework has been developed to improve forecasting accuracy, providing professionals with an innovative and practical forecasting approach.

This study aims to address three important issues of combination forecasting in the tourism context: reducing the restrictions arising from requirements related to the statistical properties of the available data, assessing the weights of single models and considering nonlinear relationships among combinations of single-model forecasts.

A three-stage multiple-attribute decision-making (MADM)-based methodological framework was proposed. Single-model forecasts were generated by grey prediction models for the first stage. Vlsekriterijumska Optimizacija I Kompromisno Resenje was adopted to develop a weighting scheme in the second stage, and the Choquet integral was used to combine forecasts nonlinearly in the third stage.

The empirical results for inbound tourism in Taiwan showed that the proposed method can significantly improve accuracy to a greater extent than other combination methods. Along with scenario forecasting, a good forecasting practice can be further provided by estimating ex-ante forecasts post-COVID-19.

The private and public sectors in economies with high tourism dependency can benefit from the proposed method by using the forecasts to help them formulate tourism strategies.

This study contributed to presenting a MADM-based framework that advances the development of a more accurate combination method for tourism forecasting.

針對旅遊需求預測, 本研究就降低對於資料統計性質的要求、模式的重要度評估, 以及各別預設值間存在的非線性關係等三項重要議題建立組合預測的研究框架。

研究方法以多屬性決策分析為基礎, 在實作上以灰色預測模式產生各別預測值、以 VIKOR 為模式發展加權方案, 再使用模糊積分以非線性方式組合預測值。

以台灣的入境旅遊需求進行分析, 並與其他組合方法相較, 發現所提出方法的預測準確度顯著較佳。與情境預測結合下, 研究結果亦可呈現新冠疫情下於各季的事前預測。

對旅遊具有高度依賴的經濟體, 所提出方法所產生的預測值有助於其公部門與私部門規劃旅遊策略。

組合預測在旅遊需求的預測上有其研究價值。本研究在旅遊預測議題提出以多屬性決策分析為基礎之框架, 在推進具高準確率組合方法的發展上作出貢獻。

La combinación de pronósticos en este estudio abordó tres cuestiones importantes para la situación del turismo: Reducir las restricciones que surgen con respecto a las estadísticas de datos disponibles, evaluar los pesos con un solo pronóstico, y considerar las relaciones no lineales entre las combinaciones con un único modelo de pronóstico.

Se propuso un marco metodológico de tres etapas basado en MADM. Un solo pronóstico fue generado mediante modelos de predicción grises para la primera etapa. Se aplicó VIKOR para desarrollar un esquema de ponderación en la segunda etapa, y la integral de Choquet se usó para combinar los pronósticos de manera no lineal en la tercera etapa.

Los resultados empíricos de la demanda turística entrante en Taiwán mostraron que el método propuesto puede mejorar efectivamente la precisión en mayor medida que otros métodos combinados. Una buena práctica del pronóstico puede proporcionar aún más, mediante las previsiones y la estimación exante de pronósticos posteriores al COVID-19.

Los sectores públicos y privados de las economías con alta dependencia del turismo pueden beneficiarse del método propuesto al usar los pronósticos para ayudarlos a formular estrategias turísticas.

Este estudio contribuye a presentar un marco basado en MADM que avanza en el desarrollo de un método de combinación más preciso para la previsión del turismo.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This 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, whereas 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 nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The 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.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The 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.