Google Trends data and transfer function models to predict tourism demand in Italy

IF 5.8 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Giovanni De Luca, Monica Rosciano
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引用次数: 0

Abstract

Purpose

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

Design/methodology/approach

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

Findings

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

Originality/value

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

预测意大利旅游需求的谷歌趋势数据和转移函数模型
目的旅游业必须采用大数据驱动的前瞻性方法,以加强在 "后可持续消费与发展 "国际格局中的决策,该格局仍具有显著的不确定性,其中一些大趋势有可能在未来几十年重塑社会。本文考虑到在互联网新数据源上应用定量分析所带来的机遇,提出了一种基于估计传递函数模型的谷歌趋势数据预测方法。利用转移函数模型将谷歌趋势数据预测转化为游客到达预测。结果该结果凸显了使用大数据驱动的预测方法所展现的潜力。在互联网搜索数据上应用转移函数模型,可以及时预测旅游流量。出现的两种情景可用于旅游业利益相关者的决策过程。从未来的角度来看,该方法路径可应用于其他旅游原产地市场、其他互联网搜索引擎或其他社会经济和环境背景。其次,该研究通过评估新数据源定量预测技术的性能,对旅游需求预测研究进行了补充。第三,这是第一篇对美国游客抵达意大利情况进行预测的论文。最后,研究结果为旅游业利益相关者提供了可用于决策的即时宝贵信息。
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来源期刊
Journal of Tourism Futures
Journal of Tourism Futures HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
15.70
自引率
6.00%
发文量
64
审稿时长
34 weeks
期刊介绍:
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