Google Trends data and transfer function models to predict tourism demand in Italy
Bibliographic Data
| ID | 19554394 |
|---|---|
| Authors | Giovanni De Luca (0000-0002-5306-7714, Parthenope University of Naples), Monica Rosciano (0000-0002-6910-9114, Parthenope University of Naples, corresponding author) |
| Year | 2024 |
| Publication date | 2024-03-21 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Tourism Futures (JOURNAL) |
| Journal identifiers | ISSN: 2055-5911 • E-ISSN: 2055-592X |
| Publisher | Emerald (PUBLISHER) |
| DOI | 10.1108/jtf-01-2023-0018 |
| OpenAlex | W4393017243 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
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
Archaeology · Data science · Econometrics · Economic geography · Economics · Geography · Regional science · Tourism · Computer Science · Data-Driven Disease Surveillance · Diverse Aspects of Tourism Research · Human Mobility and Location-Based Analysis
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |