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Article
Publication date: 28 August 2019

Gorete Dinis, Zélia Breda, Carlos Costa and Osvaldo Pacheco

This paper aims to conduct a review of the literature published, between 2006 and 2018, that used search engine data on tourism and hospitality research, namely, Google

Abstract

Purpose

This paper aims to conduct a review of the literature published, between 2006 and 2018, that used search engine data on tourism and hospitality research, namely, Google Insights for Search and Google Trends. More specifically, it intends to identify the purpose and context of the data use, ascertaining the main findings and reviewing the methodological approaches.

Design/methodology/approach

A systematic literature review of Scopus indexed research has been carried out. Given the novelty of search engine data use in tourism and hospitality research and the relatively low number of search results in Scopus, other databases were used to broaden the scope of analysis, namely, EBSCO and Google Scholar. The papers selected were subjected to content and statistical analyses.

Findings

Google Trends data use in tourism and hospitality research has increased significantly from 2012 to 2017, mainly for tourism forecasting/nowcasting; knowing the interest of users’ searches for tourist attractions or destinations; showing the relationship between the official tourism statistics and the search volume index of Google Trends; and estimating the effect of one event on tourism demand. The categories and search terms used vary with the purpose of the study; however, they mostly focus on the travel category and use the country as the search term.

Originality/value

Google Trends has been increasingly used in research publications in tourism and hospitality, but the range of its applications and methods used has not yet been reviewed. Therefore, a systematic review of the existing literature increases awareness of its potential uses in tourism and hospitality research and facilitates a better understanding of its strengths and weaknesses as a research tool.

研究目的

本文回顾2006年至2018年发表文献使用酒店旅游相关的搜索引擎数据, 即Google Insights for Search 以及Google Trends。确切地说, 本文旨在研究数据使用目的和背景, 归纳主要研究成果和研究方法。

研究设计/方法/途径

本文采用Scopus索引, 由于旅游酒店领域使用搜索引擎数据的文献较少, Scopus搜索结果样本量较低, 本文扩展到其他数据库, 即EBSCO以及Google Scholar。选定的样本文献采用文本分析和统计分析法。

研究结果

旅游酒店领域中对Google Trends数据使用的增加主要集中在2012年到2017年, 主要研究领域有(1)旅游预测/即时预报;(2)了解用户搜索旅游景点或目的地的需求;(3)官方旅游数据和Google Trends搜索量索引之间的关系;以及(4)评估大事件对旅游需求的影响。文献归类和搜索名词根据研究目的而不同。然而, 大多数文章使用‘旅游’归类以及使用国家作为搜索关键词。

研究原创性/价值

Google Trends在酒店旅游领域研究中的使用逐渐增加, 但是据作者所知, 其应用的范畴和方法仍处在起步阶段。因此, 对现有文献的系统回顾可以提高对其在旅游酒店领域中应用的认知, 并且本文结果使其作为研究工具的优劣分析更深理解。

关键词

Google Trends, Google insights for search, 搜索引擎数据, 旅游酒店研究, 系统文献回顾

Details

Journal of Hospitality and Tourism Technology, vol. 10 no. 4
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 12 June 2014

Liwen Vaughan

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine…

Abstract

Purpose

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

Design/methodology/approach

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

Findings

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

Research limitations/implications

The study is limited to only one country and to one year of data.

Practical implications

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

Originality/value

This is the first study to establish a relationship between search engine query data and business performance and position data.

Details

Online Information Review, vol. 38 no. 4
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 5 June 2020

Hanyoung Go, Myunghwa Kang and Yunwoo Nam

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and…

Abstract

Purpose

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites.

Design/methodology/approach

The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world.

Findings

This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search.

Originality/value

This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

摘要

研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的,文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知,通过对关键要素的分类,审视生态旅游和可持续发展的关系,以及衡量生态旅游基地的受欢迎程度等方法。

研究设计/方法/途径

本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析,来开发生态旅游类别和游客对生态旅游的感知。首先,我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况,以此搭建了关键生态旅游要素的类别体系,和衡量生态旅游基地的受欢迎程度。第二,我们通过使用互联网搜索数据,检测了生态旅游如何与可持续发展相连接,以及研究了搜索数据中的趋势。第三,我们使用了GIS软件来操作空间分析,对图片的空间-时间改变和游客对世界的观点做了可视化处理。

研究结果

本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化,根据谷歌趋势上的定义,被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。

研究原创性/价值

本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法,对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见,游客如何选择和感知自然景观,这对于很多群体,比如旅游行业和政府,都有着重要意义。

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Article
Publication date: 4 March 2019

Maryam Dilmaghani

The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age.

Abstract

Purpose

The purpose of this paper is to use data mined from Google Trends, in order to predict the unemployment rate prevailing among Canadians between 25 and 44 years of age.

Design/methodology/approach

Based on a theoretical framework, this study argues that the intensity of online leisure activities is likely to improve the predictive power of unemployment forecasting models.

Findings

Mining the corresponding data from Google Trends, the analysis indicates that prediction models including variables which reflect online leisure activities outperform those solely based on the intensity of online job search. The paper also outlines the most propitious ways of mining data from Google Trends. The implications for research and policy are discussed.

Originality/value

This paper, for the first time, augments the forecasting models with data on the intensity of online leisure activities, in order to predict the Canadian unemployment rate.

Details

Journal of Economic Studies, vol. 46 no. 2
Type: Research Article
ISSN: 0144-3585

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Article
Publication date: 26 August 2014

Marian Alexander Dietzel, Nicole Braun and Wolfgang Schäfers

The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve…

Abstract

Purpose

The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

Design/methodology/approach

This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices.

Findings

The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively.

Practical implications

The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions.

Originality/value

This is the first paper applying Google search query data to the commercial real estate sector.

Details

Journal of Property Investment & Finance, vol. 32 no. 6
Type: Research Article
ISSN: 1463-578X

Keywords

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Article
Publication date: 14 August 2017

Wei Shang, Hsinchun Chen and Christine Livoti

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early…

Abstract

Purpose

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.

Design/methodology/approach

Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration’s Adverse Event Reporting System.

Findings

Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.

Research limitations/implications

Validation of Google Trends data’s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.

Practical implications

Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.

Originality/value

The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.

Details

Online Information Review, vol. 41 no. 4
Type: Research Article
ISSN: 1468-4527

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Article
Publication date: 1 February 2016

Jie Qin and Tai-Quan Peng

Queries as a pioneering measure of public attention on various social issues have elicited considerable scholarly attention. The purpose of this paper is to address two…

Abstract

Purpose

Queries as a pioneering measure of public attention on various social issues have elicited considerable scholarly attention. The purpose of this paper is to address two fundamental questions, as follows: first, how do we identify niche queries that internet users search for on specific social issues?; and second, what are the measurement properties of queries data in gauging public attention on social issues?

Design/methodology/approach

The present study uses public attention on environmental issues in the USA as the empirical setting of research. An iterative framework is developed to identify niche queries to measure public attention on environmental issues. The measurement properties of queries data are assessed by comparing the dynamics of public attention on environmental issues captured by queries data with that measured by the “most important problem” (MIP) question in Gallup opinion polls.

Findings

A list of 39 niche queries that internet users search for on environmental issues is identified. The dynamics of public attention on environmental issues determined by the search trends of these 39 queries is found to positively correlate with that measured by Gallup MIP polls, whereas both dynamics can forecast each other well in a 12-month time frame.

Originality/value

The findings of the study possess methodological and practical implications. The study shows that queries data are complementary to, rather than substitutes of, public opinion polls in measuring public attention on environmental issues. The iterative framework developed in the study can be applied in future studies to help researchers identify valid queries to measure public attention on other social issues, as it can minimize researchers’ subjective biases in selecting search queries. Policymakers and environmentalists can utilize our approach to monitor the status of public attention on environmental issues and implement campaigns to mobilize favorable public opinion when the decline of public attention is predicted by the trends of web search queries.

Details

Internet Research, vol. 26 no. 1
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 3 August 2015

Bixia Xu and Zhulin Huang

This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market…

Abstract

Purpose

This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market value. It also examines whether information content and state of nature can have an impact on this relationship.

Design/methodology/approach

The paper is an empirical study using Web search volume data collected from Google Trends and financial and market data collected from Compustat.

Findings

This paper finds that investors use Web search engines as an alternative way to search for information they need, search frequency of accounting information is positively related to the explanatory power of accounting information for firm market value, the relationship is found differential between statements and categories within a statement depending on the information content and the relationship is found stronger during economic upturns.

Research limitations/implications

This paper examines 59 accounting items that are cross-firm commonly reported and that have data availability in Compustat. The external validity might be an issue.

Practical implications

This paper is of interest to standard setters, corporate management and academics who wish to understand and improve the value of accounting information in the capital market.

Originality/value

This paper is the first study which provides a comprehensive examination of the impact of investors’ information search volumes on the explanatory power of accounting information. It is also the first paper that intrudes Google Trends search volume data into accounting research.

Details

International Journal of Accounting and Information Management, vol. 23 no. 3
Type: Research Article
ISSN: 1834-7649

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Article
Publication date: 4 April 2016

Nicole Braun

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

Abstract

Purpose

The purpose of this paper is to analyze the effect of investor sentiment, measured with Google internet search data, on volatility forecasts of the US REIT market.

Design/methodology/approach

The author uses the S&P US REIT index and collects search volume data from Google Trends for all US REIT. Two different Generalized Autoregressive Conditional Heteroskedastic models are then estimated, namely, the baseline model and the Google augmented model. Using these models, one-step-ahead forecasts are conducted and the forecast accuracies of both models are subsequently compared.

Findings

The empirical results reveal that search volume data can be used to predict volatility on the REIT market. Especially in periods of high volatility, Google augmented models outperform the baseline model.

Practical implications

The results imply that Google data can be used on the REIT market as a market indicator. Investors could use Google as an early warning system, especially in periods of high volatility.

Originality/value

This is the first paper to use Google search query data for volatility forecasts of the REIT market.

Details

Journal of Property Investment & Finance, vol. 34 no. 3
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 3 April 2018

Shohreh SeyyedHosseini, Asefeh Asemi, Ahmad Shabani and Mozafar CheshmehSohrabi

According to the studies conducted in Iran, the breast cancer is the most frequent type of cancer among women. This study aimed to explore the state of health information…

Abstract

Purpose

According to the studies conducted in Iran, the breast cancer is the most frequent type of cancer among women. This study aimed to explore the state of health information supply and demand on breast cancer among Iranian medical researchers and Iranian Web users from 2011 to 2015.

Design/methodology/approach

A mixed method research is conducted in this study. In qualitative part, a focus group interview is applied to the users to identify their selected keywords searched for breast cancer in Google. The collected data are analyzed using Open Code software. In quantitative part, data are synthesized using the R software in two parts. First, users’ internet information-seeking behavior (ISB) is analyzed using the Google Trends outputs from 2011 to 2015. Second, the scientific publication behavior of Iranian breast cancer specialists are surveyed using PubMed during the period of the study.

Findings

The results show that the search volume index of preferred keywords on breast cancer has increased from 4,119 in 2011 to 4,772 in 2015. Also, the findings reveal that Iranian scholars had 873 scientific papers on breast cancer in PubMed from 2011 to 2015. There was a significant and positive relationship between Iranian ISB in the Google Trends and SPB of Iranian scholars on breast cancer in PubMed.

Research limitations/implications

This study investigates only the state of health information supply and demand in PubMed and Google Trends and not additional databases often used for medical studies and treatment.

Originality/value

This study provides a road map for health policymakers in Iran to direct the breast cancer studies.

Details

The Electronic Library, vol. 36 no. 2
Type: Research Article
ISSN: 0264-0473

Keywords

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