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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: 9 October 2017

Bodo Herzog

The purpose of this paper is to study the impact of transparency on the political budget cycle (PBC) over time and across countries. So far, the literature on electoral…

Abstract

Purpose

The purpose of this paper is to study the impact of transparency on the political budget cycle (PBC) over time and across countries. So far, the literature on electoral cycles finds evidence that cycles depend on the stage of an economy. However, the author shows – for the first time – a reliance of the budget cycle on transparency. The author uses a new data set consisting of 99 developing and 34 Organization for Economic Cooperation and Development countries. First, the author develops a model and demonstrates that transparency mitigates the political cycles. Second, the author confirms the proposition through the econometric assessment. The author uses time series data from 1970 to 2014 and discovers smaller cycles in countries with higher transparency, especially G8 countries.

Design/methodology/approach

Mathematical model and a respective econometric model testing.

Findings

First, the author shows in the theoretical model that higher transparency mitigates the PBC. Second, the author confirms the theoretical proposition through the econometric model. The author confirms that the countries with higher transparency have smaller budget cycles. Or technically, the author cannot reject the null-hypothesis that the budget cycles are different due to transparency.

Research limitations/implications

As explained in the paper: one issue is the data limitations in respect to the transparency measures. Data for Google are just available since 2004. Data for broadband-subscription are just on annual frequency. But both limitations can be tackled in the future. Hence, the findings are first evidence and a benchmark for future studies.

Practical implications

First, higher public transparency implies smaller budget cycles. In the end, this enhances the stability of economic and fiscal policy. Second, policy-makers have to consider the impact of higher transparency in respect to future election pledges. In a more transparent world, all voters can easily check the commitment of previous election pledges.

Social implications

Transparency helps to improve democracy and thus enhances the political credibility because it allows the voters to check the commitment of the elected policy-makers.

Originality/value

First, the author shows – for the first time – a reliance of the budget cycle on transparency. Second, the author is the first that build a new theoretical model that extends the existing literature in respect to transparency and the size of the budget cycle. Third, the author uses for the first time – in this literature – new internet-based data such as broadband-subscription and Google search data. Fourth, the author empirically proves the new hypothesis based on the new data sources.

Details

Journal of Economic Studies, vol. 44 no. 5
Type: Research Article
ISSN: 0144-3585

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Article
Publication date: 14 November 2016

Mark A. Harris and Amita G. Chin

This paper aims to investigate Google’s top developers’ apps with trust badges to see if they warrant an additional level of trust and confidence from consumers, as stated…

Abstract

Purpose

This paper aims to investigate Google’s top developers’ apps with trust badges to see if they warrant an additional level of trust and confidence from consumers, as stated by Google.

Design/methodology/approach

Risky app permissions and in-app purchases (IAP) from Google’s top developers and traditional developers were investigated in several Google Play top app categories, including Editor’s Choice apps. Analysis was performed between categories and developer types.

Findings

Overall, Google’s top developers’ apps request more risky permissions and IAP than do traditional developers. Other results indicate that free apps are more dangerous than paid apps and star ratings do not signify safe apps.

Research limitations/implications

Because of a limited number of Google’s top developers and Editor’s Choice apps, conclusions are drawn from a small sample of apps and not the entire market.

Practical implications

Google’s top developers’ apps are suited well for increasing revenue for Google and developers at the consumer’s expense. Consumers should be wary of top developer trust badges.

Social implications

As the lure for “top free” and “top developer” software is strong among consumers, this research contributes to societal welfare in that it makes consumers aware that Google top developer app trust badges and free apps are more dangerous than traditional developer and paid apps, as they request risky permissions at a much higher frequency. Therefore, consumers should be very careful when downloading apps that are advertised as “top free” or “top developer”.

Originality/value

Google’s top developers’ apps and Editors’ Choice apps have not been investigated from the perspective of permissions and IAP before.

Details

Information & Computer Security, vol. 24 no. 5
Type: Research Article
ISSN: 2056-4961

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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

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Article
Publication date: 7 April 2015

Nikolaos Askitas and Klaus F. Zimmermann

The purpose of this paper is to recommend the use of internet data for social sciences with a special focus on human resources issues. It discusses the potentials and…

Abstract

Purpose

The purpose of this paper is to recommend the use of internet data for social sciences with a special focus on human resources issues. It discusses the potentials and challenges of internet data for social sciences. The authors present a selection of the relevant literature to establish the wide spectrum of topics, which can be reached with this type of data, and link them to the papers in this International Journal of Manpower special issue.

Design/methodology/approach

Internet data are increasingly representing a large part of everyday life, which cannot be measured otherwise. The information is timely, perhaps even daily following the factual process. It typically involves large numbers of observations and allows for flexible conceptual forms and experimental settings.

Findings

Internet data can successfully be applied to a very wide range of human resource issues including forecasting (e.g. of unemployment, consumption goods, tourism, festival winners and the like), nowcasting (obtaining relevant information much earlier than through traditional data collection techniques), detecting health issues and well-being (e.g. flu, malaise and ill-being during economic crises), documenting the matching process in various parts of individual life (e.g. jobs, partnership, shopping), and measuring complex processes where traditional data have known deficits (e.g. international migration, collective bargaining agreements in developing countries). Major problems in data analysis are still unsolved and more research on data reliability is needed.

Research limitations/implications

The data in the reviewed literature are unexplored and underused and the methods available are confronted with known and new challenges. Current research is highly original but also exploratory and premature.

Originality/value

The paper reviews the current attempts in the literature to incorporate internet data into the mainstream of scholarly empirical research and guides the reader through this Special Issue. The authors provide some insights and a brief overview of the current state of research.

Details

International Journal of Manpower, vol. 36 no. 1
Type: Research Article
ISSN: 0143-7720

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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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Book part
Publication date: 13 November 2017

Robert Kozielski, Grzegorz Mazurek, Anna Miotk and Artur Maciorowski

It seems that the Internet boom, which started at the end of the 1990s and finished with the spectacular collapse of the so-called dotcoms, is probably over. We are…

Abstract

It seems that the Internet boom, which started at the end of the 1990s and finished with the spectacular collapse of the so-called dotcoms, is probably over. We are currently enjoying a period of fast and stable growth. This is manifested by the growing number of both Internet users and companies which – to an ever-increasing extent – use the Internet as a form of communication (both internal and external), promotion, sales etc. Expenditures on Internet advertising are growing continuously and now constitute more than 25% of all advertising expenditure. A natural consequence of this development is the need for the standardisation and organisation of the world of the Internet. These activities will result in a greater awareness of the benefits which this medium provides, increasing the possibilities of its use, and – most importantly – the opportunity to evaluate the return on investments made on the Internet. Nowadays, it is clear that many companies are striving to increase the quality of their activities on the Internet or to improve the effectiveness of such activities. As a consequence, the number of companies that look for indices which would enable the making of more precise and effective decisions in the scope of online operations is growing.

This chapter is dedicated to the phenomenon of the increasing role of the Internet in business, including the scale of its use by Polish and international companies. We present the most commonly used measures of marketing activities on the Internet and in social media. This group includes the indices which make it possible to determine whether a company actually needs a website. Other measures allow for the improvement in the effectiveness of the activity on the Internet, whereas others specify the costs of activities on the Internet and often serve as the basis for settlements between a company and advertising agencies or companies specialising in website design. It is worth emphasising that the Paid, Earned, Shared, Owned (PESO) model, worked out by Don Bartholomew,1 is the basis for creation and description of indices concerning social media. This model has gained certain popularity in the social media industry. It does not, however, specify how individual indices should be named and calculated. It maps already existing indices and adapts them to specific levels of marketing communication measurement. All the measures indicated by the author of the model have been grouped into five major areas: exposure, engagement, brand awareness, action and recommendations. This model– similarly to all models of performance measurement – inspired by the sales funnel concept, adjusts certain standard indices and proposals of measurements for specific levels. Additionally, the measures are divided into four types, depending on who the owner of the content is: Paid (P) – refers to all forms of paid content; Owned (O) – all websites and web properties controlled by a company or brand; Earned (E) – the contents about a given brand created spontaneously by Internet users; and Shared (S) – the contents shared by Internet users.

Details

Mastering Market Analytics
Type: Book
ISBN: 978-1-78714-835-2

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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

Keywords

Content available
Book part
Publication date: 30 August 2019

Gary Koop and Luca Onorante

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic…

Abstract

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

Details

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
Type: Book
ISBN: 978-1-78973-241-2

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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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