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Article
Publication date: 14 May 2018

Sulah Cho

The purpose of this paper is to utilize co-query volumes of brands as relatedness measurement to understand the market structure and demonstrate the usefulness of brand…

Abstract

Purpose

The purpose of this paper is to utilize co-query volumes of brands as relatedness measurement to understand the market structure and demonstrate the usefulness of brand relatedness via a real-world case.

Design/methodology/approach

Using brand relatedness measurement obtained using data from Google Trends as data inputs into a multidimensional scaling method, the market structure of the automobile industry is presented to reveal its competitive landscape. The relatedness with brands involved in product-harm crisis is further incorporated in empirical models to estimate the influence of crisis on future sales performance of each brand. A representative incident of a product-harm crisis in the automobile industry, which is the 2009 Toyota recall, is investigated. A panel regression analysis is conducted using US and world sales data.

Findings

The use of co-query as brand relatedness measurement is validated. Results indicate that brand relatedness with a brand under crisis is positively associated with future sales for both US and global market. Potential presence of negative spillovers from an affected brand to innocent brands sharing common traits such as same country of origin is shown.

Originality/value

The brand relatedness measured from co-query volumes is considered as a broad concept, which encompasses all associative relationships between two brands perceived by the consumers. This study contributes to the literature by clarifying the concept of brand relatedness and proposing a measure with readily accessible data. Compared to previous studies relying on a vast amount of online data, the proposed measure is proven to be efficient and enhance predictions about the future performance of brands in a turbulent market.

Details

Industrial Management & Data Systems, vol. 118 no. 4
Type: Research Article
ISSN: 0263-5577

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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: 28 September 2012

Bing Pan, Doris Chenguang Wu and Haiyan Song

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

Abstract

Purpose

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

Design/methodology/approach

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

Findings

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

Research limitations/implications

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Practical implications

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

Originality/value

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

Details

Journal of Hospitality and Tourism Technology, vol. 3 no. 3
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 10 June 2019

Dongha Kim, JongRoul Woo, Jungwoo Shin, Jongsu Lee and Yongdai Kim

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such…

Abstract

Purpose

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

Design/methodology/approach

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

Findings

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

Research limitations/implications

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

Originality/value

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

Details

Industrial Management & Data Systems, vol. 119 no. 5
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 5 September 2017

Ernesto D’Avanzo, Giovanni Pilato and Miltiadis Lytras

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey…

Abstract

Purpose

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

Design/methodology/approach

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

Findings

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

Originality/value

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

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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

Keywords

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Article
Publication date: 2 March 2015

Karim Rochdi and Marian Dietzel

– The purpose of this paper is to investigate whether there is a relationship between asset-specific online search interest and movements in the US REIT market.

Abstract

Purpose

The purpose of this paper is to investigate whether there is a relationship between asset-specific online search interest and movements in the US REIT market.

Design/methodology/approach

The authors collect search volume (SV) data from “Google Trends” for a set of keywords representing the information demand of real estate (equity) investors. On this basis, the authors test hypothetical investment strategies based on changes in internet SV, to anticipate REIT market movements.

Findings

The results reveal that people’s information demand can indeed serve as a successful predictor for the US REIT market. Among other findings, evidence is provided that there is a significant relationship between asset-specific keywords and the US REIT market. Specifically, investment strategies based on weekly changes in Google SV would have outperformed a buy-and-hold strategy (0.1 percent p.a.) for the Morgan Stanley Capital International US REIT Index by a remarkable 15.4 percent p.a. between 2006 and 2013. Furthermore, the authors find that real-estate-related terms are more suitable than rather general, finance-related terms for predicting REIT market movements.

Practical implications

The findings should be of particular interest for REIT market investors, as the established relationships can potentially be utilized to anticipate short-term REIT market movements.

Originality/value

This is the first paper which applies Google search query data to the REIT market.

Details

Journal of Property Investment & Finance, vol. 33 no. 2
Type: Research Article
ISSN: 1463-578X

Keywords

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Article
Publication date: 28 December 2020

Yong Jin Park

The purpose of the current study is to theorize and apply a socio-technological model – the powerful influence of social determinants in conditioning the effects of…

Abstract

Purpose

The purpose of the current study is to theorize and apply a socio-technological model – the powerful influence of social determinants in conditioning the effects of information attention on social outcomes. Fundamentally, this study is motivated by the idea that the social determinants of information flow can be used as a predictive tool to inform public socio-policy decisions.

Design/methodology/approach

This study draws upon digital disparity literature and uses publicly available Google search queries in exploring online information attention and its relationships to the HIV/AIDS diffusion in US cities. This study’s secondary data collected from extant sources is used to draw attention to a holistic urban ecology under which online search attention represents the variation of information access at the aggregate level.

Findings

The main finding shows that online information attention, as indicated by search trend, is far from being a simple predictor, but operates in complex interactions with existing social environments. A bivariate correlation between AIDS information search and AIDS diffusion rate was found to be significant. However, predictive multivariate models displayed robust effects of social contextual variables, such as income level and racial composition of cities, in moderating the effect of online search information flow.

Practical implications

The importance of these insights is discussed for reducing socio-health disparities at the macro-social level, and policymakers and health administrators are recommended to incubate supportive online infrastructure as an effective preventive measure at the time of a crisis.

Originality/value

The unique contribution of this study is the premise that looks at the aggregate-ecological contour of cities within which the potential benefits of information occur, instead of examining the isolated function of mediated information per se. In this vein, online information search, in lieu of the exposure to mass media message that is often measured via self-reported items, is a particularly unique and fruitful area of future inquiry that this study promotes.

Details

Aslib Journal of Information Management, vol. 73 no. 2
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 8 May 2017

Juan Liu, Xue Li and Ya Guo

This paper aims to analyze and model consumer behavior on hotel online search interest in the USA.

Abstract

Purpose

This paper aims to analyze and model consumer behavior on hotel online search interest in the USA.

Design/methodology/approach

Discrete Fourier transform was used to analyze the periodicity of hotel search behavior in the USA by using Google Trends data. Based on the obtained frequency components, a model structure was proposed to describe the search interest. A separable nonlinear least squares algorithm was developed to fit the data.

Findings

It was found that the major dynamics of the search interest was composed of nine frequency components. The developed separable nonlinear least squares algorithm significantly reduced the number of model parameters that needed to be estimated. The fitting results indicated that the model structure could fit the data well (average error 0.575 per cent).

Practical implications

Knowledge of consumer behavior on online search is critical to marketing decision because search engine has become an important tool for customers to find hotels. This work is thus very useful to marketing strategy.

Originality/value

This research is the first work on analyzing and modeling consumer behavior on hotel online search interest.

Details

International Journal of Contemporary Hospitality Management, vol. 29 no. 5
Type: Research Article
ISSN: 0959-6119

Keywords

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Article
Publication date: 12 March 2018

Divya Sharma, Agam Gupta, Arqum Mateen and Sankalp Pratap

Google commands approximately 70 per cent of search market share worldwide, resulting in businesses investing heavily in search engine advertising on Google to target…

Abstract

Purpose

Google commands approximately 70 per cent of search market share worldwide, resulting in businesses investing heavily in search engine advertising on Google to target potential customers. Recently, Google changed the way in which content and ads were displayed on the search engine results page. This reshuffling of content and ads is expected to affect the advertisers who advertise on Google and/or use it to drive traffic to their websites. The purpose of this study is to analyze the impact of these changes on various stakeholders.

Design/methodology/approach

Data have been collected from various sources on the internet including blogs and discussion forums. Netnography has been used as it allows a detailed evaluation of the consumers’ needs, wants and choices in a virtual space.

Findings

The average cost-per-click for ads on the top positions is expected to increase. Advertisers whose ads usually occupy the lower positions would be adversely affected. To counter this, more emphasis should be placed on ad extensions and on product listing ads. In addition, organizations would benefit from increased efforts on search engine optimization.

Practical implications

A variety of coping strategies have been developed that can help marketers to successfully navigate through the change, including the use of ad extensions and the use of product listing ads.

Originality/value

This practice-focused paper offers guidelines for digital marketers to use sponsored search more effectively as part of their arsenal in light of some important changes recently made by Google. The potential of netnography as a research methodology has also been expanded by using it in a novel setting and in drawing up actionable insights.

Details

Journal of Information, Communication and Ethics in Society, vol. 16 no. 1
Type: Research Article
ISSN: 1477-996X

Keywords

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