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1 – 10 of over 4000The purpose of this paper is to examine two research questions: What search engine queries lead users to the Auburn University electronic theses and dissertations (AUETDs…
Abstract
Purpose
The purpose of this paper is to examine two research questions: What search engine queries lead users to the Auburn University electronic theses and dissertations (AUETDs) collection? Do these queries vary for users in different locations and, if so, how?
Design/methodology/approach
Search engine queries used to locate the AUETDs collection were obtained from Google Analytics and were separated into groups based on user location. These queries were assigned to empirically derived categories based on their content.
Findings
Most local users’ queries contained person names, variants for thesis or dissertation, and variants for Auburn University. Over a third were queries for the AUETDs collection, while the remainder were seeking theses and dissertations from specific Auburn researchers. Most out-of-state users’ queries contained title and subject keywords and appeared to be seeking specific research studies. Queries from users located within the state but outside of the local area were intermediate between these groups.
Practical implications
Over two-thirds of visits to the AUETDs collection were made by search engine users which reinforces the importance of having repository content indexed by search engines such as Google. The specificity of their queries indicates that full-text indexing will be more helpful to users than metadata indexing alone.
Originality/value
This is the first detailed analysis of search engine queries used to locate an ETDs collection. It may also be the last, as query content for the major search engines is no longer available from Google Analytics.
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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…
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.
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Manuel Kaesbauer, Ralf Hohenstatt and Richard Reed
The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing…
Abstract
Purpose
The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.
Design/methodology/approach
In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.
Findings
The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.
Originality/value
This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.
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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 data…
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.
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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.
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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.
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Purpose — The purpose of this chapter is to give an overview of the context of Web search and search engine related research, as well as to introduce the reader to the sections…
Abstract
Purpose — The purpose of this chapter is to give an overview of the context of Web search and search engine related research, as well as to introduce the reader to the sections and chapters of the book.
Methodology/approach — We review literature dealing with various aspects of search engines, with special emphasis on emerging areas of Web searching, search engine evaluation going beyond traditional methods and new perspectives on Web searching.
Findings — The approaches to studying Web search engines are manifold. Given the importance of Web search engines for knowledge acquisition, research from different perspectives needs to be integrated into a more cohesive perspective.
Research limitations/implications — The chapter suggests a basis for research in the field and also introduces further research directions.
Originality/value of paper — The chapter gives a concise overview of the topics dealt within the book and also shows directions for researchers interested in Web search engines.
Zeljko Tekic, Andrei Parfenov and Maksim Malyy
Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and…
Abstract
Purpose
Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.
Design/methodology/approach
Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.
Findings
The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.
Practical implications
The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.
Originality/value
To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.
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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 introduced in…
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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Ze-Han Fang and Chien Chin Chen
The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines…
Abstract
Purpose
The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.
Design/methodology/approach
In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.
Findings
The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.
Originality/value
This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.
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