Search results

In 2016, Bohannon published an article analyzing the download rate of the top ten countries using the illegal Sci-Hub website. Four years later, this study approaches the search behavior of these ten countries as they query about Sci-Hub in Google's search engine, the world's most widely used search engine. The authors also tracked the possible consequences of using Sci-Hub, such as plagiarism.

The search terms “Sci-Hub”, “Plagiarism” and “Plagiarism Checker” were explored with Google Trends. The queries were performed globally and individually for the ten target countries, all categories and web searches. The time range was limited between 1/1/2016 (after the date of publication of Bohannon's work) and 29/03/2020. Data were extracted from Google Trends and the findings were mapped.

Searching for the word Sci-Hub on Google has increased nearly eightfold worldwide in the last four years, with China, Ethiopia and Tunisia having the most searches. Sci-Hub's search trends increased for most of the T10C, with Brazil and Iran having the highest and lowest average searches, respectively.

Access to the research literature is required to the progress of research, but it should not be obtained illegally. Given the increasing incidence of these problems in countries at any level of development, it is important to pay attention to ethics education in research and establish ethics committees. A comprehensive review of the research process is required to reduce the urge to circumvent copyright laws and includes training and educating research stakeholders in copyright literacy. To address these goals, national and international seriousness and enthusiasm are essential.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

The authors develop new quantitative methods to estimate the level of speculation and long-term sustainability of Bitcoin and Blockchain.

The authors explore the practical application of speculative bubble models to cryptocurrencies. They then show how the approach can be extended to provide estimated brand values using data from Google Trends.

The authors confirm previous findings of speculative bubbles in cryptocurrency markets. Relatedly, Google searches for cryptocurrencies seem to be primarily driven by recent price rises. Overall results are sufficient to question the long-term sustainability of Bitcoin with the suggestion that Ethereum, Bitcoin Cash and Ripple may all enjoy technical advantages relative to Bitcoin. Our results also demonstrate that Blockchain has a distinct value and identity beyond cryptocurrencies – providing foundational support for the second generation of academic work on Blockchain. However, a relatively low estimated long-term growth rate suggests that the benefits of Blockchain may take a long time to be fully realised.

The authors contribute to an emerging academic literature on Blockchain and to a more established literature exploring the use of Google data within business analytics. Their original contribution is to quantify the business value of Blockchain and related technologies using Google Trends

The purpose of this paper is to improve the forecast of tourism inflows into Spain by use of Google – indices on internet searches measuring the relative popularity of keywords associated with travelling to Spain.

Two models are estimated for each of the three countries with the largest tourist flows into Spain (Germany, UK and France): a conventional model, the best ARIMA model estimated by TRAMO (model 0) and a model augmented with the Google-index relating to searches made from each country (model 1). The overall performance of both models is compared.

The improvement in forecasting provided by the short-term models that include the G-indicator is quite substantial up to 2012, reducing out of sample mean square errors by 42 per cent, although their performance worsens in the following years.

Deeper study and conceptualization of sources of error in Google trends and data quality is necessary.

The paper illustrates that while this new tool can be a powerful instrument for policy makers as a valuable and timely complement for traditional statistics, further research and better access to data is needed to better understand how internet consumers’ search activities translate (or not) into actual economic outcomes.

This study aims to predict stock market crises in the Middle East North Africa (MENA) regions by leveraging the nonlinear autoregressive neural network with exogenous inputs (NARX) model with two measures of investor sentiment: the ARMS indicator and Google Trends' search volume of positive and negative words.

Employing a novel approach, this study utilizes the NARX model with ten neurons in the hidden layer and the Levenberg–Marquardt training algorithm. It evaluates model performance through learning, validation and test errors, as well as correlation analysis between predicted and actual crises.

The NARX model, incorporating investor sentiment, has proven to be a reliable tool for forecasting crises, helping market participants understand data complexity and avoid crisis consequences. The divergence in how investors interpret market news, with some focusing solely on negative developments and others valuing positive outcomes, highlights the predictive nature of the optimistic and pessimistic sentiments captured by the model.

This study advocates for integrating behavioral approaches into stock market crisis prediction, highlighting the significance of investor sentiment and deep learning. It advances crisis mechanism understanding and opens avenues in behavioral finance. Integration of these findings into finance and economics education could enhance students' risk understanding and mitigation strategies.

The adoption of NARX models, incorporating investor sentiment, empowers market participants to proactively manage crises, adjust strategies, enhance asset protection and make informed decisions. These models enable them to minimize losses, maximize returns and diversify portfolios effectively in response to market fluctuations. These insights also guide policymakers such as governments, regulatory institutions and financial organizations in formulating crisis prevention and mitigation policies, bolstering economic and financial stability.

This research reduces economic uncertainty, safeguards individuals' savings and investments and promotes a stable financial climate.

This study is one of the first attempts to demonstrate the detection and prediction of stock market crises, specifically in the MENA stock market, using the NARX model. It offers a robust forecasting model using machine learning and investor sentiment, providing decision-making support for investment strategies and policy development aimed at enhancing financial and economic stability.

This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility.

For a sample of 124 Italian FinTechs, the authors measured online visibility through their website ranking (Google PageRank) and website traffic (Google Trends). Consistent to the historical depth of these measures, the authors separately investigated the effect of equity and non-equity (contractual) agreements on online visibility by means of ordinal logistic regressions and diff-in-diff analysis.

Strategic alliances with banks enhance FinTechs' online visibility. Although both equity and contractual agreements positively influence the popularity of FinTechs' website achieved through the activity of internal and external online content creators (websites ranking), only equity agreements are effective in attracting Internet users (website traffic).

When deciding to interact with banks, FinTechs' managers should consider that equity agreements may be a powerful strategic choice for enlarging the customer base and boosting visibility of FinTechs.

Fostering strategic alliances between banks and FinTechs contributes to FinTechs' growth, generating virtuous mechanisms of innovation, financial inclusion and better allocative efficiency of the financial system.

This work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market, while providing an empirical strategy replicable by future marketing studies.

The purpose of this paper is to explore two identified knowledge gaps: first, the identification and analysis of online searching trends for Financial Technology (FinTech)-related jobs and education information in UK, and second to assess the current strength of the FinTech-related job distribution in terms of job titles and locations in UK, job market in UK and what is required to help it to grow.

Two sets of data were used in this study in order to fill the two identified knowledge gaps. First, six years’ worth of data, for the period from September 2012 to August 2018 was collected from Google Trends. This was in the form of search term keyword text. The hypothesis was designed correspondingly, and the results were reviewed and evaluated using a relevant statistical tool. Second, relevant data were extracted from the “Indeed” website (www.indeed.co.uk) by means of a simple VBA programme written in Excel. In total, the textual data for 500 job advertisements, including the keyword “FinTech”, were downloaded from that website.

The authors found that there was a continuously increasing trend in the use of the keyword “fintech” under the category “Jobs and Education” in online searching from September 2012 to August 2018. The authors demonstrated that this trend was statistically significant. In contrast, the trends for searches using both “finance” and “accounting” were slightly decreased over the same period. Furthermore, the authors identified the geographic distribution of the fintech-related jobs in the UK. In regard to job titles, the authors discovered that “manager” was the most frequently searched term, followed by “developer” and “engineer”.

Educators could use this research as a reference in the development of the portfolio of their courses. In addition, the findings from this study could also enable potential participators to reflect on their career development. It is worth noting that the motivations for carrying out an internet search are complex, and each of these needs to be understood. There are many factors that would affect how an information seeker would behave with the obtained information. More work is still needed in order to encourage more people to enter to the FinTech sector.

In the planning stage prior to launching a new course educators often need to justify the market need: this analysis could provide a supporting rationale and enable a new course to launch more quickly. Consequently, the pipeline of talent supply to the sector would also be benefitted. The authors believe this is the first time that a study like this had been conducted to explore specifically the availability and opportunities for FinTech education and retraining in UK. The authors anticipate that this study will become the primary reference for researchers, educators and policy makers engaged in future research or practical applications on related topics.

The purpose of this paper is to investigate the impacts of investor attention on stock market activity.

The authors employed the Google Search Volume (GSV) Index, a direct and non-traditional proxy for investor attention.

The results indicate a strong correlation between GSV and trading volume – a traditional measure of attention – proving the new measure’s reliability. In addition, market-wide attention increases both stock illiquidity and volatility, whereas company-level attention shows mixed results, driving illiquidity and volatility in both directions.

To the best of the authors’ knowledge, Nguyen and Pham’s (2018) study has been the only previous study identifying investor attention in Vietnam by using GSV as a proxy and examining the impacts of broad search terms about the macroeconomy on the stock market as a whole – on stock indices’ movements. The paper will contribute to this by quantifying GSV impacts on each stock individually.

The purpose of this paper is to explore the differences and similarities between computer ethics, internet ethics and cyberethics as reflected in the contents of the published literature as well as the search trends on Google.

The paper opted for an informetrics approach, and more specifically content analysis, to investigate the inter-relationships between computer ethics, internet ethics and cyberethics. The data sources for this study included Google Trends, Google Scholar and the Web of Science citation indexes. Different search queries were used, depending on the structure of each data source, to extract the relevant data sets.

Using different methods and techniques to analyse the data, the paper provides an alternative means of investigating relationships among concepts. The findings indicate that there is still no clear distinction between the concepts in terms of subject and title terms used to describe the published literature on the three concepts, as well as the research areas where the three concepts are applied. Going by the current trend, the paper envisages that cyberethics may, in the future, become a broader term to include computer ethics and internet ethics.

The data sources that were selected for the study might have not been comprehensive in the coverage of the published literature on the three concepts and therefore there is need for further research, which will expand the scope of the data sources.

The paper’s findings may apply in the practice of indexing and abstracting as well as thesaurus construction as far as the three terms are concerned.

The paper offers an alternative technique that can be used to investigate relationships among concepts. The value of the paper could include curriculum development of programmes dealing with ethical issues that arise when developing and using computers and related technologies.