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The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Microlearning has been considered as a promising topic in work-based learning. This paper aims to review the trends of microlearning in terms of related publications and Internet searches. Hopefully, the findings can serve as a reference for the education sector, government, business and academia to promote, design and use microlearning.

In this study, two sets of analysis were conducted. Firstly, the authors analysed the publication trend of microlearning. Second, the authors analysed the trend of Internet searches related to microlearning. More specifically, the authors analysed real-world data of 14 years obtained from Scopus and Google Trends for the purpose. These data include the first relevant publication found in the database.

In total, 476 relevant publications have been identified during 2006–2019. According to the findings from the analysis of the identified publications, microlearning is a relevantly new and emerging global topic involving authors, affiliations and funding sponsors from different countries. Moreover, many microlearning-related publications were conducted from perspectives of e-learning or mobile learning. Furthermore, the authors notice higher education was the most frequently mentioned education level in the identified publications. On the other hand, language learning (i.e. second language, vocabulary learning, etc.) had been mentioned more times in the titles and abstracts than other subject areas. Overall, the increasing trend of publications on “microlearning” (as a knowledge supply) is in line with the established increasing Internet searches of “microlearning” (as a practical demand) in recent years.

From the work-based learning perspective, microlearning has been considered as one of the key topics in talent development topics. Policymakers, educators, researchers and participators have the responsibility to explore how to promote, design and use microlearning to help people to learn in the right direction through valid knowledge with ethical consideration.

Although many works had been done on microlearning, there is a lack of comprehensive studies reviewing the trends of microlearning in terms of related publications and Internet searches. This study aims to fill this gap by analysing real-world data obtained from Scopus and Google Trends – these data include the first relevant publication found in the database. The authors believe this is the first time that a study has been conducted to comprehensively review the development trends of microlearning. Hopefully, this study can shed some light on related research.

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.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

This paper aims to investigate the determinants of global interest in central bank digital currency (CBDC). It assessed whether global interest in sustainable development and cryptocurrency are determinants of global interest in CBDC.

Google Trends data were analyzed using two-stage least square regression estimation.

There is a significant positive relationship between global interest in sustainable development and global interest in CBDC. There is a significant positive relationship between global interest in cryptocurrency and global interest in the Nigeria eNaira CBDC. There is a significant negative relationship between global interest in CBDC and global interest in the eNaira CBDC. There is a significant positive relationship between global interest in CBDC and global interest in the China eCNY. There is a significant negative relationship between global interest in cryptocurrency and global interest in the Sand Dollar and DCash.

The literature has not empirically examined whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC. This study fills a gap in the literature by investigating whether global interest in sustainable development and cryptocurrency are factors motivating global interest in CBDC.

This paper aims to shed light on an impact of Google keywords on the number of new businesses (and an amount of capital registered) in Vietnam, the Southeast Asian country, after the year of an entrepreneur, 2016.

This study uses a rich set of quantitative techniques from VAR Granger and threshold regression. The whole sample period covers the data (keywords, number of new businesses, an amount of capital invested to register) from the first week of 2016 to October 2018, which includes 144 observations in total.

The findings suggest that the relationship between Google does not persist in the long run. There is a short-run shock, might cause a change to the frequency of the other keywords rather than the number of firms (or an amount of capital). However, under the number of firms’ threshold, keywords have the both positive and negative impacts on entrepreneurs whereas a higher threshold of capital, keywords show their roles to predict an amount of money for registering firms.

The Vietnamese Government and executives are advised to consider the Google keywords “entrepreneur” (in Vietnamese) and “start-up”, which cause a decline in entrepreneurial movements. In addition, the current period is going to inverse from the previous one in terms of the number of firms and an amount of capital. Finally, there are two critical thresholds: 1,602 companies and 35,010m VND for the keywords' influence.

This study contributes empirical evidence of technological change and entrepreneurship and contributes to the existing literature by discussing how this relationship under the threshold.