Search results

This paper aims to explore the significance of videoconferencing in blended learning, using the technology acceptance model to investigate students’ perceptions and its impact on course engagement, student satisfaction and future technology use intention. In addition, it examines the role of teacher support in fostering interactive virtual learning experiences.

This study focuses on a cohort of international students regarding blended courses amid the COVID-19 pandemic when the conventional face-to-face components were substituted with virtual classrooms through videoconferencing. It aims to investigate how to facilitate connectivity between Southeast Asian students and their professors located in a Southern state in the USA.

This study reveals that the perceived usefulness of videoconferencing predicts future intention to use, emphasizing the vital role of teacher support in engaging students in virtual classrooms and contributing to student satisfaction.

The small sample of international students in blended courses with an American university during the COVID-19 pandemic may limit the generalization of the findings.

Videoconferencing can be a valuable tool to enhance connectedness in digital learning post pandemic.

Videoconferencing in blended learning can bridge geographical barriers and provide access to diverse learners who might otherwise have limited educational opportunities.

This study supports the integration of videoconferencing as a mechanism for providing high-quality digital learning experiences.

The main aim of the study is to explore the volatility spillover effect of cryptocurrencies (Bitcoin, Ethereum and Litecoin) on inflation volatility in India.

A popular tool, the Bivariate GARCH model (BEKK-GARCH), to study the volatility spillover effect, is applied in the study. Monthly data of cryptocurrencies and inflation (WPI and CPI indices) are gathered from 2015 to 2021.

Significant short-term responsiveness of volatility of cryptocurrencies on the inflation volatility is found. In addition to this, the significant volatility spillover effect from the cryptocurrencies to the inflation volatility is found.

The findings of the current paper can be of use for inflation management, target inflation policies and policies to contain the volatility of cryptocurrencies. The significance of the current paper is relevant as governments worldwide are officially recognizing cryptocurrencies and starting the process of launching their official virtual currency.

No other study is observed on the topic. Hence, the contribution and novelty of the findings of the current paper are very high and add value to the nonexistent literature on the topic. Lack of the number of inflation observations (data of CPI and WPI are available only in monthly frequency) crimps the model estimation. As the cryptocurrencies become old, more data points will be available by design, and such problems can be resolved, and better model estimation may be possible.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

This study aims to quantitatively consolidate the research conducted over the past four decades on how internal branding activities drive employee commitment. It summarizes several operationalizations of internal branding and tests the moderating effect of employee’s personal characteristics and job characteristics on the relationship between internal branding and employee commitment.

This paper uses meta-analysis as the research methodology. The analysis includes a sample of 65 studies (from 62 published works), yielding 226 effect sizes (coded into 82 composite effect sizes) over an aggregated sample of 21,706 respondents.

This study finds that brand communication, brand-centered human resource management (HRM), training and development, organizational support and culture, brand-centered leadership and an excellent reward system are the key operationalizations of internal branding. Furthermore, employee’s personal (education, age and gender) and job (tenure, work status and level of customer orientation) characteristics significantly moderate the internal branding–employee commitment relationship.

Limited empirical literature on some of the internal branding operationalizations such as brand-centered HRM and rewards has curbed the scope of moderator analysis.

This paper proposes some effective ways of implementing internal branding strategies and provides support for boundary conditions that brand managers should consider to strengthen the impact of internal branding activities on employee commitment.

As per the authors’ knowledge, this paper is among the few quantitative consolidations of four decades of research on the internal branding–employee commitment relationship.