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This paper aimed to determine the self- and co-regulation influences on the community of inquiry (CoI) for collaborative online learning.

A quantitative survey was used on a sample of (N = 626) enrolled postgraduate students in a South African Open Distance and e-Learning (ODeL) university. The measuring instruments were the CoI and the shared metacognitive surveys. Correlation and multiple regression analyses were used to determine the association and influence of self- and co-regulation on the CoI.

The results indicated that self- and co-regulation related to the CoI (teaching, cognitive and social) presences. In addition, the results revealed that self- and co-regulation influence the CoI presences. Self-regulation had the highest influence on teaching and cognitive presence, while co-regulation influenced social presence.

The study’s convenience sampling method from a single university limited the applicability of the findings to other online learning environments.

Higher educational teachers who encourage student self- and co-regulation may enhance their online teaching, cognitive and social presence when studying online. The research’s findings may be valuable to teachers to enable them to provide a more collaborative and interactive online learning environment and promote productive online communities.

This study contributes to the body of knowledge about the relationship between teaching, social and cognitive presence and self- and co-regulation within the CoI framework. Furthermore, there has also been limited research focussing on the dynamics of shared metacognition within the CoI framework in an ODeL context.

The purpose of this study was to obtain valuable insights into students’ engagement and experiences within the virtual learning environment, especially in the context of crises. Among the innumerable challenges people throughout the world faced during the first year of the COVID-19 pandemic, those of students in institutions of higher education needing to engage in online academic studies are of special interest. Using an online survey, this study could predict students’ online engagement during the COVID-19 pandemic through three theoretical frameworks: the students’ academic motivation to study, the Big Five personality traits, and loneliness, and with a new tool measuring the participation in the Zoom platform.

To examine the psychological and technological factors predicting the students’ engagement, this study surveyed 547 students from different academic institutions of higher learning.

Findings show that the less lonely the students felt, the less neurotic they were, and the higher they scored in levels of extroversion, agreeableness, consciousnesses and openness to experience, the greater their engagement in their academic studies. In addition, students who were older, more educated, with higher intrinsic motivation and lower lack of motivation were more engaged in their online academic studies. Finally, participating in classes through the Zoom platform and experiencing it positively was a significant predictor of higher academic engagement.

Recognizing these factors can enable educators, institutions of higher learning, counselling services and students to obtain tools for higher engagement in online learning.

This paper aims first to describe the most prevalent teachers’ and students’ behaviors in synchronous online classes in emergency remote teaching; second, to discern behavior profiles and third, to investigate what features explain the observed behaviors.

An adapted COPUS observation protocol was employed to observe 292 online classes from 146 higher education teachers.

The most prevalent behaviors were: Presenting for teachers and Receiving for students, followed by Teachers Guiding and Students Talking to Class. Furthermore, cluster analysis showed two groups: Traditional and Interactive. The variables that better explained belonging to the Interactive lecture group were disciplinary area – social sciences and humanities –and teaching in technical institutions.

In a context where higher education institutions intend to project the lessons learned into post-pandemic learning experiences, this study provides observational evidence to realize the full potential expected from online and blended teaching and learning.

Despite the prevalence of synchronous online lectures during COVID-19, there is a paucity of observational studies on the actual behaviors that occurred in this context. Most research has been based on surveys and interviews. This study addresses this gap.

The current study investigates the factors influencing graduates’ perceived employability by utilizing the stimulus-organism-response theory, in the post pandemic era.

A quantitative approach was employed to examine the hypotheses of the research framework through partial least squares structural equation modelling (PLS-SEM) on the SmartPLS software.

The result indicates that course structure has a positive effect on students’ grit and community of inquiry (CoI). Also, students’ grit and CoI have a positive relationship with students’ performance, while students’ performance has a positive relationship with perceived employability. Moreover, students’ grit, CoI and students’ performance sequentially mediated course structure and perceived employability, whereas readiness and self-directed learning strengthen the relationship between students’ performance and perceived employability.

The findings will benefit university management, government and potential employers on how confident the student is in the chances of a future career after graduating from a higher institution.

This study attempts to elucidate the role of key influencers impacting the student decision-making process of enrollment for higher education in India from the lenses of Stephen Covey's theory on circles of life.

A sample of 556 students of Delhi NCR, India was selected based on a multi-staged sampling method. PLS-SEM was subsequently applied for statistical data analysis.

This quantitative finding voiced the relationships among the constructs in the proposed theoretical framework, i.e. Stephen Covey's circle of life theory. Additionally, it tinted the crucial role of “College attributes” in reconnoitering the relationship dynamics between key influencers (Circle of Influence and Circle of Concern) and student college choice (Circle of Control).

The present study incorporates only the first-year student population of undergraduate management courses in private universities from Delhi NCR, India limiting the generalization of findings substantially.

The study garners the attention of education policymakers on the cognizance of the role played by parents and cohorts in driving the student's decision-making process of college choice under the circle of influence.

This study is pioneering research disseminating a comprehensive outlook of the circle of life theory of great Stephen Covey engrained upon a compendious conceptual model which enlightens the landscape of the decision-making process of student on enrollment under the influence of key influencers.

This paper aims to identify such “entrepreneurial skills” that the scientific community can develop within themselves through training and practice to improve their academic research translation capabilities and performance.

From a qualitative perspective, this study currently presents a few findings from some of the best government-funded laboratories in India and Japan, where they are training their young researchers to be world-class scientists as well as entrepreneurs to help them commercialize their scientific research findings from lab to market for societal benefit.

This paper intends to focus on identifying some of the “best practices” in developing such capabilities that allow “entrepreneurial scientists” to take their science to the society through entrepreneurship. This paper presents two exemplary case studies about institutions that are trying to build innovation and entrepreneurship capabilities in their research groups through continuous training and practice in the fields of biotechnology (India) and autonomous transportation (Japan).

Policymakers and top management teams at academic institutions can learn from the “best practices” on envisioning, evaluation and execution skills for designing their innovation and entrepreneurship skill-building programs for their scientific community.

This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.

This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.

The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.

This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

In order to achieve sustainable development objectives, safeguard the ecosystem, combat global warming and preserve biodiversity for a more sustainable and secure future, the ecological footprint (EF) must be reduced. Therefore, embracing holistic methods, emphasizing renewable energy (RN) and environmental taxes (ET) is crucial. Therefore, the present study aims to capture the effect of ET and RN on EF in Germany.

To achieve this aim, the novel Fourier-based Autoregressive Distributive Lag (ADL) cointegration and the time and frequency-based connections among the variables are investigated in this work throughout the 1994–2021 time span using the wavelet analytic methods, including wavelet power spectrum (WPS) and wavelet coherence (WC) methods, respectively.

The study’s results express that (1) RN, ET and EF are cointegrated in the long run; (2) EF and RN have volatility; (3) RN use in Germany prevents environmental deterioration and (4) ET decreases EF.

The research findings imply that Germany needs rigorous environmental restrictions and enforcement of alternate energy sources for energy use plans and sustainable production objectives.

To the best of our knowledge, the effect of RN and ET on EF in Germany has not been comprehensively explored by using newly developed econometrics techniques and a single dataset. Therefore, the study provides important policy implementations for the German government and is also likely to open debate on the concept.

This study examines the dynamics of the comovement and causal relationship between conventional (Bitcoin, Ethereum and Binance coin) and Islamic (OneGram, X8X token and HelloGold) cryptocurrencies.

This study uses wavelet coherence approach to examine the time-varying lead-lag relationship between conventional and Islamic cryptocurrencies. Furthermore, the authors use BEKK-GARCH model to estimate the optimal weights, hedge ratio and hedging effectiveness in pre-COVID-19 and during the COVID-19 period.

The authors find no significant comovement in pre-COVID-19. However, the authors find significant positive comovement in conventional and Islamic cryptocurrencies at the beginning of the pandemic, and in most cases, conventional cryptocurrencies are leading. X8X and HelloGold have no/weak correlation with conventional cryptocurrencies, implying that investors can diversify the risk by making an Islamic and conventional cryptocurrencies portfolio. The authors also calculate the optimal weights, hedge ratio and hedging effectiveness using the BEKK-GARCH model. Based on the optimal weights, for the portfolios of conventional–Islamic cryptocurrencies, investors are suggested to increase their investment in Islamic cryptocurrencies during the COVID-19 than normal period. The results of hedge ratios show that hedging costs are higher during COVID-19 than before.

The findings of the paper offer several practical policy implications for investors, portfolio manager, Shariah advisors and policymakers pertaining to asset allocation, risk management, forecasting and diversification. Specifically, investors can maximize the risk adjusted returns of their conventional cryptocurrencies portfolio by adding some portions of Islamic cryptocurrencies. Considering the comovement is time-varying, investors/manager should adjust their investment strategies frequently. For the entrepreneurs in crypto-industry, it is advised to introduce new Islamic cryptocurrencies, as it has a huge growth potential because of their distinct features and performance.

This is the first study that explores the linkages between conventional and Islamic cryptocurrencies, therefore this study extends the literature of Islamic finance, stablecoins and cryptocurrencies in pre-COVID-19 and during COVID-19 period. The study results provide insights to conventional crypto investor on how to manage their portfolio during normal and turbulent period.