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This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

Studies have so far focused on learning in organizations, factors affecting learning, learning effectiveness and so on but the concept of learning in a hybrid work arrangement is yet unexplored. The purpose of this study is to measure the perception of faculty members in higher education institutions towards learning in a hybrid work arrangement and also to measure the differences of perception towards hybrid work arrangement based on employees’ gender and organization type.

The data was collected from a sample of 390 faculty members composing of Assistant Professors, Associate Professors and Professors, purposely chosen from two of the premier higher education institutions (one private and one public) located in Punjab, India. A self-structured questionnaire was administered to the faculty members who are working on a regular basis and have minimum of two years of work experience with the chosen university. For analysing the collected data exploratory factor analysis and other descriptive statistics have been applied.

The findings of the survey show that in terms of gender differences, it is the female employees who are more satisfied with different aspects of hybrid/remote work arrangement as compared to male employees. In regard to organizational differences in the perception towards learning in a hybrid work arrangement it is found that public university employees have a more positive attitude so far as individual factors are concerned, but in terms of organizational factors, it is the private university that is scoring better than the public university.

The study is limited to only two higher education institutions, and its findings to be applicable in all higher education institutions, further studies may be required on a larger canvas. Future studies may be undertaken using advanced statistical tools like structural equation modelling to explore various variables associated with learning in a hybrid work arrangement.

Applicability of hybrid work arrangement is very high in higher education institutions and to the best of the authors’ knowledge, this is the first study which adds to the literature on perception of employees towards organizational learning in a hybrid work arrangement.

The study aims to examine the dark side of teleworking and tests the various factors which lead to employee exhaustion while teleworking. The study examines two key variables, i.e. initiated interdependence and professional isolation, as antecedents of emotional exhaustion amongst employees who are teleworking. The study further investigates the mediating role of psychological detachment in these relationships.

Survey data were collected from 307 employees who were teleworking for more than three months. Structural equation modeling (SEM) was used to test the proposed hypothesis.

The study found that initiated interdependence and professional isolation positively impact emotional exhaustion. These findings suggest that employees whose work is designed such that others depend on them will experience high emotional exhaustion while teleworking. Also, employees who experience professional isolation because of a lack of connection while teleworking will experience emotional exhaustion. The study also revealed the mediating role of psychological detachment in these relationships.

The study has insights for policy-making concerning telework practices.

It is one of the first studies examining the impact of teleworking in a context when it is not a choice exercised by the employees but has been imposed upon them. This study is particularly relevant in the context of the decision made by some organizations to move to telework as a permanent work format.

This paper seeks to investigate the differences in the teachers’ professional development (TPD) by mentorship in workplace. The authors examined the role of mentorship in the PD of teachers and conducted a meta-analysis of pertinent empirical data.

Using data from over 2,900 individuals, 66 experiments and 12 countries, the authors presented a meta-analysis of the association between workplace mentorship and TPD.

The authors concluded that mentoring activities could boost the TPD to some extent. It contributes positively to the discipline of science and language, kindergarten, individual mentoring and curriculum research. In addition, the periodicity should not exceed 1 year.

The results of the meta-analysis are restricted to short-term mentorship activities, and the sample size is modest. Building upon the findings from the literature review and meta-analysis, the authors delineated a research agenda for prospective investigations. This includes an imperative for further exploration into the nexus between mentoring and the PD of educators.

Based on the available literature and meta-analysis findings, the authors developed a framework for the “Experts in the classroom” TPD pattern.

This is the first meta-analysis evaluating the association between mentorship and TPD.

To encourage entrepreneurship, which accelerates economic growth by increasing employment opportunities and competitiveness, stakeholders must conduct studies and develop policies that consider both the current situation and future expectations. This study aims to examine the environmental and personal factors that influence students’ entrepreneurial intentions (EIs), using a model based on the theory of planned behaviour (TPB) and social cognitive theories (SCT).

This study proposed an institutional framework demonstrating contextual features to achieve this objective. This theoretical framework is evaluated using a sample of 375 university students in Türkiye.

The empirical findings can help policymakers develop effective policies to encourage entrepreneurship.

The study focuses on EIs; it is possible that even if a participant indicated a high EI in the survey, they will ultimately pursue a completely different career path.

The study also contributes to entrepreneurship literature studies investigating the relationships between the TPB and SCT.

By testing specific hypotheses for Türkiye, this study contributes to the demand for entrepreneurship research in countries that are major global players but have vastly different sociocultural contexts than Western countries.

The study draws a theoretical model that explains the factors affecting the EIs of university students and attempts to explain the EIs of university students with and without business education within this model.

This study aims to integrate the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory into a meta-analytic framework to synthesize green consumption literature.

By integrating the findings from 173 studies, a meta-analysis was performed adopting several analytical methods: bivariate analysis, moderation analysis and path analysis.

VBN- and TPB-based psychological factors (adverse consequences, ascribed responsibility, personal norms, subjective norms, attitude and perceived behavioral control) mediate the effects of altruistic, biospheric and egoistic values on green purchase intention. Further, inconsistencies in the proposed relationships are due to cultural factors (i.e. individualism-collectivism, power distance, uncertainty avoidance, masculinity–femininity, short- vs long-term orientation and indulgence-restraint) and countries’ human development status.

The authors selected papers published in English; hence, other relevant papers in this domain published in other languages might have been missed.

The findings are useful to marketers of green offerings in designing strategies, i.e. specific messages, targeting different customers based on countries’ cultural score and human development index, to harvest positive customer responses.

This study is the pioneering attempt to synthesize the TPB- and VBN-based quantitative literature on green consumer behavior to resolve the reported inconsistent findings.