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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.

Innovation has become a necessity for the information technology (IT) sector, especially during COVID-19 pandemic. Therefore, this study aims to investigate how knowledge sharing affects employees’ innovative work behavior (IWB). Specifically, the study examined occupational self-efficacy (as mediating mechanism) and entrepreneurial leadership (as boundary condition) to encourage IWB.

The study used social media platforms to collect data from 270 employees working in the IT sector through “google forms” on convenience basis between March and August, 2021. The study applied structural equation modeling in two stages to examine the measurement model (for uni-dimensionality) and the structural model (for hypotheses testing).

The study noted that knowledge sharing positively affects employees’ IWB and occupational self-efficacy positively explains this association. In addition, employees’ perception of entrepreneurial leadership strengthens the association between knowledge sharing and IWB.

The study collected data from a developing country during COVID-19 by using a cross-sectional design that may restrict causality. However, the findings suggest the management not only encourages knowledge sharing environment but also engages employees in various training that motivate them to experiment with new ideas and techniques.

This study extends the existing literature on knowledge sharing and IWB by exploring occupational self-efficacy as mediating mechanism and entrepreneurial leadership as a boundary condition.

The aim of the study is to examine the influence of remuneration committee (RC) characteristics, namely separation, size, independence, meetings, and female directors, on firm performance (FP) by using return on assets (ROA), return on equity (ROE) and earnings per shares (EPS). The study covers all firms being listed in Bahrain Bourse for two years which are 2020 and 2021. The results of the study show that having more directors in RC would significantly increase firm performance “ROE and EPS.” Further, having more females in RC would significantly increase firm performance “ROA.” In addition, having separate RC would significantly decrease firm performance “ROA and EPS.” Moreover, the independence of directors in RC and its frequent meetings has no significant impact on the firm’s performance. The results show that there is a need to re-evaluate the role of the RC and strengthen its effectiveness, as some of the variables examined by this study have an insignificant impact on a firm’s performance. Further, there is a need to allocate additional efforts and policies in developing corporate governance and RCs as well.

This paper aims to synthesize the diverse literature on nomination and remuneration committees and provide avenues for future research.

This study provides a comprehensive literature review of theoretical and empirical studies published in reputable international journals indexed by Scopus.

The literature review reveals several aspects of the nomination and remuneration committee. These aspects have been classified into the definition of the nomination and remuneration committee, dimensions of the nomination and remuneration committee, measurement and research review results, reasons for conflict empirical findings, company dynamics and research on moderators, as well as recommending future research.

Our literature review shows that nomination and remuneration committees play a role in improving board performance and company performance, reducing agency conflicts and improving corporate governance to provide implications for companies, regulators and investors and pave the way for future research.

This paper identifies issues related to nomination and remuneration committees, their theoretical and practical implications and avenues for future research.

This study aims to explore the unique and synergistic effects of green human resource management (GHRM) and corporate environmental ethics (CEE) on the environmental performance (EP) of manufacturing small and medium-sized enterprises (SMEs) in Yemen, a less developed country (LDC).

Through a cross-sectional survey design, data were collected from 262 manufacturing SMEs in Yemen and analyzed using “hierarchical regression analysis” via PROCESS Macro.

The empirical results showed that GHRM and CEE positively affect EP and, more importantly, that CEE and GHRM have a synergistic effect on EP.

This study makes a theoretical contribution by integrating GHRM, CEE and EP into a single framework, taking into account the perspectives of the resource-based view and the ethical theory of organizing. The results corroborate the unique and synergistic effects of GHRM and CEE on EP of SMEs in the manufacturing sector.

The results of this study offer valuable insights for SME managers/decision-makers, who are anticipated to become more interested in integrating environmental ethics into their companies. This has implications that with the consideration of CEE, SMEs can benefit from GHRM practices to improve their EP.

The study highlights the positive economic and social impact of SMEs adopting eco-friendly practices like GRHM. In today’s economy, it is not sufficient to simply strive for economic growth. It is possible for SMEs to achieve well-rounded performance by implementing the recommended framework that emphasizes the importance of social and environmental well-being.

This study advances the existing work on the impact of GHRM on EP by demonstrating the crucial role of CEE in predicting EP of manufacturing SMEs in LDCs like Yemen. Previous research on GHRM has mainly been conducted on SMEs in developed nations, which may not be entirely applicable to LDCs. It is crucial to understand this aspect in the context of LDCs so that SMEs can adopt environmental practices effectively in the future: how SMEs conserve the environment through their environmental practices.

This paper aims to investigate the relationship between employee training and bank risk to verify whether and to what extent an increase in employee training, as a soft component of total quality management (TQM), affects bank risk.

The research adopts a panel regression, based on a unique dataset of a sample of Italian banks over the period 2011–2018, to test whether employee training affects bank risk, measured alternatively in terms of Z-score, a proxy of bank stability and non-performing loans (NPLs)/gross loans ratio as a proxy of credit risk.

Research findings reveal that increasing employee training leads to growing bank stability. In contrast, credit risk is not affected by employee training. However, by investigating training heterogeneity, this study found that the increase in the number of managerial training hours, as a proxy for soft skills training, negatively impacts credit risk. Therefore, an increase in soft skills leads to a reduction in bank credit risk.

This study provides empirical evidence in support of the relationship between employee training and bank risk, which seems novel in the literature. From a managerial point of view, this study highlights the need for banks to pay attention to the skills, particularly soft skills, that banks' employees must possess to effectively manage bank risk and, more specifically, the core bank risk.

Empirical evidence on the relationship between employee training, soft/hard skills and bank risk appears limited if not absent. Therefore, the findings provide insights for a more nuanced interpretation of variables that affect bank risk.

This work aims to examine the communication on Twitter of the most responsible companies in Spain to identify the topics covered on corporate social responsibility (CSR) from the perspective of happiness and social marketing. In addition, the profiles of the messages that show an association with the impact of the messages have been identified.

An empirical analysis of the Twitter posts of Spain's ten most responsible companies has been carried out. The methodology of this work combines data mining techniques, sentiment analysis and content analysis, both from a quantitative and qualitative approach.

The results show that most brand tweets do not deal with CSR-related topics. The topics they address the most are those related to sports and the weather. From the perspective of social marketing, conversational-type tweets are the most published and have achieved the most significant reaction from the public. In addition, four messages' profiles have been identified based on the company and the emotional connotation associated with the impact, giving rise to more outstanding promotion of social causes.

Our main contribution to this work has been to value positive communication and social marketing to promote better CSR on Twitter. In this sense, it has been verified that there is a relationship between the public's reaction, the affective connotation and the company that issues the messages.