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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.

This study aims to dissect and understand the latent themes of Islamic work ethic (IWE) and explore the driving factors of IWE research.

Structural topic modeling (STM), a sophisticated machine learning technique, was used to analyze a corpus of 205 articles sourced from the Scopus database. These articles cover the 36 years of research on IWE, from 1988 to 2024. Moreover, negative binomial regression was applied to examine the driving factors of IWE research.

The STM analysis unfolds ten topics in conjunction with IWE including individual success, workplace dynamics, organizational work ethics, knowledge management, employee citizenship behavior, financial ethics, job satisfaction, organizational commitment, performance enhancement and leadership. The further STM outputs included word clouds, prevalence proportions, correlation matrix, heatmap, relationship of topics with metadata, topic prominence in the publishing journals and, finally, illustrating trends and future prospects of research on IWE. The results of negative binomial regression reveal that number of authors, article age, journal indexing, authors from multiple countries and number of references are strong drivers of fostering research in IWE, by having significant positive impacts on total citations.

The insights from this study provide valuable guidance for businesses and organizations looking to integrate IWE principles into their operations. By promoting values such as fairness, hard work and ethical behavior, organizations can foster a more inclusive and morally grounded workplace culture. This, in turn, may lead to enhanced employee satisfaction, greater organizational commitment and improved overall performance. Additionally, the emphasis on ethical practices can contribute to broader societal benefits, such as increased trust in business practices and a stronger alignment with social responsibility initiatives.

This is a unique study that explores the latent themes and characteristics of the IWE literature through STM and provides insights on the future research directions. In addition, this study also examines the driving factors of IWE research.

Drawing on conservation of resources, social cognitive and self-verification theories, the current study endeavors to extend our comprehension of the mechanisms linking Islamic work ethics (IWE) to employee ethical behavior. More specifically, the current study investigates the potential impact of IWE on employees’ ethical behavior through the serial mediating roles of moral identity and felt obligation.

By utilizing two-wave data collected from 513 employee-co-worker dyads in the education sector in Turkey, we employed AMOS to conduct a confirmatory analysis and the PROCESS macro for SPSS to test the hypothesized relationships.

The results provide evidence for our hypothesized model. Our results indicate that employees with higher levels of IWE are more likely to exhibit higher levels of ethical behavior. Our results also reveal that IWE has a significant and positive impact on employees’ moral identity and sense of obligation, which in turn enhances their ethical behavior.

By integrating multiple theories, the current research addresses a dearth in the literature and provides a nomological network from Islamic work ethics to ethical employee behavior through the serial mediating role of moral identity and felt obligation. This study adds value to the literature on human resource management and work ethics by examining how IWE affects the attitudes and behaviors of employees in both the public and private sectors. Accordingly, organizations can strengthen their workforce’s moral identities and instill a sense of obligation to behave morally by incorporating workplace ethics into HRM processes.

Abusive supervision (AS) provides insights into the darker aspects of leadership behavior and its effects on employees. Understanding and addressing AS can contribute to creating healthier work environments and promoting employee well-being. The effect of abusive leadership (AS) on counterproductive work behaviors (CWB) in nursing staff is examined through the theoretical lens of the social exchange theory.

Data were collected from 302 nursing staff working at public and private hospitals through a self-administered questionnaire. Measurement scales were adapted from the literature and the data were tested for validity and reliability before performing hypotheses testing through structural equation modeling in SmartPLS 4.0.

AS positively affects CWB, and psychological contract breach mediates this relationship. However, employees with high Islamic work ethics (IWE) are less concerned with supervisors' dysfunctional behaviors and pay less attention to them; thus, IWE buffers the effect of AS on CWBs.

A positive and supportive organizational climate is crucial for attracting and retaining skilled healthcare professionals. When healthcare professionals are subjected to abusive behaviors, their ability to share knowledge, adopt safety protocols and provide the best patient care may be hampered. Therefore, addressing AS in hospitals is vital to promoting a positive work environment, enhancing employee well-being and improving patient care.