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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.

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.