Search results

Drawing upon the cognitive-affective processing system (CAPS) framework, the current study proposes a dual-pathway model that suggests self-serving leadership has a positive influence on employee knowledge hiding. The study also examines the mediating effects of relative deprivation and emotional exhaustion, as well as the moderating effect of political skill, to provide a comprehensive understanding of these relationships.

This study employed two-wave time-lagged survey data collected from 644 employees in 118 teams within a company based in Shenzhen, China. Moreover, hierarchical linear modeling (HLM) was used to test the hypothesized relationships.

The results indicated that self-serving leadership positively influenced employee knowledge hiding, and this relationship was mediated by relative deprivation and emotional exhaustion. Additionally, political skill was found to negatively moderate both the direct relationship between self-serving leadership and relative deprivation and emotional exhaustion, and the indirect path from self-serving leadership to employee knowledge hiding through relative deprivation and emotional exhaustion.

This study makes a unique contribution to the knowledge management literature in several ways. First, it introduces self-serving leadership as a predictor of employee knowledge hiding, expanding the current understanding of this phenomenon. Second, it offers a novel conceptualization, suggesting that employees coping with self-serving leadership may experience relative deprivation and emotional exhaustion, and these factors can predict their engagement in knowledge hiding. Third, the research findings on the moderating role of political skill push the boundaries of the knowledge-hiding literature, providing new insights into the conditions under which this behavior occurs.

The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.

We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.

The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.

This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.