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The purpose of this paper is to examine the moderating role of transformational leadership on the relationship between job characteristics of both leaders and followers and workplace bullying within the workgroup. The central hypotheses were that, in a process of resource erosion, leaders’ task demands would be positively associated with workplace bullying in the workgroup, but that transformational leadership would moderate this effect, and the effect of followers’ autonomy on bullying.

Anonymous surveys were completed by 540 volunteer fire-fighters’ from 68 fire brigades and, separately, by 68 brigade captains.

The multi-level analyses show that leaders’ task demands positively predicted both bullying outcomes, after controlling for followers’ emotional demands and autonomy. Of most interest, transformational leadership moderated the influence of leaders’ task demands and followers’ autonomy on workplace bullying assessed by two approaches: self-labeling and behavioral experience. Further, a significant three-way interaction demonstrated that transformational leadership is actually associated with higher bullying as followers’ emotional demands increase under conditions wherein followers’ autonomy is constrained, but not when followers’ autonomy is high.

This study offers important practical implications in terms of leadership development in bullying prevention and reduction. For transformational leadership to be effective in reducing bullying at work, the situation must be matched to support this leadership style, or bullying could actually increase.

The study contributes to the research on workplace bullying by advancing the understanding of organizational factors that can influence bullying at work. The study also provides the first quantitative evidence of a relationship between the demands faced by leaders and the bullying experienced by members of the workgroup.

We integrate trait, individual differences and substitutes for leadership theories to examine how leader trait anger influences leader vision and follower trust in the leader across high versus low levels of organizational formalization.

We obtained data from two independent multi-source samples from different occupations and countries. Sample 1: leader–follower dyads (n = 179) collected over three measurement periods from the Philippines; Sample 2: cross-correlational sample of leaders (n = 166), their manager (n = 166) and their followers (n = 610) from Australia.

We tested our hypotheses using PROCESS (Hayes, 2018) and found that leader trait anger influenced follower trust in the leader both directly and indirectly through leader vision (Samples 1 & 2). We also found that organizational formalization neutralized the effect of leader vision on follower trust in the leader (Sample 2).

While we used a time-lagged design in Sample 1, we cannot make strong causal claims as might be the case in an experimental study, for example.

Our results highlight the adverse effect of leader trait anger on perceptions of leader vision and follower trust in the leader, which we suggest should be considered in recruitment and promotion decisions. Our findings also suggest that high levels of organizational formalization may undermine the motivational effect of leader vision on followers, which should be considered in relation to organizational rules and procedures.

This research enhances our understanding of a previously unexplored boundary condition (i.e. organizational formalization) that appears to neutralize the motivational effect of leader vision. In addition, it highlights the ubiquitous effect of trait anger, in this case undermining the effectiveness of leader vision and trust in the leader, from two different observer perspectives (i.e. leaders’ followers and managers).

Abusive supervision is associated with many detrimental consequences. In this theory-review chapter, we extend the abusive supervision literature in two ways. First, we argue that more attention needs to be given to the emotion contagion processes between the leader and followers. More specifically, leaders’ negative affect can lead to followers’ experiences of negative affect, thereby influencing followers’ perception of abusive supervision. Second, we explore how employees draw upon their cognitive prototypes of an ideal leader or Implicit Leadership Theories (ILTs) to evaluate leader behaviors. In this regard, we argue that ILTs can influence the (negative) emotional contagion process between the leaders’ negative affect and followers’ perception of abusive supervision. In our proposed model, leaders’ expressions of negative affect, via emotional contagion, influence followers’ negative affect, perception of abusive supervision, and two behavioral responses: affect- and judgment-driven. The negative emotional contagion process between the leader and followers also differs depending on followers’ susceptibility to emotional contagion and their ILTs. We conclude by discussing the theoretical and practical implications of our model.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.

Issues, which have negatively impacted a diversity of business stakeholders, suggest that business thinking and leadership behaviors surrounding a desired strategic business focus appear increasingly inadequate. For example, that integration strategies and differentiation strategies are mutually exclusive. Three issues appear to contribute to such circumstances.

First, Western strategic business frameworks are largely based on quantitative foci, and remain largely unchallenged. Second, balance between key leadership team agendas and external stakeholder expectations is usually absent. Third, there is minimal connection between what organizational cultures reward, and how human resource management prescriptions provide support.

To address such concerns and implant a renewed strategic business focus, Porter and Kramer (2006, 2011) have identified the notion of shared value, which seems an appealing means to redress business problems represented by negative multistakeholder relations; moreover, an absence of any contemporary acknowledgment of the social contract. Nevertheless, a number of elements appear to be missing from the how shared value is portrayed by Porter and Kramer (2006, 2011).

Based on Maslow’s notion of Eupsychia, and employing an Ideation approach, a renewed strategic business focus supporting the notion of shared value is presented. The renewed focus seeks to enhance Porter and Kramer’s (2011) framework, by including key features to enhance shared value, including elements of Eastern and Western philosophy, and Western organization theory.

Problematic examples, identifying the absence of shared value, and including research propositions are identified.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.