Culture is not static, however - partly because leaders can work to change cultural norms over time.

Theories of leadership often don’t hold across cultures, because these contextual factors look different across and within societies. Stereotypes about gender roles and norms that vary among women and men strongly moderate leader assessments, although evidence shows that these perceptions can be shifted.Ĭultural context: Leadership is always situational, evaluated in a particular political setting, cultural environment and through the lens of gender power relations. Instead, they are likely to ‘PIIIC’ their leaders, based on: 1) the position of a leader, which determines the source of their authority (legal-rational, traditional, charismatic) 2) their views on a particular issue 3) whether they will act in their interests 4) how far the leader matches the identity of their group and 5) the characteristics they display, including how they conduct themselves.Ĭhannels: Perceptions of leaders are rarely unmediated, because media affects matter what information is included (or not), how information is framed, and therefore whether and how information transforms individuals’ assessment of leaders.įollower identity: The assessment of leaders is also moderated by the identity of the follower, whether their gender, age and other markers. It identifies four key areas as important influences on how followers understand leadership:ĭimensions of assessment: Followers may or may not perceive the neat leadership categories researchers use to describe leaders. This paper develops a basis for thinking about how followers form perceptions of leaders, the affect this can have on leadership practices, and why this matters for development. To understand how developmental leadership works, we need to better understand one vital but often overlooked ingredient: Followers. Leadership is always, everywhere, an interaction between leaders and followers. But leaders cannot pursue real change without influencing people, or persuading them to change their ideas or behaviours.

We evaluate different JCI implementations on synthetic data and on flow cytometry protein expression data and conclude that JCI implementations can considerably outperform state-of-the-art causal discovery algorithms.What does it mean to be a leader in different places and times? How are leaders’ styles, characteristics and practices evaluated? How does history and culture affect perceptions of leadership?įor a long time, leadership studies have addressed these questions by focusing on the properties and characteristics of individual leaders, viewing leadership as a particular set of traits or behaviours. We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting. JCI can deal with different types of interventions (e.g., perfect, imperfect, stochastic, etc.) in a unified fashion, and does not require knowledge of intervention targets or types in case of interventional data. JCI is a causal modeling framework rather than a specific algorithm, and it can be implemented using any causal discovery algorithm that can take into account certain background knowledge. We introduce Joint Causal Inference (JCI), a novel approach to causal discovery from multiple data sets from different contexts that elegantly unifies both approaches. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. The gold standard for discovering causal relations is by means of experimentation. Joint Causal Inference from Multiple Contexts

Different contexts