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Research has provided accumulative evidence that the willingness of teachers to invest in organizational citizenship behavior (OCB) is a fundamental component for achieving school effectiveness. However, most studies examined OCB of the individual teacher, while neglecting the fact that such behavior might grow in a context. Furthermore, educational scholars have focused almost solely on OCB of teachers, and have almost completely neglected to address the concept through a managerial prism. By taking a contextual perspective, the purpose of this paper is to postulate a positive link between leader OCB and team OCB, and suggest that organizational justice serves as a moderator in this relationship.

Data were collected through a survey from multiple sources, to avoid one-source bias. The sample included 82 schools: 82 management teams and their 82 principals, as well as 246 teachers, who were not members of management.

Results of the hierarchical regression analysis confirmed the hypotheses. The authors found a positive association between leader OCB and team OCB and revealed that this positive relationship was significant under high levels of organizational justice, but non-significant under low levels.

The importance of leader OCB in promoting team OCB can inspire the educational system to learn how to develop organizational mechanisms that encourage principals to perform citizenship behaviors and to take this component into consideration in screening processes and succession planning.

The contribution of the study is in identifying leader OCB as a key instrument that may encourage teams to invest in OCBs. To the best of the authors’ knowledge, this is the first study ever to examine the link between leader OCB and team OCB. The finding that there is a positive association between the two constructs may imply that leader OCBs contribute to the school, not only directly, by exhibiting behaviors of helping and support, but also indirectly, through the leader’s impact on his or her team’s behavior.

Analytics technologies are profoundly changing the way in which organizations generate economic and social value from data. Consequently, the professional roles of data scientists and data analysts are in high demand in the labor market. Although the technical competencies expected for these roles are well known, their behavioral competencies have not been thoroughly investigated. Drawing on the competency-based theoretical framework, this study aims to address this gap, providing evidence of the emotional, social and cognitive competencies that data scientists and data analysts most frequently demonstrate when they effectively perform their jobs, and identifying those competencies that distinguish them.

This study is exploratory in nature and adopts the competency-based methodology through the analysis of in-depth behavioral event interviews collected from a sample of 24 Italian data scientists and data analysts.

The findings empirically enrich the extant literature on the intangible dimensions of human capital that are relevant in analytics roles. Specifically, the results show that, in comparison to data analysts, data scientists more frequently use certain competencies related to self-awareness, teamwork, networking, flexibility, system thinking and lateral thinking.

The study was conducted in a small sample and in a specific geographical area, and this may reduce the analytic generalizability of the findings.

The skills shortages that characterize these roles need to be addressed in a way that also considers the intangible dimensions of human capital. Educational institutions can design better curricula for entry-level data scientists and analysts who encompass the development of behavioral competencies. Organizations can effectively orient the recruitment and the training processes toward the most relevant competencies for those analytics roles.

This exploratory study advances our understanding of the competencies required by professionals who mostly contribute to the performance of data science teams. This article proposes a competency framework that can be adopted to assess a broader portfolio of the behaviors of big data professionals.