Digital technology has changed organizations in an irreversible way. Like the movable type printing accelerated the evolution of our history, digitalization is shaping organizations, work environment and processes, creating new challenges leaders have to face. Social science scholars have been trying to understand this multifaceted phenomenon, however, findings have accumulated in a fragmented and dispersed fashion across different disciplines, and do not seem to converge within a clear picture. To overcome this shortcoming in the literature and foster clarity and alignment in the academic debate, this paper provides a comprehensive analysis of the contribution of studies on leadership and digitalization, identifying patterns of thought and findings across various social science disciplines, such as management and psychology. It clarifies key definitions and ideas, highlighting the main theories and findings drawn by scholars. Further, it identifies categories that group papers according to the macro level of analysis (e-leadership and organization, digital tools, ethical issues, and social movements), and micro level of analysis (the role of C-level managers, leader's skills in the digital age, practices for leading virtual teams). Main findings show leaders are key actors in the development of a digital culture: they need to create relationships with multiple and scattered stakeholders, and focus on enabling collaborative processes in complex settings, while attending to pressing ethical concerns. With this research, we contribute to advance theoretically the debate about digital transformation and leadership, offering an extensive and systematic review, and identifying key future research opportunities to advance knowledge in this field.

PurposeAnalytics 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.Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThe study was conducted in a small sample and in a specific geographical area, and this may reduce the analytic generalizability of the findings.Practical implicationsThe 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.Originality/valueThis 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.

Entrepreneurs play a central role in generating and adopting both technological and non-technological innovations. However, existing research provides little guidance on how entrepreneurs interpret their endeavours to generate and implement different types of innovation. This paper addresses this void by bringing metaphor analysis into the field of innovation. We argue that the role of figurative language is important in describing the complexity that characterizes the innovation process. Examining qualitative data from episodes of innovation narrated by a sample of Italian entrepreneurs, this paper provides insights into how each type of innovation (product, marketing, process, organizational and strategic) is described differently, through the use of metaphorical language. This research advances the literature by providing a conceptualization of the different metaphor themes entrepreneurs ascribe to each type of innovation, and discusses practical implications for how the metaphorical language can be analysed and interpreted so that the innovation process can be improved.

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