A longitudinal study was conducted on the social network of a leaderless group to explore how Big Five personality traits affect leadership emergence, in the form of receiver ties (being nominated as a leader), sender ties (nominating others as leaders), and similarity effects (nominating similar/different others as leaders). Forty one students on a three-month study aboard program participated in intensive group work, and their perceptions of emergent task-and relationship-oriented leadership within these groups were assessed three times across the life cycle of the group. Results indicated that individuals scoring higher on extraversion, openness to experience, and conscientiousness were nominated more as task-and relationship-oriented leaders, whereas those who were more agreeable were more likely to emerge as relationshiporiented leaders. In terms of emergent followership, group members who were more agreeable and neurotic (and less open to experience) were less likely to follow relationship-oriented leaders, whereas more conscientious individuals were more likely to follow task-oriented leaders. With respect to the effects of complementarity and similarity, both task-and relationship-oriented leader nominations were based on dissimilar levels of agreeableness between leaders and followers, while nominated relationship-based leaders tended to have similar levels of openness to experience to followers. Implications of these results are discussed.

Organizations, particularly those for whom safety and reliability are crucial, develop routines to protect them from failure. But even highly reliable organizations are not immune to disaster and prolonged periods of safe operation are punctuated by occasional catastrophes. Scholars of safety science label this the "paradox of almost totally safe systems," noting that systems that are very safe under normal conditions may be vulnerable under unusual ones. In this paper, we explain, develop, and apply the concept of "organizational limits" to this puzzle through an analysis of the loss of Air France 447. We show that an initial, relatively minor limit violation set in train a cascade of human and technological limit violations, with catastrophic consequences. Focusing on cockpit automation, we argue that the same measures that make a system safe and predictable may introduce restrictions on cognition, which over time, inhibit or erode the disturbancehandling capability of the actors involved. We also note limits to cognition in system design processes that make it difficult to foresee complex interactions. We discuss the implications of our findings for predictability and control in contexts beyond aviation and ways in which these problems might be addressed.

Comparatively little attention has been paid to the international careers of many academics, with gender and ethnicity frequently ignored in discussions of migrant academics. Through the lenses of intersectionality, hegemonic masculinity and whiteness, this study explores experiences of migrant academics in Australia and New Zealand, understanding how gender and ethnicity intersect to shape experiences of relative privilege and disadvantage. Qualitative interviews were conducted with 30 academics at various stages of their careers in both Australia and New Zealand. The data reveals the complex patterns of (dis)advantage which characterize the experiences of migrant academics. While some migrant academics may experience disadvantage, for Anglo white male senior academics, considerable privilege is (re)produced through the migration experience. As such, this article suggests migratory experiences can be better understood through the intersectionality of hegemonic masculinity and whiteness to reveal how privilege is maintained.

In this conceptual article, the relations between sensemaking, learning, and big data in organizations are explored. The availability and usage of big data by organizations is an issue of emerging importance, raising new and old themes for diverse commentators and researchers to investigate. Drawing on sensemaking, learning, and complexity perspectives, this article highlights four key challenges to be addressed if organizations are to engage the phenomenon of big data effectively and reflexively: responding to the dynamic complexity of big data in terms of 'simplexity'; analyzing big data using interdisciplinary processes; responsible reflection on ideologies of learning and knowledge production when handling big data; and mutually aligning sensemaking with big data topics to map domains of application. The article concludes with additional implications arising from considering sensemaking in conjunction with big data analytics as a critical way of understanding unique aspects of learning and technology in the twenty-first century.

In recent years, a great deal of attention has been devoted to trying to understand the risk challenges that arise in information management, and most recently, challenges that arise due to big data. In this article, the complexities of big data for employers are explored, drawing on a risk management on Human Resources (HR) perspective and normal accident theory (NAT) to illustrate the evolving characteristics of these complexities. The paper concludes with a series of recommendations that focus on education, design in data collection, and risk management, in the hope that these recommendations enable employers to better anticipate and address emerging big data challenges.