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This chapter explores the perfect storm brewing at the interface of an increasingly organized ethics review process, grounded in principles of anonymity and informed consent, and the formation of a new digital data landscape in which vast quantities of unregulated and often personal information are readily available as research data. This new form of data not only offers huge potential for insight into everyday activities, values, and networks but it also poses some profound challenges, not least as it disrupts the established principles and structures of the ethics review process. The chapter outlines four key disruptions posed by social media data and considers the value of situational ethics as a response. Drawing on the experiences and contributions of Ph.D. students in interdisciplinary Web Science, the chapter concludes that there is a need for more sharing of the ethical challenges faced in the field by those at the ‘cutting edge’ of social media research and the development of shared resources. This might inform and speed-up the adaptation of ethics review processes to the challenges posed by new forms of digital data, to ensure that academic research with these data can keep pace with the methods and analyses being developed elsewhere, especially in commercial and journalistic contexts.

This chapter describes a neurodynamic modeling approach which may be useful for dynamically assessing teamwork in healthcare and military situations. It begins with a description of electroencephalographic (EEG) signal acquisition and the transformation of the physical units of EEG signals into quantities of information. This transformation provides quantitative, dynamic, and generalizable neurodynamic models that are directly comparable across teams, tasks, training protocols, and team experience levels using the same measurement scale, bits of information. These bits of information can be further used to dynamically guide team performance or to provide after-action feedback that is linked to task events and team actions.

These ideas are instantiated and expanded in the second section of the chapter by showing how these data abstractions, compressions, and transformations take advantage of the natural information redundancy in biologic signals to substantially reduce the number of data dimensions, making the incorporation of neurodynamic feedback into Intelligent Tutoring Systems (ITSs) achievable.

The contemporary workplace is characterized by transience: Organizational members frequently turn over and careers span multiple organizations. Consequently, workplace friendships that were once close become less close and intimate, that is they become peripheral and can deteriorate. While research has examined the benefits for employees who move on to new opportunities, less clear is how stayers, or employees who remain behind in the work setting, are affected. To understand stayers’ experiences and how they manage, we draw on theories of belongingess and to offer a three-part episodic process model, which explains how stayers’ engagement in the task and social domains are influenced. In doing so, we (1) present a dynamic view of the deterioration of dyadic relationships, highlighting how workplace relationships can change over time; (2) discuss both the depth and breadth of emotions involved for stayers; and (3) integrate a positive organizational scholarship perspective by considering both strength of friendships with other present coworkers and coping approaches of stayers as important boundary conditions, which can facilitate their recovery process. We draw attention to the broader implications of our theorizing for research on relationships and emotions, and practical implications for management.

In the aftermath of the global financial crisis, transparency became a rhetorical token used to provide a solution to financial problems. This study examines how transparency materialized in the context of the European securitization industry, which was largely blamed for the credit crunch. The authors show that although transparency was broadly associated with a political call for financial system reform, in the European securitization industry it provided the basis on which to repurpose its market infrastructure. The authors introduce the concept of transparency work to show that transparency is a market achievement organized as a standardization network of heterogeneous actors aiming at establishing a new calculative infrastructure for managing credit risk. Combining insights from information infrastructure research and Economic Sociology, the authors contribute to a distributed and networked understanding of information infrastructure development.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.