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Technical solutions can be important when key communicators take on the task of making sense of social media flows during crises. However, to provide situation awareness during high-stress assignments, usability problems must be identified and corrected. In usability studies, where researchers investigate the user-friendliness of a product, several types of data gathering methods can be combined. Methods may include subjective (surveys and observations) and psychophysiological (e.g. skin conductance and eye tracking) data collection. This chapter mainly focuses on how the latter type can provide detailed clues about user-friendliness. Results from two studies are summarised. The tool tested is intended to help communicators and journalists with monitoring and handling social media content during times of crises.

This chapter describes how researchers and developers may improve the design of technical innovations for crisis communicators by testing how user-friendly the innovation is for its intended end users. In the RESCUE project, a tool for social media information gathering was developed. During this process, tool usability was thoroughly tested. Good usability allows the user to complete tasks and achieve goals with effectiveness, efficiency and satisfaction. The purpose of the usability testing was to strive for a tool that is easy to use during demanding circumstances and contributes to a high level of situation awareness (SA) among users. SA is about being aware of what is happening around you – during, for example, emergency assignments – and what this means for your on-going work tasks. The main focus of this chapter is to describe how usability testing was applied throughout the tool development process, from the pre-production planning phase to the final phase. As a part of this, the tool features are described.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

In many security domains, the ‘human in the system’ is often a critical line of defence in identifying, preventing and responding to any threats (Saikayasit, Stedmon, & Lawson, 2015). Traditionally, such security domains are often focussed on mainstream public safety within crowded spaces and border controls, through to identifying suspicious behaviours, hostile reconnaissance and implementing counter-terrorism initiatives. More recently, with growing insecurity around the world, organisations have looked to improve their security risk management frameworks, developing concepts which originated in the health and safety field to deal with more pressing risks such as terrorist acts, abduction and piracy (Paul, 2018). In these instances, security is usually the specific responsibility of frontline personnel with defined roles and responsibilities operating in accordance with organisational protocols (Saikayasit, Stedmon, Lawson, & Fussey, 2012; Stedmon, Saikayasit, Lawson, & Fussey, 2013). However, understanding the knowledge that frontline security workers might possess and use requires sensitive investigation in equally sensitive security domains.

This chapter considers how to investigate knowledge elicitation in these sensitive security domains and underlying ethics in research design that supports and protects the nature of investigation and end-users alike. This chapter also discusses the criteria used for ensuring trustworthiness as well as assessing the relative merits of the range of methods adopted.

This chapter advances our understanding of collaborative innovation processes that span across organizational boundaries by providing an ethnographic account of idea generation dynamics in a member-initiated online songwriting community. Applying a science and technology studies perspective on processes “in the making,” the findings of this chapter reveal the generative entanglements of three processes of content-in-the-making, skill-in-the-making, and community-in-the-making that were triggered and maintained over time by temporary stabilizations of provisional, interim outcomes. These findings also elucidate interferences between these three processes, particularly when an increased focus on songs as products undermines the ongoing collaborative production of ideas. Regular interventions in the community design were necessary to simultaneously stimulate the three processes and counteract interfering tendencies that either prioritized content production, community building, or skill development, respectively. The authors conclude that firms seeking to tap into online communities’ innovative potential need to appreciate community and skill development as creative processes in their own right that have to be fostered and kept in sync with content production.

Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.

Work-life flexibility policies (e.g., flextime, telework, part-time, right-to-disconnect, and leaves) are increasingly important to employers as productivity and well-being strategies. However, policies have not lived up to their potential. In this chapter, the authors argue for increased research attention to implementation and work-life intersectionality considerations influencing effectiveness. Drawing on a typology that conceptualizes flexibility policies as offering employees control across five dimensions of the work role boundary (temporal, spatial, size, permeability, and continuity), the authors develop a model identifying the multilevel moderators and mechanisms of boundary control shaping relationships between using flexibility and work and home performance. Next, the authors review this model with an intersectional lens. The authors direct scholars’ attention to growing workforce diversity and increased variation in flexibility policy experiences, particularly for individuals with higher work-life intersectionality, which is defined as having multiple intersecting identities (e.g., gender, caregiving, and race), that are stigmatized, and link to having less access to and/or benefits from societal resources to support managing the work-life interface in a social context. Such an intersectional focus would address the important need to shift work-life and flexibility research from variable to person-centered approaches. The authors identify six research considerations on work-life intersectionality in order to illuminate how traditionally assumed work-life relationships need to be revisited to address growing variation in: access, needs, and preferences for work-life flexibility; work and nonwork experiences; and benefits from using flexibility policies. The authors hope that this chapter will spur a conversation on how the work-life interface and flexibility policy processes and outcomes may increasingly differ for individuals with higher work-life intersectionality compared to those with lower work-life intersectionality in the context of organizational and social systems that may perpetuate growing work-life and job inequality.

This paper investigates effective human-robot collaboration (HRC) and presents implications for Human Resource Management (HRM). A brief review of current literature on HRM in the smart industry context showed that there is limited research on HRC in hybrid teams and even less on effective management of these teams. This book chapter addresses this issue by investigating factors affecting intention to collaborate with a robot by conducting a vignette study. We hypothesized that six technology acceptance factors, performance expectancy, trust, effort expectancy, social support, organizational support and computer anxiety would significantly affect a users' intention to collaborate with a robot. Furthermore, we hypothesized a moderating effect of a particular HR system, either productivity-based or collaborative. Using a sample of 96 participants, this study tested the effect of the aforementioned factors on a users' intention to collaborate with the robot. Findings show that performance expectancy, organizational support and computer anxiety significantly affect the intention to collaborate with a robot. A significant moderating effect of a particular HR system was not found. Our findings expand the current technology acceptance models in the context of HRC. HRM can support effective HRC by a combination of comprehensive training and education, empowerment and incentives supported by an appropriate HR system.