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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 research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers. Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload. However, whether moderation and mediation effects exist among warning information, driver’s cognition, behavior and risky avoidance performance is unclear.

This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior. A driving simulator experiment was conducted with waring and command information.

Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario. The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk. At the same time, command information can weaken this negative effect. Moreover, improved collision avoidance behavior can be achieved through increasing drivers’ mental workload.

The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior, thus contributing to improved in-vehicle information system design.

The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’ collision avoidance ability. Through further calibration with larger sample size, the proposed structural model can be used to predict the effect of in-vehicle warnings in different risky driving scenarios.

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.

Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving.

Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system.

A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI.

This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.

This chapter analyses the Norwegian authorities’ presence on Twitter during the 22 July 2011 terrorist attacks. Twitter activity by two official institutions is analysed in particular, namely, the blood bank at Oslo University Hospital and the Norwegian Police Security Services (PST). Our findings show that the Norwegian authorities were almost completely absent on Twitter during the critical hours of the terrorist attack, and that there was no coordination and synchronisation of communication from the authorities. This official silence allowed the diffusion of speculation and misinformation to take place; these were neither corrected nor addressed, as the analysed PST case shows. In contrast, the blood bank used Twitter to mobilise blood donors to address an acute problem: a shortage of blood to treat casualties. The chapter concludes by offering recommendations to the authorities for future major incidents.

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

This paper aims to examine the types of user misbehaviours in the sharing economy (SE) context. SE offers a fruitful study setting due to the scope of potential misbehaviour and the expanded role of consumers.

The study drew on online archival data from the AirbnbHell.com website, where people share their stories about their Airbnb-related negative experiences. The authors reviewed 405 hosts’, guests’ and neighbours’ stories and coded the identified forms of misbehaviours into categories. The typology thus developed was validated in the context of the Uber Rides service.

User misbehaviours in the SE context can be distinguished based on the domain in which the user role is violated and the nature of violated norms. These two conceptual distinctions delineate a four-fold typology of user misbehaviours: illegal, unprofessional, unbefitting and uncivil behaviours.

The trustworthiness of the stories could not be assessed.

The presented typology can be used as a mapping tool that facilitates detection of the full scope of misbehaviours and as a managerial tool that provides ideas for effective management of misbehaviours that correspond to each category.

The paper presents the first empirically derived comprehensive typology of user misbehaviours in SE settings. This typology enables classification of a broad set of misbehaviours, including previously overlooked unprofessional behaviours carried out by peer-service providers. The study also puts forward a revised definition of consumer misbehaviours that encompasses the impact of misbehaviours on parties not directly involved in the SE-mediated exchange.

The importance of information systems (IS) professions is increasing. As personality–job fit theory claims, employees must have suitable personality traits for particular IS professions. However, candidates can try to fake-good on personality tests towards the desired personality type. Thus, the purpose of this study is to identify archetypal IS professions, their associated personality types and examine the reliability of the Myers–Briggs Type Indicator (MBTI) personality test in IS recruitment decisions.

The authors reviewed academic literature related to IS professions to identify job archetypes and personality traits for IS professions. Then, the authors conducted an experiment with 452 participants to investigate whether candidates can fake-good on personality tests when being tested for a particular IS profession.

The identified job archetypes were IS project manager, IS marketing specialist, IS consultant, IS security specialist, data scientist and business process analyst. The experimental results show that the participants were not able to fake-good considerably regarding their personality traits for a particular archetype.

The taxonomy of IS professions should be validated further. The experiment was executed in an educational organisation and not in a real-life environment. Actual work performance was not measured.

This study enables a better identification of suitable candidates for a particular IS profession. Personality tests are good indicators of the candidate's true personality type but must be properly interpreted.

This study enhances the existing body of knowledge on IS professions' archetypes, proposes suitable MBTI personality types for each profession and provides experimental support for the appropriateness of using personality tests to identify potentially suitable candidates.