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While research on digital dangers has been growing, studies on their respective solutions and justice responses have not kept pace. The agathokakological nature of technology demands that we pay attention to not only harms associated with interconnectivity, but also the potential for technology to counter offenses and “do good.” This chapter discusses technology as both a weapon and a shield when it comes to violence against women and girls in public spaces and private places. First, we review the complex and varied manifestations of technological gender violence, ranging from the use of technology to exploit, harass, stalk, and otherwise harm women and girls in communal spaces, to offenses that occur behind closed doors. Second, we discuss justice-related responses, underscoring how women and girls have “flipped the script” when their needs are not met. By developing innovative ways to respond to the wrongs committed against them and creating alternate systems that offer a voice, victims/survivors have repurposed technology to redress harms and unite in solidarity with others in an ongoing quest for justice.

This study investigates the relationship between feelings of loneliness in the workplace, life satisfaction, affect, hope and expressivity among hotel employees.

The research model was tested via structural equation modeling based on the empirical data collected from hotel employees in Antalya, Turkey.

The research findings suggest that emotional deprivation and social companionship have a significant impact on life satisfaction, that life satisfaction has a significant impact on positive and negative emotions, and that positive and negative emotions have the same impact on pathways and agencies.

The research findings should assist researchers and practitioners to understand the behaviors of hotel employees in continuous interaction and relationship with individuals to motivate them while providing more effective services.

Due to the conflicts between left turn traffic and opposite straight-going traffic in urban traffic network, some of the traffic lanes cannot be used to discharge vehicles during its green phases and the intersection capacity can be greatly reduced. This study/paper aims to reduce the effect of conflicts and increase its capacity through the reasonable pre-signal phase time with the exchangeable lanes.

This paper took into consideration various influence factors to intersection capacity and formulated the capacity optimization model based on 0-1 mixed-integer programming model. This model is efficiently solved by standard branch-and-bound algorithms.

The authors took an intersection as an example and solved the optimal signal timing and entrance lane capacity via this model. Then, simulations were carried out to verify the effect of the exchangeable lanes strategy of this intersection through the simulation software VISSIM and take the traffic volume and delay as outputs, which indicated that this model has better performance.

The front-end control strategy can not only exploit the full potential of the intersection but also significantly improve the operational efficiency of the intersection. It plays a positive role in improving urban intersection congestion.

Although supporting and assessing the non-academic “impact” of research are not entirely new developments in higher education, academics and research institutions are under increasing pressure to produce work that has a measurable influence outside the academy. With a view to supporting the solution of complex societal issues with evidence and expertise, and against the background of increased emphasis on impact in the United Kingdom's 2021 Research Excellence Framework (REF2021) and a proliferation of impact guides and tools, this article offers a simple, easy to remember framework for designing impactful research. We call this framework “The 7Cs of Impact” – Context, Communities, Constituencies, Challenge, Channels, Communication and Capture.

Drawing on core elements of the Policy Institute at King's College London's Impact by Design training course and the authors' practical experience in supporting and delivering impact, this paper outlines how this framework can help address key aspects across the lifecycle of a research project and plan, from identifying the intended impact of research and writing it into grants and proposals, to engaging project stakeholders and assessing whether the project has had the desired impact.

While preparations for current and future REF submissions may benefit from using this framework, this paper sets out the “7Cs” with a more holistic view of impact in mind, seeking to aid researchers in identifying, capturing, and communicating how research projects can and do contribute to the improvement in society.

This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately.

In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement.

Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice.

Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.

This study aims to deepen the understanding of insurers’ role within the return-to-work (RTW) process by uncovering and categorizing the multiple roles assumed by the insurer based on the claimant’s perceptions and identifying the underlying mechanisms that explain the relationship between perceived insurer roles and occupational rehabilitation outcomes.

The author used a qualitative approach with theory-guided content analysis to examine 24 semi-structured interviews with occupational rehabilitation claimants who had undergone occupational rehabilitation within the earnings-related pension insurance system in Finland.

The author uncovered three perceived insurer roles in relation to other stakeholders in the rehabilitation network: financier, coordinator and leader. These roles have different perceived responsibilities and influences on rehabilitation outcomes. Additionally, the author found four perceived insurer roles in relation to the claimants, which varied according to their democracy and activity levels: ally, facilitator, enforcer or enemy. Based on this study, the author recommends that insurers adopt democratic and participatory actor roles (ally and facilitator) to promote the RTW process in occupational rehabilitation.

This inaugural study applied role theory to insurers within the RTW process, developing a new framework of insurer profiles. This study reveals the dynamic nature of insurers and enhances the understanding of the connections between perceived insurer roles and rehabilitation outcomes.

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.

The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam.

The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses.

The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk.

This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards.

This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base.

The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry.

This article aims to examine whether specific job tasks measured at the individual level or personality traits are associated with wages and whether the relationship between personality traits and wages differs depending on the job tasks that individuals perform.

This study analyzes the association between job tasks and personality traits, and their interaction, with regard to wages using German employee data from 2017/2018.

Results suggest that nonroutine manual, interactive or analytic tasks are associated with significantly higher wages compared to routine manual tasks, and while extraversion and emotional stability are related to higher wages, agreeableness and openness tend to be associated with lower wages also within occupations. Moreover, the association between personality traits and wages varies depending on the job task requirements at the workplace. A high degree of extraversion in particular is associated with higher wages when the employee performs nonroutine manual, interactive or analytic tasks.

To date, especially the interaction between individual job tasks and personality traits on wages has not been extensively studied because data on both job tasks and personality at the employee level are scarce. This study contributes to the understanding of wage differences among employees.