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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 study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

This paper investigates the development of efficiency and the progress of banking integration in the European Union by checking for convergence among banks of European and Eurozone countries as well as contrasting the results with those of United States banks.

Initially, we employ the two-stage semi-parametric double bootstrap DEA method, which absorbs the effects of possible integration barriers in the measurement of efficiency. Afterwards, we apply a panel data model, in order to investigate the process of banking integration by testing for convergence and for convergent clusters in banking efficiency.

Our main findings show that the bank efficiency of the US is considerably higher than that of the Eurozone and the European Union. Although there is no evidence of convergence across the banking groups, our results indicate the presence of club convergence. We also conclude that the US banking system is closer to convergence than the Eurozone and the European Union banks. Nevertheless, this outcome is subject to change in the future due to the fact that Eurozone and European Union banks' speed of convergence is higher than that of US banks.

Our survey is unique in trying to check for convergence while controlling for country-specific and bank-specific factors that affect the efficiency of European and Eurozone banks. Moreover, recent literature does not compare the convergence of efficiency of Eurozone, European and US banking. Finally, in our paper special consideration was given to the comparison of commercial, cooperative and savings banks, as subsets of our banking groups.

Progress toward health equity is necessary to reduce health disparities, and health literacy is key to achieving this goal. Because libraries provide access to knowledge and insights about their communities, they are effective partners in advancing health equity and implementing programs to reduce health disparities. A literature review on library programs and activities that focus on promoting health equity was conducted.

A literature review on library programs and activities concerning health equity and social determinants of health was conducted. Relevant literature was identified from searches of databases, library publications and grey literature.

The authors found 224 eligible sources and many types of libraries advancing health equity. Libraries frame their role in advancing health equity through external programs in three ways: (1) providing access to high-quality health information, (2) delivering health literacy training and resources and (3) connecting their communities with community health services. Libraries also advance health equity by focusing on internal library operations and providing research services focused on cultural humility and competence as they apply to health care.

This literature review will help the National Library of Medicine (NLM) develop a strategy to support libraries advancing health equity through information made available by programs and activities of NLM and the Network of the National Library of Medicine.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

In recent years, there has been a growing dialogue around community-based and systems-based approaches to security risk management through the introduction of top-down and bottom-up knowledge acquisition. In essence, this relates to knowledge elicited from academic experts, or security subject-matter experts, practitioner experts, or field workers themselves and how much these disparate sources of knowledge may converge or diverge. In many ways, this represents a classic tension between organisational and procedural perspectives of knowledge management (i.e. top-down) versus more pragmatic and experience focussed perspectives (i.e. bottom-up).

This chapter considers these approaches and argues that a more consistent approach needs to address the conflict between procedures and experience, help convert field experience into knowledge, and ultimately provide effective training that is relevant to those heading out into demanding work situations. Ultimately, ethics and method are intricately bound together in whichever approach is taken and the security of both staff and at-risk populations depends upon correctly managing the balance between systems and communities.

Volunteers at child helplines play an important role in providing support for children, so keeping them satisfied during encounters is crucial to continue helping children. The purpose of this study is to understand how children’s perceptions of instrumental and emotional support (partner effects) influence volunteer encounter satisfaction, and whether this effect is moderated by a volunteer’s previous encounter experience and levels of interpersonal and service-offering adaptiveness.

The sample consisted of 377 dyads of 116 volunteers and 377 children from online service encounters at a child helpline. Questionnaires were used to measure satisfaction, support and volunteer adaptiveness. A multilevel model was estimated to test the hypothesized moderation effects.

This study revealed that the instrumental support partner effect positively influenced volunteer encounter satisfaction. This relationship was stronger when the previous encounter was less satisfying or for volunteers with higher interpersonal, but not higher service-offering, adaptiveness. Negative effects on the relationship between the emotional support partner effect and volunteer encounter satisfaction were found after a less satisfying previous encounter or for volunteers with higher interpersonal adaptiveness.

This study contributes to the services and volunteerism literature by providing a unique perspective on the interpersonal influence between volunteers and children during service encounters. In the context of child helplines, this paper illustrates how volunteer encounter satisfaction is a function of the intricate interplay between children’s perceptions of the service encounter and volunteers’ perceptions of previous experiences and their adaptiveness.

Understanding people’s intentions to be an internet entrepreneur is an important issue for educators, academics and practitioners. The purpose of this paper is to develop and validate a scale to measure internet entrepreneurial self-efficacy.

Based on an analysis of 356 responses, a scale of internet entrepreneurial self-efficacy is validated in accordance with established scale development procedures.

The internet entrepreneurial self-efficacy scale has 16 items under three factors (i.e. leadership, technology utilization and internet marketing and e-commerce). The scale demonstrated adequate convergent validity, discriminant validity and criterion-related validity. Nomological validity was established by the positive correlation between the scale and, respectively, internet entrepreneurship knowledge and entrepreneurial intention.

This study is a pioneering effort to develop and validate a scale to measure internet entrepreneurial self-efficacy. The results of this study are helpful to researchers in building internet entrepreneurship theories and to educators in assessing and promoting individuals’ internet entrepreneurial self-efficacy and behavior.

The purpose of this research is to systematically review the properties of supply chains demonstrating that they are complex systems, and that the management of supply chains is best achieved by steering rather than controlling these systems toward desired outcomes.

The research study was designed as both exploratory and explanatory. Data were collected from secondary sources using a comprehensive literature review process. In parallel with data collection, data were analyzed and synthesized.

The main finding is the introduction of an inductive framework for steering supply chains from a complex systems perspective by explaining why supply chains have properties of complex systems and how to deal with their complexity while steering them toward desired outcomes. Complexity properties are summarized in four inter-dependent categories: Structural, Dynamic, Behavioral and Decision making, which together enable the assessment of supply chains as complex systems. Furthermore, five mechanisms emerged for dealing with the complexity of supply chains: classification, modeling, measurement, relational analysis and handling.

Recognizing that supply chains are complex systems allows for a better grasp of the effect of positive feedback on change and transformation, and also interactions leading to dynamic equilibria, nonlinearity and the role of inter-organizational learning, as well as emerging capabilities, and existing trade-offs and paradoxical tensions in decision-making. It recognizes changing dynamics and the co-evolution of supply chain phenomena in different scales and contexts.