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Cybersecurity attacks on critical infrastructures, businesses and nations are rising and have reached the interest of mainstream media and the public’s consciousness. Despite this increased awareness, humans are still considered the weakest link in the defense against an unknown attacker. Whatever the reason, naïve-, unintentional- or intentional behavior of a member of an organization, the result of an incident can have a considerable impact. A security policy with guidelines for best practices and rules should guide the behavior of the organization’s members. However, this is often not the case. This paper aims to provide answers to how cybersecurity-related behavior is assessed.

Research questions were formulated, and a systematic literature review (SLR) was performed by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The SLR initially identified 2,153 articles, and the paper reviews and reports on 26 articles.

The assessment of cybersecurity-related behavior can be classified into three components, namely, data collection, measurement scale and analysis. The findings show that subjective measurements from self-assessment questionnaires are the most frequently used method. Measurement scales are often composed based on existing literature and adapted by the researchers. Partial least square analysis is the most frequently used analysis technique. Even though useful insight and noteworthy findings regarding possible differences between manager and employee behavior have appeared in some publications, conclusive answers to whether such differences exist cannot be drawn.

Research gaps have been identified, that indicate areas of interest for future work. These include the development and employment of methods for reducing subjectivity in the assessment of cybersecurity-related behavior.

To the best of the authors’ knowledge, this is the first SLR on how cybersecurity-related behavior can be assessed. The SLR analyzes relevant publications and identifies current practices as well as their shortcomings, and outlines gaps that future research may bridge.

This paper aims to present a comprehensive review of the laser engineered net shaping (LENS) process in an attempt to provide the reader with a deep understanding of the controllable and fixed build parameters of metallic parts. The authors discuss the effect and interplay between process parameters, including: laser power, scan speed and powder feed rate. Further, the authors show the interplay between process parameters is pivotal in achieving the desired microstructure, macrostructure, geometrical accuracy and mechanical properties.

In this manuscript, the authors review current research examining the process inputs and their influences on the final product when manufacturing with the LENS process. The authors also discuss how these parameters relate to important build aspects such as melt-pool dimensions, the volume of porosity and geometry accuracy.

The authors conclude that studies have greatly enriched the understanding of the LENS build process, however, much studies remains to be done. Importantly, the authors reveal that to date there are a number of detailed theoretical models that predict the end properties of deposition, however, much more study is necessary to allow for reasonable prediction of the build process for standard industrial parts, based on the synchronistic behavior of the input parameters.

This paper intends to raise questions about the possible research areas that could potentially promote the effectiveness of this LENS technology.

Nowadays businesses tend to compete with rivals by improving capability to meet customer demands. One of the key to improve logistics efficiency of a firm is to select appropriate supplier. In the past, to select the most suitable supplier, most people evaluated performance by using average performance or variance from historical data but did not mentioned skewness. In other words, skewness impact on supplier performance is ignored by researchers and buyers. In fact, supplier with greatest average performance does not confirm to be the most suitable one because of uncertainties which make its performance skew either to the left or right, i.e., lower or higher than expectation. Therefore, this empirical study aims to discover and determine the important role of skewness on supplier selection problem. After identifying influential criteria on supplier selection, we analyze skewness effect on suppliers’ performance in each criterion by surveying real data of suppliers’ performances. Skewness effect can be rated in 3 levels; no effect, moderately effect, and highly effect. The results show that, there is only one criterion with no skewness effect, which is price. Criteria which have high skewed performance, for both of medium-sized and large-sized buyers, are lead time, product quality and reliability, and on-time delivery. Also, skewness has higher effect on suppliers’ performance of medium-sized buyers than large-sized buyers. The conclusion surprisingly shows that, skewness is the best index to distinguish between good and bad suppliers, while mean is the worst index.

According to the “strategic focus and future orientation” principle of the integrated reporting (<IR>) framework, <IR> should provide information useful to support investors in assessing the future financial performance of organizations. This study aims to support the operationalization of this function by improving the forward-looking orientation of the integrated report.

Basing on the backward- and forward-looking disclosure in <IR> and the dynamic resource-based view (DRBV), this study develops an explorative case study building a quantitative simulation model based on an integrated report.

This study provides useful insights into how operationalizing the <IR> “future orientation” and obtaining more quantitative information on the organization’s capacity to create value in the future by applying DRBV and quantitative simulation modeling.

The article presents one case study to explore the method suggested to improve the <IR> forward-looking orientation. Additional case studies applying the same research design should be certainly useful to refine the method.

Supporting the <IR> forward-looking orientation, this study provides additional information for the decision-making process of investors, thus contributing to the efficient and productive allocation of capital.

Few studies have investigated forward-looking information in integrated reports, highlighting the existence of an “information gap” referred to such disclosure. Overcoming these previous results, the study provides useful insights on how to improve the <IR> forward-looking orientation.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

The development of green manufacturing has become essential to achieve sustainable development and modernize the nation’s manufacturing and production capacity without increasing nonrenewable resource consumption and pollution. This study investigates the effect of green industrial practices on technical efficiency for Thai manufacturers.

The study uses stochastic frontier analysis (SFA) to estimate the stochastic frontier production function (SFPF) and inefficiency effects model, as pioneered by Battese and Coelli (1995).

This study shows that, on average, Thai manufacturing firms have experienced declining returns-to-scale production and relatively low technical efficiency. However, it is estimated that Thai manufacturing firms with a green commitment obtained the highest technical efficiency, followed by those with green activity, green systems and green culture levels, compared to those without any commitment to green manufacturing practices. Finally, internationalization and skill development can significantly improve technical efficiency.

Green industry policy mixes will be vital for driving structural reforms toward a more environmentally friendly and sustainable economic system. Furthermore, circular economy processes can promote firms' production efficiency and resource use.

To the best of the authors' knowledge, this study is the first to investigate the effect of green industry practices on the technical efficiency of Thai manufacturing enterprises. This study also encompasses analyses of the roles of internationalization, innovation and skill development.