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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 study aims to introduce an affective computing‐based method of identifying student understanding throughout a distance learning course.

The study proposed a learning emotion recognition model that included three phases: feature extraction and generation, feature subset selection and emotion recognition. Features are extracted from facial images and transform a given measument of facial expressions to a new set of features defining and computing by eigenvectors. Feature subset selection uses the immune memory clone algorithms to optimize the feature selection. Emotion recognition uses a classifier to build the connection between facial expression and learning emotion.

Experimental results using the basic expression of facial expression recognition research database, JAFFE, show that the proposed facial expression recognition method has high classification performance. The experiment results also show that the recognition of spontaneous facial expressions is effective in the synchronous distance learning courses.

The study shows that identifying student comprehension based on facial expression recognition in synchronous distance learning courses is feasible. This can help instrutors understand the student comprehension real time. So instructors can adapt their teaching materials and strategy to fit with the learning status of students.

The purpose of this paper is to examine the usability of the dimensional view of data in the context of its presumed learnability.

In total, 303 participants were asked to solve 12 analytical problems in an experiment using the dimensional view of data for half of the problems and an operational view of data for the other half. Inferential statistics and structural equation modeling were performed with participants’ objective results and affective reactions.

Showing that the order of exposure to the two views of data impacts the overall usability of ad hoc querying, the study provided evidence for the learnability potential of the dimensional view of data. Furthermore, the study showed that affective reactions to the different views of data follow objective usability parameters in a way that can be explained using models from affective computing research.

The paper proposes a list of guidelines for use of the dimensional view of data in business analytics.

This study is the first to confirm the learnability of the dimensional view of data and the first to take a deeper look at affective reactions to an ad hoc business analytics solution. Also, it is one of few studies that examined the usability of different views of data directly on these views, rather than using paper representations of data models.

The paper aims to explain the limitations of existing cognitive architectures and affective models, and propose a new cognitive-affective architecture that can be integrated in real intelligent agents to make them more realistic and believable.

The paper evaluates the state of the art, and describes the design and implementation of the cognitive-affective architecture in an agent. A brief evaluation of the agent is provided.

The paper clearly states that it is possible to use cognitive architectures to help, but there is a lack of architectures that address the problem of combining cognition and emotion in agents in a unified, simplified way. A cognitive-affective architecture is useful to make believable intelligent agents in an easier way.

The paper does not explore a lot of possible future work that can be done to extend the emotional expressions of the agent, as well as including direct emotional-sensing capabilities in real time.

The paper argues about the need to include cognitive-affective architectures in modern intelligent agents. The architecture allows to influence and modify the behavior of the agent in real time, to achieve a more realistic and believable interaction with the user.

The paper remarks the importance of a cognitive-affective architecture that makes intelligent agents able to help the users in different tasks and environments.

The paper describes a new cognitive-affective architecture and its utility for modern intelligent agents. This is proven by including it in a previous agent, which boosts its behavior and emotional expression possibilities and thus improves user experience.

Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.

To identify research work on Information and Communication Technology (ICT) in the context of young girls’ affective responses to access and use of ICT in developing countries.

A literature search covering the period from early 1990s to date in fields of information science, human–computer interaction, ICT, and educational technologies was performed using relevant databases and Google Scholar. Related literature in the context of specific theoretical frameworks was retrieved and analyzed.

Abundant research exists on ICT in developing countries. However, little empirical work was found on young girls’ affective responses to access and use of ICT. The gendered digital divide in relation to ICT showed differing perspectives on this issue. Generally, affective information behavior is much less observed in empirical research than the cognitive behavior, regardless of age, gender, or culture.

Young girls’ affective responses to access and use of ICT in these countries warrant additional research in this area of study. Findings from research on youth information behavior in Western countries may have little or no bearings on youth in developing countries. The study of young girls’ access and use of ICT in developing countries should account for cultural, socioeconomic, and institutional differences among countries and between societies or communities in a given developing country.

Minimal research exists on young girls’ affective responses to accessing and using ICT in developing countries. The literature review covered in this chapter is grounded in theoretical frameworks derived from varied disciplines, including the field of library and information science.

The purpose of this paper is to explore uncertainty inherent in emotion recognition technologies and the consequences resulting from that phenomenon.

The paper is a general overview of the concept; however, it is based on a meta-analysis of multiple experimental and observational studies performed over the past couple of years.

The main finding of the paper might be summarized as follows: there is uncertainty inherent in emotion recognition technologies, and the phenomenon is not expressed enough, not addressed enough and unknown by the users of the technology.

Practical implications of the study are formulated as postulates for the developers, users and researchers dealing with the technologies of automatic emotion recognition.

As technologies that recognize emotions are becoming more and more common, and perhaps more decisions influencing people lives are to come in the next decades, the trustworthiness of the technology is important from a scientific, practical and ethical point of view.

Studying uncertainty of emotion recognition technologies is a novel approach and is not explored from such a broad perspective before.