Abstract
Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.

Abstract
In the chapter, we can find the recommendation for entrepreneurs. The authors are trying to answer the question: How should employers treat Generation Z employees? A complex analysis of the research carried out by the authors as well as other examples from Europe and other continents have been pointed out. A recommendation for enterprises has been included. © 2023 selection and editorial matter, Joanna Niezurawska, Radoslaw Antoni Kycia and Agnieszka Niemczynowicz; individual chapters, the contributors.

Abstract
This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.

Abstract
[No abstract available]

Abstract
Identifying stress in people is not a trivial or straightforward task, as several factors are involved in detecting the presence or absence of stress. The problem of detect stress has attracted much attention in the last decade and is mainly addressed with physiological signals and in a controlled ambience with specific tasks. However, the widespread use of video cameras permitted the creation of a new non-invasive data collection techniques. The goal of this work is to provide an alternative way to detect stress in the workplace without the need of specific laboratory conditions. For that, a stress detection model based on images analysed with deep learning neural networks was developed. The trained model achieved a F1 = 79.9% on a binary dataset, of stress/non-stress, with an imbalanced ratio of 0.49. This model can be used in a non-invasive application to detect stress and provide recommendations to the collaborators in the workplace in order to help them to control their stress condition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The assertive online strategies of Portuguese luxury fashion brands/designers (LFBDs) are essential for managers to succeed in the digital medium and establish long-lasting relationships with clients. This paper explores the challenges in digitizing all the dis-tinctive elements of a luxury fashion brand: the product characteristics, the luxury atmosphere, the personalization of the service, and the services provided. Fourteen Portuguese brands/designers were selected for content analysis. According to Hansen's framework, 43% of Portuguese LFBDs fail to share information about their com-panies on their websites, and only 43% include promotions. Only 36% have a com-munity outside the websites, compared to 93% of the international luxury fashion brands. Moreover, Portuguese websites lack 360 & DEG; technology and augmented reality. Considering the Digital Visual Merchandising - MVD Grid, Portuguese LFBDs do not allow multiple zooms and 2D F/T cursor change, while international brands feature these tools. Regarding the Social Media Performance Analysis Grid, the global aver-age engagement rate of Portuguese LFBDs on Facebook is 0.19% and on Instagram - 0.89%. According to Social Media Content Analysis Grid, Portuguese brands bet a lot on sharing content in story format, mainly on Instagram; however, Facebook has a higher network value. Therefore, the findings show that Portuguese LFBDs should be present and market their products online. This online presence must be supported by a multi-channel strategy while maintaining luxury product characteristics and elements of differentiation.