Abstract
Digital transformation has been one of the main trends in organizations in recent years, and digital literacy is a critical factor in the success of this transformation. Digital transformation involves the use of digital technologies to improve an organization’s processes, products, and services. For this transformation to be successful, it is necessary for employees to have knowledge of and skills in digital technologies. Digital literacy allows employees to understand technologies and their applications, know how to use them efficiently and safely, evaluate and select the most appropriate digital tools for each task, and be prepared to deal with problems and challenges that arise in the digital environment. Thus, this study is relevant because it seeks to understand how digital literacy can impact Digital Transformation in organizations and, through the construction of an explanatory model, allows the identification of variables that influence this relationship by developing strategies to improve the digital literacy of employees in organizations.

Abstract
Business Process Automation has been gaining increasing importance in the management of companies and organizations since it reduces the time needed to carry out routine tasks, freeing employees for other, more creative and exciting things. It can be applied in the most varied business areas. Organizations from any sector of activity can also adopt it. Given these benefits, the granted success in transforming business processes would be expected. However, automation initiatives still fail. Adopting this technology can raise social, technological, ethical, methodical, and organizational issues. These facts have triggered the necessity for a summary that could extract more information about how to implement Business Process Automation (BPA), attending to the administrative processes, especially when applied in Small and Medium-sized Enterprises (SMEs). This study aims not only to review the available literature on how to implement BPA but also to typify the processes that can be automated, which technologies or tools exist for making that change, and influence factors in the procedure of BPA. We have covered more than 300 research papers published between 2016 and 2023 in reputable scientific data sources like Scopus, Web of Science/Clarivate, and ScienceDirect/Elsevier. The review revealed some paths for BPA, with some common steps. In addition, some common process characteristics fundamental to automation are exposed, as well as factors that are critical to an organization for successful automation. The results indicate that BPA is an established area in Business Process Management related to technologies/tools like Robotic Process Automation (RPA) or Cognitive-Robotic Process Automation (C-RPA), Workflow Management Systems (WfMS), Enterprise Resource Planning (ERP), and Blockchain. As far as we observed, this Systematic Literature Review (SLR) is a unique study that covers all the environmental variables for applying automation in business processes.

Abstract
The speed and scale of technological change are raising concerns about the extent to which new technologies will radically transform workplaces. Competition for the best talent is being intensified, and talent management requires new approaches and innovative strategies for developing talent based on corporate culture and its unique properties. By implementing and adopting technology in Human Resources Management (HRM), organizations create a digital employee lifecycle that spans from the initial Hiring Process to encompassing areas such as Performance Management, Learning and Development until the Offboarding, shaping a Talent Management journey. Despite the implementation of technologies being a continuous practice observed in numerous organizations, there are still challenges. The HRM technological market has become massive, and concerns arise about adopting these technologies' costs, practicality, and purpose. Because of that, designing strategies for implementing technologies in HRM, specifically in talent management, is hard to overview. In this context, this document aims to present the necessity and significance in developing a framework that aggregates the implementation process of technologies in talent management supported by Design Science Research (DSR). The holistic perspective of the forthcoming framework consolidates insights into business challenges and their correlation with technology selection, technological capabilities, implementation procedures, as well as anticipated metrics and their impact.

Abstract
Knowledge of Social Engineering is crucial to prevent potential attacks related to organizational Information Security. The objective of this paper aims to identify the most common social engineering techniques, success attack factors, and obstacles, as well as the good practices and frameworks that could be adopted concerning their mitigation. As an analysis methodology, a Systematic Literature Review was carried out. The findings revealed that the discussion about SE attacks has increased and that the most imminent threat is phishing. Exploiting human vulnerabilities is a growing threat when the attack is not carried out directly through technical means. There continue to be more technical attacks than non-technical attacks. Encouraging organizational security prevention, like training, education, technical controls, process development, defense in detail, and the development of security policies, should be considered mitigating factors for the negative impact of SE attacks. Most SE frameworks/models are focused on attack techniques and methods, mostly on technical components, decorating human factor. As a novelty, we found the opportunity to develop a new framework that could improve coverage of the gaps found, supported on security international standards, that could help and support researchers in developing their work, understanding open research topics, and providing a clearer understanding of this type of threat. © 2024 by the authors. Licensee ESJ, Italy.

Abstract
Cybercrime is growing rapidly, and it is increasingly important to use advanced tools to combat it and support investigations. One of the battlefronts is the forensic investigation of mobile devices to analyze their misuse and recover information. Mobile devices present numerous challenges, including a rapidly changing environment, increasing diversity, and integration with the cloud/IoT. Therefore, it is essential to have a secure and reliable toolbox that allows an investigator to thwart, discover, and solve all problems related to mobile forensics while deciphering investigations, whether criminal, civil, corporate, or other. In this work, we propose an original and innovative instantiation of a structure in a forensic toolbox for mobile devices, corresponding to a set of different applications, methods, and best practice information aimed at improving and perfecting the investigative process of a digital investigator. To ensure scientific support for the construction of the toolbox, the Design Science Research (DSR) methodology was applied, which seeks to create new and unique artifacts, drawing on the strength and knowledge of science and context. The toolbox will help the forensic investigator overcome some of the challenges related to mobile devices, namely the lack of guidance, documentation, knowledge, and the ability to keep up with the fast-paced environment that characterizes the mobile industry and market. © 2024 by the authors. Licensee ESJ, Italy.

Abstract
Enterprise Architecture (EA) is a coherent set of principles, methods, and models that express the structure and behavior of an enterprise and its IT landscape. EA mining uses data mining techniques to automate EA models' extraction. Ontologies help to define concepts and the relationships among these concepts to describe a domain of interest. This paper presents an extensible ontology for EA mining to extract models using Application Program Interface (API) log files as the data source. The ontology development follows the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and uses OntoUML 2.0 language to ensure its expressiveness and readability. To validate its theoretical feasibility and contribution to EA modeling, it presents a simulation of the ontology application through a controlled scenario using data structures similar to an industrial case. Then, the ontology is verified and validated, checking quality ontology criteria using specialized tools for syntactic and semantic model checking, which also aids in avoiding ontology anti-patterns.