Detalhes do Documento

The purpose of the research is to determine the strategic importance of digital identity in human resources management (HRM) through exploratory qualitative research. The qualitative methodology allows us to understand the complexity of discourse and digital practices and to analyze them in depth. Thus, employing a qualitative approach brings forth the complexity posed by discourses as well as digital practices while ensuring sufficient depth of interpretation. In this case, digitalization changes a candidate’s digital footprint into a strategic asset which is very important for talent assessment during recruitment. The main methodological contribution of the paper rests upon the use of descending hierarchical classification (DHC) through the IRaMuTeQ software applied to the digital HR interviews’ corpus. Through this paper, it has been possible to identify both the opportunities and the obstacles arising from the incorporation of digital innovation, as well as ethical issues concerning the protection of personal data and algorithmic bias. The findings confirm five main classes that are divided into three thematic clusters: the evolution of organizational processes, the significant influence of digital tools on recruitment effectiveness, and potential ethical concerns related to data privacy and algorithmic fairness. The importance of this study is in its analysis of the less researched relation between digital identity and human resource management. The study employs concepts of self-presentation and social identity theories to clarify the way digital technologies influence the recruitment and assessment process. This study suggests an enhanced foundation of human resource management policies that is capable of being more practical to be implemented in real life. It questions the old models of recruitment of employees in addition to providing an outline of recommendations to make amendments to Law 09-08 in order to improve the application of the personal data and ensure that the risks of using artificial intelligence (AI) for making employment decisions are minimized. Furthermore, this research offers a methodological reflection that critically analyzes the research process, epistemological choices, and the combined use of thematic analysis and IRaMuTeQ.