CRIS

Permanent URI for this communityhttps://cris-udd.scimago.es/handle/123456789/1

Browse

Settings

Language: search.filters.language.English (United States)Subject: search.filters.subject.Artificial Intelligence

Search Results

Now showing 1 - 1 of 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Enhancing Decision-Making Through Human–AI Synergy in Smarter and Fairer Recruitment
    (Springer Nature Switzerland, 2026)
    Godínez-Oliva, Carmen
    ;
    Pusey-Alvarado, Luis
    ;
    Díaz-Álvarez, Nelson
    ;
    RUBIO PROAÑO, Andrès  
    ;
    Jadán Guerrero, Janio  
    The digitalization of recruitment processes has paved the way for the integration of artificial intelligence (AI) into hiring, transforming how organizations identify and select talent. According to a McKinsey [7] report, the use of AI has significantly reduced hiring times by automating résumé screening and candidate preselection. However, a key question arises: does this efficiency also translate into better candidate quality, reflected in performance and retention rates? This study seeks to answer the following question: how does the use of AI systems in résumé analysis impact the reduction of recruitment time and the improvement of candidate quality, as measured by the retention rate after two months? The main objective is to evaluate how the implementation of AI in selection processes influences both the speed and quality of hiring. This research is relevant because it provides empirical evidence about the benefits and limitations of AI in recruitment, allowing organizations to make more informed decisions regarding its adoption. A literature review was conducted to explore the topic from different perspectives. Preliminary findings indicate that AI can streamline processes, improve selection accuracy, and reduce bias, although concerns remain regarding algorithmic transparency and fairness. The study adopts a mixed-methods approach: in the quantitative phase, traditional and automated processes are compared in résumé analysis, psychometric testing, and interviews, measuring hiring times and retention rates. In the qualitative phase, recruiters were surveyed to gather their perceptions about the use of AI in decision-making. Preliminary results suggest a significant reduction in hiring times but similar retention rates between traditional and AI-assisted methods. Consequently, a hybrid recruitment model is proposed, in which the synergy between humans and intelligent systems strengthens decision-making, promoting more efficient, transparent, and equitable selection processes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.