Artificial Intelligence-Based Human Talent Attraction and Optimization of the Applicant Experience in Cotopaxi

Abstract

The general objective of this study was to analyze the influence of artificial intelligence-based human talent attraction on the optimization of the applicant experience in organizations in the province of Cotopaxi. A quantitative approach with a descriptive and correlational design was applied using a structured 24-item questionnaire rated on a five-point Likert scale. The sample comprised 384 applicants residing in Cotopaxi, selected through non-probability convenience sampling according to criteria related to their recent participation in selection processes. The results showed favorable assessments of evaluation speed, ease of use, alignment between assessments and vacancies, clarity of instructions, and willingness to participate again. However, limitations were identified regarding the protection of personal data and the explanation of results. The PLS-SEM model yielded a path coefficient of 0.742, a significance level below 0.001, and an R² of 0.551; thus, artificial intelligence explained 55.1% of the applicant experience. It was concluded that the useful, accessible, transparent, and reliable application of these tools significantly enhanced perceived fairness, communication, and organizational attractiveness during personnel selection in Cotopaxi.