Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method

Abstract

This study evaluates the application of Multi Attribute Utility Theory (MAUT)-based decision support systems in the recruitment process of financial managers in technology companies "TechInnovate". This study aims to identify the extent to which the use of MAUT can increase objectivity and effectiveness in candidate selection. The quantitative method was used by collecting data from 50 candidates who volunteered for this position. The assessment criteria that have been determined include Education and Qualifications, Work Experience, Analytical and Financial Skills, Communication Skills, Leadership and Team Management, Ethics and Integrity, Technology Capabilities, Adaptability and Innovation, Decision Making Ability. Data is collected through scores assigned to each criterion for each candidate, which are then aggregated taking into account the relative weights of each criterion. Statistical analysis is applied to evaluate the distribution of scores and the effectiveness of the system in selecting the most suitable candidates. Results from this study show that the application of MAUT is significant in improving objectivity in recruitment decision making, with high scores strongly correlated with expected job performance in in-depth interviews and follow-up evaluations. This study proves that integrating a systematic approach in recruitment not only strengthens the selection process but also promotes data-driven and objective decisions. The implications of this research are relevant for companies operating in dynamic and technology-centric environments, suggesting the integration of MAUT-based decision support systems as an effective strategy in the recruitment process.