The aim of this study was to investigate if a mechanical recruitment process could be a useful tool for employers to avoid discrimination. National and international law protect jobseekers from discrimination during the recruiting process. Despite this individuals frequently report that they are treated unfairly when they apply for a job. In line with this research shows that some individuals do not have the same opportunities in the labour market as the rest of the population. This study focus on discrimination based on ethnicity, age, gender or disability. Today most of the hiring decisions are based on employers professional judgement. This constitute a potential risk for discrimination since the judgement can be influenced by prejudices and stereotypes. The research shows that employers tend to measure variables which are not relevant for individuals future job performance when they make decisions in selection processes. A mechanical approach for the recruitment can broadly correct for this since it is based on standardized procedures that eliminates human judgement. The data collection of candidates strictly relate to a defined job profile which only contains criterions that are of importance for the employment. Pre-specified algorithms are later on used for combining each candidates data into an overall assessment. This regulates that the same criteria is measured for all individuals. In order to analyse the material a legal dogmatic method has been combined with a legal sociology method.

Identifying and hiring the highest performers is essential for organizations to remain competitive. Research has provided effective guidelines for this but important aspects of these evidence-based processes have yet to gain acceptance among practitioners. The general aim of this thesis was to help narrowing the gap between research and practice concerning personnel selection decisions. The first study compared the validity estimates of general mental ability (GMA) and the five factor model of personality traits as predictors of job performance, finding that, when the recently developed indirect correction for range restriction was applied, GMA was an even stronger predictor of job performance than previously found, while the predictive validity of the personality traits remained at similar levels. The approach used for data collection and combination is crucial to forming an overall assessment of applicants for selection decisions and has a great impact on the validity of the decision. The second study compared the financial outcomes of applying a mechanical or clinical approach to combining predictor scores. The results showed that the mechanical approach can result in a substantial increase in overall utility. The third study examined the potential influences that practitioners’ cognitive decision-making style, accountability for the assessment process, and responsibility for the selection decision had on their hiring approach preferences. The results showed that practitioners scoring high on intuitive decision-making style preferred a clinical hiring approach, while the contextual aspects did not impact practitioners’ preferences. While more research may be needed on practitioner preferences for a particular approach, the overall results of this thesis support and strengthen the predictive validity of GMA and personality traits, and indicate that the mechanical approach to data combination provides increased utility for organizations. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>