Personality Profiles in Online Job Advertisements: Applying a Weighted Closed Vocabulary Approach to Identify Generic Profiles and Clusters

On the background of the supplementary applicant-job fit theory, this study tests the application of a weighted closed vocabulary approach as introduced by Ostendorf on a large corpus of online job advertisements (OJA). Therefore, a large dataset of online job advertisements (OJAs) was scraped from a German job portal ( N = 151k), and a sample ( n = 3,239) was selected based upon Porter’s Value Chain and corresponding job functions. The dataset was pre-processed with techniques of natural language processing, and the predefined words extracted from the OJAs were weighted with the average subject matter expert rating, as offered in the vocabulary of Ostendorf. The results reveal that, first, 0 to 12 words describing personality are used across OJAs, second, that HR, sales, and finance positions show values higher than sample mean (3.22), third, that HR positions show loadings across all dimensions of personality above the sample means (extroversion: 2.32, agreeableness: 3.98, conscientiousness: 4.28, emotional stability: 3.54, openness: 2.0) followed by sales. The job profiles being significantly parallel on equal levels are HR clerk and referee, as well as IT, R&D, and procurement. While conscientiousness is reported to be an important predictor of performance, our study showed only low to average importance in OJAs.