EXAMINING PERSONALITY-BASED FILTERING OF CONFLICTING TECHNICAL DATA THROUGH A HUMAN RESOURCES LENS

Abstract

Employees in today’s workplaces are often data-driven due to the continuous and sometimes competing pieces of information that are often interwoven in the workplace. Individual biases shape perceptions and prioritizations of data based on personality differences. Thus, this study aims to fill this knowledge gap by investigating personality traits with the filtering of conflicting technical data and by using the human resources (HR) lens to assess and address these cognitive and behavioral differences. A personality profiling system was created for a sample of 100 participants, which was taken from an HR and IT firm. It was also observed that the participants were placed into controlled data conflict scenarios. Their performance on these scenarios was assessed about the Big Five personality traits. The study found that higher scores on the openness and conscientiousness traits were linked to higher accuracy and confidence in the data. On the other hand, neuroticism was linked to indecisiveness and over-relying on the prevailing narratives. The results demonstrate the value of personality biases in the evaluation and interpretation of data, which highlights the need for strategic HR-driven data-aided decision-making and team composition.