This study aims to investigate the relationship between specific digital handwriting characteristics and self-discipline levels to evaluate the viability of automated feature extraction as a cost-effective, complementary screening tool in human resources processes.
Handwriting samples and self-discipline scores were collected from a homogenous sample of 353 right-handed university students, representing potential entry-level job candidates. The samples were digitised and analysed based on five structural features (line slant, word slant, line spacing, word spacing and font size) using computer-aided software to eliminate subjective interpretation. One-way analysis of variance was conducted to determine statistical differences.
The findings revealed that slant characteristics are significant behavioural markers of self-discipline. Participants with an ascending line slant exhibited significantly higher self-discipline scores compared to those with a straight baseline. Similarly, individuals with upright or left-slanted handwriting scored significantly higher than those with a right-slanted style. Spatial features (line spacing, word spacing, font size) showed no statistically significant associations.
This study contributes methodologically to the literature on personnel selection and graphology by isolating self-discipline from the broader Big Five framework. Despite these contributions, several limitations should be considered when interpreting the findings, each pointing towards avenues for future investigation.
The directional dynamics of handwriting offer valuable predictive insights into self-regulative behaviour. These indicators can serve as a scalable and non-invasive tool for Human Resources (HR) professionals to cross-validate candidate profiles, particularly in pre-screening stages where identifying high self-discipline is prioritised.
By using a right-handed sample and digital feature extraction, this research provides methodological robustness often missing in graphological studies, extending signalling theory into the neuromotor domain for personnel selection.
