Gambling Is in My Genes: Correlations Between Personality Traits with Biological Basis and Digital Entertainment Choice

This study examines whether personality traits with roots in biological differences can predict individuals' preference for online gambling, a rapidly growing segment of digital entertainment. The authors surveyed 93 college students at a large Singaporean university, after excluding participants who were not of Chinese descent to control for cultural factors linked to gambling vulnerability. Two personality measures were used: Zuckerman's Sensation Seeking Scale, which captures the pursuit of novel and intense experiences, and Lang's Motivational Activation Measure, which assesses appetitive (positivity offset) and aversive (negativity bias) motivational systems. Participants also reported their online gambling behavior, spending, time per visit, future intentions, and symptoms adapted from the DSM-IV criteria for pathological gambling.

The findings were mixed across the two frameworks. Sensation seeking was not significantly associated with current online gambling behavior, but it was correlated with the likelihood of future online gambling and with pathological gambling symptoms. Positivity offset, by contrast, showed significant positive correlations with the variety of online gambling engaged in, money spent, time per visit, future intention, and pathological symptoms. Negativity bias showed no significant relationship with any gambling measure. The author concludes that the appetitive motivational system is the more consistent predictor of online gambling engagement.

These results carry practical implications. The author suggests that prevention campaigns for problem online gambling could be delivered through new media, which high positivity-offset individuals are more inclined to use, at lower cost than traditional channels. The study also notes that the same trait may predict openness to highly stimulating video games, though this remains untested. The sample is small, geographically limited, and drawn from one demographic, so generalizability is constrained, and the author recommends further work with alternative assessment tools.