Bwin Interactive Entertainment AG has paired up with an affiliate of the Harvard medical school to determine differences in online gaming behavior.
This study, by the Division On Addictions of the Cambridge Health Alliance, will solidify the relationship between Harvard Medical and Bwin Interactive as they continue their Transparency Project.
The recent study is just one more addition to the Transparency Project which was created to learn more concerning gambling addiction.
The Project began in February and has been touted as the first collection of data for public consumption, by private sectors in order to study addictive behaviors.
Benefits of this study have already reared their head as over 40,000 users have been able to commit their anonymous activity to help scientists, and gain more knowledge about the addictive behaviors associated with gambling.
In a study done by 3,445 online users over two years, it was revealed that these users exhibit rational behavior, and are only moderate users of online gambling websites.
‘In this research we provide additional evidence in support of our previous research showing that most subscribers who gamble on the Internet do so moderately,” said Howard Shaffer, Associate Professor at Harvard Medical School and Director for The Cambridge Health Alliance’s Division On Addictions.
“In fact, correlation analyses indicated that as percent lost increased, duration, total gambling sessions and total amount wagered all decreased suggesting that individuals moderated their behaviour based on their wins and their losses, exhibiting ‘rational’ betting behaviour.’
According to the study, the average player was around 28 years of age, and approximately 95 percent of the users were active gamblers for about 6 months. The study also revealed that the users wagered approximately 13 dollars per session.
‘In our intention to replace speculation with scientific evidence,” said Manfred Bodner, Co-Chief Executive Officer for Bwin.
“This study takes us a big step closer towards understanding the behaviour of online poker players. Ultimately, we are interested in developing algorithms capable of identifying behavioural patterns or identifying risk patterns associated with disordered gaming.’
