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Online poker is one of the main products of many iGaming companies. Its popularity is being fuelled by a combination of celebrities, technologies and television coverage. The ‘Poker Boom’ between 2002 and 2006 saw a surge of new players entering the game and this allowed for the creation of a new subcategory of player – the “online pro”. Online poker currently provides around 13.5% of all online gaming activity (H2 Gambling Capital, 2011) and had it not been for poker’s “Black Friday” and its subsequent actions which decimated the scale of the internet poker market in the United States (U.S.) and which saw the worldwide online poker market shrink by 18% (Online Poker Insiders, 2011), the current market share would have been much higher. However, there are already clear signs that the worldwide online poker market is recuperating (PokerWatch.EU, 2011).
While some of the biggest potential markets such as China and the U.S. prohibit many forms of online gambling, the gambling market has taken off throughout Europe and many European countries have been reforming their gambling regulations to open up markets to non-native competitors (KPMG, 2010). With poker on fire and revenues continuously on the rise (H2 Gambling capital, 2011), there is a strong and fierce competition among betting companies to attract and retain players. Some have adopted a strategy of specialising solely on online poker while others are offering poker variations that were previously unheard of on the digital platform (Jack, 2010).
Most studies in the gambling literature relate to traditional gambling. However, online poker is increasingly becoming detached from the definition of gambling (Sexton, 2009) and studies show that the personality traits and behaviour of online and offline users vary (Amichai-Hamburger, Wainapel, & Fox, 2002). Hence, the theories and findings that emerge from traditional gambling may not necessarily apply to online poker.
The scope of this study is to extend the gambling literature by providing a comprehensive picture of the online poker consumer. Using a hierarchical model of motivation and personality (The 3M Model) (Mowen, 2000), we determine which of the 17 personality traits and 3 demographic variables investigated emerge as significant predictors of online poker propensity. Additionally, we segment the online poker players into four hypothesised groups based on their propensities for skilled and chance games and investigate whether the resulting segments differ across the personality traits and motives addressed. The findings of this study could enable online poker companies to better segment the online poker market and to design more effective promotional strategies aimed at attracting and retaining consumers. They could also guide governments who may be interested in introducing de-marketing strategies or corporate social responsibility managers in managing problem gamblers responsibly. The study concludes by providing interesting avenues for further research.