Consumer Attitudes toward Online Video Game Purchases

Introduction

There are considerable debates on how the advent of Internet technologies along with its prototypical subsets impacts video games. Some scholars have recently outlined that Internet technologies broaden the massification of video games, thereby increasing the level of revenue generation (Marchand &Hennig-Thurau, 2013; Williams, 2002; Teach, 2013). As a primary source of revenue, attracting and retaining consumers remain the principal objectives of both online and offline video game retailers; however, a few retailers directly compete against each other for market share by operating purely through one channel (online or offline), and other retailers have sought to integrate their offline and online operations (Stuart, 2012). These actions have spread confusion among industry practitioners and interested parties regarding the impact that online shopping has had on the video game retail industry as a whole (Krohn, 2012; Warman, 2012; Euromonitor, 2010). This confusion combined with limited research within the video game retail industry has left industry leaders and scholars unclear as to consumer purchasing decisions in regard to online and offline shopping, and more specifically, it has spawned a debate among scholars as to the motivations driving consumers’ online video game purchases (Williams, 2002; Morgan Stanley, 2013; Zhu &Zhang, 2010; Conley et al., 2004; Alba et al., 1997; Biyalogorsky &Naik, 2003; Lovell, 2011; Kain, 2013; Warman, 2013; Flew, 2012).

Researchers suggest that online shopping facilitates easier transactions and leverages lucrative marketing opportunities within the growing online marketplace, but existing research is vague in terms of how online shopping motivates consumer purchases (Ha &Stoel, 2009; Nemat, 2011; Ling et al., 2010; Grewal et al., 2010; Enders &Jelassi, 2000; Eroglu et al., 2001). Existing research exploring online shopping and how it relates to consumer purchasing behaviour is primarily based on the theory of reasoned action (TRA), the theory of planned behaviour (TPB), and the technology acceptancemodel (TAM).All three models have been used to predict online shopping purchases based on behavioural intentions (Azjen, 1991; Davis et al., 1989; Su &Huang, 2011). It is argued that the credibility of research based on intentions is undermined because intentions do not represent conclusive actions (Pahnila &Warsta, 2010).

Similarly, controversy exists within the literature as to which research model possesses the relevant variables to accurately predict consumer purchasing behaviour (Crespo et al., 2009; Pavlou &Fygenson, 2006). Furthermore, the applicability of these deterministic models is criticised considering that the predictions simplify a complex process and risk ignoring influential subjective variables (Werner, 2004).

There is an acknowledgement among scholars in the academic community that existing research has focused on behavioural determinants of intention across generic broad categories, while neglecting which exact variables induce online shopping for specific products (Hernandez et al., 2009; Pahnila &Warsta, 2010; Wang et al., 2007). This is compounded by the fact that online shopping intention and purchase behaviour vary for individual products because of their different product characteristicsand attributes (Malik &Guptha, 2013; Keisidou et al., 2011). For example, Crespo et al. (2009) found that the impact of perceived risk on online shopping purchases varies depending on the tangibility of the product.