Antecedents and Outcomes of the Flow Experience: An Empirical Study in the Context of Online Gaming
This study uses flow theory and the technology acceptance model (TAM) to provide new insight into the impact of enjoyment, one important dimension of flow, on user satisfaction, user beliefs, and behavioral intention to use. In addition, based on the propositions that knowledge results in an increased ability for activity and that flow is an emotional state of activity, this paper adopts a process view of knowledge to examine the role of knowledge in predicting enjoyment. The foregoing concepts are represented in a nomological network of enjoyment. Associated hypotheses are tested by using questionnaire responses of 253 online game players. INTRODUCTION Online games emerged in the electronic commerce vocabulary with the birth of the World Wide Web and the business of online games has grown quickly. It is estimated that the worldwide number of online game players reached 50 million by the year 2004 and it is expected to be 114 million in the year 2006 (DFC Intelligence, 2004) Meanwhile, the global revenues of online games reached $1.9 billion in the year 2003 and they are projected to increase to about $10 billion by the year 2009 (DFC Intelligence, 2004) Those who create, sponsor, and operate online games can benefit from an improved understanding of the behaviors of online game users (Park Chen, 2007) Here, we introduce and empirically study a model that systematically links constructs of knowledge processes, flow experience, and consumer behavior in the context of online games. A key issue facing electronic commerce practitioners and researchers has been understanding consumer behavior on the Web (Jarvenpaa Todd, 1997; Koufaris, 2002; Loiacono Lin, 2005; Mahatanankoon et al. 2007) As a subject of psychology and marketing research, flow experience is recognized as an important determinant of consumer behavior (Hsu Lu, 2004; Novak et al. 2000) Flow, as a state of optimal experience, can affect the frequency and amount of time spent using online services or making online purchases through the development of customer loyalty (Choi Kim, 2004; Jarvenpaa Todd, 1997; Rice, 1997) Thus, knowing what conditions are conducive to online customers achieving optimal experience is critical to the success of an online vendor (Eighmey, 1997; Lee et al. 2003) be it in the gaming sectors or elsewhere. C. W. Holsapple J. Wu 2008 Volume 17, Numbers 3/4 286 Researchers find that knowledge results in an increased ability for activity (Akbar, 2003; Gronhaug Olson, 1999; Roth, 2003) Because a flow experience is an emotional state of activity (Chou Ting, 2003; Csikszentmihalyi, 1990) an important question is whether knowledge is useful in predicting flow experience. While one may intuitively believe that knowledge can affect flow, this important relationship has not been empirically tested. If it is an antecedent of flow experience in online games, knowledge may function as an effective lever for success in such e business endeavors. This research empirically examines the impact of knowledge on flow and the subsequent effects of flow in the context of online games. The theoretical underpinnings of this study rest on a process view of knowledge created to identify the lifecycle of knowledge (Davenport Prusak, 1998; Lee et al. 2005) flow theory founded to examine state of mind (Csikszentmihalyi, 1990) and the technology acceptance model (TAM) developed to measure user acceptance of computer systems (Davis, 1989) RESEARCH MODEL AND HYPOTHESES As a basis for the research model introduced in this section, we begin with descriptions of relevant knowledge concepts and flow theory. These are linked into a TAM like research model and nine hypotheses are developed about linkages in this model. Knowledge Concepts Emphasizing the view that knowledge is highly human related, Davenport and Prusak (1998) define it as "a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers." Knowledge is a product of human reflection and experience emphasizing understanding and sense making (why and how) while information is the awareness of something (who and what) such as patterns that individuals instill on data (Bennet Bennet, 2003; Roth, 2003) In a related vein, knowledge is that which is conveyed in representations (e.g. linguistic, symbolic, digital, mental, behavioral and material patterns) that are usable to some processor (e.g. human mind) and can be categorized as being descriptive (characterizations of the state of some system who, what and when, etc. procedural (characterizations of how to do something) or reasoning (characterizations of logic or causality) (Holsapple, 1995, 2003, 2005) A process view of knowledge indicates that knowledge is both used and embedded in various processes (i.e. in the behaviors of some processor, such as a person or organization) Nonaka (1994) presents a four fold classification of knowledge processes: socialization, combination, externalization, and internalization. Socialization is the process of transferring tacit knowledge from a tutor or a more knowledgeable individual to an apprentice through observations, imitation, and practice while these two individuals work together. Combination is the process of creating new explicit knowledge through the combination of other existing explicit knowledge. Externalization is the process of converting tacit knowledge into explicit knowledge through metaphor that enables people to experience a new behavior by making inferences from the model Online Gaming Journal of International Technology and Information Management 287 of another behavior. Finally, internalization is the process of converting explicit knowledge into tacit knowledge through action or trial and error. An alterative process view of knowledge includes following: knowledge generation, knowledge codification, knowledge sharing, and knowledge utilization (Davenport Prusak, 1998; Lee et al. 2005) In this categorization, knowledge generation is conceived as involving knowledge creation and acquisition. Knowledge creation refers the development of new content or the replacement of existing content within the tacit and explicit knowledge (Pentland, 1995) It includes the activities of deriving and discovering knowledge (Holsapple Joshi 2004) Knowledge acquisition refers to the intake or acceptance of knowledge from external sources (Davenport Prusak, 1998) Even though nothing is "generated," this classification scheme treats acquisition as being part of the knowledge generation category. Knowledge codification refers to the translation of knowledge into representations such as documents, rules, and manuals for storage in a repository. Knowledge sharing refers to transfer of knowledge between individuals (Ford, 2003) Finally, knowledge utilization is the use of knowledge to solve practical problems. Knowledge gives not only the capacity to conceptualize, but also the capacity to act (Davenport Prusak, 1998; Nosek, 2004; Roth, 2003) To perform their activities appropriately, individuals must have knowledge about the purpose of the activities, how the various activities relate to each other, and how they relate to their goals (Gronhaug Olson, 1999; Galup et al. 2004) Moreover, to fit their activities to an ever changing environment, individuals also need the knowledge about the immediate situation in which they are embedded (Gronhaug Olson, 1999) Therefore, knowledge can be viewed as a key to performing activities or to better performing activities in online gaming context, as well as many other settings. Flow Theory As the founder of flow theory, Csikszentmihalyi (1990) defines it as "the state in which people are so involved in an activity that nothing else seems to matter." Flow, characterized as a state of optimal experience, can be applied to almost any activity including making music, rock climbing, dancing, sailing, and playing chess (Csikszentmihalyi, 1990) The founder further argues that what makes these activities conducive to flow is that they make optimal experience easier to achieve, they have rules that require the learning of skills, they set up goals, they make control possible, and they provide feedback. In order to better understand flow in the context of electronic commerce and technology usage, researchers have devoted considerable effort to finding answers to these three questions: what results in flow, what is the outcome of flow, and how flow can be measured. Novak et al. (2000) conceptualize flow on the World Wide Web as a cognitive state experienced during navigation that is determined by high levels of skill/control, high levels of challenge and arousal, and focused attention, and enhanced by interactivity and telepresence. The consequents of flow included in their model are consumer behavior variables that involve online shopping and Web use applications such as the extent to which consumers search for production information and participate in chat rooms. The most important construct of the model, flow experience, is measured with a three item scale following a narrative description of flow. The findings of the C. W. Holsapple J. Wu 2008 Volume 17, Numbers 3/4 288 research suggest that skill/control, challenge, and telepresence have positive effects on flow. Results of the impact of flow on consumer behavior variables are complex: the base model gives positive outcome, but the revised model does not. In research grounded on the integrated theoretical framework of online consumer behavior, Koufaris (2002) argues that online consumer intention to return and their likelihood of making unplanned purchases can be influenced by the flow experience, as measured in three dimensions: shopping enjoyment, perceived control, and concentration/attention focus. Results o