Measuring the Flow Construct in Online Environments: A Structural Modeling Approach

Though marketers are beginning to gain an understanding of the marketing strategies that will attract visitors to Web sites (Hoffman, Novak, and Chatterjee 1995; Morr 1997; Schwartz 1996; Tchong 1998), very little is known about the factors that make using the Web a compelling experience for its users, and of the key consumer behavior outcomes of this compelling experience. Recently, the flow construct has been proposed as important for understanding consumer behavior on the World Wide Web. Although widely studied over the past twenty years, quantitative modeling efforts of the flow construct have been neither systematic nor comprehensive. In large parts, these efforts have been hampered by considerable confusion regarding the exact conceptual definition of flow. Lacking precise definition, it has been difficult to measure flow empirically, let alone apply the concept in practice. Following the conceptual model of flow proposed by Hoffman and Novak (1996), we conceptualize flow on the Web as a cognitive state experienced during navigation that is determined by: 1) high levels of skill and control; 2) high levels of challenge and arousal; 3) focused attention; and is 4) enhanced by interactivity and telepresence. Consumers who achieve flow on the Web are so acutely involved in the act of online navigation that thoughts and perceptions not relevant to navigation are screened out and the consumer focuses entirely on the interaction. Concentration on the navigation experience is so intense that there is little attention left to consider anything else and, consequently, other events occurring in the consumer’s surrounding physical environment lose significance. Self-consciousness disappears, the consumer’s sense of time becomes distorted, and the state of mind arising as a result of achieving flow on the Web is extremely gratifying. In a quantitative modeling framework, we use data collected from a large-sample Web-based consumer survey to measure flow and its influences, and fit a series of structural equation models that test Hoffman and Novak’s (1996) theory. The conceptual model is largely supported and the improved fit offered by the revised model provides additional insights into the direct and indirect influences of flow, as well as the relationship of flow to key consumer behavior and Web usage variables. A key insight from the paper is that the degree to which the online experience is compelling can be defined, measured, and shown to relate significantly to important marketing variables. As such, our flow model may be useful both theoretically and in practice as marketers strive to decipher the secrets of commercial success in interactive online environments.