Author List: Goel, Lakshmi; Johnson, Norman A.; Junglas, Iris; Ives, Blake;
MIS Quarterly, 2011, Volume 35, Issue 3, Page 749-A5.
Virtual worlds have received considerable attention as platforms for entertainment, education, and commerce. But organizations are experiencing failures in their early attempts to lure customers, employees, or partners into these worlds. Among the more grievous problems is the inability to attract users back into a virtual environment. In this study, we propose and test a model to predict users' intentions to return to a virtual world. Our model is based on the idea that users intend to return to a virtual world having conceived of it as a "place" in which they have had meaningful experiences. We rely on the interactionist theory of place attachment to explain the links among the constructs of our model. Our model is tested via a lab experiment. We find that users' intentions to return to a virtual world is determined by a state of deep involvement (termed cognitive absorption) that users experience as they perform an activity and tend to lose track of time. In turn, cognitive absorption is determined by users' awareness of whom they interact with and how they interact within a virtual world, what they interact about, and where, in a virtual sense, such interaction occurs. Our work contributes to theory in the following ways: it identifies state predictors of cognitive absorption, it conceives of virtual worlds in such a way as to account for users' experiences through the notion of place, and it explains how the properties of a virtual world contribute to users' awareness.
Keywords: cognitive absorption; intention to return; location awareness; place attachment; sense of place; social awareness; Task awareness
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#120 0.432 virtual world worlds co-creation flow users cognitive life settings environment place environments augmented second intention spatial interactivity ownership objects immersive
#140 0.145 model use theory technology intention information attitude acceptance behavioral behavior intentions research understanding systems continuance models planned percent attitudes predict
#128 0.083 dynamic time dynamics model change study data process different changes using longitudinal understanding decisions develop temporal reveal associated state identifies
#284 0.082 users user new resistance likely benefits potential perspective status actual behavior recognition propose user's social associated existing base using acceptance
#121 0.069 human awareness conditions point access humans images accountability situational violations result reduce moderation gain people features presence increase uses means
#26 0.066 business large organizations using work changing rapidly make today's available designed need increasingly recent manage years activity important allow achieve