The sale of digital items, such as avatars and decorative objects, is becoming an important source of revenue for virtual community (VC) websites. However, some websites are unable to leverage this source of revenue, and there is a corresponding lack of understanding about what motivates people to purchase digital items in VCs. To explain the phenomenon, we develop a model based on the theory of self-presentation. The model proposes that the desire for online self-presentation is a key driver for such purchases. We also hypothesize that the social influence factors of online self-presentation norms and VC involvement as well as personal control in the form of online presentation self-efficacy are antecedents of the desire for online self-presentation. The model was validated by using survey data collected from Cyworld (N = 217) and Habbo (N = 197), two online social network communities that have been pioneers in the sale of digital items. This work contributes to our understanding of the purchase of digital items by extending the theory of self-presentation and adds to the broader line of research on online identity. It also lends insights into how VC providers can tap this source of revenue.
The digital divide has loomed as a public policy issue for over a decade. Yet, a theoretical account for the effects of the digital divide is currently lacking. This study examines three levels of the digital divide. The digital access divide (the first-level digital divide) is the inequality of access to information technology (IT) in homes and schools. The digital capability divide (the second-level digital divide) is the inequality of the capability to exploit IT arising from the first-level digital divide and other contextual factors. The digital outcome divide (the third-level digital divide) is the inequality of outcomes (e.g., learning and productivity) of exploiting IT arising from the second-level digital divide and other contextual factors. Drawing on social cognitive theory and computer self-efficacy literature, we developed a model to show how the digital access divide affects the digital capability divide and the digital outcome divide among students. The digital access divide focuses on computer ownership and usage in homes and schools. The digital capability divide and the digital outcome divide focus on computer self-efficacy and learning outcomes, respectively. This model was tested using data collected from over 4,000 students in Singapore. The results generate insights into the relationships among the three levels of the digital divide and provide a theoretical account for the effects of the digital divide. While school computing environments help to increase computer self-efficacy for all students, these factors do not eliminate knowledge the gap between students with and without home computers. Implications for theory and practice are discussed.
A common classification of data models is based on their abstraction levels: physical, logical and conceptual. The user-database interaction can be similarly classified. For the conceptual-level interaction, the user and the database exchange information on the user's world, e.g., information of entities, relationships, and attributes. For the logical-level interaction, the user and the database communicate based on concepts in the database system, e.g.. relations and join operations. We expect users to be familiar with concepts in their world but not the concepts in the database system. This is especially so for infrequent or naive database users. The conceptual level should therefore be easier because it is semantically closer to the user. This deduction was tested in an experiment using the entity-relationship (ER) model for the conceptual-level model and the relational model for the logical-level model. The results were affirmative. The users at the conceptual level had 38 percent higher accuracy and 16 percent higher confidence than users at the logical level. The conceptual-level users took 65 percent less time than the logical-level users, and it took 33 percent less time to train them. The differences were statistically significant with p < 0.003. The huge differences indicate that noticeable improvements can be made by switching from the relational model to the ER model. The experiment also provided valuable data on errors commonly made by users.