Testing social information processing and technology acceptance models in a distance learning environment with a social network approach

Previous research found that attitude formation was influenced by objective characteristics of the technology system, the extent of the use, and individual differences. It is also well known that people are not always rational in selecting and using technologies, and attitudes toward and use of technology are influenced by culture, norms, social contexts, or salient others around them. Two distinctive theoretical perspectives examined these two different approaches toward technology use. While the technology acceptance model has focused on individuals' perceptions of technology in determining attitude and behavior, the social information processing perspective has investigated the role of informational social influence in shaping attitude toward technology. In this study, these two models were separately tested using a longitudinal data set collected from a distance learning class. Then, an integrated model—which predicted attitude change from individuals' beliefs about technology and social information from others—was developed and tested.; One of the primary interests of social information processing studies has been examining different mechanisms of the social influence process. With the integrated model developed in this study, relational and positional social influence mechanisms at the individual level and group level social influence mechanisms were compared. One important aspect of social influence process that previous studies have largely overlooked is the effect of communication media on social influence. Since this study was conducted in a distance learning environment, communication with remote participants primarily relied on computer-mediated communication. To test different social influence mechanisms and the media effect, this study adopted a network autocorrelation model of the social influence process. The model predicts an individual's attitude from the individual's own beliefs and social information from other people.; Results showed that although each of the technology acceptance and social information processing models alone was useful in predicting user attitude in a distance learning environment, the integrated model explained changes of user attitude better than each of the two models alone. Results also illustrated that the positional mechanism at the individual level and project team membership at the group level together with perceived usefulness of technology contributed significantly to predicting changes of user attitudes toward technology. When the social network was decomposed to within-school (face-to-face and computer-mediated) and between school (computer-mediated) networks, relational proximity was the primary channel of social influence in the within-school network and positional influence was the main channel in the computer-mediated network. It seems students in the distance learning class used computer-mediated communication for social comparison with remote classmates, and face-to-face communication for maintaining relationships with other students at the same physical location.