| Social computing has emerged to be one of the most fundamental ways of communicating, collaborating, and transferring knowledge throughout the enterprise. Many organizations have simply gone beyond using email and instant messaging to tools such as blogs, social networks, wikis, and other methods of group collaboration. Employees of these organizations are often not familiar with these technologies and how to use them, overwhelmed with the capabilities they provide, and sometimes get discouraged from using social computing which can lead to low contribution rates. The problem addressed in this study is the low contribution rates of employees resulting from using social computing technologies in the workplace. The current study examined the explanatory ability of the UTAUT factors upon social media usage/acceptance towards improving employees' likelihood of contributing to social computing technologies. The overall question addressed relative to the problem of low contribution rates is the following: What is the explanatory ability of the UTAUT factors upon social media usage/acceptance towards improving employees' likelihood of contributing to social computing technologies? One hundred six survey respondents who were current employees of local, state, and federal government agencies, lived in the Washington DC Metropolitan area, and over the age of 18 completed the questionnaire. The multiple regression analysis results indicated that the overall model was statistically significant in predicting behavioral intention and that the independent variables were capable of explaining 38% of the variance in behavioral intention. However, further analysis determined that previous experience was the only variable that proved to have any significant predictive power on behavioral intention. Future research may include focusing on a single type of government employee (local, state, or federal), a specific type of social computing, the addition of the facilitating conditions variable, and utilizing a longitudinal study with a mixed methods approach. |
