The purpose of this thesis was to establish an understanding on the productivity of Open Source Software (OSS) developer community through diffusion of innovation. It was empirically ascertained that network effect affects productivity of OSS community, which provide enough justification to view the matter of productivity through the lens of diffusion of innovation. To reach its purpose, this thesis tackled the issue in two ways: 1) It utilized a definition of IT productivity for new technologies that especially deals with Open Source Software (OSS) communities and 2) It proposed tools and methods to perform such studies. A mature OSS project called CakePHP was chosen as a case for this thesis. I compiled time-series data from the software’s source code that accounts for more than 8 years of development. The obtained raw data in a form of ‘commits’ was converted into network graph and time-series productivity data. Then, dynamic network visualization software was employed to analyze the evolution of its network structure. A quantitative regression analysis using Negative Binomial estimator was also employed to estimate the effects of individual work intensity, community work intensity and network effect on its production rate. Visual inspection on CakePHP’s adoption pattern shows that it does indeed follow S-shaped diffusion curve normally found in other innovation life cycle, though yet to complete its life cycle. The regression results suggest that individual work intensity, network effect, and community work intensity were found to have significant effects on the rate of production output. The results also suggest that individual work intensity has a positive influence on the rate of production while network effect and community work intensity was found to have negative effects. This was suspected to be caused by overdispersion on the productivity level of contributors and also on the type of releases. CakePHP underwent two diffusion phases, which are emergence phase and growth phase. These two phases exhibit very different network characteristics. On emergence phase the adoption rage, network size, number of connections, total production output and work intensity was substantially lower compared to growth phase. Members of the network especially opinion leaders and community leaders played a crucial role to drive adoption.