Revisiting Organizations as Information Processors: Organizational Structure as a Predictor of Noise Filtering

By comparing the information processing behaviors of four groups of mid-level working professionals as each undertakes a series of four complex, interdependent, computer-mediated decision-making exercises, this thesis explores 1 how processing of information in effective i.e., high-performing groups differs from the processing of information in ineffective i.e., low-performing groups, and 2 the characteristics of adaptation, from an information processing perspective, within high performing groups. The results of the exploration, though mostly inconclusive, call into question both intuition and literature regarding organizational structure as well as literature in information and knowledge sharing. It is predicted that meaningless noise information will be shared less as time passes and individuals learn. It is also hypothesized that as less noise is shared the organizations performance will increase. As an explanation, this thesis proposes that the ability to filter noise not only increases over time, but is also dependent on the organizational structure further explaining why one structure consistently outperforms another organizational structure. Further experimentation is needed to test the validity of these conjectures and bring better understanding to Organizational Theory, Information Processing and Knowledge Sharing networks.