While IS research into on-line Inter-Personal (IP) Social Networks (SN) is highly visible, there has been surprisingly little focus on the use of on-line social networks for Inter-Organizational (IO) communications, interactions, and goal achievement. We explore the issues and challenges facing organizations in their design and use of inter-organizational social network information systems (IO SNIS). Artifact design principles are drawn from a new and insightful model that contrasts the advantages of existing innovative inter-personal (IP) SNIS artifacts with Social Network Theory on differences between IP and IO Social Networks. This research extends the existing streams of IS social networking research into the inter-organizational domain and encourages additional IS research into the analysis, design, and build of artifacts that animate the social behavior of organizations. We develop a key design concept for IO SNIS and establish the design principles underlying the general artifact design and the specific design features that apply the design constructs to an exemplar IO social domain. This dissertation uses Action Design Research (ADR) approach within the Design Science Research (DSR) paradigm to formulate the research opportunity and anticipate a practice-inspired and theory-ingrained artifact. The researcher works with a practitioner team in the domain of mid-market private equity (MMPE) to explore the model and evaluate existing on-line inter-organizational artifacts to establish specific design features for an IO SNIS artifact. We find that the design principles can generalize from the IO SNIS Design Concept Model to other IO Social domains and that the design features can be used to build an instantiation of IO SNIS in the Private Equity domain.

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Since their emergence in the 1990’s, recommendation systems have transformed the intelligence of both the web and humans. A pool of research papers has been published in various domains of recommendation systems. These include content based, collaborative and hybrid filtering recommendation systems. Recommendation systems suggest items to users and their principal purpose is to increase sales and recommend items that are predicted to be suitable for users. They achieve this through making calculations based on data that is available on the system. In this study, we give evidence that the research on group recommendation systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be more beneficial for groups based on the individual interests of group members while also striving to maximise satisfaction. The matrix factorization algorithm of collaborative filtering was used to make predictions and three movie recommendation for each and every individual user. The three recommendations were of three highest predicted movies above the pre-set threshold which was three. Thereafter, four virtual groups of varied sizes were formed based on four highest predicted movies of the users in the dataset. Plurality voting strategy was used to achieve this. A publicly available dataset based on Group Recommender Systems Enhanced by Social Elements, constructed by Lara Quijano from the Group of Artificial Intelligence Applications (GIGA), was used for experiments. The developed recommendation system was able to successfully make individual movie recommendations, generate virtual groups, and recommend movies to these respective groups. The system was evaluated for accuracy in making predictions and it was able to achieve 0.7027 MAE and 0.8996 RMSE. This study was able to recommend to virtual groups to enable social network group members to engage in discussions of recommended items. The study encourages members in engaging in similar activities in their respective physical locations and then discuss on social network.