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In the preceding decade world has witnessed the emergence of a new phenomenon: a predominately mobile based computing paradigm aiming to meet the needs of the broader society termed Social Computing. This new computing paradigm has introduced a myriad of applications. Three main characteristics observed within this phenomenon were these applications gained a rapid growth, built remarkably large user communities and disrupted the traditional societal and business practices. The recent advancements in Information and Communication Technologies (ICTs) have facilitated the emergence of this new computing paradigm. Enabling technologies of this computing paradigm can be considered as broadband connectivity mobile or WiFi, frontend devices with sensors such as smart phones. The other enablers are, backend cloud computing and growth of the Web from 1.0 which was for one-way communication that wasn’t providing interactive content to 2.0 which support effective two-way rich multimedia communication. Social Computing has changed how we find accommodation like Airbnb, how we book a taxi like Uber, or how we reference a book with access over ownership by accessing only the necessary chapter for a short period on Amazon. We socially interact with peers across the globe for free using social networking site Facebook or share user generated content with a global audience on YouTube. These successful applications possess exponential growth curves and exceptionally large community sizes. Facebook initiated in 2004 by 2017 has a community of 2.2 billion users, YouTube 1.5 billion users and Airbnb 200 million users. These applications have demonstrated new approaches to enhance triple bottom line of businesses: financially, social corporate responsibly and environmentally. For example, application like Airbnb cause extra income for a host, YouTube cause empowerment through user generated content shared to international audience for free, Uber cut down fuel consumption by ride sharing. However even after a decade, designing and developing successful Social Computing applications still remain more of an art than a science with design and development success rate remaining as low as 20% as per business statistics. Scholarly literature revealed that this is due to not having a theoretical background to clearly understand what Social Computing is. This gap of not having a theoretical foundation to comprehend the observed phenomenon led to formulation of the main research question “What is Social Computing?” Scholarly literature that reported this phenomenon was limited may be due to Social Computing still being a comparatively new and emerging paradigm, yet offered important leads. Scholarly surveys exposed there is a positive correlation between Social Computing and business process improvements such as Facebook enhanced communication process and Blogger enhanced collaboration process. Scholarly literature also presented uncategorised lists of Social Computing characteristics indicating a need for a scientific categorisation. In the attempt to comprehensively answer the main research question there arose several sub research questions as: (i) What are the characteristics of Social Computing? (ii) What is the structure of a Social Computing application? (iii) What is the relationship between Social Computing and societal/business outcomes? (iv) What is the behaviour of Social Computing? and (v) How the process of achieving societal/business outcomes in a non-digital era can be mapped to the digital era? Though the reporting about this phenomenon in scholarly literature was limited grey literature published in reputed business magazines such as Economist, Harvard Business Review, Forbes and the likes largely reported the societal and business benefits due to Social Computing. Thirty-one scenarios from grey literature that reported successful applications were analysed to understand the underlying principles for success as some applications succeeded while many failed. Supported by scholarly literature success of an application in the context of this Thesis is defined as the application’s ability to build a large community and ascertain substantial societal/business outcomes. Scholarly literature also revealed main reason for application failure amongst other let downs such as financial, technical were not building a strong user community catering to user needs, user community not coming back on the application, lack of trust and reputation with partners/users, loosely affiliated user network. When choosing the thirty-one scenarios, to get a better coverage of all aspects of the phenomenon three or more articles per scenario written by different authors were selected maximising the author perceptions. Content Analysis is a methodology that helps reduce copious textual data to a manageable amount by taking a qualitative or quantitative approach and can be used inductively for Theory building and deductively for Theory validation. Thus, overarching methodological approach adopted for this research was Qualitative Content Analysis. Theory building was done inductively and data analysis helped abstract below research contributions in the light of sub research questions posed: (i) Taxonomy of Social Computing characteristics (ii) Structural Model for a Social Computing Application (iii) Multistage Causal Model for Social Computing and Societal/Business Outcomes (iv) Behavioural model for Social Computing and (v) Human Need Fulfilment Model. Validation of the above four generic Models was done deductively adopting a qualitative interpretive approach. For the validation process well representative six scenarios from the original data set was purposively selected and through these six scenarios using the four generic Models the original phenomenon with its three major observations were comprehensively explicated. As it was found the generic Models were capable of explicating the original phenomenon with its major observations through a well representative sample of scenarios, it was concluded that the generic Models are true and valid for any instance. Furthermore, validation process revealed emergence of common interaction causal pattern formation in all scenarios of the representative sample which was abstracted as an Overall Interaction and information Flow Model for Social Computing. This metamodel systematically explained what Social Computing is. Moreover, this interaction and information flow explained the unique ability of Social Computing applications to evolve over time that caused rapid growth and enormous community size that stood Social Computing apart from all other preceding computing paradigms. Behavioural aspect of the Theory further explained how aggregation of user actions evolved trust in system/users causing more transactions over time, enabled system offer personalised content to individual users, harnessed 3rd party information that unlocked additional income streams, all of which disrupted traditional societal/business practices. Thus this overall Model and other generic Models will help researchers, practitioners, business community, application designers, and the global community comprehensively comprehend what Social Computing is solving the research problem and reducing the quite high application design failure rate of 80% by large.