The current dissertation provides an examination of health information technology (HIT) by analyzing big datasets. It contains two separate essays focused on: (1) the evolving intellectual structure of the healthcare informatics (HI) and healthcare IT (HIT) scholarly communities, and (2) the impact of social support exchange embedded in social interactions on health promotion outcomes associated with online health community use. Overall, this dissertation extends current theories by applying a unique combination of methods (natural language processing, machine learning, social network analysis, and structural equation modeling etc.) to the analyses of primary datasets. The goal of the first study is to obtain a full understanding of the underlying dynamics of the intellectual structures of HI and its sub-discipline HIT. Using multiple statistical methods including citation and co-citation analysis, social network analysis (SNA), and latent semantic analysis (LSA), this essay shows how HIT research has emerged in IS journals and distinguished itself from the larger HI context. The research themes, intellectual leadership, cohesion of these themes and networks of researchers, and journal presence revealed in our longitudinal intellectual structure analyses foretell how, in particular, these HI and HIT fields have evolved to date and also how they could evolve in the future. Our findings identify which research streams are central (versus peripheral) and which are cohesive (as opposed to disparate). Suggestions for vibrant areas of future research emerge from our analysis. The second part of the dissertation focuses on comprehensively understanding the effect of social support exchange in online health communities on individual members’ health promotion outcomes. This study examines the effectiveness of online consumer-to-consumer social support exchange on health promotion outcomes via analyses of big health data. Based on previous research, we propose a conceptual framework which integrates social capital theory and social support theory in the context of online health communities and test it through a quantitative field study and multiple analyses of a big online health community dataset. Specifically, natural language processing and machine learning techniques are utilized to automate content analysis of digital trace data. This research not only extends current theories of social support exchange in online health communities, but also sheds light on the design and management of such communities.

Today scholars give particular emphasis to creativity. A definite part of scholarship inquiring particularly into the problem of human creative power covers a wide range of aspects of training for creative thinking. The present work seeks to explore the chances of fostering the cognitive and the personality components of creativity in university students. Creativity means one’s ability to perceive a problem and to generate new ideas, or to think independently and deal quickly and easily with a problem situation, or to find an original way of solving a problem, or to create novel things (Guilford, 1968b; Torrance, 1974; Sternberg, O’Hara, 1999; Sternberg et al., 2005). Ability to think creatively depends not only on one’s knowledge and skills. Rather, it is determined by one’s special ability to distinguish a problem, and to utilise, speedily and in multiple ways, information contained in tasks one has been set (Guilford, 1968; Torrance, 1962). Research outlined in the present dissertation is based on theoretical assumptions advanced by cognitive (J. P. Guilford, E. P. Torrance), humanistic, and Gestalt psychology. The present work regards divergent thinking as a cognitive component of human creative power. Divergent thinking parameters covered by the present research include the fluency, the flexibility, and the originality of thinking. Thus, this particular aspect of creativity is highlighted in this work in order to verify the chances of training for divergent thinking by... [to full text]