Below a more detailed description of my current research focus and future research streams.
My research focuses on understanding the strategic mechanisms and practices used by organizations in complex multi-actor collaborations to mobilize support and pursue big goals related to digital innovation. My first priority lies with doing highly rigorous, qualitative, empirical work. For example, my dissertation research concerns a longitudinal empirical study of a collaboration between ‘big science’ and ‘big business’ called Helix Nebula, where I analyze how large scientific institutes (CERN, EMBL, ESA) collaborate with commercial cloud computing organizations (Atos, T-Systems), and public infrastructure providers (EGI, GEANT), to develop a European scientific cloud computing infrastructure. In one empirical paper, I analyze the processes through which the organizations manage pluralism by forming emerging micro-alliances of decision-making and legitimating actors around technical options for a cloud computing infrastructure. In another empirical paper, together with Prof. Shaz Ansari, I study the aspect of goals in complex multi-actor collaborations. I analyze the micro-level strategies and framing mechanisms organizations use to keep the broad goals that motivated the collaboration relevant despite emerging conflicts on subgoals.
My research is grounded in science and technology studies and process research, and I link my work on complex multi-actor collaborations driven by digital innovation to various literatures, including organizational pluralism, resourcing, framing, and new ways of organizing. I typically do process research, and through my interest in micro-level dynamics I also have knowledge of practice theory and its relevant methodologies. I am a passionate qualitative researcher (case study and ethnographic methods). Driven by my determination for methodological rigor I have adopted digital methods for qualitative data as a complementary analysis technique. For example, for the second empirical paper of my dissertation I use Latent Dirichlet Allocation (a topic modelling technique from computer linguistics) to extract topics from 11,000 internal text documents (e-mails, documents, presentations) and use measures from communication studies and clustering methods to analyze the relationships between topics over time.