I am particularly interested in interdisciplinary research where one or both areas are leveraged to help organizations. For example, AI offers knowledge about methods and techniques to generate insights from machine-readable data, while digital innovation researchers and practitioners – and their surrounding socio-technical structures – are increasingly looking at new ways of finding and assimilating new knowledge sources to inform their ideas and develop new services.
With the increasing availability of such data, data-driven innovation is one of my key research interests. In my research projects, I focus on understanding data-driven innovation (DDI) as a phenomenon. This was initially done through looking at the areas of data science, AI and digital innovation separately to understand potential research problems and gaps.
My empirical work focused on digital services developed in smart cities, in which data and analytics played a key role in delivering value to their intended user. Through iterative analysis between the empirical data and digital service innovation theories, innovation networks was used as a theoretical frame to analyze the social and cognitive interactions between innovators, end users – as data generators, and other types of actors. In addition to being prominent network participants, end users’ role in digital service innovation is also of particular interest to me as a researcher.
Hence, I am interested in understanding the adoption barriers pertaining to the diffusion of DDIs. The whole process from discovery to post-diffusion (e.g. termination or scaling) is explored in my research, as well as how the knowledge development in that field is shaped by exploring the dualism of innovation: as a process and as an outcome. This work points to two further specialized areas of interest.
First, innovation being a complex endeavor, the range of skills, capabilities and strategies that take an idea into market are of key interest. More specifically, I am interested to understand how thought patterns such as design thinking and data thinking relate to each other in realizing potential AI benefits.
Second, in socio-technical structures such as innovation networks where both machines and humans learn and have some agency, decision-making seems to take new forms and to utilize different combinations of resources.
Thus, one of my key interests is in understanding how data-driven decision making (D3M) and DDI relate to and shape one another. In addition to studying data as an element of phenomena I study, I use it as an indispensable tool in informing my research results and turn to data science techniques in my analyses. Techniques such as clustering, association rules and topic modeling are examples of those in my toolbox towards theory development and validation.
