More often than ever before, innovation activities are crossing organizational boundaries and taking place in the spaces between formal, organizational structures. This new context for innovation activities is increasingly referred to as an innovation ecosystem. Open innovation, co-creation, user-driven innovation, API and platform economies, and business ecosystems are key drivers of the transformation. Innovation ecosystems are open, dynamic systems that cross geographical as well as organizational boundaries and include ﬁnancial, technological, and political dimensions. Talented humans have a crucial driving role in ecosystemic innovation activities. Innovation ecosystems set a new framework for analyzing, investigating, and therefore measuring innovation.
Measuring and visualizing innovation is difficult, particularly within innovation ecosystems where activities take very complex forms and even identifying all relevant actors and stakeholders is challenging. At the same time, ecosystem-level analyses of innovation ecosystem structures are imperative for three groups: innovation ecosystem scholars, policy and decision makers, and innovation ecosystem actors. Moreover, new sources of digital data on innovation activities have become available, introducing new opportunities to investigate innovation ecosystems at the ecosystem level.
In this dissertation, we seek to develop new means to utilize digital data in analyzing innovation ecosystems at the ecosystem level. We take an action design research approach to develop the means to investigate the structural properties of innovation ecosystems at the ecosystem level by using visual network analytics. We start from the realization that interconnectedness is a key property of innovation ecosystems. Addressing innovation ecosystems as networks, that is, as collections of pairs of interconnected innovation ecosystem actors, allows scholars and practitioners to gain insight into innovation ecosystem structures and the structural roles of individual ecosystem actors. To determine how innovation ecosystems should be modeled and analyzed as networks, we investigate several innovation ecosystems representing regional, metropolitan, national, and international contexts as well as investigating the context of programmatic activities that support innovation and growth. Our main objective in the dissertation is to develop a process model for data-driven visual network analytics of innovation ecosystems.
Visual network analytics is a valuable method for investigating and mapping the innovation ecosystem structure. In the proposed approach, transactional microdata on innovation ecosystem actors and their interconnections is collected from various digital sources. Innovation ecosystem actors are represented as network nodes that are connected through transactions, including investments and acquisitions and advisory, founder, and contributor affiliations. Network metrics are used to quantify actors’ structural positions. Interactive visual analytics tools are used to support the visual exploration of the innovation ecosystem under investigation by using both top-down and bottom-up strategies.
This work makes several contributions to the art and science of data-driven visual network analytics of innovation ecosystems. Most importantly, the dissertation proposes the ostinato model, an iterative, user-centric, process-automated model for data-driven visual network analytics. The ostinato model simultaneously supports the automation of the process and enables interactive and transparent exploration. The model has two phases: data collection and reﬁnement, and network creation and analysis. The data collection and reﬁnement phase is further divided into entity index creation, Web/API crawling, scraping, and data aggregation. The network construction and analysis phase is composed of ﬁltering in entities, node and edge creation, metrics calculation, node and edge ﬁltering, entity index reﬁnement, layout processing, and visual properties conﬁguration. The cycle of exploration and automation characterizes the model and is embedded in each phase.
In addition to the ostinato model, we contribute a set of design guidelines for modeling and visualizing innovation ecosystems as networks. Finally, we contribute to the empirical body of knowledge on innovation ecosystems through a series of investigations of innovation ecosystems of different levels of abstraction and complexity. Innovation ecosystem scholars, policy makers, orchestrators, and other stakeholders in the innovation ecosystem under investigation in this dissertation have subscribed to the approach presented herein. The design guidelines, together with the ostinato model, allow innovation ecosystem investigators and actors an opportunity to signiﬁcantly advance in utilizing visual network analytics in managing and orchestrating innovation ecosystems. Further research and development of supporting processes and tools are needed to take full advantage of the presented approach in analyzing, investigating, facilitating, and orchestrating interorganizational innovation activities.