Discovery and Diffusion of Digital Innovations – An Analysis of Enterprise Social Networks and Data-Driven Business Models

Digital technologies radically transform today’s organizations as they permeate both innovation processes and outcomes. While the potential of digital innovations is tremendous, many companies hardly realize the extensive benefits of digital technologies so far. Furthermore, the theoretical understanding of digital innovations is limited since scholars started to challenge the assumptions made in traditional innovation research due to digital technologies’ affordances. Therefore, this thesis seeks to improve the knowledge about digital innovations by analyzing their discovery and diffusion. The discovery of innovations relates to the development of ideas, which can result in new products, processes, or business models. It is essential to investigate companies’ innovation discovery as they often struggle to create innovative ideas and existing theory rarely incorporates the increasing diversity of employees involved in these processes. Papers A and B of this thesis address these issues by examining how Enterprise Social Networks (ESNs) facilitate employees’ innovation discovery. According to Communication Visibility Theory (CVT), the consideration of ESNs is crucial in this regard as they make employees’ everyday communication permanently visible, which provides a basis for acquiring new knowledge. Paper A validates and extends the newly developed CVT. By incorporating individuals employed in diverse contexts, it empirically supports the theory’s external validity. Therefore, different companies can draw on ESNs to foster their innovation discovery, which is made possible through improvements in employees’ meta-knowledge. Besides, the paper reveals that meta-knowledge is not merely formed in the long-run, as indicated by previous research, but in the short-run as well. Interestingly, it also shows that managers can gain more meta-knowledge using ESNs compared to non-managers, which is in contrast with prior literature’s findings. Paper B investigates when employees disclose information in ESNs, which is essential to attain high communication visibility and, in this way, to facilitate the discovery of innovations. To that end, the paper transfers theory on Online Social Networks (OSNs) to the ESN context. It finds that employees’ trusting and risk beliefs are associated with their information disclosure. Additionally, the paper reveals that a company’s group and development culture influence these beliefs, with error aversion culture transmitting the effect of development culture. Innovation diffusion relates to the distribution of a novel product, process, or business model across a group of target users. It is important to better understand the diffusion of digital innovations as companies often lack knowledge about why new offerings are rejected, which limits their chances of counteracting the underlying issues. Furthermore, digital technologies impact the innovation diffusion by blurring industry boundaries and facilitating competition. Papers C and D of this thesis investigate the diffusion of digital innovations in the context of data-driven business models. This context is especially affected by new competition arising across previous boundaries and, thus, necessary to analyze as diverse organizations have high incentives to utilize their data in new ways. Paper C analyzes which dimensions substantially differentiate between distinct data-driven business models. For this purpose, it leverages practitioners’ perceptions of business models obtained from a start-up database. Based on three identified dimensions, the paper creates a taxonomy that classifies the business models into eight ideal-typical categories. The number of business models present in each category provides insights into their diffusion. By offering basic knowledge about the nature of data-driven business models, the paper can be used as a foundation for future research that seeks to dig deeper into this new field and for companies that aim at developing data-driven business models. Paper D investigates how individuals evaluate data-driven services that are offered by highly diverse companies. Based on a qualitative study, the paper shows that individuals’ perception of fit between a service and its provider is crucial for their evaluations. It also reveals the dimensions that influence this perception. Additionally, it explores the consequences that come with a perception of fit. Using these results, the paper offers a new perspective on individuals’ service evaluations, which is vital to the diffusion of the services as well as the associated business models and helps organizations in developing and promoting data-driven services.