Digital Technologies for Digital Innovation: Unlocking Data and Knowledge to Drive Organizational Value Creation

The rise of digitization has radically transformed innovation processes of today's companies and is increasingly challenging existing theories and practices. Digital innovation can describe both the use of digital technologies during the innovation process and the outcome of innovation. This thesis aims to improve the understanding of digital innovation in today's digitized world by contributing to the theoretical and practical knowledge along the four organizational activities of the digital innovation process: initiation, development, implementation, and exploitation. In doing so, the thesis pays special attention to the use of digital technologies and tools (e.g., machine learning, online crowdsourcing platforms, etc.) that unlock knowledge and data to facilitate new products, services, and other value streams. When initiating digital innovations, organizations seek to identify, assimilate, and apply valuable knowledge from within and outside the organization. This activity is crucial for organizations as it determines how they address the increasing pressure to innovate in their industries and markets while innovation processes themselves are changing and becoming more distributed and open. Papers A and B of this thesis address this phase by examining how digital technologies are changing knowledge gathering, e.g., through new ways of crowdsourcing ideas and facilitating cooperation and collaboration among users and innovation collectives. Paper A focuses on organizational culture as a critical backdrop of digital innovations and explores whether it influences the implementation of idea platforms and, in this way, facilitates the discovery of innovations. The paper reveals that the implementation of idea platforms is facilitated by a culture that emphasizes policies, procedures, and information management. Additionally, the paper highlights the importance of taking organizational culture into account when introducing a new technology or process that may be incompatible with the existing culture. Paper B examines newly formed innovation collectives and initiatives for developing ventilators to address shortages during the rise of the COVID-19 pandemic. The paper focuses on digital technologies enabling a transformation in the way innovation collectives form, communicate, and collaborate - all during a period of shutdown and social distancing. The paper underlines the role of digital technologies and collaboration platforms through networking, communication, and decentralized development. The results show that through the effective use of digital technologies, even complex innovations are no longer developed only in large enterprises but also by innovation collectives that can involve dynamic sets of actors with diverse goals and capabilities. In addition, established organizations are increasingly confronted with community innovations that offer complex solutions based on a modular architecture characteristic of digital innovations. Such modular layered architectures are a critical concept in the development of digital innovations. This phase of the digital innovation process encompasses the design, development, and adoption of technological artifacts, which are explored in Sections C and D of this paper. Paper C focuses on the latter, the adoption of digital services artifacts in the plant and mechanical engineering industry. The paper presents an integrative model based on the Technology-Organization-Environment (TOE) framework that examines different contextual factors as important components of the introduction, adoption, and routinization of digital service innovations. The results provide a basis for studying the assimilation of digital service innovations and can serve as a reference model for informing managerial decisions. Paper D, in turn, focuses on the design and development of a technology artifact. The paper focuses on applying cloud-based machine learning services to implement a visual inspection system in the manufacturing industry. The results show, for one, the value of standardization and vendor-supplied IS architecture concepts in digital innovation and, for another, how such innovations can facilitate further innovations in manufacturing. The implementation of digital innovations marks the third phase of the digital innovation process, which is addressed in Paper E. It encompasses organizational changes that occur during digital innovation initiatives. This phase emphasizes change through digital innovation initiatives within the organization (e.g., strategy, structure, people, and technology) and across the organizational environment. Paper E investigates how digital service innovations impact industrial firms, relationships between firms and their customers, and product/service offerings. The paper uses work systems theory as a theoretical foundation to structure the results and analyze them through the lens of service systems. While this analysis helps to identify the organizational changes that result from the implementation of digital innovations, the paper also provides a basis for further research and supports practitioners with systematic analyses of organizational change. The last phase of the digital innovation process is about exploiting existing systems/data for new purposes and innovations. In this regard, it is important to better understand the improvements and effects in the domains beyond the sheer outcome of digital innovation, such as organizational learning or organizational change capabilities. Paper F of this thesis investigates the exploitation of digital innovations in the context of organizational learning. One aspect of this addresses how individuals within the organization leverage innovation to explore and exploit knowledge. Paper F utilizes the organizational learning perspective and examines the dynamics of human learning and machine learning to understand how organizations can benefit from their respective idiosyncrasies in enabling bilateral learning. The paper demonstrates how bilateral human-machine learning can improve the overall performance using a case study from the trading sector. Drawing on these findings, the paper offers new insights into the coordination of human learning and machine learning, and moreover, the collaboration between human and artificial intelligence in organizational routines.