| Abstract: |
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. |