Umeå University's logo

Digital reform of public sector organizations rests on their ability to innovate with digital technology. Since the public sector innovation literature pays limited attention to digital, and as digital innovation is rarely investigated in public sector contexts, current knowledge on digital innovation capabilities in such organizations is limited. Through a case study of digital reform in a local government organization, we identify how public sector conditions organizations' capability for digital innovation. This includes legal structures that propagate infrastructural fragmentation, resource allocation systems that restrict innovation scopes, how political pressure leads to conflicting frames of the strategic role of digital, and how public and legal liability may lead to innovation paralysis. The paper contributes to the literature by illustrating the unique conditions for digital innovation in the public sector, and to practice by highlighting key shifts that increase organizations' digital innovation capability.

IoT systems generate data that can improve organizations’ ability to adapt to new demands and proactively address emerging issues. However, to develop such abilities, organizations need to engage in change beyond technical implementations. In this paper, we study the development of a real estate company that, through the adoption of IoT, strives to increase operational agility and become data-driven. Seeking to understand the experiences of key actors across units within this organization, we identify different co-existing rationales regarding the strategic value of data and show how their operationalization in data-driven activities ultimately led to a fragmentation of the data-driven culture within the organization. Furthermore, we highlight the role of customers in the realization of value for data-driven services and how the implementation of IoT can enable new sensing capabilities but also foster expectations of a heightened organizational ability to respond.

This study explores the emergence and expansion of data network effects (DNEs) in AI platforms. Previous research has focused on direct and indirect network effects. However, the rise of AI platforms necessitates understanding DNEs for platforms’ learning and improvement. Through a longitudinal case study of a Conversational AI (CAI) platform's 12-year evolution, the study identifies generative feedback loops as the mechanism for DNEs. These loops are initiated by adding functions that enhance the platform's generative capacity, resulting in more diverse data that improves platform learning. DNEs develop through interactions with different ecosystem actors, including clients and external developers, and rely on various data sources beyond user data to enhance AI platform capabilities. This study contributes to IS literature, specifically digital platform literature, following recent calls to empirically examine DNEs to better understand how AI platforms grow and improve their algorithmic capabilities over time.

As organizations embrace artificial intelligence (AI) for business process automation, they face challenges with its adoption. Low-code platforms promise to simplify this process, but the supporting evidence is limited. We studied the adoption of a low-code conversational AI platform in four multinational companies and found three significant challenges linked to fundamental assumptions about low-code approaches. Based on this case study research, we recommend steps companies can take to guide the adoption and maximize the potential of low-code AI platforms.

Digital innovation (DI) has enabled businesses to enhance their existing market offerings by integrating digital features. Despite advanced technologies, substantial marketing efforts, and global recognition, businesses can still struggle to convince customers to adopt their digital market offerings. This process of spreading novel innovation is known as diffusion. In the fast-growing digital world, due to the unique characteristics of DI, traditional diffusion theories and models show limited explanatory power, creating challenges for researchers and practitioners alike. With the aim of exploring these challenges, we position our research within the IS literature with the following research question:"How and why do diffusion enablers and barriers emerge during digital innovation?". We conducted an interpretive case study of Company X, one of the world's largest consulting firms and an active DI practitioner. Our findings suggest that digital innovation diffusion can be enabled or hindered by several understudied interdependencies in its technological architecture. Furthermore, for successful diffusion, how DI distributes the division of labor between actors and subsequent layers must be effectively embedded and aligned for value in the clients' context in order to diffuse successfully. This study provides novel insights and compelling research avenues. 

Digital service platforms need to be embedded in external device platforms because they are not bundled with a proprietary device. From our analysis of the Spotify music streaming service, we have identified three strategic objectives that service platform providers need to pursue as they establish and scale their services. Achieving each objective will require trade-offs, and we described the tactics Spotify used to manage these trade-offs. We conclude by providing recommendations on how other service platform providers can apply these tactics

This dissertation investigates digital transformation, understood here as processes where organizational actors engage in digital innovation and transform their organizations in order to respond to change in their business and technology environments. Specifically, it examines the dynamics of digital transformation, seeking to understand the key sociotechnical elements and their relationships that drive digital transformation processes and influence how they unfold over time. To theorize the dynamics of digital transformation, I synthesize extant knowledge with contributions from four appended research papers.

The outset for theorizing in this dissertation is a body of literature that has begun to accumulate knowledge on digital transformation as a distinct phenomenon. Within this literature, I identify three main areas that are vital to understanding digital transformation processes, yet have so far not been sufficiently theorized. First, research on digital transformation often describe it as a complex and longitudinal process that involves several sequences of digital innovation, yet it has primarily been studied in the form of discrete instances of innovation decoupled in time and space. As a result, current knowledge on digital transformation as a longitudinal process is limited. Second, the literature on digital transformation emphasize that interactions between digital business and technology environments and organizations are crucial for explaining why and how digital transformation unfolds. At the same time, however, the literature has so far not been able to offer a conceptualization of these interactions in ways that make formative influence over time visible. Third, existing research on digital transformation has remained dominantly focused on the role of managers and paid limited attention to other organizational actors in digital transformation.

Addressing the limitations identified in existing digital transformation research, I draw upon established theoretical concepts and the four appended research papers to theorize a conceptual framework on digital transformation dynamics. The conceptual framework contributes to research by clarifying a set of theoretical concepts and relationships that are instrumental for addressing digital transformation as a sequential and cumulative process, and the actors, agency and actions that realize digital transformation over time. It is supportive of future theorizing of digital transformation as a subject matter related yet distinct from other forms of organizational change enabled by technology use.