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.
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.
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.
Cluster evolution research suggests that maintaining an optimal technological heterogeneity that is exploitable by cluster actors is key to sustainable cluster development. This paper argues that exploring this optimal span and its influence on local synergy creation calls for understanding the interaction between cluster actions, local conditions for collaboration, and heterogeneity requirements over time. For this purpose, a longitudinal case study is conducted, tracing the development of a digital creative cluster that has experienced the initiation, rise, and decline of local technological heterogeneity exploitation. By applying institutional logics as a sensitising device, the analysis explores how actors interact with local and theme structures in this process. Findings show how hub-firms draw on creative norms and technologies to produce situated heterogeneity requirements. These are assessed with co-location factors and accumulated experience of local collaboration to produce local organising rationales that guides decisions to engage in local collaboration.
In order to face the challenges derived from an increasingly competitive and disruptive environment, firms often engage in collaborative arrangements with other firms. While it is argued that inter-firm networks can serve as a way to catalyze innovation, to manage risks involved in R&D and to enable the creation of new value through co-creation, the causes and reasons for inter-firm collaboration are well-known. However, little effort has been focused at critically examining the challenges that co-creation brings on a network and firm-level. This research addresses this issue by taking a process perspective on the formation and development of an inter-firm network in relation to its technologically turbulent environment. Building on a case study involving firms from that network, this research shows that such arrangements may also involve challenges for participating firms. These challenges relates to a paradoxical tension between exploitation of relation-specific assets and success in the long- and short-term, but also a challenge in terms of positioning the firm within the network.
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.
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
Research on digital platform evolution is largely focused on how platform-owners leverage boundary resources to facilitate and control contributions from external developers to extend the functional diversity and scope of a digital device. However, our knowledge of the digital platforms that carve out their existence exclusively in the service layer of industry architectures, i.e. without proprietary device connections, is limited. The concept of digital service platforms directs attention to such platforms, the role of end-users as value co-creators, and devices as requisite, but not necessarily proprietary, distribution mechanisms for service. Based on a longitudinal case study of Spotify, this paper contributes by demonstrating that digital service platform evolution is characterized by specific architectural conditions that rationalize the use of boundary resources for extending scale rather than scope, and for resourcing and controlling not only developers but also end-users as a means to strategically adjust the evolutionary process.