Aya Rizk

The terminology associated with the use of information and communication technologies in the public sector has shifted over time, from information technology to digitalization. This change raises the question of whether corresponding shifts in government practices have accompanied it. To investigate this issue, we use topic modeling to analyze a corpus of IT- and digitalization strategies from Swedish local governments, produced over three decades. Our analysis reveals salient topics covered in these strategies and classifies them, displaying patterns of both continuity and change. Some of these patterns reflect dominant discourses about technological governance and the provision of digital services based on citizens’ needs, but there is also a notable absence of content related to democracy and participatory practices. Taken together, our study contributes empirically with an evolutionary perspective on digital government strategies and methodologically with the adoption of computational methods for this purpose.

Prototyping is an important part of any development activity since it supports communication and knowledge creation among the members of the developing organization. Although prototyping is well established in the development of physical products, less is known about its use and effect on digital service innovation and development. Since digital technologies today are embedded in almost every level of an organization, from its processes to its offerings, it can be argued that it is crucial for an organization to be able to handle not only digital aspects of prototyping, but also physical and digital aspects simultaneously. This study addresses this need by exploring the impact of digital technology on prototyping, answering the research question “How does digital technology affect the different elements of prototyping?” By taking a comprehensive view on prototypes, implications for development are analyzed and developed based on the complex nature and ontology of digital technology. The result encompasses a set of nine different propositions for digital prototyping which contributes to both academia and the work of practitioners.

Automation of administrative work, using Robotic Process Automation (RPA), has been highlighted as something that can help Swedish municipalities to reduce costs and improve efficiency. The use of RPA in Swedish municipalities is still in its infancy, and much can be learned from the experiences made so far. In this paper, we present an overview of the findings from a four-year case study investigating RPA development, implementation, and use in Swedish municipalities. The aim of the paper is to describe the general drive to implement RPA in Swedish municipalities, the intended uses of RPA in this context, and challenges experienced as part of RPA implementation. Based on this description, we outline a set of organizational conditions required to work with RPA in local government. Our findings illustrate that process automation is only partly about technology. The main focus in process automation must be on mapping and developing processes and routines. RPA-technology is best seen as a tool and not as an end in itself. It is therefore important to develop the employees' abilities and knowledge regarding both processes and automation, so that the employees can drive the development forward based on the challenges and opportunities that arise in their practical work.

Purpose: Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.Design/methodology/approach: A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.Findings: Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.Originality/value: The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.

Scholars have been increasingly calling for innovative research in the organizational sciences in general, and the information systems (IS) field in specific, one that breaks from the dominance of gap-spotting and specific methodical confinements. Hence, pushing the boundaries of information systems is needed, and one way to do so is by relying more on data and less on a priori theory. Data, being considered one of the most important resources in research, and society at large, requires the application of scientific methods to extract valuable knowledge towards theoretical development. However, the nature of knowledge varies from a scientific discipline to another, and the views on data science (DS) studies are substantially diverse. These views vary from being seen as a new scientific (fourth) paradigm, to an extension of existing paradigms with new tools and methods, to a phenomenon or object of study. In this paper, we review these perspectives and expand on the view of data science as a methodology for scientific inquiry. Motivated by the IS discipline’s history and accumulated knowledge in using DS methods for understanding organizational and societal phenomena, IS theory and theoretical contributions are given particular attention as the key outcome of adopting such methodology. Exemplar studies are analyzed to show how rigor can be achieved, and an illustrative example using text analytics to study digital innovation is provided to guide researchers.

The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger’s Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users’ situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research.