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Citizen-centricity is a common concept in digital government research and policy. However, there is little clarity regarding the concept in previous literature. To address this shortcoming, and build theoretical foundations for addressing both citizen-centricity and associated phenomena, we have examined how citizen-centricity is characterized in digital government research. This study is based on literature review of 66 journal articles. A combination of narrative analysis and ideal-type methodology identified themes concerning four modes of government, designated traditionalist, service-dominant, participatory, and transformative. Further analysis of associated types and research streams provides an overview of the theoretical understandings of citizen-centricity and methodological approaches applied to explore it in the literature. The findings contribute to contemporary theory on citizens in digital government by outlining an integrative framework of citizen-centricity. The paper concludes with proposals for further research, including efforts to enhance conceptual clarity and develop more dynamic theories.

While data are often depicted as raw, neutral, and mere inputs to algorithms, we build on an emerging stream of research on data work viewing data as ambivalent, performative, and embedded entities, interwoven with organizing. We argue that in the process of developing AI, where epistemic uncertainty prevails as a key organizing challenge, data work serves as an organizing principle providing the logic through which behaviors are adopted, interpretations are made, and the collective efforts of domain experts and AI experts are coordinated. Prior research suggests that active involvement of both AI and domain experts is required for developing AI. Yet, domain experts and AI experts have distinct knowledge and understandings of domain specificities, meanings of data, and AI’s possibilities and limitations. Consequently, in AI initiatives, a key organizing challenge is epistemic uncertainty, i.e., ignorance of pertinent knowledge that is knowable in principle. We build a conceptual model deciphering three key mechanisms through which data work serves as an organizing principle supporting organizations to cope with epistemic uncertainty: cultivating knowledge interlace, triggering data-based effectuation, and facilitating multi-faceted delegations. These three mechanisms emerge when domain experts and AI experts work with and on data to define and shape trajectories of an AI initiative and make decisions about AI. This chapter contributes to the nascent body of research on data work by expounding the performative role of data as a relational entity, by providing a processual view on data’s interweaving with organizing, and by deciphering data work as a collectively accomplishment.

Samhället sätter stora förhoppningar till att digitaliseringen ska utveckla hemtjänsten, men kunskap saknas om konsekvenserna för brukare, omsorgspersonal och chefer. Det finns många frågor att besvara. 

Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.

This article focuses on how recent advances in artificial intelligence (AI), particularly chatbots based on large language models (LLMs), such as ChatGPT, can be used for innovation purposes. The article begins with a brief overview of the development and characteristics of generative AI (gAI). Elaborating on the implications of gAI, we provide examples to demonstrate four mechanisms of LLMs: translation, summarization, classification, and amplification. These mechanisms inform a framework that highlights how LLMs enable the creation of innovative solutions for organizations through capacities in two dimensions: context awareness and content awareness. The strength of LLMs lies in the combination of capacities in both these dimensions, which enables them to 'comprehend' and amplify content. Four managerial suggestions are presented, ranging from starting out with small-scale projects and data exploration, to scaling through integration efforts and educating prompt engineers. By presenting the framework, recommendations, and examples of use cases in various contexts, the article contributes to the emerging literature on gAI and innovation.

Contemporary welfare organizations engage in various evaluation practices to assess the quality of their services. In this paper we report a qualitative exploration of how technology-enabled evaluations are understood by organizational members who participate in quality assurance activities in Swedish social services. The study contributes to critical information systems literature, focusing on the tensions professionals experience in relation to the digital systems they use for evaluations. For example, "quantities" take precedence over the qualities of such work, as information systems constrain ambitions to realize knowledge-based social services. The results reveal three tensions in professionals' evaluation-related activities arising from conflicting uses or desires. One is between desires for flexible systems that enable reflection and standardized digital support systems. Another is between uses or desires for indicators that are meaningful at the operational level and for general, comparable measures at the management level. The third is between desires to use evaluation procedures for learning and control. The study contributes to both theory and practice related to technology-enabled evaluation of welfare services, and critical perspectives on information systems.

With recent advances in artificial intelligence, machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for students with diverse educational backgrounds. The approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate no-code AI in their courses, and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.

Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) such as machine learning (ML) systems into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article we outline three key challenges and discuss how an emerging form of AI platforms – ‘no-code AI’ – may help organizations to address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining important remaining challenges associated with no-code AI and MLOps we propose three managerial recommendations. By doing so, we provide insights into an important novel, emerging phenomenon in AI software and set the stage for further research in the area.

Substantial research has been conducted to investigate the value that Enterprise Architecture (EA) can generate for organizations. However, there is also a need to empirically explore the mechanisms involved in creating this value. Against this backdrop, this paper aims to answer the research question: “Which mechanisms contribute to generating value through using Enterprise Architecture in government?” The research was conducted through a survey administered to Swedish government organizations, directed by a public value framework. The data analysis was conducted using descriptive statistics and an inductive analysis of open-text answers. The findings reveal values associated with the use of EA in government and corresponding value-generating mechanisms. Core activities in the municipalities consist of establishing digital value chains where values are generated for citizens and the internal administration. National agencies engage more in creating strategic value through intrinsic enhancements enabled via EA to establish organizational commonalities. Our findings informed a conceptual framework, which encompasses organizing principles, core EA activities, and applications. This research contributes to the literature on the use of EA in government by highlighting EA activities related to the strategic orientation of organizational operations and enablers for deriving valuable results from these activities. Our framework, informed by theories of public value and results from practice, provides a roadmap for public managers to plan and operationalize their architectural work. By doing so, we contribute to establishing an important link between research on EA in the public sector and public value theory. We conclude the paper with suggesting additional research on two identified research gaps: 1. Using EA for participatory processes, 2. Further investigation of evaluation practices.