Digitalization drives societal transformation to reform existing practices in the evolving environment. Central to this transformation is the creation of interoperable digital public services across diverse organizations, increasingly guided by human-centric principles and life-event orientation. This paper explores the complex process of achieving digital service innovation, emphasizing the need for inter-organizational balancing between radical transformation and efficiency through collective ambidexterity, where multiple ecosystem actors coordinate to balance innovation and efficiency simultaneously. While previous research predominantly studied ambidexterity at the organizational level, focusing on its antecedents, mechanisms, and outcomes, our study extends this inquiry to the broader ecosystem. Via a single-case study, we investigate how collective ambidexterity can be governed in a large-scale digital service ecosystem. To address the research question, we developed a multi-level conceptual model of governing mechanisms, antecedents, and outcomes of collective ambidexterity across three analytical levels: the ecosystem, organization group, and organization. Our theoretical contribution is twofold. First, we enhance conceptual clarity on collective ambidexterity and show how Modes of Collaboration (MoC) can facilitate innovation and efficiency of human-centric digital services throughout the three levels of governance. Second, the resulting governance model emphasizes the need to connect centralized, decentralized, and group-level governance strategies for developing digital services—to achieve and govern collective ambidexterity in the development of digital services in the public sector.

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems.

Knowledge graphs (KGs) structure knowledge to enable the development of intelligent systems across several application domains. In industrial maintenance, comprehensive knowledge of the factory, machinery, and components is indispensable. This study defines the objectives for evolvable KGs, building upon our prior research, where we initially identified the problem in industrial maintenance. Our contributions include two main aspects: firstly, the categorization of learning within the KG construction process and the identification of design objectives for the KG process focusing on supporting industrial maintenance. The categorization highlights the specific requirements for KG design, emphasizing the importance of planning for maintenance and reuse.

Digital public service development toward human-centric service portfolios requires radical changes and innovative collaboration among multiple actors. Such a collaboration involving public and private service providers and NGOs has turned out to be challenging. On the organizational level, long-term sustainability requires dialectical reasoning and synthesis between innovation and efficiency toward organizational ambidexterity. However, public organizations face the need for mandated efficiency, which hinders innovativeness. In addition, the need for interoperable human-centric services at the ecosystem level introduces additional complexity. To evaluate how to address the emerging tensions in complex public service ecosystems in developing interoperable human-centric digital service portfolios, we conducted a single-case study with multilevel analysis in a large-scale public service ecosystem in Finland. We found that dialectical tensions emerging on various forums, where differing goals, views, and values meet, are intertwined within and across multiple ecosystem levels and that the synthesis of these tensions can be more than the sum of their parts. We introduce the concept of multilectic forums and theorize how they emerge continuously and simultaneously in public service ecosystems between the public and private service providers and NGOs while human-centric digital service portfolios are developed. We propose that governing intertwined tensions on multilectic forums can result in innovative and efficient digital services and collectively ambidextrous ecosystems.

The digital transformation era calls for digital innovations affecting the public sector, but such organizations often focus more on the efficiency and exploitation of current resources, ascribing less attention to explorative innovation. Innovation in new capabilities and technologies often require inter-organizational collaboration. In this context, public sector organizations do not operate in isolation—often engaging in interactions within their service sectors. While the contemporary literature on organizational ambidexterity primarily focuses on the balancing opportunities of a focal organization, radical sector-level development requires collaboration among various actors to achieve collective ambidexterity. Drawing on the research question of the influence of inter-organizational collaboration on the outcomes of digital initiatives, this exploratory case study on the Finnish water sector found that simultaneously operating modes of inter-organizational collaboration can facilitate different aspects of collective ambidexterity in an inter-organizational field and, consequently, the expected benefits from initiatives. Our theoretical contribution lies in demonstrating collaboration modes that can enhance specific aspects of collective ambidexterity. Accordingly, we suggest that collective ambidexterity through deliberately adopted modes of inter-organizational collaboration requires field-level facilitation in parallel with organizational ambidexterity. Furthermore, consequent practical implications for managing such collaborations are suggested for policymakers, interest groups, and the public sector.

Decisions continue to be an essential topic of utmost importance in every research field and era. However, while decision research has extensively offered a wide range of theories, it remains delved in the past, and needs robustness to sustain the future of data-driven decision-making, encompassing topics and technologies such as big data, analytics, machine learning, and automated decisions. Nowadays, decision processes have evolved, the role of humans as decision makers has changed and become inevitably intertwined with the support of machines, rationalities are no longer limited in the same way, data has become an abundant commodity, and the optimizing of decisions is not so far-fetched a tale as it once was in classical times. Accordingly, there is a dire need for new theories to support new phenomena. This paper aims to propose a modern data-driven decision theory, DECAS, to support the new elements of today’s decisions. Our theory extends upon classical decision theory by proposing three main claims: the (big) data and analytics should be considered as separate elements along with the decision-making process, the decision maker, and the decision; the appropriate collaboration between the decision maker and the analytics (machine) can result in a “collaborative rationality,” extending beyond the bounded rationality which decision makers were classically characterized by; and finally, the proper integration of the five elements, and the correct selection of data and analytics, can lead to more informed, and possibly better, decisions.  Hence, the theory is elaborated in the paper, and introduced to some data-driven decision examples.