Timo Koivumäki

Purpose: Artificial intelligence (AI) is increasingly integrated into healthcare, but technological capabilities alone are insufficient for successful implementation. This study examines the implementation challenges that arise in collaboration between AI experts and healthcare professionals (HCPs) and explores how these challenges can be addressed through business model innovation by changing the content, structure and governance of the business model activity system. Design/methodology/approach: Drawing on the NASSS (non-adoption, abandonment, scale-up, spread and sustainability) framework, a qualitative approach was adopted using semi-structured interviews with four healthcare professionals and five AI professionals in Finland. The findings were then used to define challenge areas and changes in the activity system for business model innovation. Findings: The study identifies implementation challenges related to demand-side value, the scope of AI tool use, trust in AI and stakeholder interaction. Interpreting these findings through the activity-system perspective shows how changes in the content, structure and governance of business model activities can address these challenges by improving collaboration between AI experts and HCPs throughout AI development and implementation. Originality/value: This study contributes to the business model literature by linking AI implementation challenges to the activity-system perspective of business model innovation. It further contributes by demonstrating how changes in the content, structure and governance of business model activities can practically address these challenges through improved collaboration between AI experts and healthcare professionals.

By late 2026, the mandate for European Digital Identity Wallets (EUDI Wallets) under the revised eIDAS framework will fundamentally affect the interaction model of the Finnish national Kanta ecosystem. This study presents a scoping review of 74 studies, following the PRISMA-ScR reporting guideline, to map the determinants of citizen adoption when health-related authentication and attribute presentation are mediated through the wallet paradigm. We propose a descriptive conceptual framework that integrates the Unified Theory of Acceptance and Use of Technology (UTAUT2) with Privacy Calculus Theory to explain user behavior across three interacting layers: citizen-level psychology, the functional wallet paradigm, and the institutional governance ecosystem. Our findings reveal that adoption in Finland is not a struggle for basic digitization, but a complex migration of established habits into a decentralized model. The framework distinguishes between the wallet as an identity and consent broker for cloud-based services (Role A) and as a secure local container for verifiable credentials like e-prescriptions, vaccinations and the European Health Insurance Card (Role B). We analyze the “Root of Trust” established by national authorities, including the Digital and Population Data Services Agency (DVV), the Finnish Transport and Communications Agency (Traficom), and the National Police Board, and contrast this with the psychological impact of systemic data breaches, such as the Vastaamo incident. Beyond technical utility, the synthesis highlights the role of “Warm Experts” in bridging the digital divide for citizens over age 75. To our knowledge, this is the first review focused specifically on determinants of Finnish citizen adoption of the EUDI Wallet for health services and on translating those determinants into a wallet-specific conceptual framework. The resulting synthesis provides a roadmap for service designers to use selective disclosure and purpose-limited requests to strengthen perceived safety and digital equity in the high-sensitivity domain of healthcare.

Technologically enabled smart hospitals are emerging as a future trend in modern healthcare, yet their objectives and the role of advanced connectivity such as 6G remain unclear. This study explores the concept of smart hospitals and their aims for future patient care from technological, business, and regulatory perspectives, considering diverse stakeholder viewpoints. To address this, we propose a five-layered “C-service” business model for deploying 6G systems to support smart hospital objectives. The connectivity layer ensures ultra-reliable, low-latency communication for real-time monitoring and telemedicine. The computing layer enables distributed edge processing for rapid decision-making and immersive applications such as AR-assisted surgery. The content layer leverages integrated sensing and communication capabilities in 6G to facilitate secure transmission of medical data and imaging. The context layer interprets this data through AI-driven analytics and digital twinning, supporting personalized care and predictive diagnostics. Finally, the commerce layer integrates multi-layered communication and data platforms for efficient healthcare transactions, reducing administrative burdens and enabling new business models for value-based care. By minimizing hospital admissions through proactive health management, 6G-enabled smart hospitals empower patients and seamlessly integrate services across physical and virtual environments. This framework highlights how next-generation connectivity can transform healthcare delivery, improve operational efficiency, and create sustainable business models for future smart hospitals.

Objective: The integration of Artificial Intelligence (AI) in healthcare offers opportunities to transform patient care, improve efficiency, and support clinical decision-making. Yet, its implementation is hindered by technical, organizational, and collaborative challenges. This study explores the key success factors for sustainable AI adoption from the perspectives of healthcare professionals (HCPs) and AI experts, with the aim of identifying alignments and gaps that influence collaboration and sustainability. Methods: Guided by the NASSS framework, semi-structured interviews were conducted with four HCPs and five AI experts with experience related to AI technologies in the healthcare sector. Thematic analysis was applied to examine stakeholder perspectives, focusing on gaps and alignments between stakeholders and identifying common patterns. Results: Transparency leading to trust was the most emphasized factor by both AI experts and HCPs. Alignments were also found in recognizing the importance of interorganizational cooperation, demand-side value, scope of use, usability, responsibility and redefining the roles. Gaps included challenges in cooperation with HCPs, misunderstandings between stakeholders and the need for interdisciplinary experts, insufficient training, concerns over data quality and privacy, and limited attention to usability. Based on the found patterns, a framework is proposed to strengthen collaboration between AI experts and HCPs, enabling effective and sustainable AI implementation in healthcare. Conclusion: This study extends the NASSS framework to a dual-stakeholder context, offering insights into the alignments and gaps between HCPs and AI experts. The proposed framework supports improved collaboration, guiding healthcare professionals, AI developers, and managers toward effective, sustainable AI implementation and fostering mutual understanding for successful adoption.

As digital transformation (DT) sweeps over society, the exploitation of digital solutions is obvious, especially for large enterprises. Unfortunately, small- and medium-sized enterprises (SMEs) struggle with DT because of limited resources, understanding, and implementation skills. Thus, SME companies need both methods and tools to proceed with DT as well as support to exploit them. This article presents a study in which adult learners with professional experience are trained to use a digitalization development method and tools to analyze target organizations’ digitalization state and identify improvement ideas. Thirty trained digital evangelists used the tools and methods while conducting digitalization status analyses in eleven organizations. The study results show that the method and tools work in the context presented in this research. The study’s findings are beneficial for the educational professionals interested in educating students towards helping SME organizations along their digitalization pathway.

Personal data has become an important resource in today’s market and in platform business models. In fact, the essence of many platform business models is to create value for an individual by offering a free service and to capture value by collecting and selling collected data. In this study, we analyse the drivers for platform business model innovation in the context of personal data used in the healthcare sector. Platform business models are shaped and designed with respect to the internal and external drivers in the market. However, few studies have increased our understanding of the drivers for platform business model innovation. For that, we conducted an exploratory study for five personal data platform providers that enable individuals to control the use of their personal data in digital services by interviewing the personal data platform providers and using company presentation material. The findings show that by adopting a human-centred approach to personal data, giving control over data to the individual, personal data platform providers change how value is created and captured. This research has managerial implications and contributes to the business model innovation literature by extending the current knowledge about drivers for personal data-based platform business model innovation in the healthcare sector.

Purpose: Covid has increased the usage of multisided digital platforms. For companies, this has become a business opportunity. Data usage on platforms requires that platform companies co-create services for common customers. In this case, the target is not to make the same value proposition but rather to use the resources such as data, knowledge, technology, or institutions in a complementary manner. Platforms are characterized as a combination of hardware and software including standards, interfaces, and rules making it possible for different ecosystem players to complement and interact in the ecosystem. Current ecosystems include several platforms that do not work without resource integration. The purpose of this study is to increase understanding what do we mean by resource complementarity in service ecosystems.Design/methodology/approach: This study was done via an in-depth qualitative case study in which a health service ecosystem co-creating technological surgery innovation was used as a unit of analysis.Findings: The authors’ findings suggest that key resource capabilities, to enable complementarity in service ecosystems, are motivation, knowledge, skills, data and complementary designed technology components.Research limitations/implications: The authors’ study increases theoretical understanding of what does one mean by construct of resource complementarity.Practical implications: From a managerial perspective, it is shown that organizations need to develop adaptive capabilities to utilize internal and external competencies and resources and enable co-creative processes within the service ecosystem.Originality/value: Very few empirical studies in the marketing literature have focused on multi-sided digital platforms and their resource complementarity in the data-driven healthcare ecosystem settings.