Internet of things (IoT) technology must provide users with benefits that outweigh the costs and risks for it to be widely accepted and adopted. New functionality is continuously added, and costs decrease with the increasing volumes and technological advances; however, the risks also increase as technology becomes increasingly integrated into human lives. This survey study of 251 respondents investigated how users and different user groups perceive IoT security and privacy in the home domain, their trust in the involved parties, and how different types of measures, as suggested by the literature, can affect user adoption. The results indicate that people are particularly concerned about security and privacy and are fearful of breaches and misuse. A large majority of the people do not believe the products to be secure. Less than a third of the users trust manufacturers, service providers, and governments to uphold their privacy; this remarkably hinders IoT adoption.

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.

Purpose: This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field. Design/methodology/approach: The research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI. Findings: This emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection. Originality/value: The proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.

The rapid evolution of labor market demands, driven by technological and societal transformations, has intensified the need for higher education to foster future-oriented competencies. Frameworks such as Education 4.0 emphasize the development of abilities, skills, attitudes, and values; such as problem-solving, critical thinking, adaptability, and curiosity; alongside disciplinary knowledge. However, translating these competencies into course-level practice remains a challenge for educators, especially with regard to course materials and assessment methods. This study addresses this gap by proposing and evaluating a structured, instructor-led method for course redesign aligned with the Education 4.0 framework (E4CAM). Through an iterative research process involving literature reviews, expert workshops, and in-depth interviews, E4CAM integrates self-assessment checklists and targeted action guidelines to support competency integration. E4CAM was evaluated through two workshops and experimental applications involving university instructors. Results indicate E4CAM's practical relevance and adaptability across course levels and disciplines. By enabling instructors to systematically align course content and assessments with Education 4.0 competencies, the proposed approach offers a scalable tool for enhancing pedagogical practices and advancing competency-based education in higher education.