Aaron Antonovsky wrote extensively, although disjointedly, about the roles of culture in salutogenesis. This paper provides a synopsis of his work in this arena. A literature review identified those of his English language writings in which culture was a subject, and relevant text segments were analysed using an inductive followed by a deductive method. Using thematic network analysis, text segments were sorted inductively by open coding and then analysed. This was followed by deductive text segment coding guided by the constructs of the salutogenic model of health. The analysis revealed that Antonovsky had an expansive interest in the roles of culture in salutogenesis. His writings included attention to the role of culture in: (a) shaping life situations; (b) giving rise to stressors and resources; (c) contributing to life experiences of predictability, load balance and meaningful roles; (d) facilitating the development of the sense of coherence and (e) shaping perceptions of health and well-being. Antonovsky’s writings about culture were sometimes conjectural, as well as being obviously influenced by his life experience in the USA and then in Israel, and by the spirit of the times in which he lived. However, he also drew extensively on his own and others’ empiricism, leading him to view culture as an integral aspect of the salutogenic model of health. The present analysis provides salutogenesis scholars with a roadmap of Antonovsky’s reflections, ponderings and conclusions about culture in the context of salutogenesis. It provides assistance in the form of an overview of Antonovsky’s treatment of culture in the context of salutogenesis.

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Many important decisions in daily life are made with the help of advisors, e.g., decisions about medical treatments or financial investments. Whereas in the past, advice has often been received from human experts, friends, or family, advisors based on artificial intelligence (AI) have become more and more present nowadays. Typically, the advice generated by AI is judged by a human and either deemed reliable or rejected. However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI. Therefore, the aspired goal should be to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Specifically, that means that humans should rely on the AI in the presence of correct advice and self-rely when confronted with incorrect advice, i.e., establish appropriate reliance (AR) on AI advice on a case-by-case basis. Current research lacks a metric for AR. This prevents a rigorous evaluation of factors impacting AR and hinders further development of human-AI decision-making. Therefore, based on the literature, we derive a measurement concept of AR. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly. In this article, we derive the measurement concept, illustrate its application and outline potential future research.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; Empirical studies in HCI; • Computing methodologies → Artificial intelligence.

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How do manufacturing small and medium-sized enterprises perceive and realize the potential of emerging technologies for the innovation of smart product-service systems? We address this question by conducting nine expert interviews with representatives in the manufacturing sector. We apply qualitative content analysis to identify current practices, affordances, and constraints in the adoption of technologies to evolve offerings towards smart product-service systems.Building on this inductive empirical approach, we postulate three overarching affordances and four constraints that companies perceive in this process. We conclude by reflecting on applying affordances as our theoretical lens and postulate a multi-level approach to grasp and outline the multi-faceted implications of emerging digital technologies on organizations.

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