Knowledge

Artificial Intelligence, now commonly called AI, is having an increasingly big impact on society. There are fears that may be negatives or downsides, especially when Artificial Intelligence is used unethically. But how are humans guiding these machines to know whether the choice, the decision, is ethical? Since 2007, one way to check the ethicality of any choice has been to apply the JUSTICE model. This framework helps practitioners decide whether a specific action is or is not ethical by looking through one or more of the seven JUSTICE lenses: Justice, Utilitarian, Spiritual Values, TV rule or Transparency, Influence, Core, and Emergency. Now, in this era of increasing prevalence of Artificial Intelligence, with humans making decisions often together with machines, can the JUSTICE framework still be useful? Yes, it can. We look at each of those seven components. Each may give guidance in some situations. Of the seven, it seems that T or the TV test is most likely to give guidance in this new era.

Small- and medium-sized enterprises (SMEs) are increasingly embracing digital transformation (DT) to remain competitive; however, the enabling role of knowledge management (KM) remains underexplored. This systematic literature review investigates how KM supports DT in SMEs, focusing on strategic processes, tools, barriers, and policy contexts. A structured search was conducted in Google Scholar, Scopus, and Web of Science using the string: (“knowledge management” OR “KM”) AND (“digital transformation” OR “DT”) AND (“small and medium enterprises” OR “SME”). The search yielded 32,547 results, from which 19 studies met the eligibility criteria (English, 2020–2025, KM–DT focus, clear methodology). Results indicate that KM supports DT primarily through change management (31.58%), innovation enablement (21.05%), as well as improved decision-making and agility (15.79%). The most cited tools include KM systems, AI/analytics, and collaborative platforms. Major barriers include limited resources, lack of digital skills, and poor KM culture. Critical success factors identified are leadership commitment (26.32%) and strategic alignment (21.05%). Theoretical foundations are dominated by the Resource-Based View and Dynamic Capabilities Theory. While KM is proven to be a strategic driver of DT in SMEs, more empirical and policy-grounded studies are needed. This review provides a framework to guide future research and inform SME practitioners and policymakers.

Almost all societal grand challenges, whether concerning the environment, health, well-being, or the development of sustainable economic models, have at their heart a need to understand people’s behaviour. However, uniting data and insights across disparate fields requires an explicit and shared understanding of concepts, variables, and ideas (e.g., how to characterise and differentiate behaviours). Ontologies provide a mechanism for creating this explicit and shared understanding and are starting to be developed and used in the social and behavioural sciences. This paper proposes an online co-design approach to use and develop ontologies of behaviour to specify the characteristics of behaviour (e.g., habitual, changeable, effortless) and studies that investigate behaviour as part of a project designed to understand how behaviours are related. We report on our experience of collaborative co-development of ontologies using real-time interactive tools and reflect on the benefits and challenges of our approach. We also offer a set of recommendations for researchers interested in applying such methods to co-develop ontologies. The work contributes to efforts to understand the characteristics of behaviour and enable these to be used to understand questions about behaviour (e.g., is poor sleep associated with greater engagement in habitual behaviours?).