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This article focused on two main topics currently on among many agendas: smart cities and Artificial Intelligence (AI). The growing interest in the former is due to these cities’ multidimensionality and adaptability in terms of residents’ needs and the requirements of each municipality's reality. AI, in turn, currently plays a transformative, disruptive role in various areas by performing “smart” tasks, thereby facilitating the automation of processes and differentiation initiatives. Smart city strategic planning, especially in the long term, will most likely have to deal with adaptations to AI. Thus, research is needed that can contribute to a more holistic view of these topics and support decision-making processes in these areas. Based on the epistemological principles of the multiple-criteria decision analysis approach (MCDA), this article developed and tested a dynamic analysis system that allows smart city initiatives to address the challenges of adapting to AI. The proposed system highlights the cause-and-effect relationships in this context. The article included an application of the decision-making trial and evaluation laboratory (DEMATEL) technique. The procedural steps followed to implement this methodology were enhanced by close collaboration with an experienced decision maker, who has coordinated various projects in this research context. The proposed system's contributions and limitations were also analyzed in this article.

Innovation is seen today as an indispensable practice if an organization wishes to achieve competitive advantage and sustainable success in the marketplace. The development of new products and services and new technologies that enable those products and services seem to be the crown jewel of innovation. The management of these technologies, more specifically the process through which technologies are transferred, has been the focus of scholarly debates and studies for several decades. Currently, there are no quantitative, comprehensive, and easily applicable models in the literature that researchers and practitioners can use to assess organizations’ technology transfer capabilities. This article has sought to delineate important factors and perspectives applicable to technology transfer, and to use those in order to assess an organization's capabilities, through the application of a hierarchical decision modeling model to create a technology transfer score. The research model is applied to a utility company in the Pacific Northwest of the United States, and the results provide insights on how to measure and interpret technology transfer capabilities as well as a starting point for technology managers to improve those capabilities.

Fusion energy has the potential to address long-term energy requirements and climate change. However, fusion energy is characterized by a “development divide” between the International Thermonuclear Experimental Reactor (ITER) consortium members and the non-ITER “Global South” states. It is beset with problems, which can be divided into geopolitical, geo-economic, geo-sociocultural, and geo-technological (GEO-PEST). Geopolitical problems include cooperation on fusion energy development between ITER members (of the Global North) and the Global South on fusion development. Geo-economic problems include the cost of funding ITER versus newly emerging private-sector fusion companies. Geo-sociocultural problems include the requirement to maintain the peacebuilding tradition of nuclear energy (embodied by the Cold War-era “Atoms for Peace” initiative), as well as public perceptions of radiation. Geo-technological problems include tokamak lock-in, fuel type, the viability of “compact reactors,” and disruptive technology events. In this article, we outline these problems and discuss how to address them in a timely fashion via an external independent review mechanism, modeled on the International Energy Agency's “Global Commission for Urgent Action on Energy Efficiency,” established in 2019 and due to deliver recommendations in 2020.