Are the digital platforms that we know here to stay? Both empirical insights and theoretical works suggest digital platform stability. Digital platforms such as Airbnb, Netflix and Taobao have experienced tremendous success, and the theoretical works on modularity and multisided markets depict competitive platform landscapes as controlled by a hegemonic platform leader. However, platform history chronicles multiple cases of leadership shifts in platform ecosystems. In this paper, we coin these situations ‘platform overthrows’ and uncover the relevant strategies for both a challenger to conduct a platform overthrow and for a platform leader to resist it. To do so, we conducted an inductive protocol to reinterpret 27 already published cases of platform overthrow attempts. Our results suggest that, during a platform overthrow attempt, both players articulate functional expansion and technical genericity. Accordingly, we formulate propositions that account for the strategies at play during a platform overthrow attempt. We discuss these results regarding the digital platform literature on competition and conclude that the study of platform overthrow can yield useful insights on the future of digital platforms.

The boreal forest is subject to various anthropogenic disturbances, including logging, mining, and hydroelectricity production and transport. These disturbances affect Indigenous communities and the culturally salient species they depend on for the practice of traditional and subsistence activities. <i>Rhododendron groenlandicum</i> is one such species whose leaves are used to treat various ailments, due to their concentration in biologically active chemicals such as flavonoids. Our objective was to assess the effect of anthropogenic disturbances on the chemical properties of <i>R. groenlandicum</i> on the territories of three Indigenous communities. Leaf samples were collected near mines, under hydroelectric power lines, and in non-disturbed sites. Our results showed that variations in flavonoid concentration were mainly related to territory (R² = 0.43, P = 0.0005), while disturbance type had a smaller effect (R² = 0.18, P = 0.02). Samples from Nemaska, the northernmost territory with the most open forest stands, had higher concentrations of epicatechin (+23%, P = 0.03). Quercetin-3-glucoside concentrations were lower near mines (-19%, P = 0.01). The effects of disturbances on the chemical signature of <i>R. groenlandicum</i> are complex, and a complete assessment of the consequence of industrial activity on Indigenous landscape value must take into account other culturally salient species.

Generative design (GD) algorithms is a fast growing field. From the point of view of Design Science, this fast growth leads to wonder what exactly is 'generated' by GD algorithms and how? In the last decades, advances in design theory enabled to establish conditions and operators that characterize design generativity. Thus, it is now possible to study GD algorithms with the lenses of Design Science in order to reach a deeper and unified understanding of their generative techniques, their differences and, if possible, find new paths for improving their generativity.In this paper, first, we rely on C-K ttheory to build a canonical model of GD, based independent of the field of application of the algorithm. This model shows that GD is generative if and only if it builds, not one single artefact, but a “topology of artefacts” that allows for design constructability, covering strategies, and functional comparability of designs. Second, we use the canonical model to compare four well documented and most advanced types of GD algorithms. From these cases, it appears that generating a topology enables the analyses of interdependences and the design of resilience.

Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.