Designing coopetition for radical innovation: An experimental study of managers' preferences for developing self-driving electric cars

The major premise of this study is that managers purposefully shape the business context for radical innovation. Particularly, the strategic option of developing radical innovation in collaboration with direct competitors offers opportunities otherwise unattainable. We tap into its cognitive underpinnings by running an experimental study of coopetition design for radical innovation. We have collected 5760 binary decisions from a sample of 160 managers. Their indications are used to run a choice-based conjoint analysis in order to identify utilities attributed to coopetition-shaping decisions in a radical innovation project (using a scenario of self-driving/electric cars produced by VW, Daimler or Tesla). We use Hierarchical Bayes Multinomial Logit Regression to test a set of four hypotheses, each addressing a different coopetition factor to unveil manager's preferences in coopetition design for radical innovation. Our findings pinpoint a clear preference for network coopetition, using formal governance, and being based on intensive knowledge sharing. Contrary to prior literature, market uncertainty does not appear to significantly influence coopetition design for radical innovation

On the Robustness of Quality Measures for GANs

This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fr\'echet Inception Distance (FID). Analogous to the vulnerability of deep models against a variety of adversarial attacks, we show that such metrics can also be manipulated by additive pixel perturbations. Our experiments indicate that one can generate a distribution of images with very high scores but low perceptual quality. Conversely, one can optimize for small imperceptible perturbations that, when added to real world images, deteriorate their scores. We further extend our evaluation to generative models themselves, including the state of the art network StyleGANv2. We show the vulnerability of both the generative model and the FID against additive perturbations in the latent space. Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception. We validate the effectiveness of the robustified metric through extensive experiments, showing it is more robust against manipulation.Comment: Accepted at the European Conference in Computer Vision (ECCV 2022

Japanese source (Let's have the proper number of children and raise them well!): Family planning and nation-building in South Korea, 1961-1968

10.1007/s12280-008-9054-5East Asian Science, Technology and Society23361-37