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The ability to create tacit knowledge is important to the competitive advantage of firms in general but is critical to the survival and growth of small and medium‐sized enterprises (SMEs). Consequently, SME strategic orientations that facilitate tacit knowledge creation, especially in hostile environments, are important factors that can enhance SME competitiveness. This paper shows that while an entrepreneurial orientation (EO) and environmental hostility are positively related to an SME's cultivation of tacit knowledge, market orientation (MO) is negatively related to SME's tacit knowledge. Additionally, we find that in benign environments, the relationship between an SME's MO and tacit knowledge becomes more strongly negative than in hostile environments.

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Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce classification errors through tiny, almost imperceptible, perturbations. Vulnerability to such "adversarial attacks", or "adversarial examples", has been conjectured to be due to the excessive linearity of deep networks. In this paper, we study this phenomenon in the setting of a linear classifier, and show that it is possible to exploit sparsity in natural data to combat ∞-bounded adversarial perturbations. Specifically, we demonstrate the efficacy of a sparsifying front end via an ensemble averaged analysis, and experimental results for the MNIST handwritten digit database. To the best of our knowledge, this is the first work to show that sparsity provides a theoretically rigorous framework for defense against adversarial attacks.

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