Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo

Dose‐response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose‐response model parameters are estimated using limited epidemiological data is rarely quantified. Second‐order risk characterization approaches incorporating uncertainty in dose‐response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta‐Poisson dose‐response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta‐Poisson dose‐response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta‐Poisson dose‐response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta‐Poisson model are proposed, and simple algorithms to evaluate actual beta‐Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta‐Poisson dose‐response model parameters is attributable to the absence of low‐dose data. This region includes beta‐Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.     

Liability of foreignness as a barrier to knowledge spillovers: Lost in translation?

Multinational corporations (MNC) search increasingly for lead market knowledge and technological expertise around the globe. We investigate whether their subsidiaries gain access to these valuable sources of host country knowledge to the same degree as domestic rivals. We develop a theoretical framework for “why” and “how” a lack of embeddedness and legitimacy (liability of foreignness) may translate into additional obstacles for foreign subsidiaries. We test these hypotheses empirically using a broad dataset of more than 1100 firms in Germany. We find that MNCs can compete on an equal footing with host country competitors when it comes to generating impulses for innovations from universities. They are significantly challenged by liabilities of foreignness, though, when host country customers are involved. The disadvantages are especially pronounced when the host country industry is at the technological forefront. We suggest that the disadvantages arising from liability of foreignness in the host country are especially relevant when promising lead customers have to be identified and their tacit and often unarticulated impulses have to be transferred, understood and prioritized. Management recommendations are developed on the basis of these results.