Completing the market orientation matrix: The impact of proactive competitor orientation on innovation and firm performance

The concept of market orientation comprises four components: customer and competitor orientations, each with a proactive and responsive dimension. Studies have considered both responsive and proactive customer orientation. Competitor orientation, however, has been investigated more narrowly. Research has focused specifically on its responsive dimension, a firm's posture of quickly responding to its competitors' actions and their offerings; but has largely disregarded proactive competitor orientation, a firm's posture towards altering the market's competitive behavior in its favor. This study investigates the role of responsive and proactive competitor orientation on influencing innovation and firm performance, as well as the mediating effects of technology and learning orientation. Utilizing a unique dataset that combines primary and time-lagged secondary data from 306 firms, we find that both responsive and proactive competitor orientation are observable drivers of performance in the market, but in notably different ways. Proactive competitor orientation drives innovation performance, directly and through technology orientation. Responsive competitor orientation, instead, enhances firm performance through learning orientation. By providing insights about the proactive side of competitor orientation, this study supplements and completes the so called “market orientation matrix”. This framework provides guidance for leaders to develop and manage a practical application of, and future research on market orientation

Langevin Thermostat for Rigid Body Dynamics

We present a new method for isothermal rigid body simulations using the quaternion representation and Langevin dynamics. It can be combined with the traditional Langevin or gradient (Brownian) dynamics for the translational degrees of freedom to correctly sample the NVT distribution in a simulation of rigid molecules. We propose simple, quasi-symplectic second-order numerical integrators and test their performance on the TIP4P model of water. We also investigate the optimal choice of thermostat parameters.Comment: 15 pages, 13 figures, 1 tabl

On the terminal velocity of sedimenting particles in a flowing fluid

The influence of an underlying carrier flow on the terminal velocity of sedimenting particles is investigated both analytically and numerically. Our theoretical framework works for a general class of (laminar or turbulent) velocity fields and, by means of an ordinary perturbation expansion at small Stokes number, leads to closed partial differential equations (PDE) whose solutions contain all relevant information on the sedimentation process. The set of PDE's are solved by means of direct numerical simulations for a class of 2D cellular flows (static and time dependent) and the resulting phenomenology is analysed and discussed.Comment: 13 pages, 2 figures, submitted to JP

Kernel Learning for Explainable Climate Science

The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes from the complex spatial-temporal distribution of precipitation across the basin. In this work we propose Gaussian processes with structured non-stationary kernels to model precipitation patterns in the UIB. Previous attempts to quantify or model precipitation in the Hindu Kush Karakoram Himalayan region have often been qualitative or include crude assumptions and simplifications which cannot be resolved at lower resolutions. This body of research also provides little to no error propagation. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. This allows the posterior function samples to adapt to the varying precipitation patterns inherent in the distinct underlying topography of the Indus region. The input dependent lengthscale is governed by a latent Gaussian process with a stationary squared-exponential kernel to allow the function level hyperparameters to vary smoothly. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint spatio-temporal reconstruction. We benchmark our model with a stationary Gaussian process and a Deep Gaussian processes.Comment: 16th Bayesian Modelling Applications Workshop at UAI, 2022 (Eindhoven, Netherlands

Convergence of the stochastic Euler scheme for locally Lipschitz coefficients

Stochastic differential equations are often simulated with the Monte Carlo Euler method. Convergence of this method is well understood in the case of globally Lipschitz continuous coefficients of the stochastic differential equation. The important case of superlinearly growing coefficients, however, has remained an open question. The main difficulty is that numerically weak convergence fails to hold in many cases of superlinearly growing coefficients. In this paper we overcome this difficulty and establish convergence of the Monte Carlo Euler method for a large class of one-dimensional stochastic differential equations whose drift functions have at most polynomial growth.Comment: Published at http://www.springerlink.com/content/g076w80730811vv3 in the Foundations of Computational Mathematics 201

Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise and non-conservative force

We discuss the ergodic properties of quasi-Markovian stochastic differential equations, providing general conditions that ensure existence and uniqueness of a smooth invariant distribution and exponential convergence of the evolution operator in suitably weighted $L^{\infty}$ spaces, which implies the validity of central limit theorem for the respective solution processes. The main new result is an ergodicity condition for the generalized Langevin equation with configuration-dependent noise and (non-)conservative force

A Comparative Study of Cell Specific Effects of Systemic and Volatile Anesthetics on Identified Motor Neurons and Interneurons of Lymnaea stagnalis (L.), Both in the Isolated Brain and in Single Cell Culture

1. A comparative descriptive analysis of systemic (sodium pentobarbital, sodium thiopentone, ketamine) and volatile (halothane, isoflurane, enflurane) general anesthetics revealed important differences in the neuronal responses of identified motor neurons and interneurons in the isolated central nervous system (CNS) and cultured identified neurons in single cell culture of Lymnaea stagnalis (L.).2. At high enough concentrations all anesthetics eventually caused cessation of spontaneous or evoked action potentials, but volatile anesthetics were much faster acting. Halothane at low concentrations caused excitation, thought to be equivalent to the early excitatory phase of anesthesia. Strong synaptic inputs were not always abolished by pentobarbital.3. There were cell specific concentration-dependent responses to halothane and pentobarbital in terms of membrane potential, action potential characteristics, the after hyperpolarization and patterned activity. Individual neurons generated specific responses to the applied anesthetics.4. The inhalation anesthetics, enflurane, and isoflurane, showed little concentration dependence of effect, in contrast to results obtained with halothane. Enflurane was faster acting than halothane and isoflurane was particularly different, producing quiescence in all cells types studied at all concentrations studied.5. Halothane, enflurane, the barbiturate general anesthetics, pentobarbital, and sodium thiopentone and the dissociative anesthetic ketamine, produced two distinctly different effects which could be correlated with cell type and their location in the isolated brain: either a decline in spontaneous and evoked activity prior to quiescence in interneurons or paroxysmal depolarizing shifts (PDS) in motor neurons, again prior to quiescence, which were reversed when the anesthetic was eliminated from the bath. In the strongly electrically coupled motor neurons, VD1 and RPD2, both types of response were observed, depending on the anesthetic used. Thus, with the exception isoflurane, all the motor neurons subjected to the anesthetic agents studied here were capable of generating PDS in situ, but the interneurons did not do so.6. The effects of halothane on isolated cultured neurons indicates that PDS can be generated by single identified neurons in the absence of synaptic inputs. Further, many instances of PDS in neurons that do not generate it in situ have been found in cultured neurons. The nature of PDS is discussed