The mediation between participative leadership and employee exploratory innovation: Examining intermediate knowledge mechanisms

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.We examine mediation effects of coworker knowledge sharing and absorptive capacity on the participative leadership–employee exploratory innovation relationship in R&D units of Taiwanese technology firms. Deploying a time-lagged questionnaire method implemented over four business quarters, data is generated from 1600 paired samples (managers and employees) in R&D units of Taiwanese technology firms. The structural equation modeling results reveal that (1) participative leadership is positively related to employee exploratory innovation; (2) coworker knowledge and (3) absorptive capacity partially mediate the relationship between participative leadership and employee exploratory innovation independently; and, (4) coworker knowledge sharing in combination with absorptive capacity partially mediates this relationship. The results extend previous research on participative leadership and innovation by demonstrating that participative leadership is related to employee exploratory innovation (Lee and Meyer-Doyle, 2017; Mom et al., 2009).Results also confirm that participative leadership drives employee exploratory innovation through employee absorptive capacity. This reinforces the need highlighted by Lane et al. (2006) to investigate the role of absorptive capacity at the individual-level. Collectively, while participative leadership is important for employee exploratory innovation it is the knowledge mechanisms existing and interacting at the employee-level that are central to generating increased employee exploratory innovation from this leadership approach

Berry Phase Effects on Electronic Properties

Ever since its discovery, the Berry phase has permeated through all branches of physics. Over the last three decades, it was gradually realized that the Berry phase of the electronic wave function can have a profound effect on material properties and is responsible for a spectrum of phenomena, such as ferroelectricity, orbital magnetism, various (quantum/anomalous/spin) Hall effects, and quantum charge pumping. This progress is summarized in a pedagogical manner in this review. We start with a brief summary of necessary background, followed by a detailed discussion of the Berry phase effect in a variety of solid state applications. A common thread of the review is the semiclassical formulation of electron dynamics, which is a versatile tool in the study of electron dynamics in the presence of electromagnetic fields and more general perturbations. Finally, we demonstrate a re-quantization method that converts a semiclassical theory to an effective quantum theory. It is clear that the Berry phase should be added as a basic ingredient to our understanding of basic material properties.Comment: 48 pages, 16 figures, submitted to RM

Data-driven Estimation of the Power Grid Inertia with Increased Levels of Renewable Generation Resources

The thesis investigates methods for estimating inertia in systems at different levels of renewable energy penetrations. Estimating renewable generators\u27 inertia is challenging because their structures differ from traditional generators. Moreover, the power generated from renewable energy resources is not stable, depending on weather conditions. When a power grid has a disturbance, photovoltaic inverter control influences a power grid inertia by different controllers, such as power factor and reactive power control, to bring a power grid back to a steady state. The changing reactive power impacts the frequency, which strongly relates to inertia and increases the inertia estimation problem. Several papers proposed different approaches to estimating renewable generators\u27 inertia. The two main categories of estimating inertia are model-based and measurement-based methods. The model-based methods mimic an actual renewable generator behavior to calculate inertia. It is a complicated model specialized for specific renewable devices, but unlike the measurement-based methods, it can estimate the inertia in the steady state. The measurement-based methods find the patterns in measured data and use classification or regression functions to calculate inertia. A measurement model can monitor a power grid in real time. However, the method needs parameter oscillation, representing power imbalance in a power grid. This thesis proposes three measurement-based models to estimate inertia for systems under levels of photovoltaic systems: Symbolic Aggregate Approximation, Back Propagation Neural Network, and Minimum Volume Enclosing with a Gradient Descent Machine Model. The measurement-based inertia estimation models need large-scale system measurement data. PowerWorld Simulator has a function to analyze the transient stability, which is utilized in this thesis to generate simulated data for this. Reducing photovoltaic output power can mimic the impact of weather changes. Different types of photovoltaic controllers have various behavior. The Symbolic Aggregate Approximation transfers continuous data into discrete data. The advantage of this method over other techniques is its ability to compress large-scale data and the reduced data storage requirements. Hence, the model demonstrates the best performance for estimating the inertia. The Minimum Volume Enclosing Ellipsoid visualizes measurement data, including frequency, generator output power, and bus voltage, on a 3-dimensional space. The volume of the enclosed ellipsoid is the output that yields label inertia. During a fault in a power system, the volume of the ellipsoid increases. The Gradient Descent Model estimates an optimal regression curve to match volume with label inertia as the estimated inertia. The Back Propagation Neural Network is a nonlinear classification method. With multiple layers and neurons, this method can efficiently cluster complex input features, such as the frequency of all buses and generator output power. The error between the estimated inertia and the label inertia is used to modify the branches\u27 weight to reduce error. The disadvantage of the second and third models is that they do not have a better performance than the first one

Statistical Characterization and Prediction for a Stochastic Sea Environment

Designing marine and maritime systems requires the probabilistic characterization of sea waves in the time-history and spectral domains. These probabilistic models include parameters that can be empirically estimated based on limited data in durations, locations and applicability to particular designs. Characterizing the statistical uncertainties associated with the parameters and the models is an essential step for risk-based design methods. A framework is provided for characterizing and predicting the stochastic sea-state conditions using sampling and statistical methods in order to associate confidence levels with resulting estimates. Sea-state parameters are analyzed using statistical confidence intervals which give a clear insight for the uncertainties involved in the system. Hypothesis testing and goodness-of-fit are performed to demonstrate the statistical features. Moreover, sample size is required for performing statistical analysis. Sample size indicates the number of representative and independent observations. Current practices do not make a distinction between the number of discretization points for numerical computations and the number of sampling points, i.e. sample size needed for statistical analysis. Sample size and interval between samples to obtain independent observations are studied and compared with existing methods. Further, spatial relationship of the sea-state conditions describes the wave energy transferred through the wave movement. Locations of interest with unknown sea-state conditions are estimated using spatial interpolations. Spatial interpolation methods are proposed, discussed, and compared with the reported methods in the literature. This study will enhance the knowledge of sea-state conditions in a quantitative manner. The statistical feature of the proposed framework is essential for designing future marine and maritime systems using probabilistic modeling and risk analysis