The effect of transformational leadership on organizational change: Evidence from Chinese manufacturing firms

Focused on the theme of the influence of transformational leadership on organizational change in Chinese private enterprises, this thesis identifies the paths of factors (transformational leadership, organizational learning, organizational innovative climate and organizational innovation) influencing organizational performance, and adopts survey and case study methods to verify them. Several conclusions are made. First, this thesis further confirms that transformational leadership positively and significantly affects organizational innovation. Second, organizational learning mediates the relationship between transformational leadership and organizational innovation. Organizational innovative climate does not moderate the relationship between organizational learning and organizational learning. Finally, organizational innovation has a positive impact on organizational performance, and it further plays a mediating role in the positive relationship between organizational learning and organizational performance.Focada no tema da influência da liderança transformacional na mudança organizacional em empresas privadas chinesas, esta tese identifica os caminhos dos fatores (liderança transformacional, aprendizagem organizacional, clima organizacional inovador e inovação organizacional) que influenciam o desempenho organizacional e adota métodos de pesquisa e estudo de caso para verificá-los. Várias conclusões são feitas. Em primeiro lugar, esta tese confirma ainda mais que a liderança transformacional afeta positiva e significativamente a inovação organizacional. Em segundo lugar, a aprendizagem organizacional medeia a relação entre liderança transformacional e inovação organizacional. O clima organizacional inovador não modera a relação entre a aprendizagem organizacional e a aprendizagem organizacional. Finalmente, a inovação organizacional tem um impacto positivo no desempenho organizacional e, além disso, desempenha um papel mediador na relação positiva entre aprendizagem organizacional e desempenho organizacional

Valuation and Risk Measurement of Guaranteed Annuity Options under Stochastic Environment

This thesis develops stochastic modelling frameworks for the accurate pricing and risk management of complex insurance products with option-embedded features. We propose stochastic models for the evolution of the two main risk factors, the interest rate and mortality rate, which could also have a correlation structure. For the valuation problem, a general framework is put forward where correlated interest and mortality rates are modelled as affine-diffusion processes. A new concept of endowment-risk-adjusted measure is introduced to facilitate the calculation of the GAO value. As a natural offshoot of addressing GAO valuation, we derive the convex-order upper and lower bounds of GAO values by employing the comonotonicity theory. As an alternative to affine structure, we construct a more flexible modelling framework that incorporate regime-switching dynamics of interest and mortality rates governed by a continuous-time Markov chain. The corresponding endowment-risk-adjusted measures are constructed and employed to obtain more efficient GAO pricing formulae. An extension of the previous modelling set-up is further developed by integrating the affine structure and regime-switching feature. Both interest and mortality risk factors follow correlated affine structure whilst their volatilities are modulated by a Markov chain process. The change of probability measure technique is again utilised to generate pricing expressions capable of significantly cutting down computing times. Finally, the risk management aspect of GAO is investigated by evaluating various risk measurement metrics. The bootstrap technique is used to quantify standard error for the estimates of risk measures under a stochastic modelling framework in which death is the only decrement

Coordination and Privacy Preservation in Multi-Agent Systems

This dissertation considers two key problems in multi-agent systems: coordination (including both synchronization and desynchronization) and privacy preservation. For coordination in multi-agent systems, we focus on synchronization/desynchronization of distributed pulse-coupled oscillator (PCO) networks and their applications in collective motion coordination. Pulse-coupled oscillators were originally proposed to model synchronization in biological systems such as flashing fireflies and firing neurons. In recent years, with proven scalability, simplicity, accuracy, and robustness, the PCO based synchronization strategy has become a powerful clock synchronization primitive for wireless sensor networks. Driven by these increased applications in biological networks and wireless sensor networks, synchronization of pulse-coupled oscillators has gained increased popularity. However, most existing results address the local synchronization of PCOs with initial phases constrained in a half cycle, and results on global synchronization from any initial condition are very sparse. In our work, we address global PCO synchronization from an arbitrary phase distribution under chain or directed tree graphs. More importantly, different from existing global synchronization studies on decentralized PCO networks, our work allows heterogeneous coupling functions and perturbations on PCOs\u27 natural frequencies, and our results hold under any coupling strength between zero and one, which is crucial because a large coupling strength has been shown to be detrimental to the robustness of PCO synchronization to disturbances. Compared with synchronization, desynchronization of PCOs is less explored. Desynchronization spreads the phase variables of all PCOs uniformly apart (with equal difference between neighboring phases). It has also been found in many biological phenomena, such as neuron spiking and fish signaling. Recently, phase desynchronization has been employed to achieve round-robin scheduling, which is crucial in applications as diverse as media access control of communication networks, realization of analog-to-digital converters, and scheduling of traffic flows in intersections. In our work, we systematically characterize pulse-coupled oscillators based decentralized phase desynchronization and propose an interaction function that is more general than existing results. Numerical simulations show that the proposed pulse based interaction function also has better robustness to pulse losses, time delays, and frequency errors than existing results. Collective motion coordination is fundamental in systems as diverse as mobile sensor networks, swarm robotics, autonomous vehicles, and animal groups. Inspired by the close relationship between phase synchronization/desynchronization of PCOs and the heading dynamics of connected vehicles/robots, we propose a pulse-based integrated communication and control approach for collective motion coordination. Our approach only employs simple and identical pulses, which significantly reduces processing latency and communication delay compared with conventional packet based communications. Not only can heading control be achieved in the proposed approach to coordinate the headings (orientations) of motions in a network, but also spacing control for circular motion is achievable to design the spacing between neighboring nodes (e.g., vehicles or robots). The second part of this dissertation is privacy preservation in multi-agent systems. More specifically, we focus on privacy-preserving average consensus as it is key for multi-agent systems, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual nodes (agents) to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In our work, we propose a novel average consensus algorithm for time-varying directed graphs which can protect the privacy of participating nodes\u27 initial states. Leveraging algorithm-level obfuscation, the algorithm does not need the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can guarantee privacy of participating nodes without compromising the accuracy of consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable privacy through compromising accuracy in obtained consensus value. The approach is able to protect the privacy of participating nodes even in the presence of multiple honest-but-curious nodes which can collude with each other