Job Satisfaction and Its Relationships with Age, Gender and Educational Background in a Vietnamese Context : A thesis presented in partial fulfilment of the requirement for the degree of Master of Business Studies (Management) at Massey University, Manawatu New Zealand

The present study aims at examining the reliability and validity of a Vietnamese version of the Job Satisfaction Survey (JSS) which was developed by Spector (1997). It also reveals the current overall job satisfaction and investigates the relationship between job satisfaction and age, gender, and educational background among a specific community, the auditors and ex-auditors in Vietnam. With these goals, a quantitative cross-sectional design has been employed for the research. A pilot study with 68 Vietnamese respondents establishes a solid foundation for the final Vietnamese-translated version of the JSS. In the main study, a sample of 202 Vietnamese auditors and ex-auditors is recruited. The JSS in Vietnamese demonstrates a high internal consistency with the Cronbach’s alpha coefficient of α = .91. Moreover, an exploratory factor analysis reports an underlying construct of nine dimensions, which is similar to the original version of the JSS. The convergent and divergent validity of the scale are also analysed and return satisfactory results. The present research suggests that the auditors and ex-auditors in Vietnam are generally satisfied with their jobs and, surprisingly, the auditors are reported to be happier than their ex-colleagues in every job aspect. There is no relationship found between the overall job satisfaction and age or gender for this specific community, while a significant correlation between job satisfaction and educational background is confirmed. However, the women of this community are reported to be more likely to experience a lower level of job satisfaction when they get older or when they have a better educational background. The present study provides audit companies in Vietnam with recommendations for improving the job satisfaction of their employees. Its findings suggest that these firms should pay more attention to their older female employees as well as the ones with higher educational backgrounds due to their vulnerability to a lower level of job satisfaction than the opposite gender. Furthermore, directions and indications for future research are also offered in the present dissertation

Poverty Targeting and Impact of a Governmental Micro-credit Program in Vietnam

It is argued that without collateral the poor often face binding borrowing constraints in the formal credit market. This justifies a micro-credit program, which is operated by the Vietnam Bank for Social Policies to provide the poor with preferential credit. This paper examines poverty targeting and impact of the micro-credit program. It is found that the program is not very pro-poor in terms of targeting. Among the participants, the non-poor account for a larger proportion of loans. The non-poor also tend to receive larger amounts of credit compared to the poor. However, the program has positive impact on poverty reduction of the participants. This positive impact is found for all the three Foster-Greer-Thorbecke poverty measures.Micro-credit, poverty, poverty targeting, impact evaluation, instrumental variables, fixed-effect model

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

New Algorithms in Computational Microscopy

Microscopy plays an important role in providing tools to microscopically observe objects and their surrounding areas with much higher resolution ranging from the scale between molecular machineries (angstrom) and individual cells (micrometer). Under microscopes, illumination, such as visible light and electron-magnetic radiation/electron beam, interacts with samples, then they are scattered to a plane and are recorded. Computational microscopy corresponds to image reconstruction from these measurements as well as improving quality of the images. Along with the evolution of microscopy, new studies are discovered and algorithms need development not only to provide high-resolution imaging but also to decipher new and advanced research. In this dissertation, we focus on algorithm development for inverse problems in microscopy, specifically phase retrieval and tomography, and the application of these techniques to machine learning. The four studies in this dissertation demonstrates the use of optimization and calculus of variation in imaging science and other different disciplines.Study 1 focuses on coherent diffractive imaging (CDI) or phase retrieval, a non-linear inverse problem that aims to recover 2D image from it Fourier transforms in modulus taking into account that extra information provided by oversampling as a second constraint. To solve this two-constraint minimization, we proceed from Hamilton-Jacobi partial differential equation (HJ-PDE) and its Hopf-Lax formula. Introducing generalized Bregman distance to the HJ-PDE and applying Legendre transform, we derive our generalized proximal smoothing (GPS) algorithm under the form of primal-dual hybrid gradient (PDHG). While the reflection operator, known as extrapolating momentum, helps overcome local minima, the smoothing by the generalized Bregman distance is adjusted to improve convergence and consistency of phase retrieval.Study 2 focuses on electron tomography, 3D image reconstruction from a set of 2D projections obtained from a transmission electron microscope (TEM) or X-ray microscope. Notice that current tomography algorithms limit to a single tilt axis and fail to work with fully or partially missing data. In the light of calculus of variations and Fourier slice theorem (FST), we develop a highly accurate tomography iterative algorithm that can provide higher resolution imaging and work with missing data as well as has capability to perform multiple-tilt-axis tomography. The algorithm is further developed to work with non-isolated objects and partially-blocked projections which have become more popular in experiment. The success of real space iterative reconstruction engine (RESIRE) opens a new era to the study of tomography in material science and magnetic structures (vector Tomography).Study 3 and 4 are applications of our algorithms to machine learning. Study 3 develops a backward Euler method in a stochastic manner to solve K-mean clustering, a well-known non-convex optimization problem. The algorithm has been shown to improve minimums and consistency, providing a new powerful tool to the class of classification techniques. Study 4 is a direct application of GPS to deep learning gradient descent algorithms. Linearizing the Hopf-Lax formula derived in GPS, we derive our method Laplacian smoothing gradient descent (LSGD), simply known as gradient smoothing. Our experiment shows that LSGD has the ability to search for better and flatter minimums, reduce variation, and obtain higher accuracy and consistency