Organizational, job, and supervisory antecedents and consequence of job embeddedness: the case of Vietnam

A recent major development in the turnover literature is the introduction of the Job Embeddedness (JE) construct. JE is a multidimensional construct conceptualized as the combined forces that tend to keep an employee from leaving his or her job. Research has demonstrated that JE predicts voluntary turnover above and beyond the variables used in traditional turnover models. However, since it is a relatively new construct, JE has received very limited study, especially across cultures. Further research is needed in order to understand both antecedents and consequences of JE. This dissertation, therefore, investigates a range of presumed organizational, job, and supervisory antecedents and consequence of JE in the context of Vietnam. The objectives of the study include (1) examining how human resource practices such as perceived supervisor support, organizational rewards, growth opportunity, training, and organizational justice, impact JE; (2) investigating how job characteristics such as skill variety, task significant, task identity, autonomy, and feedback influence JE; and (3) exploring whether perceived organizational support mediates the relationships between these organizational factors and JE; and (4) testing the relationship between JE and turnover intention in Vietnam. The study used a sample of 304 employees from a state-owned company in Hanoi, Vietnam to test fourteen hypotheses. The results indicated that human resource practices, including organizational rewards, growth opportunities, and procedural justice, and job characteristics, directly influence JE. In addition, perceived organizational support was found to mediate the relationships between organizational rewards and JE and between procedural justice and JE. The results also provided support for a significant and negative relationship between JE and intention to quit. The findings of this study, therefore, contribute to understanding the theoretical network of JE, as well as to helping managers find ways and conditions to retain valuable employees

JOB CHARACTERISTICS, JOB EMBEDDEDNESS, AND TURNOVER INTENTION: THE CASE OF VIETNAM

Job Embeddedness (JE) has recently become an important construct in the voluntary turnover research. However, there are a very limited number of studies on the antecedents of JE as well as studies across cultures. This study, therefore, investigated how job characteristics influence JE and turnover intention in the context of Vietnam. The results indicated that job characteristics not only directly influence JE, but also affect turnover intention via the mediation effect of JE. Implications, limitations of the study, and future research are discussed

Intent Recognition Of Rotation Versus Translation Movements In Human-Robot Collaborative Manipulation Tasks

The goal of this thesis is to enable a robot to actively collaborate with a person to move an object in an efficient, smooth and robust manner. For a robot to actively assist a person it is key that the robot recognizes the actions or phases of a collaborative tasks. This requires the robot to have the ability to estimate a person’s movement intent. A hurdle in collaboratively moving an object is determining whether the partner is trying to rotate or translate the object (the rotation versus translation problem). In this thesis, Hidden Markov Models (HMM) are used to recognize human intent of rotation or translation in real-time. Based on this recognition, an appropriate impedance control mode is selected to assist the person. The approach is tested on a seven degree-of-freedom industrial robot, KUKA LBR iiwa 14 R820, working with a human partner during manipulation tasks. Results show the HMMs can estimate human intent with accuracy of 87.5% by using only haptic data recorded from the robot. Integrated with impedance control, the robot is able to collaborate smoothly and efficiently with a person during the manipulation tasks. The HMMs are compared with a switching function based approach that uses interaction force magnitudes to recognize rotation versus translation. The results show that HMMs can predict correctly when fast rotation or slow translation is desired, whereas the switching function based on force magnitudes performs poorly

Essays on Corporate Finance and Governance

Thesis advisor: Philip E. StrahanIn my first essay, I explain the rise of transferable managerial skills in the CEO market. I show that growing competition in the product markets is a key factor driving the increased importance of CEOs’ transferable managerial skills, specifically industry-transferable skills. To rule out the endogeneity of CEO-firm matching, I exploit the exogenous shocks of the Canada-United States Free Trade Agreement (FTA) of 1989 and the deregulatory policy in the 1990s. I show that CEOs with these skills outperform in competitive markets and are a good match for firms’ innovation-based competition strategy. In my second essay, we explain why firms in the same board-interlock networks tend to have similar corporate governance practices. Specifically, we utilize a novel instrument based on staggered adoptions of universal demand laws across states to identify causal peer effects in firms’ decisions to adopt various governance provisions. We find that a firm’s propensity to adopt these provisions increases after other firms in the same board interlock network choose to adopt similar policies. The impact of universal demand laws on the incentives faced by directors as they seek to maximize their career outcomes is a likely mechanism explaining these effects. In my third essay, I identify the effects of the gender of CEOs’ offspring on corporate performance. First, acquisitions, debt and equity offerings made by CEOs with more daughters are better received by the market. Second, CEOs with more daughters are less likely to overpay the targets, and better use newly raised capital. Third, CEOs’ daughter(s) decrease(s) corporate litigation risk. In sum, the gender of a child is arguably a random and natural experiment, which shows a clear effect on CEOs’ behavior.Thesis (PhD) — Boston College, 2017.Submitted to: Boston College. Carroll School of Management.Discipline: Finance

In silico extension on the antidiabetic potential of Euonymus laxiflorus natural compounds onto the inhibitability against protein tyrosine phosphatase 1B

Euonymus laxiflorus bioactive compounds 1-β-D-glucopyranosyloxy-3,5-dimethoxy-4-hydroxybenzene (1), Walterolactone A/B β-D-pyranoglucoside (2), Gallocatechin (3), Leonuriside A (4), Methyl galloate (5), and Catechin (6) were experimentally evidenced for their multi-inhibition against α-glucosidase and α-amylase. In this work, they were subjected to a combination of computational platforms on tyrosine phosphatase 1B (UniProtKB-PTP1B). As the results, the overall potentiality for bio-inhibitory applications is primarily evaluated by the order: 1 (DSaverage -12.2 kcal.mol-1; polarisability 45.5 Å; no toxicity; ground-state energy -1222.73 a.u.; dipole moment 0.989 Debye) > 2 (DSaverage -9.7 kcal.mol-1; polarisability 39.4 Å; no toxicity; ground-state energy -1070.08 a.u.; dipole moment 6.726 Debye) > 4 (DSaverage -9.1 kcal.mol-1; polarisability 45.1 Å; no toxicity; ground-state energy -1222.73 a.u.; dipole moment 4.895 Debye). Altogether, the retrievals encourage further attempts to test the inhibitory effects of 2 against tyrosine phosphatase 1B and improve the dipole moment of 1 to enhance its biological applicability

Insights into Hydration Dynamics and Cooperative Interactions in Glycerol-Water Mixtures by Terahertz Dielectric Spectroscopy.

We report relaxation dynamics of glycerol-water mixtures as probed by megahertz-to-terahertz dielectric spectroscopy in a frequency range from 50 MHz to 0.5 THz at room temperature. The dielectric relaxation spectra reveal several polarization processes at the molecular level with different time constants and dielectric strengths, providing an understanding of the hydrogen-bonding network in glycerol-water mixtures. We have determined the structure of hydration shells around glycerol molecules and the dynamics of bound water as a function of glycerol concentration in solutions using the Debye relaxation model. The experimental results show the existence of a critical glycerol concentration of ∼7.5 mol %, which is related to the number of water molecules in the hydration layer around a glycerol molecule. At higher glycerol concentrations, water molecules dispersed in a glycerol network become abundant and eventually dominate, and four distinct relaxation processes emerge in the mixtures. The relaxation dynamics and hydration structure in glycerol-water mixtures are further probed with molecular dynamics simulations, which confirm the physical picture revealed by the dielectric spectroscopy

Interfacial Layers between Ion and Water Detected by Terahertz Spectroscopy

Dynamic fluctuations in hydrogen-bond network of water occur from femto- to nano-second timescale and provides insights into structural/dynamical aspects of water at ion-water interfaces. Employing terahertz spectroscopy assisted with molecular dynamics simulations, we study aqueous chloride solutions of five monovalent cations, namely, Li, Na, K, Rb and Cs. We show that ions modify the behavior of surrounding water molecules and form interfacial layers of water around them with physical properties distinct from that of bulk water. Small cations with high charge densities influence the kinetics of water well beyond the first solvation shell. At terahertz frequencies, we observe an emergence of fast relaxation processes of water with their magnitude following the ionic order Cs>Rb>K>Na>Li, revealing an enhanced population density of weakly coordinated water at ion-water interface. The results shed light on the structure breaking tendency of monovalent cations and provide insights into the properties of ionic solutions at the molecular level

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure