Trust, Organizational Controls, Knowledge Acquisition from the Foreign Parents, and Performance in Vietnamese International Joint Ventures

Successful adaptation in strategic alliances "calls for a delicate balance between the twin virtues of reliability and flexibility" [Parkhe 1998]. On one hand, the joint venture must be flexible enough to respond to the uncertainties of competitive business environments because it is not feasible to plan for every possible contingency. Yet, on the other hand, unfettered flexibility invites dysfunctional behavior, such as opportunism and complacency. This delicate balance accompanies a parallel balance between trust and control of the joint venture. The primary goal of this study is to empirically examine this relationship in the context of Vietnamese international joint ventures (IJVs) by building on the model of knowledge acquisition and performance in IJVs established by Lyles and Salk [1996]. This study makes three major contributions to the literature. First it confirms several findings of the original Lyles and Salk study [1996]. Second, we strengthen Lyles and Salk's original model by incorporating multiple measures of both interorganizational trust and control as independent variables. Finally, this study represents one of the first in-depth examinations of business in the emerging Vietnamese economy.http://deepblue.lib.umich.edu/bitstream/2027.42/39713/3/wp329.pd

Project knowledge into project practice: generational issues in the knowledge management process

This paper considers Learning and Knowledge Transfer within the project domain. Knowledge can be a tenuous and elusive concept, and is challenging to transfer within organizations and projects. This challenge is compounded when we consider generational differences in the project and the workplace. This paper looks at learning, and the transfer of that generated knowledge. A number of tools and frameworks have been considered, together with accumulated extant literature. These issues have been deliberated through the lens of different generational types, focusing on the issues and differences in knowledge engagement and absorption between Baby Boomers, Generation X, and Generation Y/Millennials. Generation Z/Centennials have also been included where appropriate. This is a significant issue in modern project and organizational structures. Some recommendations are offered to assist in effective knowledge transfer across generational types.Accepted manuscrip

Specialization and Variety in Repetitive Tasks: Evidence from a Japanese Bank

Sustaining operational productivity in the completion of repetitive tasks is critical to many organizations' success. Yet research points to two different work-design related strategies for accomplishing this goal: specialization to capture the benefits of repetition or variety to keep workers motivated and allow them to learn. In this paper, we investigate how these two strategies may bring different benefits within the same day and across days. Additionally, we examine the impact of these strategies on both worker productivity and workers' likelihood of staying at a firm. For our empirical analyses, we use two and a half years of transaction data from a Japanese bank's home loan application processing line. We find that over the course of a single day, specialization, as compared to variety, is related to improved worker productivity. However, when we examine workers' experience across days we find that variety, or working on different tasks, helps improve worker productivity. We also find that workers with higher variety are more likely to stay at the firm. Our results identify new ways to improve operational performance through the effective allocation of work.Job Design, Learning, Productivity, Specialization, Turnover, Variety, Work Fragmentation

Trust, Organizational Controls, Knowledge Acquisition from the Foreign Parents, and Performance in Vietnamese International Joint Ventures

Successful adaptation in strategic alliances "calls for a delicate balance between the twin virtues of reliability and flexibility" [Parkhe 1998]. On one hand, the joint venture must be flexible enough to respond to the uncertainties of competitive business environments because it is not feasible to plan for every possible contingency. Yet, on the other hand, unfettered flexibility invites dysfunctional behavior, such as opportunism and complacency. This delicate balance accompanies a parallel balance between trust and control of the joint venture. The primary goal of this study is to empirically examine this relationship in the context of Vietnamese international joint ventures (IJVs) by building on the model of knowledge acquisition and performance in IJVs established by Lyles and Salk [1996]. This study makes three major contributions to the literature. First it confirms several findings of the original Lyles and Salk study [1996]. Second, we strengthen Lyles and Salk's original model by incorporating multiple measures of both interorganizational trust and control as independent variables. Finally, this study represents one of the first in-depth examinations of business in the emerging Vietnamese economy.

Multi-Task Learning For Option Pricing

Multi-task learning is a process used to learn domain-specific bias. It consists in simultaneously training models on different tasks derived from the same domain and forcing them to exchange domain information. This transfer of knowledge is performed by imposing constraints on the parameters defining the models and can lead to improved generalization performance. In this paper, we explore a particular multi-task learning method that forces the parameters of the models to lie on an affine manifold defined in parameter space and embedding domain information. We apply this method to the prediction of the prices of call options on the S&P index for a period of time ranging from 1987 to 1993. An analysis of variance of the results is presented that shows significant improvements of the generalization performance. L'apprentissage multi-tâches est une manière d'apprendre des particularités d'un domaine (le biais) qui comprend plusieurs tâches possibles. On entraîne simultanément plusieurs modèles, un par tâche, en imposant des contraintes sur les paramètres de manière à capturer ce qui est en commun entre les tâches, afin d'obtenir une meilleure généralisation sur chaque tâche, et pour pouvoir rapidement généraliser (avec peu d'exemples) sur une nouvelle tâche provenant du même domaine. Ici cette commonalité est définie par une variété affine dans l'espace des paramètres. Dans cet article, nous appliquons ces méthodes à la prédiction du prix d'options d'achat de l'indice S&P 500 entre 1987 et 1993. Une analyse de la variance des résultats est présentée, démontrant des améliorations significatives de la prédiction hors-échantillon.option call pricing, multi-task learning, artificial neural networks, valorisation d'options d'achat, apprentissage multi-tâches, réseau de neurones artificiels

Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances

In today's digital world, we are confronted with an explosion of data and models produced and manipulated by numerous large-scale IoT/cloud-based applications. Under such settings, existing transfer evolutionary optimization frameworks grapple with satisfying two important quality attributes, namely scalability against a growing number of source tasks and online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to big source instances as well as simultaneously curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this paper, we take a quantum leap forward in enabling two orders of magnitude scale-up in the number of tasks; i.e., we efficiently handle scenarios with up to thousands of source problem instances. We devise a novel transfer evolutionary optimization framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source problem instances, of which only a small fraction show source-target relatedness. The experimental results strongly validate the efficacy of our proposed framework with two salient features of scalability and online learning agility.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm pseudocode