市场导向与组织绩效的关系--环境与组织学习的影响

国外学者对市场导向是否影响组织绩效进行了大量的实证研究,至今没有定论,许多学者仍非常关注这个问题.改革开放以来,我国许多企业开始引入市场导向的管理观念,然而这种观念是否影响以及如何影响企业的绩效?国内外学术界尚未发表以中国大陆数据为基础的实证结果.本文以我国华南地区企业为调查对象,对市场导向是否以及如何通过组织学习影响组织的绩效,以及环境在其中的作用进行实证研究.结果表明,环境变动越快,企业的市场导向程度越高,企业的市场导向对组织绩效没有显著直接影响,但是市场导向却可以通过组织学习对组织绩效产生显著影响.     

效忠主管与员工工作绩效的关系:反馈寻求行为和权力距离的影响

效忠主管一直是华人组织经久不衰的议题,尽管已有文献认为员工效忠主管有助于提升其工作绩效,然而关于效忠主管通过怎样的具体机制来影响员工工作绩效却并未得到系统的阐释。本文以14家企业的360名员工为样本,探讨了效忠主管对工作绩效的影响机制,尤其是研究员工反馈寻求行为在其中的中介作用及员工权力距离的调节作用。分析结果表明,效忠主管对员工工作绩效、反馈寻求行为都有显著的正向影响;反馈寻求行为在效忠主管与工作绩效关系中起完全中介作用。另外,员工的权力距离负向调节了效忠主管与反馈寻求行为的正相关关系,即员工的权力距离越低,两者的正向相关关系越强。     

南京国民政府时期民事诉讼法制变革原因之探析

南京国民政府依靠军事力量取得全国统治权,政治的统一为民事诉讼法制的变革提供了前提和保证。政治统一又推动着国家社会经济的发展,这是民事诉讼法制变革的现实基础。而随着不同历史阶段社会矛盾的转变,南京国民政府的民事诉讼法制建设也受到一定程度的影响,推动或是抑制了民事诉讼法制变革与发展的进程。     

2017年轮国际比较项目住房服务比较方法及其对中国的影响

住房服务比较一直是国际比较项目(ICP)中的一个难点领域,即将结束的2017年轮ICP在实践中也十分关注住房领域,并做出了系列改进。本文主要梳理现阶段区域住房服务比较中的3种方法和亚洲开发银行创建的新方法的基本原理、优缺点及适用范围,以及介绍全球住房服务比较中的区域链接方法、剖析方法存在的问题;同时,本文进一步评估了亚太区域比较方法和区域链接方法对中国的可能影响。研究发现,现阶段的区域住房服务比较方法和区域链接方法都存在一定的不足,亚太区域比较方法以及ICP区域链接方法都存在低估中国住房服务购买力平价的可能。本文提出,在中国可以提供准确的租金数据背景下,为避免亚太区域因整体数据质量较差而被迫选择较差的方法所带来的不利影响,中国应在亚太区域尝试寻求比较方式的转变,以提高自身比较结果的准确性。     

海外上市地点和企业投资:纳斯达克、香港、新加坡上市企业比较

本文研究海外上市地点与企业投资之间的关系。香港和新加坡在信息环境和制度环境方面相差不大,而纳斯达克在这两个方面均存在显著优势。良好的信息环境和制度环境可以减弱信息不对称的程度,缓解企业面临的代理问题,从而减少企业投资不足或者投资过度的行为。与此相一致,我们发现相比在香港和新加坡上市而言,在纳斯达克上市的企业其投资—现金流敏感性更低,规模小、上市时间短、应计项高、杠杆率低的企业受影响更大。进一步研究发现,在纳斯达克上市既可以缓解企业的投资不足,也可以降低企业的投资过度,这表明纳斯达克信息环境优势和制度环境优势共同作用提高了企业的投资效率。     

The spokane heart study: weibull regression and coronary artery disease

Coronary artery calcium is a marker of coronary artery disease and measures the progression of atherosclerosis. It is measured by electron beam computed tomography, and the measured amount of coronary artery calcium is highly skewed to the right and left censored. The distribution of coronary artery calcium appears to be Weibull. We propose a Weibull regression model and we analyze the data using these techniques. Our analysis is based on data from the Spokane Heart Study, which is a cohort of about a thousand subjects that are assessed every two years for coronary artery calcium and risk factors of coronary artery disease. The major focus of the heart study is to determine the natural history of atherosclerosis in its early phase, and we analyze the data as a cross-sectional study with 859 subjects. We would also like to highlight the use of Weibull regression techniques in situations like this, where we have extreme right skewed data. Our main emphasis will be on examining the effect of the traditional risk factors of age, gender, lipid profile (cholesterol and HDL), patient history of lipid abnormality, hypertension, and smoking, and other family history risks on coronary artery calcium. We found that the most important factors influencing the disease were age, sex, and patient history of smoking and lipid abnormality.     

Quasi-Likelihood Approach to Bioavailability and Bioequivalence Analysis

The outcomes AUCT (area-under-curve from time zero to time t) of n individuals randomized to one of two groups TR or RT, where the group name denotes the order in which the subjects receive a test formulation (T) or a reference formulation (R), are used to assess average bioequivalence for the two formulations. The classical method is the mixed model, for example, proc mixed or proc glm with random statement in SAS can be used to analyze this type of data. This is equivalent to the marginal likelihood approach in a normal–normal model. There are some limitations for this approach. It is not appropriate if the random effect is not normally distributed. In this article, we introduce a hierarchical quasi-likelihood approach. Instead of assuming the random effect is normal, we make assumptions only about the mean and the variance function of the random effect. Our method is flexible to model the random effect. Since we can estimate the random effect for each individual, we can check the adequacy of the distribution assumption about the random effect. This method can also be used to handle high-dimensional crossover data. Simulation studies are conducted to check the finite sample performance of the method under various conditions and two real data examples are used for illustration.     

Focused Information Criterion and Model Averaging in Quantile Regression

In this article, we study model selection and model averaging in quantile regression. Under general conditions, we develop a focused information criterion and a frequentist model average estimator for the parameters in quantile regression model, and examine their theoretical properties. The new procedures provide a robust alternative to the least squares method or likelihood method, and a major advantage of the proposed procedures is that when the variance of random error is infinite, the proposed procedure works beautifully while the least squares method breaks down. A simulation study and a real data example are presented to show that the proposed method performs well with a finite sample and is easy to use in practice.     

An Exponential-Gamma Convolution Model for Background Correction of Illumina BeadArray Data

Illumina BeadArrays are becoming an increasingly popular Microarray platform due to its high data quality and relatively low cost. One distinct feature of Illumina BeadArrays is that each array has thousands of negative control bead types containing oligonucleotide sequences that are not specific to any target genes in the genome. This design provides a way of directly estimating the distribution of the background noise. In the literature of background correction for BeadArray data, the information from negative control beads is either ignored, used in a naive way that can lead to a loss in efficiency, or the noise is assumed to be normally distributed. However, we show with real data that the noise can be skewed. In this study, we propose an exponential-gamma convolution model for background correction of Illumina BeadArray data. Using both simulated and real data examples, we show that the proposed method can improve the signal estimation and detection of differentially expressed genes when the signal to noise ratio is large and the noise has a skewed distribution.