浅谈企业文化六大功能的落地生根

企业文化是企业的灵魂。员工高幸福指数离不开企业文化导向、约束、激励、凝聚、协调、辐射等六大功能。通过文化引领、心理关怀等措施,浓厚人文关怀氛围,增强员工归属感,从而使企业文化之功能落地生根。     

论农业科技园的建立理论基础与功能

文章阐述了建立农业科技园的理论基础,包括农业区位理论、长极理论、创新理论、高新技术改造传统农业理论和国际合作生产理论等,并探讨了按照上述理论建立起来的农业科技园所具有的一般功能。     

《长恨歌》的叙事视角转换及其作用

不同的叙事视角具有不同的功能。在白居易的《长恨歌》中,叙事视角呈现出由第三人称全知叙事逐渐向人物叙事转换的趋势,在这个过程中,由于两种视角功能各不相同,使诗歌的主题出现了由批判讽刺向同情惋惜的转移。而叙事视角转换的推动力,则来自“人”在叙事中的核心地位。     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.     

On Goodness of Fit for Operational Risk

The Advanced Measurement Approach (AMA) to operational risk, as described by the Basel Committee on Banking Supervision ( 2011 ), provides a framework meant to be used by banks for establishing the capital required to be set aside to cover worst‐case operational loss scenarios. The problems raised by an AMA approach are primarily statistical in nature, and many lie at the frontier of statistical research. The aim of this paper is to contribute to one of the more pressing challenges of an AMA, namely that of testing the goodness of fit (GoF) of a distributional family to operational loss data. Our focus is on extending certain classically known tests, such as that of Anderson–Darling, with particular emphasis on the right tails of the distributions. The nature of such GoF tests is examined in detail, and computational efficiency of the procedures is taken into account. We also propose a novel saddlepoint approximation method for assessing the asymptotic null distributions of the test statistics based on the eigenvalues of covariance kernels estimated via a jackknife and influence function‐based approach.