Keyword ：

工作意义 工作状态 过剩经济 经济时代 人力资源管理 认知 时代背景 引导 员工激励 自我价值

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Abstract ：

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.

Keyword ：

2-tuple fuzzy linguistic model Analytic hierarchy process (AHP) individual selection numerical scale prioritization method

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Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the ARIMA and TF-epsilon-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.

Keyword ：

chaotic immune algorithm (CIA) electric load forecasting seasonal adjustment support vector regression (SVR)

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Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow. (C) 2011 Elsevier Inc. All rights reserved.

Keyword ：

Back-propagation neural network BPNN Holt-Winters (HW) Hybrid evolutionary algorithms Hybrid genetic algorithm-simulated annealing algorithm (GA-SA) SARIMA Seasonal Holt-Winters (SHW) Support vector regression Traffic flow forecasting

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Despite being widely discussed in the literature, the impact of managerial discretion on firm performance remains inconclusive. In this study it is proposed that market competitiveness plays an important role in determining the influence of perceived managerial discretion on firm performance. In a survey of 169 top managers of Chinese enterprises, it was found that in highly competitive markets, perceived managerial discretion was positively related to firm performance. In contrast, where there is little competition, perceived managerial discretion and firm performance were negatively correlated. These results suggest that companies operating in highly competitive markets should permit managers greater discretion to make strategic choices to react to the dynamics of the environment. Conversely, companies in less competitive markets should limit managerial discretion to prevent managers abusing this power for personal benefits.

Keyword ：

agency theory China firm performance managerial discretion market competition

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This paper discusses chain of command networks that are most likely to exhibit the scale-free (SF) property in organizational networks, explaining why organizational networks do not show SF distributions. We propose an evolving hierarchical tree network model without explicit preferential attachment. The model simulates several kinds of chain of command networks with the span of control ranging from extreme homogeneity to extreme heterogeneity. In addition to traditional degree distribution, a new kind of cumulative-outdegree distribution p(K (cum) =k (cum) ) is introduced and discussed that gives a probability that a randomly selected node has exactly k (cum) children nodes. Theoretical analysis and simulation results show that even if the network size is large enough to meet the demand of large-scale networks, the SF property can emerge only when a hierarchical tree lies in two extreme situations: (1) the exact same span of control exists at all levels of an organization; (2) the node outdegree (i.e. span of control) distribution obeys a power-law distribution. The empirical investigations show that real organization networks are between the two extreme situations. This is why organizational networks in reality do not show an SF degree distribution or SF cumulative-outdegree distribution. This finding shows that the SF property is the consequence of extreme situations, even though it is very common in nature and in society. In fact, the SF property is of no value in the study of problems in organizations.

Keyword ：

Complex networks Cumulative-outdegree distribution Degree distribution Hierarchical organization Scale-free networks

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When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Martinez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale. We find that the key of computational techniques based on linguistic 2-tuples is to set suitable numerical scale with the purpose of making transformations between linguistic 2-tuples and numerical values. By defining the concept of the transitive calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.

Keyword ：

Computing with words (CW) linguistic modeling linguistic variables numerical scale transitive calibration

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利用因子分析法将影响组织变革的指标变量综合简化,甄别主要影响因素;借助于结构方程模型刻画组织变革的影响因素对于组织变革过程以及组织绩效的影响程度,验证了组织变革动力因素中组织内部因素作用大于外部因素的假设,指出充分利用变革的动力因素比极力排斥阻力因素的组织绩效更高,从而形成基于创新的,强调组织知识和结构创新的新创科技企业组织变革模式.

Keyword ：

结构方程模型 因子分析 组织变革

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一、组织学习测度的相关文献综述 (一)基于组织的测度研究 在基于组织的组织学习测度研究中,相关的研究主要集中在三个方面,即以企业或者其下属部门为研究对象的宏观研究、以个人为研究对象的微观研究以及混合了两种方法的中观研究.

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本文是人力资本定价课题的基础研究.文章分析了见诸文献的人力资本分类方法,认为按照人力资本知识技能特征进行分类对人力资本定价或收益确定并无实际操作意义.文章提出,应该在组织环境或背景下,按照人力资本在组织中所扮演的"角色"考察人力资本的分类.在研究了各类组织中人力资本的分布情况后,文章提出组织中的人力资本应分为主持者、支撑者和参与者三个层次(或三种角色).由于不同组织中相同层次的人力资本价值差别可能很大,因此,三个层次的划分只是确定了一个中位概念,各个层次是有一定的扩展范围的,此谓"三层中位扩展分类法".在本分类法下,即可以将人力资本收益与其价值创造统一起来,进行客观、理性的人力资本定价,同时,对组织的收益分配也有一定的指导和参考价值.

Keyword ：

层次 人力资本类型 收益 中位扩展 组织

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