资质过剩感知与员工时间侵占行为的关系研究:自我决定感和威权领导的作用

本文以资源保存理论和自我决定理论为切入点,探讨了资质过剩感知与员工时间侵占行为的关系及自我决定感和威权领导在两者之间的作用机制。以402名企事业员工为对象的调查研究显示:资质过剩感知正向影响员工时间侵占行为;自我决定感在资质过剩感知与员工时间侵占行为之间起到了部分中介作用;威权领导调节了资质过剩感知与自我决定感之间的关系,同时调节了资质过剩感知通过自我决定感的降低影响员工时间侵占行为的间接效应。     

股权结构对信息技术产业上市公司绩效的影响研究

选取中国2012-2016年A股信息技术产业不同股权性质上市公司为样本,采用Pearson相关性检验和SPSS多元回归分析方法,探讨股权结构对公司绩效的影响,并提出对策建议.研究发现:信息技术类上市公司的两权分离率对股权结构和公司绩效的关系具有调节效应;股权制衡度与信息技术类国有企业绩效有显著正相关关系;信息技术类民营企业两权不分离时,股权集中度与绩效有正相关关系,两权分离时的股权制衡度与绩效有负相关关系.     

不同行业员工责任绩效比较及其与企业经济绩效的关系研究

本文以随机抽取的食品、石化、金属、地产和电子五个行业共773家上市公司为样本,以样本公司2012年的财务数据为依据,通过运用层次分析、方差分析、相关分析和回归分析等方法,对不同行业员工责任绩效的差异性进行比较,并对不同行业员工责任绩效和企业经济绩效的相关关系进行了实证研究。统计分析结果显示:不同行业之间,员工的责任绩效具有显著差异性;对于不同行业,员工责任绩效与企业经济绩效之间都呈现显著正相关的关系。     

民企家长型领导、组织承诺与组织绩效的关系研究

以广东、广西68家民营企业的159名管理者及员工的调查结果为样本,实证分析了民企家长型领导、组织承诺与组织绩效的关系.研究发现:家长型领导行为中德行领导对组织承诺及组织绩效具有较大程度的影响,仁慈领导能够有效地促进员工组织承诺的提升并改善组织绩效,而威权领导则不利于员工组织承诺的提升.组织承诺在德行领导和仁慈领导与组织绩效的关系中有着积极的中介作用,在威权领导与组织绩效间的关系不明确.     

监察员背景特征对民航安全监管绩效的影响研究

为探索民航安全监察员背景特征对民航安全监管绩效的影响作用.系统分析了衡量民航安全监管绩效的维度,并构建了其与监察员性别、年龄、学历、专业背景、企事业单位工作经历和籍贯6个背景特征之间的假设关系,通过问卷调查的方式收集数据,利用单因素方差检验和独立样本T检验验证了原假设.结果表明:不同背景特征的监察员对民航安全监管绩效的影响存在显著性差异.年龄差异体现在监管效能上,性别差异体现在监管效能和监管成本方面,是否具有民航企事业单位工作经历体现在监管效率上.在民航安全监察效能方面,男性要高于女性,40~50岁的监察员对监管效能的影响最高;在民航安全监管成本控制方面,女性要优于男性;具有民航企事业单位工作经历的监察员的监管效率高于不具有类似工作经历的人员.不同年龄段、学历和籍贯的监察员对监管效率均不存在显著性差别.     

总体报酬与员工创新行为的关系研究:组织自尊和工作投入的作用

本研究基于社会信息加工理论,构建了一个以组织自尊为中介变量、工作投入为调节变量的总体报酬影响员工创新行为的两阶段调节中介模型,并采用时间滞后法获得来自多个行业与地区的140家企业470位非研发人员及其领导的多时点配对调查数据对模型进行检验,旨在揭示中国情境下总体报酬激发员工创新行为的作用机制和边界条件。实证研究发现,总体报酬与员工的创新行为、组织自尊显著正相关,组织自尊完全中介了总体报酬与创新行为的关系,工作投入正向调节了总体报酬和组织自尊的关系、以及组织自尊和员工创新行为的关系,并进一步正向调节了总体报酬通过组织自尊对创新行为的间接影响。     

基于混合方法的工作乐趣分类模型构建及其效用的实证研究

本文采用混合研究的方法,分析目前两岸企业工作乐趣的开展类型和效果。在构建类型学概念模型基础上,修订中华工作乐趣测量量表并检验不同乐趣活动对员工的绩效影响差异。数据分析结果表明社交型、放松型、辅助型和福利型乐趣的四类结构信效度良好,但不同类型乐趣对员工满意度、任务绩效和组织公民行为的影响存在明显差异。     

双组织承诺对工作绩效的影响研究:基于潜在剖面分析和因子混合分析

以1395名工会会员为样本,基于资源保存理论研究了双组织承诺对工作绩效的影响.个体中心的研究结果显示:以企业和工会为目标的双组织承诺可以分为单边企业承诺、单边工会承诺、完全承诺、不承诺和中等企业-弱工会承诺五个剖面;完全承诺员工的任务绩效显著高于单边企业承诺和单边工会承诺的员工,但他们的周边绩效没有显著差别,不承诺员工的任务绩效和周边绩效都最差.本研究拓展了承诺研究的深度,并为企业和工会合作提升员工工作绩效提供理论支持.     

组织支持感及支持性人力资源管理对员工工作绩效的影响

通过对626名企事业单位大学以上文化程度知识员工的问卷调查,采用相关分析和结构方程模型的多重数据处理方法,构建和检验了多维组织支持感对于支持性人力资源管理影响员工工作绩效的中介作用模型。研究结果显示,支持性人力资源管理实践(具体包括上级支持、参与决策、组织公正等)对于提高员工组织支持感具有积极影响,同时,组织支持感在支持性人力资源管理实践与员工工作绩效(含任务绩效和情境绩效)之间起着重要中介作用。     

服务品牌资产、顾客满意与品牌忠诚的关系研究:基于顾客的视角

通过市场调查,从顾客的视角,实证研究了服务品牌资产、顾客满意和品牌忠诚三者之间的关系,以及转换成本和消费者特征在顾客满意和品牌忠诚之间的调节作用.研究结果表明:基于顾客的服务品牌资产包括物理质量、人员行为、理想自我一致性、品牌认同、生活方式一致性五个维度;物理质量和生活方式一致性对顾客满意具有显著性影响;物理质量、人员行为、品牌认同、生活方式一致性对品牌忠诚具有显著性影响;顾客满意在服务品牌资产对品牌忠诚的影响中起到了部分中介作用;转换成本对顾客满意与品牌忠诚之间关系具有调节作用,但调节效应不显著;消费者特征对顾客满意与品牌忠诚之间关系具有部分调节作用.     

考虑情感的三策略囚徒困境博弈模型与合作演化

基于社会比较情境下强者和弱者之间产生的同情、欺凌、漠然、尊敬、嫉妒和畏惧等6种情感,建立个体情感特征的定量表达方法。考虑情感驱动的三策略囚徒困境博弈模型,设计了6种情感与合作、背叛及孤独3个博弈策略之间的对应关系和定量表达机制。采用二维格子网络、随机网络和无标度网络等三种网络模型进行演化博弈仿真分析。结果显示:较大的背叛成功诱惑促进了采取背叛策略的情感类型发展演化和繁荣稳定,系统的最终演化状态都体现出程度不同的负性情感特征;空间网络结构对情感类型和合作行为的演化存在重要影响,无标度网络结构能够导致具有一定良性特征的情感类型演化稳定,并带来较高的合作水平;从社会比较及由此产生的对比效应和同化效应的角度,对三种网络下不同的情感演化状态进行了相应阐释。     

Hybrid chaos optimization algorithm with artificial emotion

Much research on Artificial Intelligence (AI) has been focusing on exploring various potential applications of intelligent systems. In most cases, the researches attempt to model human intelligence by mimicking the brain structure and function, but they ignore an important aspect in human learning and decision making: the artificial emotion. In this paper, we present a new unconstrained global optimization method, hybrid chaos optimization algorithm with artificial emotion (HCOAAE), which avoids trapping to local minima, and improves convergence in large space and high-dimension optimization problems. The main purpose of artificial emotion is to mimic decision making behavior process of humans, to choose most suitable parameters of HCOAAE and decide whether to change current search strategy or not in the next iteration. Numerical simulations of 13 benchmark functions with different dimensions are used to test the performance of HCOAAE. Experimental results show that the proposed method significantly outperforms the existing methods in terms of convergence speed, computational effectiveness, and numerical stability.     

基于股吧情感本体与传播的股市短线预测

为研究股吧文本中蕴含的股民情感对股市短线走势的影响,使用情感倾向点互信息算法构建面向股市的情感词典,分别考虑情感本体与情感来源传播建立面向股市的加权情感倾向得分模型,并进一步建立ARMA-GARCHX模型以分析股民情感与股市短线走势之间的关系.研究显示：面向股市的情感词典相比于传统情感词典具有更强的适应性,使用加权情感倾向得分模型建立的预测模型对股市走向的预测相比于传统方法具有更优良的预测效果.     

Hierarchical forecasting based on AR-GARCH model in a coherent structure

This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1, 1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.     

Measuring air routes performance using a fractional network data envelopment analysis model

This paper aims to propose a unified framework of data envelopment analysis (DEA) model so as to facilitate legitimate comparisons of performance among the production and service processes as well as overall operations. The fractional measure network DEA (FNDEA) model is constructed by analytically stratifying the structure of an operational network according to the transport service characteristics of air routes. We compare the proposed model with separate multi-stage DEA evaluation results by using an actual data set from the domestic air routes of an airline in Taiwan. The results show that the FNDEA model is significantly different from the separate multi-stage DEA model in magnitude of performance scores. Managerial strategies for the improvement of performance for the airline are also suggested.     

A locally linear neuro-fuzzy model for supplier selection in cosmetics industry

Supplier selection and evaluation is a complicated and disputed issue in supply chain network management, by virtue of the variety of intellectual property of the suppliers, the several variables involved in supply demand relationship, the complex interactions and the inadequate information of suppliers. The recent literature confirms that neural networks achieve better performance than conventional methods in this area. Hence, in this paper, an effective artificial intelligence (AI) approach is presented to improve the decision making for a supply chain which is successfully utilized for long-term prediction of the performance data in cosmetics industry. A computationally efficient model known as locally linear neuro-fuzzy (LLNF) is introduced to predict the performance rating of suppliers. The proposed model is trained by a locally linear model tree (LOLIMOT) learning algorithm. To demonstrate the performance of the proposed model, three intelligent techniques, multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network and least square-support vector machine (LS-SVM) are considered. Their results are compared by using an available dataset in cosmetics industry. The computational results show that the presented model performs better than three foregoing techniques.     

Network DEA model for supply chain performance evaluation

This paper constructs an alternative network DEA model that embodies the internal structure for supply chain performance evaluation. We take the perspective of organization mechanism to deal with the complex interactions in supply chain. Three different network DEA models are introduced under the concept of centralized, decentralized and mixed organization mechanisms, respectively. Efficiency analysis including the relationship between supply chain and divisions, and the relationship among the three different organization mechanisms are discussed. As a further extension, we investigate internal resource waste in supply chain.     

Mathematical literacy in undergraduates: role of gender,emotional intelligence and emotional self-efficacy

This empirical study explores the roles that Emotional Intelligence (EI) and Emotional Self-Efficacy (ESE) play in undergraduates’ mathematical literacy, and the influence of EI and ESE on students’ attitudes towards and beliefs about mathematics. A convenience sample of 93 female and 82 male first-year undergraduates completed a test of mathematical literacy, followed by an online survey designed to measure the students’ EI, ESE and factors associated with mathematical literacy. Analysis of the data revealed significant gender differences. Males attained a higher mean test score than females and out-performed the females on most of the individual questions and the associated mathematical tasks. Overall, males expressed greater confidence in their mathematical skills, although both males’ and females’ confidence outweighed their actual mathematical proficiency. Correlation analyses revealed that males and females attaining higher mathematical literacy test scores were more confident and persistent, exhibited lower levels of mathematics anxiety and possessed higher mathematics qualifications. Correlation analyses also revealed that in male students, aspects of ESE were associated with beliefs concerning the learning of mathematics (i.e. that intelligence is malleable and that persistence can facilitate success), but not with confidence or actual performance. Both EI and ESE play a greater role with regard to test performance and attitudes/beliefs regarding mathematics amongst female undergraduates; higher EI and ESE scores were associated with higher test scores, while females exhibiting higher levels of ESE were also more confident and less anxious about mathematics, believed intelligence to be malleable, were more persistent and were learning goal oriented. Moderated regression analyses confirmed mathematics anxiety as a negative predictor of test performance in males and females, but also revealed that in females EI and ESE moderate the effects of anxiety on test performance, with the relationship between anxiety and test performance linked more to emotional management (EI) than to ESE.     

基于四个维度的绩效定量考核模型及模糊分类

从四个新维度划分了绩效考核指标内容及其绩效分数的类别,这四个维度指定性、定量、投票和附加分等四个方面,分别涵盖了目前党政领导班子和领导干部综合考核评价中大部分指标,是不同类别考核指标的综合反映.本文通过原始考核数据拟合和变量相关性分析挖掘其变化规律,给出了基于特定地区考核原始数据的绩效考核定量模型,测算各个变量之间的弹性系数值,并使用一种具有模糊关系的区域绩效考核方法,对考核指标体系本身做出评估.     

A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting

This paper built a hybrid decomposition-ensemble model named VMD-ARIMA-HGWO-SVR for the purpose of improving the stability and accuracy of container throughput prediction. The latest variational mode decomposition (VMD) algorithm is employed to decompose the original series into several modes (components), then ARIMA models are built to forecast the low-frequency components, and the high-frequency components are predicted by SVR models which are optimized with a recently proposed swarm intelligence algorithm called hybridizing grey wolf optimization (HGWO), following this, the prediction results of all modes are ensembled as the final forecasting result. The error analysis and model comparison results show that the VMD is more effective than other decomposition methods such as CEEMD and WD, moreover, adopting ARIMA models for prediction of low-frequency components can yield better results than predicting all components by SVR models. Based on the results of empirical study, the proposed model has good prediction performance on container throughput data, which can be used in practical work to provide reference for the operation and management of ports to improve the overall efficiency and reduce the operation costs.