企业非伦理行为的演化博弈分析

企业兼具逐利的动机和同情心。随着市场社会的发展和企业影响力的增大,社会对企业的期望逐渐超越了纯经济的范畴,希望企业成为一个负责任的企业公民。作者借助博弈论方法,通过对信息维度、揭发方利益和当事方利益的分析,揭示在企业公民概念框架下的基本疑问:行业内企业非伦理行为(潜规则)为何能够存在?其生成和消亡机制是什么?通过分析表明,信息的可获得性和可证明性、揭发方成本和收益、被揭发方的收益和风险决定了行业非伦理行为是否能够持续存在。     

理解的三个过程

本文论述了阅读理解的三个过程 :语义辨析、语法分析和逻辑分析 ,并指出这三个过程相互影响 ,相互作用 ,缺一不可     

Exploring the Effects of Conditioning Factors on the Strain–Crime Association: A Test on Incarcerated Chinese Women

A key assumption of general strain theory (GST) is that various factors condition the effects of strains on crime. Past research examining this conditioning hypothesis tended to focus on youth samples and use gender as a control variable. Using survey data from Chinese female inmates, this study tests the strain–crime relationship posited in GST as well as the hypothesized effects of the conditioning factors. Regression results show that different types of strains have distinct effects on respondents’ odds to commit violent over property crimes. Conditioning factors are found to moderate the strengths of strains toward respondents’ likelihood to commit property crimes over violent crimes.     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.

Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).     

"市场-政策"双重导向对农业企业绩效的影响机制研究——以泛长三角地区农业龙头企业为例

经济转型时期,市场机制与政策机制呈现出共存性与互补性特征.本文以泛长三角地区农业龙头企业为样本,通过构建以动态能力为中介变量的理论模型,实证检验了市场导向、政策导向与农业企业绩效的关系.结果显示,(1)市场导向与企业绩效有显著正相关关系,这种关系可以是直接的也可以是间接的,政策导向与企业绩效有显著的直接正相关关系;(2)动态能力在市场导向与企业绩效关系之间起着部分中介作用,但在政策导向与企业绩效之间的中介作用不显著;(3)市场导向与政策导向对农业企业绩效的影响具有结构差异性,市场导向程度高的企业长期绩效较好,政策导向程度高的企业短期绩效较好,具备双重导向的企业长短期绩效均较好.     

转型背景下中国企业战略研究:特征、方法及主题分析——基于英文文献的探讨

尽管转型经济下的中国企业战略管理日益成为国内外学者关注的前沿领域,但在这一领域国内外研究仍然缺乏有效的联系和沟通.对于国外学者已有的研究成果,我国战略研究学者至今还缺少全面认识和深入梳理.为了弥补这一缺憾,本文通过广泛搜集1978年至2008年30年间国际优秀期刊上发表的、研究转型经济下中国企业战略问题的英文文献,评价了其文献数量变化、期刊、学者和机构的贡献以及研究主题、研究理论基础、采用的研究方法及研究风格,提炼和分析了国外学者在中国企业战略研究中的成果和观点,最后对未来本土学者如何实现将研究与国际接轨、突出自身研究特色提出了相关看法和建议.     

An Examination of the Relationship Between Absorptive Capacity and Organizational Learning,and a Proposed Integration

Since its inception, the concept of absorptive capacity has been closely linked with notions of organizational learning. Yet the precise nature of the relationship between these two concepts has never been established. This relationship is examined in a variety of ways, and it is suggested that the literature on these two concepts shares a conceptual affinity which needs to be delineated. It is suggested that absorptive capacity (a dynamic capability) is a concrete example of organizational learning that concerns an organization's relationship with new external knowledge. Using the 4I Model for organizational learning ( Crossan, M.M., Lane, H.W. and White, R.E. (1999 ). An organizational learning framework: from intuition to institution. Academy of Management Review, 24, 522–537) and Zahra and George's conceptualization of absorptive capacity ( Zahra, S.A. and George, G. (2002 ). Absorptive capacity: a review, reconceptualization, and extension. Academy of Management Review, 27, 185–203), this paper proposes an integration of the two concepts.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.