The Ethical Foundations of Risk Analysis

In the field of risk analysis, the normative value systems underlying accepted methodology are rarely explicitly discussed. This perspective provides a critique of the various ethical frameworks that can be used in risk assessments and risk management decisions. The goal is to acknowledge philosophical weaknesses that should be considered and communicated in order to improve the public acceptance of the work of risk analysts.     

社会网络对企业管理人员职业生涯成功影响的实证研究

本文从社会网络的角度出发,基于社会交换理论,建立了社会网络对企业管理人员职业生涯成功的作用模型,并指出社会网络对职业成功两者之间的关系是通过网络利益(资源和职业支持)的中介作用实现的.本文以407位企业管理人员为研究对象,采用结构方程模型对假设模型进行检验,研究结果验证了网络利益(资源和职业支持)的中介效应.研究同时得出,社会网络各指标会对网络利益产生影响.相对于网络规模和网络异质性,网络密度和关系强度对于网络利益的获取有更为重要的影响.在网络利益对职业生涯成功作用的过程中,网络资源仅对管理人员职业满意度产生显著的正向影响,而职业支持对管理人员的职业生涯成功各指标都有显著的影响.文章最后指出了研究的局限性和未来研究方向.     

The wireless network jamming problem

In adversarial environments, disabling the communication capabilities of the enemy is a high priority. We introduce the problem of determining the optimal number and locations for a set of jamming devices in order to neutralize a wireless communication network. This problem is known as the wireless network jamming problem. We develop several mathematical programming formulations based on covering the communication nodes and limiting the connectivity index of the nodes. Two case studies are presented comparing the formulations with the addition of various percentile constraints. Finally, directions of further research are addressed.     

知识型团队动态能力构建、团队体系模型与创新运作模式研究

知识型团队是现代组织执行复杂创新任务的重要组织形式.本文从分析知识型团队与知识工作任务的匹配关系入手,在引入知识链及知识链网络等概念的基础上,探讨了基于知识链的知识型团队的动态能力构建机制,分析了围绕知识链网络的知识团队结构体系模型及创新运作机理.     

Network learning: Episodes of interorganizational learning towards a collective performance goal

Little is known about learning processes in horizontal networks. This study focuses on networks as learning entities, i.e. learning by multiple organizations as a group, and the mechanisms involved in developing and addressing a network-level performance goal. By using a narrative approach, we gather in-depth primary data from network members to examine: how do firms engage in network learning? and, how is network learning coordinated towards a performance goal in a horizontal inter-firm network? Our findings comprise two learning episodes: ‘learning how to compete’ and ‘learning how to perform’. These episodes help us to understand network learning processes; the relationship between network learning and organizational learning; and the regulatory role a hub firm provides towards a collective performance goal.     

Building interfirm leadership: A relational identity perspective

While leadership has been recognized as an approach to facilitating network orchestration, little is known about the mechanism through which a hub firm enhances interfirm leadership. Grounded on the theory of social identity and network orchestration, we develop a framework of the enhancement of interfirm leadership, proposing that the tendency of building leadership rests on the hub firm's ability to shape the partners' relational identity. We identify three types of role-adoption that indicate seven actions a hub firm might take to establish such an identity. We suggest that the mechanism through which leadership emerges is contingent on relationship duration, dependence asymmetry, and competition intensity.     

The efficiency of mutual fund companies: Evidence from an innovative network SBM approach

This paper is the first DEA (Data Envelopment Analysis) evaluation of the efficiency of a sample of mutual fund companies in a large Euro fund industry, i.e. Spain. Our novel network model links the efficiency of the core activities (portfolio management and marketing/distribution) of a mutual fund company to the efficiency of its operational management function. Our results highlight the important networking effects at the marketing stage and question the prime role of portfolio management skills in the overall efficiency of a mutual fund company. The evaluation of robust efficiency clusters indicates that the efficiency rankings obtained with our network model persist significantly along our sample period. Finally, the application of the SBM (Slacks-Based Measure) Variation III to our network model allows us to identify a large number of globally inefficient but locally efficient companies with reference to competitors with similar management resources.     

Who Cares About Your Big Day? Impact of Life Events on Dynamics of Social Networks

With online social networking having revolutionized the way in which individuals communicate and interact with each other, there is heightened research interest in the dynamics of social networks. This article seeks to contribute to this stream of research by addressing the key question of the impact of major life events, such as getting married or graduating from college, on social network evolution. Consistent with prior studies on the evolution of individuals’ social networks, we specifically focus on two key attributes of an individual's network: indegree of ties and relational embeddedness. By longitudinally analyzing the network activities of a large‐scale online social network, we find that the indegree of ties increased significantly following a major life event, and that this impact was stronger for more active users in the network. Interestingly, we also find that the broadcast of major life events served to revive dormant ties as reflected by a decrease in embeddedness following a life event. We also found that one‐time life events such as weddings had a greater impact than recurring life events such as birthdays on the evolution of a user's social network. From a research perspective, our study contributes to existing research by focusing on a user's communication network as opposed to friendship network and by emphasizing how exogenous life events add a different dimension to user communication patterns. The importance of life events on social network evolution has important implications for practice as well by providing insights to advertisers as well as to social networking sites.     

基于自组织算法的改进型GAANN预测模型

在传统的基于GA算法人工神经网络的基础上作了改进,将训练集分为两部分,在前一训练集训练后获得的网络基础上使用后一训练集进行进一步的训练获得更为优化的网络结构。针对复杂系统建模输入节点难以确定的问题,提出将其与自组织理论相结合,首先使用GMDH方法获得神经网络的初始化节点,然后使用训练好的神经网络模型进行预测。最后,将由此建立的预测模型应用于国家粮食产量预测,取得了令人满意的效果。     

Evaluating the Evaluators: Should Investors Trust Corporate Governance Metrics Ratings?

Companies are under increasing pressure to have their corporate governance rated by an independent corporate governance metrics firm, such as Institutional Shareholder Services (ISS) or Governance Metrics International (GMI). These rating firms claim to be able to determine how effective and responsive a company’s board is. Institutional investors have begun using these board governance ratings when deciding which firms to include in their stock portfolios. This paper considers whether investors, many of whom claim to be socially responsible, should be relying upon board governance metrics. We find that these metrics are not good indicators of either the quality of a firm’s earnings or of its ethics.