Diffusion of environmental business practices: A network approach

The purpose of this research is to build a conceptual foundation that examines network effects on the diffusion of environmental business practices (EBP) among suppliers. This research extends a network perspective to adoption of an environmental business practice across a large network of suppliers. The context of EBP is used to better understand adoption of a complex business practice, with perceived costs that are often greater than the perceived benefit. Variation in the level of structural and relationship embeddedness affect network diffusion of environmental business practices differently. Increased levels of structural and relational embeddedness are proposed to be positively associated with diffusion of EBP. From a practical standpoint, firms that leverage embeddedness may facilitate higher diffusion and adoption of environmental business practices. This facilitation may lead network actors to engage in EBP, and leverage benefits that may stem from these practices. This research introduces the concept of embeddedness to the environmental supply chain literature. Practicing managers can use the findings in this research to better position themselves within a network to diffuse EBP. This research also helps managers understand how supply chain members that are weakly connected to the primary network are important for introducing new ideas and innovations.     

Fostering product innovation in industry networks: the mediating role of knowledge integration

The locus of innovation has shifted from single entrepreneurial firms to groups of networked firms. Inter-organizational cooperation rather than competition to exploit the value of knowledge through new product innovation lies at the heart of the knowledge-based economy. This paper focuses on the phenomenon of network product innovation and the holistic integration of distributed knowledge across organizational boundaries to foster production innovation. A new construct, knowledge integration, is found to have a strong positive impact on new product performance. Resource complementarity, market orientation, and information sharing are three antecedents that positively affect knowledge integration across organizational boundaries. Survey data also suggest that knowledge integration serves as a mediator between the three antecedents and product innovation performance. Managerial implications and future research directions are discussed.     

Learning from the alien: knowledge relationships with temporary workers in network contexts

This paper contributes to our understanding of the role of temporary workers in learning for innovation in network contexts. The strategy literature advocates that the knowledge and talent of workers is vital to competitive success yet at the same time temporary workers are becoming more prevalent, making relationships with workers more ephemeral. These shifting competitive and employment contexts make insights into learning from, and with, contractual nomads paramount. The setting is the UK television industry, where such workers are dominant. Internationally, creative industries are a prime site of networks where temporary workers are brought together for specific projects. In the television industry, a range of knowledge attachment employee relationships have been developed by firms to ‘contain’ and sustain worker contributions in terms of knowledge and talent. These relationships are identified as freelance, regular contract, golden handcuffs and invest in knowledge firm. This paper highlights the learning implications both positive and negative arising from these different ways of relating to temporary workers thereby extending our understanding of employee relationships built on mutuality.     

初识无线传感器网络

文章简要介绍了无线传感器网络的构成,其出现对社会发展的影响;重点介绍了无线传感器网络的应用领域及其现阶段存在的一些问题。     

Toward a structural view of co-opetition in supply networks

Co-opetition, or simultaneous competition and cooperation, in the supply chain management literature has been treated as a dyadic relational phenomenon where the buyer's strategy is considered to be the primary driver. In this paper, we move beyond the dyadic view and propose a theory of co-opetition in supply networks. We argue that as firms within a supply network interact over time to access, share, and transform resources, new ties between firms are formed and existing ties dissolve, giving rise to co-opetition dynamics at the network level. Taking a configurational approach, we employ the inter-related dimensions of ties between firm, firm-level task, network-level objective, and governance to specify four practical supply network archetypes that cover a wide range of economic activities. We then explain how coopetitive relationships may evolve in these supply network archetypes. Specifically, we discuss how relationships form or dissolve in these archetypes and how local structural changes lead to co-opetition dynamics at the network level. We also discuss the implications of such dynamics from a managerial perspective.     

Neighbourhood Effects as Indirect Effects: Evidence from a Dutch Case Study on the Significance of Neighbourhood for Employment Trajectories

One of the key challenges in the study of neighbourhood effects on work is to understand the pathways through which disadvantaged neighbourhoods impact the employment opportunities of residents. Endogenous explanations for neighbourhood effects focus on social life in these neighbourhoods, identifying mechanisms of social isolation, deviant work ethics and neighbourhood disorder. This article studies these mechanisms in a low‐income neighbourhood in the Netherlands. The case study shows that unfavourable socioeconomic outcomes can be indirect and unintended consequences of actions and choices in everyday life that are not directly concerned with work. Nevertheless, these individual actions and choices reflect local social practices that are influenced by the marginalized context in which residents lead their lives.     

Alignment,integration, and consistency in HRM across multi‐employer networks

Most studies of human resource management (HRM) have been conducted within the context of the single employing organization, which is strange given the recent growth in multi‐employer networks. In this study, the authors examine whether alignment, integration, and consistency—concepts central to or implicit in most analyses of HRM—has meaning and relevance in the multi‐employer context. They focus specifically on networks in which collaboration is intended to deliver high levels of product quality or customer service, precisely where one might expect employers would be attracted to “strong” HRM systems. Data was collected via interviews and document analysis in four networks, spanning both the public and private sectors in the United Kingdom. Despite a set of potentially favorable conditions within these networks to promote alignment, integration, and consistency, implementation was impeded by other equally powerful forces, including differences in employer goals within networks, especially between public and private sector organizations; intraorganizational tensions within internal labor markets for organizations involved in networks; using divergent HR policies between organizations within multi‐employer networks; and contradictions between the pursuit of “among employee” or “temporal” consistency for workers. Rather than prescribing a one‐size‐fits‐all solution for these problems, it is argued that detailed analysis of each network is necessary. ©2011 Wiley Periodicals, Inc.     

网络环境下地方志资源的开发与利用

互联网的诞生改变了人类的生活方式,也提高了人们的工作效率和能力。互联网环境下,加强地方志资源的开发与利用,将对本地经济发展和社会进步起到积极的推动作用。     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Automatic neural network modeling for univariate time series

Artificial neural networks (ANNs) are an information processing paradigm inspired by the way the brain processes information. Using neural networks requires the investigator to make decisions concerning the architecture or structure used. ANNs are known to be universal function approximators and are capable of exploiting nonlinear relationships between variables. This method, called Automated ANNs, is an attempt to develop an automatic procedure for selecting the architecture of an artificial neural network for forecasting purposes. It was entered into the M-3 Time Series Competition. Results show that ANNs compete well with the other methods investigated, but may produce poor results if used under certain conditions.