Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

社交网络、投资者关注与股价同步性

随着以微博、微信为代表的社交网络信息平台在中国的崛起,形成了新媒体时代下信息资讯生成与扩散的完整传播链条,深刻地影响着金融市场参与主体的学习认知习惯、投资决策理念、交易行为模式,最终影响不同金融资产的价格波动规律． 本文在新媒体时代情景下,以社交网络信息披露与传播平台为切入点,基于信息关注度、信赖度、更新频率等三层维度,构建社交网络微博信息质量指标体系,研究社交网络信息质量与股价同步性的内在关联关系． 研究表明: 微博信息质量与股价同步性有着显著的高度负向线性关联性,并且呈现出非线性 U 型关系． 即随着社交网络信息质量水平的提升,股价同步性逐渐降低到达最小值,而后又逐渐提高． 研究结论为证明上市公司社交网络微博平台对股价同步性有较强影响力,提供了中国金融市场的证据．     

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.     

Crossing borders with social media: Online social networks and FDI

Social media is embedded in today's internationalization strategy. Companies extend their reach into foreign countries by posting and tweeting. Firms also enhance their mobile capabilities in foreign markets (e.g., knowledge and reputation) through user-generated content in online social networks. Levering on the capabilities-based theory of the multinational enterprise, this paper builds upon a resource-based, industry/network-based, and institution-based view framework. The study provides a comprehensive conceptual and empirical model to explain the effect of social networks on foreign direct investment. Empirical analysis in a global panel dataset of >4500 multinational enterprises suggests that online social networks' activity stimulates foreign capital expenditure and new affiliates. In addition, the article explores the relevance of customer capabilities along with sectoral and institutional moderating effects.     

遗传算法优化神经网络及信用评价研究

研究关于公司神经网络信用评估问题的现状,提出遗传算法辅助网络训练策略(优化后的网络称为进化网络),克服传统网络建模中产生的局部极小缺陷。建立了适合于我国商业企业的信用评分指标体系;然后依据该指标体系建立了基于进化神经网络的信用评估模型;最后,利用样本公司实际指标数据对该模型的评分效果进行了比较研究。     

A comparison of Japanese failure models: Hybrid neural networks,logit models,and discriminant analysis

This article looks at the ability of a relatively new technique, hybrid artificial neural networks (ANNs), to predict Japanese banking and firm failures. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for one year prior to the event. This suggests that for researchers, policymakers, and others interested in early warning systems, the hybrid network may be a useful tool for predicting banking and firm failures.     

Wide Diameters of Cartesian Product Graphs and Digraphs

In graph theory and study of fault tolerance and transmission delay of networks, connectivity and diameter of a graph are two very important parameters and have been deeply studied by many authors. Wide diameter combining connectivity with diameter is a more important parameter to measure fault tolerance and efficiency of parallel processing computer networks and has received much attention in the recent years. Diameter with width k of a graph G is defined as the minimum integer d for which between any two distinct vertices in G there exist at least k internally disjoint paths of length at most d. In the present paper, the tight upper bounds of wide diameter of the Cartesian product graphs are obtained. Some known results can be deduced or improved from ours.     

城市路网运输性能退化成本评估模型

路网的运行受到诸多因素的影响而使得路网运输性能发生退化,分析了路网运输性能退化的影响因素和作用机理,给出了路网运输性能退化及退化的极端情况路段失效的成本分析过程框架.基于BPR路阻函数和用户均衡分配理论分析路网运输性能退化的成本函数,建立了拥堵情况下城市路网路段失效的成本评估模型.最后,通过实例分析了路网运输性能退化对出行成本影响的变化规律,给出了路网运输性能退化的极端情况路段失效成本评估的具体过程,研究表明路网运输性能退化对出行成本具有很大的影响,二者成显著的正相关关系.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Improving operational plant performance in international manufacturing networks: the effects of integrative capabilities and plant roles

Abstract

This study adopts the resource-based view to examine how two integrative capabilities of a plant embedded in a manufacturing network enhance operational performance: the capability to connect with other plants in its manufacturing network (a networking capability, NC), and the capability to integrate manufacturing processes with new product development processes (a new product development capability, NPDC). Our model tests the indirect effect of NC on cost, quality, flexibility and delivery through NPDC. Furthermore, we test if the relationship between NC and NPDC depends on the strategic role of the plant. Using survey data from a sample of 521 plants, we confirmed the mediating effect of NPDC. However, the moderation effect was not confirmed. Our findings reveal that plants can deploy NPDC capabilities, fostered by NC, independently from their role in the network. This study contributes to the understanding of plant capabilities and plant roles in manufacturing networks.