The emergence of multi-sided platform MNEs: Internalization theory and networks

The rise of the digital economy provides firms across the globe with unique business opportunities. Companies such as Facebook, Alibaba, and Uber are competing in a new multi-sided platform world; the primary focus of these firms, from their inception, is to provide digital infrastructure, information and technology—intangible assets that enable direct interaction or value creation across platforms by linking different user group and complementors, often at the international level. Building on data drawn from multinational multisided Platform corporations (MMPCs) operating in China, we combine insights from internalization theory and network effects in understanding the value creation of such firms. We explore the boundaries of these new “breed” of MNEs in exploiting firm-specific advantages (FSAs) and in creating new knowledge between headquarters and subsidiaries. The findings suggest that internalization theory needs to shift its focus from the ‘boundaries of the firm’ to the ‘boundaries of the local network’. By integrating their internal and external networks of knowledge in adapting their business models in host markets, this new breed of MNEs is more likely than the traditional one to gain a sustainable competitive advantage in the new information age.     

基于卷积神经网络的列车检测算法研究

列车检测作为列车自动驾驶的核心技术,可以有效地降低列车追尾等事故造成的人身危险和财产损失。为实现精准的列车检测,选用改进的卷积神经网络(PVANET)对输入图像进行特征提取,在此基础上,采用候选区域网络,从生成的特征图里滑动搜索,判断出图像中可能为列车的区域位置,并进一步采用快速区域卷积神经网络对每个候选区域进行分类,计算出其所属类别的置信度,同时精确定位列车。经验证,该方法适应范围广、鲁棒性高,可以有效地检测不同环境光强及不同朝向的列车,保障列车安全,为列车自动驾驶及辅助驾驶提供安全保障。     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Interbank tiering and money center banks

This paper provides evidence that interbank markets are tiered rather than flat, in the sense that most banks do not lend to each other directly but through money center banks acting as intermediaries. We capture the concept of tiering by developing a core-periphery model, and devise a procedure for fitting the model to real-world networks. Using Bundesbank data on bilateral interbank exposures among 2000 banks from 1999 to 2012, we find strong evidence of tiering in the German banking system. This extent of tiering is unlikely to arise in standard random networks. Indeed, we show that bank specialization and balance sheet variables predict how banks position themselves in the interbank market. This link provides a promising avenue for understanding the formation of financial networks.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.     

The relationship between brokers’ influence, strength of ties and NPD project outcomes in innovation-driven horizontal networks

This paper examines the impact of a broker's perceived use of power – position (i.e., coercive, reward and legitimate) and personal (i.e., expert, information and referent) – on strength of ties between network members and new product development (NPD) project outcomes. Our sample consists of 100 individuals drawn from 42 organizations that were involved in different innovation-driven horizontal networks. The results of structural equation modeling suggest that the perceived use of both personal power bases and position power bases by the broker are positively related to the strength of ties between members. Strength of ties, on the other hand, is positively related to NPD project outcomes of design performance and development time. Finally, results show that the relationships between a broker's use of different power bases and NPD project outcomes are fully mediated by the strength of ties between networks members. Implications for research, theory, and practice are discussed.     

In search of efficient network structures: the needle in the haystack

Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of non-trivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced by Jackson and Wolinsky (1996), for which the efficient networks are known over the whole state space of the parameters’ values. This approach allows us to obtain new exploratory results in the case of the linear-spatialized connections model proposed by Johnson and Gilles (Rev Econ Des 5:273–299, 2000), for which the efficient allocation of bilateral connections is driven by contradictory forces that push either for a centralized structure around a coordinating agent, or for only locally and evenly distributed connections. Murat Yıldızoğlu gratefully acknowledges the support of the CCRDT program of Aquitaine Region.     

基于粗神经网络的港口集装箱吞吐量预测

运用粗糙集神经网络建立了广州港集装箱吞吐量的预测模型,预测了2007～2010年的集装箱吞吐量。该预测方法融合了粗糙集理论与神经网络方法具有的优点,具有很强的学习与泛化能力,非常适合处理多因素、非线性的复杂系统。预测结果对广州港的发展有较强的借鉴作用,可以为广州港未来发展提供参考。     

生态视角下协同创新网络构建及演替研究

创新驱动已成为社会经济发展的主要驱动力,而以多元主体为基础的协同创新网络正成为创新模式的发展趋势。首先,通过对生态群落和协同创新网络的构成要素及要素间关系进行比较发现,二者具有较强相似性,因而成熟的生态群落理论可以为协同创新网络的构建和发展提供理论借鉴。然后,在生态群落多样性、层次性、协同竞争性、开放性和动态性等特征的指导下构建了协同创新网络。在此基础上,结合生态群落的演替进程,阐述了协同创新网络演替的5个阶段。最后,提出了加速协同创新网络演替的政策建议。     

铁路行包运量预测模型研究

铁路行包运量预测是以运输需求和内部供给为导向,综合考虑各种影响因素,对行包运量现状和发展的正确把握.探讨利用人工神经网络结合主成分分析的方法,建立铁路行包运量预测模型,解释并预测行包专列开行后铁路行包运量的增长趋势.实例分析的仿真结果表明,采用主成分分析法的广义回归神经网络模型结构简洁、预测精度高、收敛速度快,对相关铁路部门和企业的决策具有参考意义.