Innovation in the bioeconomy – dynamics of biorefinery innovation networks

The bioeconomy has become a central concept in many strategies for future economic development, emphasising an increasing need for collaboration across industries and sectors for innovation. This paper unpacks aspects of collaboration in the bioeconomy by looking at the development of innovation networks for biorefinery technologies from 2004 to 2014 based on innovation project data from Swedish public funding agencies using a stochastic actor-oriented model for network analysis. The analysis shows that although the network grew significantly during the time period, indicating an increasing interest in biorefinery technology innovation, inter-sectoral collaboration is not favoured over intra-sectoral collaboration. As is known from previous work on social networks trust-building is a key driver for collaboration, as actors tend to form collaborations with previous partners or indirectly connected partners, creating clustered networks.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Spatial movement patterns among intra-destinations using social network analysis

To explore popularly visited tourist locations, travel movement patterns, and movement points, this study collected samples of 321 Chinese tourists and 337 Japanese tourists who were visiting major tourist destinations in Seoul and its vicinity in South Korea. Results of analyzing movement patterns showed that Japanese tourists tend to be clustered around popular attractions, whereas Chinese tourists tend to spread over a larger area of attractions. Some specific shopping and amusement attractions were the locations most popularly visited by both groups. The start points and end points in the two groups’ itineraries were dissimilar overall, even though their patterns were similar in regard to major preferred tourist attractions. Thus, the findings of this study have the potential to contribute to understanding spatial mobility in a tourism destination through tracking tourists’ movement patterns.     

Do ongoing networks block out new friends? Reconciling the embeddedness constraint dilemma on new alliance partner addition

Research Summary: This study addresses a theoretical dilemma regarding how alliance network constraint (reflected by network cohesion) affects a firm’s alliance formation with new partners. Using a network pluralism approach, we separate a firm’s ego alliance network into two activity‐based networks—an exploratory network and an exploitative network—based on the primary value chain activity involved in each alliance. We argue that the cohesion of exploratory or exploitative networks has an inverted U‐shaped effect on the addition of new partners in the same activity‐based network, and a positive effect on the addition of new partners in the other network. Results based on data from the biotechnology industry largely support our predictions with one exception. Our study contributes to both scholarly understanding of network embeddedness and alliance practice. Managerial Summary: The structure of firms’ ongoing alliance networks may have paradoxical implications for their efforts to search for and form alliance with new partners. That is, when a firm’s alliance partners are tightly connected with each other, the cohesive network tends to both encourage and impede the focal firm to add new partners. We resolve this dilemma by showing that when a firm is deeply entrenched in a cohesive alliance network conducting a certain type of activities (e.g., R&D activities), it may not easily add new R&D alliance partners. However, it may still be able to escape from the cohesive R&D alliance network by seeking new partners conducting other activities (e.g., manufacturing activities).     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

Corporate social responsibility and competitive advantage: A developing country perspective

Corporate social responsibility (CSR) has attracted wider research interests over recent decades. While some studies have examined the impact of CSR activities on firm competitive advantage (CA), the findings so far remain contradictory. Moreover, the role of export orientation, firm strategy, and structure on the association between CSR and CA has not been explicitly examined. Thus, the purpose of this study is to examine the moderating role of export orientation, firm strategy, structure, and firm size on the association between CSR and CA. Using a sample of 179 responses from management staff in organizations across five sectors in a developing country context of Ghana, the study found positive effects of CSR on CA. The study contributes to the resource‐based view (RBV) scholarship by confirming the important complementary effect of export orientation and organizational structure as important resources and capabilities on the CSR–competitiveness relationship. However, no evidence of a moderating effect of firm strategy, or firm size on the CSR–CA relationship was found. These findings are instructive, impactful, and enrich the existing literature on CSR and strategy. Implications for theory and practice are also discussed.     

Managerial Overconfidence,Self-Attribution Bias,and Downwardly Sticky Investment: Evidence from Korea

The extant literature on behavioral corporate finance has explored the effects of overconfidence on investment–cash flow sensitivity (ICS) to explain overinvestment, yet it has overlooked the asymmetric behavior of investments in relation to changes in cash flow levels. This study examines whether investments behave asymmetrically responding to changes in cash flows and, if so, how managerial overconfidence affects asymmetric ICS. Using a sample of KOSPI and KOSDAQ firms in Korea, we find the incidence of downwardly sticky ICS in unconstrained firms. We then find that overconfident managers encourage ICS to be stickier than their rational peers do in unconstrained firms. Finally, we find that managerial overconfidence intensified by self-attribution bias induces ICS to get even stickier, suggesting more explicit evidence of corporate investment distortions. The results of alternative tests using the asymmetric models of Homburg and Nasev (2008) are qualitatively consistent with prior results. Overall, our findings imply a higher incidence of excessive investment commitments driven by overconfident managers.     

黑龙江旅游网络关注度时空演变特征研究

探究黑龙江旅游网络关注度时空演变特征对黑龙江旅游业良性发展具有重要意义.利用百度指数获取2011—2018年全国和分地区黑龙江旅游网络关注度逐日搜索指数数据.从时间分布、空间分布、时空集聚3个方面研究其演变特征.研究表明:2011—2018年全国黑龙江旅游网络关注度年际变化总体呈现上升趋势,月际变化呈"W"形;2011—2018年31个省区市黑龙江旅游网络关注度空间差异较大、季节性影响显著;东部地区黑龙江旅游网络关注度偏高,西部地区黑龙江旅游网络关注度的季节差异最明显;东部地区内的黑龙江旅游网络关注度差异性最小,西部地区内差异性最大;黑龙江旅游网络关注度具有正空间自相关,部分东部、中部地区表现为高高集聚,多数西部地区表现为低低集聚.     

Is there a diversification “cost” of Shari’ah compliance? Empirical evidence from Malaysian equities

Islamic equity portfolios work with a smaller investment universe given the filtering of non-Shari’ah compliant stocks. It has been theoretically argued that this culminates in suboptimal portfolio diversification, which in turn adversely affects risk-adjusted returns. We offer empirical evidence that such a conceived portfolio diversification “penalty” is far from a foregone conclusion, at least empirically. Our results tend to indicate that Islamic portfolios are not invariably handicapped in terms of portfolio diversification. We also explored dimensions that may account for differences in the relative investment performance between Islamic and conventional portfolios, such as portfolio constraints, short selling and market conditions. We believe this paper is among the first to apply substantial empirical analysis specifically with respect to the portfolio diversification perspective on Islamic equity investments.     

Situating the Asian Infrastructure Investment Bank in the context of global economic governance

The creation of the Asian Infrastructure Investment Bank (AIIB)was welcomed by the World Bank but opposed by the Obamaadministration. The paper explains China’s positive relationshipwith the Bank and the Organization for Economic Cooperation andDevelopment (OECD) in terms of the mission of the Bank, sharedby the OECD, to develop and deepen the global economy. The AIIBand the related Belt and Road initiative promise to do this throughinvestment in infrastructure and connectivity in and around thepoorly integrated Eurasian landmass. But while the current Chineseleadership has supported an inclusive global economy based uponfree trade and supported by multilateral institutions, China’s controlof resources outside the multilateral framework and adherence topractices that challenge liberal principles prompt suspicions thatthese commitments are either disingenuous or anyway subjectto reversal. In itself, therefore, the AIIB provides no conclusiveevidence either way on China’s future course.