Identifying the ‘knowledge base’ or ‘intellectual structure’ of research on international business, 2000–2015: A citation/co-citation analysis of JIBS

The purpose of this study consists in analyzing the existing literature on international business from all the research papers published in Journal of International Business Studies (JIBS) during the period of time comprised between 2000 and 2015. The authors apply bibliometric methods to that end –in particular, document citation and co-citation analyses (DCAs)– so that they can try and identify the main research lines within the aforementioned scientific field or domain; in other words, the ‘intellectual structure’ of the discipline examined. Social networks analysis was also used to carry out the representation or visualization of such structure or knowledge base. The highly interdisciplinary nature of the research undertaken, since the study field still keeps its characteristic diversity and interdisciplinarity or the need to integrate a variety of theoretical frameworks or approaches into the development of a widely-accepted theory about the field under study become evident. Amongst these theories would be the new institutional theory, the strategic theory or perspective or others such as the transaction cost theory or the agency theory, applied to the context of internationalization and to the field of international business, on whose integration the future development of this field may largely depend.     

Structural holes and hedge fund return comovement: evidence from network-connected stock hedge funds in China

Using data from a new hedge fund database, we examine the impact of social networks on the return comovement of stock hedge funds in China. We use structural holes in the college alumni networks of managers to measure the managers’ social network positions. We perform an empirical analysis on a sample of 3,012 hedge fund products in China from 2010 to 2017. We find that greater structural holes are associated with higher return comovement. The positive impact of the structural holes on return comovement is not affected by market cycles, a manager's major in college, or his or her abilities.     

Does ethnic diversity decrease economic interactions? Evidence from exchange networks in rural Gambia

Using a unique dataset collected in 59 rural Gambian villages, we study how ethnic heterogeneity is related to the structure of four economic exchange networks: land, labour, inputs and credit. We find that different measures of village‐level ethnic fragmentation are mostly uncorrelated with network structure. At a more disaggregated level, household heads belonging to ethnic minorities are not less central than those from the predominant ethnicity in any of the networks and, at the dyadic level, the fact that two households share ethnicity is not an economically significant predictor of link formation. Our results indicate that, in the particular setting of our study, the structure of the exchange networks is better defined by other variables than ethnicity and that ethnic heterogeneity is unlikely to be a driver for sub‐optimal economic exchanges.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

机动车尾气污染危害的生成机理与治理发展

本文介绍了汽车尾气对人类健康和生态环境的危害,及其生成机理和影响因素并简述汽车尾气治理与发展。     

Network games with incomplete information

We consider a network game with strategic complementarities where the individual reward or the strength of interactions is only partially known by the agents. Players receive different correlated signals and they make inferences about other players’ information. We demonstrate that there exists a unique Bayesian-Nash equilibrium. We characterize the equilibrium by disentangling the information effects from the network effects and show that the equilibrium effort of each agent is a weighted combinations of different Katz–Bonacich centralities.     

Cultural embeddedness in supply networks

Recent studies on structural and relational embeddedness suggest that favorable position and connections in supply networks benefit a firm. While fruitful, this focus misses the motivations that prompt firms to take economic action in the first place. Understanding cultural embeddedness provides insight into why individuals and firms behave as they do and how their behavior can influence network structure. Contrary to the belief that firms act solely for profit and growth, we note that cultural contents such as values, social issues and political ideologies explain firms’ motives and guide their economic activities. We explore the role of cultural embeddedness through a grounded study of Country Natural Beef, a sustainability-oriented agricultural cooperative in the western United States. This supply network demonstrates strongly competing cultural claims among its members as well as a unique institutionalized culture. Cultural interactions at the node and network levels explain the functioning of and changes to the network. Through interviews, analysis of archival information and direct observation of pivotal events over a period of 5 years, we unpack cultural embeddedness and take an incremental step toward a theory of cultural embeddedness in cooperative supply networks.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.