失败学习对企业绩效的影响机制研究

已有文献认为失败学习对企业绩效具有重要作用，但失败学习通过何种途径促进企业绩效提升的研究并不完善。基于失败学习理论，引入资源拼凑和机会识别作为中介变量，构建失败学习影响企业绩效的多路径模型，探索失败学习对企业绩效的驱动路径及内在机理。实证结果表明：失败学习对企业绩效具有显著积极作用，资源拼凑和机会识别分别在失败学习与企业绩效之间起中介作用，资源拼凑和机会识别在失败学习对企业绩效驱动过程中存在链式中介作用，战略柔性能够强化资源拼凑与企业绩效之间的关系，并正向调节资源拼凑的中介作用。研究结论拓展了失败学习对企业绩效的影响路径，对企业复苏和成长具有重要启示。     

国际策略情境、国际双元学习与后发企业创新赶超

整合国际策略与双元学习理论，构建国际策略情境、国际双元学习的平衡和联合与后发企业创新赶超之间的关系模型。基于长三角地区327家外向型制造企业（高新技术企业）问卷调查数据，发现国际策略情境以及国际双元学习平衡和联合均显著正向影响企业创新赶超，国际策略情境对国际双元学习平衡和联合均具有显著正向影响，并且国际双元学习平衡和联合均在国际策略情境与创新赶超之间具有部分中介作用。研究结果揭示了国际化视域下双元学习与后发企业创新赶超的内在影响机制，延展了企业国际化、组织学习和创新赶超等相关领域理论空间，对于本土企业有效实施创新赶超具有启示意义。     

Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Learning from R&D outsourcing vs. learning by R&D outsourcing

We analyze how research and development (R&D) outsourcing influences product innovation. We propose a separation between learning from R&D outsourcing, whereby the firm improves its ability to innovate by using outsourced R&D directly in new products, from learning by R&D outsourcing, whereby the firm indirectly uses outsourced R&D by integrating it with internal R&D to create new products. Building on the knowledge-based view, we argue that learning from R&D outsourcing is likely to have an inverse U-shaped relationship with product innovation, because the initial benefits of using outsourced component R&D knowledge to innovate products is eventually outweighed by the hollowing out of the firm's ability to innovate. In contrast, we propose that learning by R&D outsourcing is likely to have a U-shaped relationship with product innovation, because the initial challenges of integrating internal and external R&D are eventually overcome, resulting in more innovations. Finally, we distinguish between domestic and foreign R&D outsourcing and propose a liability of foreignness in R&D outsourcing as it has a lower impact on new products than domestic R&D outsourcing. The empirical analysis shows that outsourced R&D has an inverted U-shaped relationship with the number of new products, while the interaction between outsourced R&D and internal R&D has a U-shaped relationship with the number of new products. It also shows that domestic outsourced R&D has a higher positive impact on the number of new products than foreign outsourced R&D.     

Enhancing adaptive transfer of cross-cultural training: Lessons learned from the broader training literature

Drawing insights from the broader training literature, we argue that evaluation of cross-cultural training effectiveness should adopt comprehensive criteria, including cognitive, skill-based, and affective learning outcomes as well as adaptive transfer. We propose that the integration of an error management supplement in cross-cultural training can enhance trainee acquisition of self-regulation skills and self-efficacy that facilitate adaptive application of learning to novel cultural situations. In addition to the traditional error management training designs (i.e., positive error framing), the current paper describes additional design elements to promote acquisition of cognitive strategies, prevent premature automaticity, alleviate concerns about error occurrence during learning, and enhance readiness to transfer. In addition, we offer propositions regarding the effects of the supplement on learning and transfer outcomes, along with implications for future research and practice on cross-cultural training.     

知识型员工双重社会网络对企业创新绩效作用机制研究

基于企业基础资源观和组织学习理论，从知识型员工个人和组织社会网络两个方面构建知识型员工双重社会网络影响企业创新绩效的理论模型，分析知识共享、组织学习及资源整合在员工双重社会网络对企业创新绩效影响机制中的作用。结果表明：知识型员工双重社会网络对科技型企业创新绩效的作用路径有3条，资源获取与整合、知识共享与学习及员工动态创新能力分别在其中发挥中介作用；在不同类型组织文化环境中，知识型员工双重社会网络对企业创新绩效的作用特征、作用重点以及作用机制存在显著差异，内部整合维度主要通过知识共享和组织学习影响企业创新绩效，外部适应维度主要通过隐性知识传播和资源整合影响企业创新绩效。