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1.
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。  相似文献   
2.
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。  相似文献   
3.
This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.  相似文献   
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The quest for authenticity in dining experiences has become increasingly important. This paper explores authenticity dimensions that are of value to customers in dining experiences, and by that gains a multi-dimensional understanding of authenticity in this context. Following an integrated learning approach using text mining and classification techniques, this paper explores and confirms different dimensions of authenticity by identifying and classifying authenticity judgements in online restaurant reviews. The results suggest that authenticity is a multi-dimensional concept encompassing Authenticity of the Other, Authenticity of the Producer, and Authenticity of the Self as first-level dimensions. Additionally, besides historical and categorical authenticity which have been previously explored in the literature, a new type of authenticity - Deviated Authenticity - emerged as a second-level dimension falling under Authenticity of the Other. This paper enhances existing conceptualisations of authenticity and establishes avenues for exploring the multi-dimensionality of other consumer research concepts using user-generated content.  相似文献   
6.
网络化边界为创业模式与情境要素变革背景下的组织学习效力分析提供了更具时效性的研究视角。在孵企业为上述视角的落地,进而还原复杂情境下多维组织学习方式对企业价值创造活动的协同作用过程提供了丰富的实证脚本。基于235家企业的实证结果显示:内外部学习不仅能单独提升在孵企业创造力,还具有协同促进效应,而该过程会受到内外部环境的共同调节作用。孵化网络异质性越低、环境动态性越强,在孵企业内部学习与创造力间的正向关系越强;孵化网络异质性越高、环境动态性越弱,在孵企业外部学习与创造力间的正向关系越强。进一步对内外部学习协同效应进行调节作用检验,发现只有当孵化网络异质性低或是环境动态性弱时,才有利于发挥在孵企业内部学习与外部学习对创造力的协同促进作用。研究结论为组织学习、环境要素与创业孵化理论融合提供了较为完整的研究框架,并为创业企业学习战略与孵育机制设计提供了更具靶向性的决策依据。  相似文献   
7.
Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.  相似文献   
8.
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。  相似文献   
9.
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.  相似文献   
10.
Machine learning (ML) methods are gaining popularity in the forecasting field, as they have shown strong empirical performance in the recent M4 and M5 competitions, as well as in several Kaggle competitions. However, understanding why and how these methods work well for forecasting is still at a very early stage, partly due to their complexity. In this paper, I present a framework for regression-based ML that provides researchers with a common language and abstraction to aid in their study. To demonstrate the utility of the framework, I show how it can be used to map and compare ML methods used in the M5 Uncertainty competition. I then describe how the framework can be used together with ablation testing to systematically study their performance. Lastly, I use the framework to provide an overview of the solution space in regression-based ML forecasting, identifying areas for further research.  相似文献   
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