创新政策组合：研究述评与未来展望

创新政策组合是当前政策研究领域的热点问题，已经得到越来越多学者的关注。通过梳理国内外创新政策组合相关研究文献，阐述政策组合概念起源和内涵，对政策组合文献的发文数量、重要文献等进行统计和分析。在此基础上，进一步厘清创新政策组合的发展脉络及理论框架，并从政策组合的维度、要素、过程和特征4个方面分析创新政策组合研究现状。最后，针对现有研究不足，提出未来研究方向，为进一步拓展该领域研究提供理论基础和知识储备。     

军民科技协同创新要素融合机制研究

构建一体化国家战略体系和能力是新时代实施军民融合战略的重要目标，军民科技协同创新则是实现该目标的必然选择。基于扎根理论建立军民科技协同创新影响因素体系，通过分析影响因素间的交互关系构建系统动力学模型。根据模型对案例进行仿真分析，考察单因素作用、多因素协同作用、多因素非协同作用条件下协同创新水平变化情况。从军民科技协同创新内涵、结构、功能实现3个层面揭示要素融合机制。据此，为国家军民深度融合提供相应政策建议。     

Avoiding digitalization traps: Tools for top managers

Digital transformation is fundamentally changing the business landscape. It is also affecting the roles of top managers within firms. Our survey of more than 160 senior managers in Europe suggests that digitalization, rather than encouraging more decentralized forms of management, will lead to an expanded role for headquarters and further empowerment of top managers. While acknowledging the benefits of the digital transformation, in this Executive Digest we identify five key challenges for newly empowered top managers and offer solutions for these digitalization traps.     

Comparison of dimension reduction methods for DEA under big data via Monte Carlo simulation

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.     

Shifts in consumer behavior towards organic products: Theory-driven data analytics

Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.     

大数据技术下消费者个人信用信息共享的治理机制

大数据技术下,消费者个人信用信息可以通过共享被加工成知识,从而促进消费者人格的发展以及征信等行业的升级。从本质来讲,信息共享发挥着知识服务的功能。然而,在云计算和区块链等大数据技术支持下,信息共享呈现出信息的集中收集与传输、分节点存储与利用特征,这在方便信息共享与知识服务的同时,造成了共享者组织有序与共享整体高效之间难以调和的矛盾,共享知识服务功能的实现由此受阻。在对我国治理共享实证资料进行文本分析、对现有共享治理机制效果进行测评的同时,重点借鉴欧美共享治理经验特别是英国沙箱监管模式,并运用知识治理理论进行研究发现,与我国现有治理机制相比,知识治理机制更适合化解当前面临的矛盾,有利于实现大数据技术下信息共享的知识服务功能。根据知识治理机制,我国宜加强对共享者准入与退出的外部监管,引导共享者自我完善内部组织并寻求彼此协作,以确保共享有序;同时提高消费者对共享的参与度,合理配置消费者和共享者对信息享有的权利,强化权利保护与侵权规制,激励信息共享高效开展。在知识治理机制实施过程中,治理者既要借助法律等正式制度的规制作用,也要借助行业自律规范的引导作用。在评估知识治理机制绩效时,要着重考察信息共享对相关行业发展的促进作用以及消费者对知识服务的满意度。     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Forecasting value at risk and expected shortfall with mixed data sampling

I propose applying the Mixed Data Sampling (MIDAS) framework to forecast Value at Risk (VaR) and Expected shortfall (ES). The new methods exploit the serial dependence on short-horizon returns to directly forecast the tail dynamics of the desired horizon. I perform a comprehensive comparison of out-of-sample VaR and ES forecasts with established models for a wide range of financial assets and backtests. The MIDAS-based models significantly outperform traditional GARCH-based forecasts and alternative conditional quantile specifications, especially in terms of multi-day forecast horizons. My analysis advocates models that feature asymmetric conditional quantiles and the use of the Asymmetric Laplace density to jointly estimate VaR and ES.     

Incorporating satisfaction measures into a restaurant productivity index

The increasing stature of the foodservice industry in the global service economy suggests that productivity analyses—similar to those performed in non-service-based settings—would benefit multi-unit operators by maximizing their desirable operational outcomes while minimizing expenses and other detrimental conditions such as low job satisfaction. This paper suggests that such analyses might be possible through the application of a holistic productivity metric—one that includes traditional operational variables such as revenue, profit, food cost, and labor cost, and previously ignored variables such as guest and employee satisfaction as well as retention equity. Through data gathered from a single chain's 36 corporate-owned, same-brand casual-theme restaurants located in metropolitan centers across the United States, we found that factors leading to maximum outputs such as controllable profit and retention equity include employee satisfaction in addition to expected variables such as cost of goods sold and number of seats. Most notably, employee satisfaction as an input proved to be the most volatile variable in maximizing operational outputs.