Post-script—Retail forecasting: Research and practice

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.     

基于关键节点与关键路径的专利集群网络演进研究

专利作为技术和知识的重要载体，是研究技术演进和产业发展的重要信息源。目前缺乏针对专利集群网络中关键节点与关键路径中节点的比较分析和技术知识挖掘。构建基于关键节点和关键路径的专利集群网络演进模型，从两个层面综合分析技术演进特征。检索德温特数据库(Derwent Innovation Index)得到碳化硅肖特基势垒二极管（SiC-SBD）相关专利作为实证数据，时间跨度为1986-2017年。结果表明，SiC-SBD专利集群网络经历了萌芽期、成长期、成熟期和衰退期，其中，萌芽期专利主要涉及基础技术，成长期为器件结构，以完善器件结构、改进二极管性能为主要研究方向。近年来SiC-SBD专利年增长率下降，技术发展速度放缓，专利价值更多体现在其商业价值上。SiC-SBD专利集群网络关键节点与关键路径中节点重合度较高，核心专利识别可通过多视角分析得到。通过关键节点与关键路径分析专利集群网络演进过程，有助于更全面呈现技术演进过程，为决策者识别核心专利、预测技术发展提供参考。     

区域知识产权保护发展的空间格局及结构体系研究

以我国各省知识产权保护制度为研究对象，创新性地将探索性空间数据分析方法与社会网络分析方法相结合，基于地理邻近视角，验证了区域知识产权保护的空间相关性、空间集聚特征和空间溢出效应。同时，突破地理近邻效应的局限，解析区域知识产权保护的空间关联特征。结果表明：我国各省知识产权保护具有全局自相关性，相似地区间存在空间集聚效应，不同发展程度地区的空间关联性质不同；网络化后的区域知识产权保护各节点间联系紧密、网络结构稳定，并且可以确定核心行动者和边缘行动者角色；长三角、珠三角、环渤海等较发达地区与其它地区之间存在较多溢出关系。     

国家高新区设立能够提升城市全要素生产率吗？——基于261个地级市TFP分解数据的路径分析

国家高新区是培育经济增长新动能，实现区域经济高质量发展的重要政策载体，文章利用2001-2014年我国261个城市面板数据，通过渐进双重差分法研究国家高新区设立对城市全要素生产率的影响。研究发现：研究期内国家高新区的设立对城市全要素生产率造成显著的负面影响；从TFP的分解项来看，高新区设立促进了所在城市规模效率提高，却抑制了技术进步和技术效率提高；异质性分析结果表明上述作用具有显著城市等级异质性，在高行政等级城市设立高新区对城市全要素生产率具有促进作用。进一步运用工业企业数据库、微观土地转让数据、城市专利数据等进行机制分析发现，企业间资源错配和土地价格扭曲降低了技术效率，而技术创新下降和产业升级钝化是抑制技术进步的主要机制。     

The moderating effect of average wage and number of stores on private label market share: A hierarchical linear model analysis

Existing research on private label market share is primarily in the context of the Western market. The Chinese market context research is scarce, although private labels are developing rapidly in the past several years. This study investigates how the average wage and number of stores affect the Chinese market's private label market share. More importantly, this paper examines the moderating effect of the average wage and the number of stores on the relationship between the private label market share and product assortment as well as the relationship between the private label market share and pricing. Data collected from a Chinese supply chain dyad is analyzed to study category management using hierarchical linear models. The results reveal that the average wage and the number of stores positively affect the private label market share. Furthermore, the average wage enhances the negative effect of the number of brands, weakens the negative effect of the private label price, weakens the positive effect of national brand price. Meanwhile, the number of stores enhances the positive effect of the SKU proliferation of private label, enhances the negative effects of the number of brands, and enhances the negative effect of the private label price. This study contributes to category management. Furthermore, the findings will be valuable to domestic and international grocery marketers and retailers operating private labels in China.     

COVID19: Holiday intentions during a pandemic

The travel, tourism and hospitality industries have been the worst affected of the world's major economic sectors during the COVID19 pandemic, which has had a devastating effect on both destinations and organisations. Drawing from a sample (N = 385) of adult permanent residents of Athens, Greece, the study examines the impact of COVID19 upon holiday intention. The chaordic systems are evaluated through the use of fuzzy-set Qualitative Comparative Analysis, whilst the study also used Necessary Condition Analysis for the calculation of the size effects of the examined conditions. The findings reveal two sufficient complex configurations leading to holiday intention: (i) holiday risks, and (ii) impact of COVID19. Based on the results, the article also offers a set of managerial implications. The contribution of the study is to both theoretical and methodological tourism domains.     

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities

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.     

Understanding the trend to mobile-only internet connections:A decomposition analysis

A growing portion of internet users rely solely on mobile devices such as smartphones for their online access. The percentage of “mobile-only” households increased from 9% in 2011 to 20% in 2015, more than doubling in only four years. As this shift continues, it leads to the question of what factors are driving the rise in mobile-only adoption. Using nationally representative data, this study uses logistic regressions and a decomposition technique to understand the trend. The decomposition reveals that a significant portion (65%) of the growth was due to an increase in the download speeds of mobile networks. An increased acceptance of mobile-only access by households aged 55 and older was also partly responsible. Understanding (and developing a response to) the trend towards mobile-only adoption will be important as organizations and governments continue to work to close the digital divide.     

Panel LM unit root tests with level and trend shifts

This paper considers a Lagrange multiplier (LM) based panel unit root test that allows for heterogeneous structural breaks in both the intercepts and slopes of a series. We note that many popular time series variables are likely to exhibit changing means and/or trends over time. Given that the usual tests will depend on the nuisance parameters indicating the locations of the trend breaks, we adopt a transformation procedure that makes our new panel unit root tests invariant to the nuisance parameters. To illustrate the importance of the power gain provided by our test, we examine the convergence hypothesis using relative ratios of per capita health care expenditures in 20 OECD countries. Our results provide evidence that the convergence hypothesis is supported.     

The Impact of Public Expenditure and International Trade on Agricultural Productivity in China

The “industry nurturing agriculture” reforms and World Trade Organization accession led to dramatic growth in public expenditure and international trade in China’s agricultural sector. This article aims to estimate the effects of public expenditure and trade on agricultural productivity in China for 2004–2015. A semi-parametric production function with shape constraints is introduced to derive more accurate productivity before the productivity determinants are analyzed with an emphasis on public expenditure and trade. The empirical result shows that public expenditure and exports can effectively improve agricultural productivity, while imports have no significant effects. Policy implications are discussed in the context of supply-side reforms.