中国高端装备制造业自主技术创新效率稳定性及影响因素研究

在全面实施创新驱动发展战略的时代背景下，揭示中国高端装备制造业自主技术创新效率稳定性，测度相关因素的影响强度与方向，对揭示中国高端装备制造业自主技术创新规律，制定其发展策略具有重要指导意义。聚焦中国高端装备制造业自主技术创新效率稳定性及影响因素，采用DEA-Malmquist指数方法和Tobit回归模型进行实证研究，结果表明，中国高端装备制造业自主技术创新效率存在一定波动性，且各细分行业技术创新效率内部稳定性较差，产业自主技术创新效率整体稳定性不高；自主技术创新投入因素和环境影响因素对产业自主技术创新效率的影响具有较高的异质性，其中创新资金投入和人力资源投入对自主技术创新综合效率、纯技术效率及规模效率具有正向影响，而新产品开发投入和企业规模对自主技术创新综合效率、纯技术效率和规模效率的影响具有异质性，政策因素与市场因素分别具有显著和非显著负向影响，外源性创新因素具有显著正向影响，且影响强度较大。在此基础上，提出调整政府对高端装备制造业创新支持模式、加强外源性创新与智力引进等策略建议。     

基于状态空间模型的政产学研资协同创新四螺旋影响因素实证研究

在三螺旋理论框架下，搭建政府、产业、大学、研究机构和资本部门四螺旋模型，运用文献归纳法选取多元主体协同创新6类影响因素，以主体间协同程度测度结果为因变量基础，利用状态空间模型动态分析这些因素影响方向、大小和趋势。实证结果表明，中国经济因素表现为促进作用持续提升；中国技术市场规模和创新产业规模表现为先抑制后促进，且促进作用不断提升；中国产品市场需求和创新投入表现为抑制作用持续提升；中国创新产出在时间上表现为促进作用先提升后下降并最终稳定的状态。最后，提出不同政策建议，以促进中国政产学研资协同创新体系后续发展。     

Pitching research: ‘qualitative cousins’ and the ‘extended family’

This study has two related goals – one very specific and one more general. The specific goal is to constructively engage with Lodhia (2017) on the issue of whether and to what extent ‘qualitative cousins’ are well served by Faff's (2015) original pitching research (PR) template. Using this ‘cousins’ focus as a primer, the more general goal is to update the agenda created by Faff (2015) and in so doing, explore the ‘extended family’ of PR work/resources now available.     

Drivers of research impact: evidence from the top three finance journals

We study the characteristics of all published papers in the top three finance journals (JF, JFE and RFS), and how these paper characteristics affect the number of citations in Google Scholar and the Web of Science database. First, we find the characteristics in the universalist perspective remain constant while the characteristics in the constructivist and presentation perspectives increase over time. Second, some characteristics are significantly different between the high-impact and the low-impact papers. Third, paper quality, research method, journal placement and paper age are the most important drivers. Last, different drivers play different roles in different journals.     

Risks from self-referential peer review echo chambers developing in research fields: 2018 Keynote Address presented at The British Accounting Review 50th Anniversary Celebrations,British Accounting and Finance Association Annual Conference,London

Denigration of academic experts and expertise, amid a resurgence of political populism, poses a challenge to the legitimacy of academic research. Addressing this challenge requires us to continually demonstrate the importance of basing policy interventions on reliable evidence, rather than unevidenced assertions that gain traction through communication echo chambers. However, unconscious confirmation biases in collection and analysis of evidence can impair the reliability of our research insights. A key source of such confirmation biases are unchallenged ideologies and other taken-for-granted assumptions underlying any research (sub)field. This essay argues that informal and formal peer review processes at many stages of research need to highlight and challenge both conscious selectivity bias and unconscious confirmation bias. However, they are unlikely to do so where researchers only take on board feedback from peers in the same (sub)field who share ideological commitments and taken-for-granted assumptions. In such circumstances, self-referential peer review echo chambers can develop that entrench rather than challenge weaknesses in a research (sub)field. This can be a major risk to the effectiveness and reputation of any academic research (sub)field; a risk we need to confront.     

烹饪原料在烹饪加工过程中水溶性维生素的保存方法研究

水溶性维生素是一类重要的食品营养成分,其含量少、稳定性差、在高温下易被破坏。因此,在烹饪过程中,要注重对水溶性维生素的保护。本文从饮食烹饪角度出发,分析日常烹饪方法对食物中水溶性维生素的破坏,并提出科学合理的烹饪方法。     

绿色环保军用食品的研究与进展

军用野战食品是保证战士饮食的重要食物,对提高战士的身体素质、提升军队的战斗力具有至关重要的作用。各国军队首脑对此都十分重视,将此项内容作为军队建设的重要内容。基于此,本文综述了相关学者对军用食品的研究,以期为相关学者提供借鉴。     

Steady state adjusting trends using a data-driven local polynomial regression

Economic variables usually follow a dynamic trend pattern. However, it is difficult to estimate this trend precisely as numerous economically- and statistically-based estimation methods exist. This contribution proposes a data-driven nonparametric trend that is local polynomial, to improve arbitrary trend estimations of commonly used methods concerning the selection of the smoothing parameter and the dependence structure. An iterative plug-in (IPI) algorithm determines the bandwidth endogenously and allows a theory-based interpretation of the length of growth processes. This length of the bandwidth reflects the lengths of the steady state periods. Consequently, the bandwidth identifies the time period of stable economic conditions and can detect economic changes. To demonstrate the power of this estimation approach, an extensive simulation study is performed. Furthermore, examples using US and UK GDP data along with a guide for the optimal choice of algorithms for empirical applications are provided. This proposed method yields new insights for growth dynamics, cyclical movements and their dependence.     

Permit markets,carbon prices and the creation of innovation clusters

Innovation clusters combining public and private effort to develop breakthrough technologies promise greater technological advances to slow down climate change. We use a multi-country model with an emission trading system to examine whether and how international climate policy can incentivize countries to create such innovation clusters. We find that a minimal carbon price is needed to attract applied research firms, but countries may nevertheless fail to invest in complementary research infrastructure. We construct a mechanism that leads to innovation clusters when emissions targets are set before uncertainty surrounding technological developments is resolved. It is a combination of low permit endowments for the country with the lowest costs to build the needed infrastructure, compensation for this country by profits from permit trade, and maximal possible permit endowments for the remaining countries. We outline how the EU-ETS can be further refined according to this mechanism.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.