组织模块化对研究型大学技术转移绩效的影响

技术转移是将社会科技资源转化为实体经济效益的重要途径。当前，我国研究型大学技术转移渠道不畅、技术转移绩效不高、大学科技成果成功产业化“最后一公里”问题凸显。基于现实需求和研究缺口，构建组织模块化对研究型大学技术转移绩效影响的理论模型。在此基础上，收集我国38所研究型大学样本数据，采用SPSS24.0和AMOS软件进行结构方程路径分析，论证和检验组织模块化对于研究型大学技术转移绩效的影响机制。结果表明：组织模块化对研究型大学技术转移绩效具有显著正向影响；技术创新能力在组织模块化与技术转移绩效之间起显著中介作用；知识共享的调节作用主要体现在组织模块化独立性、响应性与技术创新能力之间的关系上，其对组织模块分工性与技术创新能力关系的影响作用不显著。     

Organization,technology and management innovations through acquisition in China’s pork value chains: The case of the Smithfield acquisition by Shuanghui

This paper reviews the changes in Shuanghui’s operations after the Smithfield acquisition as well as transformations in China’s pork industries. As income grows and diets change, there has been an increasing demand for high-quality pork and more processed pork products. However, China’s swine and pork industries are still at an early stage of development as evidenced by the low market concentration, intensive use of labour, a proliferation of intermediaries, and low levels of technology. Bounded by the established consumer preferences for freshness and the status quo in the midstream and upstream segments, Shuanghui is in a slow process of upgrading its domestic operations with Smithfield’s leading brands, import of chilled and frozen pork, advanced technologies, and the vertically integrated business model after this acquisition. In particular, it remains unclear whether Shuanghui can follow Smithfield’s experience to establish stable and safe hog supplies by multiplying self-owned hog farms, maintain and expand its current contracts with large-scale commercial hog farms, or rely more on global sourcing of pork primarily from the U.S.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Forecast quality improvement with Action Research: A success story at PharmaCo

There is a gap in the forecasting research surrounding the theory of integrating and improving forecasting in practice. The number of academically affiliated consultancies and knowledge transfer projects that there are around, due to a need for improvements in forecast quality, would suggest that many interventions and actions are taking place. However, the problems that surround practitioner understanding, learning and usage are rarely documented. This article takes the first step toward trying to rectify this situation by using the specific case study of a fully engaged company. A successful action research intervention in the Production Planning and Control work unit improved the use and understanding of the forecast function, contributing to substantial savings, enhanced communication and improved working practices.     

科技服务业产出测算：指标确立、数据收集与未来预测

科技服务业产出测算是对科技服务业产出数据的统计与分析，加强科技服务业产出数据的规范统计和应用分析可为我国政府调整产业政策提供重要数据支撑。以科技服务业产出测算为研究对象，结合学者观点、国家标准及各级政府相关产业政策，提出将分支产业作为初始指标的横向统计思路，构建科技服务业产出测算指标体系。在数据收集方面，强调要做好“收”与“填”两个基本工作；在指标确立和数据收集基础上，进一步提出未来预测的两种方法，以此作为科技服务业产出数据预测的初步探索；提出应从统一数据统计标准、完善数据调查与公开制度、加强数据资源开发3个方面建立健全我国科技服务业产出数据测量与管理体制机制。     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Customer reactions to self-checkout discontinuance

Self-service technologies (SSTs) increasingly permeate retail space. Yet, sometimes retailers decide to revert to human-delivered service mode by discontinuing their incumbent SST. In this study, we examine how self-checkout (SCO) discontinuance affects customers’ perceptions of SCO technology and purchase behavior. We conduct a natural field experiment by surveying two groups of customers pre- and post- SCO discontinuance: treatment group (who experience discontinuance) and control group (who do not experience discontinuance). Leveraging difference-in-differences analyses, we find that SCO discontinuance results in decreases in customers’ satisfaction with technology, intentions to use technology, perceived simplicity of technology, and basket size. Our results inform managers of the potential downsides of discontinuing SST and provide corroborating evidence of the technology’s benefits.     

产业共性技术政府支持性研发外包合同影响因素研究

建立产业共性技术研发外包博弈模型，从政府视角研究以共性技术产出最大化为目标的最优政府支持性研发外包合同，并对最优外包合同影响因素进行分析。研究表明：足够多的政府预算是共性技术政府支持性最优研发外包合同存在的前提，最优合同条款随政府预算的增大而增多，企业保留收益增大倒逼政府预算增加；企业最优知识技术投入与共性技术价值系数以及从研发中分享的收益份额正相关，与政府支付的固定研发费用负相关；政府最优知识技术投入与固定研发费用及单位投入成本系数负相关；企业最优投入与政府投入成本系数负相关，与政府投入对研发成功的贡献系数正相关，且企业最优投入随其研发能力的增强而加大。最后，根据研究结论，从政府预算、项目前景及支持性资源分配3个方面提出共性技术研发外包合同实施的政策建议。     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.