Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.     

A systematic approach to conducting review studies: An assessment of content analysis in 25 years of IB research

Content analysis has become a popular method for qualitative and quantitative analyses in management and international business (IB) research. It is increasingly used in literature reviews to assess extant knowledge and understand intellectual structures. However, it is often poorly understood and incorrectly applied. In this article, we identify benchmark criteria and develop coding schemes that IB scholars can use in review studies. We also demonstrate the application of content analysis through a review of content analysis–based articles, published in the top eight IB journals from 1991 to 2015.     

STOCK‐BOND CO‐MOVEMENTS AND FLIGHT‐TO‐QUALITY IN G7 COUNTRIES: A TIME‐FREQUENCY ANALYSIS

This paper examines co‐movement between stock returns and changes in 10‐year government bond yields as well as flight‐to‐quality behaviour in G7 countries. We conduct the wavelet squared coherence analysis to explore the dynamics in both time and frequency domain. Our results provide evidence of positive co‐movements, which vary over time and across investment horizon. The higher co‐movement is found to be more concentrated in the lower frequency bands. We further analyse the dynamic nature of the scale‐dependent wavelet correlations and find that the correlations are highly volatile and significantly increase across different time scales during the episodes of equity market turbulence. The increase in correlations reflects flights from stocks to safer bond investments as a result of dramatic changes in investor sentiment and risk aversion at times of market stress.     

中国北方大规模生猪养殖技术效率研究

[目的]通过对我国北方地区大规模生猪养殖技术效率进行测度,以期发现影响其技术效率增长的主要因素,为我国现阶段生猪养殖业战略性区划调整"南猪北进"工作提供实践指导和理论支持。[方法]根据随机前沿分析方法 (SFA)基本原理,运用对数型柯布—道格拉斯生产函数及我国北方大规模生猪养殖数据构建中国北方大规模生猪养殖随机前沿分析模型,对我国北方大规模生猪养殖技术效率进行测度并据此提出相关建议。[结果]北方地区大规模生猪养殖技术效率值纵向上来看整体在不断下降,而从地域上来看内蒙古自治区和东北地区技术效率值较高,北京与西北地区技术效率值较低。[结论]内蒙古自治区与东北地区适宜作为大规模生猪养殖承载地,与国家政策具有一致性;华北地区与西北地区大规模生猪养殖技术效率测度值较低,存在较大改进空间;针对该现状提出构建经济效益与环境保护并重的规模生态生猪养殖体系。     

河北省北部山区农村生态环境治理及绩效评价

[目的]农村生态环境是一个复杂的系统,严重的生态环境问题制约着农村经济的发展以及新农村建设,探讨其治理和绩效,有利于解决生态环境建设中的各种问题,对于区域发展具有重要的理论和实践意义。[方法]文章通过构建农村生态环境治理及绩效评价指标体系,采用层次分析法确定各个指标的权重以及灰色关联法分析各指标间的灰色关联度。[结果]河北省北部山区农村生态环境治理绩效评价指标体系要素层中权重值大小依次为生态经济(0. 483 9)、生态环境(0. 273 6)、生态人居(0. 137 1)、生态保护(0. 105 4),说明该地区农村经济水平已达到一定水平,但生态环境、生态保护等仍有待提高。灰色关联法计算结果表明河北省北部山区生态环境中关联系数最高的是畜禽粪便处理率(0. 754),生态经济中关联系数最高的是农民人均纯收入(0. 624),生态保护中关联系数最高的是化肥施用量(0. 846),生态人居中关联系数最高的是饮用水合格率(0. 682),而关联度大小依次为生态保护(0. 724)、生态环境(0. 662)、生态人居(0. 573)、生态经济(0. 543),说明该地区生态环境治理仍有很大空间。[结论]研究结果指出河北省北部山区农村生态环境治理的不足,为进一步完善区域生态环境具有重要的指导意义。     

Information disclosure quality: correlation versus precision

We investigate how a multidimensional disclosure quality (i.e., correlation and precision) determines an optimal information disclosure strategy. We find that, for an infinitely lived, unlevered firm with market perfection, a truth‐telling disclosure is optimal at increasing the expected firm value. However, for a finitely lived, levered firm in the presence of market imperfections (e.g., bankruptcy cost), the optimal disclosure quality depends negatively on the level of imperfections. Once we consider the agency problem, such dependence can become positive, thereby highlighting the importance of a proper managerial‐incentive scheme to align the information disclosure interests of managers and shareholders.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

The impact of German-speaking online media on tourist arrivals in popular tourist destinations for Europeans

This empirical study analyzes the relationship between the sentiments in online media with regard to travel destinations and corresponding tourist arrivals. We expect the media reports on political and economic instability and turmoil to enhance tourist arrival nowcasts and forecasts, as they can probably complement them with information on disruptions and shocks. Therefore, we believe this research will help to build better models for tourism demand nowcasting and forecasting. We use the sentiment in the German-speaking online media because the German-speaking region is the most populated in Europe and has the largest group of travelers visiting destinations in and around Europe.

An artificial neural network is used to analyze the mood of the media. The software classifies news items regarding potential tourist destinations with either positive or negative labels. The number of positive and negative news items is used to build sentiment indices for popular tourist destinations for Europeans.

Our results show strong correlations between the mood concerning tourist destinations and tourist arrivals in these countries. Indeed, disruptions and shocks prevalent in the news are reflected in similar ratios in both tourist arrivals and sentiment indices. These results can be used as a new explanatory variable for tourism demand modelling.     

Discouraging Rainforest Transformation: An Ex-ante Policy Impact Analysis

Despite extensive efforts made by national and international certification agencies, Indonesian smallholder farmers’ participation in palm oil certification schemes adoption remains low. A fundamental obstacle is the smallholder practice of rainforest transformation into oil palm plantation which is forbidden by the agencies. In this context, we investigate three policies that could lead to a reduction in rainforest deforestation by smallholders: price premium on certified palm oil; the provision of environmental information; contributor recognition. In order to evaluate the influence of the policies ex-ante, we conduct a social dilemma experiment involving rubber and oil palm smallholders in Jambi Province, Sumatra, Indonesia. The findings indicate that the price premium and provision of context-specific environmental information could reduce rainforest transformation. However, a statistically significant effect of contributor recognition was not found.     

Economic policy uncertainty and mergers and acquisitions: Evidence from China

This study examines the relationship between economic policy uncertainty and mergers and acquisitions (M&As) in China. Using all listed Chinese companies on the Shanghai and Shenzhen Stock Exchanges as well as 4188 ​M&A deals from the period of 2001–2018, we show that Chinese firms are more likely to make acquisitions during periods of high economic policy uncertainty, which contradicts the behavior of US firms. We further show that state-owned enterprises (SOEs) are less likely than non-SOEs to make acquisitions during periods of high economic policy uncertainty. SOEs are less likely to use only cash for their acquisitions during periods of high economic policy uncertainty. These results indicate the prudence of SOEs regarding acquisitions relative to non-SOEs during periods of high economic policy uncertainty. Moreover, acquisitions during periods of high economic policy uncertainty are associated with an increase in shareholder wealth for acquirers, and this wealth effect is more pronounced for SOEs.