Effective HR implementation: the impact of supervisor support for policy use on employee perceptions and attitudes

Abstract

Effective implementation of human resource (HR) policies appears to depend on supervisors’ active involvement in the intervention process. Following recommendations of a recent intervention evaluation framework, we examine how perceived supervisor support during the implementation of a work-life intervention helps to change participants’ perceptions of organizational family supportiveness, and how this ultimately changes participants’ engagement and turnover intentions. A three-wave longitudinal study in a professional services firm (N = 434) that has formally involved supervisors in the process of a work-life intervention showed support for our study hypotheses. Supervisor support for policy use influenced employees’ positive work–home culture perceptions, which in turn strengthened employees’ work engagement and diminished their turnover intentions over time. We discuss practical implications and give recommendations on the future design of HR interventions and related policies.     

关于加快农田水利供给侧改革的几点思考

我国灌溉面积位居世界第一,主要矛盾由总量不足转变为结构性矛盾,深入推进供给侧结构性改革提上议程。针对存在的主要结构性矛盾,研究提出系统解决方案,即调整结构、去除库存、提升品质、降低成本、补齐短板。调整结构重点是严格落实"以水定地",着力以水资源、水生态、水环境承载能力倒逼灌溉面积调整;去除库存重点是依法实施"僵尸"工程降等报废,及时办理注销登记;提升品质重点是大力发展高效节水灌溉,提升灌溉服务质量;降低成本重点是深化国有灌区管理体制改革,创新"最后一公里"管理模式;补齐短板重点是坚持统分结合、重点在统的原则推进农田水利产权改革,深化农业水价综合改革,建立准确补贴机制。     

普惠金融与县域资金外流：减贫还是致贫*——基于中国592个国家级贫困县的研究

本文利用全国592个国家级贫困县的数据，采用空间计量模型实证分析了普惠金融对县域资金外流的影响，并验证了贫困县资金外流是否会产生致贫效应。本文研究发现：如果普惠金融只注重解决贫困地区对金融机构的接触性排斥，会进一步加剧资金外流，对减贫产生负向影响，即存在显著的致贫效应。这种致贫效应具有明显的空间外溢性，邻近县域的贫困状况在很大程度上会彼此“传染”，并具有空间衰减特征的地理边界。因而需要客观认识普惠金融的本质，有针对性地选择恰当有效的实施载体，解决好对信贷产品等关键金融服务的使用性排斥问题。     

Economic Impacts of the Possible China–US Trade War

This article uses a multi-country global general equilibrium (GE) model to numerically simulate the effects of possible China–US trade wars. We introduce an endogenous trade imbalance structure with trade cost into the model which helps to explore both tariff and non-tariff trade war effects. Our simulation results show that China will be significantly hurt by the China–US trade war, but negative impacts are affordable. The US can gain under unilateral sanction measures to China, but will lose if China takes retaliation measures. Comparing the effects under mutual trade war, China will lose more than the US. Introducing non-tariff barrier trade wars will intensify the negative effects, and comparatively negative effects to China are larger than to the US. Mexico’s involvement in trade war with the US will strengthen the negative effects and comparatively hurt the US more. Under non-cooperative and cooperative Nash bargaining equilibrium, the US can gain more than China in trade war negotiation, which means the US has stronger bargaining power than China. Additionally, trade wars between China and the US will hurt most countries and the world especially in GDP and manufacturing employment, but benefit their welfare and trade.     

颠覆性创新四阶段扩散过程模型

基于颠覆性创新、创新扩散、创新模糊前端等相关理论，从全流程角度创新性地构建了颠覆性创新四阶段扩散过程模型，包括颠覆性创意产生、颠覆性创新产品开发、侵蚀非主流市场、占据主流市场4个阶段，各阶段之间相互关联、循环往复，并受到技术、市场、政策、竞争等外部环境因素影响。选取液晶电视机和山寨手机作为高端和低端两个颠覆性创新典型案例，通过对其进行验证性分析，构建具体的高端与低端颠覆性创新四阶段扩散过程模型。     

Reorganizing School Lunch for a More Just and Sustainable Food System in the US

Public school lunch programs in the United States are contested political terrains shaped by government agencies, civil society activists, and agri-food companies. The particular organization of these programs has consequences for public health, social justice, and ecological sustainability. This contribution draws on political economy, critical food studies, and feminist economics to analyze the US National School Lunch Program, one of the world's oldest and largest government-sponsored school lunch programs. It makes visible the social and environmental costs of the “heat-and-serve” economy, where widely used metrics consider only the speed and volume of service as productive work. This study demonstrates that such a narrow understanding of the labor of lunch devalues care and undercuts the potential for school food provisioning to promote ecological and feminist goals. Further, it proposes a “high road” alternative and outlines an agenda for reorganizing school food provisioning to maximize care in all its dimensions.     

Does the Eco‐Management and Audit Scheme Foster Innovation in European Firms?

This paper studies whether environmental management systems can spur eco‐innovation, analyzing EMAS (Eco‐Management and Audit Scheme) adoption and patented innovations (at the European Patent Office) at firm level. It uses an original panel database of 30 439 European firms belonging to all sectors from 2003 to 2012. An original instrumental variable is implemented to control for potential endogeneity. The analysis reveals that EMAS adoption is conducive to more innovation at the firm level. The results vary across countries and sectors. In particular, EMAS is positively related to green patents for medium and low technology manufacturing. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

新时代耕地占补平衡的逻辑根源、模式探索与管理创新——基于“新时代耕地占补平衡方式改进与管理创新研讨会”的思考

研究目的:对耕地占补平衡的发展阶段与政策绩效、存在问题与逻辑根源进行分析,对改进方式进行探讨,进而提出管理创新建议,为新时代耕地占补平衡政策实施提供科学依据,为促进城乡融合、扶贫攻坚和乡村振兴提供参考。研究方法:综合分析与系统推理。研究结果:(1)耕地占补平衡存在实质性不平衡、时空性不平衡、政策性弱化等微观、中观及宏观层面问题;(2)在数量方面,通过盘活宅基地增量、存量和完善易地调剂方式,统筹城乡和区域平衡来实现数量占补平衡。(3)在质量方面,通过优化建设用地约束指标、完善耕地质量评价、规范耕作层保护与再利用等促进耕地质量占补平衡。(4)在生态方面,通过完善指标调剂的价格内涵、扩大耕地补偿标准范围、转变耕地补充途径等实现耕地生态占补平衡。研究结论:建议做好顶层设计,抓好规划引领,构建耕地占补平衡与城乡要素流动的平衡机制,建立耕地占补平衡后续生态管护长效机制,提升软硬实力等。     

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.