“功能性表达性模型”与中国警察信任——基于全国样本的多重中介与调节模型分析

功能性模型认为，提升警察信任的主要方法在于预防和控制犯罪，降低犯罪率与提升公众安全感；而表达性模型认为，提升警察信任更应注重维护社会正义与社区凝聚力。基于提出促进中国警察信任度提高的理论模型，提升公众获得感、幸福感、安全感，实现国家和社会整体安全的最终目的，采用结构方程模型验证“功能性-表达性模型”与中国警察信任关系的结果表明，功能性模型与表达性模型内部因素之间存在显著路径关系，同时功能性模型显著影响表达性模型指标进而改变公众对警察的信任程度。经调节效应分析后发现，主观社会阶层负向调节被害经历到犯罪恐惧感的直接路径，社交媒体使用负向调节犯罪恐惧感到警察信任的直接路径；在主观社会阶层和社交媒体使用的共同调节作用下，“被害经历→犯罪恐惧感→警察信任”中介路径效应值，随社交媒体使用频率的提升而增大，随主观社会阶层的升高而减小。     

Genetic distance,international experience and the performance of cross-border R&D for EMNEs

Cross-border R&D can contribute to the enhancement of independent innovation capabilities of emerging markets multinational enterprises (EMNEs) by benefiting from knowledge management. However, scant research exists examining the location impact of cross-border R&D for EMNEs on performance implications. This paper fills this important theoretical gap by building upon the literature of genetic distance in connection with knowledge management. We use a panel data of Chinese high-tech listed companies to empirically examine the impact of genetic distance on the performance of cross-border R&D and the role played by international experience. Our results reveal a positive relationship between genetic distance and the performance of cross-border R&D. Importantly, we highlight the motivation for cross-border R&D of EMNEs to acquire technical knowledge magnifies the positive effects of genetic distance and performance. Furthermore, our analysis indicates that international experience significantly enhanced the positive effect of genetic distance on cross-border R&D performance. We conclude this paper by discussing theoretical contributions to genetic distance, international management and knowledge management, as well as practical implications for cross-border R&D of EMNEs.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Regression analysis of case-cohort studies in the presence of dependent interval censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.     

An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.     

Reviews of Academic English Listening Tests for Non-Native Speakers

This article presents a review of 20 tests designed for assessing the academic English listening skill of second or foreign language learners. The available test information has been systematically condensed in purpose, listening construct, task characteristics, and validity evidence. It was found that most of the tests were developed for proficiency and placement purposes in academic contexts, with few of the tests serving for making workplace decisions. Also, global, local, and inferential skills constitute the construct in most listening comprehension tests. A practical approach for justifying the uses of these tests for different stakeholders is discussed. This review is a valuable resource for educators, administrators, test developers, and researchers looking for a comprehensive analysis of existing English tests that assess listening comprehension in second or foreign language learners.     

考虑消费者双重偏好的低碳供应链减排策略研究

为了削弱传统零售商在渠道博弈中的垄断地位，制造商尝试建立在线渠道与零售商展开竞争。本文在考虑消费者低碳偏好的基础上，又将消费者的渠道偏好进行区分，通过不同决策模式的对比及数值算例分析，寻求低碳供应链最优的减排边界。研究表明：1分散决策时，制造商的低碳决策会受消费者渠道偏好的影响；集中决策时，制造商的低碳决策保持不变。2不同决策模式的减排边界由渠道偏好决定，渠道偏好较大时，分散决策的减排边界更高；渠道偏好较低时，集中决策的减排边界更高；渠道偏好中立时，两者的减排边界相同。3制造商可以通过升级低碳技术、实行低碳化管理等方式降低减排成本，同时通过低碳产品认证、广告宣传等方式提高消费者对低碳产品的认可程度。     

基于大数据合作资产的适应性创新--数字经济的创新逻辑(二)

万物互联时代,数据被认为是与土地、劳动、资本、知识、技术和管理并重的第七大生产要素。当前,多样化的商业实践创新对数据价值的深度挖掘正在突破经济学在传统意义上对数据及其价值的限定,然而,理论上对此还缺乏相应的概念凝练和探讨。本文从企业与消费者价值共创角度提出大数据合作资产的概念,并探讨基于大数据合作资产和适应性创新的数字经济创新逻辑。研究表明:大数据合作资产是一种互动性资源,是企业与消费者在数字化服务交互中成为能够被另一方所拥有和利用并能创造当前或未来经济收益的数字化资产;单纯拥有异质资源不一定会形成合作资产,只有当异质化资源被有效整合、且被使用于企业与消费者服务交换中,才能体现为行动者创造收益的价值潜力;协同演化促进了企业与消费者对彼此的适应性调整,基于大数据合作资产激发以即时调整、即时反馈和难以预测为特征的适应性创新,进而促进了数字经济创新。     

Parenting Practices,Autonomous Motivation,and Adolescent Diet Habits

Guided by self‐determination theory, the aim of this study was to examine whether adolescents’ autonomous motivation for making healthy diet choices mediates the association between parenting practices regarding diet and adolescent food choices. We analyzed data from the NCI FLASHE study, a survey of demographically diverse parents and their adolescent children (N = 1,646 dyads). Path models supported self‐determination theory’s assertion that indirect, encouraging parenting practices fostered internal motivation; in contrast, regulation was both positively and negatively linked to motivation depending on the model. Models also yielded direct paths between parenting practices and healthy and unhealthy diet choices. Overall, regardless of what predicted motivation, adolescents’ autonomous motivation was linked with their consumption of healthier foods and drinks. As such, cultivating a sense of internal motivation during these formative years may put young people on a path toward healthy patterns of self‐regulation later in life.