财务柔性能力与中国上市公司持续性创新——兼论协调创新效应与自适应效应

基于财务柔性组织理论,本文利用2007-2017年我国A股上市非金融类公司微观数据,从理论和实证检验分析了企业财务柔性能力对持续性创新的影响,结果显示企业财务柔性对持续性创新存在正向促进作用。进一步,本文探讨了财务柔性可能存在的“协调创新效应”和“自适应效应”两种机制。协调创新效应相关检验结果显示企业储备的财务柔性能力能有效协调内外部资源,释放融资约束的负向影响,从而正向促进企业持续性创新。检验结果还显示,企业主动地调节财务柔性储备,能够在不同维度环境的不确定性影响下发挥“自适应效应”,助力企业实现持续创新的目标。总体而言,本文结论改善了企业断点式或阶段式的创新投入方式,切实提升了企业自主研发能力和质量,对`实现我国创新“量质平衡”有重要意义。     

企业在线社群内容组合策略的影响研究

现代企业通过建立在线社群实现与消费者的互动,希望在向消费者提供服务的同时进行更好的营销,然而如何提供在线社群中的营销与服务内容却是一大难题.本文在营销—服务二元理论的基础上,提出了在线社群内容二元性的平衡维度与结合维度概念,并研究平衡维度与结合维度如何影响销售业绩与消费者的满意度.结合机器学习方法,本文发现,平衡维度对消费者满意度和销售绩效有提高作用,但是,结合维度对消费者满意度及企业绩效的影响呈倒U型;另外,企业员工的技能水平对内容二元性策略的效果有着显著的调节作用.研究结论对企业理解在线社群中的营销内容与服务内容之间的二元关系,以及内容提供策略的价值机制有重要的指导意义.     

Oppositional Girlhoods and the Challenge of Relational Politics

Over the last five years, scholars from a wide variety of disciplines have problematized the discourse of “adolescent female exceptionalism” (Switzer in Fem Theory 14(3):345–360, 2013, p. 4) popularized by the NIKE Foundation’s Girl Effect. Arriving at similar conclusions, scholars point to the artificial neocolonial divisions between ‘the West’ and ‘the rest’ of the world animated by an ‘invest in girls’ logic. This paper endeavors to move beyond the “oppositional girlhoods” (Bent in Sociol Stud Child Youth 16(1):3–20, 2013a, p. 7) bind of girl effects discourse to propose that differentially positioned and experienced girlhoods might be better understood as transnational, relational cultural formations. Drawing from our empirical research with girls in North America and Sub-Saharan Africa, we consider the implications of girls’ increasing global visibility as the ‘saviors of humanity’ from different geopolitical contexts. We then go further to suggest the oppositional girlhoods frame assumes reductive, apolitical, and ahistorical claims of divergence between girlhoods in the Global North and Global South “with highly unequal effects” (Gonick et al. in Girlhood Stud 2(3):1–9, 2009, p. 3). By countering the normative construction of global girlhoods as mutually exclusive forms of personhood and historical experience, our project authorizes a new understanding of girlhoods as mutually constituted and relationally contingent. It is from within this relational framework that we propose new directions for thinking about girlhoods transnationally.     

目标取向对员工创新行为的影响机制研究——基于隐性知识共享行为的视角

在目前中国经济创新驱动转型的时代,企业对员工个人创新行为的依赖度越来越高.文章基于11家企业184名被试的问卷调查数据,采用结构方程分析方法,探索员工不同的目标取向通过隐性知识共享行为对创新行为的影响机制.研究结果表明,学习目标取向对隐性知识共享行为产生正向影响;绩效证明取向对隐性知识共享行为产生正向影响;绩效回避取向对隐性知识共享行为的影响效果不显著;同时,隐性知识共享行为对员工创新行为有显著影响,目标取向通过隐性知识共享行为对员工创新行为起间接作用.该研究结论进一步拓展了隐性知识共享行为在目标取向和员工创新行为中间接作用的研究领域,也为创新驱动型组织建设提供理论指导依据.     

A log rank type test in observational survival studies with stratified sampling

In randomized clinical trials, the log rank test is often used to test the null hypothesis of the equality of treatment-specific survival distributions. In observational studies, however, the ordinary log rank test is no longer guaranteed to be valid. In such studies we must be cautious about potential confounders; that is, the covariates that affect both the treatment assignment and the survival distribution. In this paper, two cases were considered: the first is when it is believed that all the potential confounders are captured in the primary database, and the second case where a substudy is conducted to capture additional confounding covariates. We generalize the augmented inverse probability weighted complete case estimators for treatment-specific survival distribution proposed in Bai et al. (Biometrics 69:830–839, 2013) and develop the log rank type test in both cases. The consistency and double robustness of the proposed test statistics are shown in simulation studies. These statistics are then applied to the data from the observational study that motivated this research.     

Boosting multi-state models

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.     

Cox regression with missing covariate data using a modified partial likelihood method

Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard function with the idea being to profile out this function before carrying out the estimation of the parameter of interest. In this step one uses a Breslow type estimator to estimate the cumulative baseline hazard function. We focus on the situation where the observed covariates are categorical which allows us to calculate estimators without having to assume anything about the distribution of the covariates. We show that the proposed estimator is consistent and asymptotically normal, and derive a consistent estimator of the variance–covariance matrix that does not involve any choice of a perturbation parameter. Moderate sample size performance of the estimators is investigated via simulation and by application to a real data example.     

Statistical inference for homologous gene pairs between two circular genomes: a new circular–circular regression model

In this paper, we investigate the problem of determining the relationship, represented by similarity of the homologous gene configuration, between paired circular genomes using a regression analysis. We propose a new regression model for studying two circular genomes, where the Möbius transformation naturally arises and is taken as the link function, and propose the least circular distance estimation method, as an appropriate method for analyzing circular variables. The main utility of the new regression model is in identification of a new angular location of one of a homologous gene pair between two circular genomes, for various types of possible gene mutations, given that of the other gene. Furthermore, we demonstrate the utility of our new regression model for grouping of various genomes based on closeness of their relationship. Using angular locations of homologous genes from the five pairs of circular genomes (Horimoto et al. in Bioinformatics 14:789–802, 1998), the new model is compared with the existing models.     

Non-parametric regression on compositional covariates using Bayesian P-splines

Methods to perform regression on compositional covariates have recently been proposed using isometric log-ratios (ilr) representation of compositional parts. This approach consists of first applying standard regression on ilr coordinates and second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts. This gives easy-to-interpret parameters indicating the relative effect of each compositional part. In this work we present an extension of this framework, where compositional covariate effects are allowed to be smooth in the ilr domain. This is achieved by fitting a smooth function over the multidimensional ilr space, using Bayesian P-splines. Smoothness is achieved by assuming random walk priors on spline coefficients in a hierarchical Bayesian framework. The proposed methodology is applied to spatial data from an ecological survey on a gypsum outcrop located in the Emilia Romagna Region, Italy.     

A flexible generalization of the skew normal distribution based on a weighted normal distribution

The skew normal distribution of Azzalini (Scand J Stat 12:171–178, 1985) has been found suitable for unimodal density but with some skewness present. Through this article, we introduce a flexible extension of the Azzalini (Scand J Stat 12:171–178, 1985) skew normal distribution based on a symmetric component normal distribution (Gui et al. in J Stat Theory Appl 12(1):55–66, 2013). The proposed model can efficiently capture the bimodality, skewness and kurtosis criteria and heavy-tail property. The paper presents various basic properties of this family of distributions and provides two stochastic representations which are useful for obtaining theoretical properties and to simulate from the distribution. Further, maximum likelihood estimation of the parameters is studied numerically by simulation and the distribution is investigated by carrying out comparative fitting of three real datasets.