多元文化经历对领导者能力及发展的影响

随着全球化发展的深入, 越来越多的企业领导者具有多元文化经历。多元文化经历是指个体具有直接或间接与外国文化元素或人群进行交互的经历。拟从领导者发展(leader development)和领导力发展(leadership development)两个层面, 全面地探讨多元文化经历对企业领导者能力形成及发展的影响：第一, 探讨多元文化经历对领导者能力发展(包括个人层面、关系层面和社会层面能力)的影响及内部机制; 第二, 探讨多元文化经历对领导力发展(包括领导力涌现、领导力选拔和领导力效能)的影响及内部机制。项目成果不仅能够丰富领导者能力形成与发展等组织管理相关理论, 还能为企业的全球化管理及国际化人才培养实践提供建议。     

团队文化多样性对团队创新的影响及作用机制

随着经济全球化和跨国企业的迅速发展, 越来越多的企业中开始出现具有多样性文化背景的团队。企业管理者期待具有不同文化背景的员工能在团队工作中贡献新的观点和视角, 从而促进团队创新。因此, 探讨团队文化多样性与团队创新之间的影响关系已成为近年来学术界和企业界共同关注的焦点问题。所以, 采用社会分类-信息加工的理论视角, 首先厘清团队文化多样性的理论框架, 并进一步检验团队文化多样性与团队创新之间的影响关系及内在机制; 并且, 分别从团队内部和团队外部因素出发, 探讨团队文化多样性与团队创新影响关系中的边界条件。在实践上, 研究结论也将为企业有效管理跨文化团队, 推动企业创新提供一定的理论指导。     

睡眠剥夺与购物后悔：来自大规模个体层面数据的证据

睡眠剥夺正在成为人们面临的一种普遍问题,但是已有研究很少将睡眠剥夺与消费者行为联系起来。本文采用真实购物情境下的大规模消费者个体层面数据(N=1625472),通过建立计量经济学模型来研究睡眠剥夺与购物后悔的关系。结果表明,睡眠剥夺显著地增加了消费者的购物后悔倾向,具体在行为上表现为增加了商品的退货概率和加快了商品的退货速度。此外,商品的价格正向调节了睡眠剥夺对购物后悔的影响,而促销优惠金额的比例则负向调节了睡眠剥夺对购物后悔的影响。     

Authority Argument Schemes,Types, and Critical Questions

Authority arguments generate support for claims by appealing to an agent’s authority status, rather than to reasons independent of it. With few exceptions, the current literature on argument schemes acknowledges two basic authority types. The epistemic type grounds in knowledge, the deontic type grounds in power. We review how historically earlier scholarship acknowledged an attractiveness-based and a majority-based authority type as equally basic type. Crossing these with basic speech act types thus yields authority argument sub-schemes. Focusing on the epistemic-assertive sub-scheme (‘an epistemic authority A_E asserts a proposition P’), we apply a meta-level approach to specifying critical questions. Results improve the evaluation of this sub-scheme and show how similar improvements are obtainable for other schemes.

     

Peirce Knew Why Abduction Isn’t IBE—A Scheme and Critical Questions for Abductive Argument

Whether abduction is treated as an argument or as an inference, the mainstream view presupposes a tight connection between abduction and inference to the best explanation (IBE). This paper critically evaluates this link and supports a narrower view on abduction. Our main thesis is that merely the hypothesis-generative aspect, but not the evaluative aspect, is properly abductive in the sense introduced by C. S. Peirce. We show why equating abduction with IBE (or understanding them as inseparable parts) unnecessarily complicates argument evaluation by levelling the status of abduction as a third reasoning mode (besides deduction and induction). We also propose a scheme for abductive argument along with critical questions, and suggest retaining abduction alongside IBE as related but distinct categories.     

Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model

A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model. Moreover, this study is one of few studies that specifically compared the performance of full-information maximum likelihood (FIML) estimation versus robust weighted least squares (WLS) estimation when the normality assumption is violated. The research also manipulated the number of nonnormal latent trait dimensions. Results showed that FIML consistently outperformed WLS when there were one or multiple skewed latent trait distributions. More interestingly, the bias of the discrimination parameters was non-ignorable only when the corresponding factor was skewed. Having other skewed factors did not further exacerbate the bias, whereas biases of boundary parameters increased as more nonnormal factors were added. The item parameter standard errors recovered well with both estimation algorithms regardless of the number of nonnormal dimensions.