国家内部地域文化差异及其对组织的影响

近年来,跨文化管理研究越来越关注国家内部（次国家层面）地域文化差异。在类似中国这样幅员辽阔历史悠久的多民族国家中,经济发展带来的人口流动和企业的跨地域布局使得员工与管理者都需要面对不同外部地域文化环境和组织内部人员地域多样化的挑战,研究国家内部地域文化差异及其对组织的影响有重要意义。 现有实证研究的一个重要共识是国家内部地域文化差异普遍存在于亚洲、欧洲和南北美洲各国。覆盖的国家类型多样,既有美国、巴西等地理跨度较大或有多民族多宗教历史文化背景的国家,也有日本、德国等地理跨度较小或历史上民族和信仰成分较单一的国家;既包括高度工业化的发达国家,也有发展中国家;既有集体主义倾向的东南亚和南美国家,也有个人主义倾向的北美和西欧国家。差异的内容主要包括霍夫斯泰德和施瓦茨的文化价值观维度,尤其是个人主义-集体主义。此外,不同国家之间内部地域文化差异的程度也显著不同。 聚焦中国大陆的研究主要关注了大陆地域文化区的划分和地域文化差异的内容维度。研究早期主要是从理论上根据影响文化的外部因素（如地理环境特征）讨论了文化差异的存在和文化区域划分,后来直接根据价值观相似度对不同省市或地区进行聚类;近年来,少数研究进一步依据多个指标综合划分地域文化区,并开始假设和验证不同区域价值观对个人和组织的影响。本文整合现有的几种中国大陆地域文化区划,用层次聚类法,获得了有较大共识的8个文化区。现有实证研究结果表明,中国大陆在集体主义、长期导向、风险偏好、儒家价值观等文化价值观维度存在地域差异。 有关国家内部地域文化差异如何影响组织行为和企业经营管理的实证文献仍然非常有限。 从跨地区文化比较的视角看,国家内部不同地区的文化价值观有可能影响当地人群的消费模式、员工行为、管理风格和决策（如投资）,进而影响企业生产效率和创新。从跨地区文化互动研究看,母公司企业文化与子公司所在地文化互动融合的模式有可能影响子公司经营绩效。最后,国家内部地域文化差异的程度不仅有可能影响本国企业跨地区布局的方式,还有可能影响企业跨境投资方式、企业国际化程度和经营绩效。 未来研究应更多的关注个人主义-集体主义以外的文化维度,探索主位视角来丰富研究理论和测量方法,尤其亟需更多对地域文化差异和跨地域互动在个人,团队和组织层面的结果的研究。最后,文化随着经济活动、政治变化、人口流动、对外交流互动而不断演化。在经济持续发展、商业活动地理布局不断拓展、人员流动极大增加、改革开放进一步深化的大背景下,我国大陆各区域及港澳台地区的地域文化价值观的演变,及其组织中不同层面的行为和结果的影响也值得探究。     

Dealing with Wicked Problems: Conducting a Causal Layered Analysis of Complex Social Psychological Issues

Causal layered analysis (CLA) is an emerging qualitative methodology adopted in the discipline of planning as an approach to deconstruct complex social issues. With psychologists increasingly confronted with complex, and “wicked” social and community issues, we argue that the discipline of psychology would benefit from adopting CLA as an analytical method. Until now, the application of CLA for data interpretation has generally been poorly defined and overwhelming for the novice. In this paper we propose an approach to CLA that provides a method for the deconstruction and analysis of complex social psychological issues. We introduce CLA as a qualitative methodology well suited for psychology, introduce the epistemological foundations of CLA, define a space for it adoption within the discipline, and, outline the steps for conducting a CLA using an applied example.     

What is “Intervention”?

I begin by noting that several theologians and others object to special divine action (divine intervention and action beyond conservation and creation) on the grounds that it is incompatible with science. These theologians are thinking of classical Newtonian science; I argue that in fact classical science is in no way incompatible with special divine action, including miracle. What is incompatible with special divine action is the Laplacean picture, which involves the causal closure of the universe. I then note that contemporary, quantum mechanical science doesn't even initially appear to be incompatible with special divine action. Nevertheless, many who are well aware of the quantum mechanical revolution (including some members of the Special Divine Action Project) still find a problem with special divine action, hoping to find an understanding of it that doesn't involve divine intervention. I argue that their objections to intervention are not sound. Furthermore, it isn't even possible to say what intervention is, given the quantum mechanical framework. I conclude by offering an account of special divine action that isn't open to their objections to intervention.     

Extinction in human learning and memory

In two experiments, participants were given extinction training in a human causal learning task. In both experiments, three critical experimental cues were paired with different outcomes in a first phase of training and were then extinguished in a second phase. Three control cues were given the same treatment in the first phase of training, but were not then presented in the second phase. Participants' ability to correctly identify the outcome with which each cue had been paired in the first phase was lower for extinguished than for control cues. Causal attributions to the extinguished cues were also lower than those to the control cues, a difference that correlated with outcome memory. These data are consistent with the idea that extinction in causal judgement is due, at least in part, to a failure to remember the cue–outcome relationship encoded in the first phase of training.     

When is a cause the “same”? Incoherent generalization across contexts

A theory or model of cause such as Cheng's power (p) allows people to predict the effectiveness of a cause in a different causal context from the one in which they observed its actions. Liljeholm and Cheng demonstrated that people could detect differences in the effectiveness of the cause when causal power varied across contexts of different outcome base rates, but that they did not detect similar changes when only the cause–outcome contingency, ?p, but not power, varied. However, their procedure allowed participants to simplify the causal scenarios and consider only a subsample of observations with a base rate of zero. This confounds p, ?p, and the probability of an outcome (O) given a cause (C), P(O|C). Furthermore, the contingencies that they used confounded p and P(O|C) in the overall sample. Following the work of Liljeholm and Cheng, we examined whether causal induction in a wider range of situations follows the principles suggested by Cheng. Experiments 1a and 1b compared the procedure used by Liljeholm and Cheng with one that did not allow the sample of observations to be simplified. Experiments 2a and 2b compared the same two procedures using contingencies that controlled for P(O|C). The results indicated that, if the possibility of converting all contexts to a zero base rate situation was avoided, people were sensitive to changes in P(O|C), p, and ?p when each of these was varied. This is inconsistent with Liljeholm and Cheng's conclusion that people detect only changes in p. These results question the idea that people naturally extract the metric or model of cause from their observation of stochastic events and then, reasonably exclusively, use this theory of a causal mechanism, or for that matter any simple normative theory, to generalize their experience to alternative contexts.     

Locating Potential Leverage Points In A Systems Thinking Causal Loop Diagram Toward Policy Intervention

Outcomes depending on various interacting and adaptive parts and actors in a volatile, uncertain, complex, and ambiguous (VUCA) world are challenging to analyze using traditional thinking. Hence, Systems Thinking (ST) can be applied as a tool to aid policy makers in designing interventions that anticipates various ways in which a system might react to policy changes through the practice of integrating multiple perspectives into a causal loop diagram (CLD). We formalize criteria for identifying potential leverage points (PLP) in a CLD. Identifying PLPs enable policy makers to apply ST tools to devise strategies that are capable of addressing VUCA problems.     

Infrastructure and Scaffolding: Interpretation and Change of Research Involving Human Genetic Information

This essay addresses the relationship of interpretation to change, at two levels. One level concerns the revolutionary claims of molecular biology and biotechnology about using genetic information, read literally or with a minimum of interpretation, to reshape human life. The other level concerns the relationship in social studies of science and technology (STS) between interpreting projects in the life sciences and influencing their direction. On that level, the essay is experimental, employing a series of vignettes that introduce themes and questions—scaffolding—intended to stimulate readers to make their own connections between interpretation and change, in science, STS, and society. The vignettes in Part 1, which range from treatment of individuals with PKU or MAOA genes to personalized medicine and biobanks, indicate in different ways that the use of genetic information always requires social infrastructure. Once attention is given to the actual or implied social infrastructure, the prospect of reshaping life using human genetic information raises more questions than it answers. This thread carries over into Part 2, which speaks to an area of STS that needs more development, namely, conceptualizing the structure of the social context of scientific and technological developments and the nature of human agency in the ongoing restructuring of that context. The vignettes create a picture in which the influence on science of an STS interpretation will, like any effort to produce change, depend on how it links with other engagements and with the heterogeneous components that make up ongoing, intersecting processes of science in society.     

Children’s developing understanding of the relation between variable causal efficacy and mechanistic complexity

Two experiments investigated 3–4-year-olds’ ability to infer the causal mechanisms for a pair of lights. In both experiments the exterior of the two lights appeared identical. In Experiment 1, one light displayed a stable activation pattern of a single color while the other light displayed a variable pattern of activation by cycling through a series of different colors (i.e., a more varied effect). Children were asked to judge which light had a more complex internal structure. Four-year-olds were more likely to match the light with the more variable effect with a more complex internal mechanism and the light with the more stable effect with a less complex mechanism. Three-year-olds’ responses were at chance. Experiment 2 replicated this finding when the activation patterns of the two lights were described verbally but never demonstrated. Taken together, these results suggest that 4-year-olds appreciate that the variability of an object’s causal efficacy is related to the complexity of its internal mechanistic structure.     

Two causal theories of counterfactual conditionals

Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause occurs after the event of the then-clause), and Experiment 4 shows the same is true of forward counterfactuals. An amended version of one of the approaches, however, can provide a more accurate account of these data.