网络消费者仿冒品购买意图的实证研究——基于TAM和TPB集成视角

基于TAM和TPB集成的视角,以仿冒品为研究对象,从网络消费者的感知利益和感知风险出发,构建网络消费者仿冒品购买意图的概念模型,从而分析网络消费者仿冒品购买意图的形成路径。在设计相关变量测量量表的基础上,通过问卷调查获取相关数据,用SPSS19.0进行问卷信度及效度分析后,利用AMOS19.0验证概念模型。实证研究结果表明:网络消费者的感知利益、感知有用性和感知易用性影响仿冒品购买态度;而购买态度、感知有用性、主观规范性和感知行为控制影响网络消费者的购买意图,不过网络消费者的感知风险对仿冒品的购买态度影响不显著。因此,对品牌企业而言,应注重提高其网络平台的感知易用性,充分关注网络消费者的感知利益,建立查假打假职能部门,实现品牌企业联合协同打假。     

网购退货后续购买行为的实证研究

互联网时代下,对于消费者来说网上购物已成趋势。而退货作为网购过程的一部分,影响着消费者的满意度和后续购买行为,已成为不可避免和亟待解决的问题。目前国内关于有过退货经历的消费者其后续购买行为及所创造的价值研究甚少。笔者从退货后续购买行为这一角度展开实证研究,选取退货经历为自变量,研究在退货政策宽松程度这一调节变量下消费者的后续购买行为(购买次数、数量和金额)。实证结果表明:对于有过退货经历的消费者而言,宽松的退货政策,有助于良性的后续购买行为。本文提出,零售商应重塑对于退货问题的看法和意识,提升消费者的退货体验,以期将退货成本转化为新的利益增长点,增强消费粘性。     

产品伤害危机下消费者风险感知对购买意愿的影响：一个有调节的中介模型

产品伤害危机爆发后，消费者有着怎样的心理变化，存在哪些因素影响消费者对危机品牌的态度?论文基于“刺激—加工—响应”(S-O-R)理论，以风险感知为自变量，以消费者宽恕为中介变量，以品牌熟悉度为调节变量，以产品伤害危机爆发前后消费者购买意愿变化为因变量建立了研究模型，然后通过问卷调研和Bootstrap等方法对研究模型进行了实证检验。检验结果表明：风险感知对消费者购买意愿有显著影响；消费者宽恕中介了风险感知对购买意愿的影响；品牌熟悉度在风险感知对购买意愿的影响中起到调节作用。     

华东地区消费者民族中心主义研究

本文在文献综述的基础上,以华东地区为例,构建了消费者民族中心主义研究模型,并提出了消费者民族中心主义与对待国外产品的态度的假设,并运用相关分析和可靠性分析等实证研究方法研究了消费者民族中心主义、对待国外产品态度、购买国外产品行为之间的关系。     

华东地区消费者民族中心主义研究

本文在文献综述的基础上,以华东地区为例,构建了消费者民族中心主义研究模型,并提出了消费者民族中心主义与对待国外产品的态度的假设,并运用相关分析和可靠性分析等实证研究方法研究了消费者民族中心主义、对待国外产品态度、购买国外产品行为之间的关系。     

香奈儿品牌形象对消费者购买意愿的影响分析

中国消费者为现阶段全球最大的奢侈品消费群体。自香奈儿进入中国市场以来,因其高贵、优雅、简约的设计风格受到中产阶级以上女性消费群体的欢迎和喜爱。在奢侈品行业中,品牌形象作为企业重要的无形资产,成为了品牌竞争的另一领域。基于上述背景,论文以“香奈儿品牌形象对消费者购买意愿的影响分析”为选题,以贝尔模型为研究基础,使用问卷调查法获取数据,运用SPSS 19.0统计软件对数据进行处理分析,并结合消费者行为学相关理论,研究品牌形象的不同变量对于消费者购买行为产生的影响。     

融资约束对企业可持续增长的影响——基于上市公司的实证分析

企业增长的关键是实现销售增长与企业资源的相互匹配,否则,可能得不偿失。因此,可持续增长思想的确立对企业战略目标的实现至关重要。文章以实证研究为主,结合理论分析,首先选出可能影响可持续增长的自变量,包括11个指标;然后运用皮尔逊相关分析和逐步回归法,对融资约束的各替代变量如何影响上市公司可持续增长进行实证分析;最后得出盈利能力、公司规模、发展能力与可持续增长正相关,债务状况、国有化程度与可持续增长负相关的结论。     

创业营销对创业绩效的影响研究:一个有调节的中介模型

先前学者们对于创业营销的研究大多聚焦于定性研究,文章从创业营销的创业导向与市场导向入手,通过实证研究的方法探究创业营销对创业绩效的提升机制。以创业导向作为自变量、市场导向作为中介变量、顾客导向为调节变量,构建一个有调节的中介模型。通过问卷搜集中国297家企业的数据检验假设,结果表明:创业导向、市场导向均正向影响创业绩效,且当市场导向作为中介变量时,对两者之间对创业绩效的总效应更加明显;顾客导向的调节作用并不明显。最后,提出了管理建议与未来的研究方向。     

对外贸易与我国经济增长关系的实证研究基于VAR模型

随着改革开放的深入,对外贸易已成为中国经济增长的一个重要推动力.本文以1997-2017年的中国统计数据为样本,运用stata15软件对相关变量进行回归分析.利用VAR模型对我国对外贸易与经济增长的关系进行了分析和研究,并通过实证数据研究得出了相关结论.最后基于分析结论提出中国应如何发展对外贸易以更好地推动经济增长的建...     

国家品牌效应及其调节变量研究

在当今国际竞争环境下,国家品牌正变得越来越重要。本文使用文献资料法,指出国家品牌包括3个层面:整体国家形象、整体产品形象和类别产品形象。消费者对这3个层面的整体感知影响他们的购买决策,这就是国家品牌效应。国家品牌效应受到一些变量的调节作用:消费者品牌熟悉度、认知需求、介入度会调节国家品牌和消费者感知、评价、态度之间的关系;消费者民族中心主义、爱国热情、敌意会调节消费者感知、评价、态度和消费者购买意向之间的关系;经济发展水平、文化类型、产品类型是整体框架的调节变量。     

Theoretical considerations and simulation models related to the method of Sonquist and Morgan

The tree which results from the application of the Sonquist and Morgan method is based on the principle of dichotomizing the population at each point according to one of the independent variables in such a way as to explain as much variance of the dependent variables as possible. Confronted before applying this tree-analysis-method, with a distribution of the dependent variable, we may imagine that this distribution was the result of some process Pj. The present paper describes such processes, in which each element of the population is going through a sequence of nodes at each of which the value of the dependent variable is modified. These modifications can be expressed by certain types of regressive equations. The nodes can be considered as constituting a tree structure. If we are analysing the resulting distribution, the tree underlying the above described process may come out. However, the regressive functions involved are not determined. Furthermore, not tree-like processes may bring upon a certain distribution. Thus the tree-analyses can reveal only part of the processes which led to a given distribution. The explanation of such processes by soical science theories is studied.     

Who volunteers? Constructing a hybrid theory

This paper describes three major theoretical perspectives in research on volunteering: social theories that stress the importance of context, roles, and integration; individual characteristic theories that emphasize values, traits, and motivations; and resource theories that focus on skills and free time. It unites research from multiple disciplines into a single hybrid model, performs a preliminary test of the model on a nationally representative US dataset, and concludes with recommendations for scholars and practitioners. Using the 1995 Midlife in the US dataset, we operationalized concepts from each theoretical category and found that variables measuring each perspective played a substantial and independent role in predicting volunteering. Our hybrid model, which includes significant variables from each theory, offers some directions for recruitment and retention by showing how social roles and networks can constrain or encourage volunteering at different stages of the life course. As social roles and networks are both highly predictive and easily observed, volunteer managers can use them to recruit and retain volunteers. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessing social identity and collective efficacy as theories of group motivation at work

The increasing reliance globally on teams raises the question whether existing theories of work motivation remain applicable as bases for inspiring people to perform. There is some support for the view that well-known theories of work motivation were conceived with individuals as the unit of analysis, and that they may not apply for groups at work. Some authors have added a cultural dimension to this argument, contending that existing theories have ignored cultural practice in eastern countries, where collectivism at work is the normal mode. In light of arguments of this nature, this article critically examines the merits of two theories, social identity, and collective efficacy, as bases of group work motivation, and provides a model that can serve to organize studies on teamwork processes and outcomes, and for designing studies that compare the two types of groups, or that look individually at the workings of a particular type. The model includes four types of variables, namely contextual, independent, moderator, and dependent.     

Foreign capital inflows,domestic savings and the price of political stability in the sudan: Weisskopf revisited

This paper reworks Weisskopf's estimates of the effect of foreign capital inflow on domestic savings for a later time period. The Sudan is presented as an example of a public sector dominated economy, dependent on one major export crop and politically unstable. While Weisskopf's savings function had an indication of a negative relationship between public sector savings and official foreign capital inflow, problems of collinearity between the independent variables cast doubt on its utility for analysis of economies dependent on limited primary exports. The negative relationship between public sector savings and official is explained in terms of the expansion of the state's bureaucracy and military.     

成都市房价影响因素的回归分析与事后模拟

运用SPSS软件,以GDP、市区居民人均可支配收入、住宅投资完成额、住宅施工面积、住宅竣工面积和市区人口为自变量,采用1997～2005年9年的数据,建立了多元线性回归模型,对成都市房价这一因变量进行了模拟分析。模型通过各种检验,得出了影响成都市住房价格最重要的因素是市区人口和住宅施工面积这一结论;最后,并运用模型做了事后模拟,其结果比较接近市场价格。     

Limited-dependent rational expectations models with future expectations

This paper examines limited-dependent rational expectations (LD-RE) models containing future expectations of the dependent variable. Limited dependence is of a two-limit tobit variety which may, for example, arise as a result of a policy of imposing limits on the movement of the dependent variable by means of marginal as well as intramarginal interventions. We show that when the forcing variables are serially independent the model has an analytical solution which can be computed by backward recursion. With serially correlated forcing variables, we discuss an approximate solution method, as well as a numerically exact method that, in principle, can be implemented by stochastic simulation, although in practice it is limited by available computational capacity. The paper discusses some properties of the approximate solutions and reports the results of a limited number of Monte Carlo experiments in order to illustrate the computational feasibility of using the exact solution when the fundamentals are serially independent and the approximate solution when they are serially correlated.     

Measurement error in causal analysis of panel data: Attenuated versus inflated relationships

The presence of random measurement error is commonly thought to cause attenuation of statistical relationships. While this is an unquestionable truth in bivariate analysis, it cannot be generalized to the multivariate case without qualification. This paper shows that measurement error may give rise to overestimates of parameters in causal analysis whenever there is more than one independent variable and the independent variables are correlated. If the independent variables are not measured with the same amount of reliability, there may also be considerable error in estimates of the relative magnitude of their impact. Both problems are particularly serious when the amount of measurement error is large relative to some of the causal effects such as in panel analysis with lagged dependent variables.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Boolean comparative analysis of qualitative data

This paper explores the use of Boolean logic in the analysis of qualitative data, especially on the basis of so-called process theories. Process theories treat independent variables as necessary conditions which are binary rather than variable in nature, while the dependent variable is a final cause. In this respect, Boolean comparison appears to be a rigorous method for testing process theories on the basis of qualitative evidence, for example, from case studies. It is argued that Boolean logic may compensate for some of the weaknesses of the conventional approach to process studies — going back to Stuart Mill's (1843) system of logic — by systematically comparing observations without forsaking complexity too much. In addition, Boolean logic systematically structures the kind of interpretive dialogue between theory and evidence typically found in qualitative research. Finally, a procedure for using Boolean analysis is outlined. This procedure involves systematic attempts to falsify and identify hypotheses on the basis of truth tables constructed from qualitative data.     

On direction of dependence

Under the assumption of the existence of linear relationship between two random variables, new formulas are introduced to express the coefficient of correlation. One of these formulas, the fourth power of the correlation coefficient is used to determine the direction of dependency between two random variables. Also an interpretation of the correlation coefficient as an asymmetric function of kurtosis coefficient and skewness coefficient of dependent variable and independent variable is provided. In the absent of the intercept in linear regression, the correlation coefficient is also expressed as a ratio of coefficients of variation between independent and dependent variables.