感知风险对在线旅游产品购买意愿的影响分析

旅游电子商务发展迅猛,互联网购物平台的虚拟性和远程性强化了消费者感知风险,它也成为影响旅游电子商务发展的重要因素之一.文章聚焦消费者感知风险,运用SPSS 19.0及AMOS 21.0软件,构建以5个维度的感知风险为自变量、购买意愿为因变量、信任为调节变量的结构方程模型,探讨变量间相关关系.结果发现:感知风险对信任的影响差异较大,仅经济风险感知与信任呈显著负相关;感知风险对购买意愿总效应皆呈负相关;信任作为中介变量存在明显的调节作用,有效地降低了感知风险对购买意愿的负向影响.     

Semiparametric regression of clustered current status data

This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.     

金融发展与区域创新的交互影响

基于2008—2017年长三角地区41个地级市的数据，通过构建联立方程模型，考察金融发展与区域创新之间的交互影响，探讨两者的空间溢出效应与地区交互影响。研究发现：金融发展对区域创新的正向促进作用不受时间跨度影响，但区域创新对金融发展的影响存在时间跨度异质性，短期内区域创新对金融发展影响不确定，而长期内区域创新反向促进金融发展，忽略这种反向影响会显著高估金融发展对区域创新的促进作用；区域创新与金融发展均存在正向空间外溢性，临近地区金融发展促进本地区的区域创新，但临近地区区域创新对本地区的金融发展作用并不显著。金融发展与区域创新应当融合联动，互利共生，实现双赢。     

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.     

浅谈矩阵特征值的估计

矩阵的特征值问题是矩阵计算的一个重要方向,在众多的领域中都得到了应用。在这样的大背景下,有必要深入地研究矩阵的特征值的估计问题。基于此,本文将结合国内外对于矩阵的特征值的估计方面的研究,以及一些具体的应用实例,来深入地探索矩阵的特征值的估计问题。     

服装系列设计方法之“抽象方程”论

通过长期的实践教学经验总结,将服装系列设计方法与数学方程联系在一起,形成服装系列设计的"抽象方程"理论,使服装系列设计具有了定向、定量、明确、有序的特征,力求寻找一条由简到繁、由易到难、循序渐进的学习实践方法,帮助服装设计初学者尽早摆脱设计瓶颈,步入设计正轨。     

The effect of different data structures,sample sizes on model fit measures

In this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.     

Hierarchical likelihood approach to non-Gaussian factor analysis

Factor models, structural equation models (SEMs) and random-effect models share the common feature that they assume latent or unobserved random variables. Factor models and SEMs allow well developed procedures for a rich class of covariance models with many parameters, while random-effect models allow well developed procedures for non-normal models including heavy-tailed distributions for responses and random effects. In this paper, we show how these two developments can be combined to result in an extremely rich class of models, which can be beneficial to both areas. A new fitting procedures for binary factor models and a robust estimation approach for continuous factor models are proposed.     

Did small and medium enterprises maintain better jobs during the early years of the recession? Job quality multidimensional evidence from Spain

While the literature has indeed confirmed a general tendency linking small and medium enterprises (SMEs) to a dynamic of greater job creation, there is little available evidence on what has happened to job quality since the financial crisis. Through a representative sample of 5311 employees in 2008 (first year of job destruction) and 4925 employees in 2010 (last year for which data were available), and using a two-stage structural equation model, this article empirically analyses the multidimensional determinants of job quality, by enterprise-size class, in Spain. The research has revealed three main results. First, job quality in Spain improved in all enterprises, regardless of their size, during the early years of the recession. Second, the greatest improvements were found in SMEs. Although job quality was already better in SMEs than in large enterprises in 2008, the differences between them subsequently widened. Third, this accelerated divergence was explained by the following dimensions: working conditions, work intensity, health and safety at work, and work–life balance. These dimensions were much more positive in SMEs. Employment-related public policy should therefore focus more specifically on SMEs. There are two reasons for this. First, despite the recession, SMEs have shown themselves to be key factors in the explanation of job quality. Second, by making changes to their value generation model, they could continue to drive the creation of better quality jobs.     

Concept mapping as an approach for expert-guided model building: The example of health literacy

Concept mapping served as the starting point for the aim of capturing the comprehensive structure of the construct of ‘health literacy.’ Ideas about health literacy were generated by 99 experts and resulted in 105 statements that were subsequently organized by 27 experts in an unstructured card sorting. Multidimensional scaling was applied to the sorting data and a two and three-dimensional solution was computed. The three dimensional solution was used in subsequent cluster analysis and resulted in a concept map of nine “clusters”: (1) self-regulation, (2) self-perception, (3) proactive approach to health, (4) basic literacy and numeracy skills, (5) information appraisal, (6) information search, (7) health care system knowledge and acting, (8) communication and cooperation, and (9) beneficial personality traits. Subsequently, this concept map served as a starting point for developing a “qualitative” structural model of health literacy and a questionnaire for the measurement of health literacy. On the basis of questionnaire data, a “quantitative” structural model was created by first applying exploratory factor analyses (EFA) and then cross-validating the model with confirmatory factor analyses (CFA). Concept mapping proved to be a highly valuable tool for the process of model building up to translational research in the “real world”.