The influencing factors of satisfaction and lending intention in online lending investment: an empirical study based on the Chinese market

This paper develops a platform‐based influencing factors model which considers value perception, risk prevention measure, non‐default experience, trust and incentive gap, to better examine the impact of platforms on investors’ satisfaction and lending intention based on the Chinese market. The results reveal that the first four factors positively influence the satisfaction of the investors, while the incentive gap has a negative impact, and there is a positive association between investors’ satisfaction and lending intention. Some specific features of China’s online lending market are identified, which provides valuable insights for online lending platforms and the government.     

基于SARIMA-SVR组合模型的 空气质量指数预测

空气质量指数(AQI)在波动中既具有整体的时间序列线性特征和明显的季节性波动周期,又具有多种因素影响的不确定性,为了提高AQI的预测精度,基于Ri386 3.3.3和Matlab R2014a两种编程软件,提出了一种同时具有线性和非线性的复合特征的时间序列预测模型——SARIMA-SVR组合模型。以太原市2014年1月—2019年7月的AQI月均值数据为基础,利用SARIMA时间序列模型进行线性预测,利用SVR模型对残差进行非线性预测,加和得到组合预测模型的预测结果,分析比较SARIMA,SVR和SARIMA-SVR这3种模型的预测结果和平均绝对百分比误差。结果表明,组合预测模型发挥了2种模型各自的优势,相较于单一预测模型的预测结果而言,其预测精度更高,稳定性更好。通过此模型得到的空气质量预测结果不仅可为人们的日常生活提供指导,而且可为大气污染的防治工作提供科学依据和借鉴意义。     

儒家传统文化与公司违规行为*——基于中国家族上市公司的分析

本文按照“文化-行为”的逻辑，探讨了作为非正式制度的重要组成部分之儒家传统文化对公司违规行为的影响。本文以2007—2014年沪深两市的中国家族企业上市公司为研究样本，考察了儒家传统文化对上市公司违规行为的影响以及其与正式制度（法律）的交互作用。研究发现，儒家传统文化影响力越强，上市公司违规行为发生的概率越低；并且在儒家传统文化与正式制度（法律）两者交互叠加作用时上市公司违规行为发生的概率更低。     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Mobile Commerce Website Success: Antecedents of Consumer Satisfaction and Purchase Intention

With the rapid development of apparel mobile commerce in the United States, more companies view mobile commerce as a new source of competitive advantage. Despite the importance of apparel mobile website quality and its effect on consumer satisfaction and future purchase stimulus, extant research has paid little attention to these topics. This study proposes a website quality–consumer satisfaction–purchase intention research model based on the self-regulatory process theory. Six dimensions of apparel mobile website quality—website visual appeal, apparel visual appeal, brand trust, website information quality, website response time, and website security—were investigated. In all, 293 eligible responses were collected via an online survey. Multiple regression analysis was utilized to test the proposed relationships. Results reveal that website information quality, website visual appeal, apparel visual appeal, and website security positively affect consumer satisfaction toward apparel mobile commerce websites, while website response time and brand trust show insignificant impacts on consumer satisfaction. With higher satisfaction on an apparel mobile commerce website, consumers are more likely to purchase apparel through the website.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

Do ongoing networks block out new friends? Reconciling the embeddedness constraint dilemma on new alliance partner addition

Research Summary: This study addresses a theoretical dilemma regarding how alliance network constraint (reflected by network cohesion) affects a firm’s alliance formation with new partners. Using a network pluralism approach, we separate a firm’s ego alliance network into two activity‐based networks—an exploratory network and an exploitative network—based on the primary value chain activity involved in each alliance. We argue that the cohesion of exploratory or exploitative networks has an inverted U‐shaped effect on the addition of new partners in the same activity‐based network, and a positive effect on the addition of new partners in the other network. Results based on data from the biotechnology industry largely support our predictions with one exception. Our study contributes to both scholarly understanding of network embeddedness and alliance practice. Managerial Summary: The structure of firms’ ongoing alliance networks may have paradoxical implications for their efforts to search for and form alliance with new partners. That is, when a firm’s alliance partners are tightly connected with each other, the cohesive network tends to both encourage and impede the focal firm to add new partners. We resolve this dilemma by showing that when a firm is deeply entrenched in a cohesive alliance network conducting a certain type of activities (e.g., R&D activities), it may not easily add new R&D alliance partners. However, it may still be able to escape from the cohesive R&D alliance network by seeking new partners conducting other activities (e.g., manufacturing activities).     

基于BIM技术的医院建筑设备运维管理研究

近年来,随着科学技术的不断发展,我国医疗水平也在逐步提升,医院建设标准越来越高,医院建筑设备越来越复杂,建筑设备运维管理的问题逐渐突显出来.本文通过分析研究现阶段医院建筑设备运维管理存在的问题,结合BIM技术的优势,提出在医院建筑设备运维管理中引入BIM技术,对医院建筑设备的运维管理方式、管理手段进行优化和改进,从而提高其运维效率、降低运维成本,为医院的长远可持续发展提供保障.     

高管校友关系与业绩预测披露行为

本文考察高管对个人社会关系的依赖是否对上市公司的信息环境产生冲击;具体的,本文系统论证了高管校友关系和上市公司信息披露的重要载体—管理层业绩预测之间的关系.结果显示,当公司与其供应商的高管之间存在校友关系或校友关系更广泛时,公司发布管理层业绩预测的概率和频数均显著下降,即高管校友一定程度上取代了业绩预测,成为了供应链上的信息流通渠道.进一步分析显示,当公司的私有信息成本较高或经营环境更不确定时,校友关系对业绩预测的替代作用更强;而当供应商议价能力较强时,这种替代作用显著下降.此外,本文发现高管校友关系也显著降低了业绩预测发布的自愿性和及时性.最后,本文发现校友关系引起的业绩预测披露减少,削弱了外部信息使用者获取公司层面信息的程度,表现为股价同步性和分析师预测分歧度加大.本文结果表明,高管对个人社会关系的依赖降低了上市公司的信息环境质量,可能使中小投资者处于更弱势的地位,一定程度上损害了市场公平性.本文将个人社会关系的经济影响拓展到信息环境领域,为社会关系如何影响公开信息披露行为提供了新的经验证据.     

基于跨境社交平台的产品推广模式研究

论文调查分析了近年来热度较高的跨境社交媒体平台以及相关使用数据,利用相关性分析方法分析了多个平台的数据,在此过程中发现了各平台间具有相当高的互惠性,并运用互惠比例和推广方式适配度模型完成了对跨境社交平台的运行模式以及推广方式的对比分析,最终研究得出跨境社交媒体平台的产品推广模式。