Complex Network of Aviation E-Services in the Belt and Road Initiative: A Heuristic Study of Small Data based on Block Modeling

ABSTRACT

The aviation e-service system is an important part of support for business communication in regions covered by Belt and Road initiative. In exploring the role of airline companies in this system, we examine the structure of the network, which consists of airlines interconnections based on small data of individual attributes of aviation companies in aviation e-services, and study these interconnections across different groups using block modeling. The heuristic solution of airline companies in development of international communication and cooperation is further discussed. Among our policy implications for global managers, we suggest the enhancement of cross-regional cooperation among airlines.     

Cluster Analysis for mixed data: An application to credit risk evaluation

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.     

Can stable environmental protection officials’ tenure reduce illegal emissions?

Based on the day-night PM_2.5 difference of the nearest atmospheric monitoring points to measure whether there is illegal emission, this paper explores the impact of environmental protection officials' tenure (EPLT) on illegal emissions. The results show that there is a positive correlation between EPLT and illegal emissions. Stable EPLT can reduce illegal emissions by increasing environmental information disclosure and environmental subsidies. It can increase illegal emissions by weakening public pollution reporting and media supervision, environmental law enforcement and promoting corruption. In addition, EPLT can increase illegal emissions as EPLT is less than 3.55 years, while EPLT can increase illegal emissions as EPLT exceeds 6.21 years. Further, when mayor's tenure and EPLT are both stable, the promotion effect of EPLT on illegal emission is stronger. Stable company director's tenure can also aggravate the promotion effect of EPLT on illegal emissions.     

Are USDA reports still news to changing crop markets?

This study investigates whether major USDA reports still provide important news to changing crop markets. The news component of each report, or market “surprise,” is measured as a difference between the USDA estimate and its private expectation in corn, soybeans, and wheat markets. Changes in the relevance of USDA information are assessed by examining changes in the magnitude of market surprises and shifts in the futures price reaction to these surprises, which isolates the impact of each report. The stable size of market surprises over time suggests that competition from alternative data sources has not reduced the news component of USDA crop reports. Increasing price reaction to most reports, including those facing competition from alternative information sources, suggests that value of public information may be enhanced in uncertain markets affected by structural changes.     

欧盟视阈下在线平台中个人数据的增值税处理

近年来,在线平台的迅猛发展推动了全球经济数字化加速转型,但同时也引发了包括增值税政策适用在内的诸多挑战.通过研究《欧盟增值税指令》和欧州法院的判例法,以及分析个人数据与在线平台运行之间的关系可以推断出,个人数据是在线平台提供访问服务构成的增值税应税范围内的供应,且与消费者是否向在线平台支付金钱形式的对价无关.在实践中,使用客观价值方法来确定在线服务供应的应税金额存在实际困难,而使用直接联系标准解决易货交易问题,会导致增值税税基在本质上得到扩张.因此,建议当前不要将在线平台访问服务的供应视为一项应税交易.此外,对于商家为获得消费者数据而向平台付款的情形,因其不符合直接联系标准,也不应被视为服务对价.     

浅谈基于大数据背景下的数字经济发展——以数字电网为例

我们已经进入了一个全新的大数据信息化时代,充满了更多的机遇与挑战。大数据及数字经济的出现极大程度地改变了传统技术及工作思维的方式。数字经济是继工业和农业之后的又一大经济形态,为社会的经济增长提供了新的动力。通过分析电网行业传统的科学技术及作业方式,论文简要阐述了基于大数据信息化发展下电网的影响,并认为信息化技术的发展对于数字经济有着至关重要的影响及重要性。最后论文讨论了数字经济发展存在的问题及方向。     

An innovation to improve health outcomes in Amajuba district,KwaZulu-Natal,South Africa

Systems’ thinking places high value on understanding the context. This study focused on the collection of disaggregated data in order to understand the context, to facilitate improvement of health outcomes. The aim of this article was to assess the implementation of municipal ward-based health data collection (disaggregated data) and health care workers’ perceptions of this data collection process. This cross-sectional study used mixed methods in Amajuba district. The participants were professional nurses at the Primary Health Care level. Of the 131 respondents, 123 (93.9%) collected municipal ward-based health data, and found it useful. Opportunities for improving data collection were identified. Disaggregation of the data at ward level contributes to a better understanding of the target population’s health, assists planning for health needs and enables provision of targeted interventions in order to improve health outcomes, to prevent financial regression and waste of health resources.     

Human resource management practices and voluntary turnover: a study of internal workforce and external labor market contingencies

We tested relationships between employee quit rates and two bundles of human resource (HR) practices that reflect the different interests of the two parties involved in the employment relationship. To understand the boundary conditions for these effects, we examined an external contingency proposed to influence the exchange-based effects of HR practices on subsequent quit rates – the local industry-specific unemployment rate – and an internal contingency proposed to shape employees’ conceptualization of their exchange relationship – their employment status (i.e. full-time, part-time and temporary employment). Analyses of lagged data from over 200 Canadian establishments show that inducement HR practices (e.g. extensive benefits) and performance expectation HR practices (e.g. performance-based bonuses) had different effects on quit rates, and the former effect was moderated by unemployment rate. The effects of HR practices on quit rates did not differ between FT and PT employees, but a different pattern of main and interactive effects was found among temporary workers. These findings suggest that employees’ exchange-based decisions to leave may be less affected by the number of hours they expect to work each week, and more by the number of weeks they expect to work.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.