基于边缘层活动的战略决策模式

安东尼—安索夫—安德鲁斯范式确定了经典的战略决策模式，高层管理者成为战略决策的权力中心、信息中心与知识中心，处于公司边缘层的员工行为对战略决策的影响常常被忽视。欧洲研究者提出“活动基础观”这一新的战略范式，边缘层行为对公司战略决策的重要意义日益受到重视。边缘层行为是市场导向的、反复试错的结果，公司总部常常在与边缘层的相互冲突、相互学习中发现新战略。     

高职教育实践性教学初探

加强高等职业教育的实践性教学,是高等职业教育教学改革的一个关键的突破口。只有加强实践性教学的探讨和创新,才能突出高职教育的特色。要积极探讨建立实践性教学为主的模式和运行机制,如实践性教学导师制、学生模拟实习公司等。同时,要处理好理论教学与实践教学的关系,处理好培养人才"宽"与"窄"的关系。     

贵州经济增长的影响因素研究:计量模型及实证分析

贵州目前的主要经济指标与全国平均水平相比仍有很大差距.借鉴C-D生产函数,选取物质资本、人力资本以及劳动力作为经济增长的主要影响因素,建立经济增长的模型并对这些因素进行实证分析,得出的结论是:贵州要保持经济的持续增长,需要从树立科学的发展观、加快产业结构调整、重视人力资本投资以提高劳动者素质、降低农村人口比重、加快城镇化进程以及进行制度创新等方面进行努力.     

西部地区形象塑造与设计模式探研

文章从西部地区形象塑造与设计模式应考虑的因素入手,探讨了西部地区形象塑造与设计的模式,并对西部地区形象塑造与设计的经济战略进行了分析,认为只有大力发展西部经济,以良好的经济发展势头,带动西部地区形象的塑造,其它问题才会迎刃而解.     

中国通货膨胀成因的实证分析

通过对可能影响我国通货膨胀的因素,包括经济增长、货币供应量、居民消费水平和工资的格兰杰因果分析和自回归分布滞后模型的拟合,可知我国通货膨胀和货币供应量、居民预期有密切关系,而和其他因素没有显著关系.     

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。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

A graduates ’ satisfaction index for the evaluation of the university overall quality

In the higher educational setting, students provide a relevant contribution to the quality of educational services. In such a context, the measurement of the perceived quality and related satisfaction for the university experience are of primary interest to evaluate the efficiency and efficacy of the learning processes. In this contribution, we aim at assessing the overall quality of the graduates’ university experience in terms of internal and external efficacy by applying the ECSI (European Customer Satisfaction Index) methodology, based on structural equation models and primarily developed in the context of customer satisfaction. For this aim, we propose a modified ECSI model tailored for the higher educational setting, explicitly taking into account the differences among groups of degree program. The study is carried out on data collected by the AlmaLaurea surveys at the University of Florence (Italy) in the period 2014–2017 and concerns a sample of more than 2,000 graduates. We find out eight latent variables that contribute to define the overall quality of university experience. These variables are differently affected by the type of degree program, with the highest levels of external efficacy observed for degree programs belonging to the educational, health, and engineering groups. It also turns out that interventions on the internal efficacy (i.e., quality of hardware and quality of humanware) have a direct positive effect on the university (i.e., loyalty) and an indirect positive effect on the society (i.e., external efficacy).     

Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model

Election forecasting has become a fixture of election campaigns in a number of democracies. Structural modeling, the major approach to forecasting election results, relies on ‘fundamental’ economic and political variables to predict the incumbent’s vote share usually a few months in advance. Some political scientists contend that adding vote intention polls to these models—i.e., synthesizing ‘fundamental’ variables and polling information—can lead to important accuracy gains. In this paper, we look at the efficiency of different model specifications in predicting the Canadian federal elections from 1953 to 2015. We find that vote intention polls only allow modest accuracy gains late in the campaign. With this backdrop in mind, we then use different model specifications to make ex ante forecasts of the 2019 federal election. Our findings have a number of important implications for the forecasting discipline in Canada as they address the benefits of combining polls and ‘fundamental’ variables to predict election results; the efficiency of varying lag structures; and the issue of translating votes into seats.     

How does news sentiment affect the states of Japanese stock return volatility?

This paper examines the impact of public news sentiment on the volatility states of firm-level returns on the Japanese Stock market. We firstly adopt a novel Markov Regime Switching Long Memory GARCH (MRS-LMGARCH), which is employed to estimate the latent volatility states of intraday stock return. By using the RavenPack Dow Jones News Analytics database, we fit discrete choice models to investigate the impact of news sentiment on changes of volatility states of the constituent stocks in the TOPIX Core 30 Index. Our findings suggest that news occurrence and sentiment, especially those of macro-economic news, are a key factor that significantly drives the volatility state of Japanese stock returns. This provides essential information for traders of the Japanese stock market to optimize their trading strategies and risk management plans to combat volatility.