基于BP网络对我国通货膨胀率的预测及其分析

本文首先由我国的CPI计算出通货膨胀率,综合分析了我国通货膨胀率和GDP增长率数据。其次,用带有动量项和自适应学习率的BP神经网络预测出2008～2009年我国CPI,从而推算出2008～2009年我国通货膨胀率分别是4.99%和4.91%左右。并提出了一些相应的政策建议。     

武汉市人口与零售商业网点分布优化配置研究

本文根据湖北和武汉统计年鉴等资料中的人口数据和社会商品零售额数据 ,拟合出反映人口分布和商业网点分布的计量模型。然后根据模型的含义 ,并综合考虑其它影响商业网点配置的因素 ,提出适合武汉市人口分布的商业网点优化配置的合理性建议     

Spread spectrum of knowledge network based on information integration environment

The concept and the characteristic of spread spectrum technology of knowledge network based on information integration environment are given at first. And the basic principle of the wireless network of knowledge and information is recommended. Then a project alternatives using the spread spectrum technology and wireless network is given to build a across-the abroad knowledge network system.     

Managing and financing metropolitan public services in China: experience of the Pearl River Delta region

Under China’s current fiscal policies and inter-governmental relations, it is a significant challenge to finance and deliver public services across jurisdictions. This challenge was met in the Pearl River Delta region in southern China with a collaborative governance approach. Directives from higher-level governments and horizontal inter-city fiscal arrangements were successfully combined to deliver public services. Effective networks should be developed to improve co-ordination and collaboration in delivering cross-jurisdictional public services.     

碳市场建立对非控排企业绿色创新的影响研究

以尚未纳入碳交易体系的上市公司为样本,基于溢出效应框架分析碳市场建立对非控排企业环保决策的影响。研究发现,客户被纳入碳市场交易促进了非控排供应商企业的绿色创新。其作用机制在于,碳交易下的控排企业绿色创新具有基于生产网络的压力效应和动力效应,能够激发关联非控排企业的绿色创新意愿,并助推其绿色创新能力提升。这一溢出效应有助于实现非控排企业绿色创新增量提质,且该过程具有方向性,碳市场建立并不存在基于生产网络“自上而下”的治理效果外溢,相关影响在非控排企业客户集中度较高、绿色创新基础较好及环境不确定性较高时更加明显。此外,碳市场建立的绿色创新溢出效应对于推动非控排企业可持续发展具有积极作用。研究结论为高效发挥碳市场绿色治理效果、深化环保改革以促进经济高质量发展提供了管理启示。     

社会资本研究综述

社会资本是近年来学术界研究的热潮,内容涉及微观、中观以及宏观各个层次,取得了一定的成果,但同时也存在一些不足,诸如概念不统一、缺乏有效的测量以及研究中忽视其消极功能等。与此同时,社会资本作为一个外来分析工具和理论模型,面临着研究的本土化问题。     

浅析网络政治动员

网络政治动员对国家政治安全具有双重影响,因此,我们要畅通网络政治参与渠道,加强对网络信息的监控和管理,发挥网络政治动员的正面作用。     

Can information and communication overload influence smartphone app users' social network exhaustion,privacy invasion and discontinuance intention? A cognition-affect-conation approach

Thanks to the expansion of network technologies and the coupling of smartphone hardware, younger generations have unparalleled access to a diverse range of information and interactive communication opportunities. Despite voluminous research on the causes of social network exhaustion and privacy invasion, limited academic attention has been devoted to understanding the driving forces and contributing factors for discontinuing usage of smartphone app. Drawing on the cognition-affect-conation paradigm, the present research proposes a conceptual research model to examine how information overload and communication overload effect social network exhaustion, privacy invasion, and ultimately, smartphone app users' intentions to discontinue usage. The study evaluates data from 442 smartphone app users, employing statistical analyses to demonstrate that both information overload and communication overload significantly impact users' exhaustion and privacy invasion. Additionally, social network exhaustion and privacy invasion are two major predictors of smartphone app discontinuance intention. By identifying the influential factors and causes of younger generations' discontinuous usage intention of smartphone app, this study meets the need for a thorough assessment of the social network exhaustion and privacy invasion phenomena. The findings may potentially enhance comprehension of the discontinuation intention phenomenon in the context of cognitive overload. Furthermore, they may provide novel insights for practitioners regarding effective strategies conducive to managing mobile social network adoption behavior, as well as the preservation of engagement levels among young users in mobile-saturated societies.     

E-commerce sellers' ratings: Is user feedback adequate?

The literature on the theory of public procurement points out two well-known informational problems arising out of information asymmetry: (i) adverse selection and (ii) moral hazard. To reduce these issues and foster credibility and trust in the procurement process while maintaining quality and efficiency in public procurement, e-procurement platforms have turned to reputation or rating systems. Therefore, the research and design of such rating systems are crucial. In this study, we discuss the theoretical underpinnings of procurement and employ the information-theoretic, regression analysis, artificial neural network and principal component analysis (PCA) approaches to estimate the weights of the variables entering the rating system. Using real data from Government e-Marketplace, a business-to-business public e-commerce portal, we empirically determine the weights of the rating variables derived from the transaction-level and user feedback data for sellers. The weights obtained from the PCA are the most applicable compared with the other three methods. We compare the old rating system with the newly proposed design using the Wilcoxon signed-rank test. This results in a statistically significant difference between the two ratings. The canonical correlation and Wilks' trial reveal that the ratings derived from transaction-level data and user feedback are uncorrelated to a great extent. Hence, considering only transaction-level data or user feedback is unlikely to divulge sellers' intrinsic worth. E-commerce platforms can use this approach to quickly implement methods to obtain rating scores on a real-time basis for sellers on online platforms.