老旧小区改造模式化推广探究--西安碑林区老旧小区改造为例

在我国社会建设与发展中,对于城镇老旧小区的改造是一项重大的民生工程以及发展工程,可以有效满足社会人民群众对于美好生活的需要,全面推进惠民生、扩内需,助推城市的更新以及开发建设方式的转型,可以促使我国民生工程取得更加良好的成效。文章首先探讨与分析老旧小区改造的重要意义,老旧小区模式化推广的必要性,文中通过实际案例分析,最后提出相应的建议,希望可以为相关企业老旧小区改造工程全面推进贡献出绵薄之力。     

The legacy of state socialism on attitudes toward immigration

Does the politico–economic system affect preferences for immigration? In this study, I show that individuals exposed to life under state socialism have formed and persistently hold different attitudes toward immigration. By exploiting the division and reunification of Germany, I estimate the influence of state socialism on attitudes toward immigration. Drawing on rich individual panel data, I find that East Germans who lived under state socialism, are 15 percent more likely to oppose immigration than West Germans who spent their entire life in a democratic, capitalist country. This difference in attitudes toward immigration is persistent over time and across space, and largest for cohorts born and raised under state socialism. This gap in attitudes can be traced back to a longer-term deterioration in trust. Evidence from members of a group that opposed the authoritarian system highlights the importance of state socialist ideology for attitude formation.     

Technological character,function type,and the longevity of standardized knowledge

This study seeks to determine the validity of the current de jure standards management system. The de jure standard is an important tool for innovation policy. However, its review interval has been fixed in the management system and maintained without the use of empirical analysis to guide its development. Therefore, this study (1) examines the factors that affect the longevity of the standards, and (2) outlines methods for improving how the de jure standards are managed. Results indicate that design and mark standards influence the longevity of standardized knowledge. This is notable, given that design for innovation is an emerging area of research that is commonly studied through the analysis of design patent data. Taken together, this study’s major findings are twofold. First, different technological categories have significantly different effects on longevity. Because the longevity of some technological sectors is naturally longer than others, there exists a need for a more flexible interval system. Second, the longevity of the mark and design standard is longer than the longevity of other types of standards. Both developing and developed countries utilize the de jure standard, so the policy implications of these findings are widely applicable.     

女性高管对企业研发创新的影响：一个有调节的中介模型

企业创新是企业持续发展的内在动力,也是推动国家产业升级、建设创新型国家的重要举措.近年来女性高管比重不断提高,学术界开始关注女性高管对企业研发创新的影响.以沪深两市2010—2018年A股上市公司为研究样本,实证检验女性高管对企业研发创新投入的影响.结果表明:女性高管负向影响企业研发创新投入;其负向作用以风险承担为中介实现;企业所有制调节了中介过程的后半段路径.     

试论土壤调查、土地类型与耕地评价兼论倪绍祥先生的土地评价思想

目的 以倪绍祥先生土地评价思想概括和我国土壤普查、土地类型述评为依据，探讨我国耕地评价工作完善路径和方向。方法 文章采用文献研究和对比分析方法。结果 （1）倪先生经过多年研究实践，形成以土壤调查为前提的，土地类型单元为基础，开展土地评价的系统思想。（2）我国土壤调查、土地类型工作中断，致使农用地分等定级、土地质量地球化学等工作缺乏扎实的数据基础，使得现有的耕地评价成果难以满足新时代耕地资源“三位一体”保护要求，也妨害国家对自然资源的科学管理。（3）国家生态文明建设、国土资源科技创新和自然资源管理机构职责整合等宏观背景，客观上要求将土地评价与土壤类型、土地类型相联系，夯实耕地评价的科学基础，创新耕地质量评价体系。（4）科学组织第三次土壤普查，服务于耕地评价实践，将土壤资源融入自然资源统一管理成为必然趋向。结论 倪绍祥先生有关土地评价思想，对于完善我国土地评价理论和方法体系，创新性开展第三次土地利用现状调查中的耕地质量调查专题，适应自然资源统一管理新形势具有重要借鉴价值。亟需科学组织新的土壤普查和土地类型调查研究，更新完善原有的土壤资源图和报告，深化中小尺度的土地类型调查研究，不仅深化人们对土地资源的客观认识，也为新的耕地质量评价提供客观、科学、高精度的数据基础提供可能。     

Cross-national differences in stakeholder management: Applying institutional theory and comparative capitalism framework

Drawing upon the research in institutional theory and comparative capitalism, the present study investigates how cross-national differences in the political, business, and economic institutional contexts of the United States, Italy, and Japan are associated with the ways in which companies in each of these countries prioritize and engage in their stakeholder engagement activities (SEAs). Using Porter and Kramer's framework, which classifies corporate social responsibility (CSR) activities as falling into four categories (good citizenship, mitigating harm from value chain, transforming value chain activities, and strategic philanthropy), we investigate how companies in the United States, Japan, and Italy prioritize and engage in these four SEAs. An analysis of data collected from 340 companies across these countries reveals that while companies in each of these three countries undertake the four types of SEAs, the prioritization and prevalence of the four types of SEAs vary from one country to the other, in ways that align with the prevailing institutional contexts of each country. The results contribute to a more nuanced understanding of why and how companies' approaches to CSR differ across countries. From a practitioner's perspective, the findings highlight the cultural specificity of CSR, implying that despite the global nature of CSR, the implementation of CSR needs to be tailored to a country's context.     

External pressure or internal culture? An innovation diffusion theory account of small retail businesses’ social media use

COVID-19 put unprecedented external pressure on small businesses to adopt or increase use of social media while not all small businesses are internally ready for this rapid change. This study investigated the roles of external pressures and organizational culture of openness and learning in driving small retail business owners'/managers' social media use decisions by impacting their perceived usefulness and barriers, based on the innovation-decision process model from the diffusion of innovations theory and the theory of reasoned action. An online survey with structured measurements was administered to 411 U.S. small retail business owners/managers. Results from structural equation modeling revealed that external pressures positively influenced small business owners'/managers’ perceived usefulness and barriers and social media use intention. In addition, the culture of openness and learning positively influenced the perceived usefulness while mitigating the perceived barriers, thereby directly and indirectly influencing the social media use intention. Theoretical and managerial implications are discussed.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand?

Retailing strategy is one of the most crucial factors for industries. A proper retailing strategy can help to enhance consumer service and increase the industry's profit. An improved approach to retailing is suggested in this research to deliver superior customer service while maximizing profits in a dynamic system. The study analyzes a retailing strategy for a demand with cross-price elasticity upon the retail price. A product's cross-price elasticity and the system reliability are critical factors in retailing. Understanding the cross-price elasticity of demand between products helps retailers to make pricing decisions that maximize profits by maintaining demand. Imperfect products are produced due to an imperfect production system. The imperfect ones must be adjusted with some costs to make them perfect for better retailing. The system failure rate is crucial for retailing under cross-price elasticity of demand patterns. Production system reliability, cross-price elasticity of demand, and consumer service are all essential factors that can impact a company's success in the market. The production rate is considered time- and system failure rate-dependent. Contradictory to the literature, a dynamical system is proposed for improved retail management, which is solved using the Euler-Lagrange theory. Finally, one can achieve the expected maximum profit for this retail system with optimum selling prices for different products by reducing the system failure rate. Some numerical illustrations with graphical representations are provided to validate the current study. Numerical examples show that applying cross-price elasticity of demand for more than two identical products provides 35% more profit for the retail industry than a single type of product.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.