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21.
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. 相似文献
22.
Motivated by the European sovereign debt crisis, we propose a hybrid sovereign default model that combines an accessible part taking into account the evolution of the sovereign solvency and the impact of critical political events, and a totally inaccessible part for the idiosyncratic credit risk. We obtain closed‐form formulas for the probability that the default occurs at critical political dates in a Markovian setting. Moreover, we introduce a generalized density framework for the hybrid default time and deduce the compensator process of default. Finally, we apply the hybrid model and the generalized density to the valuation of sovereign bonds and explain the significant jumps in long‐term government bond yields during the sovereign crisis. 相似文献
23.
《Journal of Retailing》2019,95(4):111-115
Sensory elements are an important aspect of both offline and online retail stores and can non-consciously influence consumer judgments and purchase behavior. In offline settings, ambient factors like scent, lighting, and music have been shown to influence customers’ shopping experiences and their buying behaviors. For online retail outlets, sensory factors related to color, display patterns, and layouts can have significant effects on consumer behavior. Sensory elements have strong managerial relevance since they can non-consciously influence behaviors and because they are relatively easy to change; for example, it requires minimal effort to change the lighting or the music at a physical retail store or the display pattern for an online store. The eight papers in this special issue address theoretical and practical issues related to how different sensory elements can strongly influence retail practices. These papers focus on underexplored topic areas and also on emerging technologies, which stimulate thoughts for further research in these areas. 相似文献
24.
智慧居家养老服务的协同供给可以形成社会联动效应,降低公共服务成本。运用公共服务协同供给分析框架,通过交叉分类方法从协同主体的利益与目标两个变量的耦合性出发分析广西钦州市智慧居家养老项目中养老服务主体间不同种类的协同关系,可以从加强政府领导、形成合作联盟、建立市场标准、创新扶持政策4个方面完善政府、企业、社会组织之间不同类型的协同供给,实现社会资源的高效利用以及向智慧居家养老服务的发展和转变。 相似文献
25.
《International Journal of Research in Marketing》2022,39(2):522-540
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. 相似文献
26.
Catapults(弹射中心)作为一种新型研发组织形式,在英国创新发展战略的实施中扮演着重要角色。精准的组织功能定位、高效的组织管理运作模式和紧密的网络化组织关系,是Catapults组织管理模式创新的重要三大重要内容。为我国新型研发机构的培育和发展提供了良好的参考样本。 相似文献
27.
[Objective] This study aims to investigate how place attachment and the quality of experience affect consumers' intention to repurchase Geographical Indication (GI) agricultural products. [Methodology] Based on the special functional value and symbolic meaning of GI agricultural products, we constructed a five-dimensional model of place attachment for GI agricultural products consumers. Expanding on the original two-dimensional place attachment model, we introduced three connection concepts: nature, society, and emotion. We also combined this model with the quality of experience model and consumer purchase intention model to form a research model of repurchase intention for GI agricultural products. A questionnaire survey was used to collect data (n = 340), and a partial least squares structural equation model was used to test the empirical evidence. [Research results] (1) Place attachment to Geographical Indication agricultural products significantly affects the quality of experience. (2) Quality of experience significantly affects consumer trust and repurchase intention. (3) Consumer trust significantly affects repurchase intention. (4) There is a mediating effect of experience quality between place attachment and repurchase intention; there is a mediating effect of trust in the relationship between quality of experience and repurchase intention. (5) There is a chain mediating effect of “place attachment→quality of experience→consumer trust→repurchase intention” in the model. 相似文献
28.
AbstractThe detrimental effects traditionally assigned to warfare in the development of pre-industrial economies have obscured the prominent role that military entrepreneurs played in economic development in this period. Historiography minimises the extent to which war and the concomitant strengthening of the central state provided a whole new range of opportunities for capital investment, a tendency that has been strengthened by the paradigm of Redlich’s ‘decline of the soldier-entrepreneur’ and the technological determinism of the debate on the Military Revolution among others. The aim of this introduction is to look into the background of this relative lack of interest and to reaffirm the mutual dependence of eighteenth-century state-formation and the business of war. 相似文献
29.
在全面实施创新驱动发展战略的时代背景下,揭示中国高端装备制造业自主技术创新效率稳定性,测度相关因素的影响强度与方向,对揭示中国高端装备制造业自主技术创新规律,制定其发展策略具有重要指导意义。聚焦中国高端装备制造业自主技术创新效率稳定性及影响因素,采用DEA-Malmquist指数方法和Tobit回归模型进行实证研究,结果表明,中国高端装备制造业自主技术创新效率存在一定波动性,且各细分行业技术创新效率内部稳定性较差,产业自主技术创新效率整体稳定性不高;自主技术创新投入因素和环境影响因素对产业自主技术创新效率的影响具有较高的异质性,其中创新资金投入和人力资源投入对自主技术创新综合效率、纯技术效率及规模效率具有正向影响,而新产品开发投入和企业规模对自主技术创新综合效率、纯技术效率和规模效率的影响具有异质性,政策因素与市场因素分别具有显著和非显著负向影响,外源性创新因素具有显著正向影响,且影响强度较大。在此基础上,提出调整政府对高端装备制造业创新支持模式、加强外源性创新与智力引进等策略建议。 相似文献
30.
《International Journal of Forecasting》2022,38(1):240-252
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. 相似文献