The effect of control deprivation on consumers’ adoption of no-pain,no-gain principle

Consumers often base their judgments on a no-pain, no-gain principle—that is, one must pay a cost in order to achieve a beneficial outcome. For example, they infer the quality of a product from its price and judge a bad-tasting medicine to be more effective than a tasty one. Although the use of this principle to infer the value of a product or service has been observed in several domains, the processes that underlie its use have not been fully explored. We find that when people feel out of control, they tend to use the principle because it exemplifies a causal relationship between actions and outcomes and endorsing it reaffirms their belief that they have control over the outcomes of their behavior. Our findings have implications for how marketers might position products and services to attract consumers who perceive themselves as having different levels of control.     

智慧居家养老服务协同供给的优化路径研究——以广西钦州市智慧居家养老项目的实践经验为例

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。     

论数据权益的刑法保护——民刑交错视角下的法益保护论探讨

关于“数据权益的刑法保护”问题，在理论层面上，可从“刑法解释论”和“刑法立法论”两种路径进行探讨。“刑法解释论”探讨路径以现行《刑法》罪名体系为出发点，主要任务是揭示哪些罪名的犯罪构成之设置是以“数据权益”为规范保护目的；“刑法立法论”探讨路径是在刑法立法层面上探讨立法者为在刑法政策上实现数据权益的刑法保护这一目的，动用刑罚禁止何种行为类型才具正当性的问题。然而，两种探讨路径都将依赖于“数据权益”的内涵确定以及法益格的认定问题，换言之，是刑法法益理论在数据权益刑法保护问题上的具体运用。《民法典》第127条并非是关于数据的确权性规定，且在民法理论界关于数据权益的法律属性存在诸多争议的状况下，“数据权益”内涵不明、法律属性不清导致其并不具备作为刑法保护客体的“法益”格。当前刑法理论界存在的关于“数据权益”的刑法类法益保护模式探讨路径，是对刑法法益保护方式的误读，并未触及“数据权益刑法保护”之规范层面问题的实质。“数据安全”法益观的探讨路径则不仅混淆了现象层面与规范层面上的“数据犯罪”概念的界分，也由于没有论证“数据安全”如何能够构成刑法的保护法益，而导致结论过于武断。在理论探讨上，应该明确作为行为客体的物理层面上的“数据”概念与作为保护客体的法益层面上的“数据权益”概念之间的区别，据此明确“数据权益的刑法保护”在规范层面上是否构成新问题。     

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.     

知识型员工冗余对中小上市公司二元创新的影响：环境特征的调节作用

以277家中小板和创业板上市公司2012-2017年平衡面板数据为样本，利用泊松模型，实证分析知识型员工冗余与企业二元创新的关系，并探讨外部环境特征（要素市场发育程度和市场不确定性）的调节机理。研究表明：企业应保持一定的知识型员工冗余，促进企业二元式创新，过多或过少的知识型员工冗余均不利于企业开展创新活动；不同的环境特征对二者间关系的调节作用存在异质性，要素市场发育程度会减弱知识型员工冗余与探索式创新、利用式创新间关系，而市场不确定性会加强知识型员工冗余与探索式创新、利用式创新间关系。     

险资股权投资的结构性改革研究*——基于险资投资的倒U型效应分析

保险资金作为重要的机构投资者能否发挥促进公司绩效提升的作用在已有研究和监管政策制定上都存在较大争议。本文以2005-2017年A股上市公司为研究样本，发现险资持股比例对上市公司绩效以及估值影响存在显著的倒U型曲线效应，相应机制研究表明险资持股通过显著影响两类代理成本的渠道发挥治理作用。在适度持股比例以下，险资增持能够降低被投资公司管理费用率和其他应收款占比，并提高资产周转率，从而降低两类代理成本；持股超过一定比例反而造成两类代理成本上升。险资持股比例变化对保险机构调研次数的影响则从公司治理参与角度印证了以上结论。对倒U型曲线效应的深入分析既有助于深化对机构投资者影响公司绩效的多重效应和机制的认识，同时为在金融机构层面落实金融供给侧结构性改革、增强险资服务实体经济能力提供参考。     

我国“一带一路”沿线地区风险投资时空分布及影响因素研究

深入分析我国“一带一路”沿线地区风险投资时空格局及影响因素，有助于促进创新创业与企业发展、提高区域科技创新水平。运用空间分析方法和空间计量模型，对2003-2018年我国“一带一路”沿线139个城市风险投资的时空分布特征及影响因素进行分析，结果表明：①沿线地区的风险投资发展具有不均衡性，呈现为沿海高、西部低的分异格局；②风险投资的全局自相关系数均为正，呈现出明显的空间集聚特征；③风险投资机构数、国家高新区数量、风险投资退出、政府引导基金发展、财政科技支出对区域风险投资具有显著促进作用，环境污染对风险投资有一定抑制作用。随着ESG投资理念逐渐获得认可，沿线地区可以通过提升绿色发展水平促进风险投资发展。     

技术价值分界及其决策的伦理指向研究——以保罗的后果不确定技术价值分界理论为例

合理的技术价值界限是理性决策的依据。对于复杂的新技术，尤其是后果不确定技术，不同主体间价值认知分歧直接导致价值界限不清，成为理性决策的难点。以保罗的实用对话伦理理论为认知路径，讨论此类技术价值分界方法。通过比较功利主义、自由至上主义在价值认识和判断上的后果差异，揭示后果不确定技术价值在不同主体间漂移的原因，探索在不同价值主体间产生伦理维度的技术价值界限方法。在开放式语义环境中，不同价值主体之间的共识即是技术价值的伦理界限，可为决策公正提供伦理支撑，避免因主体缺失而导致在应用中发生技术价值过度偏失和决策失当，为我国发展诸如转基因水稻等后果不确定技术决策提供可操作性的方法论借鉴。