寡头竞争市场下共同配送定价方法研究

共同配送采用多个企业联合组织起来实施配送活动的方式，其优势越来越受到重视，关于共同配送的理论研究也越来越多，但是对于定价问题还一直是个难题。在前人研究的基础上，引入共同配送服务质量因素，建立寡头竞争市场下共同配送定价模型，并设计粒子群算法，采用帕累托改进方法和总收益最大解方法对模型进行具体求解，得到相关的结论，最后验证了模型和算法的有效性。     

人工智能与中国企业参与全球价值链分工

人工智能是各国构筑核心竞争力的重要模块,更是关乎产业转型和价值链升级的主要抓手。本文使用International Robot Federation提供的机器人数据、中国海关数据库和中国工业企业数据库提供的企业数据构成了研究使用的高度细化的微观企业数据,在实证层面检验了人工智能对中国企业全球价值链参与的影响,数据跨度为2000—2013年。本文的主要结论是:①人工智能显著促进了中国企业参与全球价值链分工,且在多重稳健性检验和考虑工具变量的因果识别后仍然显著。②人工智能对中国企业全球价值链嵌入的影响目前主要集中在加工贸易企业。③2008年国际金融危机以后,人工智能对中国参与价值链的促进效应得到进一步释放。④人工智能对价值链参与的影响主要是通过两个渠道实现:一是替代从事低端环节生产的劳动力来降低企业成本;二是提高企业的生产率来增强企业的竞争力。⑤针对行业层面的数据分析发现,人工智能会显著促进行业的全球价值链位置提升。因此,在劳动力成本不断提高的现实情况下,重视创新发展,就是要抓住人工智能高速发展的契机,减少人工投入、提高企业的生产率,从而助力中国企业以更高水平融入全球价值链分工体系。     

Effects of voice assistant recommendations on consumer behavior

The present study compares the influence of text-based recommendations; traditionally known as online consumer reviews, and the influence of voice-based recommendations provided by voice-driven virtual assistants on consumer behaviors. Based on media richness theory, the research model investigates how voice versus text modality influences consumers' perceptions of credibility and usefulness, as well as their behavioral intentions and actual behaviors. In addition, the study analyses if these relationships vary based on the type of product and compares the influence of masculine and feminine voices. Two studies were conducted using between-subjects experimental designs, partial least squares-structural equation modeling, and logistic regression. The core finding is that voice-based recommendations are more effective than online consumer reviews in altering consumer behaviors. In addition, the first study showed that the influence of recommendations on behavioral intentions is mediated by consumers' perceptions of their credibility and usefulness. The second study confirmed, in a realistic setting, that voice-based recommendations affect consumer choices to a greater extent. Recommendations for search products and provided by males are also found to be more effective. These results contribute to the voice assistant and e-WOM literature by highlighting the effectiveness of voice-based recommendations in predicting consumer behaviors, confirming that credibility and usefulness are key factors that determine the influence of recommendations, and showing that recommendations are more effective when they focus on search products.     

国外人工智能开源生态运营模式剖析

开源正成为新一代人工智能发展的全球趋势。本文从开源政策、开源组织、开源基础设施三个方面,基于典型实践案例,总结了国外人工智能开源生态的运营模式和主要特征。研究发现,欧美等发达国家的人工智能开源生态已进入到一个元素基本完整、运营基本顺畅的发展阶段,并通过所有权中立、社区治理专业化、顶尖企业引领、商业模式创新等方式实现开源生态的可持续运营。建议围绕打造良好的人工智能开源政策环境、推动开源组织有序分工合作、夯实开源基础设施等方面,进一步完善我国人工智能开源生态。     

英国人工智能路线图及对我国的启示

人工智能是新一轮科技革命和产业变革的必争领域之一。英国高度重视人工智能发展,成立了专门的人工智能办公室,并于2021年1月发布人工智能路线图。本文介绍了英国人工智能发展基本情况和路线图的主要内容,并对中国的人工智能发展提出了建议。     

印度情报侦察装备的现状与未来发展

印度建立的情报侦察体系具有较强的实战能力,具备全天候的战略战术侦察能力.基于印度情报侦察发展的借鉴作用,介绍了印度天基、空基、陆基、海基的情报侦察装备的现状,并预测了印度情报侦察装备系统未来的发展趋势,最后总结了印度情报侦察的发展特点和经验.     

人工智能、侵蚀性竞争和行业质量阶梯

本文采用国际机器人联合会提供的工业机器人跨国跨行业安装数据，考察了人工智能对行业质量阶梯的影响与作用机制，并在此基础上探究了侵蚀性竞争的调节作用及其引致的质量集聚效应。研究结果发现，人工智能显著缩短了行业质量阶梯，提升了产品质量，且这一作用在一系列检验后依然稳健；基于内生中介效应模型的结果表明，人工智能对质量阶梯的影响是通过增加中间品使用、提高生产率和改变资本密集度实现的。侵蚀性竞争的存在，不仅负向调节了人工智能对行业质量阶梯的作用，还引发了质量维度上的地理转移现象，导致高质量产品的产销活动转变为以高质量国家为中心进行的区域性活动。探究人工智能对行业质量阶梯的影响，既有助于厘清人工智能的经济效应，也有助于提升产品国际竞争力，助推贸易高质量发展。     

Interpretable selective learning in credit risk

Forecasting credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution because of its accuracy and interpretability. Although complex machine learning models may improve accuracy over simple logistic regressions, their interpretability has prevented their use in credit risk assessment. We introduce a neural network with a selective option to increase interpretability by distinguishing whether linear models can explain the dataset. Our methods are tested on two datasets: 25,000 samples from the Taiwan payment system collected in October 2005 and 250,000 samples from the 2011 Kaggle competition. We find that, for most of samples, logistic regression will be sufficient, with reasonable accuracy; meanwhile, for some specific data portions, a shallow neural network model leads to much better accuracy without significantly sacrificing interpretability.     

The influence of chatbot humour on consumer evaluations of services

Technological advances have enabled firms to automate customer service by employing artificial intelligence (AI) chatbots. Despite their many potential benefits, interactions with chatbots may still feel machine-like and cold. The current study proposes the use of humour by chatbots as a gateway to humanizing them and thereby enhancing the customer experience. Across three experimental studies, the results reveal that (i) the use of humour enhances service satisfaction when it is used by a chatbot but not when it is used by a human agent, (ii) this chatbot humour effect is serially mediated by enhanced perceptions of anthropomorphism and interestingness of the interactions with the chatbot, and (iii) whilst both positively and negatively valenced chatbot humour may enhance the interestingness of the interactions, socially appropriate (i.e., affiliative) humour as opposed to inappropriate (i.e., aggressive) humour leads to enhanced service satisfaction. This study extends the understanding of the humanization processes of chatbots and provides guidelines for how firms should use chatbot humour to positively influence consumers' service satisfaction.