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原文：Laudenbach C, Siegel S. Personal communication in an automated world: evidence from loan repayments[J]. The Journal of Finance, 2024.
Personal communication in an automated world: evidence from loan repayments
ABSTRACT:We examine the effect of personal, two-way communication on the payment behavior ofdelinquent borrowers. Borrowers who speak with a randomly assigned bank agent are significantly more likely to successfully resolve the delinquency relative to borrowers who do notspeak with a bank agent. Call characteristics related to the human touch of the call, such as the likeability of the agent’s voice, significantly affect payment behavior. Borrowers whospeak with a bank agent are also significantly less likely to become delinquent again. Ourfindings highlight the value of a human element in interactions between financial institutionsand their customers.
自动化世界中的个人交流：来自贷款偿还的证据
摘要：文章研究了个人双向沟通对拖欠借款人支付行为的影响。相对于不与银行代理交谈的借款人，与随机分配的银行代理交谈的借款人更有可能成功解决拖欠问题。与呼叫人情味相关的呼叫特征，如代理人声音的可爱程度，会显著影响支付行为。与银行代理人交谈的借款人再次拖欠贷款的可能性也大大降低。文章的发现强调了金融机构与其客户间互动中，人这一因素的价值。
亮点：（1）研究结论为我们提供了一个有趣的启示：“人与人的联结”依然重要且不可或缺。（2） 研究结果提供了与亲社会行为和信守承诺行为一致的重要证据，这种行为部分是通过个人、双向沟通而不是机器生成的单向沟通引起的，并且随着感知到的社会距离的减小而增加。（3）大多数研究都集中在AI的优势（减少行为偏见，改善财务决策）上。而文章揭示了即使成本低得多的信息传输唾手可得，昂贵的个人沟通仍可能继续发挥作用，为理解技术和新通信格式对消费者金融的影响提供了新的视角。
02
原文：Eisfeldt A L, Schubert G. AI and Finance[R]. National Bureau of Economic Research, 2024.
AI and Finance
ABSTRACT: What are the effects of recent advances in Generative AI on the value of firms? Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI. Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics. Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT. Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies.
人工智能与金融
摘要：生成式人工智能的最新进展对企业价值有何影响？文章的研究根据美国上市公司员工对生成式人工智能的接触情况，为这一问题提供了定量答案。文章新颖的公司层面员工对生成式人工智能接触情况的衡量标准已通过收益电话会议数据得到验证，并且与公司和行业层面的特征具有直观的关系。使用人工智能减去人类投资组合（包括接触率较高的多头公司和接触率较低的空头公司），文章发现，在ChatGPT发布后，接触率较高的公司每天获得的超额回报比接触率较低的公司高出0.4%。尽管投资者普遍认为这一消息对接触率较高的公司来说是好消息，但不同行业和同一行业内的情况存在很大差异，这与生成式人工智能技术的巨大颠覆潜力相一致。
亮点：（1）论文结合Lightcast的技能需求数据和LinkedIn的就业结构数据，分析了ChatGPT发布前后金融行业对生成式人工智能相关技能的需求变化，为研究企业成果提供了独特的机会。（2）作者创新性地开发了一种基于职业任务的企业技术暴露测量方法，通过O*NET数据库对职业任务的分析，量化企业中不同任务受到生成式人工智能影响的程度，并结合企业的就业结构数据计算企业整体的技术暴露水平。（3）研究专注于衡量企业对生成式人工智能的敞口，并评估投资者对技术冲击的反应，为研究颠覆性技术对公司估值的影响提供了思路。
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原文：Kemp A. Competitive advantage through artificial intelligence: Toward a theory of situated AI[J]. Academy of Management Review, 2024, 49(3): 618-635.
Competitive advantage through artificial intelligence: Toward a theory of situated AI
ABSTRACT: How can firms establish competitive advantages using artificial intelligence (AI)? Although AI is beginning to permeate business activities, our understanding of how AI can be used to create unique value is limited. To address this void, I introduce the concept of situated AI and illuminate its importance for establishing AI-driven competitive advantages. The paper highlights the organizational activities involved in situating AI—specifically, grounding, bounding, and recasting. It also explains the conditions in which these situating activities better enable firms to develop AI-driven capabilities that are firm-specific, cost-effective, and appropriate for opportunities in the strategic environment. Thus, this paper provides an integrative framework for connecting a firm’s AI pursuits to competitive advantage.
通过人工智能的竞争优势：迈向情境AI理论
摘要：企业如何利用人工智能（AI）建立竞争优势？尽管AI开始渗透到商业活动中，但我们对如何使用AI创造独特价值的理解是有限的。为了解决这一空白，本文引入了“情境AI”（Situating AI）的概念，并阐明了它对建立AI驱动的竞争优势的重要性。本文重点介绍了情境AI所涉及的组织活动，特别是基础、边界和重塑。它还解释了在哪些条件下，这些情境活动能更好地帮助企业发展AI驱动的能力，这些能力是企业特有的、具有成本效益的，并且适合战略环境中的机遇。因此，本文提供了一个将企业的AI追求与竞争优势联系起来的综合框架。
亮点：（1）本文提供了一个整合性框架，将企业的AI追求与竞争优势联系起来，旨在解释企业如何以及在何时可以利用AI建立竞争优势。（2）作者提出了三种情境活动（基础、边界和重塑）并解释了这些活动如何帮助企业在AI的通用性、显性性和短视性等战略限制中找到竞争优势。（3）研究整合了战略管理中的多个基础概念，如组织学习、知识管理、交易成本等，为未来研究提供了新的出发点。
本文仅用于学术交流
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总编：游家兴
解析：陈思佳
审校：刘晗 谭传兴
编辑：潘芳妍
