【作者】 陈凯；

【导师】 张兮；

【作者基本信息】 天津大学 ， 管理科学与工程， 2021， 硕士

【摘要】 人工智能的发展为商业伦理教育课程带来了新的机遇和挑战,由联合国提出的负责任的管理教育原则(PRME)明确提出要创新责任管理的教学方法,国际高等商学院协会(AACSB)设定标准包括课程要培养学生使用新兴技术,哈佛大学、加州大学和麻省理工大学等顶级大学尝试将推荐系统应用到商业伦理教育课程中,许多在线教育平台也提供在线的商业伦理课程为大学认证学分。随着推荐系统越来越多地应用于商业伦理教育,关于推荐系统对商业伦理教育的影响研究变得十分重要。然而,虽然有关于推荐系统对商业伦理带来影响的研究,但是关于算法过滤对商业伦理的有效性研究尚少。将推荐系统应用到商业伦理教育是否能产生积极作用还缺少实证性的研究。本文实证研究了使用推荐系统构建商业伦理教育课程是否真的有利于商学院学生的商业伦理培养。本研究利用说服理论和详尽可能性模型解释推荐系统提供的信息如何影响企业社会责任(CSR)的态度和意愿,以及不同算法逻辑的推荐系统的影响。本研究设计了一个企业社会责任案例推荐系统,并基于真实案例教学课程实施了一个随机现场实验。研究发现推荐系统根据不同的信息处理路径(边缘路径或中心路径)提升商学院学生的企业社会责任态度和企业社会责任意愿,并且系统期望确认可调节这些影响。同时,比较不同逻辑的推荐算法发现,专家监督的推荐算法对企业社会责任态度的影响比兴趣监督的推荐算法和无监督的聚类推荐算法更显著。本研究有助于企业社会责任教育领域设计先进的教学工具和优化教学过程。更多还原

【Abstract】 The development of Artificial Intelligence has brought new opportunities and challenges to business ethics education.The Principles for Responsible Management Education(PRME)clearly proposes to create innovated educational approaches for responsible management.The Association to Advance Collegiate Schools of Business(AACSB)requires curriculum content to cultivate agility with current and emerging technologies.Top universities such as Harvard University,University of California,and Massachusetts Institute of Technology try to apply recommender systems to business ethics education.Many platforms also provide online business ethics courses for university certification.As recommender systems are increasingly applied in business ethics education,research on the impact of recommender systems on business ethics education has become very important.However,there is still a lack of empirical research on whether the application of recommender systems to business ethics education can produce a positive effect.This article empirically studies whether using recommender algorithms to construct education content is really conducive to the cultivation of business ethics.The elaboration likelihood model is utilized to understand how information provided by recommender systems impacts corporate social responsibility(CSR)attitude and intention,and the effects of recommender systems with different principles.We designed a CSR case recommender system and implemented a randomized field experiment based on a real case-based teaching course.We found that recommender systems facilitate students’ positive CSR attitude change and CSR intention depending on the different information-processing routes(peripheral or central)and system expectation confirmation moderates these influences.Meanwhile,experts-supervised algorithm has a more significant impact on CSR attitude change than interest-supervised algorithm and unsupervised algorithm.Our study contributes to CSR education field on how to design the advanced teaching tools and optimize the teaching process.更多还原