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人与机器,谁的建议更容易被采纳?不同决策情境下建议者类型对建议采纳的影响研究

Whose suggestion is easier to be adopted for man and machine?A study on the influence of advisor types on advice taking in different decision makings
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摘要 在数字经济时代,人类会更加面临“有限处理能力”和“无限增长信息”的矛盾,而利用机器帮助提高决策者决策质量是解决或缓解这一矛盾的途径之一。本研究从接纳建议的角度研究人们对于人机工作的态度,通过引入“机器”作为建议者,比较人们在接纳机器和人提出建议上的差别以及重要的认知条件。研究的主要发现包括:(1)决策者在主观决策情境中会倾向于人的建议,而在客观决策情境中则倾向于机器的建议;(2)在客观决策情境中,高认知闭合需要的个体对人和机器建议的采纳无显著差异,而低认知闭合需要的个体会更倾向于采纳机器的建议;(3)在客观预测情境中,决策者在困难任务时更倾向于采纳机器的建议,而在简单任务时,对人和机器建议的采纳无显著差异;(4)在主观决策情境中,对人的建议的采纳不受建议框架的影响,但对于机器提出的建议在负性建议框架描述更容易采纳。研究的理论贡献在于:丰富和完善了不同决策情境下建议者类型(人/机器)对建议采纳的影响的理论体系;研究的实践意义是:帮助决策者更充分地利用机器来辅助决策,更全面地了解机器建议过程中的作用机制与路径,提高了决策质量。 In the era of digital economy,a large amount of information has brought great challenges to decision-makers;how to distinguish effective information from massive information is an important problem faced by individuals.However,for individuals with limited information processing capacity,it is difficult to effectively screen effective information from massive information,that is,decision-makers face the contradiction between“limited processing capacity”and“unlimited information growth.”When using machines to make decisions,improving the decision quality of decision makers is an effective way to solve this problem.The rapid development of the new generation of information technology represented by artificial intelligence and big data,as well as its mutual penetration with all sectors of society,has greatly promoted the digital transformation and upgrading of enterprises,and also provided decision-makers with a richer source of advice.As one of the important topics in the decision-making field,advice taking has attracted more and more attention,and its influencing factors have been widely studied.Previous studies have shown that advice taking is affected by the judge′s features,task features,advice features and the advisor factors.In recent years,there are more and more studies on advice taking by different proponents,but most of them focus on the characteristics of the advisor itself.However,the progress of artificial intelligence technology makes decision-makers have more reference suggestions in the decision-making process,with the suggestions by machines being one of the important factors that cannot be ignored.Therefore,in order to better explore the role of the“external brain”of the machine in promoting decision-making when information is overloaded,this paper selects the types of advisor(human/machine)as an independent variable for exploring the behavior of decision-makers when adopting suggestions from different sources;it also further explores the interaction between the need for cognitive clo
作者 惠青山 赵俊峰 姜红梅 苟思颖 易文璋 张慧君 HUI Qingshan;ZHAO Junfeng;JIANG Hongmei;GOU Siying;YI Wenzhang;ZHANG Huijun(School of Management,Guangdong University of Technology,Guangzhou 510520,China;School of Foreign Studies,Shunde Polytechnic,Foshan 528333,China)
出处 《管理工程学报》 CSCD 北大核心 2024年第1期74-87,共14页 Journal of Industrial Engineering and Engineering Management
基金 广东省哲学社会科学规划项目(GD20CGL62、GD20CGL17)。
关键词 建议采纳 建议者类型 认知闭合需要 任务难度 建议框架 Advice taking Types of advisor Need for cognitive closure Task difficulty Suggestion framework
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