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Article(id=1316695226262074229, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1754323200000, receivedDateStr=2025-08-05, revisedDate=1764172800000, revisedDateStr=2025-11-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1791854204954, onlineDateStr=2026-10-13, pubDate=1768406400000, pubDateStr=2026-01-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791854204954, onlineIssueDateStr=2026-10-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791854204954, creator=13701087609, updateTime=1791854204954, updator=13701087609, issue=Issue{id=1316695224617914963, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='1', pageStart='1', pageEnd='224', issueExtLink='null', onlineDate='null', pubDate='1768406400000', pubDateStr='2026-01-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791854204563, creator='13701087609', updateTime=1791854713654, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1316697359963239159, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1316697359963239160, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=29, endPage=40, ext={EN=ArticleExt(id=1316695228782850934, articleId=1316695226262074229, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=How Artificial Intelligence reshapes decision-making patterns: A systematic literature review based on Human-AI power dynamics, columnId=1316695225842651733, journalTitle=Studies in Science of Science, columnName=Forums of Science and Technology, runingTitle=null, highlight=null, articleAbstract=

In an era characterized by intensifying globalization, rapid technological iteration, and geopolitical volatility, traditional organizational decision-making paradigms grounded in the theory of “bounded rationality” are facing unprecedented challenges. Decision-makers are increasingly constrained by inherent cognitive limits when processing real-time, high-dimensional information, leading to satisficing behaviors that are frequently insufficient for today’s high-risk, complex, and feedback-delayed environments. While Artificial Intelligence (AI) has emerged as a transformative force capable of reshaping the epistemological foundations of strategy and risk management, the theoretical mechanisms and boundary conditions governing “human-AI symbiosis” remain underexplored. Existing literature often treats AI as a monolithic variable or categorizes collaboration solely based on automation levels, failing to elucidate the underlying power dynamics—specifically, the allocation of decision rights and the locus of agency—that define the collaborative interface. Consequently, there is a critical need for a systematic framework that deconstructs how AI reconfigures the structural and procedural dimensions of organizational decision-making beyond mere technical functionalism. To address this significant theoretical gap, this study conducts a systematic literature review of 85 core articles published in UTD24 premier management journals between 2020 and 2025. Utilizing bibliometric analysis and systematic coding, the research maps the trajectory of AI application in organizational decision-making, identifying key thematic clusters such as information processing, machine learning, and trust dynamics. The central contribution of this work is the construction of the novel “Interaction-Support-Influence” classification framework, anchored in the power relations between humans and machines. This typology transcends prior functional descriptions to reveal distinct logics of agency and collaboration. The “Interaction Mode” represents a form of shared agency characterized by a recursive loop of output, feedback, and modification; here, decision power is dynamically shared, and AI acts as a reflexive partner that challenges human assumptions and co-creates solutions through bidirectional engagement. The “Support Mode” characterizes scenarios where AI functions as an augmentative tool for information processing or option generation, yet the final fiat remains exclusively with the human agent, focusing on enhancing efficiency and accuracy while retaining human cognitive sovereignty. The “Influence Mode” delineates scenarios where AI operates as an environmental variable or architect, shaping the decision-making context through information filtering or algorithmic choice architecture without explicit participation in the final choice, thereby structurally constraining human agency through latent algorithmic antecedents. The synthesis reveals that the efficacy of these modes is contingent upon a complex interplay of internal and external factors. Internally, individual traits such as algorithm aversion or appreciation, alongside organizational capabilities like absorptive capacity and strategic inertia, dictate adoption success. Externally, macro-institutional factors, including regulatory frameworks and cultural contexts, significantly bound the scope and legitimacy of collaboration. Regarding outcomes, the study distinguishes between performance metrics and behavioral evolution, highlighting a “double-edged sword” effect: while AI integration generally enhances financial efficiency and decision quality, it simultaneously introduces risks such as cognitive atrophy, algorithmic bias, and the erosion of unique human tacit knowledge. Despite these theoretical advances, the review identifies significant lacunae in current scholarship, particularly regarding the lack of integration between AI implementation and core organizational strategy. Existing research remains fixated on a “human-in-the-loop” paradigm that underestimates AI’s potential agency and neglects the profound impact of macro-environmental variables. To advance the field, this paper proposes a comprehensive future research agenda structured around two critical dimensions: the deep integration of AI with organizational strategy and the expansion of novel AI strategic horizons. First, regarding the deep integration of AI and strategy, the study calls for moving beyond static adoption to investigate dynamic human-machine authority allocation, the impact of macro-institutional variables—such as geopolitical shifts and cultural contexts—and the profound reconfiguration of organizational structures and relational networks. Second, regarding the expansion of new AI horizons, the paper emphasizes the urgent need to examine AI’s disruptive role across four emerging frontiers: facilitating scalable innovation and application, redefining power dynamics within platform ecosystem governance, managing systemic risks and compliance, and addressing the paradoxes of AI-driven ESG sustainability. Ultimately, this research extends the theoretical boundaries of sociotechnical systems in management, offering practitioners a diagnostic logic for designing differentiated human-AI mechanisms that align with specific strategic imperatives, thereby facilitating a shift from simple tool adoption to deep structural integration and sustainable competitive advantage.

, authors=Bing-wen ZHAO1, Hong-zhen LEI2, Hao YU1, authorsList=Bing-wen ZHAO, Hong-zhen LEI, Hao YU, authorCompany=null, correspAuthors=Hao YU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1316695233300116353, articleId=1316695226262074229, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=人工智能如何重塑决策模式——基于人机权力关系的系统性文献综述, columnId=1316695226064949848, journalTitle=科学学研究, columnName=专稿, runingTitle=null, highlight=null, articleAbstract=

在全球化与数字化加剧的复杂不确定环境下,传统基于“有限理性”的决策模式难以应对实时、高维信息处理需求,人工智能(AI)正深刻重塑组织决策范式。组织需要引入AI提升决策效能,但其作用机制与适用边界尚缺乏系统性理论支撑。基于2020-2025年间发表于UTD24管理学期刊的85篇核心文献,文章系统厘清了AI在组织决策中的应用路径与研究进展,并依据人机权力关系,创新性地构建了“交互—支持—影响”人机决策模式分类框架。该框架突破了以往研究单纯依赖自动化程度划分的局限,揭示了不同模式下决策影响因素与作用结果的系统性差异。目前研究对AI主导模式、宏观情境及关系影响的探讨尚存不足。未来研究应聚焦AI与组织战略的深度整合及AI新视野的拓展,以深入揭示AI如何重构组织决策的底层范式与组织结构基础。此研究结论在理论上扩展了协同决策边界,在实践上为企业设计差异化人机协同机制提供参考,并为后续人机决策提供新的研究思路。

, authors=赵秉文1, 雷宏振2, 余昊1, authorsList=赵秉文, 雷宏振, 余昊, authorCompany=null, correspAuthors=余昊, authorNote=

赵秉文(2002-),女,博士研究生。

雷宏振(1966-),男,教授、博士生导师。

, correspAuthorsNote=
余昊(1996-),男,博士研究生, E-mail:。
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Literature review on Human-Machine Decision-Making

, figureFileSmall=null, figureFileBig=null, tableContent=
分类 主要研究问题 涉及理论 作者&年份
交互模式 如何进行交互式人机协作?
·人工智能分析师何时会超越人类分析师?“人+机器”能否提高预测准确性?
·基于机器学习的系统如何促进专业人员的反思实践?
·机器学习开发者如何在独立创造知识的同时,又不脱离相关领域,并应对其中的矛盾关系
AI能否胜任某些工作?
·大型语言模型(LLMs)是否适合评估战略决策?
·大规模语言模型(LLMs)能否取代人类参与者进行市场研究中的感知分析?
·与传统人类众包相比,生成式人工智能如何从解决方案的新颖性、价值和质量的维度增强创造性问题解决?
·大型语言模型(LLMs)能否“准确”反映人类的跨期偏好和决策?
AI与人类交互带来何种影响?
·人工智能的采用如何影响竞争能力和绩效?		 ·资源基础观
·反思性实践理论
·战略远见		 Abdel-Karim等(2023); Cao等(2024); van den Broek等(2021); Doshi等(2025); Li等(2024); Boussioux等(2024); Goli等(2024); Krakowski等(2023)
支持模式 何种因素影响人类使用AI工具?
·决策中的享乐/功利属性权衡如何会影响人们对人工智能推荐的偏好或抵制?
·如果强大的人工智能辅助工具与一个人与同行相比的独特优势相一致呢?他们是否会支持整合这种有能力创造公平竞争环境的算法辅助工具?
·是什么促使医疗保健市场中出现“检测不足”的情况?
·为什么司机对算法反感?何时算法厌恶和算法欣赏更为普遍?
AI辅助是否能产生更优的效果?
·人工智能建议如何影响人类决策的准确性和人类的独特知识?
·具有不同水平和类型经验的知识工作者如何与人工智能合作以提高生产力?
·为员工提供人工智能协助是否以及在多大程度上改善服务结果?
·人工智能反馈会提高还是降低员工绩效?
·人工智能能否帮助人类员工提高创造力和绩效?
·决策者是否能从人工智能支持的计算机模拟培训中获益?
·数字咨询工具如何改变消费者行为,哪些类型的消费者可能会使用这些工具?
人类应如何有效利用AI提高效率?
·人类专家要如何才能根据黑盒机器的输入转化自己的知识?
·虚拟形象的要素分类以及在实践中虚拟形象如何有效地实现绩效成果?
·企业如何采用算法解决方案来创建并持续改进需要熟练工作的知识密集型任务?		 ·算法厌恶
·算法赞赏
·组织学习		 SimanTov-Nachlieli等(2025); Miao等(2022); Longoni等(2022); Kim等(2022); Dai等(2020); Amaya等(2024); Liu等(2023); Jussupow等(2024); Bundorf等(2024); Lebovitz等(2022); Fuegener等(2021); Wang等(2024); Tong等(2021); Jia等(2024); Gaessler等(2023)
影响模式 AI的出现对是否人类产生影响?
·生成式人工智能模型对大型在线平台上自由职业者就业结果的短期影响如何?
·突破性发明的出现是否会影响开发人员研究问题的复杂性?
·人工智能技术如何对企业产生经济影响?
·设计师如何应对引入人工智能系统的设计任务?在引入人工智能系统后,成功设计师的行为与其他设计师的行为有何不同?
·人工智能分析在促进经历过首次公开募股的企业创新方面有何作用?
·什么会影响机器人投资,以及随着机器人的应用,就业和组织形式发生了哪些变化?
人类如何应对环境中的AI变化?
·工人流动性如何影响新技术的采用?
·监管者如何构建防御性行为以应对算法变化?		 ·内隐偏见理论
·知识基础观
·威胁理论		 Chen等(2024); Dixon等(2021); Marti等(2024); Hui等(2024); Zhang等(2022); Babina等(2024); Lysyakov等(2023); Wu等(2024)
), ArticleFig(id=1316695238014514088, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695226262074229, language=CN, label=表1, caption=

人机决策文献梳理

, figureFileSmall=null, figureFileBig=null, tableContent=
分类 主要研究问题 涉及理论 作者&年份
交互模式 如何进行交互式人机协作?
·人工智能分析师何时会超越人类分析师?“人+机器”能否提高预测准确性?
·基于机器学习的系统如何促进专业人员的反思实践?
·机器学习开发者如何在独立创造知识的同时,又不脱离相关领域,并应对其中的矛盾关系
AI能否胜任某些工作?
·大型语言模型(LLMs)是否适合评估战略决策?
·大规模语言模型(LLMs)能否取代人类参与者进行市场研究中的感知分析?
·与传统人类众包相比,生成式人工智能如何从解决方案的新颖性、价值和质量的维度增强创造性问题解决?
·大型语言模型(LLMs)能否“准确”反映人类的跨期偏好和决策?
AI与人类交互带来何种影响?
·人工智能的采用如何影响竞争能力和绩效?		 ·资源基础观
·反思性实践理论
·战略远见		 Abdel-Karim等(2023); Cao等(2024); van den Broek等(2021); Doshi等(2025); Li等(2024); Boussioux等(2024); Goli等(2024); Krakowski等(2023)
支持模式 何种因素影响人类使用AI工具?
·决策中的享乐/功利属性权衡如何会影响人们对人工智能推荐的偏好或抵制?
·如果强大的人工智能辅助工具与一个人与同行相比的独特优势相一致呢?他们是否会支持整合这种有能力创造公平竞争环境的算法辅助工具?
·是什么促使医疗保健市场中出现“检测不足”的情况?
·为什么司机对算法反感?何时算法厌恶和算法欣赏更为普遍?
AI辅助是否能产生更优的效果?
·人工智能建议如何影响人类决策的准确性和人类的独特知识?
·具有不同水平和类型经验的知识工作者如何与人工智能合作以提高生产力?
·为员工提供人工智能协助是否以及在多大程度上改善服务结果?
·人工智能反馈会提高还是降低员工绩效?
·人工智能能否帮助人类员工提高创造力和绩效?
·决策者是否能从人工智能支持的计算机模拟培训中获益?
·数字咨询工具如何改变消费者行为,哪些类型的消费者可能会使用这些工具?
人类应如何有效利用AI提高效率?
·人类专家要如何才能根据黑盒机器的输入转化自己的知识?
·虚拟形象的要素分类以及在实践中虚拟形象如何有效地实现绩效成果?
·企业如何采用算法解决方案来创建并持续改进需要熟练工作的知识密集型任务?		 ·算法厌恶
·算法赞赏
·组织学习		 SimanTov-Nachlieli等(2025); Miao等(2022); Longoni等(2022); Kim等(2022); Dai等(2020); Amaya等(2024); Liu等(2023); Jussupow等(2024); Bundorf等(2024); Lebovitz等(2022); Fuegener等(2021); Wang等(2024); Tong等(2021); Jia等(2024); Gaessler等(2023)
影响模式 AI的出现对是否人类产生影响?
·生成式人工智能模型对大型在线平台上自由职业者就业结果的短期影响如何?
·突破性发明的出现是否会影响开发人员研究问题的复杂性?
·人工智能技术如何对企业产生经济影响?
·设计师如何应对引入人工智能系统的设计任务?在引入人工智能系统后,成功设计师的行为与其他设计师的行为有何不同?
·人工智能分析在促进经历过首次公开募股的企业创新方面有何作用?
·什么会影响机器人投资,以及随着机器人的应用,就业和组织形式发生了哪些变化?
人类如何应对环境中的AI变化?
·工人流动性如何影响新技术的采用?
·监管者如何构建防御性行为以应对算法变化?		 ·内隐偏见理论
·知识基础观
·威胁理论		 Chen等(2024); Dixon等(2021); Marti等(2024); Hui等(2024); Zhang等(2022); Babina等(2024); Lysyakov等(2023); Wu等(2024)
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人工智能如何重塑决策模式——基于人机权力关系的系统性文献综述
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赵秉文 1 , 雷宏振 2 , 余昊 1
科学学研究 | 专稿 2026,44(1): 29-40
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科学学研究 |专稿 2026 , 44 (1) : 29 -40
人工智能如何重塑决策模式——基于人机权力关系的系统性文献综述
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赵秉文1, 雷宏振2, 余昊1
作者信息
  • 1 上海交通大学安泰经济与管理学院, 上海 200030
  • 2 陕西师范大学国际商学院, 陕西西安 710119
通讯作者:
余昊(1996-),男,博士研究生, E-mail:。
作者简介:

赵秉文(2002-),女,博士研究生。

雷宏振(1966-),男,教授、博士生导师。

How Artificial Intelligence reshapes decision-making patterns: A systematic literature review based on Human-AI power dynamics
Bing-wen ZHAO1, Hong-zhen LEI2, Hao YU1
Affiliations
  • 1 Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
  • 2 International Business School, Shaanxi Normal University, Xi’an 710119, China
出版时间: 2026-01-15
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摘要
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在全球化与数字化加剧的复杂不确定环境下,传统基于“有限理性”的决策模式难以应对实时、高维信息处理需求,人工智能(AI)正深刻重塑组织决策范式。组织需要引入AI提升决策效能,但其作用机制与适用边界尚缺乏系统性理论支撑。基于2020-2025年间发表于UTD24管理学期刊的85篇核心文献,文章系统厘清了AI在组织决策中的应用路径与研究进展,并依据人机权力关系,创新性地构建了“交互—支持—影响”人机决策模式分类框架。该框架突破了以往研究单纯依赖自动化程度划分的局限,揭示了不同模式下决策影响因素与作用结果的系统性差异。目前研究对AI主导模式、宏观情境及关系影响的探讨尚存不足。未来研究应聚焦AI与组织战略的深度整合及AI新视野的拓展,以深入揭示AI如何重构组织决策的底层范式与组织结构基础。此研究结论在理论上扩展了协同决策边界,在实践上为企业设计差异化人机协同机制提供参考,并为后续人机决策提供新的研究思路。

人工智能  /  决策  /  文献综述

In an era characterized by intensifying globalization, rapid technological iteration, and geopolitical volatility, traditional organizational decision-making paradigms grounded in the theory of “bounded rationality” are facing unprecedented challenges. Decision-makers are increasingly constrained by inherent cognitive limits when processing real-time, high-dimensional information, leading to satisficing behaviors that are frequently insufficient for today’s high-risk, complex, and feedback-delayed environments. While Artificial Intelligence (AI) has emerged as a transformative force capable of reshaping the epistemological foundations of strategy and risk management, the theoretical mechanisms and boundary conditions governing “human-AI symbiosis” remain underexplored. Existing literature often treats AI as a monolithic variable or categorizes collaboration solely based on automation levels, failing to elucidate the underlying power dynamics—specifically, the allocation of decision rights and the locus of agency—that define the collaborative interface. Consequently, there is a critical need for a systematic framework that deconstructs how AI reconfigures the structural and procedural dimensions of organizational decision-making beyond mere technical functionalism. To address this significant theoretical gap, this study conducts a systematic literature review of 85 core articles published in UTD24 premier management journals between 2020 and 2025. Utilizing bibliometric analysis and systematic coding, the research maps the trajectory of AI application in organizational decision-making, identifying key thematic clusters such as information processing, machine learning, and trust dynamics. The central contribution of this work is the construction of the novel “Interaction-Support-Influence” classification framework, anchored in the power relations between humans and machines. This typology transcends prior functional descriptions to reveal distinct logics of agency and collaboration. The “Interaction Mode” represents a form of shared agency characterized by a recursive loop of output, feedback, and modification; here, decision power is dynamically shared, and AI acts as a reflexive partner that challenges human assumptions and co-creates solutions through bidirectional engagement. The “Support Mode” characterizes scenarios where AI functions as an augmentative tool for information processing or option generation, yet the final fiat remains exclusively with the human agent, focusing on enhancing efficiency and accuracy while retaining human cognitive sovereignty. The “Influence Mode” delineates scenarios where AI operates as an environmental variable or architect, shaping the decision-making context through information filtering or algorithmic choice architecture without explicit participation in the final choice, thereby structurally constraining human agency through latent algorithmic antecedents. The synthesis reveals that the efficacy of these modes is contingent upon a complex interplay of internal and external factors. Internally, individual traits such as algorithm aversion or appreciation, alongside organizational capabilities like absorptive capacity and strategic inertia, dictate adoption success. Externally, macro-institutional factors, including regulatory frameworks and cultural contexts, significantly bound the scope and legitimacy of collaboration. Regarding outcomes, the study distinguishes between performance metrics and behavioral evolution, highlighting a “double-edged sword” effect: while AI integration generally enhances financial efficiency and decision quality, it simultaneously introduces risks such as cognitive atrophy, algorithmic bias, and the erosion of unique human tacit knowledge. Despite these theoretical advances, the review identifies significant lacunae in current scholarship, particularly regarding the lack of integration between AI implementation and core organizational strategy. Existing research remains fixated on a “human-in-the-loop” paradigm that underestimates AI’s potential agency and neglects the profound impact of macro-environmental variables. To advance the field, this paper proposes a comprehensive future research agenda structured around two critical dimensions: the deep integration of AI with organizational strategy and the expansion of novel AI strategic horizons. First, regarding the deep integration of AI and strategy, the study calls for moving beyond static adoption to investigate dynamic human-machine authority allocation, the impact of macro-institutional variables—such as geopolitical shifts and cultural contexts—and the profound reconfiguration of organizational structures and relational networks. Second, regarding the expansion of new AI horizons, the paper emphasizes the urgent need to examine AI’s disruptive role across four emerging frontiers: facilitating scalable innovation and application, redefining power dynamics within platform ecosystem governance, managing systemic risks and compliance, and addressing the paradoxes of AI-driven ESG sustainability. Ultimately, this research extends the theoretical boundaries of sociotechnical systems in management, offering practitioners a diagnostic logic for designing differentiated human-AI mechanisms that align with specific strategic imperatives, thereby facilitating a shift from simple tool adoption to deep structural integration and sustainable competitive advantage.

Artificial Intelligence  /  decision-making  /  literature review
赵秉文, 雷宏振, 余昊. 人工智能如何重塑决策模式——基于人机权力关系的系统性文献综述. 科学学研究, 2026 , 44 (1) : 29 -40 .
Bing-wen ZHAO, Hong-zhen LEI, Hao YU. How Artificial Intelligence reshapes decision-making patterns: A systematic literature review based on Human-AI power dynamics[J]. Studies in Science of Science, 2026 , 44 (1) : 29 -40 .
正文
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传统“有限理性”理论指出,决策者受限于信息获取、处理与判断能力,往往难以实现最优决策,而仅能达成“满意化”选择[1]。随着市场不确定性攀升、技术迭代加速及地缘政治变动,组织战略决策呈现出高风险、高复杂及反馈滞后等特征[2]。传统依赖人类经验与直觉的决策模式日益显现其局限性,如何提升决策效能已成为管理领域的关键议题[3]。在此背景下,人工智能(AI)凭借其在认知、预测及判断等环节的优势,正迅速成为组织应对复杂环境的重要赋能手段[4,5]。现有研究已证实,AI在战略制定、风险管理等核心领域发挥着日益重要的作用[6]。
相对于传统技术,AI展现出在学习、适应及不确定环境中寻求最优解的自主性[5],随之而来需要考虑在实践中如何与AI协同。尽管现有研究肯定了AI对组织绩效的积极影响,但仍存在明显的理论缺口:未能系统解析“协同”的本质。一是在理论框架层面,现有文献多将AI视为被动工具,缺乏基于权力视角解析“人机互动主导权”的分析框架[7];二是在影响因素识别方面,既有探讨集中于微观层面的个体与组织变量[8],忽视了宏观制度逻辑与文化情境对协同模式的规制作用;三是结果碎片化,未能系统整合AI介入决策后引发的多维效应。
上述局限表明,深入揭示AI重塑决策的内在机制,必须将一个模糊的“协同”概念,细化为更具体的分类框架,以便于进一步回答:“目前处于哪种协同模式中”“为什么这种模式在这里有效或无效”“为了实现某一目标,我们应该选择并构建哪种模式”。因此,有必要以“人机权力关系”为核心基础,直面决策协同的本质,构建“交互-支持-影响”的分类框架。该分类框架能够揭示从人机交互到隐性影响不同协同模式下的差异化作用,为整合从微观个体到宏观环境的多层次前因变量提供统一的理论框架,并系统性地预测和解释AI对组织绩效、行为模式及结构关系的多元化影响。
本文基于2020至2025年间发表于UTD24管理学期刊的85篇核心文献,系统梳理AI在组织决策中的应用路径与研究进展。本文的贡献体现在三个方面:第一,本文基于人机权力关系,将现有文献整合为“交互-支持-影响”的人机决策模式分类框架,分析了不同模式下人机交互的前因与影响。第二,识别并区分了内部因素与外部因素两个前因维度。第三,识别了绩效产出与行为演进两个结果维度,系统阐释了人机决策模式的多元化影响效应。本文通过揭示AI如何塑造组织决策过程,为未来相关研究提供系统性的理论图谱与实践启发。
1 研究背景
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1.1 概念界定
人工智能(Artificial Intelligence, AI) 这一术语最早可追溯至1955年的《达特茅斯夏季人工智能研究项目提案》。该提案初步将AI定义为:“使机器能够模拟人类智能的各个方面,包括学习、推理、问题解决、感知和语言使用等能力,并最终使机器能够像人类一样完成需要智能的任务”[9]。相较于传统技术,当代AI的核心特征在于其具备动态学习与自主适应能力。AI不再是被动工具,而是展现出更强的自主性[5]。
1.2 文献检索方法
为系统、全面地涵盖人工智能在决策领域相关研究,本研究以Web of Science核心合集数据库为来源,检索了UTD24期刊2020年1月至2025年4月间发表的相关文献。初步检索共获得596篇在线发表文章。检索关键词包括:“AI”“Artificial Intelligence”“Machine Learning”“Deep Learning”“LLM”“Large Language Model”。
通过筛选摘要与全文,本研究纳入AI在组织决策中的实证、理论及案例研究,排除了纯技术性或与决策过程无关的文献。为聚焦于“影响人机协作的因素”及“AI对决策结果的影响”两大核心问题,本研究最终确定85篇文章作为分析样本。
1.3 计量分析
本研究采用CiteSpace文献计量学工具,分析样本文献的分布特征与网络结构。对85篇样本文献进行期刊来源与学科分布分析。结果显示,文献分布于16种UTD24管理学期刊。其中,刊载文献数量大于等于10篇的期刊包括:Information Systems Research、Management Science、Organization Science和Strategic Management Journal。从年份分布看,相关文献数量呈显著增长趋势,反映了该主题在管理学界的热度持续上升。基于合作者共现网络分析,本研究发现AI决策研究领域呈现出多中心化、跨区域协作的特征,形成了涵盖方法开发、理论探索与实证应用的研究生态。关键词的出现频率、中心性分析显示:“决策”和“管理”中心性最高,验证了样本与研究主题的高度关联性。关键词聚类勾勒出关键主题,如信息过程、机器学习、大语言模型和信任,揭示了焦点的研究领域。
2 内容分析
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2.1 整体概述
如图1所示,为系统剖析人工智能(AI)与人类在决策场景中的协同本质,本文基于人机决策权力关系的差异,构建了“交互-支持-影响”人机协同决策模式分类框架。该框架依据“决策过程实质参与”与“决策权归属”为边界划分了三种人机权力关系的决策模式,明确了人机协作中决策权分配与作用方式的根本性差异。权力关系揭示人机协同本质,决定了决策过程中人与AI的职责边界、互动逻辑与责任归属,是理解协同效率、可信度与伦理风险的关键。从人机权力视角出发,有助于超越技术功能的表层描述,深入分析“谁在决策”“如何决策”以及“权力如何在人机之间流动”等根本问题。具体而言,交互模式下,AI与人类共同参与决策的构建、推演与确定环节,形成一个包含输出、反馈与修正的双向闭环。决策权在双方间动态流动与共享,二者对决策过程与结果拥有近乎同等的影响力。研究多关注反思性实践与混合决策效能[7];支持模式下,AI承担信息处理、方案生成或建议提供等辅助性、执行性任务,旨在“增强”人类决策者。其核心互动是单向的:AI输出,人类接收并最终裁定。决策权归属人类,AI不承担决策职责。研究多集中于系统准确性[10]及个体使用的差异化效果[11];影响模式下,AI不直接参与具体的决策环节,而是通过塑造决策者所依赖的信息环境、认知框架或行为选项,间接地影响人类的决策偏好与最终选择。AI作为决策背景条件,预先定义了人类的决策空间,决策过程、决策权在形式上完全由人类控制,但其决策空间已被AI预先定义。研究多围绕算法偏见[12]或AI对企业行为的宏观影响[13,14]。综上所述,这三种模式构成了一个从显性到隐性、从直接到间接、从高AI权力到低AI权力的人机协同决策模式,为厘清混杂的研究发现提供了清晰的理论框架。
2.2 主要理论梳理
通过对已有文献的系统梳理,本文提炼出三种人机决策模式常用理论。
(1)交互模式理论视角
交互模式聚焦于人机互动过程,构建了多层次的理论解释:微观层面,反思性实践理论视AI为认知触发器以引发人类深度思考[7]。宏观层面,资源基础观将人机互动视为竞争优势的新来源[15]。随着AI交互模式的引入,传统理论的关注点已从解释特定行为转向设计与管理人机交互关系。
(2)支持模式理论视角
支持模式聚焦于AI作为辅助工具如何作用于决策。个体层面,算法欣赏/厌恶理论探究了影响用户抵触或信赖AI工具的因素[16]。组织层面,组织学习理论一方面解析了AI如何克服人类训练伙伴的稀缺性[17],另一方面也警示了AI替代决策可能加剧组织学习的短视倾向[18]。在支持模式下,经典的组织决策理论面临新的独特情景:决策不再仅是信息处理或政治过程,已成为人与算法间认知权威的博弈,这要求理论模型纳入AI新变量以拓展解释边界。
(3)影响模式理论视角
影响模式探究AI作为环境变量如何潜移默化地影响人类决策者。组织战略层面,资源基础观关注构建“AI创新能力”战略资产[19]。个体认知与行为层面,内隐偏见理论解释了初期算法厌恶及随经验衰减的机制[12]。在影响模式下,AI使环境具备了主动塑造能力,理论情景随之转向探索个体在AI算法隐形影响下的自我调节与行为适应。
2.3 分类分析——三种人机决策模式
如表1所示,本文梳理了三种人机决策模式下的主要研究问题。
(1)人机决策交互模式下的文献整合
①影响人机决策交互模式的因素
人机决策交互效能受外部环境与内部机制两方面影响。外部环境方面,行业衰退期的人机协同系统预测准确率显著超越任一独立方[20]。内部机制研究则呈现多层次特征:个体层面,机器学习系统通过诱发反思性实践优化人类决策[7]。组织层面,AI分析师的超额收益具情境依赖性,人机协同效应源于人机特征的动态互补[20]。
②人机决策交互模式的结果
人机交互模式对决策绩效与行为均产生深刻影响。在绩效层面,首先,对决策质量的影响仍不统一。一方面,人机互补重构竞争优势[15,21]。另一方面,机器学习“知其然”与专家实践的“知其所以然”存在认知断层[22]。其次,人机交互还可提升员工绩效和企业财务表现[8]。
(2)人机决策支持模式下的文献整合
①影响人机决策支持模式的因素
当前关于人机决策支持模式影响因素的研究主要聚焦于内部视角。个体层面,能力特质是影响人类使用AI支持决策的重要因素。不同特质的个体从AI支持中获益存在差异[23]。高绩效员工则可能因感知到地位损失而抵触AI[16]。此外,个体对算法与人类的感知差异影响其决策[24]。组织层面,战略惯性突破程度决定AI技术应用深度[25]。
②人机决策支持模式的结果
支持模式对绩效、行为多维度影响。从绩效层面来看,人机决策支持模式能够影响个体或组织的财务与经济绩效。AI辅助可提升员工创造力和绩效,或对高技能专家更为有利[26],或资深员工受益更低[27]。组织层面,AI协同效果存在条件性[28]。
在决策质量方面,支持模式下人机协同的正负效应并存。现有研究证实算法能显著提升决策质量[23],并可作为有效训练伙伴[17]。然而,AI建议虽提升个体准确性,但降低群体效率和独特知识[10]。组织层面,人类与机器学习差异可能降低组织惯例多样性[18]。
在用户和市场评价方面,企业与客户互动中,AI技术可提升服务质量和消费者满意度[29],但负面因素同样存在:客户可能对AI产生负面评价[30]。
(3)人机决策影响模式下的文献整合
①影响人机决策影响模式的因素
影响模式的驱动因素主要源于个体行为态度、组织战略与外部环境。个体层面,对AI的隐性偏见加剧算法厌恶,但积极经历可缓解[12]。组织层面,企业采用机器人旨在提升产品与服务质量,而非仅为降低成本[31]。在外部环境方面,促进工人流动的变革会抑制企业采用机器学习[32]。
②人机决策影响模式的结果
在财务与经济层面,AI投资通过产品创新提升企业增长[14,31]。机器人投资在提升总就业的同时减少了管理者,推动了组织调整[31]。然而,生成式AI的普及对在线平台的自由职业者产生冲击,尤其是顶尖人才的就业和收入[13]。在决策质量方面,AI有助于提升个人工作质量[33]。行为层面,AI失误会引发“算法转移”效应,人为监督可缓解[34]。
综上所述,文献揭示人机协同决策是一个复杂动态系统。其效能并深受个体认知与态度、组织战略与结构,以及外部环境的多层次因素共同驱动。协同结果呈现显著的双重性:既能通过优势互补提升决策质量、创新能力与财务表现;也可能因算法偏见、认知断层或不良互动,引发锚定效应、算法厌恶乃至效率降低。因此,未来决策主体需思考不同模式下如何实现人机协同,最大化协同价值,从而构建可持续的竞争优势。
3 研究不足与未来研究方向
收起
3.1 现有文献的不足
基于“交互-支持-影响”框架对文献的系统整合,本研究发现,现有研究存在两大层面的局限。其一,在对核心决策过程的理解上,缺乏与组织战略的深度整合;其二,在研究视野的广度上,未能充分响应AI技术发展所带来的范式变革机遇。
(1)AI与组织战略整合的不足
现有文献在对人机协同本身的理解上,存在以下三个相互关联的不足:
研究层次方面,首先,研究普遍局限于“人类主导”范式,多探讨AI作为伙伴[15,21]、辅助工具[23]或影响因素[13]。未来亟需探讨AI作为“管理者”或“联席决策者”的角色配置及其对组织代理链条、权力结构与问责机制的冲击。其次,现有文献过度聚焦于人机协同决策的内部影响因素,对外部环境因素,尤其是制度、文化等宏观层面变量的关注相对匮乏。虽有研究指出市场因素的影响[20],但对宏观力量的系统性探讨严重匮乏,而这些变量是连接协同与战略的关键桥梁。最后,现有研究焦点过度集中于人机决策对个体与组织绩效、人类AI采纳行为的影响,却忽略了AI系统引入对更广泛组织与社会关系网络的深刻影响。同时,研究也未能充分细分“人类”角色,对于AI开发者、管理者、使用者、受决策影响者在决策过程中的不同作用、权利与责任,缺乏细致的区分与探究。
研究变量方面,人机决策交互模式下,相关研究多采用人与AI的简单实验对比[8,35],虽然揭示了效能差异,但普遍缺乏对为何及何种条件下的影响因素等深层问题的解答。人机决策支持模式下,虽有一定研究积累,但过度集中于个体心理与态度等内部因素[11,23]。对于数据治理框架、算法审计要求、技术基础设施成熟度等技术与治理变量,以及它们与组织战略的匹配关系,探讨明显不足。此外,关于人机协同的边界条件的深入研究仍显薄弱。人机决策影响模式下,大多描述了AI采纳的宏观结果[14,31],但严重缺乏对前因机制及情境变化的深入剖析。
研究理论方面,AI与传统管理学理论的结合仍显不足。现有人机决策的理论将AI技术视为个体、组织的重要资源[15];聚焦组织的学习过程[17,18]等。未来可结合代理理论,探讨AI作为代理人时代理成本与激励机制的变化;结合制度理论,关注组织采纳AI的模仿性、强制性与规范性压力,丰富理论适用场景。此外,现有文章对理论大多只简单引用,缺少深入对传统的管理学理论进行拓展。
(2)AI视野扩展不足
超越对人机协同微观机制的深化探讨,本研究发现在一个更宏观的层面,现有研究在整体视野与研究范式上存在明显局限,未能充分响应AI技术,特别是生成式AI的突破性进展,为管理学理论及实践所带来的范式级变革机遇,忽视了AI正带来的颠覆性战略情境。具体而言,本文挖掘出现有文献在以下四个前沿战略视野上研究尚有不足:
第一,对AI的落地应用与创新价值挖掘不足。现有研究虽验证了人机协同在特定任务中的效能[35],但忽视了AI规模化落地所需的深层组织变革,且未能将其置于核心创新要素地位,以探讨其如何重构价值主张、赋能新型商业模式。
第二,对AI驱动的平台与生态系统治理探讨较少。一方面,目前研究未能探讨如何利用AI构建自适应、高透明且公平的治理机制;另一方面,研究严重忽略了AI驱动的算法如何重塑平台内部的权力格局与价值分配。同时,对于AI引入或放大的生态系统风险,以及如何利用AI增强生态韧性与价值共创效率,现有研究也尚未展开讨论。
第三,对AI赋能的全面风险管理与合规范式缺乏系统性探讨。现有研究在风险层面多关注算法偏见等微观问题[24],而缺乏从企业整体风险与合规管理的宏观视角,系统性思考AI如何管理风险,以及如何管理AI自身的新型风险。
第四,对AI与战略可持续性与ESG的联系薄弱。尽管可持续发展已成为核心战略议题,但现有研究严重忽略了AI在驱动ESG绩效与长期战略可持续性方面的关键作用。
3.2 未来研究议程
如图5所示,综合考虑现有研究的局限性与AI能力边界的持续扩展,拟提出人机决策下未来的研究方向以对人机决策的框架进一步扩充。
(1)AI与组织战略的深度整合
未来研究应从人机决策模式创新、前因因素拓展、结果因素深化等方面推进AI与组织战略的深度整合。
第一,拓展人机决策模式维度。未来应进一步拓展AI主导的人机决策维度。需系统探究AI主导模式的情境适用性,明确其所需的技术成熟度、治理保障与伦理边界。其次,应超越“人类主导”或“AI主导”的简单二分法,深入探索“动态权责分配”或“情境适配权责分配”模式。具体而言:一是决策模式的情境化配置,未来研究需关注组织如何依据战略目标、决策任务属性以及AI系统的成熟度与可信度,灵活配置AI在交互、支持与影响模式中的角色权重。二是AI角色的动态演化,探讨在外部环境变迁与组织战略调整过程中,AI的功能定位如何随之演变。例如当组织战略由效率优化转向颠覆性创新时,AI的角色如何从流程自动化、预测分析向创意生成、战略情景模拟甚至自主探索新机会转化。研究应进一步剖析此演化过程中的关键触发因素以及组织能力瓶颈与所需的治理结构调整。三是赋能条件的协同机制,针对AI成功应用的个体、组织与技术层面的关键赋能条件,未来研究应聚焦于解析这些条件间复杂关系。研究应致力于构建动态适配模型,并探索通过组织设计和技术手段系统性培育优化这些条件路径。
第二,纳入宏观因素。未来研究应进一步关注制度政策、国际形势、文化差异等宏观环境因素对人机决策的影响。一是制度政策的多层次影响,未来需深入考察国家、区域及全球层面的AI制度、政策治理框架如何影响组织的人机决策战略。二是地缘政治格局的影响,未来研究需探究地缘政治对AI技术供应链、数据跨境流动及特定类型AI选择与部署的影响。例如关键AI技术与数据资源的供应链韧性如何影响组织的技术路线图;数据主权与跨境流动限制如何迫使组织调整其全球化运营与决策架构;特定地缘政治集团内的技术标准联盟或互操作性要求如何影响AI系统的选择与集成;组织如何评估与应对因地缘政治导致的AI技术获取受限或“断供”风险等。三是普惠性与数字鸿沟的战略考量,研究需将AI普惠性与弥合数字鸿沟纳入战略视野。探讨组织在追求商业价值的同时,如何通过AI应用设计、运营模式及伙伴关系促进社会包容性,并分析此类实践对品牌声誉、人才吸引、长期社会许可乃至新市场开拓的战略价值。四是跨文化情境的深度探究,在跨国企业背景下研究人机决策,有必要引入文化视角。研究需超越表面的适配,深入探讨霍夫斯泰德文化维度如何影响组织内部不同层级、部门对AI决策的接受度、信任度及期望。
第三,深化关系与结构影响。未来研究应关注AI决策对组织关系和结构的影响。具体应从以下维度展开:一是组织关系的动态变化,内部视角下,关注人机决策下团队成员间关系、团队成员与管理者间关系的演变,并进一步探究AI如何改变部门间协作模式、管理层级关系及不同经验员工间的知识权力格局。需特别关注“开发者-使用者-决策者”链条中存在的期望差异、沟通障碍、责任模糊与潜在冲突,以及如何通过治理机制与协作实践进行协调。外部视角下,系统分析AI如何重塑企业与供应链伙伴、客户、监管机构及社会公众间的关系性质与互动模式。研究AI驱动的平台型组织如何通过数据与算法重新定义生态系统参与者(互补者、用户)间的依赖性与权力结构。二是组织结构与流程的演化,未来应深入研究AI如何成为组织结构设计与业务流程重组的关键输入变量。三是人机协同绩效评估与责任归属,未来人机决策研究需建立科学量化AI在复杂决策任务中贡献的方法。针对人机紧密协作导致的决策责任模糊问题,探索适应不同AI自主程度和任务风险等级的动态责任分配框架。
(2)AI的全新视野扩展
未来应积极拓展以下前沿议题:AI落地应用效果与创新、AI情境下的平台与生态系统治理、AI赋能的风险与合规管理以及AI驱动的战略可持续性与ESG。
第一,AI的落地应用效果与创新。AI技术从实验室潜力转化为企业真实生产力/创新能力,是企业赢得竞争优势的核心挑战。未来研究应进一步探讨以下问题:一是在技术可行性研究基础上,未来工作应深入剖析AI在不同行业、不同规模企业以及不同文化/制度背景下落地应用的独特挑战、适配模式与价值创造机制。例如:研究供应链金融风控模型如何适应区域金融监管差异;探索中小企业利用云化AI服务克服资源限制的“普惠化”路径;分析文化差异对员工接受AI决策建议的影响机理等。二是需系统识别并分析AI规模化落地的组织障碍,并研究有效的变革管理策略、领导力范式、学习型组织构建方法及激励体系重构方案以克服这些障碍。三是需实证检验不同类型创新任务下,不同人机协同模式对创新绩效的影响机制。深入研究AI如何作为核心创新引擎驱动创新过程。四是重点研究AI如何作为核心支撑要素赋能新型商业模式,分析其对价值主张重构、关键活动与资源重组、收入模式创新以及可持续竞争优势来源的影响。
第二,AI情境下平台与生态系统治理。AI深刻改变了平台与生态系统的结构、互动和治理逻辑。未来研究需进一步探讨以下问题:一是研究在构建自适应治理机制中的作用。探索去中心化治理与中心化协调的混合模式,研究AI在自动化规则执行、动态激励调整、争议智能仲裁中的应用。关注算法透明度、可解释性与用户参与度在治理机制中的平衡。二是深入研究AI驱动的算法如何重塑平台内部的权力格局,影响平台所有者、互补者、用户之间的议价能力、依赖关系和价值分配。分析算法决策的透明度、可解释性、可竞争性对权力制衡的影响,并探讨利用AI实现更公平价值分配的路径。三是探究AI如何通过智能匹配供需、优化资源配置、促进知识流动、预测生态演化趋势等途径提升生态系统的价值共创效率和规模。研究AI如何赋能生态系统实现更快的集体学习、更敏捷的集体适应以及更有效的集体创新。
第三,AI赋能的风险与合规管理。AI既是风险管理工具,也引入新风险。未来研究可进一步探讨以下问题:一是研究如何利用AI实现风险管理从被动响应向主动预测、从局部风险向系统性风险、从低频评估向实时监控的范式跃迁。探索AI在识别新型风险中的作用,并构建基于AI的企业级风险预警与控制塔。二是深入探讨AI技术迭代对组织伦理治理能力提出的新挑战。研究如何系统性地构建兼顾多维度的可信赖AI系统,关注伦理原则向工程实践和操作流程的落地转化。三是研究组织如何应对碎片化且快速演变的全球AI监管环境。未来应探索利用AI实现自动化合规检查、法规智能解读、风险自评估、报告生成,以构建动态合规能力,及跨国/跨行业监管协调、国际标准制定以及监管科技在平衡创新激励与风险管控中的作用。四是深入研究AI系统自身带来的独特风险的管理策略:包括模型风险、操作风险等,并整合上述风险,研究董事会与高管层在AI风险管理中的协同、风险偏好与容忍度的设定、风险报告与披露机制。
第四,AI驱动的战略可持续性与ESG。AI在推动ESG绩效和战略可持续性方面潜力巨大。未来研究需进一步探讨以下问题:一是深入研究AI在优化能源效率、减少资源消耗与浪费、精准管理环境影响等方面的具体路径与效果。二是系统研究如何设计或应用AI,以深入分析并缓解AI应用加剧社会不公的风险、促进普惠金融与普惠服务、改善员工福祉、支持社区发展、保障产品安全与负责任营销。三是研究AI如何提升公司治理透明度、加强风险管理改善董事会监督效能、促进商业伦理实践。四是需高度重视并研究AI应用于ESG领域时产生的悖论效应。核心挑战在于算法偏见等技术风险对社会(S)维度的潜在威胁,需研究有效的偏见检测、缓解和影响评估方法。同时,也应关注技术应用落差可能导致的ESG绩效分化等议题。
4 结论
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本综述以“人机协同决策模式”为核心,回顾了AI从辅助工具向战略合作者的演变。本文提出“嵌入路径—机制反应—绩效后果”三段式逻辑模型,以刻画AI的协作方式与价值实现机制,并强调了组织结构与制度环境的调节作用。AI系统的嵌入受多重因素影响。三种模式下,AI的作用各异:交互模式适用于高复杂度任务;支持模式侧重于提升效率;影响模式则通过认知与行为倾向间接作用于决策。
此外,AI在组织中的深度嵌入不仅改变了个体决策行为与认知过程,还对组织结构、学习机制与文化规范提出新的挑战与要求。技术的不透明性与自动化程度提升,在增强决策效率的同时,也对责任归属、算法信任与制度合法性带来了新的张力。组织需要在灵活性与规范性之间寻求平衡,建立机制化的AI治理体系,以实现技术整合与制度创新的动态耦合。
本文贡献在于:第一,本文基于人机权力关系,将现有文献整合为“交互-支持-影响”的人机决策模式分类框架,分析了不同模式下人机交互的前因与影响;第二,识别并区分了内部因素与外部因素两个前因维度,其中内部因素进一步细分为个体、组织、技术三个层次,外部因素则聚焦于环境动态与市场竞争;第三,识别了绩效产出与行为演进两个结果维度,系统阐释了人机决策模式的多元化影响效应。
实践层面,本文建议管理者在任务特性、治理机制与制度约束之间寻求动态匹配,推进AI在战略流程中的嵌入与协同,并注重AI系统与组织文化、结构及流程的耦合,以实现技术整合与组织转型的协同共振。
未来研究应进一步探讨AI与组织战略的深度整合与人工智能的全新视野扩展,深入揭示AI如何重塑组织决策的范式与结构基础。
基金
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  • 国家自然科学基金面上项目(72372102)
文献
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2026年第44卷第1期
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  • 接收时间：2025-08-05
  • 首发时间：2026-10-13
  • 出版时间：2026-01-15
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  • 收稿日期：2025-08-05
  • 修回日期：2025-11-27
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国家自然科学基金面上项目(72372102)
作者信息
    1 上海交通大学安泰经济与管理学院, 上海 200030
    2 陕西师范大学国际商学院, 陕西西安 710119

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余昊(1996-),男,博士研究生, E-mail:。
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