生成式人工智能队友如何影响团队新产品创意生成?基于团队过程的视角

doi:10.3724/SP.J.1042.2025.1684

摘要/Abstract

摘要：

以生成式人工智能(亦称生成式AI)技术为支撑的生成式AI智能体具有令人惊叹的智能涌现能力, 可以帮助人类突破思维定势, 成为企业新产品开发团队新成员——生成式AI队友。但其商业实践效果及对团队创造力的影响却存在争议。本研究遵循团队效能IPO模型的理论逻辑框架, 针对团队新产品创意生成“发散-收敛-形成”三个阶段, 分3个研究从团队视角深入探究生成式AI队友对团队新产品创意生成绩效的影响。其中, 研究1聚焦创意发散阶段, 从团队任务过程视角探究生成式AI队友抑制作用的认知固化机制, 并识别其缓解策略; 研究2关注创意收敛阶段, 从团队情感过程视角探讨生成式AI队友强化作用的社会认同机制, 并提出其增强策略; 研究3针对创意形成阶段, 系统探索生成式AI队友的双刃剑效应, 并构建其应对策略。本研究突破了前人仅从个体层面探讨AI智能体影响的研究局限, 将人机协作策略由一人一机情境拓展至多人一机情景, 不仅为企业新产品开发团队有效利用生成式AI队友提供了实践启示, 也为我国政府全面开展“人工智能+”行动提供重要决策参考。

关键词: 生成式人工智能, 新产品开发团队, 新产品创意生成, 人机团队协作策略

Abstract:

Generative AI agents, leveraging their natural language capabilities for human interaction and emergent intelligence that helps humans transcend cognitive fixedness, are increasingly participating autonomously in interactions and collaborations within enterprise new product development (NPD) teams, akin to human members. This creates a novel "multiple-humans-one-machine" collaborative context. Generative AI teammates have become significant new members of NPD teams as well. However, their practical effectiveness and impact on team creativity remain contentious. Existing research regrettably exhibits a threefold disconnection. Firstly, NPD team related research remains confined to contexts involving exclusively human members, overlooking the novel team paradigm introduced by the integration of AI agents. Secondly, AI agent related research predominantly stagnates at the individual level of analysis, lacking a comprehensive team-level perspective. Thirdly, Human-AI collaboration related research is largely limited to dyadic human-AI interaction, failing to extend into the complexities of multi-human-AI teams. Consequently, following the theoretical logic of the Input-Process-Output (IPO) model of team effectiveness, this study investigates the influence of generative AI teammates on team performance in new product idea generation from the team perspective. Based on the three distinct phases of new product idea generation: divergence, convergence, and formation, this study includes three sub-studies.

Sub-study 1 focuses on the divergence phase. It explores the cognitive fixation mechanism underlying the inhibitory effect of generative AI teammates from the perspective of team task processes and identifies related mitigation strategies. The study posits that the superior information processing and logical articulation capabilities of generative AI teammates not only foster team consensus but also discourage human members from voicing unique ideas and intuitions, thereby exacerbating team cognitive fixation and inhibiting the diversity of ideas generated by human members. Compared with interactive groups, this inhibitory effect of generative AI teammates will be effectively mitigated in nominal groups.

Sub-study 2 examines the convergence phase. It investigates the social identification mechanism underlying the reinforcing effect of generative AI teammates from a team affective process perspective and proposes related enhancement strategies. As human members perceive generative AI teammates as lacking value judgments, subjective preferences, and emotional capabilities, the study posits that human members perceived team social identification - including emotional exchange - among themselves is heightened after generative AI teammates joined their team. This consequently enhances the convergence of idea adoption among human members. The reinforcing effect of generative AI teammates in the convergence phase can be further amplified by high team diversity beliefs.

Sub-study 3 addresses the formation phase. It systematically explores the double-edged sword effect of generative AI teammates and constructing corresponding coping strategies. The study contends that generative AI teammates, by inhibiting the diversity of ideas generated in the divergence phase and enhancing the convergence of idea adoption in the convergence phase, ultimately increase the speed of team new product idea generation while decreasing the quality of team new product idea generation in the formation phase. Furthermore, when human members receive generative AI skill training, the positive effect of generative AI teammates on the team new product idea generation speed will be further strengthened, while the negative effect of generative AI teammates on the team new product idea generation quality will be effectively alleviated.

This study specifically focuses on the role of generative AI teammates as new members within NPD teams. It reveals the mechanisms through which generative AI teammates influence team interaction and collaboration among human members at the team level, and constructs optimized collaborative strategies for generative AI teammates operating within the multiple-humans-one-machine context. Consequently, this research not only enriches the theoretical understanding of team effectiveness models and expands human-AI collaboration strategies from "one-human-one-machine" to "multiple-humans-one-machine" contexts, but also provides significant practical implications for enterprises seeking to effectively leverage generative AI teammates in NPD teams and offer significant decision-making references for the Chinese government in implementing its “AI+” initiative.

Key words: generative artificial intelligence, new product development team, new product idea generation, human-AI team collaboration strategy

中图分类号:

B849:C93

郑宇, 谌怡, 吴月燕. (2025). 生成式人工智能队友如何影响团队新产品创意生成?基于团队过程的视角. 心理科学进展 , 33(10), 1684-1697.

ZHENG Yu, CHEN Yi, WU Yueyan. (2025). How do generative AI teammates affect team new product idea generation? A perspective from team process. Advances in Psychological Science, 33(10), 1684-1697.

图/表 5

图1 生成式AI队友示意图注：图中一人一机情景指一个人类与一个生成式AI智能体进行协作, 多人一机情景指多个人类同时与一个生成式AI智能体进行互动合作(Dennis et al., 2023; 许为等, 2024)。其中, 生成式AI智能体以生成式AI技术底层算法模型为支撑, 具有嵌入式智能工具、虚拟机器人、实体机器人等表现形式(罗映宇等, 2023)。图中以虚拟机器人形象作为生成式AI智能体示例。图中表示生成式AI队友同时与多个人类成员进行双向交互, 表示人类成员间进行双向沟通。

图2 总体研究逻辑框架图

图3 研究1理论模型图

图4 研究2理论模型图

图5 研究3理论模型图注：虚框内为研究1与研究2的研究构想。

参考文献 51

[1]	陈玮奕, 刘新梅, 张新星. (2019). 关系冲突认知差异有助于团队创造力—— 一个被调节的中介作用模型. 科学学与科学技术管理, 40(6), 125-139.
[2]	多里·克拉克, 托马斯·查莫罗·普雷穆日奇.(2023). AI时代的5种新技能. 2023-07-28取自 https://www.hbr-caijing.com/#/article/detail?id=480593
[3]	何贵兵, 陈诚, 何泽桐, 崔力丹, 陆嘉琦, 宣泓舟, 林琳. (2022). 智能组织中的人机协同决策:基于人机内部兼容性的研究探索. 心理科学进展, 30(12), 2619-2627. doi: 10.3724/SP.J.1042.2022.02619
[4]	李宁, 邓文明. (2023). 团队的未来:领导者如何与AI携手前行. 清华管理评论, 12(9), 94-99.
[5]	李倩, 龚诗阳, 李超凡. (2019). 团队文化多样性对团队创新的影响及作用机制. 心理科学进展, 27(9), 1521-1539. doi: 10.3724/SP.J.1042.2019.01521
[6]	李树文, 罗瑾琏, 张志菲. (2023). AI能力如何助推企业实现价值共创——基于企业与客户间互动的探索性案例研究. 中国工业经济, 40(5), 174-192.
[7]	刘松博, 程进凯, 王曦. (2022). 虚拟团队共享式领导的双刃剑效应研究:基于调适性结构理论的视角. 心理科学进展, 30(8), 1770-1784. doi: 10.3724/SP.J.1042.2022.01770
[8]	栾琨, 谢小云. (2014). 国外团队认同研究进展与展望. 外国经济与管理, 36(4), 57-64.
[9]	罗映宇, 朱国玮, 钱无忌, 吴月燕, 黄静, 杨智. (2023). 人工智能时代的算法厌恶:研究框架与未来展望. 管理世界, 39(10), 205-233.
[10]	玛丽亚姆·阿拉维, 乔治·韦斯特曼.. (2023). 生成式AI如何改变知识工作. 2024-12-12取自 https://www.hbr-caijing.com/#/article/detail?id=480849
[11]	任宗强, 刘冉. (2017). 人机交互模式下企业知识管理平台研究. 技术与创新管理, 38(5), 526-529.
[12]	舒丽芳, 王魁, 吴月燕, 陈斯允. (2024). 人工智能指导对消费者长期目标追求的多阶段影响机制. 心理科学进展, 32(3), 451-464. doi: 10.3724/SP.J.1042.2024.00451
[13]	托因·伊彭, 丹尼尔·芬肯施塔特, 乔希·福尔克, 洛克什·文卡塔斯瓦米.(2023). AI“生成”你独有的创造力. 2023-08-11取自 https://www.hbr-caijing.com/#/article/detail?id=480672
[14]	王才, 周文斌, 赵素芳. (2019). 机器人规模应用与工作不安全感——基于员工职业能力调节的研究. 经济管理, 41(4), 111-126.
[15]	王楠, 陈详详, 孙百惠, 陈劲. (2021). 谁的创意更具价值—内外部领先用户的对比研究. 南开管理评论, 24(5), 51-62.
[16]	吴小龙, 肖静华, 吴记. (2023). 当创意遇到智能:人与AI协同的产品创新案例研究. 管理世界, 39(5), 112-126.
[17]	肖静华, 吴瑶, 刘意, 谢康. (2018). 消费者数据化参与的研发创新——企业与消费者协同演化视角的双案例研究. 管理世界, 34(8), 154-173.
[18]	许为, 高在峰, 葛列众. (2024). 智能时代人因科学研究的新范式取向及重点. 心理学报, 56(3), 363-382. doi: 10.3724/SP.J.1041.2024.00363
[19]	姚加权, 张锟澎, 郭李鹏, 冯绪. (2024). 人工智能如何提升企业生产效率?——基于劳动力技能结构调整的视角. 管理世界, 40(2), 101-116.
[20]	周详. (2021). 集体智慧在合作创新中的生成与应用. 北京: 人民出版社.
[21]	Brodbeck F. C., Kerschreiter R., Mojzisch A., & Schulz- Hardt S. (2007). Group decision making under conditions of distributed knowledge: The information asymmetries model. Academy of Management Review, 32(2), 459-479.
[22]	Criscuolo P., Dahlander L., Grohsjean T., & Salter A. (2017). Evaluating novelty: The role of panels in the selection of R&D projects. Academy of Management Journal, 60(2), 433-460.
[23]	Dennis A. R., Lakhiwal A., & Sachdeva A. (2023). AI Agents as team members: Effects on satisfaction, conflict, trustworthiness, and willingness to work with. Journal of Management Information Systems, 40(2), 307-337.
[24]	Diehl M., & Stroebe W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of Personality and Social Psychology, 53(3), 497-509.
[25]	Duan Y., Edwards J. S., & Dwivedi Y. K. (2019). Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
[26]	Dwivedi Y. K., Kshetri N., Hughes L., Slade E. L., Jeyaraj A., Kar A. K., … Wright R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(4), Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
[27]	Eling K., Langerak F., & Griffin A. (2015). The performance effects of combining rationality and intuition in making early new product idea evaluation decisions. Creativity and Innovation Management, 24(3), 464-477.
[28]	Fugener A., Grahl J., Gupta A., & Ketter W. (2022). Cognitive challenges in human-artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678-696.
[29]	Gallupe R. B., Bastianutti L. M., & Cooper W. H. (1991). Unblocking Brainstorms. Journal of Applied Psychology, 76(1), 137-142. pmid: 2016214
[30]	Harreis H., Koullias T., Roberts R., & Te K. (2023). Generative AI: Unlocking the future of fashion. Retrieved January 19, 2025, from https://www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-the-future-of-fashion#/
[31]	Hentout A., Aouache M., Maoudj A., & Akli I. (2019). Human-robot interaction in industrial collaborative robotics: A literature review of the decade 2008-2017. Advanced Robotics, 33(15-16), 764-799.
[32]	Hutson J. (2023). AI and the creative process: Part three. Retrieved January 19, 2025, from https://daily.jstor.org/ai-and-the-creative-process-part-three/
[33]	Kaufman J. C., & Beghetto R. A. (2009). Beyond big and little: The four C model of creativity. Review of General Psychology, 13(1), 1-12.
[34]	Kihlstrom J. (1987). The cognitive unconscious. Science, 237(4821), 1445-1452. pmid: 3629249
[35]	Klotz A. C., Hmieleski K. M., Bradley B. H., & Busenitz L. W. (2014). New venture teams: A review of the literature and roadmap for future research. Journal of Management, 40(1), 226-255.
[36]	Kushwaha A. K., & Kar A. K. (2021). MarkBot-A language model-driven chatbot for interactive marketing in post- modern world. Information Systems Frontiers, 26(3), 857-874.
[37]	Longoni C., Bonezzi A., & Morewedge C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. doi: 10.1093/jcr/ucz013
[38]	Mahzoon H., Ogawa K., Yoshikawa Y., Tanaka M., Ogawa K., Miyazaki R.,... Ishiguro H. (2019). Effect of self- representation of interaction history by the robot on perceptions of mind and positive relationship: A case study on a home-use robot. Advanced Robotics, 33(21), 1112-1128.
[39]	Mathieu J., Maynard M. T., Rapp T., & Gilson L. (2008). Team effectiveness 1997-2007: A review of recent advancements and a glimpse into the future. Journal of Management, 34(3), 410-476.
[40]	Metcalf L., Askay D. A., & Rosenberg L. B. (2019). Keeping humans in the loop: Pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review, 61(4), 84-109. doi: 10.1177/0008125619862256
[41]	Miron-Spektor E., Erez M., & Naveh E. (2011). The effect of conformist and attentive-to-detail members on team innovation: Reconciling the innovation paradox. Academy of Management Journal, 54(4), 740-760.
[42]	Nataraj P. (2022). 6-Month review: GitHub copilot elicits mixed reactions from developers. Retrieved January 19, 2025, from https://analyticsindiamag.com/global-tech/6-month-review-github-copilot-elicits-mixed-reactions-from-developers/
[43]	Noy S., & Zhang W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. doi: 10.1126/science.adh2586 pmid: 37440646
[44]	Qin X., Chen C., Yam K. C., Cao L. M., Li W. L., Guan J., … Lin Y. Q. (2022). Adults still can't resist: A social robot can induce normative conformity. Computers in Human Behavior, 127(2), Article 107041. https://doi.org/10.1016/j.chb.2021.107041
[45]	Raisch S., & Fomina K. (2024). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441-464. https://doi.org/10.5465/amr.2021.0421
[46]	Raisch S., & Krakowski S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210.
[47]	Rietzschel F. E., Nijstad A. B., & Stroebe W. (2006). Productivity is not enough: A comparison of interactive and nominal brainstorming groups on idea generation and selection. Journal of Experimental Social Psychology, 42(2), 244-251.
[48]	Shrestha Y. R., Ben-Menahem S. M., & Von Krogh G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. doi: 10.1177/0008125619862257
[49]	Simons T., & Peterson R. (2000). Task conflict and relationship conflict in top management teams: The pivotal role of intragroup trust. Journal of Applied Psychology, 85(1), 102-111. doi: 10.1037/0021-9010.85.1.102 pmid: 10740960
[50]	van Dick R., van Knippenberg D., Hägele S., Guillaume Y. R. F., & Brodbeck F. C. (2008). Group diversity and group identification: The moderating role of diversity beliefs. Human Relations, 61(10), 1463-1492.
[51]	Złotowski J., Yogeeswaran K., & Bartneck C. (2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human-Computer Studies, 100(4), 48-54.