Generative artificial intelligence is becoming deeply embedded in the knowledge workflows of virtual teams, yet how it affects team innovation performance through knowledge integration lacks direct team-level evidence. Drawing on the knowledge-based view, this study develops a dual-path model linking generative AI knowledge integration to virtual team innovation performance through transactive memory systems and knowledge dependence, with team virtuality as a boundary condition. Using survey data from 455 virtual team members, we find that generative AI knowledge integration significantly improves team innovation performance. Both transactive memory systems and knowledge dependence mediate this effect; contrary to the expectation that dependence constitutes a drain, the mediating effect of knowledge dependence is stronger than that of transactive memory systems. Team virtuality positively moderates the main effect and specifically strengthens the knowledge-dependence path. These findings reveal the dual mechanisms through which generative AI shapes team innovation and inform the deployment of AI in virtual team contexts.

Jiaying Zeng. The Impact of Generative Artificial Intelligence Knowledge Integration on Virtual Team Innovation Performance: The Dual-Path Mechanism of Transactive Memory Systems and Knowledge Dependence. Academic Journal of Business & Management (2026), Vol. 8, Issue 10: 24-32. https://doi.org/10.25236/AJBM.2026.081004.

[1] Martins LL, Gilson LL, Maynard MT. Virtual teams: what do we know and where do we go from here? Journal of Management. 2004;30(6):805-835.

[2] Dell'Acqua F, Ayoubi C, Lifshitz H, et al. The cybernetic teammate: a field experiment on generative AI reshaping teamwork and expertise. Cambridge, MA: National Bureau of Economic Research; 2025. NBER Working Paper No. 33641.

[3] China Daily. AI “co-scientist” reshapes research collaboration. Available from: https://cn.chinadaily.com.cn/a/202507/08/WS68809428a310028a84abf545.html [Accessed 21st October 2026].

[4] State Administration for Market Regulation, Standardization Administration of China. Cybersecurity technology—Security basic requirements for generative AI services: GB/T 45654—2025. Beijing: Standards Press of China; 2025.

[5] Li YH, Xue JX. How does the use of generative AI affect employee innovation performance? A heuristic–shortcut orientation perspective. Nankai Business Review. 2026.

[6] Zhang H, Zhu L, Zhang A, et al. The influence of generative artificial intelligence usage on employees' innovative job performance. PLoS One. 2026. DOI: 10.1371/journal.pone.0327786.

[7] Noy S, Zhang W. Experimental evidence on the productivity effects of generative artificial intelligence. Science. 2023;381(6654):187-192.

[8] Doshi AR, Hauser OP. Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances. 2024;10(28):eadn5290.

[9] Reif JA, Cummings JN. The generative AI dilemma in knowledge-intensive teams. Academy of Management Review. 2026. DOI: 10.5465/amr.2024.0538.

[10] Alavi M, Tiwana A. Knowledge integration in virtual teams: the potential role of KMS. Journal of the American Society for Information Science and Technology. 2002;53(12):1029-1037.

[11] Mesmer-Magnus JR, DeChurch LA, Jimenez-Rodriguez M, et al. A meta-analytic investigation of virtuality and information sharing in teams. Organizational Behavior and Human Decision Processes. 2011;115(2):214-225.

[12] Li H, Song FB, Guo CH. The impact of human–generative AI collaboration on team innovation performance: a knowledge brokerage perspective. Research and Development Management. 2025;37(3):27-39.

[13] Grant RM. Toward a knowledge-based theory of the firm. Strategic Management Journal. 1996;17(S2):109-122.

[14] Okhuysen GA, Eisenhardt KM. Integrating knowledge in groups: how formal interventions enable flexibility. Organization Science. 2002;13(4):370-386.

[15] Davenport TH, Prusak L. Working knowledge: how organizations manage what they know. Boston: Harvard Business School Press; 1998.

[16] Belbaly Aissa N, Gurăau C, Psychogios A, et al. Transactional memory systems in virtual teams: communication antecedents and the impact of TMS components on creative processes and outcomes. Technological Forecasting and Social Change. 2022;174:121235.

[17] Yin M, Jiang S, Niu X. Can AI really help? The double-edged sword effect of AI assistant on employees' innovation behavior. Computers in Human Behavior. 2024;150:107987.

[18] Raisch S, Krakowski S. Artificial intelligence and management: the automation-augmentation paradox. Academy of Management Review. 2021;46(1):192-210.

[19] Kong W, Hu H, Liu Y, et al. How different forms of human-AI interaction shape team innovation: the mediating role of knowledge domain coupling in AI-enabled new product development. Systems. 2026;14(7):824.

[20] Wei X, Wang L, Lee LK, et al. The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: an experimental study. International Journal of Educational Technology in Higher Education. 2025;22(1):38.

[21] Liu LM, He YQ, Hu MY. Active learning or deliberate avoidance? The influence of AI algorithmic monitoring on employee innovation performance. Science of Science and Management of Science and Technology. 2024;45(10):181-197.

[22] Zheng Y, Chen Y, Wu YY. The impact of generative AI teammates on team new product idea generation performance: a team process perspective. Advances in Psychological Science. 2025; 33(10):1684-1697.

[23] Boussioux L, Lane JN, Zhang M, et al. The crowdless future? Generative AI and creative problem-solving. Organization Science. 2024;35(5):1589-1607.