Measuring the impact of human–AI collaboration on knowledge diffusion in new product development projects

Abstract

Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management. We propose a comprehensive framework to explore the impact of human–AI collaboration on organizational knowledge diffusion. First, we develop a knowledge diffusion model based on continuous human–AI interactions, and we use the Agent-Based Modeling (ABM) method to simulate the diffusion process within the collaborative team and assess diffusion rates and efficiency based on knowledge levels. Second, we examine the interdependencies among members under different roles of AI, integrating AI cognitive capabilities, human–AI cognitive trust, and task interdependencies, and build a tie strength measurement model from the Social Network Analysis (SNA) perspective. Third, an entropy-based model is introduced to measure AI’s cognitive capability, accounting for project complexity and AI-generated solution uncertainty. We also establish a dynamic cognitive trust model that incorporates both the dynamic nature of trust in human–AI interactions and AI’s cognitive capability. Task interdependencies are assessed through a multi-dimensional activity network, and visualized by the Dependency Structure Matrix (DSM) method. Finally, an industrial example is provided to demonstrate the proposed model. Results show that organizational knowledge diffusion performs best when AI acts both as a collaborator and a tool. Moreover, this paper provides new insights, including how trust and task interdependencies significantly impact knowledge diffusion in human–AI collaborative organizations.

Graphical abstract

Keywords

human–AI collaboration / knowledge diffusion / trust / Agent-Based Modeling (ABM) / product development project

References

[1] Akula A R, Wang K, Liu C, Saba-Sadiya S, Lu H, Todorovic S, Chai J, Zhu S C, (2022). CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models. iScience, 25( 1): 103581

[2] Anantrasirichai N, Bull D, (2022). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55( 1): 589–656

[3] Arias-Pérez J, Huynh T, (2023). Flipping the odds of AI-driven open innovation: The effectiveness of partner trustworthiness in counteracting interorganizational knowledge hiding. Industrial Marketing Management, 111: 30–40

[4] Beaulieu T, Dupin-Bryant P, Olsen D, (2017). Dynamic SQL knowledge as a mechanism for increasing individual absorptive capacity. International Journal of Management & Information Systems, 21( 2): 27–40

[5] Boyacı T, Canyakmaz C, de Véricourt F, (2024). Human and machine: The impact of machine input on decision making under cognitive limitations. Management Science, 70( 2): 1258–1275

[6] Capestro M, Rizzo C, Kliestik T, Peluso A M, Pino G, (2024). Enabling digital technologies adoption in industrial districts: The key role of trust and knowledge sharing. Technological Forecasting and Social Change, 198: 123003

[7] Choung H, David P, Ross A, (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 39( 9): 1727–1739

[8] Chowdhury S, Budhwar P, Dey P K, Joel-Edgar S, Abadie A, (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. Journal of Business Research, 144: 31–49

[9] Evans M M, Frissen I, Choo C W, (2019). The strength of trust over ties: Investigating the relationships between trustworthiness and tie-strength in effective knowledge sharing. Electronic Journal of Knowledge Management, 17( 1): 19–33

[10] Farquhar S, Kossen J, Kuhn L, Gal Y, (2024). Detecting hallucinations in large language models using semantic entropy. Nature, 630( 8017): 625–630

[11] Frankel A, Kamenica E, (2019). Quantifying information and uncertainty. American Economic Review, 109( 10): 3650–3680

[12] Gama F, Magistretti S, (2025). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 42( 1): 76–111

[13] Glikson E, Woolley A W, (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14( 2): 627–660

[14] Haefner N, Wincent J, Parida V, Gassmann O, (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162: 120392

[15] Jarrahi M H, (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61( 4): 577–586

[16] Jarrahi M H, Askay D, Eshraghi A, Smith P, (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66( 1): 87–99

[17] Kiesling E, Günther M, Stummer C, Wakolbinger L M, (2012). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20( 2): 183–230

[18] KyungNKwonH E (2022). Rationally trust, but emotionally? The roles of cognitive and affective trust in laypeople’s acceptance of AI for preventive care operations. Production and Operations Management: poms.13785

[19] Ng S W T, Zhang R, (2025). Trust in AI chatbots: A systematic review. Telematics and Informatics, 97: 102240

[20] Olaisen J, Revang O, (2018). Exploring the performance of tacit knowledge: How to make ordinary people deliver extraordinary results in teams. International Journal of Information Management, 43: 295–304

[21] Omrani N, Rivieccio G, Fiore U, Schiavone F, Agreda S G, (2022). To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181: 121763

[22] Pan M, Chandrasekaran A, Hill J, Rungtusanatham M, (2022). Multidisciplinary R&D project success in small firms: The role of multiproject status and project management experience. Production and Operations Management, 31( 7): 2806–2821

[23] Park S, Puranam P, (2024). Vicarious learning without knowledge differentials. Management Science, 70( 5): 2999–3019

[24] Paschen J, Wilson M, Ferreira J J, (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63( 3): 403–414

[25] Qiao T, Shan W, Zhang M, Liu C, (2019). How to facilitate knowledge diffusion in complex networks: The roles of network structure, knowledge role distribution and selection rule. International Journal of Information Management, 47: 152–167

[26] Sankar C P, Thumba D A, Ramamohan T R, Chandra S S V, Satheesh Kumar K, (2020). Agent-based multi-edge network simulation model for knowledge diffusion through board interlocks. Expert Systems with Applications, 141: 112962

[27] Sassine J G, Rahmandad H, (2024). How does network structure impact socially reinforced diffusion. Organization Science, 35( 1): 52–70

[28] Schelble B G, Lopez J, Textor C, Zhang R, McNeese N J, Pak R, Freeman G, (2024). Towards ethical AI: Empirically investigating dimensions of AI ethics, trust repair, and performance in human–AI teaming. Human Factors, 66( 4): 1037–1055

[29] Seeber I, Bittner E, Briggs R O, De Vreede T, De Vreede G J, Elkins A, Maier R, Merz A B, Oeste-Reiß S, Randrup N, Schwabe G, Söllner M, (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57( 2): 103174

[30] Song W, Ming X, Xu Z, (2013). Risk evaluation of customer integration in new product development under uncertainty. Computers & Industrial Engineering, 65( 3): 402–412

[31] Thomas A, Gupta V, (2022). Tacit knowledge in organizations: bibliometrics and a framework-based systematic review of antecedents, outcomes, theories, methods and future directions. Journal of Knowledge Management, 26( 4): 1014–1041

[32] Todo Y, Matous P, Inoue H, (2016). The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks. Research Policy, 45( 9): 1890–1906

[33] Westphal M, Vössing M, Satzger G, Yom-Tov G B, Rafaeli A, (2023). Decision control and explanations in human–AI collaboration: Improving user perceptions and compliance. Computers in Human Behavior, 144: 107714

[34] Xu L, Ding R, Wang L, (2022). How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles. Scientometrics, 127( 3): 1353–1379

[35] YadkoriY AKuzborskijIGyörgyASzepesváriC (2024). To Believe or Not to Believe Your LLM. arXiv preprint arXiv:2406.02543

[36] Yang Q, Yang N, Browning T R, Jiang B, Yao T, (2022). Clustering product development project organization from the perspective of social network analysis. IEEE Transactions on Engineering Management, 69( 6): 2482–2496

[37] Zhang J, Adomavicius G, Gupta A, Ketter W, (2020). Consumption and performance: Understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Information Systems Research, 31( 1): 76–101

[38] Zhang W, Zhang W, Daim T, Yalçın H, (2025). AI challenges conventional knowledge management: light the way for reframing SECI model and Ba theory. Journal of Knowledge Management, 29( 5): 1618–1654

[39] Zou X Q, Yang Q, (2019). R & D project portfolio selection based on domination and diffusion relationship in the project network. Chinese Journal of Management Science, 27: 198–209

[40] Zou X Q, Yang Q, Wang Q R, (2023). Analyzing the knowledge diffusion among multiple projects and its impact on the program. Operations Research and Management Science, 32( 2): 261–270

RIGHTS & PERMISSIONS

Higher Education Press