Abstract
The mechanism of knowledge diffusion in collaborative innovation projects has long been a controversial topic, partly due to the lack of attention to actors. The characteristics of actors in projects directly affect individual diffusion behaviour and, in turn, the whole process of knowledge diffusion. The interactions among actors in knowledge diffusion can be abstractly expressed as a network. In prior research, the network characteristics related to knowledge diffusion are confined to the dimensions of network relationships, and the attributes of network nodes have received limited attention. For this reason, this paper focuses on two characteristics, network density and project roles, which are argued to have a considerable correlation with knowledge diffusion in collaborative innovation projects. As knowledge diffusion in a project is a complex and dynamic process, an agent-based modelling approach was employed to construct a simulation model of knowledge diffusion. A case study was conducted that included three parallel simulation experiments with different network characteristics. The results show that (a) the adjustment of network density within a specific range is positively correlated with the extent of knowledge diffusion; (b) the role division of network nodes has a negative impact on the overall extent of knowledge diffusion; and (c) the role division of network nodes has a particular moderating effect on the relationship between density and the extent of diffusion. This research reveals the mechanism of knowledge diffusion in collaborative innovation projects, which provides theoretical guidance for designing the relational structure and the roles of actors in practice.
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References
Ajmal, M. M., & Koskinen, K. U. (2008). Knowledge transfer in project-based organizations: An organizational culture perspective. Project Management Journal, 39(1). https://doi.org/10.1002/pmj.
Akbar, H., & Mandurah, S. (2014). Project-conceptualisation in technological innovations: A knowledge-based perspective. International Journal of Project Management, 32(5), 759–772. https://doi.org/10.1016/j.ijproman.2013.10.002
Allen, V. L., Vliert, E. V. D., & SpringerLink (Online service). (1984). Role transitions: Explorations and explanations. Boston, MA: Springer US
Anand, A., Centobelli, P., & Cerchione, R. (2020). Why should I share knowledge with others? A review-based framework on events leading to knowledge hiding. Journal of Organizational Change Management, 33(2), 379–399. https://doi.org/10.1108/JOCM-06-2019-0174
Anderson, H., Havila, V., Andersen, P., & Halinen, A. (1998). Position and role-conceptualising dynamics in business networks. Scandinavian Journal of Management, 14(3), 167–186. https://doi.org/10.1016/S0956-5221(97)00037-7
Bahar, D., & Rapoport, H. (2018). Migration, knowledge diffusion and the comparative advantage of nations. The Economic Journal, 128(612), F273–F305.
Barkoczi, D., & Galesic, M. (2016). Social learning strategies modify the effect of network structure on group performance. Nature Communications, 7(1). https://doi.org/10.1038/ncomms13109.
Bock, Zmud, Kim, & Lee. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87–111. https://doi.org/10.2307/25148669.
Bolton, G. E., Katok, E., & Ockenfels, A. (2004). How effective are electronic reputation mechanisms? An experimental investigation. Management Science, 50(11), 1587–1602. https://doi.org/10.1287/mnsc.1030.0199
Breschi, S., & Lissoni, F. (2009). Mobility of skilled workers and co-invention networks: An anatomy of localised knowledge flows. Journal of Economic Geography, 9(4), 439–468. https://doi.org/10.1093/jeg/lbp008
Brettel, M., Heinemann, F., Engelen, A., & Neubauer, S. (2011). Cross-functional integration of R&D, marketing, and manufacturing in radical and incremental product innovations and its effects on project effectiveness and efficiency: Cross-functional integration. The Journal of Product Innovation Management, 28(2), 251–269. https://doi.org/10.1111/j.1540-5885.2011.00795.x
Burt, R. (2004). Structural holes and good Ideas1. The American Journal of Sociology, 110(2), 349–399. https://doi.org/10.1086/421787
Busby, J. S. (2019). The co-evolution of competition and parasitism in the resource-based view: A risk model of product counterfeiting. European Journal of Operational Research, 276(1), 300–313. https://doi.org/10.1016/j.ejor.2018.12.039.
Cacciatori, E., Tamoschus, D., & Grabher, G. (2012). Knowledge transfer across projects: Codification in creative, high-tech and engineering industries. Management Learning, 43(3), 309–331. https://doi.org/10.1177/1350507611426240
Cai, B., Huang, X., & Chen, G. (2017). Knowledge diffusion performance of innovation network: A study based on organization evolution theory. Journal of Discrete Mathematical Sciences and Cryptography, 20(6–7), 1541–1549. https://doi.org/10.1080/09720529.2017.1390838
Chakkol, M., Selviaridis, K., & Finne, M. (2018). The governance of collaboration in complex projects. International Journal of Operations & Production Management, 38(4), 997–1019. https://doi.org/10.1108/IJOPM-11-2017-0717
Chen, Y., Lin, T., & Yen, D. C. (2014). How to facilitate inter-organizational knowledge sharing: The impact of trust. Information & Management, 51(5), 568–578. https://doi.org/10.1016/j.im.2014.03.007
Chinowsky, P., & Taylor, J. E. (2012). Networks in engineering: An emerging approach to project organization studies. Engineering Project Organization Journal, 2(1–2), 15–26. https://doi.org/10.1080/21573727.2011.635647
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8), 1557–1575. https://doi.org/10.1016/j.jedc.2003.04.002
Davila, G. A., Durst, S., & Varvakis, G. (2018). Knowledge absorptive capacity, innovation, and firm’s performance: Insights from the south of Brazil. International Journal of Innovation Management, 22(2), 1850013. https://doi.org/10.1142/S1363919618500135
De Zubielqui, G. C., Jones, J., & Statsenko, L. (2016). Managing innovation networks for knowledge mobility and appropriability: A complexity perspective. Entrepreneurship Research Journal, 6(1), 75–109. https://doi.org/10.1515/erj-2015-0016
Del Chiappa, G., & Baggio, R. (2015). Knowledge transfer in smart tourism destinations: Analysing the effects of a network structure. Journal of Destination Marketing & Management, 4(3), 145–150. https://doi.org/10.1016/j.jdmm.2015.02.001
Derakhshan, R., Turner, R., & Mancini, M. (2019). Project governance and stakeholders: a literature review. International Journal of Project Management, 37(1), 98–116. https://doi.org/10.1016/j.ijproman.2018.10.007.
Ding, R., Wang, L., Sun, T., Gao, S., & Qian, C. (2016). Network dynamic analysis method for project governance risk control. In Proc., DEStech transactions on computer science and engineering (MCSSE) (pp. 184–193). Lancaster, PA: DEStech Publication.
Du, J., & El-Gafy, M. (2015). Using agent-based modeling to investigate goal incongruence issues in proposal development: Case study of an EPC Project. Journal of Management in Engineering, 31(6), 05014025. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000343
Elche, D., García-Villaverde, P. M., & Martínez-Pérez, Á. (2018). Inter-organizational relationships with core and peripheral partners in heritage tourism clusters. International Journal of Contemporary Hospitality Management, 30(6), 2438–2457. https://doi.org/10.1108/IJCHM-11-2016-0611
Erkutlu, H., & Chafra, J. (2015). The effects of empowerment role identity and creative role identity on servant leadership and employees’ innovation implementation behavior. Procedia—Social and Behavioral Sciences, 181, 3–11. https://doi.org/10.1016/j.sbspro.2015.04.860
Freeman, C. (1991). Networks of innovators: A synthesis of research issues. Research Policy, 20(5), 499–514. https://doi.org/10.1016/0048-7333(91)90072-X
Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research. Journal of Product Innovation Management, 22(5), 380–398. https://doi.org/10.1111/j.1540-5885.2005.00136.x
Gilsing, V., & Nooteboom, B. (2005). Density and strength of ties in innovation networks: An analysis of multimedia and biotechnology. European Management Review, 2(3), 179–197. https://doi.org/10.1057/palgrave.emr.1500041
Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37(10), 1717–1731.
Havakhor, T., & Sabherwal, R. (2018). Team processes in virtual Knowledge teams: The effects of reputation signals and network density. Journal of Management Information Systems, 35(1), 266–318. https://doi.org/10.1080/07421222.2018.1440755
Havakhor, T., Soror, A. A., & Sabherwal, R. (2018). Diffusion of knowledge in social media networks: Effects of reputation mechanisms and distribution of knowledge roles. Information Systems Journal, 28(1), 104–141. https://doi.org/10.1111/isj.12127
Heikkinen, M. T., Mainela, T., Still, J., & Tähtinen, J. (2007). Roles for managing in mobile service development nets. Industrial Marketing Management, 36(7), 909–925. https://doi.org/10.1016/j.indmarman.2007.05.014
Herath, D., Costello, J., & Homberg, F. (2007). Team problem solving and motivation under disorganization—An agent-based modeling approach. Team Performance Management, 23(1/2), 46–65. https://doi.org/10.1108/TPM-10-2015-0046
Heydebrand, W., & Mirón, A. (2002). Constructing Innovativeness in New-Media Start-Up Firms. Environment and Planning a: Economy and Space, 34(11), 1951–1984. https://doi.org/10.1068/a3558
Hirshman, B. R., St. Charles, J., & Carley, K. M. (2011). Leaving us in tiers: Can homophily be used to generate tiering effects? Computational and Mathematical organization Theory, 17(4), 318–343. https://doi.org/10.1007/s10588-011-9088-4
Hurmelinna-Laukkanen, P., & Nätti, S. (2018). Orchestrator types, roles and capabilities—a framework for innovation networks. Industrial Marketing Management, 74, 65–78. https://doi.org/10.1016/j.indmarman.2017.09.020
Jasny, L., Sayles, J., Hamilton, M., Gomez, L. R., Jacobs, D., Prell, C., Matous, P., Schiffer, E., Guererro, A. M., & Barnes, M. L. (2021). Participant engagement in environmentally focused social network research. Social Networks, 66, 125–138. https://doi.org/10.1016/j.socnet.2021.01.005
Jiang, S., & Chen, H. (2019). Examining patterns of scientific knowledge diffusion based on knowledge cyber infrastructure: A multi-dimensional network approach. Scientometrics, 121(3), 1599–1617. https://doi.org/10.1007/s11192-019-03242-5
Kahn, & Ken. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with netlogo. Physics Today, 68(8), 55–55. https://doi.org/10.1063/PT.3.2884
Kasvi, J. J. J., Vartiainen, M., & Hailikari, M. (2003). Managing knowledge and knowledge competences in projects and project organizations. International Journal of Project Management, 21(8), 571–582. https://doi.org/10.1016/S0263-7863(02)00057-1
Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. (2012). Agent-based simulation of innovation diffusion: A review. Central European Journal of Operations Research, 20(2), 183–230. https://doi.org/10.1007/s10100-011-0210-y
Kim, C., & Lee, J. (2018). The effect of network structure on performance in South Korea SMEs: The moderating effects of absorptive capacity. Sustainability, 10(9). https://doi.org/10.3390/su10093174.
Korkmaz, G., Kuhlman, C. J., Goldstein, J., & Vega-Redondo, F. (2020). A computational study of homophily and diffusion of common knowledge on social networks based on a model of facebook. Social Network Analysis and Mining, 10(1). https://doi.org/10.1007/s13278-019-0615-5.
Kunpeng, Y., Jiafu, S., & Hui, H. (2017). Simulation of collaborative product development knowledge diffusion using a new cellular automata approach. Advances in Production Engineering & Management, 12(3), 265–273. https://doi.org/10.14743/apem2017.3.257.
Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and inter organizational learning. Strategic Management Journal, 19(5), 461–477. https://doi.org/10.1002/(SICI)1097-0266(199805)19:53.3.CO;2-C
Lefebvre, V. M., Sorenson, D., Henchion, M., & Gellynck, X. (2016). Social capital and knowledge sharing performance of learning networks. International Journal of Information Management, 36(4), 570–579. https://doi.org/10.1016/j.ijinfomgt.2015.11.008
Lin, J., & Yang, C. (2020). Heterogeneity in industry—university R&D collaboration and firm innovative performance. Scientometrics, 124(1), 1–25. https://doi.org/10.1007/s11192-020-03436-2
Lin, M., & Li, N. (2010). Scale-free network provides an optimal pattern for knowledge transfer. Physica A: Statistical Mechanics and Its Applications, 389(3), 473–480. https://doi.org/10.1016/j.physa.2009.10.004
Liu, T., & Tang, L. (2020). Open innovation from the perspective of network embedding: Knowledge evolution and development trend. Scientometrics, 124(2), 1053–1080. https://doi.org/10.1007/s11192-020-03520-7
Manzo, G., Gabbriellini, S., Roux, V., Mbogori, M., & F. N. (2018). Complex contagions and the diffusion of innovations: Evidence from a Small-N study. Journal of Archaeological Method and Theory, 25(4), 1109–1154. https://doi.org/10.1007/s10816-018-9393-z
Mao, C., Yu, X., Zhou, Q., Harms, R., & Fang, G. (2020). Knowledge growth in university-industry innovation networks—results from a simulation study. Technological Forecasting & Social Change, 151, 119746. https://doi.org/10.1016/j.techfore.2019.119746
Meagher, K., & Rogers, M. (2004). Network density and R&D spillovers. Journal of Economic Behavior & Organization, 53(2), 237–260. https://doi.org/10.1016/j.jebo.2002.10.004
Montgomery, J. D. (1998). Toward a role-theoretic conception of embeddedness. The American Journal of Sociology, 104(1), 92–125. https://doi.org/10.1086/210003
Moreira, S., Markus, A., & Laursen, K. (2018). Knowledge diversity and coordination: The effect of intrafirm inventor task networks on absorption speed. Strategic Management Journal, 39(9), 2517–2546. https://doi.org/10.1002/smj.2914
Mowery, D. C., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2), 77–91.
Mueller, M., Bogner, K., Buchmann, T., & Kudic, M. (2017). The effect of structural disparities on knowledge diffusion in networks: An agent-based simulation model. Journal of Economic Interaction and Coordination, 12(3), 613–634. https://doi.org/10.1007/s11403-016-0178-8
Muller, E., & Peres, R. (2019). The effect of social networks structure on innovation performance: A review and directions for research. International Journal of Research in Marketing, 36(1), 3–19. https://doi.org/10.1016/j.ijresmar.2018.05.003
Müller, R., Zhai, L., Wang, A., & Shao, J. (2016). A framework for governance of projects: Governmentality, governance structure and projectification. International Journal of Project Management, 34(6), 957–969. https://doi.org/10.1016/j.ijproman.2016.05.002
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. The Academy of Management Review, 23(2), 242–266. https://doi.org/10.2307/259373
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. https://doi.org/10.1016/j.ijinfomgt.2019.01.016
Rahmandad, H., & Sterman, J. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014. https://doi.org/10.1287/mnsc.1070.0787
Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25(6), 541–562. https://doi.org/10.1002/smj.398
Roper, S., & Hewitt-Dundas, N. (2015). Knowledge stocks, knowledge flows and innovation: Evidence from matched patents and innovation panel data. Research Policy, 44(7), 1327–1340. https://doi.org/10.1016/j.respol.2015.03.003
Salehi, F., Zolkiewski, J., Perks, H., & Bahreini, M. A. (2018). Exploration of capability and role development in an emerging technology network. Journal of Business & Industrial Marketing, 33(7), 931–944. https://doi.org/10.1108/JBIM-09-2017-0211
Scholz, R., Triulzi, G., & Pyka, A. (2014). R&d and knowledge dynamics in university-industry relationships in biotech and pharmaceuticals: An agent-based model. International Journal of Biotechnology, 13(1/3), 137–179.
Singh, J., & Marx, M. (2013). Geographic constraints on knowledge spillovers: Political borders vs. spatial proximity. Management Science, 59(9), 2056–2078. https://doi.org/10.1287/mnsc.1120.1700.
Singh, J., Hansen, M. T., & Podolny, J. M. (2010). The world is not small for everyone: Inequity in searching for knowledge in organizations. Management Science, 56(9), 1415–1438.
Staples, D. S., & Webster, J. (2008). Exploring the effects of trust, task interdependence and virtualness on knowledge sharing in teams. Information Systems Journal, 18(6), 617–640. https://doi.org/10.1111/j.1365-2575.2007.00244.x
Story, V., Hart, S., & O’Malley, L. (2009). Relational resources and competences for radical product innovation. Journal of Marketing Management, 25(5–6), 461–481.
Story, V., O’Malley, L., & Hart, S. (2011). Roles, role performance, and radical innovation competences. Industrial Marketing Management, 40(6), 952–966. https://doi.org/10.1016/j.indmarman.2011.06.025
Swanson, J. (2002). Business dynamics-systems thinking and modeling for a complex world. Journal of the Operational Research Society, 53(4), 472–473. https://doi.org/10.1057/palgrave.jors.2601336
Teece, D. (1993). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 22(2), 112–113. https://doi.org/10.1016/0048-7333(93)90063-N.
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. https://doi.org/10.1016/j.respol.2016.06.008
Tortoriello, M. (2015). The social underpinnings of absorptive capacity: The moderating effects of structural holes on innovation generation based on external knowledge. Strategic Management Journal, 36(4), 586–597. https://doi.org/10.1002/smj.2228
Tsai, W. (2001). Determinants and consequences of employee displayed positive emotions. Journal of Management, 27(4), 497–512. https://doi.org/10.1177/014920630102700406
Wang, C., Rodan, S., Fruin, M., & Xu, X. (2014). Knowledge networks, collaboration networks, and exploratory innovation. Academy of Management Journal, 57(2), 484–514. https://doi.org/10.5465/amj.2011.0917
Wang, L., Gao, S., Ding, R., & Sun, T. (2017). Research on dynamic evaluation of project goverance under the information network perspective—a case study on collaborative innovation projects of industrial technology research institute. Science & Technology Progress and Policy, 34(04), 31–39.
Wei, Y., & Miraglia, S. (2017). Organizational culture and knowledge transfer in project-based organizations: Theoretical insights from a Chinese construction firm. International Journal of Project Management, 35(4), 571–585. https://doi.org/10.1016/j.ijproman.2017.02.010
Zaheer, A., & Bell, G. G. (2005). Benefiting from network position: Firm capabilities, structural holes, and performance. Strategic Management Journal, 26(9), 809–825. https://doi.org/10.1002/smj.482
Zhang, H. P. (2015). An agent-based simulation model for supply chain collaborative technological innovation diffusion. International Journal of Simulation Modelling, 14(2), 313–324. https://doi.org/10.2507/IJSIMM14(2)CO6
Zhang, J., Jiang, H., Wu, R., & Li, J. (2018). Reconciling the dilemma of knowledge sharing: A network pluralism framework of firms’ R&D alliance network and innovation performance. Journal of Management, 45(7), 2635–2665. https://doi.org/10.1177/0149206318761575
Zhu, H., & Ma, J. (2018). Knowledge diffusion in complex networks by considering time-varying information channels. Physica a: Statistical Mechanics and Its Applications, 494, 225–235. https://doi.org/10.1016/j.physa.2017.12.046
Zobel, A., Lokshin, B., & Hagedoorn, J. (2017). Formal and informal appropriation mechanisms: The role of openness and innovativeness. Technovation, 59, 44–54. https://doi.org/10.1016/j.technovation.2016.10.001
Zwikael, O., & Smyrk, J. (2015). Project governance: Balancing control and trust in dealing with risk. International Journal of Project Management, 33(4), 852–862. https://doi.org/10.1016/j.ijproman.2014.10.012
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We would especially like to thank all the interviewees, editors, reviewers and Dr. Ying Li for the provided suggestions.
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This work was supported by the National Natural Science Foundation of China (No. 71572094), National Natural Science Foundation of China (No. 72171134), Department of science & technology of Shandong Province (No. 2021RZA01014) and Natural Science Foundation of Shandong Province (No. ZR202102250148, No. ZR202102240063).
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All authors are responsible for the completeness of the data and the accuracy of the data analysis. All authors developed the idea of the study and participated in its design and coordination. LX performed the experiments and drafted the manuscript. LW provided a critical review of the coding and writing. RD contributed to the interpretation of the data and substantially revised the manuscript. All authors read and approved the final manuscript.
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Xu, L., Ding, R. & Wang, L. How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles. Scientometrics 127, 1353–1379 (2022). https://doi.org/10.1007/s11192-021-04255-9
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DOI: https://doi.org/10.1007/s11192-021-04255-9