How innovation community embeddedness impacts firms’ innovation performance: Evidence from the global 3D printing industry

Abstract

The acceleration of digitalization and networking in the global landscape has been prompting organizations to connect into innovation communities beyond geographic boundaries within innovation ecosystems. These communities, consisting of firms that collaborate frequently, serve as a vital sub-environment for co-innovation and value creation. Despite the significant role played by these innovation communities, the impact of a firm’s embeddedness within these communities on its innovation performance remains underexplored. This paper addresses this gap by examining the effects of both within-community and cross-community embeddedness on firm innovation, with a specific focus on the contingency of collaboration complementarity. We introduce a conceptual model analyzing the effects of both relational and structural embeddedness within and across communities. An empirical study is conducted using 22 years of panel data from the global 3D printing industry. We construct patent collaboration networks among 6,109 relevant organizations over 5-year windows and identify innovation communities in each network through topological clustering algorithms. A negative binomial regression model is employed to test our hypotheses. Our findings reveal that firms benefit from both within-community and cross-community embeddedness. Notably, firms with higher collaboration complementarity experience greater benefits from within-community relational embeddedness and cross-community structural embeddedness, while those with lower complementarity gain more from cross-community relational embeddedness. This research enriches the innovation ecosystem literature by introducing an innovation community perspective and highlighting how embeddedness, coupled with collaboration orientation, drives firm-level innovation. Additionally, it offers insights into how firms can leverage collaborations and optimize their positions within innovation ecosystems to enhance their innovation performance.

Graphical abstract

Keywords

innovation ecosystem / innovation community / collaboration complementarity / 3D printing industry

References

[1] Agarwal S, Kapoor R, (2023). Value creation tradeoff in business ecosystems: Leveraging complementarities while managing interdependencies. Organization Science, 34( 3): 1216–1242

[2] Aggarwal V A, (2020). Resource congestion in alliance networks: How a firm’s partners’ partners influence the benefits of collaboration. Strategic Management Journal, 41( 4): 627–655

[3] Ahuja G, (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly, 45( 3): 425–455

[4] Aizawa A, (2003). An information-theoretic perspective of tf-idf measures. Information Processing & Management, 39( 1): 45–65

[5] Allison P D, Waterman R P, (2002). Fixed-effects negative binomial regression models. Sociological Methodology, 32( 1): 247–265

[6] Balachandran S, Hernandez E, (2018). Networks and innovation: Accounting for structural and institutional sources of recombination in brokerage triads. Organization Science, 29( 1): 80–99

[7] Baldwin C Y, Bogers M L A M, Kapoor R, West J, (2024). Focusing the ecosystem lens on innovation studies. Research Policy, 53( 3): 104949

[8] Blondel V D, Guillaume J L, Lambiotte R, Lefebvre E, (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics, 2008( 10): P10008

[9] Clauset A, Newman M E, Moore C, (2004). Finding community structure in very large networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 70( 6): 066111

[10] Clement J, Shipilov A, Galunic C, (2018). Brokerage as a public good: The externalities of network hubs for different formal roles in creative organizations. Administrative Science Quarterly, 63( 2): 251–286

[11] D’Ippolito B, Ruling C, (2019). Research collaboration in large scale research infrastructures: Collaboration types and policy implications. Research Policy, 48( 5): 1282–1296

[12] Ennen E, Richter A, (2010). The whole is more than the sum of its parts—or is it? A review of the empirical literature on complementarities in organizations. Journal of Management, 36( 1): 207–233

[13] Fleming L, Mingo S, Chen D, (2007). Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52( 3): 443–475

[14] Furlotti M, Soda G, (2018). Fit for the task: Complementarity, asymmetry, and partner selection in alliances. Organization Science, 29( 5): 837–854

[15] GeorgiouAArenas D (2023). Community in organizational research: A review and an institutional logics perspective. Organization Theory, 4(1): 26317877231153189

[16] 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

[17] Gonzalez-Brambila C N, Veloso F M, Krackhardt D, (2013). The impact of network embeddedness on research output. Research Policy, 42( 9): 1555–1567

[18] Granovetter M, (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91( 3): 481–510

[19] Grigoriou K, Rothaermel F T, (2017). Organizing for knowledge generation: Internal knowledge networks and the contingent effect of external knowledge sourcing. Strategic Management Journal, 38( 2): 395–414

[20] Gu J, Zhang F, Xu X, Xue C, (2023). Stay or switch? The impact of venture capitalists’ movement across network communities on enterprises’ innovation performance. Technovation, 125: 102770

[21] Guan J, Liu N, (2016). Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy. Research Policy, 45( 1): 97–112

[22] Gulati R, Sytch M, Tatarynowicz A, (2012). The rise and fall of small worlds: Exploring the dynamics of social structure. Organization Science, 23( 2): 449–471

[23] Han S, Lyu Y, Ji R, Zhu Y, Su J, Bao L, (2020). Open innovation, network embeddedness and incremental innovation capability. Management Decision, 58( 12): 2655–2680

[24] Hannen J, Antons D, Piller F, Salge T O, Coltman T, Devinney T M, (2019). Containing the not-invented-here syndrome in external knowledge absorption and open innovation: The role of indirect countermeasures. Research Policy, 48( 9): 103822

[25] Iurkov V, Koval M, Zakaryan A, (2023). The role of network community characteristics for firms’ rapid business scaling. Technological Forecasting and Social Change, 196: 122838

[26] Jacobides M G, Cennamo C, Gawer A, (2018). Towards a theory of ecosystems. Strategic Management Journal, 39( 8): 2255–2276

[27] Jiang R J, Tao Q T, Santoro M D, (2010). Alliance portfolio diversity and firm performance. Strategic Management Journal, 31( 10): 1136–1144

[28] Jin J L, Wang L, (2021). Resource complementarity, partner differences, and international joint venture performance. Journal of Business Research, 130: 232–246

[29] Kapoor R, (2018). Ecosystems: Broadening the locus of value creation. Journal of Organization Design, 7( 1): 12

[30] Knoke D, Yang S, (2019). Social Network Analysis: Thousand Oaks, CA: SAGE Publications.

[31] Kobarg S, Stumpf-Wollersheim J, Welpe I M, (2019). More is not always better: Effects of collaboration breadth and depth on radical and incremental innovation performance at the project level. Research Policy, 48( 1): 1–10

[32] Kwak K, Kim W, Park K, (2018). Complementary multiplatforms in the growing innovation ecosystem: Evidence from 3D printing technology. Technological Forecasting and Social Change, 136: 192–207

[33] Lyu Y, He B, Zhu Y, Li L, (2019). Network embeddedness and inbound open innovation practice: The moderating role of technology cluster. Technological Forecasting and Social Change, 144: 12–24

[34] Makri M, Hitt M A, Lane P J, (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31( 6): 602–628

[35] Marić J, Opazo-Basáez M, Vlačić B, Dabić M, (2023). Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams. Technological Forecasting and Social Change, 193: 122605

[36] Mazzola E, Perrone G, Kamuriwo D S, (2015). Network embeddedness and new product development in the biopharmaceutical industry: The moderating role of open innovation flow. International Journal of Production Economics, 160: 106–119

[37] Ning L, Guo R, (2022). Technological diversification to green domains: Technological relatedness, invention impact and knowledge integration capabilities. Research Policy, 51( 1): 104406

[38] Obstfeld D, (2005). Social networks, the tertius iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50( 1): 100–130

[39] Phaal R, Chaskel C, Gonzalez Nakazawa R, Ross J, (2024). Roadmapping Roadmapping: Strategic planning for roadmapping systems. Frontiers of Engineering Management, 11( 3): 516–527

[40] Pinkse J, Vernay A L, D’Ippolito B, (2018). An organizational perspective on the cluster paradox: Exploring how members of a cluster manage the tension between continuity and renewal. Research Policy, 47( 3): 674–685

[41] Reiter A, Stonig J, Frankenberger K, (2024). Managing multi-tiered innovation ecosystems. Research Policy, 53( 1): 104905

[42] Rong K, Ren Q, Shi X, (2018). The determinants of network effects: Evidence from online games business ecosystems. Technological Forecasting and Social Change, 134: 45–60

[43] Rowley T J, Greve H R, Rao H, Baum J A, Shipilov A V, (2005). Time to break up: Social and instrumental antecedents of firm exits from exchange cliques. Academy of Management Journal, 48( 3): 499–520

[44] Schilling M A, Fang C, (2014). When hubs forget, lie, and play favorites: Interpersonal network structure, information distortion, and organizational learning. Strategic Management Journal, 35( 7): 974–994

[45] Schilling M A, Phelps C C, (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53( 7): 1113–1126

[46] Shipilov A, Gawer A, (2020). Integrating research on interorganizational networks and ecosystems. Academy of Management Annals, 14( 1): 92–121

[47] Soda G, Stea D, Pedersen T, (2019). Network structure, collaborative context, and individual creativity. Journal of Management, 45( 4): 1739–1765

[48] Soda G, Tortoriello M, Iorio A, (2018). Harvesting value from brokerage: Individual strategic orientation, structural holes, and performance. Academy of Management Journal, 61( 3): 896–918

[49] Song G, Min S, Lee S, Seo Y, (2017). The effects of network reliance on opportunity recognition: A moderated mediation model of knowledge acquisition and entrepreneurial orientation. Technological Forecasting and Social Change, 117: 98–107

[50] Sytch M, Tatarynowicz A, (2014). Exploring the locus of invention: The dynamics of network communities and firms’ invention productivity. Academy of Management Journal, 57( 1): 249–279

[51] Sytch M, Tatarynowicz A, Gulati R, (2012). Toward a theory of extended contact: The incentives and opportunities for bridging across network communities. Organization Science, 23( 6): 1658–1681

[52] Ter Wal A L J, Alexy O, Block J, Sandner P G, (2016). The Best of both worlds: The benefits of open-specialized and closed-diverse syndication networks for new ventures’ success. Administrative Science Quarterly, 61( 3): 393–432

[53] Toh P K, Miller C D, (2017). Pawn to save a chariot, or drawbridge into the fort? Firms’ disclosure during standard setting and complementary technologies within ecosystems. Strategic Management Journal, 38( 11): 2213–2236

[54] Upham S P, Rosenkopf L, Ungar L H, (2010). Innovating knowledge communities. Scientometrics, 83( 2): 525–554

[55] Vasudeva G, Zaheer A, Hernandez E, (2013). The embeddedness of networks: Institutions, structural Holes, and innovativeness in the fuel cell industry. Organization Science, 24( 3): 645–663

[56] Wang H, Zheng L J, Zhang J Z, Kumar A, Srivastava P R, (2024). Unpacking complementarity in innovation ecosystems: A configurational analysis of knowledge transfer for achieving breakthrough innovation. Technological Forecasting and Social Change, 198: 122974

[57] Wang J, Yang N, (2019). Dynamics of collaboration network community and exploratory innovation: The moderation of knowledge networks. Scientometrics, 121( 2): 1067–1084

[58] Wang W, Lu S, (2021). University-industry innovation community dynamics and knowledge transfer: Evidence from China. Technovation, 106: 102305

[59] Whalen R, (2018). Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system. Research Policy, 47( 7): 1334–1343

[60] Wiedmer R, Griffis S E, (2021). Structural characteristics of complex supply chain networks. Journal of Business Logistics, 42( 2): 264–290

[61] WohlersT TInternational A (2024). Wohlers Report 2024: Analysis. Trends. Forecasts. 3D Printing and additive manufacturing state of the industry: global state of the industry. Fort Collins, CO: Wohlers Associates

[62] Wu X, Adbi A, Mahmood I P, (2024). The social structure of insiders and outsiders: Toward a network community perspective on firm performance. Academy of Management Journal, 67( 4): 903–932

[63] Xu G, Dong F, Feng J, (2022). Mapping the technological landscape of emerging industry value chain through a patent lens: An integrated framework with deep learning. IEEE Transactions on Engineering Management, 69( 6): 3367–3378

[64] Xu G, Hu W, Qiao Y, Zhou Y, (2020). Mapping an innovation ecosystem using network clustering and community identification: A multi-layered framework. Scientometrics, 124( 3): 2057–2081

[65] Xu L, Yang S, Liu Y, Newbert S L, Boal K, (2023). Seeing the forest and the trees: exploring the impact of inter-and intra-entrepreneurial ecosystem embeddedness on new venture creation. Academy of Management Journal, 66( 6): 1954–1982

[66] Yan S, Almandoz J, Ferraro F, (2021). The impact of logic (in) compatibility: Green investing, state policy, and corporate environmental performance. Administrative Science Quarterly, 66( 4): 903–944

[67] Yan Y, Zhang J, Guan J, (2020). Network Embeddedness and innovation: Evidence from the alternative energy field. IEEE Transactions on Engineering Management, 67( 3): 769–782

[68] YoungeK AKuhn J M (2016). Patent-to-patent similarity: A vector space model. Available at SSRN 2709238

[69] Zaheer A, Bell G G, (2005). Benefiting from network position: Firm capabilities, structural holes, and performance. Strategic Management Journal, 26( 9): 809–825

[70] Zhang J M, Jiang H, Wu R, Li J Z, (2019). 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

[71] Zhou Y, Zhou R, Chen L, Zhao Y, Zhang Q, (2022). Environmental policy mixes and green industrial development: An empirical study of the Chinese textile industry from 1998 to 2012. IEEE Transactions on Engineering Management, 69( 3): 742–754

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Supplementary material is available in the online version of this article at https://doi.org/10.1007/s42524-025-4188-x and is accessible for authorized users.

Competing Interests

The authors declare that they have no competing interests.

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