Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1232 2024-02-19 09:39:50 |
2 layout & references Meta information modification 1232 2024-02-21 01:46:16 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Zhang, X.; Cui, R.; Ji, Y. Innovation Ecosystem and Its Compositions in Network Perspective. Encyclopedia. Available online: https://encyclopedia.pub/entry/55152 (accessed on 17 April 2024).
Zhang X, Cui R, Ji Y. Innovation Ecosystem and Its Compositions in Network Perspective. Encyclopedia. Available at: https://encyclopedia.pub/entry/55152. Accessed April 17, 2024.
Zhang, Xiaohang, Ran Cui, Yajun Ji. "Innovation Ecosystem and Its Compositions in Network Perspective" Encyclopedia, https://encyclopedia.pub/entry/55152 (accessed April 17, 2024).
Zhang, X., Cui, R., & Ji, Y. (2024, February 19). Innovation Ecosystem and Its Compositions in Network Perspective. In Encyclopedia. https://encyclopedia.pub/entry/55152
Zhang, Xiaohang, et al. "Innovation Ecosystem and Its Compositions in Network Perspective." Encyclopedia. Web. 19 February, 2024.
Innovation Ecosystem and Its Compositions in Network Perspective
Edit

Innovation ecosystems (IE) have gained significant attention due to the pivotal role of innovation in implementing sustainability. Network science serves as a crucial perspective for studying the innovation ecosystem, acting as a driving force for innovation development.

5G innovation ecosystem (IE) social network knowledge network ecological niche knowledge proximity

1. Introduction

Innovation ecosystems (IE) have gained significant attention in the past decade [1] due to the pivotal role of innovation in implementing sustainability [2][3][4]. Recognized as a new driver of economic growth and a pathway to sustainable competitive advantage [5][6], scholars have sought to define IE concepts and components through literature reviews and qualitative research [7][8]. However, given diverse research focuses and theoretical foundations, the study of IE frameworks is currently marked by “diversity and contention”. The composition and boundaries of IE have yet to be unified and established through quantitative research [9].
The subject of innovation, such as scientists, inventors, companies, research institutions, government departments, and investors, drives and influences the development of innovation through various interactions. Interactions, which encompass collaboration, competition, coopetition, and more, serve as the core driving force for promoting innovation. And the object of innovation, including knowledge, products, and services, represents both the resources and outcomes of innovation. Hence, at the conceptual level, based on quadruple and quintuple helix innovation systems, the innovation ecosystem is complex and heterogeneous [10], comprising actors, activities, and artifacts [7].
Network science serves as a crucial perspective for studying the innovation ecosystem, acting as a driving force for innovation development [11][12]. However, existing research on innovation networks commonly utilizes single-layer or homogeneous networks [13][14], which presents certain limitations in characterizing and describing the complexity and heterogeneity of the innovation ecosystem framework [15][16]. It is necessary to introduce multi-layered and heterogeneous networks for future inquiries into its structure and patterns.
Moreover, the utilization of multi-source heterogeneous data facilitates accurate identification and in-depth exploration of the distribution and proximity of actors and artifacts, interconnected through various activities. This approach establishes a robust data foundation and technical support for uncovering the collaborative mechanisms within the innovation ecosystem [17].

2. Innovation Ecosystem and Its Compositions in Network Perspective

The innovation ecosystem concept arises from the integration of “business ecosystems” [18] and value co-creation process [19]. It has become a new research perspective in innovation and innovation management [8][20][21].
Etzkowitz and Leydesdorff (2000) proposed the Triple Helix innovation model consisting of university–industry–government, emphasizing the importance of knowledge generation and innovation in the economy [22][23]. Subsequently, Carayannis and Campbell (2006) added the ‘media-based and culture-based public’ and ‘civil society’ as a fourth helix to the Triple Helix model, forming the Quadruple Helix innovation model [24]. This model highlights the need for the sustainable innovation of knowledge in the economy to evolve in conjunction with knowledge society and democracy. Furthermore, considering the importance of the ‘natural environments of society’, Carayannis, E.G., et al. (2012) proposed the more comprehensive Quintuple Helix innovation model [25][26]. This model emphasizes the crucial role of socio-ecological transformation in driving innovation. Therefore, innovation ecosystems, based on both democracy and ecology, exhibit broader scope, increased complexity, diversity, and heterogeneity.
Researchers defined the composition and boundaries of IE architecture according to different research preferences and goals [9] through literature review and bibliometric studies [27][28]. Based on previous research, Granstrand and Holgersson (2020) [7] proposed the “3A” architecture of innovation ecosystems, including participants, artifacts, and activities. However, quantitative research in this area is limited, with most studies being theoretical. 
Innovation actors collaborate to form social networks, while knowledge elements combine to form knowledge networks. Both are innovative interactive networks. Previous social network research has explored cooperative relationships, information flows, and decision-making processes among countries, organizations, and individuals, respectively [29][30][31]. However, a comprehensive analysis of innovative social networks at these levels is lacking. Knowledge elements are the preliminary conclusions held by research communities in scientific and technological fields [32]. They encompass facts, theories, methods, and procedures related to specific topics [33][34]. The innovation of emerging technologies relies on combining or recombining these knowledge elements, making them crucial for innovation [34][35]. In fact, existing knowledge network research has divided knowledge elements into scientific knowledge and technical knowledge. Scientific knowledge elements focus on knowledge discovery [36], while technological knowledge elements emphasize ownership and application [37]. Both types of knowledge are important in the innovation ecosystem [32][38] and are interconnected with the relationships between innovation actors. Several studies have examined networks of scientific knowledge elements [39][40], while others have focused on networks of technological knowledge elements [27][41][42]. Previous studies have examined scientific knowledge element networks [39] or technical knowledge element networks [17][43].
Research specifically analyzing and comparing these two types of knowledge meta-networks is still limited. The network characteristics of scientific and technological knowledge need to be integrated and mined. Furthermore, collaborative interactions among innovators [44] and their impact on the potential to combine scientific and technological knowledge are key factors in promoting innovation. 

3. Multilayer Heterogeneous Network for Innovation and Data

The social network formed by innovators and the knowledge network composed of knowledge elements have a double embedded relationship [32]. At the same time, they exhibit heterogeneity and decoupling [15]. Previous research has explored the impact of dual embedded relationships in heterogeneous networks on organizational innovation [45], emphasizing that the degree of integration of social networks and the diversity of knowledge networks are two driving factors for organizational-level innovation. Over time, they evolve in a spiral pattern [26]. Thus, the IE construction includes two heterogeneous networks: social network and knowledge network.
Innovation collaborations can be organized and managed at multiple levels of analysis [29] with a multilevel network perspective [46][47]. However, most studies on innovative complex networks focus on single-layer and homogeneous networks, ignoring the underlying characteristics of real complex innovation ecosystems. Combining the above studies and the diversity and heterogeneity of innovation ecosystems, it becomes imperative to utilize multi-layer heterogeneous networks [16][48] to study and understand the IE framework.
Furthermore, utilizing multivariate heterogeneous datasets allows for improved resolution and consideration of multiple characteristics of real innovation ecosystems [49]. Bibliometrics [50] and patent data [27] have been widely used in innovation research to assess and characterize technological innovations. Researchers integrated different heterogeneous data to improve the validity and credibility of the study [16] and analyzes a novel IE framework for emerging technologies [51].

4. Key Attributes of Innovation Ecosystems: Ecosystem Niche and Knowledge Proximity

An innovation ecosystem niche, as a fundamental element of the structuralist approach to ecosystems, refers to the specific position of actors and artifacts within the flow of overall system activity [5]. It is related to strategic resources and direction [52] and has a positive impact on the exploration and utilization of innovation [53]. Assessing the positioning of innovation subjects and products in the ecosystem and distinguishing the focus subjects and internal logic of the innovation ecosystem are of great significance to value creation [36].
Hub nodes and bridging nodes occupy special ecological niches in the ecosystem framework [53]. Hub nodes have a large number of connections and play a critical role in strong connections. Collaboration between Hub nodes drives technological progress, promotes cooperation and standardization, identifies key areas and innovators of the technology ecosystem, and guides and supports technological development and innovation. On the other hand, bridging nodes have the ability to bridge different sub-networks in weak connections, facilitating information transfer and facilitating collaboration between different actors or knowledge areas. Network features, degree [32], and betweenness centrality [54] are used to identify these nodes.
Knowledge proximity, which focuses on the similarity of knowledge structure and technological experience among innovation actors, has received significant attention in recent innovation network research [55][56]. Exploring the proximity of knowledge in the innovation ecosystem is beneficial to the interactive learning, knowledge acquisition and innovation success of innovative subjects.

References

  1. Robertson, J.; Caruana, A.; Ferreira, C. Innovation performance: The effect of knowledge-based dynamic capabilities in cross-country innovation ecosystems. Int. Bus. Rev. 2023, 32, 101866.
  2. Silvestre, B.S.; Tirca, D.M. Innovations for sustainable development: Moving toward a sustainable future. J. Clean. Prod. 2019, 208, 325–332.
  3. Manioudis, M.; Meramveliotakis, G. Broad strokes towards a grand theory in the analysis of sustainable development: A return to the classical political economy. New Political Econ. 2022, 27, 866–878.
  4. Klarin, T. The Concept of Sustainable Development: From its Beginning to the Contemporary Issues. Zagreb Int. Rev. Econ. Bus. 2018, 21, 67–94.
  5. Adner, R. Ecosystem as Structure: An Actionable Construct for Strategy. J. Manag. 2017, 43, 39–58.
  6. Dattee, B.; Alexy, O.; Autio, E. Maneuvering in Poor Visibility: How Firms Play the Ecosystem Game when Uncertainty Is High. Acad. Manag. J. 2018, 61, 466–498.
  7. Granstrand, O.; Holgersson, M. Innovation ecosystems: A conceptual review and a new definition. Technovation 2020, 90–91, 102098.
  8. De Vasconcelos Gomes, L.A.; Flechas, X.A.; Facin AL, F.; Borini, F.M. Ecosystem management: Past achievements and future promises. Technol. Forecast. Soc. Chang. 2021, 171, 120950.
  9. Oh, D.-S.; Phillips, F.; Park, S.; Lee, E. Innovation ecosystems: A critical examination. Technovation 2016, 54, 1–6.
  10. Hou, H.; Shi, Y. Ecosystem-as-structure and ecosystem-as-coevolution: A constructive examination. Technovation 2021, 100, 102193.
  11. Aarikka-Stenroos, L.; Ritala, P. Network management in the era of ecosystems: Systematic review and management framework. Ind. Mark. Manag. 2017, 67, 23–36.
  12. Canestrino, R.; Carayannis, E.G.; Magliocca, P. The Noncontextual Drivers of Innovation: Development and Validation of the 5H-INN Survey. IEEE Trans. Eng. Manag. 2022, 71, 1422–1438.
  13. Scott, S.; Hughes, M.; Ribeiro-Soriano, D. Towards a network-based view of effective entrepreneurial ecosystems. Rev. Manag. Sci. 2021, 16, 157–187.
  14. Mannucci, P.V.; Perry-Smith, J.E. “Who Are You Going to Call?” Network Activation in Creative Idea Generation and Elaboration. Acad. Manag. J. 2022, 65, 1192–1217.
  15. Brennecke, J.; Rank, O. The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study. Res. Policy 2017, 46, 768–783.
  16. Xu, G.; Wu, Y.; Minshall, T.; Zhou, Y. Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China. Technol. Forecast. Soc. Chang. 2018, 136, 208–221.
  17. Tsujimoto, M.; Kajikawa, Y.; Tomita, J.; Matsumoto, Y. A review of the ecosystem concept—Towards coherent ecosystem design. Technol. Forecast. Soc. Chang. 2018, 136, 49–58.
  18. Moore, J.F. Predators and prey: A new ecology of competition. Harv. Bus. Rev. 1993, 71, 75–86.
  19. Adner, R.; Kapoor, R. Innovation ecosystems and the pace of substitution: Re-examining technology S-curves. Strat. Manag. J. 2016, 37, 625–648.
  20. Ritala, P.; Almpanopoulou, A. In defense of ‘eco’ in innovation ecosystem. Technovation 2017, 60–61, 39–42.
  21. Arribas-Ibar, M.; Nylund, P.A.; Brem, A. The Risk of Dissolution of Sustainable Innovation Ecosystems in Times of Crisis: The Electric Vehicle during the COVID-19 Pandemic. Sustainability 2021, 13, 1319.
  22. Etzkowitz, H.; Leydesdorff, L. The dynamics of innovation: From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Res. Policy 2000, 29, 109–123.
  23. Etzkowitz, H.; Zhou, C. Innovation incommensurability and the science park. R D Manag. 2018, 48, 73–87.
  24. Carayannis, E.G.; Campbell, D.F.J. ‘Mode 3’ and ‘Quadruple Helix’: Toward a 21st century fractal innovation ecosystem. Int. J. Technol. Manag. 2009, 46, 201–234.
  25. Carayannis, E.G.; Barth, T.D.; Campbell, D.F.J. The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. J. Innov. Entrep. 2012, 1, 2.
  26. Carayannis, E.G.; Grigoroudis, E.; Campbell, D.F.J.; Meissner, D.; Stamati, D. The ecosystem as helix: An exploratory theory-building study of regional co-opetitive entrepreneurial ecosystems as Quadruple/Quintuple Helix Innovation Models. R D Manag. 2018, 48, 148–162.
  27. Aaldering, L.J.; Leker, J.; Song, C.H. Competition or collaboration?—Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: A patent-based approach. J. Clean. Prod. 2019, 212, 362–371.
  28. Benitez, G.B.; Ayala, N.F.; Frank, A.G. Industry 4.0 innovation ecosystems: An evolutionary perspective on value cocreation. Int. J. Prod. Econ. 2020, 228, 107735.
  29. Radziwon, A.; Bogers, M. Open innovation in SMEs: Exploring inter-organizational relationships in an ecosystem. Technol. Forecast. Soc. Chang. 2019, 146, 573–587.
  30. Prokop, V.; Hajek, P.; Stejskal, J. Configuration Paths to Efficient National Innovation Ecosystems. Technol. Forecast. Soc. Chang. 2021, 168, 120787.
  31. Xie, Q.J.; Su, J. The spatial-temporal complexity and dynamics of research collaboration: Evidence from 297 cities in China (1985–2016). Technol. Forecast. Soc. Chang. 2021, 162, 15.
  32. Wang, C.; Rodan, S.; Fruin, M.; Xu, X. Knowledge Networks, Collaboration Networks, and Exploratory Innovation. Acad. Manag. J. 2014, 57, 484–514.
  33. Maruccia, Y.; Solazzo, G.; Del Vecchio, P.; Passiante, G. Evidence from Network Analysis application to Innovation Systems and Quintuple Helix. Technol. Forecast. Soc. Chang. 2020, 161, 120306.
  34. Siegel, D.S.; Guerrero, M. The Impact of Quarantines, Lockdowns, and ‘Reopenings’ on the Commercialization of Science: Micro and Macro Issues. J. Manag. Stud. 2021, 58, 1389–1394.
  35. Scaringella, L.; Radziwon, A. Innovation, entrepreneurial, knowledge, and business ecosystems: Old wine in new bottles? Technol. Forecast. Soc. Chang. 2018, 136, 59–87.
  36. Ritala, P.; Huizingh, E. Business and network models for innovation: Strategic logic and the role of network position. Int. J. Technol. Manag. 2014, 66, 109.
  37. De Vasconcelos Gomes, L.A.; Salerno, M.S.; Phaal, R.; Probert, D.R. How entrepreneurs manage collective uncertainties in innovation ecosystems. Technol. Forecast. Soc. Chang. 2018, 128, 164–185.
  38. Hage, J.; Mote, J.E.; Jordan, G.B. Ideas, innovations, and networks: A new policy model based on the evolution of knowledge. Policy Sci. 2013, 46, 199–216.
  39. Ardito, L.; Ferraris, A.; Petruzzelli, A.M.; Bresciani, S.; Del Giudice, M. The role of universities in the knowledge management of smart city projects. Technol. Forecast. Soc. Chang. 2019, 142, 312–321.
  40. Zhou, Y.; Zang, J.; Miao, Z.; Minshall, T. Upgrading Pathways of Intelligent Manufacturing in China: Transitioning across Technological Paradigms. Engineering 2019, 5, 691–701.
  41. Hagedoorn, J.; Cloodt, M. Measuring innovative performance: Is there an advantage in using multiple indicators? Res. Policy 2003, 32, 1365–1379.
  42. Tsujimoto, M.; Matsumoto, Y.; Sakakibara, K. Finding the ‘boundary mediators’: Network analysis of the joint R&D project between Toyota and Panasonic. Int. J. Technol. Manag. 2014, 66, 120–133.
  43. Lu, Y. Implementing blockchain in information systems: A review. Enterp. Inf. Syst. 2022, 16, 2008513.
  44. Lanzolla, G.; Pesce, D.; Tucci, C.L. The Digital Transformation of Search and Recombination in the Innovation Function: Tensions and an Integrative Framework*. J. Prod. Innov. Manag. 2020, 38, 90–113.
  45. Guan, J.; Liu, N. Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy. Res. Policy 2016, 45, 97–112.
  46. Capponi, G.; Corrocher, N. Patterns of of collaboration in mHealth: A network analysis. Technol. Forecast. Soc. Chang. 2022, 175, 121366.
  47. Feng, Z.; Cai, H.; Chen, Z.; Zhou, W. Influence of an interurban innovation network on the innovation capacity of China: A multiplex network perspective. Technol. Forecast. Soc. Chang. 2022, 180, 14.
  48. Han, J.; Tang, X.; Yu, L. Research on identification of potential partnership based on link prediction with multilayer networks. Syst. Eng.-Theory Pract. 2021, 41, 1049–1060.
  49. Boccaletti, S.; Bianconi, G.; Criado, R.; del Genio, C.; Gómez-Gardeñes, J.; Romance, M.; Sendiña-Nadal, I.; Wang, Z.; Zanin, M. The structure and dynamics of multilayer networks. Phys. Rep.-Rev. Sect. Phys. Lett. 2014, 544, 1–122.
  50. De Vasconcelos Gomes, L.A.; Facin, A.L.F.; Salerno, M.S.; Ikenami, R.K. Unpacking the innovation ecosystem construct: Evolution, gaps and trends. Technol. Forecast. Soc. Chang. 2018, 136, 30–48.
  51. Awano, H.; Tsujimoto, M. The creation and capture of value through open platform: The business model utilising two-sided markets by managing standardisation. Int. J. Serv. Technol. Manag. 2021, 27, 280–306.
  52. Hoffmann, W.; Lavie, D.; Reuer, J.J.; Shipilov, A. The interplay of competition and cooperation. Strat. Manag. J. 2018, 39, 3033–3052.
  53. Xie, X.; Wang, H. How to bridge the gap between innovation niches and exploratory and exploitative innovations in open innovation ecosystems. J. Bus. Res. 2021, 124, 299–311.
  54. Muller, E.; Peres, R. The effect of social networks structure on innovation performance: A review and directions for research. Int. J. Res. Mark. 2019, 36, 3–19.
  55. Runiewicz-Wardyn, M. The role proximity plays in university-driven social networks. The case of the US and EU life-science clusters. J. Entrep. Manag. Innov. 2020, 16, 167–196.
  56. Gerli, F.; Calderini, M.; Chiodo, V. An ecosystemic model for the technological development of social entrepreneurship: Exploring clusters of social innovation. Eur. Plan. Stud. 2021, 30, 1962–1984.
More
Information
Subjects: Communication
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , ,
View Times: 56
Revisions: 2 times (View History)
Update Date: 21 Feb 2024
1000/1000