Skip to main content
SpringerLink
Log in

The effect of social networks, organizational coordination structures, and knowledge heterogeneity on knowledge transfer and aggregation

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

Previous research has established the benefits of knowledge for firm competitive advantage. Knowledge does not, however, seamlessly transfer around an organization. Research suggests that the organizations coordination structures, the heterogeneity of knowledge within an organization, and social network structure are three critical factors that can enable and constrain the transfer and aggregation of knowledge that are held by individuals and units. These three factors, however, have rarely been examined together. We use an agent based model to simulate different configurations of the three factors. We find that in decentralized coordination structures, when there is a relatively high degree of knowledge homogeneity across units, there is an advantage for actors to have a social network structure that crosses unit boundaries. This is not the case in a centralized coordination structure where there is an advantage for actors to have social network structures that remain within unit boundaries. The exception is when actors have cross-unit brokerage ties that are embedded in social networks that have a small world structure, regardless of knowledge heterogeneity. At the unit level, we find that for both centralized and decentralized reporting structures, variability of knowledge aggregation across units is higher when there is greater knowledge homogeneity between units. Overall, our results are robust to various changes in the initial parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data generated and analysed during this study as well as the original code algorithm is available from the authors upon request.

Notes

  1. An additional aspect of knowledge that has relevance for knowledge transfer is whether knowledge is explicit, i.e., it contains facts and symbols which are easier to transfer; or whether it is tacit i.e., it is know-how such as being able to solve a particular problem and is more difficult to transfer, (Kogut and Zander 1992; Nonaka and Takeuchi 1995). We treat tacit versus explicit knowledge as a potential extension to our theorization and expand upon this in the discussion section.

  2. Knowledge can transfer through the coordination structure such as via an email from one person to all people lower in the coordination structure. In this paper, however, we limit ourselves to modelling knowledge transfer through dyadic social network ties in an organization.

  3. The number of runs in a simulation model is decided based on the tradeoff between the variation in observed results over different runs, and the computing time. The higher is the variation, the higher should be the number of runs to overcome the effects of patterns caused by different random seeds. Given that the observed variability in results was very low, and the computing times was long, 20 simulation runs was decided on heuristically.

  4. We examine average knowledge aggregation rather than the net amount of knowledge in the organization because each actor could recombine different pieces of knowledge in different ways to solve problems and innovate.

  5. Shannon entropy index is calculated as:\(H=-\sum _{i=1}^{s}\left({p}_{i}){(\mathit{ln}p}_{i}\right)\), where pi denotes the proportion of unit i in total knowledge aggregation, and ln pi is the natural logarithm of this proportion.

  6. The density is based on a core-periphery unit network structure with six core, and 14 peripheral units (see Sect. 4.2.4 for extensions of these parameters). This gives a total of approximately 39 ties in the centralized structure. For a decentralized structure, this converts to a network density of 0.20.

References

  • Ahrweiler P, Gilbert N, Pyka A (2016) Joining Complexity Science and Social Simulation for Innovation Policy: Agent-based Modelling using the SKIN Platform. Cambridge Scholars Publishing, Newcastle upon Tyne

    Google Scholar 

  • Argote L, Ingram P, Levine JM, Moreland RL (2000) Knowledge transfer in organizations: Learning from the experience of others. Organ Behav and Human Dec Proc 82(1):1–8

    Google Scholar 

  • Argote L, McEvily B, Reagans R (2003) Managing knowledge in organizations: An integrative framework and review of emerging themes. Management Sci 49(4):571–582

    Google Scholar 

  • Borgatti SP, Everett MG (2000) Models of core/periphery structures. Soc Networks 21(4):375–395

    Google Scholar 

  • Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895

    Google Scholar 

  • Brass DJ, Galaskiewicz J, Greve HR, Tsai W (2004) Taking stock of networks and organizations: A multilevel perspective. Acad Management J 47(6):795–817

    Google Scholar 

  • Brennecke J, Sofka W, Wang P, Rank ON (2021) How the organizational design of R&D units affects individual search intensity–A network study. Res Policy 50(5):104219

    Google Scholar 

  • Breschi S, Lissoni F, Malerba F (2003) Knowledge-relatedness in firm technological diversification. Res Policy 32(1):69–87

    Google Scholar 

  • Brusoni S, Prencipe A, Pavitt K (2001) Knowledge specialization, organizational coupling, and the boundaries of the firm: Why do firms know more than they make? Admin Sci Quart 46(4):597–621

    Google Scholar 

  • Brusoni S, Cassi L, Tuna S (2021) Knowledge integration between technical change and strategy making. J Evol Econ 31(5):1521–1552

    Google Scholar 

  • Burt RS (1992) Structural holes: The social structure of competition. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Burt RS (2004) Structural holes and good ideas. Amer J Sociol 110(2):349–399

    Google Scholar 

  • Burt RS, Kilduff M, Tasselli S (2013) Social network analysis: Foundations and frontiers on advantage. Annual Rev Psych 64:527–547

    Google Scholar 

  • Carlile PR (2004) Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries. Organ Sci 15(5):555–568

    Google Scholar 

  • Cassi L, Zirulia L (2008) The opportunity cost of social relations: On the effectiveness of small worlds. J Evol Econ 18(1):77–101

    Google Scholar 

  • Chang M, Harrington JE Jr (2003) Multimarket competition, consumer search, and the organizational structure of multi-unit firms. Management Sci 49(4):541–552

    Google Scholar 

  • Cohen WM, Levinthal DA (1990) Absorptive capacity: A new perspective on learning and innovation. Admin Sci Quart 35(1):128–152

    Google Scholar 

  • Cohendet P, Llerena P (2003) Routines and incentives: The role of communities in the firm. Ind Corp Change 12:271–297

    Google Scholar 

  • Coleman JS (1988) Social capital in the creation of human capital. Amer J Sociology 94:S95–S120

    Google Scholar 

  • Cowan R, Jonard N (2004) Network structure and the diffusion of knowledge. J Econ Dynamics and Control 28:1557–1575

    Google Scholar 

  • Cowan R, Jonard N, Ozman M (2004) Knowledge dynamics in a network industry. Tech Forecasting Soc Change 71:469–484

    Google Scholar 

  • Cross RL, Parker A (2004) The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business School Press, Cambridge, MA

    Google Scholar 

  • Ebadi YM, Utterback JM (1984) The effects of communication on technological innovation. Management Sci 30(5):572–585

    Google Scholar 

  • Eisenhardt KM, Martin JA (2000) Dynamic capabilities: What are they? Strategic Management J 21(10–11):1105–1121

    Google Scholar 

  • Fang C, Lee J, Schilling MA (2010) Balancing exploration and exploitation through structural design: The isolation of subgroups and organizational learning. Organ Sci 21(3):625–642

    Google Scholar 

  • Feld SL (1981) The focused organization of social ties. Amer J Sociology 86(5):1015–1035

    Google Scholar 

  • Feld SL (1982) Social structural determinants of similarity among associates. Amer Sociol Rev 47(6):797–801

    Google Scholar 

  • Fiol CM, Lyles MA (1985) Organizational Learning Acad Management Rev 10(4):803–813

    Google Scholar 

  • Fleming L, Waguespack DM (2007) Brokerage, boundary spanning, and leadership in open innovation communities. Organ Sci 18(2):165–180

    Google Scholar 

  • Fleming L, King C III, Juda AI (2007a) Small worlds and regional innovation. Organ Sci 18(6):938–954

    Google Scholar 

  • Fleming L, Mingo S, Chen D (2007b) Collaborative brokerage, generative creativity, and creative success. Admin Sci Quart 52(3):443–475

    Google Scholar 

  • Galbraith JR (1973) Designing complex organizations. Addison-Wesley, Reading, MA

    Google Scholar 

  • Garcia R (2005) Uses of agent-based modeling in innovation/new product development research. J Product Innov Manag 22(5):380–398

    Google Scholar 

  • 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. Res Policy 37(10):1717–1731

    Google Scholar 

  • Gould RV, Fernandez RM (1989) Structures of mediation: A formal approach to brokerage in transaction networks. Sociol Methodol 19:89–126

    Google Scholar 

  • Granstrand O (1998) Towards a theory of the technology-based firm. Res Policy 27(5):465–489

    Google Scholar 

  • Granstrand O, Patel P, Pavitt K (1997) Multi-technology corporations: Why they have “distributed” rather than “distinctive core” competencies. California Manag Rev 39(4):8–25

    Google Scholar 

  • Grant RM (1996) Toward a knowledge-based theory of the firm. Strateg Manag J 17(S2):109–122

    Google Scholar 

  • Halevy N, Halali E, Zlatev JJ (2019) Brokerage and brokering: An integrative review and organizing framework for third party influence. Acad Management Ann 13(1):215–239

    Google Scholar 

  • Hansen MT (1999) The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Admin Sci Quart 44(1):82–111

    Google Scholar 

  • Hansen MT (2002) Knowledge networks: Explaining effective knowledge sharing in multiunit companies. Organ Sci 13(3):232–248

    Google Scholar 

  • Hargadon AB (2002) Brokering knowledge: Linking learning and innovation. Res Organ Behav 24:41–85

    Google Scholar 

  • Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manag Rev 32(4):1229–1245

    Google Scholar 

  • Kleinbaum AM, Stuart TE (2014) Network responsiveness: The social structural microfoundations of dynamic capabilities. Acad Manag Perspect 28(4):353–367

    Google Scholar 

  • Kleinbaum AM, Tushman ML (2007) Building bridges: The social structure of interdependent innovation. Strateg Entrep J 1(1–2):103–122

    Google Scholar 

  • Kogut B, Zander U (1992) Knowledge of the firm, combinative capabilities, and the replication of technology. Organ Sci 3(3):383–397

    Google Scholar 

  • Kostova T, Marano V, Tallman S (2016) Headquarters–subsidiary relationships in MNCs: Fifty years of evolving research. J World Business 51(1):176–184

    Google Scholar 

  • Krackhardt D, Hanson JR (1993) Informal networks. Harvard. Bus Rev 71(4):104–111

    Google Scholar 

  • Lawrence PR, Lorsch JW (1967) Differentiation and integration in complex organizations. Admin Sci Quart 12(1):1–47

    Google Scholar 

  • Lomi A, Lusher D, Pattison PE, Robins G (2014) The focused organization of advice relations: A study in boundary crossing. Organ Sci 25(2):438–457

    Google Scholar 

  • Macy MW, Willer R (2002) From factors to actors: Computational sociology and agent-based modeling. Annual Rev Sociol 28(1):143–166

    Google Scholar 

  • Maoret M, Tortoriello M, Iubatti D (2020) Big fish, big pond? The joint effect of formal and informal core/periphery positions on the generation of incremental innovations. Organ Sci 31(6):1538–1559

    Google Scholar 

  • March JG, Simon HA (1958) Organizations. Wiley, New York

    Google Scholar 

  • Marengo L (1992) Coordination and organizational learning in the firm. J Evol Econ 2(4):313–326

    Google Scholar 

  • Maurer I, Bartsch V, Ebers M (2011) The value of intra-organizational social capital: How it fosters knowledge transfer, innovation performance, and growth. Organ Studies 32(2):157–185

    Google Scholar 

  • McEvily B, Soda G, Tortoriello M (2014) More formally: Rediscovering the missing link between formal organization and informal social structure. Acad Management Ann 8(1):299–345

    Google Scholar 

  • Morone P, Taylor R (2012) Proximity, knowledge integration and innovation: An agenda for agent-based studies. J Evol Econ 22:19–47

    Google Scholar 

  • Mowery DC, Oxley JE, Silverman BS (1998) Technological overlap and interfirm cooperation: Implications for the resource-based view of the firm. Res Policy 27(5):507–523

    Google Scholar 

  • Nonaka I, Takeuchi H (1995) The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press, New York

    Google Scholar 

  • Nooteboom B (1998) Cost, quality and learning based governance of buyer-supplier relations. In: Colombo MG (ed) The changing boundaries of the firm. Routledge, London, pp 187–208

    Google Scholar 

  • Nooteboom B, Van Haverbeke W, Duysters G, Gilsing V, Van den Oord A (2007) Optimal cognitive distance and absorptive capacity. Res Policy 36(7):1016–1034

    Google Scholar 

  • Obstfeld D (2005) Social networks, the tertius iungens orientation, and involvement in innovation. Admin Sci Quart 50(1):100–130

    Google Scholar 

  • O’Leary MB, Mortensen M, Woolley AW (2011) Multiple team membership: A theoretical model of its effects on productivity and learning for individuals and teams. Acad Manag Rev 36(3):461–478

    Google Scholar 

  • Ozman M (2010) The knowledge base of products: Implications for organizational structures. Org Studies 31(8):1129–1154

    Google Scholar 

  • Parker A, Tippmann E, Kratochvil R (2019) Accessing diverse knowledge for problem solving in the MNC: A network mobilization perspective. Glob Strateg J 9(3):423–452

    Google Scholar 

  • Paruchuri S, Awate S (2017) Organizational knowledge networks and local search: The role of intra-organizational inventor networks. Strateg Manag J 38(3):657–675

    Google Scholar 

  • Pavitt K (1998) Technologies, products and organization in the innovating firm: What Adam Smith tells us and Joseph Schumpeter doesn’t. Ind Corp Change 7(3):433–452

    Google Scholar 

  • Phelps C, Heidl R, Wadhwa A (2012) Knowledge, networks, and knowledge networks: A review and research agenda. J Manag 38(4):1115–1166

    Google Scholar 

  • Reagans R, McEvily B (2003) Network structure and knowledge transfer: The effects of cohesion and range. Admin Sci Quart 48(2):240–267

    Google Scholar 

  • Robins G, Pattison P, Woolcock J (2005) Small and other worlds: Global network structures from local processes. American J Soc 110(4):894–936

    Google Scholar 

  • Schilling MA, Phelps CC (2007) Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Sci 53(7):1113–1126

    Google Scholar 

  • Schoenmakers W, Duysters G (2006) Learning in strategic technology alliances. Tech Analysis Strat Manag 18(2):245–264

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell System Tech J 27(3):379–423

    Google Scholar 

  • Soda G, Zaheer A (2012) A network perspective on organizational architecture: Performance effects of the interplay of formal and informal organization. Strateg Manag J 33(6):751–771

    Google Scholar 

  • Stovel K, Shaw L (2012) Brokerage. Annual. Rev Sociol 38:139–158

    Google Scholar 

  • Szulanski G (1996) Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strateg Manag J 17(S2):27–43

    Google Scholar 

  • Szulanski G (2002) Sticky knowledge: Barriers to knowing in the firm. Sage, London

    Google Scholar 

  • Tanriverdi H, Venkatraman N (2005) Knowledge relatedness and the performance of multibusiness firms. Strateg Manag J 26(2):97–119

    Google Scholar 

  • Taylor A, Greve HR (2006) Superman or the fantastic four? Knowledge combination and experience in innovative teams. Acad Manag J 49(4):723–740

    Google Scholar 

  • Tesfatsion L (2003) Agent-based computational economics: Modeling economies as complex adaptive systems. Inf Sci 149(4):262–268

    Google Scholar 

  • Thompson JD (1967) Organizations in action: Social science bases of administrative theory. McGraw-Hill, New York

    Google Scholar 

  • Tortoriello M, Reagans R, McEvily B (2012) Bridging the knowledge gap: The influence of strong ties, network cohesion, and network range on the transfer of knowledge between organizational units. Organ Sci 23(4):1024–1039

    Google Scholar 

  • Tushman ML (1977) Special boundary roles in the innovation process. Admin Sci Quart 22(4):587–605

    Google Scholar 

  • Tushman M, Nadler DA (1978) Information processing as an integrating concept in organizational design. Acad Manag Rev 3(3):613–624

    Google Scholar 

  • Tushman ML, Scanlan TJ (1981) Boundary spanning individuals: Their role in information transfer and their antecedents. Acad Manag J 24(2):289–305

    Google Scholar 

  • Uzzi B, Spiro J (2005) Collaboration and creativity: The small world problem. Amer J Sociol 111(2):447–504

    Google Scholar 

  • Wang P, Robins G, Pattison P, Lazega E (2013) Exponential random graph models for multilevel networks. Soc Netw 35(1):96–115

    Google Scholar 

  • Watts DJ (1999) Networks, dynamics, and the small-world phenomenon. Amer J Sociol 105(2):493–527

    Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440

    Google Scholar 

  • Wuyts S, Colombo MG, Dutta S, Nooteboom B (2005) Empirical tests of optimal cognitive distance. J Econom Behav Organ 58(2):277–302

    Google Scholar 

  • Zappa P, Lomi A (2015) The analysis of multilevel networks in organizations: Models and empirical tests. Org Res Methods 18(3):542–569

    Google Scholar 

  • Zappa P, Robins G (2016) Organizational learning across multi-level networks. Soc Networks 44:295–306

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université Paris-Saclay, Univ Evry, IMT-BS, LITEM, 91025, Evry-Courcouronnes, France

    Muge Ozman

  2. Durham University Business School, Mill Hill Lane, Durham, DH1 3LB, UK

    Andrew Parker

Authors
  1. Muge Ozman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Parker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muge Ozman.

Ethics declarations

Ethical conduct

This research involves experiments neither with humans nor animals.

Conflict of interest

The authors have no conflicts of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ozman, M., Parker, A. The effect of social networks, organizational coordination structures, and knowledge heterogeneity on knowledge transfer and aggregation. J Evol Econ 33, 249–278 (2023). https://doi.org/10.1007/s00191-023-00811-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-023-00811-z

Keywords

JEL Classification

Search

Navigation