IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v8y2002i4d10.1023_a1025464501110.html
   My bibliography  Save this article

Finding Optimal Targets for Change Agents: A Computer Simulation of Innovation Diffusion

Author

Listed:
  • Dirk Maienhofer

    (University of Michigan, SI-CREW)

  • Thomas Finholt

    (University of Michigan, SI-CREW)

Abstract

We introduce a diffusion of innovation model based on a network threshold approach. Realistic network and threshold data were gathered regarding the diffusion of new software tools within part of a large organization. Novel model features are a second threshold for innovation rejection and a memory that allows actors to take trends into account. Computer simulations produce expected outcomes, such as the S-shaped diffusion curve, but also diffusion breakdown and oscillations. We define and compute the quality of change agent targets in terms of the impact targeted actors have on the diffusion process. Our simulations reveal considerable variance in the quality of actors as change agent targets. Certain actors can be singled out as especially important to the diffusion process. Small changes in the distribution of thresholds and changes in some parameters, such as the sensitivity for trends, lead to significant changes in the target quality measure. To illustrate these interdependencies we outline how the impact of an actor targeted by a change agent spreads through the network. We thus can explain why a good change agent target does not necessarily need to be an opinion leader. Simulations comparing the effectiveness of randomly selected targets versus a group of good change agent targets indicate that the selection of good targets can accelerate innovation diffusion.

Suggested Citation

  • Dirk Maienhofer & Thomas Finholt, 2002. "Finding Optimal Targets for Change Agents: A Computer Simulation of Innovation Diffusion," Computational and Mathematical Organization Theory, Springer, vol. 8(4), pages 259-280, December.
    • Handle: RePEc:spr:comaot:v:8:y:2002:i:4:d:10.1023_a:1025464501110
    DOI: 10.1023/A:1025464501110
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1025464501110
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1025464501110?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eric Abrahamson & Lori Rosenkopf, 1997. "Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation," Organization Science, INFORMS, vol. 8(3), pages 289-309, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ning Nan & Robert Zmud & Emre Yetgin, 2014. "A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 52-88, March.
    2. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhiang (John) Lin & James A. Kitts & Haibin Yang & J. Richard Harrison, 2008. "Elucidating strategic network dynamics through computational modeling," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 175-208, September.
    2. Yuzhuo Cai & Borja Ramis Ferrer & Jose Luis Martinez Lastra, 2019. "Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems: Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-23, August.
    3. Cappetta, Rossella & Cillo, Paola & Ponti, Anna, 2006. "Convergent designs in fine fashion: An evolutionary model for stylistic innovation," Research Policy, Elsevier, vol. 35(9), pages 1273-1290, November.
    4. Christoph Safferling & Aaron Lowen, 2011. "Economics in the Kingdom of Loathing: Analysis of Virtual Market Data," Working Paper Series of the Department of Economics, University of Konstanz 2011-30, Department of Economics, University of Konstanz.
    5. Inoue, Yuki & Tsujimoto, Masaharu, 2018. "New market development of platform ecosystems: A case study of the Nintendo Wii," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 235-253.
    6. Cai, Yuzhuo, 2023. "Towards a new model of EU-China innovation cooperation: Bridging missing links between international university collaboration and international industry collaboration," Technovation, Elsevier, vol. 119(C).
    7. Xue, Chaokai & Jiang, Ping & Dang, Xinghua, 2019. "The dynamics of network communities and venture capital performance: Evidence from China," Finance Research Letters, Elsevier, vol. 28(C), pages 6-10.
    8. den Hartigh, E. & Langerak, F. & Commandeur, H.R., 2002. "The Effects of Self-Reinforcing Mechanisms on Firm Performance," ERIM Report Series Research in Management ERS-2002-46-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Luo, Yu & Peng, Yuchao & Zeng, Lianyun, 2021. "Digital financial capability and entrepreneurial performance," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 55-74.
    10. Xiong, Hang & Payne, Diane & Kinsella, Stephen, 2016. "Peer effects in the diffusion of innovations: Theory and simulation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 1-13.
    11. Heejung Byun & Tae-Hyun Kim, 2017. "Identity Claims and Diffusion of Sustainability Report: Evidence from Korean Listed Companies, 2003–2010," Journal of Business Ethics, Springer, vol. 140(3), pages 551-565, February.
    12. Stefan Jonsson, 2009. "Refraining from Imitation: Professional Resistance and Limited Diffusion in a Financial Market," Organization Science, INFORMS, vol. 20(1), pages 172-186, February.
    13. Ayu Pratiwi & Aya Suzuki, 2017. "Effects of farmers’ social networks on knowledge acquisition: lessons from agricultural training in rural Indonesia," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 6(1), pages 1-23, December.
    14. Bulent Ozel, 2012. "Collaboration structure and knowledge diffusion in Turkish management academia," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(1), pages 183-206, October.
    15. Manuel Portugal Ferreira & Fernando Ribeiro Serra & Emerson Maccari, 2013. "When the innovator fails to capture rents from innovation," Working Papers 101, globADVANTAGE, Polytechnic Institute of Leiria.
    16. Manuel Portugal Ferreira & Ana Teresa Tavares & William Hesterly & Sungu Armagan, 2006. "Network and firm antecedents of spin-offs: Motherhooding spin-offs," FEP Working Papers 201, Universidade do Porto, Faculdade de Economia do Porto.
    17. Harrison, Spencer Huber & Nurmohamed, Samir, 2023. "Dirty creativity: An inductive study of how creative workers champion new designs that are stigmatized," Organizational Behavior and Human Decision Processes, Elsevier, vol. 175(C).
    18. José Alberto Bayo-Moriones & Gilberto Carvalho-Vasconcelos & Fernando Lera-López, 2005. "A firm-level analysis of differences between adopters and non-adopters of ICT," ERSA conference papers ersa05p645, European Regional Science Association.
    19. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    20. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comaot:v:8:y:2002:i:4:d:10.1023_a:1025464501110. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.