IDEAS home Printed from https://ideas.repec.org/a/taf/servic/v30y2009i10p1757-1771.html
   My bibliography  Save this article

Knowledge transfer in a tourism destination: the effects of a network structure

Author

Listed:
  • Rodolfo Baggio
  • Chris Cooper

Abstract

Tourism destinations have a necessity to innovate in order to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory, they represent the nodes within the system. The paper shows how epidemic diffusion models can act as analogies for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations, the case demonstrates how the Elba network can be optimized. Overall, this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.† -super-†An earlier version of this paper has been presented at the IASK Advances in Tourism Research 2008 Conference, Aveiro, Portugal, 26--28 May 2008.

Suggested Citation

  • Rodolfo Baggio & Chris Cooper, 2009. "Knowledge transfer in a tourism destination: the effects of a network structure," The Service Industries Journal, Taylor & Francis Journals, vol. 30(10), pages 1757-1771, November.
  • Handle: RePEc:taf:servic:v:30:y:2009:i:10:p:1757-1771
    DOI: 10.1080/02642060903580649
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02642060903580649
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/02642060903580649?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. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    2. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    3. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517, Decembrie.
    Full references (including those not matched with items on IDEAS)

    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. Dror Kenett & Shlomo Havlin, 2015. "Network science: a useful tool in economics and finance," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 14(2), pages 155-167, November.
    2. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    3. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    4. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    5. repec:hal:spmain:info:hdl:2441/5bnglqth5987gaq6dhju3psjn3 is not listed on IDEAS
    6. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    7. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    8. Paul L. Borrill & Leigh Tesfatsion, 2011. "Agent-based Modeling: The Right Mathematics for the Social Sciences?," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 11, Edward Elgar Publishing.
    9. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    10. Yoo, Seung Han, 2014. "Learning a population distribution," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 188-201.
    11. Cees Diks & Cars Hommes & Valentyn Panchenko & Roy Weide, 2008. "E&F Chaos: A User Friendly Software Package for Nonlinear Economic Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 221-244, September.
    12. Serena Brianzoni & Roy Cerqueti & Elisabetta Michetti, 2010. "A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 165-188, February.
    13. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    14. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    15. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    16. Theodore Tsekeris & Klimis Vogiatzoglou, 2011. "Spatial agent-based modeling of household and firm location with endogenous transport costs," Netnomics, Springer, vol. 12(2), pages 77-98, July.
    17. Pascal Seppecher & Isabelle Salle & Dany Lang, 2019. "Is the market really a good teacher?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 299-335, March.
    18. LeBaron Blake & Winker Peter, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 141-148, April.
    19. Daniele Giachini, 2018. "Rationality and Asset Prices under Belief Heterogeneity," LEM Papers Series 2018/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    20. Доможиров Д. А. & Ибрагимов Н. М. & Мельникова Л. В. & Цыплаков А. А., 2017. "Интеграция подхода «затраты – выпуск» в агент-ориентированное моделирование. Часть 1. Методологические основы. Integration of input–output approach into agent-based modeling. Part 1. Methodological pr," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(1), pages 86-99.
    21. Witte, Björn-Christopher, 2012. "Fund managers - Why the best might be the worst: On the evolutionary vigor of risk-seeking behavior," Economics Discussion Papers 2012-20, Kiel Institute for the World Economy (IfW Kiel).

    More about this item

    Statistics

    Access and download statistics

    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:taf:servic:v:30:y:2009:i:10:p:1757-1771. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/FSIJ20 .

    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.