IDEAS home Printed from https://ideas.repec.org/p/ems/eureri/1494.html
   My bibliography  Save this paper

How to Determine the Increasing Returns Sensitivity of Your Industry?

Author

Listed:
  • Klein, M.H.
  • den Hartigh, E.
  • Commandeur, H.R.
  • Langerak, F.

Abstract

Increasing returns means that self-reinforcing mechanisms are at work within firms and markets. These mechanisms come in four forms: scale effects, learning effects, network effects and social interaction effects. Some industries are more sensitive to increasing returns than others. It is important that managers are able to assess the increasing returns sensitivity of their industry. Therefore we have developed an analytical tool that allows managers to assess their industry’s sensitivity to increasing returns. Four case studies are used to illustrate this typology. The analytic tool shows that an industry has high increasing returns sensitivity if a combination of the following situations exists: 1) high fixed costs and low, or even zero, variable costs, indicating a high sensitivity to scale effects, 2) a high level of complexity of the business process and/or the products, indicating a high sensitivity to learning effects, 3) low product utility and high network utility, indicating a high sensitivity to network effects and finally, 4) a high degree of social involvement by customers and potential customers, indicating a high sensitivity to social interaction effects.

Suggested Citation

  • Klein, M.H. & den Hartigh, E. & Commandeur, H.R. & Langerak, F., 2004. "How to Determine the Increasing Returns Sensitivity of Your Industry?," ERIM Report Series Research in Management ERS-2004-047-STR, 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.
    • Handle: RePEc:ems:eureri:1494
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/1494/ERS%202004%20047%20STR.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. den Hartigh, Erik & Langerak, Fred, 2001. "Managing increasing returns," European Management Journal, Elsevier, vol. 19(4), pages 370-378, August.
    2. Jean-Noël Kapferer & Gilles Laurent, 1985. "Measuring consumer involvement profiles," Post-Print hal-00786781, HAL.
    3. Stremersch, S., 2001. "Essays on marketing strategy in technology-intensive markets," Other publications TiSEM 51d17923-2aae-485b-a59b-5, Tilburg University, School of Economics and Management.
    4. 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.
    5. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    6. Raphael Amit, 1986. "Cost leadership strategy and experience curves," Strategic Management Journal, Wiley Blackwell, vol. 7(3), pages 281-292, May.
    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. 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.
    2. den Hartigh, E. & Langerak, F. & Commandeur, H.R., 2000. "A Managerial Perspective on the Logic of Increasing Returns," ERIM Report Series Research in Management ERS-2000-48-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.
    3. 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.
    4. 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.
    5. Rong, Ke & Xiao, Fei & Zhang, Xiaoyu & Wang, Jingjing, 2019. "Platform strategies and user stickiness in the online video industry," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 249-259.
    6. Papachristos, George, 2017. "Diversity in technology competition: The link between platforms and sociotechnical transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 291-306.
    7. Grazia Cecere & Nicoletta Corrocher & Cédric Gossart & Muge Ozman, 2014. "Lock-in and path dependence: an evolutionary approach to eco-innovations," Journal of Evolutionary Economics, Springer, vol. 24(5), pages 1037-1065, November.
    8. Joachim Henkel & Jörn Block, 2013. "Peer influence in network markets: a theoretical and empirical analysis," Journal of Evolutionary Economics, Springer, vol. 23(5), pages 925-953, November.
    9. Parente, Ronaldo C. & Geleilate, José-Mauricio G. & Rong, Ke, 2018. "The Sharing Economy Globalization Phenomenon: A Research Agenda," Journal of International Management, Elsevier, vol. 24(1), pages 52-64.
    10. Paul Steffens & Per Davidsson & Jason Fitzsimmons, 2009. "Performance Configurations over Time: Implications for Growth– and Profit–Oriented Strategies," Entrepreneurship Theory and Practice, , vol. 33(1), pages 125-148, January.
    11. Knudsen, Eirik Sjåholm & Belik, Ivan, 2023. "The Achilles heel of interconnected user networks: Network firms and the vulnerability of rapid decline," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    12. Carrillo-Hermosilla, Javier, 2006. "A policy approach to the environmental impacts of technological lock-in," Ecological Economics, Elsevier, vol. 58(4), pages 717-742, July.
    13. Jie Zhang & Lingfeng Dong & Ting Ji, 2023. "The Diffusion of Competitive Platform-Based Products with Network Effects," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
    14. Jeho Lee & Jaeyong Song & Jae-Suk Yang, 2016. "Network structure effects on incumbency advantage," Strategic Management Journal, Wiley Blackwell, vol. 37(8), pages 1632-1648, August.
    15. Eocman Lee & Jeho Lee & Jongseok Lee, 2006. "Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias," Management Science, INFORMS, vol. 52(12), pages 1838-1848, December.
    16. Lori Rosenkopf & Eric Abrahamson, 1999. "Modeling Reputational and Informational Influences in Threshold Models of Bandwagon Innovation Diffusion," Computational and Mathematical Organization Theory, Springer, vol. 5(4), pages 361-384, December.
    17. Inwon Kang & Deokhee Cheon & Matthew Shin, 2011. "Advertising strategy for outbound travel services," Service Business, Springer;Pan-Pacific Business Association, vol. 5(4), pages 361-380, December.
    18. Wiebke Roß & Jens Weghake, 2018. "Wa(h)re Liebe: Was Online-Dating-Plattformen über zweiseitige Märkte lehren," TUC Working Papers in Economics 0017, Abteilung für Volkswirtschaftslehre, Technische Universität Clausthal (Department of Economics, Technical University Clausthal).
    19. Marta Gancarczyk, 2010. "Model schyłku i odrodzenia klastrów," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 1-21.
    20. María Fernanda Viecens, 2009. "Compatibility with Firm Dominance," Working Papers 2009-12, FEDEA.

    More about this item

    Keywords

    increasing returns; industry sensitivity; learning effects; network effects; scale effects; social interaction effects;
    All these keywords.

    JEL classification:

    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

    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:ems:eureri:1494. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/erimanl.html .

    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.