IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v59y2024ics1544612323011509.html
   My bibliography  Save this article

Proximity in consumer network and company new products decisions

Author

Listed:
  • Ma, Yonghong
  • Zhao, Zhihua

Abstract

This paper examines the effects of proximity in the consumer network structure on the diffusion of firms' new products. We find that the average path length of network on new products diffusion is unrelated to consumer preferences. The average path length of consumer network decreases the efficiency of new product diffusion. But under the similar preferences of consumers, the trend of changes of consumers network average degrees and network rewiring probability are the same as the new products diffusion efficiency, and the results will be opposite under the differentiation preferences of consumers.

Suggested Citation

  • Ma, Yonghong & Zhao, Zhihua, 2024. "Proximity in consumer network and company new products decisions," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323011509
    DOI: 10.1016/j.frl.2023.104778
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323011509
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104778?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. 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.
    2. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    3. Gavious, Ilanit & Hirsh, Nimrod & Kaufman, Dan, 2015. "Innovation in pyramidal ownership structures," Finance Research Letters, Elsevier, vol. 13(C), pages 188-195.
    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. Chorowski, Michał & Nowak, Andrzej & Andersen, Jørgen Vitting, 2023. "What makes products trendy: Introducing an innovation adoption model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    2. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    3. 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.
    4. Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    5. Lea Sonderegger-Wakolbinger & Christian Stummer, 2015. "An agent-based simulation of customer multi-channel choice behavior," 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. 23(2), pages 459-477, June.
    6. Claudia Dislich & Elisabeth Hettig & Jan Salecker & Johannes Heinonen & Jann Lay & Katrin M Meyer & Kerstin Wiegand & Suria Tarigan, 2018. "Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    7. 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.
    8. Paolo Pellizzari & Elena Sartori & Marco Tolotti, 2015. "Trade-In Programs in the Context of Technological Innovation with Herding," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 219-230, Springer.
    9. Lei Xu & Ronggui Ding & Lei Wang, 2022. "How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1353-1379, March.
    10. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    11. Lixin Zhou & Jie Lin & Yanfeng Li & Zhenyu Zhang, 2020. "Innovation Diffusion of Mobile Applications in Social Networks: A Multi-Agent System," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    12. Hang Xiong & Puqing Wang & Georgiy Bobashev, 2018. "Multiple peer effects in the diffusion of innovations on social networks: a simulation study," Journal of Innovation and Entrepreneurship, Springer, vol. 7(1), pages 1-18, December.
    13. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    14. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," 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. 25(1), pages 203-230, March.
    15. Chen, Chaojung & Watanabe, Chihiro & Griffy-Brown, Charla, 2007. "The co-evolution process of technological innovation—An empirical study of mobile phone vendors and telecommunication service operators in Japan," Technology in Society, Elsevier, vol. 29(1), pages 1-22.
    16. Haris Krijestorac & Rajiv Garg & Vijay Mahajan, 2020. "Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls," Information Systems Research, INFORMS, vol. 31(2), pages 449-472, June.
    17. Bichraoui-Draper, Najet & Xu, Ming & Miller, Shelie A. & Guillaume, Bertrand, 2015. "Agent-based life cycle assessment for switchgrass-based bioenergy systems," Resources, Conservation & Recycling, Elsevier, vol. 103(C), pages 171-178.
    18. Elena Karnoukhova & Anastasia Stepanova & Maria Kokoreva, 2018. "The Influence Of The Ownership Structure On The Performance Of Innovative Companies In The Us," HSE Working papers WP BRP 70/FE/2018, National Research University Higher School of Economics.
    19. Velickovic, Stevan & Radojicic, Valentina & Bakmaz, Bojan, 2016. "The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 130-140.
    20. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.

    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:eee:finlet:v:59:y:2024:i:c:s1544612323011509. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.