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The wider, the better? The interaction between the IoT diffusion and online retailers’ decisions

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  • Hu, Sen
  • Hu, Bin
  • Cao, Ya

Abstract

Diffusion process is a hot topic in various subjects. However, much existing literature ignores the dynamic influence of the environment on the diffusion process and the impact of the diffusion process on the environment. Taking the IoT diffusion process in the e-commerce industry as example, we investigate the dynamic interaction between the IoT diffusion process and the external environment, i.e., the adjustment of retail prices and the profit of an online retailer through a simulation model. The simulation model combines the price adjustment mechanism and the diffusion evolution mechanism. We obtain two main conclusions. First, price adjustment of retailers blocks the IoT diffusion process, and a higher online shopping disutility level could amplify the impediment influence of price adjustment. Second, the IoT diffusion is of influence on the pricing and profit of the online retailers. The influence type (i.e., positive or neglect, monotonous or non-monotonous) depends on the special circumstance. Under some conditions, a wider diffusion does not inevitably result in a larger profit of the online retailer. This shows us that a wider or faster diffusion is not always better.

Suggested Citation

  • Hu, Sen & Hu, Bin & Cao, Ya, 2018. "The wider, the better? The interaction between the IoT diffusion and online retailers’ decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 196-209.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:196-209
    DOI: 10.1016/j.physa.2018.06.008
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    1. Li, Ming & Liu, Run-Ran & Peng, Dan & Jia, Chun-Xiao & Wang, Bing-Hong, 2018. "Roles of the spreading scope and effectiveness in spreading dynamics on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1239-1246.
    2. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    3. Weon Sang Yoo & Eunkyu Lee, 2011. "Internet Channel Entry: A Strategic Analysis of Mixed Channel Structures," Marketing Science, INFORMS, vol. 30(1), pages 29-41, 01-02.
    4. Sebastiano A. Delre & Wander Jager & Marco A. Janssen, 2007. "Diffusion dynamics in small-world networks with heterogeneous consumers," Computational and Mathematical Organization Theory, Springer, vol. 13(2), pages 185-202, June.
    5. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    6. Eunkyu Lee & Richard Staelin & Weon Sang Yoo & Rex Du, 2013. "A “Meta-Analysis” of Multibrand, Multioutlet Channel Systems," Management Science, INFORMS, vol. 59(9), pages 1950-1969, September.
    7. Li, Xun & Cao, Lang, 2016. "Diffusion processes of fragmentary information on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 624-634.
    8. Ádám Novotny & Lóránt Dávid & Hajnalka Csáfor, 2015. "Applying RFID technology in the retail industry – benefits and concerns from the consumer’s perspective," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 17(39), pages 615-615, May.
    9. Agha Mohammad Ali Kermani, Mehrdad & Fatemi Ardestani, Seyed Farshad & Aliahmadi, Alireza & Barzinpour, Farnaz, 2017. "A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 570-582.
    10. Abhijit Banerjee & Arun G. Chandrasekhar & Esther Duflo & Matthew O. Jackson, 2012. "The Diffusion of Microfinance," NBER Working Papers 17743, National Bureau of Economic Research, Inc.
    11. Gao, Lei & Li, Ruiqi & Shu, Panpan & Wang, Wei & Gao, Hui & Cai, Shimin, 2018. "Effects of individual popularity on information spreading in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 32-39.
    12. Suo, Qi & Guo, Jin-Li & Shen, Ai-Zhong, 2018. "Information spreading dynamics in hypernetworks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 475-487.
    13. Wang, Tao & He, Juanjuan & Wang, Xiaoxia, 2018. "An information spreading model based on online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 488-496.
    14. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    15. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    16. Krafft, Manfred & Goetz, Oliver & Mantrala, Murali & Sotgiu, Francesca & Tillmanns, Sebastian, 2015. "The Evolution of Marketing Channel Research Domains and Methodologies: An Integrative Review and Future Directions," Journal of Retailing, Elsevier, vol. 91(4), pages 569-585.
    17. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
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