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What makes products trendy: Introducing an innovation adoption model

Author

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  • Chorowski, Michał
  • Nowak, Andrzej
  • Andersen, Jørgen Vitting

Abstract

Here we propose and study a new model of innovation adoption, IA. Our hypothesis is that individuals’ decisions regarding the purchase of a new product, are driven by the perceived type of adoption trend. Our assumptions split adopters into four groups, Innovators, Early Adopters, Majority, and Laggards, based on their innovativeness, and assign particular preferences for various adoption trends based on their psychological profile. We have built several mathematical models to test our hypothesis and generated forecasts for retail sales of products sold in a supermarket chain in Poland. The performance in sales forecasting of our IA model, points to evidence of customers’ behavior as described by our hypotheses, and the usefulness in quantifying psychological behavior in a general social context of innovation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:616:y:2023:i:c:s0378437123001760
    DOI: 10.1016/j.physa.2023.128621
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    1. Christian Helmers & Mark Rogers, 2010. "Innovation and the Survival of New Firms in the UK," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 36(3), pages 227-248, May.
    2. Palm, A., 2020. "Early adopters and their motives: Differences between earlier and later adopters of residential solar photovoltaics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Arkadiusz Kijek & Tomasz Kijek, 2010. "Modelling of innovation diffusion," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 20(3-4), pages 53-68.
    4. 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.
    5. Andrzej Nowak & Wieslaw Bartkowski & Katarzyna Samson & Agnieszka Rychwalska & Marta Kacprzyk & Magda Roszczynska-Kurasinska & Magdalena Jagielska, 2013. "No Need For Speed: Modeling Trend Adoption In A Heterogeneous Population," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-28.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Elena Higueras-Castillo & Sebastian Molinillo & J. Andres Coca-Stefaniak & Francisco Liébana-Cabanillas, 2020. "Potential Early Adopters of Hybrid and Electric Vehicles in Spain—Towards a Customer Profile," Sustainability, MDPI, vol. 12(11), pages 1-18, May.
    8. Bettencourt, Luís M.A. & Cintrón-Arias, Ariel & Kaiser, David I. & Castillo-Chávez, Carlos, 2006. "The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 513-536.
    9. Zarazua de Rubens, Gerardo, 2019. "Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market," Energy, Elsevier, vol. 172(C), pages 243-254.
    10. Yang, Jianmei & Yao, Canzhong & Ma, Weicheng & Chen, Guanrong, 2010. "A study of the spreading scheme for viral marketing based on a complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 859-870.
    11. Hielke Buddelmeyer & Paul H. Jensen & Elizabeth Webster, 2010. "Innovation and the determinants of company survival," Oxford Economic Papers, Oxford University Press, vol. 62(2), pages 261-285, April.
    12. 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.
    13. Catherine M. Banbury & Will Mitchell, 1995. "The effect of introducing important incremental innovations on market share and business survival," Strategic Management Journal, Wiley Blackwell, vol. 16(S1), pages 161-182.
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