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Predicting early adoption of successive video player generations

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  • van Rijnsoever, Frank J.
  • Oppewal, Harmen

Abstract

Early adopters play an important role in the innovation diffusion process. Over the past decades, many factors have been identified as predictors for early adoption of innovations. Less attention has been paid to the relationship between the early adoption of one generation of a specific product and the early adoption of successive product generations. This paper analyzes how early adoption of a new product generation depends on ownership, purchase experience and adoption times for previous generations of the same product. The paper develops predictive models of early adoption for four generations of video player products, based on a survey among 815 Australian consumers. The model allows the testing of various hypotheses. It is shown that previous generation variables outperform conventional socio-demographic and psychographic variables in predicting early adoption but also that the two variable types complement each other. The best predicting models include both previous generation and socio/psychographic variables. It is concluded that previous generation models have substantial merits for new product forecasting as they are more parsimonious than conventional models and the data required to estimate them is relatively easy to obtain.

Suggested Citation

  • van Rijnsoever, Frank J. & Oppewal, Harmen, 2012. "Predicting early adoption of successive video player generations," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 558-569.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:3:p:558-569
    DOI: 10.1016/j.techfore.2011.08.008
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    Citations

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    Cited by:

    1. van Rijnsoever, Frank J. & van den Berg, Jesse & Koch, Joost & Hekkert, Marko P., 2015. "Smart innovation policy: How network position and project composition affect the diversity of an emerging technology," Research Policy, Elsevier, vol. 44(5), pages 1094-1107.
    2. Michelsen, Carl Christian & Madlener, Reinhard, 2016. "Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany," Energy Policy, Elsevier, vol. 89(C), pages 95-105.
    3. Ozgur Dedehayir & Roland J. Ortt & Carla Riverola & Francesc Miralles, 2017. "Innovators And Early Adopters In The Diffusion Of Innovations: A Literature Review," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 21(08), pages 1-27, December.
    4. van Rijnsoever & Marius Meeus & Roger Donders, 2012. "The effects of economic status and recent experience on innovative behavior under environmental variability: an experimental approach," Innovation Studies Utrecht (ISU) working paper series 12-01, Utrecht University, Department of Innovation Studies, revised Jan 2012.
    5. van Rijnsoever, Frank J. & Meeus, Marius T.H. & Donders, A. Rogier T., 2012. "The effects of economic status and recent experience on innovative behavior under environmental variability: An experimental approach," Research Policy, Elsevier, vol. 41(5), pages 833-847.
    6. van Rijnsoever, Frank J. & van Mossel, Allard & Broecks, Kevin P.F., 2015. "Public acceptance of energy technologies: The effects of labeling, time, and heterogeneity in a discrete choice experiment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 817-829.
    7. Heidenreich, Sven & Killmer, Jan F. & Millemann, Jan A., 2022. "If at first you don't adopt - Investigating determinants of new product leapfrogging behavior," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    8. Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    9. Xinxue (Shawn) Qu & Aslan Lotfi & Dipak C. Jain & Zhengrui Jiang, 2022. "Predicting upgrade timing for successive product generations: An exponential‐decay proportional hazard model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2067-2083, May.
    10. Barnes, Stuart J. & Pressey, Andrew D., 2016. "Cyber-mavens and online flow experiences: Evidence from virtual worlds," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 285-296.

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