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Predicting the diffusion of LCD TVs by incorporating price in the extended Gompertz model

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  • Tsai, Bi-Huei

Abstract

In addition to reflecting how price decline stimulates consumption behavior, an effective diffusion model of liquid crystal display televisions (LCD TVs) featured by coexistence of multi-generational LCD TVs could also forecast accurately LCD TV sales under a market segmentation framework. As technological advances have led to a substantial decline in the price of LCD TVs, the diffusive prediction of LCD TVs based on prior conventional models, which neglect how price affects the LCD diffusions, must be biased. Based on anchoring and adjustment theory, this work develops extended Gompertz models that incorporate the consumers’ comparison of the initial prices with the later reduced prices to analyze the purchasing decisions of consumers for various sized LCD TVs from psychological perspectives. The effective ranges of market penetration rate are located under which the estimated parameters conform to the theoretical assumptions of product diffusion. Their forecasting accuracy is examined by further comparing prediction errors of the conventional Gompertz model and the extended Gompertz model. Empirical results indicate a significantly positive correlation between price reduction and LCD TV sales. Additionally, the market penetration rate is higher for smaller-sized LCD TVs than for larger-sized ones, implying that smaller-sized LCD TVs have reached market saturation, while larger-sized LCD TVs still have remaining market potential. Furthermore, the comparison results demonstrate that the effectiveness of the extended Gompertz model in predicting future shipment orbits of LCD TVs is superior to that of the conventional Gompertz model, since the extended model incorporates price factors.

Suggested Citation

  • Tsai, Bi-Huei, 2013. "Predicting the diffusion of LCD TVs by incorporating price in the extended Gompertz model," Technological Forecasting and Social Change, Elsevier, vol. 80(1), pages 106-131.
  • Handle: RePEc:eee:tefoso:v:80:y:2013:i:1:p:106-131
    DOI: 10.1016/j.techfore.2012.07.006
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    Citations

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    Cited by:

    1. Wang, I. Kim & Seidle, Russell, 2017. "The degree of technological innovation: A demand heterogeneity perspective," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 166-177.
    2. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    3. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2020. "Modeling technology diffusion: a study based on market coverage and advertising efforts," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 154-162, July.
    4. Shi, Xiaohui & Chumnumpan, Pattarin, 2019. "Modelling market dynamics of multi-brand and multi-generational products," European Journal of Operational Research, Elsevier, vol. 279(1), pages 199-210.
    5. Bi-Huei Tsai, 2017. "Predicting the competitive relationships of industrial production between Taiwan and China using Lotka–Volterra model," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2428-2442, May.
    6. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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