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OLED TV technology forecasting using technology mining and the Fisher-Pry diffusion model

Yonghee Cho (Department of Engineering and Technology Management, Portland State University, Portland, Oregon, USA)
Tugrul Daim (Department of Engineering and Technology Management, Portland State University, Portland, Oregon, USA)

Foresight

ISSN: 1463-6689

Article publication date: 11 April 2016

1679

Abstract

Purpose

Due to rapid technological evolution driven by display manufacturers, the television (TV) market of flat panel displays has been fast growing with the advancement of digital technologies in broadcasting service. Recently, organic light-emitting diode (OLED) successfully penetrated into the large-size TV market, catching up with light-emitting diode (LED)-liquid-crystal display (LCD). This paper aims to investigate the market penetration of OLED technologies by determining their technology adoption rates based on a diffusion model.

Design/methodology/approach

Through the rapid evolution of information and communication technology, as well as a flood of data from diverse sources such as research awards, journals, patents, business press, newspaper and Internet social media, data mining, text mining, tech mining and database tomography have become practical techniques for assisting the forecaster to identify early signs of technological change. The information extracted from a variety of sources can be used in a technology diffusion model, such as Fisher-Pry where emerging technologies supplant older ones. This paper uses a comparison-based prediction method to forecast the adoption and diffusion of next-generation OLED technologies by mining journal and patent databases.

Findings

In recent years, there has been a drastic reduction of patents related to LCD technologies, which suggests that next-generation OLED technology is penetrating the TV market. A strong industry adoption for OLED has been found. A high level of maturity is expected by 2026.

Research limitations/implications

For OLED technologies that are closely tied to industrial applications such as electronic display devices, it may be better to use more industry-oriented data mining, such as patents, market data, trade shows, number of companies or startups, etc. The Fisher-Pry model does not address the level of sales for each technology. Therefore, the comparison between the Bass model and the Fisher-Pry model would be useful to investigate the market trends of OLED TVs further. Another step for forecasting could include using industry experts and a Delphi model for forecasting (and further validation).

Originality/value

Fisher-Pry growth curves for journal publications and patents follow the expected sequence. Specially, journal publications and patents growth curves are close for OLED technologies, indicating a strong industry adoption.

Keywords

Acknowledgements

This research is funded by Energy Trust of Oregon.

Citation

Cho, Y. and Daim, T. (2016), "OLED TV technology forecasting using technology mining and the Fisher-Pry diffusion model", Foresight, Vol. 18 No. 2, pp. 117-137. https://doi.org/10.1108/FS-08-2015-0043

Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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