Forecasting the Imminent Market Shares Pertinent to HTC Corporation Domestically: The Vanguard Regarding Taiwan’s Smartphone Industry

Forecasting the Imminent Market Shares Pertinent to HTC Corporation Domestically: The Vanguard Regarding Taiwan’s Smartphone Industry

Yi-Fen Chen (Chung Yuan Christian University, Taiwan), Chang-Lung Hsieh (Ming Chuan University, Taiwan), Chia-Wen Tsai (Ming Chuan University, Taiwan), Wen-Yu Chen (Chinese Culture University, Taiwan) and Wei-Hung Lin (Chung Yuan Christian University, Taiwan)
Copyright: © 2012 |Pages: 7
DOI: 10.4018/jtd.2012010101
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Abstract

The development of the smartphone will intensify in the future. Recently, many Taiwan’s manufacturers are investing in the smartphone market. The present research used the grey envelope analysis to forecast the smartphone industry market share of High Tech Computer Corporation (HTC) in Taiwan. The average residual error of up and down envelope is 6.1825% from 2003 to 2007, and the predicted market share in 2012 is 3.334%. The forecasting results showed that the market share of HTC will decrease in the future. The founding of research offers meaningful information for HTC to decide the new strategy. For government, the result could also help to implement adequate policies to support the development of smartphone industry in the future.
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Gm (1, 1) Envelope Analysis Model

The GM (1, 1) model was first introduced in 1982 by Professor Julong Deng to analyze systems with poor information (Deng, 1982). A grey system is one in which some of the information is known and some is unknown (Lin & Yang, 2004). Any random variation in a system is treated as the variation of the grey value within a certain range, and any random process is considered a time-varying grey process by the grey system theory. Instead of using a statistical model, grey theory uses grey generating techniques such as Accumulated Generating Operation (AGO) to transform the stochastic raw data into a more regular series. Grey prediction is one of the most important components of grey system theory. It utilizes past and current known or indeterminate information to establish a grey model to extend the past information into the future so that the grey model can be used to predict future variation tendencies of the system output. The key operation in the construction of a grey model is the use of discrete time sequence data to construct an ordinary differential equation. AGO and Inverse Accumulated Generating Operation (IAGO) are the basic tools for determining the grey forecasting model. The most extensively used grey forecasting model is GM (1, 1) (Lin, Liou, & Huang, 2011). Compared to other non-analytical methods, such as neural networks and regressive analysis, the grey model has the following characteristics: (i) a small data set (i.e., 4-6 data), (ii) less computation, (iii) high precision (in small data), and (iv) the ability to adjust and correct the model (Deng, 1989).

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