Building the Artificial Neural Network Pricing model for High-Tech Consumable Product - Case of Digital Still Camera

• Main Authors: Bo-Sheng Hwang, 黃勃盛
• Other Authors: Chee-Wha Yann
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/86712232850544700240

碩士 === 國防管理學院 === 資源管理研究所 === 94 === Following the arrival of high-tech era, consumer electronic products face the challenge of higher price, shorter life cycle, and higher technique level; the single cost-based pricing model can no longer predict the price of high-tech products. Under the condition of multi-channel distribution and disruptive innovation, the actual retail price varies according to individual, timing, and location, which leads past consumer-oriented pricing model to extreme challenge. High-tech product suppliers need a model that can precisely capture market dynamics and forecast the trend of price, and further help them evaluate the dimensions of technology development and create competitive advantage. This study is based on the characteristics pricing model reflecting consumer pricing, it utilizes the artificial neural networks that is capable of data mining to explore the relations between critical product attributes and prices. The empirical case study chooses Digital Still Camera as an example, and hence constructs an artificial neural network model capable of forecasting. The scope of study includes the top three market share brands and their products released in past two years. After the relationships between the camera and characteristic attributes are captured, a sensitive analysis is carried out, and the competitive technology strategy is developed.