預測在現代化管理中佔有重要的一席之地，舉凡：目標設定、策略形成、計畫擬定都需要倚賴正確的預測，以幫助決策者做出適當的決定，進一步提升決策的品質。Bass於1969年提出著名的擴散模型─「新產品成長模式」，其主要功能即是發展產品的生命週期，並且提供新產品首次購買銷售量之預測。本研究之目的是建構一個以產品屬性為基礎的新產品擴散模式之係數估計方法，配合類神經網路的學習能力，以提供一個能夠在產品上市前即可估計係數的方法，並且與學者最常使用之非線性最小平方法進行比較。本研究採用倒傳遞網路之基本架構，建立產品屬性之擴散模式係數估計方法，並以Bass基本擴散模式來進行實證分析。實證中分別採用不同數目的產品屬性作為輸入層神經原，來建立倒傳遞網路之架構，並使用十種科技性產品進行網路訓練的工作，最後以三種科技性產品銷售量驗證模式之可行性。從實證分析之結果，本研究歸納出以下兩點結論：(1) 本研究所建構模式估計出之係數，與以往學者最常使用之非線性最小平方法估計出之係數，使用在測試之三個產品銷售量預測上，在預測能力上有相似之表現。(2)本研究提出之係數估計方法，在產品尚未上市時，即可進行初期銷售量預測。

中文部分

周淑娟，科技性產品擴散模式之比較，國立成功大學工業管理研究所碩士論文，民國八十五年六月。

陳順宇、鄭碧娥，STATISTICA手冊(一版)，華泰，台北，民國八十八年。

郭建中譯，行銷學上冊(一版)，揚智文化，台北，民國八十九年。

麥素蓮譯，行銷學(一版)，美商麥格羅．希爾國際股份有限公司台灣分公司，台北，民國九十年。

葉怡成，類神經網路模式應用與實作（七版），儒林，台北，民國九十年。

魏仲政，以動態控制為基礎之產品擴散模式，國立成功大學工業管理研究所碩士論文，民國八十九年六月。

羅華強，類神經網路-MATLAB的應用(一版)，清蔚科技，台北，民國九十年。

英文部分

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Kalish, S. and L. Lilien, “A marketing entry timing model for new technologies,”
Management Science, Vol. 32, No.2, 1986, pp. 194-205.

Lilien, G. L., A. G. Rao, and S. Kalish, “Bayesian estimation and control of detailing effort in a repeat purchase diffusion environment,” Management Science, Vol. 27,
No. 5, 1981, pp. 493-506.
Mahajan, V., E. Muller, and F. M. Bass, “New-product diffusion models in marketing : a review and directions for research,” Journal of Marketing, Vol.54, 1990, pp. 1-26.

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Mahajan, V., E. Muller and Y. Wind, New-product diffusion models, 2000, Boston: Kluwer

Norton, J. A. and F. M. Bass, “A diffusion theory model of adoption and substitution for successive generations of high-technology products,” Management Science,
Vol. 33, No. 9, 1987, pp. 1069-1086.

Parker, P. M., “Price elasticity dynamics over the adoption life cycle,” Journal of Marketing Research, Vol. 34, 1994, pp. 385-367.

Peterson, R., and V. Mahajan, “Multi-product growth models,” Research in Marketing, Vol. 1, 1978, pp. 201-231.

Robinson, B. and C. Lakhani, “Dynamic price models for new-product planning,”
Management Science, Vol. 21, No. 10, 1975, pp. 1113-1122.
Rogers, E. M., “Diffusion of Innovation”, 1995, Free Press

Saton, D., “A discrete Bass model and its parameter estimation,” Journal of the Operations Research Society of Japan, Vol. 44, No. 1, 2001, pp. 1-17.

Schmittlein, D. C., and V. Mahajain, “Maximum likelihood estimation for an innovation diffusion model of new-product acceptance,” Marketing Science, Vol. 1, No.1 , pp. 57-78.

Simon, H. and K. Sebastian, “Diffusion and advertising: the German telephone campaign,” Management Science, Vol. 33, No. 4, 1987, pp. 451-466.

Srinivasan, V., and C. H. Mason, “Nonlinear least squares estimations of new-product diffusion models,” Marketing Science, Vol. 5, No. 2, 1986, pp. 169-178.

Teng, T. C., V. Grover, and W. Guttler, “Information technology innovations: general diffusion patterns and its relations to innovation characteristics,” IEEE Transactions on Engineering Management, Vol. 49, No. 1, 2002, pp. 13-27.

Weerahandi, S. and S. R. Dalal, “A choice-based approach to the diffusion of a service: forecasting fax penetration by market segments, ” Marketing Science, Vol. 11, No. 1, 1992, pp. 39-53.