Abstract
In the ‘knowledge economies’ era, most managers have discovered that
technology can be considered as the key asset in sustaining the competitive
advantage of their corporations. Many researchers have tried to discuss
the relationships between technological performance and other influential
factors, such as strategic management, information resources, etc.
But they do not mention the issues concerning how each dimension
influences innovation performance and how to forecast innovation
performance based on these dimensions. This study presents a forecasting
model that predicts innovation performance using technical informational
resources and clear innovation objectives. Specifically, we propose
a neural network approach, which utilizes the Back-Propagation Network
(BPN) to solve this problem. Also we examine the results and compare
them to those attained using the statistical regression method. The
result shows that the BPN method outperforms the statistical regression
method as far as forecasting performance concerned. With this method,
a decision maker can predict innovation performance and adjust allocated
resources to match his/her company's innovation objectives.
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