Mat Junoh, Mohd Zukime and Siraj, Fadzilah and Abdul Manaf, Arman Hadi and Ab. Halim, Mohd Suberi and Abdullah, Muhammad Safizal (2017) Predicting the GDP of the new economy based on the human capital using neural network approach. Asian Journal of Management Sciences & Education Vol. 6(3) July 2017, 6 (3). pp. 88-99. ISSN 2186-845X

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Abstract

Human capital has become important because knowledge is a critical ingredient for gaining competitive advantages, particularly in the New Economy era.It has been described as becoming the preeminent resource for creating economic wealth. To date, several studies have been conducted to determine the relationship between human capital and company performance.The relationship between human capital and economic growth has been explored.However, past literature reveals that artificial intelligence techniques have not been utilized in understanding the effect of human capital on economic growth.Artificial intelligence techniques such as neural networks have been successfully applied to business and financial problems.To this end, the neural networks approach was used to determine the impact of human capital on the New Economy.This paper discusses the results of the exploratory study for predicting demand for human capital. Data from 1971 to 1996 was collected for this study.The variables used for the prediction were based on Canadian’s Human Capital Measurement as suggested by Laroche and Merrette (2000).The exploratory study indicated that neural network is a potential approach for predicting the GDP based on human capital. In conjunction with neural network approach, statistical methods were also used to explain the relationships between variables in the study.

Item Type:	Article
Uncontrolled Keywords:	Neural Network, GDP, Prediction, Human Capital, New Economy
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	13 Feb 2018 00:37
Last Modified:	13 Feb 2018 00:37
URI:	https://repo.uum.edu.my/id/eprint/22979

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