Title: An artificial neural network-based DSS to prioritise information technology and its complementary investments in industrial firms
Authors: Abbas Keramati; Navid Mojir; Masoud Afshari-Mofrad; Iman Jahanandish; Ali Derakhshani
Addresses: Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran. ' Yale School of Management, P.O. Box 208200, New Haven, CT 06520-8200, USA. ' Information Technology Management Department, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran. ' Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran. ' Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran
Abstract: The impact of IT usage on performance and other organisation's outputs is an important issue for both practitioners and academics. Evidence shows that despite a vast percentage of firm's budget which has spent on IT is continuing increasingly, there are some failures of firms in obtaining the benefits of these expenditures within expected period. To solve this problem, managers have to consider complementary investments. In this paper a decision support system is developed for prioritising investments on the information technology (IT) and its complementary investment using data of 102 car part suppliers in Iran. This software is developed using an artificial neural network and results are validated finally. One of the main specifications of this DSS is investigating on IT aspects at the firm level, which can help top management during decision making process to allocate budget properly in the most significant aspects of IT investment inside their own company.
Keywords: information technology; IT investment; process orientation; decision support systems; DSS; artificial neural networks; ANNs; ranking; Iran; automotive suppliers; automobile industry; decision making.
International Journal of Business Information Systems, 2012 Vol.9 No.2, pp.149 - 168
Published online: 16 Aug 2014 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article