Using Artificial Neural Networks to Determine the Extent to Which the Private Sector in the KSA Benefit from Quantitative Methods in Decision-Making | ||||
| المجلة العلمية للدراسات التجارية والبيئية | ||||
| Article 61, Volume 12, Issue 3, July 2021, Page 175-212 PDF (986.94 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/jces.2021.204613 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| FUAD Awwad* | ||||
| Quantitative Analysis Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia, | ||||
| Abstract | ||||
| The goal of this research was to investigate the actuality of employing quantitative tools in decision making. The most major issues that impede the adoption of quantitative approaches in the private sector in Saudi Arabia are the key sources of managers' knowledge, decision-making styles, identifying decision-making methodologies, and sectors of applications. Data were acquired by a questionnaire survey of 594 managers, and data were analyzed descriptively and inferentially. According to the findings, 80.1 percent of respondents use quantitative techniques in decision-making; the primary sources of quantitative method expertise were university study (34%), followed by applied practice (26%). Furthermore, 42 percent of participants used experience as a decision-making tool. Statistical analysis was utilized as a quantitative method, accounting for 17% of the total. The most common fields of use for quantitative approaches were profitability analysis (12%) and inventory control (11%). With 30%, the most common reason preventing the use of quantitative approaches was a lack of professionals. The most common benefit of applying quantitative methods mentioned by participants was an increase in profitability (28%). In this study, we used two techniques: neural network and logistic regression. We concluded that the most important variables that have an impact on making the right decision in the private sector are academic qualification (46.6 percent), knowledge of quantitative methods (20 percent), the extent of application of quantitative methods (17.8 percent), and finally age (15.6 percent). | ||||
| Keywords | ||||
| Neural Network; Logistic Regression; Quantitative Methods; Information Technology; Decisions Support System | ||||
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