Abstract
Evaluating company growth potential has moved away from traditional financial focused ratios and ratios analysis that has origins in the early twentieth-century economics. However, these conventional methods might not be accurate in measuring such efficient factors as this combined proposed framework of Data Envelopment Analysis (DEA) and improved mathematical models do. The present research focuses on the prospect of growth of companies through evaluating the performance of 40 DMUs in terms of efficiency DEA and MMTs. DEA is used to determine the efficient DMUs while SFA underline the factors such as investment on research and development, effective marketing strategies and qualified human resource as the determinants of efficiency. Bootstrap Confidence Intervals (BCIs) are employed for the purpose of improving the efficiency scores precision. The integration of DEA with MMTs presents useful information on the identification of the key drivers of the growth and the effective formulation of efficient and competitive strategies for the managers and policymaker. The study emphasizes the need to apply these combined methodologies as valid in the growth evaluation of companies with implications to strategic decisions. This proposed framework can be used in finance, marketing, healthcare, and operations management to compare the current organizational effectiveness and future development.
Recommended Citation
Alqahtani, Fahad F.
(2025)
"Data Envelopment Analysis using Stochastic Frontier Analysis and Bootstrap Confidence Intervals,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
2, Article 11.
DOI: https://doi.org/10.52866/2788-7421.1254
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/11
