Detecting spatial and temporal myopia using machine learning algorithms

Authors

  • Rakan Alsarayreh Irbid National University, Jordan
  • Hazem Almahameed Irbid National University, Jordan
  • Doua Alhajahjeh The World Islamic Sciences and Education University, Jordan
  • Yousef Ali Mohammad Alrefai Irbid National University, Jordan
  • Rawan Samih Mansour Jerash University, Jordan

DOI:

https://doi.org/10.37868/sei.v8i1.id726

Abstract

This study aims to examine the ability of machine learning algorithms to detect strategic myopia in organizations. As it consists of two variables, the first machine learning algorithms as independent variable with two dimensions: Decision trees classification and K- Means clustering, while the second variable is strategic myopia as dependent variable with two dimensions: spatial and temporal myopia. This study adopted a quantitative approach, and a publicly available HR dataset obtained from Kaggle was used to ensure data privacy. The dataset, which has been used in this study, represents the organizational internal factors with 14,999 employees’ records. Both decision trees and K-means were applied to the internal factors’ datasets, showing the likelihood of employees staying in the organizations and clustering the customers into three clusters. The study revealed that both decision trees and k-means can help organizations in detecting spatial and temporal myopia, and the researchers recommended that organizations should integrate machine learning algorithms in their decision-making processes.

Published

2026-02-16

How to Cite

[1]
R. Alsarayreh, H. Almahameed, D. Alhajahjeh, Y. A. M. Alrefai, and R. Mansour, “Detecting spatial and temporal myopia using machine learning algorithms”, Sustainable Engineering and Innovation, vol. 8, no. 1, pp. 27-38, Feb. 2026.

Issue

Vol. 8 No. 1 (2026)

Section

Articles

Copyright (c) 2026 Rakan Alsarayreh, Hazem Almahameed, Doua Alhajahjeh, Yousef Ali Mohammad Alrefai, Rawan Samih Mansour

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