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Classification of Lighting Design Aspects in Relation to Employees’ Productivity in Saudi Arabia

Author

Listed:
  • Ghada Abdulrahman Najjar

    (Department of Interior Design, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia)

  • Khaled Akkad

    (Department of Engineering Management, Prince Sultan University, P.O. Box 66833, Riyadh 11586, Saudi Arabia)

  • Ahdab Hashim Almahdaly

    (East Consulting Engineering Company, P.O. Box 1973, Riyadh 11441, Saudi Arabia)

Abstract

Though the average employee spends a third of their day inside an office, designing a productive workspace can be challenging for designers. However, lighting design is a critical factor for the wellbeing of the employee. With the increasing number of local and international companies opening in Saudi Arabia, it is important to study the effect of natural and artificial lighting on the productivity of employees in the office environment. It is essential to consider that employee productivity leads to economic productivity. A questionnaire was shared with the employees of the head office of Ensan Charity for Orphans Care to collect data on the preferences of staff on the current lighting design in their offices. Office design is one of the most important aspects in need of special attention, since employees spend more than eight hours daily at their offices. Lighting design is one of the key aspects of office design that has a direct impact on employees’ satisfaction and productivity. The aim of this study was to discover employees’ preferences for office design in Saudi Arabia. The collected data are analyzed to uncover employee preferences as well as to predict two key design aspects using machine-learning techniques. The two design aspects of concern are direct sunlight in the office environment and manual control of light intensity. This research aimed to help improve the design of the office environment according to employees’ preferences and international standards through investigating sustainable lighting design elements. A further challenge to be overcome was the need for further data collection as it relates to the two design aspects mentioned above. This paper demonstrates relatively high prediction accuracies of the mentioned design considerations using a variety of machine-learning algorithms.

Suggested Citation

  • Ghada Abdulrahman Najjar & Khaled Akkad & Ahdab Hashim Almahdaly, 2023. "Classification of Lighting Design Aspects in Relation to Employees’ Productivity in Saudi Arabia," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3614-:d:1070093
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    References listed on IDEAS

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    1. Jiangping Gao & Xiangyang Shi & Linghui Li & Ziqiang Zhou & Junfeng Wang, 2022. "Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    2. Aritra Ghosh & Abdelhakim Mesloub & Mabrouk Touahmia & Meriem Ajmi, 2021. "Visual Comfort Analysis of Semi-Transparent Perovskite Based Building Integrated Photovoltaic Window for Hot Desert Climate (Riyadh, Saudi Arabia)," Energies, MDPI, vol. 14(4), pages 1-13, February.
    3. Darcin Akin & Virginia P. Sisiopiku & Ali H. Alateah & Ali O. Almonbhi & Mohammed M. H. Al-Tholaia & Khaled A. Alawi Al-Sodani, 2022. "Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia," Sustainability, MDPI, vol. 14(24), pages 1-36, December.
    4. Qinghua Zhang & Zhu Liang & Wei Liu & Weiping Peng & Houzan Huang & Shouwen Zhang & Lingwei Chen & Kaihua Jiang & Lixing Liu, 2022. "Landslide Susceptibility Prediction: Improving the Quality of Landslide Samples by Isolation Forests," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    5. Rongpeng Zhang & Carolina Campanella & Sara Aristizabal & Anja Jamrozik & Jie Zhao & Paige Porter & Shaun Ly & Brent A. Bauer, 2020. "Impacts of Dynamic LED Lighting on the Well-Being and Experience of Office Occupants," IJERPH, MDPI, vol. 17(19), pages 1-27, October.
    6. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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    1. Carlos Velásquez & Francisco Espín & María Ángeles Castro & Francisco Rodríguez, 2024. "Energy Efficiency in Public Lighting Systems Friendly to the Environment and Protected Areas," Sustainability, MDPI, vol. 16(12), pages 1-15, June.

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