Proposing a Method Based on Artificial Neural Network for Predicting Alignment between the Saudi Nursing Workforce and the Gig Framework

Reem AL-Dossary, Abdulilah Mohammad Mayet (), Javed Khan Bhutto, Neeraj Kumar Shukla, Ehsan Nazemi () and Ramy Mohammed Aiesh Qaisi
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Reem AL-Dossary: Nursing Education Department, Nursing College, Imam Abdulrahman Bin Faisal University, Dammam 34221, Saudi Arabia
Abdulilah Mohammad Mayet: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Javed Khan Bhutto: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Neeraj Kumar Shukla: Electrical Engineering Department, King Khalid University, Abha 61411, Saudi Arabia
Ehsan Nazemi: Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City 700000, Vietnam
Ramy Mohammed Aiesh Qaisi: Department of Electrical and Electronics Engineering, College of Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia

Sustainability, 2023, vol. 15, issue 17, 1-12

Abstract: The goal of the present investigation is to assess the applicability of the Gig Economy Framework (GEF) to the nursing workforce in Saudi Arabia. In order to learn more about the viability of the gig economy paradigm for the nursing profession, this study employed a cross-sectional survey technique. The survey asked questions specific to the nursing profession in Saudi Arabia and the GEF, while also taking into account other relevant variables. This nurse survey was sent to 102 Saudi Arabian hospitals’ HR departments. After removing invalid and missing data, 379 responses remained. The gig economy’s impact on everyday living and professional growth differed significantly between groups. After processing the data, we inputted them into a multi-layer perceptron (MLP) neural network to find relationships between responses to surveys and compatibility with the GEF. There were 20 inputs to this neural network and four possible outputs. The results of the network are the answers to questions about how the gig economy might affect four areas—life, financial management, and personal and professional comfort and development. Outputs 1–4 were predicted with 96.5%, 96.5%, 99.2%, and 99.2% accuracy, respectively. The primary issues with the nursing workforce in Saudi Arabia may be addressed with the use of gig economy elements. As a result, it is crucial to provide a trustworthy, intelligent strategy for foreseeing the gig economy’s framework’s alignment.

Keywords: gig economy framework; nursing workforce; Saudi Arabia; MLP neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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