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The Impact of Cloud Computing Adoption on Firm Performance Among SMEs in Palestine: A Machine Learning Approach

Author

Listed:
  • Kawther Mousa

    (University of Science and Technology, Beijing, China)

  • Zenglian Zhang

    (University of Science and Technology, Beijing, China)

  • Eli Sumarliah

    (Al-Ihya Islamic University, Indonesia)

  • Ihab K. A. Hamdan

    (University of Science and Technology, Beijing, China)

Abstract

This study seeks to identify the driving factors of cloud computing adoption (CCA) among SMEs, and examines the extent to which CCA shape SME performance. Data was gathered from 212 Halal SMEs in Palestine. This study uses a two-phase investigative method to test the research model through integrating structural equation modeling (SEM) and machine learning. SEM findings show that perceived benefit, facilitating states, server location, perceived cost, upper management support, perceived quality, and cloud providers' support significantly affect CCA. Besides, the article verifies that CCA positively shapes SMEs' performance. Machine learning findings unravel perceived benefit as the strongest determinant of CCA. This study is an initial attempt to develop a conceptual framework that hypothesizes the links between technological-organizational-environmental (TOE) factors and SMEs' intent for CCA in Palestine and provides empirical evidence regarding these links.

Suggested Citation

  • Kawther Mousa & Zenglian Zhang & Eli Sumarliah & Ihab K. A. Hamdan, 2024. "The Impact of Cloud Computing Adoption on Firm Performance Among SMEs in Palestine: A Machine Learning Approach," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 20(1), pages 1-24, January.
    • Handle: RePEc:igg:jiit00:v:20:y:2024:i:1:p:1-24
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    References listed on IDEAS

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    1. Elie Sumarliah & Tieke Li & Bailin Wang & Indriya Indriya, 2021. "An Examination of Halal Fashion Supply Chain Management Risks Based on the Fuzzy Best-Worst Approach," Information Resources Management Journal (IRMJ), IGI Global, vol. 34(4), pages 69-92, October.
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