Sri Lankan SMEs' Performance Through Cloud Computing Adoption: An SEM-ANN Analysis

被引:0
|
作者
Nawaz, Samsudeen Sabraz [1 ]
Thelijjagoda, Samantha [2 ]
机构
[1] South Eastern Univ Sri Lanka, Dept MIT, Oluvil 32360, Sri Lanka
[2] Sri Lanka Inst Informat Technol, Dept Comp Syst Engn, Malabe 10115, Sri Lanka
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Cloud computing; neural network; PLS-SEM; SMEs; performance; Sri Lanka; TECHNOLOGY ACCEPTANCE MODEL; USER ACCEPTANCE; INFORMATION-TECHNOLOGY; UNIFIED THEORY; DETERMINANTS; EXTENSION; BUSINESS; SERVICES; TOE;
D O I
10.1109/ACCESS.2025.3564339
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study identifies the determinants of cloud computing adoption and its effect on the performance of Sri Lankan small and medium-sized enterprises (SMEs). The Technology-Organization-Environment (TOE) framework, Technology Acceptance Model (TAM), and individual context were used to derive the study variables. This quantitative cross-sectional study adopted items from previous validated studies. Google Form was employed to collect data, and 418 responses were received from Sri Lankan SMEs. Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4 and Artificial Neural Network (ANN) analysis via IBM SPSS 29 were used for data analysis. Based on the results, all hypotheses are confirmed except for one, and SME performance is significantly affected by cloud computing adoption. This study adds to the existing empirical evidence on cloud computing adoption by introducing an all-inclusive model that integrates the TOE, TAM, and individual factors. This demonstrates the effectiveness of the PLS-SEM/ANN hybrid methodology in analysing the determinants of cloud computing adoption. The significance of top management as a factor is highlighted by providing training and education to employees. Managers can benefit from this result by improving cloud computing adoption among SMEs in Sri Lanka. This is the first study of its kind in Sri Lanka, integrating the TOE, TAM, and individual variables and using a hybrid methodology combining PLS-SEM and ANN.
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页码:75444 / 75461
页数:18
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