Empirical analysis of factors in user control model for cloud data migration

This study aims to develop, with empirical validation, a model of the factors influencing user control during on-premises-to-cloud data migration in Software-as-a-Service (SaaS) environments. The model is grounded in the technology-organisation-environment framework and control theory. The research examines how security, cost, legal compliance, and personnel knowledge affect user control outcomes through standards and performance as control metrics. A quantitative research approach was employed, using survey data collected from 55 cloud computing professionals selected through purposive sampling. The data were analysed using descriptive statistics in SPSS, and their structural relationships were evaluated through Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS. The results indicate that cost significantly influences both standards and performance, while security significantly affects standards but not performance. Legal compliance shows a significant relationship with performance but not standards, whereas personnel knowledge does not exhibit a significant effect on either standards or performance. Additionally, standards were found to have a significant impact on performance, confirming their role as a critical control mechanism. The measurement model demonstrated strong reliability and validity, with Cronbach's alpha values ranging from 0.753 to 0.955 and factor loadings for all indicators exceeding 0.7, confirming validity. The study contributes to the cloud computing domain by providing a proposed model for assessing user control during migration execution, extending beyond traditional cloud adoption frameworks. Practically, the findings offer guidance to organisations in prioritising cost management, legal compliance, and structured standards in managing cloud data migration processes. The model can be applied as a diagnostic and decision-support tool for improving transparency, accountability, and performance in cloud data migration projects.

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