## 1 Introduction
For this, the sequential multi-method research design, suggested by [PERSON] and [PERSON] [45], was implemented as it is suitable for enabling a deeper understanding of the subject under investigation. In this study, PLS-SEM is applied for corroborating the validity of the causal relationships through the assessment of the goodness of the model's fit. Following this, PLS-SEM analysis of supported relationships along with PLS-SEM analysis of significant variables were utilized as the neural network structure inputs for estimating how CC is adopted in HEIs. Merging these two approached provides a significant benefit for utilizing a new method to evaluate CC adoption in which one method benefits help in balancing out the other method drawbacks [45]. Generally, we contribute to research in different ways. First, this research contributes to the body of knowledge within information systems (IS) field surrounding technology adoption. This study provided empirical literature within IS, especially CC, and it provided an extensive model that integrates the TOE framework and DOI model. Second, this study provided an assessment for CC adoption in HEIs, and more revitalization of the CC and intent of decision-makers to utilize CC in HEIs. Third, the DOI model and TOE framework incorporation improved the ability to explain the proposed model with 81% of the dependent variable's variance, which shows that the model's ability for prediction is powerful and remarkable. Fourth, this research used a hybrid approach for the integration of PLS-SEM and ANN to validate the proposed model and to give priority to the factors that impact CC adoption, through the identification of the relative importance of every factor. Conventional statistical approaches are valid and necessary; thereby, offering a powerful foundation in previous IS adoption studies. The suggestion of this research is the need to reinterpret past works on IS adoption through the combination of linear and non-linear approaches in order to provide outstanding strength to technology adoption. Fifth, since HEI is a promising market for cloud service providers, this study is remarkably impactful for cloud providers and technology practitioners as it will help in the recognition of the factors that influence the adoption of CC. Likewise, cloud providers need to provide a clear instruction or navigation system in guiding users in HEIs to operate the services smoothly,thereby increasing the assurance that cloud technology is used. Finally, the research results will help decision-makers with the assessment of cloud technology, organization, and environments during the decision of the adoption of CC. Furthermore, decision-makers may use this proposed framework for the investigation of other IT/IS adoption procedures. The rest of this study is arranged as follows: Section 2 reviews previous studies on CC adoption. Section 3 presents the hypotheses of the study and discusses the model development. Section 4 highlights the research methodology and Section 5 explains the analytical approaches, including PLS-SEM and neural network. The discussion of the study is presented in Section 6, and Implications are presented in Section 7. Conclusion, limitations, and future research directions are discussed at the end of the study.