## 7 Conclusions  
The realization of the benefits of HPC has been somewhat limited due partly to the large start-up costs required. This paper leverages HOT-fit and DOI (i) to identify the determinants of cloud computing adoption for HPC, and (ii) elicit expert opinions on the issues facing HPC in the cloud in the near and long-term future. The paper makes two important contributions. First, the study incorporates DOI with HOT-fit theories to provide a holistic view of determinants of cloud computing adoption for HPC. As indicated earlier, there is a dearth of research on cloud computing adoption in the HPC area. Thus, this study provides important insights and answers calls for clarification on the drivers and inhibitors of cloud computing adoption in differing contexts [31]. Second, this paper contributes both to literature and practice by providing insights for cloud service providers with findings suggesting that cloud service providers should emphasize the indirect benefits of adopting cloud computing for HPC in their communications to potential customers and specifically address issues and concerns relating to data privacy and security. However, as is the case with all studies, this paper is not without its limitations. These include the use of single-informat, the small sample size, and potential over-representation in the sample from the academic community. The results would be more robust with data from multiple respondents and from a larger sample. When considering the diversity of the sample, a comparison of attitudes towards different cloud deployment models (public, private, hybrid, federated, and community) and service models (IAAS, PAAS, SAAS) may prove insightful. Furthermore, future research could explore the role of these factors over time to see which factors are more influential in the short and long term. It is our hope that this research can inform future research which seeks to explore the adoption and assimilation of new technologies in complex contexts.  
Conceptualization, T.L., G.F., and A.G.; methodology, T.L. and G.F.; data collection, T.L. and A.G.; analysis, T.L., A.G., G.F., and P.R.; resources, T.L. and P.R.; discussion, T.L., A.G., G.F., and P.R.; writing--original draft preparation, T.L., A.G., G.F., and P.R.; writing--review and editing, T.L., A.G., G.F., and P.R. All authors have read and agreed to the published version of the manuscript.  
This work is partially funded by the European Union's Horizon 2020 Research and Innovation Programme through the CloudLightning project ([[http://www.cloudlightning.eu](http://www.cloudlightning.eu)]([http://www.cloudlightning.eu](http://www.cloudlightning.eu))) under Grant Agreement Number 643946,the Irish Institute of Digital Business, and by the Irish Centre for Cloud Computing and Commerce (IC4), an Enterprise Ireland/IDA technology centre.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ BDA & Big Data Analytics \\ CEO & Chief Executive Officer \\ CTO & Chief Technology Officer \\ DOI & Diffusion of Innovation \\ HOT-fit & Human, Organizational, and Technological fit \\ HPC & High Performance Computing \\ IAAS & Infrastructure As A Service \\ IC4 & Irish Center for Cloud Computing and Commerce \\ ICT & Information and Communications Technology \\ IDC & International Data Corporation \\ IS & Information Systems \\ IT & Information Technology \\ PAAS & Platform As A Service \\ SAAS & Software As A Service \\ TAM & Technology Acceptance Model \\ TOE & Technological, Organizational, and Environmental \\ TRA & Theory of Reasoned Action \\ UTAUT & Unified Theory of Technology Acceptance and Usage \\ \end{tabular}