## 4 Factors Influencing Cloud Adoption for HPC  
### Methodology  
A questionnaire was designed to test the proposed framework presented above. The first stage of questionnaire design involved sourcing validated items to represent all of the variables in the framework. All items were sourced from the technology adoption literature and adapted to fit the context of HPC. Perceived innovativeness (four items), HPC competence (six items), IT/IS competence (five items), complexity (five items), and adequate resources (four items) were adapted from [9]. The six items used to measure indirect benefits were adapted from [9; 43]. Top management support (three items) and compatibility (four items) were adapted from [9; 37]. The four items to measure reliability and security were adapted from [45], while cost reduction items were adapted from [9; 45]. Each item was measured on a five-point Likert scale. The dependent variable in this study was one self-developed item which asked respondents if their organization had implemented cloud computing for HPC workloads. The second step involved pilot testing. This study was part of a broader international project, CloudLightning [46]. The questionnaire was pilot tested among international academics in the consortium from technical and business disciplines to explore comprehension. Following a number of minor wording changes, the questionnaire was pilot tested among industry members of the project consortium. The third step involved recruiting respondents. Given that HPC is a small knowledge-intensive market, a database was developed to recruit HPC decision makers, which included C-suite employees across various industries from oil and gas to genomics, as well principal investigators at universities. Decision makers were identified through an online search. The survey was distributed to 619 HPC decision-makers worldwide using publicly available email addresses. A total of 121 participants completed the survey. This response rate of 19.55% was deemed adequate given the relatively small number of organizations in the HPC market. Among the sample, 53.30% are based in the European Union, 36.10% in North America and the remainder worldwide. Respondents' organizational contexts include academic (58.20%), commercial (27.90%), and government (9.80%). Among these respondents, 45.1% indicate that they have adopted cloud computing for HPC in their organization, while 54.9% indicate they have not yet adopted. Respondents reported a range of relevant job titles including CEO, CTO, Professor, Researcher, Scientist, Director, Head of Research and Development to name but a few. This distribution allows us to compare the factors predicting adoption for adopters and non-adopters. As the response rate was slightly lower than the recommended threshold (36+/\(-\)13) [47], non-response bias was tested in order to check the representativeness of the responses in this study. Following [PERSON] et al. [48], we tested the non-response bias by comparing the organizational variables from the early respondents with the late respondents. The number of HPC users, the weekly HPC usage, and the respondent's familiarity with HPC were used as benchmarking organizational variables. The \(t\)-test results showed that there was no significant difference between early and late respondents across any of these variables. Therefore, the sample was deemed representative. Exploratory factor analysis was carried out in SPSS 23 to test the reliability and validity of the measures for all key constructs in the new context of HPC. Factor analysis with principal-component factoring method and VARIMAX rotation was used to test discriminant and convergent validity. The Kaiser-Meyer-Olkin (KMO) values of the three dimensions were above the threshold of 0.70, indicating that these items were suitable for conducting factor analysis. Three factors were generated under the human dimension; personal innovativeness, HPC technical competence, and IT/IS technical competence.