## 2 Literature Review
### Technology Adoption Research in Cloud Computing  
As our review failed to identify any studies which have previously investigated the factors influencing decisions to adopt cloud computing for HPC applications, it was important to review what theories have been utilized in the broader cloud computing context. A recent review of the literature on cloud computing adoption found that many studies fail to utilize the prevailing technology adoption theories [31]. Furthermore, those studies that do adopt a guiding framework lack a unified approach with studies adapting and combining elements of DOI, TOE, and HOT-fit theories. Among these theories, the influential factors in cloud computing can be categorized into four groups: (i) human factors such as personal innovativeness, perceived technical competence [9; 30]; (ii) organizational factors including adequacy of resources, top management support, perceived indirect benefits, and relative advantage [7; 9; 32]; (iii) technological factors such as perceptions of the innovation's complexity, compatibility, and reliability and security [3; 5; 33]; and (iv) environmental factors such as competitive pressure, government policy, and partner support [2; 7; 9; 29]. The existing literature leveraging these theories to understand cloud computing adoption is outlined in Table 2. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Author(s)** & **Dimensions/Factors Included** \\ \hline \multicolumn{3}{c}{**Theories based on the individual**} \\ \hline Theory of Reasoned action & & Individuals’ beliefs \\ (TRA) & [PERSON] and [PERSON] (1975) & Attitude toward behavior (ATT) \\  & & Subjective norm (SN) \\  & & Behavioral intention \\ \hline Technology acceptance model & [PERSON] et al. (1989) & Perceived usefulness (PU) \\ (TAM) & & Perceived ease of use (PEOU) \\ \hline Theory of Planned behavior & [PERSON] (1991) & Attitude toward behavior adapted from TRA \\ (TFB) & [PERSON] (1991) & Subjective norm adapted from TRA (SN) \\  & & Perceived behavioral control (PBC) \\ \hline UTAUT & [PERSON] et al., (2003) & Performance Expectancy (PE) \\  & & Effort Expectancy (EE) \\  & & Social Influence (SI) \\  & & Facilitating Conditions (FC) \\  & & Moderators: gender, age, experience, \\  & & Voluntariness \\ \hline \multicolumn{3}{c}{**Theories based on the innovation and wider environment**} \\ \hline Diffusion of Innovations & & Relative advantage \\  & & Ease of use \\  & & Image \\  & & Visibility \\ (DOI) & & Compatibility \\  & & Results demonstrability \\  & & Voluntariness of Use \\ \hline Technology-Organization-Environment & [PERSON] and [PERSON] (1990) & Technology: innovation characteristics, availability \\ (TOE) & & Organization: size, management support, \\  & & organizational resources \\  & & External Environment: firm size, external pressure, \\  & & industry, regulation, financial \\ \hline Human-Organization-Technology Fit & [PERSON] et al. (2008) & Human: user satisfaction \\ (HOT-fit) & & Organization: structure, environment \\  & & Technology: quality driven factors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technology adoption theories. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
As highlighted above, there are a number of combinations of these theories, and conflicting categorization of variables as human, organizational, or technical factors. However, several observations can be made. Firstly, there is no dominant model for exploring technology adoption, but there are many similarities among the theories operationalized. Furthermore, the trend of combining numerous theories is evident in this context. This approach is appropriate due to the complementarity of many of the popular technology adoption theories. For instance, DOI, TOE, and HOT-fit theories complement each other, to provide a comprehensive understanding of the key determinants of technology adoption [3, 7, 9]. Secondly, in terms of the human factors and HOT-fit, [PERSON] et al.