# Continuance Use of Cloud Computing in Higher Education Institutions: A Conceptual Model
## 2 Background and Related Work
However, certain studies have concentrated their empirical understanding of technology adoption into a single phase (e.g., adoption or pre-/post-adoption) [14]. This perspective is consistent with the wide-ranging stages of technology adoption that have been investigated in previous studies [93; 94; 95] (see Figure 1). Certain studies have reported that, in terms of the methods that are available in technology adoption research, many are limited because they do not differentiate between changes in the significance of factors in the different phases of adoption [96]. Therefore, opportunities, as well as suitable research settings for exploring continuance as the last phase of adoption, have been limited. In Table, previous studies have adapted various IS theories and empirically analyzed CC in different contexts from an individual or organizational viewpoint in the pre-adoption or post-adoption (i.e. continuance use) phase. However, no empirical study was found measuring the continuance use of CC in HEIs. Therefore, the main contribution of this study is to develop an instrument and conceptualize a model to measure the continuance use of CC in the context of HEIs. Beside this context-specific contribution, our study also reduces the gap of related to organizational IS continuance research. A range of theories have been used to study adoption (e.g., UTAT or TAM), continuance (e.g., ISC), and discontinuance at the individual level. Contrastingly, scholars have used theories such as TOE, DOI, and social contagion to examine adoption from an organizational perspective. Additionally, the ISS and ISD models have been leveraged to investigate continuance and discontinuance respectively, at the organizational level. Table 2 provides an overview of the various theoretical approaches that have been used in the literature to examine the lifecycle of an IS. Dissimilar to studies that have focused on the individual level, those that have addressed continuance and discontinuance at the organizational level are few and far between [9; 14; 16; 97]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Level of Analysis**} & \multicolumn{2}{c}{**Adoption Phase**} & \multicolumn{3}{c}{**Theoretical Perspective**} & \multicolumn{3}{c}{**Type**} \\ \hline  & **IND** & **ORG** & **PRE** & **POST** & **ISC** & **ISS** & **ISD** & **TOE** & **OTH** & **EMP** & **THEO** \\ \hline
[14] & & \(\surd\) & & \(\surd\) & & \(\surd\) & \(\surd\) & & & \(\surd\) & \\
[72] & & \(\surd\) & & \(\surd\) & \(\surd\) & & & \(\surd\) & & \  
After initially adopting an IS, a user decides to either continue or discontinue the IS adoption. Contrastingly, this is unlikely to retire or replace their on-premise CC services [106]. Nevertheless, since almost all CC services involve a subscription model, and since this study's focal point is the issue of continuance, the aim of this study is not to examine factors that influence the use of CC services in HEIs. Instead, the main area of focus in the current study is the set of constructs that contribute to IS continuance. An implication of this is that it is possible to evaluate success and system performance, which is dissimilar to the pre-adoption phase in which only expectations can be used to estimate usage. This also enables the integration of post-adoption variables as predictors of IS adoption continuance, thereby exerting far-reaching impacts on model development.