Cloud adoption decision model

This section presents the first model in the Framework, the strategic level cloud adoption decision model, which was developed based on the primary and secondary research. The cloud adoption model presented here integrates an AHP approach with CBR and uses the five factors described further below in the section headed Phase One. The decision model is shown in Figure 6-3.

The AHP approach will support the decision makers in weighting criteria to evaluate and select the best IT services delivery model. However, one criticism made in much of the literature on AHP is that judgments based on the expertise of the decision maker and the knowledge available is limited, particularly when dealing with uncertainty (Dağdeviren et al., 2009). For this reason, as discussed in 6.6, this study proposes that previous cases should be used to support help decision makers to understand and weight criteria and to validate their results

In addition, the CBR approach is able to handle incomplete and imprecise data (Işıklar et al., 2007) because gaps in the data for any given case can be filled in by reference to similar cases. Combining the AHP approach with CBR provides users with a knowledge base to support decision making. The decision as to whether to migrate to the cloud is a strategic decision which may not occur more than once in an enterprise’s lifecycle. This means that users may lack the necessary underpinning knowledge to develop appropriate weightings; this is one of the limitations of the AHP approach. Using CBR to provide a knowledge base gives users access to information about decisions taken in similar and different contexts and allows users access to a wider range of experiences.

 Phase 1: case based reasoning component

This phase developed the case base to store previous cases. Each case is indexed with five attributes, each of which has a pre-defined value. The attributes used are enterprise size, sector type, enterprise status and IT maturity rate and level of technology diffusion. The attributes chosen were identified from the literature and validated during fieldwork which confirmed these factors as relevant to cloud adoption decision making. These attributes used to retrieve similar cases.

Enterprise size: this was identified as a key determinant of cloud adoption by previous

studies (Alshamaila et al., 2013; Avram, 2014), but our primary research found no statistically significant relationship between enterprise size and cloud adoption, although this may reflect factors specific to Saudi Arabia. We did find a relationship between enterprise size and the selection of cloud deployment model. We included enterprise size partly because of the findings from the literature review and also because this would help decision makers match cases to their own organisations.

Industry sector: cloud adoption rates have been shown to vary between sectors (Low et

al., 2011), which was supported by our primary research.

Enterprise status: the literature shows that start-up enterprises find it easier to adopt

cloud computing than established enterprises (Alshamaila et al., 2013; Gupta et al., 2013); this was supported by our primary research.

Enterprise readiness: IT enterprise readiness has been shown to affect the adoption of a

cloud computing environment (Khajeh-Hosseini et al., 2012), and was identified in our primary research as an important factor affecting cloud adoption decision making.

The complexity of existing system: as discussed in section 4.4.2.1, the complexity of existing systems and the implications for migrating these systems to a cloud environment is one of the main factors inhibiting a move to cloud computing.

Technology diffusion: technology diffusion in general and specifically for cloud computing varies between developing and developed countries (Molla & Licker, 2005; Avram, 2014); this influences the cloud adoption decision. Technology diffusion may also be an issue within economies as well as between economies. Our primary research shows that the technology diffusion varies between the major cities and rural cities in Saudi Arabia. Including technology diffusion as one of the indexed attributes contributes towards the generalisability of the framework.

Phase 2: AHP model

In this phase the AHP model was developed. The AHP model (Figure 6-3) uses pairwise comparison to weight the criteria, sub-criteria and alternatives. Level 1 in the model presents the problem-solving goal; Level 2 presents the criteria; and Level 3 presents the alternatives for the problem solution, which for this research have been identified as providing an in-house service, adopting a traditional outsourcing solution or migrating to a cloud computing solution.

The criteria in the second level of the AHP model are based on five factors derived from the literature and the primary research: technical, organisational, security, economic and regulatory. Each criterion has a set of sub-criteria, which provide more detailed factors for decision making and the sub-criteria were also identified from the literature review and the primary research.

 Phase 3: integration

This phase combines the CBR element with the AHP element. Using the AHP model described in Phase 2, pairwise comparisons are performed for sub-criteria with respect to the main criteria (parent in the hierarchy), while pairwise comparisons are performed for criteria with respect of the goal. AHP provides two methods for weighting alternatives, absolute and relative measurement.

Relative measurement performs the pairwise comparisons between the alternatives with respect to each criterion. The use of absolute measurement allows alternatives to be ranked with a standard scale (Saaty, 1994). The absolute approached reduces the decision time and is easier to use by decision makers, supporting the customisation of the model. Therefore absolute measurement was used in this research.

The first step in the model is comparing the new case with stored cases and finding similar cases, as shown in Error! Reference source not found.. When the similar case is found, the AHP will be run to weight the criteria. One of the features of using CBR is to validate the decision with similar cases. Therefore, the AHP result will be compared with the result of the similar case, and if the decision makers are satisfied with the result, the new case will be added to the case base; otherwise the AHP process is repeated.

If the new case is not similar to the stored cases, the decision maker can choose to run the AHP and add the case as a new case to case base. In addition, the CBR will store the details of each case including decision, selection of cloud deployment and services models and the issues that associated with cloud adoption and how they solve these issues and make them available to use with other cloud adoption projects. The process is illustrated in Error! Reference source not found..

Figure 6-4: Flow chart of the process of cloud adoption decision model