Cloud Computing Adoption

Among the participants, only 36.8% reported that their organisations have adopted some Cloud Computing services (as a contrast, 84% of American healthcare organisations have adopted some Cloud Computing services (HIMSS, 2016)). While 28.90% of the healthcare organisations in Saudi Arabia are planning to adopt Cloud Computing, 11.90% will not adopt Cloud Computing solutions; 22.40% of the respondents do not know about their organisation’s intention towards or use of Cloud Computing. Figure 5.6 presents the percentage responses to this question.

Figure 5.6 Plan for Cloud Computing Adoption among Saudi Healthcare Organisations

36.80% 28.90% 22.40% 11.90% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% We have already adopted some Cloud Computing services.

We intend to adopt Cloud Computing services in the next 2

years.

I do not know. We do not intend to adopt any Cloud Computing services for the foreseeable future.

The participants considered some services and systems of Saudi healthcare organisations to be moving to Cloud Computing. The business processes that respondents most recommended should be moved to the cloud were Electronic Health Record (EHR) (62% of participants), followed by human resources processes (50.7%) and Pharmacy Management System (46.3%). Other HIS systems that respondents suggested should be Cloud-based systems were Laboratory Information System (39.8%), Radiology Information System (36.3%), Computerised Physician Order Entry System (CPOE) (36.3%), and Picture Archiving and Communication System (PACS) (30.3%). The participants also considered other administrative systems could to move the cloud, such as Payroll (41.8%), Accounting and Finance (36.8%) and Procurements (28.9%); 32.8% of the respondents also indicated the possibilities of using Cloud Computing services when developing healthcare applications and services. Other applications including email were also mentioned by 4.5% of the respondents. Figure 5.7 shows the participants’ views of the possible IT services and systems that healthcare organisations could move to the cloud.

Figure 5.7 Possible IT services and systems to move to Cloud Computing 41.8% 50.7% 28.9% 36.8% 32.8% 66.2% 36.3% 39.8% 46.3% 30.3% 36.3% 4.5% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% PAYROLL HUMAN RESOURCES PROCUREMENTS ACCOUNTING AND FINANCE APPLICATION DEVELOPMENT ON THE CLOUD ELECTRONIC HEALTH RECORD (EHR) RADIOLOGY INFORMATION SYSTEM LABORATORY INFORMATION SYSTEM PHARMACY MANAGEMENT SYSTEM PICTURE ARCHIVING AND COMMUNICATION SYSTEM …

COMPUTERISED PHYSICIAN ORDER ENTRY SYSTEM … OTHER

Overall Findings

The goal of this research is to identify factors that influence the adoption of Cloud Computing in Saudi Arabia’s healthcare organisations. The author has accomplished this by analysing the data collected from the survey. Among the five dimensions, the most important one is Business (mean= 3.90), then Technology (mean= 3.62), followed by Organisation (mean= 3.49), Environment (mean= 3.47) and finally Human (mean= 3.36). Table 5.6 shows the analysis of factors affecting Cloud Computing adoption in Saudi healthcare organisations.

Table 5.6 Analysis of factors affecting Cloud Computing adoption in Saudi healthcare organisations

The results show that the five most critical factors affecting the decision of Cloud Computing adoption in Saudi healthcare organisations are: soft financial analysis, relative advantage, hard financial analysis, attitude towards change and business ecosystem partners’ pressure. Figure 5.8 also represents a graphical view of the overall results.

Context Variables Mean Standard Division

S.D. Rank

Business 3.90/1

Soft financial analysis 3.99 0.907 1

Hard financial analysis 3.81 0.918 3

Technology 3.62/2 Relative advantage 3.93 0.958 2 Compatibility 3.47 1.016 7 Technology readiness 3.46 1.154 8 Organisation 3.49/3

Attitude towards Change 3.65 1.057 4

Top management support 3.32 1.284 12

Environment 3.43/4

Business ecosystem partners’ pressure 3.52 0.952 5 External expertise 3.49 0.969 6 Regulation compliance 3.29 1.139 13 Human 3.36/5 Internal expertise 3.40 1.064 9 Decision-makers’ innovativeness 3.36 1.294 10

Figure 5.8 Ranking the overall contexts affecting Cloud Computing Adoption in Saudi Healthcare Organisations

Comparison between Different Groups

The questionnaire results showed that Saudi healthcare organisations are divided into three categories. The first category is the organisations that have already adopted some Cloud Computing solutions (36.8%). The second category is the organisations that are planning to adopt Cloud Computing (28.90). The third category is the organisations that do not intend to adopt any Cloud Computing services for the foreseeable future (11.90%). The researcher used an Analysis of Variance (ANOVA) test to determine any useful information that explained the difference between the three categories. ANOVA is a statistical test that assesses the means between the groups in which the authors are interested and examines whether any of those means are significantly different from each other (Saunders et al., 2009). Table 5.7 shows the means for the different categories based on organisations’ adoption status for all constructors. Table 5.8 presents the mean of the contexts for the three categories of the Saudi healthcare organisations based on their Cloud Computing adoption status.

Table 5.7 Factors across different groups

** _{p< 0.05.}

Table 5.8 Contexts across different groups

** _{p< 0.05.} Context Constructor Adopter Planning to adopt Rejecter P value Mean S.D. Mean S.D. Mean S.D.

Business SA 4.02 0.732 4.07 0.809 3.79 0.850 0.923 HA 3.83 0.681 3.89 0.732 3.69 0.673 0.930 Technology RA 3.91 0.749 4.16 0.901 3.69 0.804 0.017** CO 3.73 0.700 3.49 0.937 3.18 0.997 0.002** TR 3.75 0.764 3.44 0.982 3.14 0.529 0.103 Organisation CR 3.95 0.918 3.64 1.029 2.98 0.972 0.011** TS 3.83 1.120 3.09 1.218 2.48 1.078 0.007** Environment TP 3.64 0.627 3.54 0.702 3.08 0.800 0.130 EE 3.53 0.927 3.49 0.814 3.13 0.912 0.929 RC 3.44 0.903 3.33 0.899 2.83 0.927 0.213 Human IE 3.64 0.909 3.40 0.912 3.23 0.821 0.003** CI 3.78 1.107 3.21 1.246 2.92 1.283 0.038** PE 3.66 0.836 3.14 0.821 3.02 1.118 0.015** Context

Adopter Planning to adopt Rejecter

P value Mean S.D. Mean S.D. Mean S.D.

Business 3.93 0.645 3.98 0.684 3.74 0.654 0.822 Technology 3.80 0.523 3.70 0.772 3.34 0.491 0.025** Organisation 3.89 0.905 3.36 1.021 2.73 0.853 0.023** Environment 3.56 0.626 3.49 0.630 3.10 0.660 0.807 Human 3.69 0.789 3.25 0.814 3.06 0.953 0.089

The results show that there are significance differences in seven factors, which are: RA: Relative advantage (p=0.017); CO: Compatibility (p=0.002); CR: Attitude towards Change (p=0.011); TS: Top management support (p=0.007); Decision-makers’ innovativeness (p=0.038); IE: Internal expertise (p=0.003); and PE: Prior technology experience (p=0.015). Figure 5.9 presents a graphical view of the factors affecting Cloud Computing adoption in Saudi healthcare organisations based on the three groups (i.e. Adopter, Planning to adopt and Rejecter groups). Among the five contexts, there was a significant difference in Technology (p=0.021) and organisation (p=0.023), as shown in Figure 5.8 (see Appendix B for further details of the ANOVA test).

• Adopter Group

This category represents Saudi healthcare organisations that have adopted some Cloud Computing services. For this category, the most important perspective is Business (mean= 3.93), then Organisation (mean= 3.89), followed by Technology (mean= 3.80), Human (mean= 3.69) and finally Environment (mean= 3.56); see Table 5.8. As shown in Figure 5.9, Soft financial analysis (mean = 4.02), Attitude towards change (mean= 3.95), Relative advantage (mean= 3.91), Top management support and hard financial analysis with the same mean value (3.83), and Decision-makers’ innovativeness are the most important factors for the adopter group. The results also show that the less important factors for the adopter group are External expertise (mean= 3.53) and Regulation compliance (mean= 3.44).

Figure 5.9 Factors affecting Cloud Computing adoption in Saudi healthcare organisations among different groups

0 1 2 3 4 5

Soft financial analysis

Attitude towards change Relative advantage

Top management support Hard financial analysis Decision-makers’

innovativeness Technology readiness Compatibility

Prior technology experience Internal expertise Business ecosystem partners's

pressure

External expertise Regulation compliance

• Planning to Adopt Group

This category comprises Saudi healthcare organisations that are planning to adopt Cloud Computing in the future. For this group, the business dimension (mean=3.98) and technology dimension (mean=3.70) came first and second respectively, followed by the environment dimension (mean= 3.49) and Organisation (mean= 3.36), and finally human dimension (mean= 3.25); see Table 5.8. Regarding the factors, Figure 5.9 shows that relative advantage (mean= 4.16) followed by soft financial analysis (mean= 4.07), hard financial analysis (mean= 3.89), Attitude towards change (mean= 3.64) and Business Ecosystem Partners Pressure (3.54) are the most important factors among Saudi healthcare organisations that are intending to adopt

Cloud Computing. For this group, Prior technology experience (mean= 3.14) and Top

management support (mean= 3.09) are found to have less impact among other factors.

• Rejecter Group

This group contains Saudi healthcare organisations that have decided not to adopt Cloud Computing. For this group, business (mean= 3.74) and technology (mean= 3.34) contexts were considered as the first and second dimensions respectively. The environment context (mean= 3.10) was ranked as the third important dimension for the rejecter group followed by the human context (mean= 3.06) and finally the organisation context (mean= 2.73); see Table 5.8. For this group, Soft Financial Analysis (mean= 3.79), Hard Financial Analysis (mean= 3.69) and Relative advantage (mean= 3.69) were considered as the factors that have a high impact when making the decision whether or not to adopt Cloud Computing. However, Decision-makers’ innovativeness (mean= 2.92), Regulation Compliance (mean=2.83) and top management support (mean= 2.48) were ranked as factors with lower mean values, as shown in Figure 5.9.