Analysis of preparedness towards adopting cloud computing technology: A case of Kenya medical Research Institute/Centres for Disease Control at the Kisia Station, Kisumu County, Kenya

Abstract

Cloud computing is a new term in the computing world and it signals the advent of a new computing paradigm. Adopting cloud computing as an organization wide strategy requires careful planning, creation of roadmap and phased execution. KEMRI/CDC is currently struggling with massive volumes of paperwork stored in huge filing safes and occupying rooms and a lot of space. The purpose of this study was to do an analysis of preparedness towards adopting cloud computing technology at the Kenya Medical Research Institute/Centres for Disease Control at the Kisian Station, Kisumu County, Kenya. The objectives of the study were: to establish the extent to which infrastructure as a component of preparedness would influence adoption of cloud computing technology in KEMRI/CDC, to assess how staff knowledge as a component of preparedness would influence the adoption of cloud computing in KEMRI/CDC, to examine the extent to which staff skills as a component of preparedness would influence adoption of cloud computing technology in KEMRI/CDC and to examine the extent to which staff attitude as a component of preparedness would influence adoption of cloud computing technology in KEMRI/CDC. The findings of this study would help organizations on the verge of adopting cloud computing, to better understand the underlying factors that need to be considered for it to be successful. This study was informed by the technology Acceptance Model. A descriptive survey study design was employed in implementing the study. The target population was all KEMRI/CDC staff stationed at CGHR campus in Kisian. A sample size of 305 was used from a target population of 1300 staff. 305 questionnaires were administered and 300 (98.4%) returned for analysis. Proportional sampling method was adopted during data collection in the different department. Structured questionnaire were used to collect the required data. Pilot testing of the data collection tool was done before the actual data collection process to validate the tool. Data collected were coded, scanned using Teleforms software and extracted into Ms Access to create the database. Data was then cleaned and analyzed using SAS and the results presented in tables. Descriptive and inferential statistical analysis was used to come up with associations between the variables (Chi Square), Odds Ratios and frequency distribution regarding preparedness towards adoption of cloud computing. The study showed that: majority of projects stored their data in network drives 237(79%), a good indication that KEMRI/CDC has servers with capabilities of storing large amounts of data. It established that there was an association between field of specialization and knowledge of cloud computing (chi=87.13, p<0.001) and that those who knew cloud computing were nearly 14 times more likely to be prepared to adopt cloud computing (OR=14.52, 95% CI [7.91, 26.64], p<0.001). The study found that there was a significant association between age and the thought that KEMRI/CDC was prepared to adopt cloud computing (chi=13.34, p=0.04). This study recommends upgrading the internet bandwidth from the service provider in order to efficiently handle cloud computing and that basic trainings on ICT processes to staff and regular awareness training on new technologies to be introduced in KEMRI/CDC to be able to empower all staff in all cadres and ages to appreciate the advent of new technologies.