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.
Publisher
University of Nairobi