|

EDITED BY

Sonika Tyagi,
Monash University, Australia
REVIEWED BY

Vinaytosh Mishra,
Fore School of Management, India
Patrice Ngangue,
Centre Intégré Universitaire de Santé et de
Services Sociaux de la Capitale-Nationale
(CIUSSSCN), Canada
*CORRESPONDENCE
Humphrey Muki Sabi
humphreysabi@gmail.com
SPECIALTY SECTION

This article was submitted to Health
Informatics, a section of the journal Frontiers in
Digital Health

TYPE Original Research
PUBLISHED 21 November 2022
DOI 10.3389/fdgth.2022.1017231

Adoption of electronic medical
records in developing countries
—A multi-state study of the
Nigerian healthcare system
Christie Divine Akwaowo1,2, Humphrey Muki Sabi3*,
4
5
Nnette Ekpenyong , Chimaobi M. Isiguzo , Nene
6
7
2
Francis Andem , Omosivie Maduka , Emem Dan ,
2,8
2
9
Edidiong Umoh , Victory Ekpin and Faith-Michael Uzoka
1

Community Medicine Department, University of Uyo, Uyo, Nigeria, 2Health Systems Research Hub,
University of Uyo Teaching Hospital, Uyo, Nigeria, 3ICT Department, The ICT University, Yaounde,
Cameroon, 4Community Health Department, University of Calabar, Calabar, Nigeria, 5Department of
Surgery, Federal Medical Centre, Owerri, Nigeria, 6Hopsital’s Management Board, Akwa Ibom State
Ministry of Health, Uyo, Nigeria, 7Department of Preventive and Social Medicine, University of Port
Harcourt Teaching Hospital, Port Harcourt, Nigeria, 8Fisheries and Aquaculture, University of Uyo, Uyo,
Nigeria, 9Department of Mathematics and Computing, Mount Royal University, Calgary, Alberta, Canada

RECEIVED 11 August 2022
ACCEPTED 28 October 2022
PUBLISHED 21 November 2022
CITATION

Akwaowo CD, Sabi HM, Ekpenyong N,
Isiguzo CM, Andem NF, Maduka O, Dan E,
Umoh E, Ekpin V and Uzoka F-M (2022)
Adoption of electronic medical records in
developing countries—A multi-state study of
the Nigerian healthcare system.
Front. Digit. Health 4:1017231.
doi: 10.3389/fdgth.2022.1017231
COPYRIGHT

© 2022 Akwaowo, Sabi, Ekpenyong, Isiguzo,
Andem, Maduka, Dan, Umoh, Ekpin and Uzoka.
This is an open-access article distributed under
the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.

Frontiers in Digital Health

Electronic medical records (EMR) are extensively used in developed countries
to manage patient records and facilitate consultations and follow-up of
treatment. This has resulted in centralised databases where different services
and clinicians can quickly access patient data to support healthcare delivery.
However, adoption and usage of EMR in developing countries is not
common and, in most cases, non-existent. Clinicians are dependent on
patients keeping their own records manually with no centralised database to
manage and control the patient medical history. The key objective of this
study was to investigate the propensity of clinicians and senior management
personnel in healthcare facilities to adopt EMR and evaluate the contextual
factors that impact or impede adoption. Using Davis’s technology adoption
model extended with other factors, this study determined if contextual or
situational factors are associated with barriers that impede adoption of EMRs
in developing countries. Using a cross-sectional quantitative research
approach, a questionnaire was designed to collect data across four states in
the Niger Delta region of Nigeria. Stratiﬁed random sampling was used to
select healthcare facilities that participated in the survey and selection of
respondents from each healthcare facility. Data was collected by trained
research assistants and a total of 1,177 valid responses were received and
analysed using factor analysis and multiple regression analysis. The results
from the analysis show that usefulness, critical success factors, awareness
and relative advantage signiﬁcantly inﬂuence clinicians’ intention to adopt
EMRs. Surprisingly, infrastructure availability was not statistically signiﬁcant.
Meanwhile, risk and data security both negatively inﬂuence adoption,
indicating that user perception of risk and safety of their data decreases their
propensity to adopt EMRs. The results from this study suggests that
usefulness and anticipated success factors in facilitating operations within
healthcare facilities have a great inﬂuence on user adoption of EMRs.
Awareness, training and education of users on the effectiveness of EMRs and
their usefulness will increase adoption. The results will be beneﬁcial in

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10.3389/fdgth.2022.1017231

helping government and healthcare leaders formulate policies that will guide and
support adoption of EMR. Other policy recommendations and suggestions for future
research were also proffered.
KEYWORDS

electronic medical records, digital health, medical informatics, adoption of eHealth, Nigeria,
developing countries

Introduction

Recent reports show that the ﬁt between information
technology and the clinical work system will lead intended
end users to accept or reject it, to use it or misuse it, to
incorporate it into their routine or work around it (8). The
successful implementation of innovative medical technologies,
depends on acceptance by medical staff, such as doctors,
nurses and other clinicians. The decision to accept or reject a
new technological innovation depends on several inﬂuencing
factors. For example, Ross et al. in a review of forty-four
studies from North America and Europe identiﬁed barriers to
implementation of EMR as adaptability, complexity, cost,
external policy and incentives (9). Other contextual factors
include organizational and social environments, cultures, state
of the economy, medical liability and processes by which
innovation is introduced (10, 11). Other barriers identiﬁed are
personal attributes such as knowledge, beliefs, computer skills,
experience, resistance to change and lack of health care
providers’ input in design and implementation of the EMR
(12, 13). An effective EMR is one that serves its intended
purposes after implementation, hence uptake of an EMR is
just as important its performance.
Several studies have analysed the behavioural intentions of
healthcare professionals to accept and use a new health
information technology. In van der Meijden et al. (14), several
factors which inﬂuenced the success of health information
systems were reported, including: System quality attributes like
ease of use, response time, useability; information quality
attributes like completeness, accuracy of data, legibility; Usage
attributes like number of entries, frequency of use, duration of
use; User attributes like user satisfaction, attitude, user
friendliness; Individual impact attributes like changes to work
patterns, documentation frequency, time of day for documenting;
and organizational impact attributes such as impact on patient
care, communication and collaboration, reduction of staff as well
as time saving (14). Chae et al. (15) reported that larger hospital
size and having top management support was signiﬁcantly
associated with adoption and utilization of EMR by health
professionals. This may be because as hospital sizes get bigger,
the adoption rate of EMR increased to improve efﬁciency of
hospital operations and patient management (15).
A mismatch between EMR functionality and the needs of
health workers in practice was identiﬁed as a common
deterrent to its uptake in O’Donnell et al. (13). EMRs were
often viewed as lacking an easily accessible overview of key

The health sector has recently seen numerous technological
innovations designed to streamline the process of healthcare
delivery for the health providers and consumers. Electronic
medical record (EMR) is one of such innovations. This
innovation has helped to reduce medication errors, adverse drug
reactions, and to improve compliance to practice guidelines
among health care professionals (1). For instance, by making
patients’ information available electronically, health care
information systems can help to prevent ordering of duplicate
tests and procedures, thereby reducing expenditures on health
care service (2). Hence, these innovations have been crucial to
improving patient safety and quality of health in today’s world.
The usual entry point for information technology (IT) in
the health sector is the EMR. EMRs are increasingly being
utilized worldwide, especially in developed countries.
Developing countries have been found to lag behind due to
organizational, ﬁnancial and infrastructural factors (3). The
national e-health strategy toolkit which was developed by
WHO and ITU has deﬁned EMR as a computerized medical
record used to capture, store, and share information among
healthcare providers in an organization, supporting the delivery
of health services to patients (4). EMRs have been proven to
improve quality of care and patient outcome by keeping
health care providers better informed, improving the workﬂow
process, improving communication between clinicians,
improving compliance with best practices and reducing
medical errors. Moreover, EMRs also allow patients to access
their own medical records easily and from research stand
point, it allows easy access to data (5–7).
In both public and private sectors, health care providers are
being encouraged to migrate from paper-based health records to
electronic storage of patient information and computer-aided
decision support systems. This is partially due to a growing
recognition that a better information technology infrastructure
is essential to addressing certain health-related national
concerns such as the need to improve the safety and quality
of health care, and rising health care costs (5). However,
adoption of EMR among health workers has been slow in
developing countries like Nigeria. Adoption is of course more
than designing or purchasing a reasonably functional
technology, but also about the acceptance and use of the
system by the health workers.

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10.3389/fdgth.2022.1017231

patient data such as family histories and past medical histories.
Furthermore, it was reported that physicians found it difﬁcult to
record certain types of information such as emerging diagnoses
and/or vague symptoms, especially for potentially sensitive or
stigmatising diagnoses (13). In Nigeria, although positive
attitudes and perception towards EMR was seen in the
Northwest, poor knowledge was prevalent among the health
workers, especially among the doctors (16). This therefore can
be a major barrier to the adoption and utilization of EMR by
health professionals. A review of the adoption of electronic
health records (EHR) in Africa also found that it has not
been widely implemented or adopted in sub-Saharan Africa
(17). In another study carried out in Cameroon to develop
and test an EHR system locally, it was found that there are
many contextual challenges which required modelling of the
system to ﬁt the local medical practice in place and using
terminology that is tailored and appropriate for their
operations (18).
The main objective of this study is to investigate the factors
that inﬂuence the decision of healthcare professionals to adopt
and use an EMR system by answering the following research
question: What are the factors that inﬂuence the decision of
health workers to adopt and use EMR in developing countries?
Different models have been theorized to examine adoption
of technology in an organizational setting. These have been
done with the intent of understanding the attitudes, the use
intentions and the behaviour towards the adoption of new
technologies (11). One of the most notable is the application
of the Technology Acceptance Model (TAM) to the prediction
and explanation of end-user reactions to health IT. TAM is
based on the theory of reasoned action (TRA) and
hypothesizes that Perceived Usefulness and Perceived Ease of
Use, are of primary relevance for technology acceptance. How
useful technology is perceived to be is subjective and depends
on how the individual thinks their jobs will be enhanced by
the technology, while perceived ease of use is the subjective
assessment of the effort required to learn and use the new
technology. The balance between these two determines the
acceptance of the technology by individuals, and their
motivation to use the technology. External factors, such as
support measures, have a positive effect on the perception of
usefulness and on understanding a technology (11, 16). In a
review of TAM usage in health care, it was able to predict
30%–70% of variance of behavioural Intention to Use (16).
Another of such theories is the Theory of Reasoned Action
which was developed by Martin Fishbein and Icek Ajzen in
1975. It is a popular model from social psychology which is
concerned with the determinants of consciously intended
behaviours (19). TRA was proposed after the researchers had
difﬁculty explaining the discrepancy between attitude and
behaviour in previous models. The model was developed to
explain whether individual behaviour such as adoption and
utilization of a new technology is driven by behavioural

Frontiers in Digital Health

intentions, with individual attitude, subjective norms
surrounding the performance of the behaviour and ease of use
of the technology affecting behaviour (19). Theory of planned
behaviour has also been employed in explaining models of
adoption of technology and it goes a step further than the
TRA by incorporating “perceived behavioural control”,
accounting for situations where the target individuals don’t
have total control over the behaviour (20).
The uniﬁed technology acceptance and use of technology
(UTAUT) model was published in 2003 and is based on an
analysis and comparison of up to eight technology acceptance
models, including TAM, TAM2, the Theory of Reasoned
Action and the Diffusion of Innovation Theory (21). This
theory describes 4 major factors that underpin acceptance of
health technology: performance expectancy, effort expectancy,
social inﬂuence, and facilitating conditions. Performance
expectancy is the expectation that the system will do the job it
was set out to do, and bring about improvement. Effort
expectancy is the expectation that the system is easy to use.
Social inﬂuence is the perception of the extent to which
others believe a new technology should be used and lastly,
facilitating conditions are deﬁned as the degree to which a
user believes that an organizational and technical
infrastructure can support the use of the new technology (11,
19, 20).
The technology acceptance model (TAM) (22) has been
extended with additional contextual and situational constructs
for this study. TAM posits that adoption of new technology is
inﬂuenced by user’s perception of the usefulness of the new
technology and ease of use of the new technology. The ability
to explain individual behavioural intentions to use new
technology is based on its usefulness and ease of use, and has
been widely used by many information systems (IS)
researchers to explain adoption of many technologies (23–25).
We have extended the TAM constructs with awareness, risks,
data security, infrastructure and relative advantage to
investigate the propensity of healthcare workers in medical
facilities in Nigeria to adopt and use EMRs. A proposed
conceptual adoption model has been developed based on the
chosen theoretical model (TAM) and the additional factors
used to extend the model for our research as shown in
Figure 1. The model shows the relationship between the
independent variables that can inﬂuence adoption of EMR
and user decision to adopt and use EMR. The model
proposes that relative advantage mediates usefulness of EMR
and hence impacts user propensity to adopt and use EMR.
The model also shows the moderating factors that can
inﬂuence user decision to adopt EMR such as age, gender and
years of experience in the medical ﬁeld.
We proposed the following hypotheses which will be tested
in the data analysis to answer our key research question on the
critical factors that inﬂuence the adoption and usage of EMR in
developing countries. The hypotheses address the relationships

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10.3389/fdgth.2022.1017231

FIGURE 1

EMR adoption model.

between the independent variables that impact adoption of
EMR and the dependent variables “intent to adopt EMR” and
“intent to use EMR”.
The main reason individuals, organizations and
governments embark on adoption of new technology is to
facilitate their work and improve performance and
proﬁtability. Adoption of new technologies often requires a
change in the status-quo and doing things differently. The
ease of use and usefulness of new technology greatly
determines user willingness to adopt and use the technology
(22). The way new technology will be more efﬁcient than
previous methods and help users gain advantage over its
competitors hence it will inﬂuence the decision to adopt the
technology. We therefore posit that:

having prior knowledge and visibility of the beneﬁts of using
the new technology (26). Hence, we posit that:
H4: Awareness will positively inﬂuence user decision to adopt
EMR
Users of new technology are usually apprehensive about the
security risks and safety of their information as it is stored and
transmitted through the internet or cloud (27). Despite the
growing popularity of EMR due to its ability to allow quick
and easy access to patient data, users are still reluctant to
adopt it due to data security and privacy concerns (28). We
posit that:
H5: Absence of risk will positively inﬂuence user decision to
adopt EMR
H6: Data safety will positively inﬂuence user decision to adopt
EMR

H1: Usefulness will positively inﬂuence user decision to adopt
EMR
H2: Ease of use will positively inﬂuence user decision to adopt
EMR
H3: Relative advantage will positively inﬂuence user decision to
adopt EMR

Infrastructural availability, which encompasses stable
electricity supply, good telecommunication networks, and
high-speed internet or broadband networks, play a crucial role
in the smooth operation of EMR. Sub-Sahara African
countries have very poor infrastructures and have been faced
with problems of stable electricity over the years. Availability
of appropriate infrastructure is crucial for the adoption of
new technological innovations (29). We posit that:

Users will usually adopt and use technology that they have
witnessed being used by others or have access to observe the
new technology in action. Since some of these new
technologies can impact user operations if not used correctly,
users will usually be sceptical to adopt and use it without

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H7: Availability of good infrastructure will positively inﬂuence
user decision to adopt EMR

validity of the tool for our data collection process. Research
assistants were recruited from each state and trained on the
data collection tool. The questionnaire was transformed and
then loaded unto the ODK (Open data kit) which was used
by the research assistants for data collection. The ODK is an
easy-to-use data collection app that runs on the android
operating system. The advantage of using the ODK is that
data can be collected and stored when the tablet is ofﬂine and
when connected to the internet, the data is automatically
saved on a central database (33). Each research assistant was
given an android tablet with the ODK running on it for the
data collection.
This study was conducted in four states in the Niger Delta
region viz: Akwa-Ibom, Cross Rivers, Rivers and Imo states.
A Stratiﬁed random sampling method was used to select
respondents for this study. The target population was all
medical facilities in the four states purposefully selected for
the study based on accessibility and budget availability. Using
a two-step stratiﬁcation process, the researchers came up with
a stakeholder grouping to ensure all stakeholders were
represented in the data collection. End users of EMR being
Clinicians (doctors, nurses, laboratory scientists/technologists
and Pharmacists) and administrators at managerial level were
selected from the secondary and tertiary medical facilities. At
the primary health facilities, the ofﬁcers in charge and the
community health extension workers (CHEWs) were the
respondents.
Respondents were randomly selected from the various
stakeholder groups in each medical facility. The survey was
administered by the research assistants and responses entered
into the ODK. Self-administration was not used because
respondents were not trained on how to use the ODK. The
data collection was carried out over a period of 6 weeks and all
data was then collated in the ODK database and transferred
into an excel spreadsheet for processing and analysis. In total,
1,177 valid responses were received for analysis.

In order for senior management and other stakeholders to
recommend or adopt new technology, there must be some
critical success factors that are used to guide their decision.
Technologies that align favourably with all or most of the
critical success factors outlined by the organisation will most
likely be adopted. Hence, we posit that:
H8: Alignment of EMR technology with business deﬁned
critical success factors will positively inﬂuence user decision
to adopt EMR
Previous studies have highlighted key innovation diffusion
factors which include relative advantage and emphasised the
impact of relative advantage on user perception of usefulness
of new technology (30). Therefore, we posit that:
H9: Relative advantage positively mediates user decision to
adopt and use EMR
Research has shown that age, gender and years of experience
of users can have an impact on their decision to adopt and use
new technology (31). Moreover, users that are already
comfortable with a particular system or way of working for
years might be reluctant to change and adopt a new system
(30). Meanwhile, it is very common for younger people and
male users to adapt and adopt new technology. We therefore
posit that:
H10a: Age will moderate impact of usefulness on user decision
to adopt EMR
H10b: Age will moderate impact of ease of use on user decision
to adopt EMR
H10c: Gender will moderate impact of usefulness on user
decision to adopt EMR
H10d: Gender will moderate impact of ease of use on user
decision to adopt EMR
H10e: Gender will moderate user perception of risk on their
decision to adopt EMR
H10f: Experience will moderate impact of ease of use on user
decision to adopt EMR
H10g: Experience will moderate user perception of risk on
decision to adopt EMR
H10h: Experience will moderate user perception of success
factors on decision to adopt EMR

Data analysis
Statistical Package for Social Sciences (SPSS) was used to
perform the data analysis. A reliability test on the full dataset
resulted in a Cronbach Alpha value of 0.94, hence conﬁrming
the reliability of the data for further analysis. The data
analysis and results were divided into three sections to cover
descriptive analysis, factor analysis and multiple regression
analysis.

Materials and methods
We adopted a quantitative research approach for this study
because it allowed us to efﬁciently collect responses from a large
sample across four states in Nigeria in a cost-effective and
timely manner (32). A survey instrument was designed and
used for the data collection. Prior to using the survey tool for
the data collection, it was peer reviewed by adoption experts
and a pilot test carried out to ensure the reliability and

Frontiers in Digital Health

Descriptive analysis
Respondent proﬁles and the various characteristics of their
medical facilities are shown in Table 1. Most of the

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TABLE 1 Descriptive statistics (N = 1,177).

Frequency

Percentage

Frequency

Male

462

39.3

21–30

139

11.8

Female

715

60.7

31–40

426

36.2

41–50

386

32.8

51–60

218

18.5

8

0.7

Gender

Percentage

Age Group

State
AkwaIbom

295

25.1

>60

Cross River

254

21.6

Experience

Imo

285

24.2

<1

17

1.4

Rivers

343

29.1

1–5

233

19.8

6–10

348

29.6

11–15

304

25.8

Others

Job Title

16–20

86

7.3

>20

189

16.1

Diploma

143

12.1

BSc

552

46.9

MSc

216

18.4

PhD

41

3.5

Fellowship

112

9.5

Other

113

9.6

Admin

83

7.1

Doctor

329

28

Nurse

435

37

Qualiﬁcation

Pharmacist

119

10.1

Lab Tech

165

14

Other

46

3.9

Computer Level
Beginner

208

17.7

Intermediate

641

54.5

Advance

286

24.3

Expert

42

3.6

Rank

Ownership

Ofﬁcer

13

1.1

Registrar

36

3.1

S Registrar

46

3.9

Consultant

57

4.8
1.2

Public

687

58.4

Private

398

33.8

S Consultant

14

Mission

92

7.8

Ch Consultant

14

1.2

Med Ofﬁcer

122

10.4

S Med Ofﬁcer

21

1.8

Classiﬁcation
Primary

287

24.4

Principal Med Off

6

0.5

Secondary

487

41.4

No Rank

848

72

Tertiary

403

34.2
Patient Numbers
<20

358

30.4

<5

36

3.1

21–50

356

30.2

5–10

244

20.7

51–100

236

20.1

11–20

317

26.9

101–200

133

11.3

21–50

435

37

201–300

52

4.4

>50

145

12.3

301–400

21

1.8

401–500

12

1

>500

9

0.8

Facility Age

No of Facilities
None

12

1

One

32

2.7

Two

59

5

Admin

549

46.6

Three

127

10.8

IT Dept

79

6.7

>Four

736

62.5

Committee

254

21.6

Unknown

211

17.9

Government

237

20.1

Other

58

4.9

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Tech Selection

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respondents were female (60.7%) with the majority falling in the
31–50 years old age group (69%) which is consistent with the
optimal working age group in the country. Most of the
medical facilities had public ownership (58.4%). It was
interesting to see that most of the medical facilities (46.6%)
depend on the administration to make decisions on new
technology adoption while very few (6.7%) relied on their IT
department for such decisions. The analysis also shows that
more nurses (37%) responded to the survey followed by
doctors (28%) and 72% did not provide any rank. Only
medical doctors are given Ranks, hence only the 329 medical
doctors provided responses for rank while others left it blank.

variables based on the survey tool can be found in Appendix
A. Measured variables that had loading coefﬁcients less than
0.4 were suppressed during the factor analysis hence only
variables with loading above 0.4 were returned. The results
show that usefulness of EMR based on the survey item
“Overall, I believe EMR will be useful for my job at my
medical facility” had the highest factor loading. Meanwhile
the lowest loading was for the item “EMR will facilitate
patient consultations within my medical facility”.

Regression analysis
In order to choose the type of regression analysis we could
perform on the dataset; we needed to determine if the data was
normally distributed or not. Since we had over 100 respondents
(1,177 respondents), test of normality was performed on the
data using the Kolmogorov-Smirnov test which is used for
datasets greater than 100. The variables were transformed by
calculating the means and the test for normality performed
on the transformed data. The results showed that all variables
were signiﬁcant (p < 0.05) hence indicating that the data was
not normally distributed. Since the data is not normally
distributed, we carried out ordinal regression analysis instead
of linear regression analysis.
The ordinal regression analysis model ﬁtting information
(Table 2) shows a signiﬁcant improvement in ﬁt of the ﬁnal
model over the null model [χ 2(107) = 1,169.904, p < .001].
Meanwhile the goodness-of-ﬁt test results show a Pearson chisquare test [χ 2 (7,981) = 12,284.829, p < .001] and Deviance
chi-square test [χ 2 (7,981) = 1,957.612, p > .05]. The nonsigniﬁcant test result indicate that the model ﬁts the data well
(37). The Pseudo R-square value (Nagelkerke value) for this
test (R2 = .674) indicates that 67.4% of the variance in user
intention to adopt and use EMR can be explained by the
independent variables considered in this study. The test of
parallel lines was not signiﬁcant (p > .05) hence indicating
that we have not violated the test of proportional odds, hence
the results obtained from the ordinal regression can be used
to expound the impact of the independent variables on user
intention to adopt and use EMR.
In order to conﬁrm the results from the ordinal regression
analysis before proceeding to interpret the results, we further
carried out the ordinal regression using generalised linear
model and ordinal logistic response. The results were
consistent with the initial ordinal regression analysis but
additionally provided a test for the proposed model constructs
to indicate which variables were statistically signiﬁcant in
inﬂuencing user decision to adopt and use EMR. The model
test results show that awareness, risk, relative advantage,
usefulness and critical success factors were statistically
signiﬁcant as factors that impact adoption of EMR.
Meanwhile, infrastructure, data security and ease of use were

Factor analysis
In order to perform factor analysis on our data, we executed
the Kaiser-Meyer-Olkin (KMO) test of sampling adequacy and
Bartlett’s test of sphericity. This resulted in a KMO value of
0.940 and a Bartlett’s test of sphericity (approx. Chi-square)
value of 24,369.354 (Appendix B). The KMO value is above
the 0.6 threshold required for good factor analysis and lies in
the “marvellous” range (>0.9) as deﬁned by Kaiser and Rice
(34). Moreover, the result was signiﬁcant (p < .001) indicating
that our variables are signiﬁcantly correlated and the sampling
was adequate to perform factor analysis.
Exploratory factor analysis was performed on the dataset
using principal component analysis (PCA) in order to extract
the signiﬁcant variables that impact user propensity to adopt
EMR. The analysis was performed on 31 measured variables
from our survey tool resulting in ﬁve factors being extracted
(see Appendix C), which had eigenvalues greater than 1 (35).
This is further supported by the scree plot (Appendix D)
which shows the ﬁve factors retained with eigenvalues ≥1
based on the eigenvalue cut-off rule. However, it is worth
noting that all the proposed components for this study loaded
under the 5 factors retained after the factor analysis. Hence,
regression analysis was carried out on the full dataset since no
components were eliminated by the factor analysis.
The correlation matrix shown in Appendix E was used to
determine if there was correlation between the component’s
variables or not. According to Tabachnick and Fidell (36), if
the absolute value of correlations exceeds.32, then there is a
10% (or more) overlap in variance among factors, hence, the
need to do oblique rotation. Since our matrix shows values
greater than this threshold, we further executed a promax
rotation on the data to identify and name the measured
variables that loaded on the retained components.
Appendix F shows the factor loading communalities for the
5 factors that were retained from the factor analysis and rotated
using promax method to allow for ease of analysis and naming.
The factors were named based on the majority of items that
loaded for those factors. The full details of the measured

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TABLE 2 Ordinal regression results.

Mediation and moderation tests

Item

Measure

Value

DF

Sig

Model Fitting info

Chi-square χ 2

1,169.904

107

.000

Goodness-of-ﬁt

Pearson χ2
Deviance χ2

12,284.829
1,957.612

7,981
7,981

.000
1.000

Test of parallel lines

General χ2

111.704

749

1.000

Pseudo R-squared

Nagelkerke

.674

In our proposed model, we posited that relative advantage
will mediate user perception of usefulness of EMR. We also
posited that age, gender and experience have moderating
effects on user intention to adopt and use EMR. The results
of the mediation and moderation analysis are shown in
Appendix G and Appendix H respectively. The results show
that the direct impact of both usefulness and relative
advantage on adoption and usage of EMR are signiﬁcant (p
< .001). To calculate the mediation effect of relative advantage
on usefulness of EMR, a Sobel test (39) was conducted to
determine if this mediation was signiﬁcant and the mediation
coefﬁcient was also computed (see row UFxRA  AD in
Appendix F) resulting a positive coefﬁcient of 0.096. Hence,
we can conclude that relative advantage has a positive
signiﬁcant mediating impact on usefulness of EMR.
The moderation test was done by calculating the interaction
term between the moderating variable and the moderated
variable and then using SPSS to run a simple linear regression
on the variables (Appendix H). The moderation test revealed
that age, gender and experience do not have direct signiﬁcant
impact on user decision to adopt and use EMR. However,
gender has a signiﬁcant impact (p < .05) on user perception
on ease of use of EMR and hence inﬂuence it adoption. Also,
experience has signiﬁcant impact (p < .05) on user perception
of risk when it comes to the adoption of EMR. Meanwhile,
the moderation effects of age on usefulness, age on ease of
use, gender on usefulness, gender on risk, experience on ease
of use and experience on critical success factors were
statistically not signiﬁcant thereby rejecting the hypotheses
that posited that these factors will have a moderating impact
on user decision to adopt and use EMR.

not statistically signiﬁcant in inﬂuencing user adoption of
EMR, see Table 3. We further performed a test for
collinearity to ensure that the variables are not highly
correlated. The tolerance values for all the independent
variables were above the 0.10 threshold and the variance
inﬂation factor (VIF) was less than the maximum permitted
value of 10.0 (38). This therefore indicates that our data
was not correlated and can be used to analyse the impact of
the independent variables on our dependent variable
(intent to adopt EMR).
The Exp(β) values represent the odds ratios that reﬂect the
multiplicative changes in the odds of being in a higher category
on the dependent variable for every one unit increase in the
dependent variable, holding all other independent variables
constant. So, odds ratios greater than 1 will represent
increasing probability of impacting the dependent variable
while odds ratios of less than 1 will represent decreasing
probability of impacting the dependent variable. The results
therefore show that usefulness has the highest impact to
increase adoption of EMR with an odds ratio of 18.357. This
indicates that user propensity to adopt EMR increases by a
factor of 18.357 for every one unit increase in user perception
of its usefulness. This is followed by critical success factors
with odds ratio of 2.404, awareness with odds ratio of 1.525
and relative advantage with odds ratio of 1.427. Even though
infrastructure availability and ease of use have odds ratios
above 1, they are not statistically signiﬁcant. Meanwhile, risk
and data security both have odds ratios below 1 with negative
coefﬁcients. This indicates that user perception of risk and
safety of their data when using EMR decreases their
propensity to adopt EMR.

Hypotheses testing
The results from the model test using ordinal regression
analysis, mediation test and moderation tests were used to test
the hypotheses that were propounded in the conceptual

TABLE 3 Model test.

Coefﬁcient β

Exp (β)

Awareness

.422

1.525

13.911

Risk

−.516

.597

35.308

Infrastructure

.079

1.082

.897

Variable

Chi-Square χ2

Tolerance

VIF

Sig

.640

1.562

.000

.719

1.391

.000

.577

1.733

.344

Data security

−.134

.875

1.929

.547

1.828

.165

Relative advantage

.356

1.427

8.429

.492

2.032

.004

Ease of use

.110

1.116

.362

.372

2.688

.547

Usefulness

2.910

18.357

278.476

.411

2.432

.000

Critical success factors

.877

2.404

57.972

.582

1.718

.000

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TABLE 4 Hypotheses test results.

Hypothesis

Path

Path
Coefﬁcient

pvalue

Discussion
Hypothesis
Status

H1

UF → AD

2.910

.000

Supported

H2

EU → AD

.110

.547

Not Supported

H3

RA → AD

.356

.004

Supported

H4

AW → AD

.422

.000

Supported

H5

RS → AD

−0.516

.000

Supported

H6

DS → AD

−0.134

.165

Not Supported

H7

IF → AD

.079

.344

Not Supported

H8

CS → AD

.877

.000

Supported

H9

UFxRA →
AD

.096

.000

Supported

H9a

AGE_UF →
AD

−0.016

.480

Not Supported

H9b

AGE_EU →
AD

−0.010

.732

Not Supported
Not Supported

H9c

GEN_UF

−0.064

.171

H9d

GEN_EU

−0.191

.001

Supported

H9e

GEN_RS

.059

.194

Not Supported

H9f

EXP_EU

−0.018

.391

Not Supported

H9g

EXP_RS

−0.050

.003

Supported

H9h

EXP_CS

−0.007

.694

Not Supported

Poor health information system has been identiﬁed as a
major challenge in the health-care system in many subSaharan African countries including Nigeria. Although, EMR
is an important tool to improve access to patient information
with attendant improved quality of care, EMR has not been
widely implemented/adopted in sub-Saharan Africa especially
Nigeria. Thus, this study sought to identify factors that affect
the adoption of EMR in Nigeria.
The demographics of this study reveals some interesting
ﬁndings about the Nigerian health sector. In this study, we
deliberately set out to study a good mix of decision makers
and system users. Majority of the respondents were female,
between the 31–50 age group. Most of the medical facilities
were in the public sector. While this is consistent with the age
of the working class in Nigeria, the female preponderance
presupposes that there are more females in the facilities.
However, further analysis suggests they are not equitably
distributed in the different cadres, being skewed towards the
lower cadres as compared to administrative/managerial
positions (see Appendix I). This is in keeping with studies
that show that although women make up a large proportion
of the health workforce, very few are in senior managerial/
leadership positions (40). It was interesting to see that while
most of the medical facilities depended on the administration
to make decisions on new technology adoption, very few
(6.7%) relied on their IT department for such decisions. The
reasons may be due to the weak ICT systems currently in
place as most of the facilities had ICT units manned by only
one or two staff as opposed to having IT departments.
Although the study targeted administrators and those who
were able to make decisions in the system, the researchers
were surprised to see that only 28% of the respondents were
medical doctors. This conﬁrms the global shortage of
physicians and the capital ﬂight of physicians in Nigeria.
Our study results also show that the majority of respondents
had intermediate level of computer literacy which can directly
impact their willingness to adopt and use EMR systems. This
is consistent with studies conducted on computer and internet
use by doctors in a Nigerian teaching hospital which revealed
that the overall proﬁciency of the respondents in computerbased competencies was below average with only 26.7%
sufﬁciently familiar with computer tools to perform advanced
tasks (18, 41). A study of three hospitals in Ethiopia also
revealed that health professionals who were computer literate
are more likely to adopt EMR than their counterparts who are
not computer literate.
In our study, usefulness of new technology was an
important facilitator to user willingness to adopt and use the
technology. It had the highest impact to increase adoption of
EMR with an odds ratio of 18.357, which indicates that user

UF, Usefulness; AD, Adopt & Use EMR; EU, Ease of use; RA, Relative advantage;
AW, Awareness; RS, Risk; DS, Data security; IF, Infrastructure; CS, Critical
success factors; GEN, Gender; EXP, Years of experience.
Bold letters represent statistically signiﬁcant values.

design. Table 4 provides the results of the hypotheses tests and
shows that eight of the proposed hypotheses were supported (p
< 0.05). Usefulness of EMR (H1) had the highest coefﬁcient
thereby indicating that users are more inclined to adopt and
use EMR due to its usefulness. This was followed by critical
success factors (H8) which indicates that users will only adopt
EMR if it can fulﬁl certain conditions that will ensure success
of their operations. Surprisingly, ease of use (H2) was not
deemed to be very crucial for the adoption and usage of EMR
hence the hypothesis was not supported despite many prior
studies showing that ease of use is relevant to user adoption
and use of new technology. The mediation test shows that
relative advantage does have a signiﬁcant positive impact on
the usefulness of EMR (β = 0.096, p = 0.000). The results also
show that age did not have any moderating impact on
usefulness or ease of use of EMR meanwhile gender
signiﬁcantly mediates the impact of ease of use of EMR and
experience was also found to signiﬁcantly mediate perception
of risk in the adoption and use of EMR. It is also worth
noting that risk (H5) has a signiﬁcant negative impact on user
decision to adopt and use EMR. This is consistent with prior
adoption studies which have highlighted user reluctance to
adopt new technology if there is a perception of risks to their
operations or data.

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propensity to adopt EMR increases by a factor of 18.357 for
every one unit increase in user perception of its usefulness.
This is comparable with a study done in Taiwan among
Nurses (42) and in another study done in Sri Lanka, where
human factors such as lack of awareness on the beneﬁts of
EMR, lack of knowledge and experience on how to use EMR,
negative perceptions and attitudes on EMR were vital barriers
to successful implementation and adoption of EMR (43).
Also, a study among Physicians in Iran showed that perceived
usefulness has a direct and signiﬁcant effect on physicians’
attitudes toward EMRs’ adoption (44). A systematic review by
Ross et al. (9) showed that usefulness was an important factor
in adoption of EMR. These however contrast with a study in
Skopje, Macedonia among health professionals which showed
that usefulness of a new technology was not an important
factor in its adoption (45).
Perceived ease of use and awareness which are important
factors for adoption in this study have been linked with
training, knowing the beneﬁts of EMR, access to information,
improved knowledge and experience of EMR. Surprisingly,
ease of use (H2) was not deemed to be very crucial for the
adoption and usage of EMR hence the hypothesis was not
supported despite many prior studies showing that ease of use
is relevant to user adoption and use of new technology (43).
A study on acceptance of health information technology
among health professionals revealed that ease of use was an
important factor on the adoption of EMR (45). In other
studies, ease of use was a signiﬁcant contributor to adoption
of EMR (42, 46).
Critical success factors that guide management and
stakeholders’ decisions to adopt new technology was
signiﬁcant in this study and had an odds ratio of 2.404 which
indicates that user propensity to adopt EMR increases by a
factor of 2.404 for every one unit increase in user perception
of its critical success factor. Management and stakeholder
engagement and support increases adoption and
implementation of EMR through fostering a sense of
ownership, building conﬁdence, acceptance, enjoyment and
self-pride (9). Organizational leadership had been shown to
play a great role in providing resources to adopt and use
EMRs. Results from a study on Jordan hospitals revealed a
positive relationship between management support and
adoption of new technology (47). Pantuvo et al. (48) revealed
that the critical success factors for adoption and
implementation of EMR in Nigeria were enforceable legislation.
Awareness with odds ratio of 1.525 was signiﬁcant and
indicates that user propensity to adopt EMR increases by a
factor of 1.525 for every one unit increase in user awareness.
Previous study had linked awareness to having good attitude
towards EMR. A study of three hospitals in Ethiopia showed
that health professionals who had good attitude towards EMR
were 1.56 times more likely to readily adopt EMR compared
to those health professionals who had poor attitude (3).

Frontiers in Digital Health

Access to information and knowledge increases the adoption
of EMR (41). This may be due to the fact that health
professionals who have information on EMR or witnessed
EMR being used before with the visibility of its beneﬁt may
have the tendency to accept the advantage of technology and
likely to be ready for EMR adoption. Relative advantage,
which is the way new technology will perform better than the
one being replaced, will also inﬂuence decisions to adopt the
technology. In this study, relative advantage had an odds ratio
of 1.427 which indicates that user propensity to adopt EMR
increases by a factor of 1.427 for every one unit increase in
user perception of its relative advantage. This is similar to the
study by Scott et al. (49) on uptake of the Canadian Heart
Health Kit which found that relative advantage signiﬁcantly
inﬂuences physicians’ intention to use the new innovation.
The mediation test shows that relative advantage also has a
signiﬁcant positive impact on the usefulness of EMR.
Previous authors had described available resources to
include the availability of suitable infrastructure important for
implementation of EMR successfully. Infrastructure features
included electricity supply, available bandwidth, access to
reliable internet connectivity, access to computers, electrical
power and access to phone lines and mobile phones (9). In
Nigeria, lack of constant supply of electricity (48) as well as
poor internet services are barriers to successful adoption and
implementation of a nationwide EMR (41). Infrastructural
availability is a major inhibitory factor to the adoption of
EMR in Nigeria as documented by other studies but was
found not to be statistically signiﬁcant in our study. Many
hospitals in Nigeria depend mainly on alternative power
supply commonly called “generator” for their operations,
EMR may not be used consistently because of the constant
power outage. Also, internet connectivity is very low in
Nigeria. A study conducted on the use of health information
and communication technologies by health workers in seven
state hospitals and a private hospital in the South Western
Zone, Ogun State, Nigeria, reported that only one of the
hospitals examined was connected to the internet, and none
of them had a website (50). Previous study showed that health
professionals who believed that there was good technical
Infrastructure for EMR system were 1.78 times more likely to
adopt EMR system (3).
In this study risk and safety of data were negatively
correlated with adoption of EMR as they both had negative
coefﬁcients. This indicates that user perception of risk and
safety of their data when using EMR decreases their
propensity to adopt EMR. This is comparable with a
systematic review on factors that inﬂuence the implementation
of e-health, where concerns over patients’ privacy, security
threats and perceived threats over patients and health
professionals’ relationship were seen as barriers to adoption of
EMR (9). A study on factors affecting the adoption of EMR
by nurses in Tamil Nadu, India, showed that risk was a

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barrier to adoption of EMR (46). In this study, the moderation
test revealed that age, gender and experience do not have direct
signiﬁcant impact on user decision to adopt and use EMR. This
is in contrast to a study done in Ethiopia that revealed that
health professionals aged 30–34 years were 52% less likely to
be ready for EMR system than younger health professionals
(3) and another systematic review that showed that younger
primary care physicians were more inclined to use EMR than
older physician (13). This maybe so because younger
respondent had multiple access to internet than older
respondent (51). More so, younger people naturally tend to
have more motive, interest, and readiness to accept new
technology developments than older people (3).
The results of our study also showed that age did not have
any moderating impact on usefulness or ease of use of EMR.
Biruk et al. (3) found out that male health professionals were
1.87 times more likely to be ready to adopt EMR system than
female health professionals. However, in our study, gender
had a signiﬁcant impact (p < .05) on user perception on ease
of use of EMR and hence inﬂuence it adoption. This may be
so because several studies have shown that males are more
proﬁcient in computer use than females (41, 51). Also,
experience has signiﬁcant impact (p < .05) on user perception
of risk when it comes to the adoption of EMR.

operations, however, this remains a huge challenge, especially
in peripheral facilities as EMR may not be used consistently
because of the constant power outage. Lack of functional
websites is also a challenge for majority of the hospitals.

Policy recommendations
We therefore make the following policy recommendations:

Government
The key role of the government in health would be
improved formulation of policies on EMR adoption with
regulatory bodies to oversee its implementation. In Nigeria,
EMR is a new technology in the nascent stages of adoption in
several states. Regulation is also rudimentary. The government
thus should be actively involved, designating appropriate
agencies to partner with the federal ministry of health to
develop regulatory policies and guidelines. More so, there
should be increased funding to health and functional health
records department in all health facilities. The government,
management and stakeholders should ensure improvement of
infrastructures for the adoption, implementation and
sustainability of EMR. Stable electricity to health facilities
should be prioritized. Government should fast-track the
development of an e-health infrastructure, such as internet
backbone and satellite technology, as well as the spread of
high-speed broadband data services. Develop legislative and
implementation frameworks for public-private partnerships in
the health sector in order to attract private sector investment
and ensure the sustainability of EMR projects. Regulation of
data safety, protection and Risk management should be
addressed to encourage stakeholder buy-in for adoption of
EMR.

Conclusion
This study set out to explore the factors that inﬂuence the
decision of healthcare professionals to adopt and use an EMR
system in developing countries. A conceptual adoption model
was developed based on an extended TAM model which
included constructs of awareness, risks, data security,
infrastructure and relative advantage to investigate the
propensity of stakeholders in Nigeria to adopt and use
electronic medical records. The results of our model testing
shows that usefulness has the highest impact on adoption of
EMR. This is followed by critical success factor, awareness
and relative advantage. However, though infrastructure
availability and ease of use were also important in the model,
they were not statistically signiﬁcant. Meanwhile, user
perception of risk and safety of their data when using EMR
decreases user propensity to adopt EMR.
Our results suggest that the Nigerian health care delivery
system is ready to adopt EMR. However, several challenges
remain for the successful adoption and implementation of
EMR in the Niger Delta region and by implication similar
contexts in developing countries. In this study, infrastructural
availability being lack of or poor internet connectivity and
lack of constant supply of electricity were not seen as barriers
to successful adoption and implementation of an EMR
system. While this may be due to dependence by many
hospitals in Nigeria on alternative power supply for their

Frontiers in Digital Health

Healthcare facility management
The management should also prioritize and invest in EMR
implementation and maintenance of innovative EMR systems
that will lead to more efﬁcient service delivery at their
facilities. The investment will cover training, infrastructure
installation and maintenance. Management should set up an
effective system that will deal with feedbacks promptly.
In preparation for the adoption of digital interventions
especially EMRs in LMICs, hospital management need to be
ready to commit the resources and investments to make them
successful. Topmost on the priority list would be electrical
power supply and deploying adequately trained staff to run
functional ICT departments. This would be key to the smooth
functioning of any EMR system.
Critical success factor such as early involvement of
stakeholders to build up the requirements of end users and
reduce resistance to change is highly recommended. The
perceived beneﬁts of EMR should be identiﬁed and

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communicated to stakeholders as much as possible. The
hospital management should be involved in enforcing policies
on EMR, training and motivating the workforce and have
sustainable funding for EMR. Training and retraining of
medical personnel should also be encouraged through inschool training on medical informatics, in-service training on
ICT and continuing medical education.
To ensure effective implementation, Training of all personnel
on management of the system should also be encouraged, besides
in-service training on ICT medical informatics. There is also need
to integrate in-school training on medical informatics in medical
and allied medical courses as all members of the health team
would be end users of any EMR system.
EMR system vendors should demonstrate its usefulness and
ease of use to the healthcare workers beyond reasonable doubt.
Safety of data should be sacrosanct. This should guide their
decision on which type to EMR program to buy into. This
process should be rigorously and painstakingly done because
one breach is enough to lead to system collapse, loss of
conﬁdence and numerous litigations. The administrator
should have a mechanism at inception of EMR that will work
at identifying, categorizing and assessing cyber risks.
Mitigation of risks should be prompt and demonstrable so as
to engender conﬁdence in the system.

use today. Hence, we also encourage future research to focus
on adoption of medical decision support systems and
telemedicine in similar contexts.

Data availability statement
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.

Ethics statement
The studies involving human participants were reviewed
and approved by University of Uyo Teaching Hospital, Uyo
Institutional Health Research Ethical Committee (IHREC).
Ref: UUTH/AD/S/96/VOL.XXI/564. The patients/participants
provided their written informed consent to participate in this
study.

Author contributions
CDA: Study conception and objectives, questionnaire design,
coordination of pilot and full data collection, writing of parts of
discussion and conclusion. HMS: Questionnaire design,
Conceptual design and hypotheses formulation, pilot data
analysis, writing of methodology, data analysis and writing of
results section, abstract and manuscript formatting. NE: Study
objectives, Questionnaire design, Data collection, Writing part of
the discussion writing of results section, abstract and manuscript
formatting policy recommendations. CMI: Study objectives,
Questionnaire design, Data collection, Writing part of the
discussion writing of results section, abstract and manuscript
formatting policy recommendations. NFA: Questionnaire design,
Writing part of the discussion, abstract and manuscript
formatting, policy recommendations. OM: Questionnaire design,
manuscript formatting. ED: Writing of introduction and
literature review, data collection. EU: Loading of questionnaire
into ODK, managing data collected, exporting of data collected
into Excel format ready for analysis, Coordination of ﬁeld
project, ensuring integrity of data. VE: Writing of introduction
and literature review. FMO: Study conception and design, overall
manuscript proofreading and correction. All authors contributed
to the article and approved the submitted version.

Individual
At individual level, our results showed that critical success
factor showed a positive impact on ease of adoption. This
suggests that both the end users and administrators should
understand and promote EMR systems, with adequate
reference to successful systems to motivate their staff to adopt
and use it. Improving awareness will encourage staff to invest
their time and ﬁnances to educate and upgrade themselves in
preparation for adoption of IT innovations which is critical to
improved patient care and service delivery.

Research implications
We recommend further research on readiness to adopt
other digital innovations for the health system including
medical decision-making systems and telemedicine.

Limitations and future research
This study was cross-sectional quantitative research focused
only on the Niger Delta region of Nigeria. Due to budgetary and
time constraints, it could not be extended to the western and
northern states. However, we are of the opinion that the
adoption challenges faced in the states selected for this study
will be very similar to those faced across the whole country.
We will therefore recommend further research with a national
coverage. Our study also focused just on the adoption of EMR
but there are many other medical decision support systems in

Frontiers in Digital Health

Funding
This is the ﬁrst of a series of papers on adoption of
technological innovations in LMICs. This study is a subset of
preliminary papers on a grant from the New Frontiers in
Research Fund of Canada. New Frontiers in Research Fund

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10.3389/fdgth.2022.1017231

Exploration (Toronto, Ontario, CAt GRANT_NUMBER:
#102079).

Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their
afﬁliated organizations, or those of the publisher, the editors
and the reviewers. Any product that may be evaluated in this
article, or claim that may be made by its manufacturer, is not
guaranteed or endorsed by the publisher.

Conﬂict of interest
The authors declare that the research was conducted in the
absence of any commercial or ﬁnancial relationships that could
be construed as a potential conﬂict of interest.

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Appendix A Experimental variables

I have known about EMR for some time now

EMRAW1

I know of the possibilities and ﬂexibilities offered by using EMR

EMRAW2

I have used EMR before

EMRAW3

I am currently using EMR within my medical facility

EMRAW4

EMR will lead to time saving managing patient records

EMRAW5

EMR will facilitate patient consultations within my medical facility

EMRAW6

I feel that EMR are safe and secure to use

EMRRS7

I believe that there are no risks when using EMR

EMRRS8

I believe that my medical facility has the necessary infrastructure needed to adopt EMR

EMRIF9

I believe that patient data stored on EMR is safe and secure

EMRDS10

I believe that using EMR will give my medical facility an edge over others not using it

EMRRA11

I believe interaction with EMR will be clear and understandable

EMREU12

I believe it will be easy for EMR to do what I want from it

EMREU13

I believe that learning to use EMR will be easy for me

EMREU14

Overall, I believe that EMR will be easy to use

EMREU15

Using EMR will improve the quality of the work I do at my medical facility

EMRUF16

Using EMR will give me greater control over my patients’ records

EMRUF17

Using EMR will enable me complete tasks quickly

EMRUF18

Using EMR will increase productivity for staff at my medical facility

EMRUF19

Overall, I believe EMR will be useful for my job at my medical facility

EMRUF20

I will recommend adoption and usage of EMR at my medical facility

EMRAD21

I intend to use EMR system when implemented at my medical facility

EMRAD22

My medical facility has the appropriate change management process to facilitate the adoption of EMR system

EMRCS23

My medical facility’s business culture will not interfere with the adoption of the EMR software

EMRCS24

In my opinion, the top management in my medical facility support the adoption of EMR software

EMRCS25

There is an effective communication structure in my medical facility to encourage adoption of EMR software

EMRCS26

My medical facility business vision and missions supports the adoption of EMR software

EMRCS27

The EMR software is appropriate for our business functions

EMRCS28

Appendix B KMO and Bartlett’s test
Kaiser-Meyer-Olkin measure of sampling adequacy

.884

Bartlett’s Test of Sphericity

Approx. Chi-square
DF
Sig.

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5,043.015
465
.000

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Appendix C Total variance explained

Component

Initial Eigenvalues

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

12.329

39.772

39.772

7.233

23.332

23.332

2

3.064

9.883

49.655

5.911

19.069

42.401

3

1.937

6.248

55.902

2.540

8.193

50.595

4

1.514

4.884

60.786

2.513

8.108

58.703

5

1.266

4.085

64.870

1.912

6.168

64.870

6

.956

3.083

67.953

7

.892

2.878

70.832

8

.757

2.440

73.272

9

.699

2.254

75.527

–

–

–

–

–

–

–

–

29

.204

.659

98.935

30

.193

.621

99.556

31

.138

.444

100.000

Extraction Method: Principal Component Analysis.

Appendix D Scree plot

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Appendix E Component correlation matrix
Component

1

2

3

4

5

1

1.000

.549

.541

.453

.082

2

.549

1.000

.395

.328

.242

3

.541

.395

1.000

.359

−.085

4

.453

.328

.359

1.000

.202

5

.082

.242

−.085

.202

1.000

Extraction Method: Principal Component Analysis.
Rotation Method: Promax with Kaiser Normalization.

Appendix F Factor loading communalities for ﬁve-factor principal component
analysis with promax rotation EMR adoption items
Label
Items

Usefulness of EMR
Factor 1

EMRUF20

.893

EMRUF19

.860

EMREU14

.850

EMRUF18

.817

EMREU15

.805

EMRUF16

.792

EMRUF17

.787

EMREU13

.701

EMREU12

.635

EMRAD22

.622

EMRAD21

.619

EMRRA11

.536

EMR Critical success
Factor 2

EMRCS26

.849

EMRCS27

.815

EMRCS30

.803

EMRCS24

.788

EMRCS29

.775

EMRCS31

.772

EMRCS25

.765

EMRCS23

.744

EMRCS28

.690

Awareness of EMR
Factor 3

EMRAW1

.893

EMRAW2

.843

EMRAW5

.551

EMRAW6

.413

Risks, Data security & Infrastructure
Factor 4

EMRRS8

.862

EMRRS7

.675

EMRDS10

.624

EMRIF9

.453

Usage Experience
Factor 5

EMRAW3

.877

EMRAW4

.798

Extraction Method: Principal Component Analysis.
Rotation Method: Promax with Kaiser Normalization.
a. Rotation converged in 6 iterations.

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Appendix G Mediation effects

Model

Appendix H Moderation effects

B

Sig.

R2

R2 Change

B

Std. Error

t

Sig.

Model

UF  RA

.762

.028

27.345

.000

AGE  AD

.018

.215

.533

RA  AD

.126

.024

5.358

.000

AGE UF  AD

−0.016

.480

.533

.000

UF  AD

.737

.029

25.596

.000

AGE EU  AD

−0.010

.732

.334

.000

UFxRA  AD

.096

.018

5.155

.000

GEN

−0.045

.103

.534

GEN_UF

−0.064

.171

.534

.001

GEN_EU

−0.191

.001

.334

.006
.001

GEN_RS

.059

.194

.020

EXP

−0.004

.737

.334

EXP_EU

−0.018

.391

.334

.000

EXP_RS

−0.050

.003

.019

.007

EXP_CS

−0.007

.694

.299

.000

Appendix I Gender vs. Job title and rank classiﬁcation
RANK
GENDER

OFF

REG

SR REG

MALE

10

21

33

FEMALE

3

15

13

CON

SR CON

CF CON

MED OFF

SR MED OFF

41

7

16

7

PRIN MED OFF TOT

12

76

14

4

218

2

46

7

2

111

JOB TITLE
GENDER

ADM DOC

NUR

PHARM

LAB SCI

OTH

TOTAL

MALE

41

218

60

60

69

14

462

FEMALE

42

111

375

59

96

32

715

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