CHAPTER FOUR
RESEARCH FINDINGS AND DISCUSSION
4.1
Introduction
The general objective of this research was to analyze the factors that affect the successful
implementation of information technology projects by commercial banks in Ethiopia. This
was guided by the specific objectives which were to examine the influence of project
procurement management, project scope management, project management methodology
and executive/top management commitment on implementation of information
technology projects and also establish the moderating effect of project risk on the factors
of implementation of information technology projects by commercial banks in Ethiopia.
This chapter outlines the response rate, assesses the reliability and validity of the research
instrument, it indicates the demographics of the respondents, it details both descriptive
and inferential statistics and also shows the research findings and discussions.
The researcher distributed two hundred and five (205) questionnaires out of which one
hundred and ninety (190) were filled which represented 92% of the total questionnaires
distributed. According to Kothari (2014), 50% response rate is considered average, 60%
to 70% is considered adequate while anything above 70% is considered to be an excellent
response rate. Bell (2014) indicated that for a social science study, anything above 60%
response rate is adequate for making significant conclusions. Attaining 92% was therefore
an excellent response rate which could be attributed to the data collection procedure
employed whereby the researcher personally administered the questionnaires through
drop and pick method and made follow up calls to clarify on any queries as well as to
prompt the respondents to fill the questionnaires. The personal involvement of the
researcher therefore contributed to the high response rate.
Fig 4. 1: Response Rate
Source: SPSS output of the survey, 2021
4.2. Demographics Information
4.2.1 Gender distribution
The descriptive statistics of the study indicated that 130 respondents were male
representing 68.4% while 60 respondents were female representing 31.6%. This is an
indication that the study involves more males. However, since both genders are
represented in the sample, the study will not suffer from gender biases. This implies that
more women are now emerging as user support staff and liaison officers with technical
skills necessary for effective implementation of IT projects (Eason, 2014).
The gender distribution is shown in Table 4.1;
Gender
Frequency
Percentage
130
68.4
60
31.6
190
100
Male
Female
Total
Table 4.1: Gender of respondents
Source: SPSS output of the survey,2021
Fig 4.2: Gender of the respondents
Source: SPSS output of the survey,2021
4.2.3 Age distribution
Table 4.2 indicates that majority of the respondents were aged between 20 and 30 years
constituting 41.6% of total respondents. Respondents between 30 and 40 years
constituting 26.8%. Respondents between 40 and 50 years constituting 18.9% of total
respondents whereas those above 50 years constituted 12.6%. The statistics are a
confirmation that majority of bank workers are youthful with those above 50 years being
continually eased out either by natural attrition or by being incentivized to take voluntary
early retirement. Younger staff are perceived to have better technical competencies and
pursue change initiatives that come with implementation of new IT projects (Kwahk &
Kim, 2008).
Age
20-30
31-40
41-50
Frequency
Percentage
Above 50
79
51
36
24
190
41.6
28.6
18.9
12.6
100
Total
Table 4.2: Distribution of age in years
Source: SPSS output of the survey,2021
4.1.3 Education Level of the Respondents
As shown by Table 4.3, from the research findings, majority of the respondents (58.4%)
hold bachelor’s degree, certificate level with 0.5%, whereas 41.1% of the respondents
indicated their level of education as masters and above. This indicates that the
respondents were educated well enough to understand the questions and thus would give
credible results.
Education
Frequency
Certificate
1
Degree
111
Master
78
190
Total
Table 4.3: Educational level of the respondents
Percentage
.5
58.4
41.1
100.0
Source: SPSS output of the survey,2021
4.1.4. Employee’s Job Experience
As shown by Table 4.4, 40% have served the organization for 6 to 10 years, 24.7% of the
respondents indicated that they have served the company for less than five years, 25.8 %
indicated to have served in the organization for eleven to fifteen years, 6.3 % indicated
to have served in the organization for sixteen to twenty years, whereas 3.2% of the
respondents indicated to have served for greater than twenty-one years. This implies that
the majority of the respondents in selected Ethiopian commercial banks, had worked for
a considerable period of time and therefore they were in a position to give credible
information relating to this study.
Experience
Frequency
≤5
47
6-10
76
11-15
49
16-20
12
21 and above
6
190
Total
Table 4.4: Employee’s job experience
Percent
24.7
40.0
25.8
6.3
3.2
100.0
Source: SPSS output of the survey,2021
4.2.4 Respondent’s Department
Table 4.3 shows the functional positions held by the respondents in their respective
workplaces. There was a near even distribution of respondents amongst IT experts
(22.1%), procurement management teams (16.8%), business team (6.8%), finance team
(16.8%), project management office (11.6%), HR expert (9.5%) and R&D (16.3%). This
is due to their routine involvement in active implementation of IT projects unlike Business
Relationship Managers whose role was mostly interfacing IT and business relationship
management, advisory and user acceptance testing. The wide array of participants
constitutes a critical mass that understands the overall needs of their institutions and can
guide in achieving success of IT projects (Jain & Metkewar, 2016).
Department
IT
Procurement
management
Business
team
Finance
Project office
HR expert
R&D
Total
Frequency
Percentage
42
32
22.1
16.8
13
6.8
32
22
18
31
190
16.8
11.6
9.5
16.3
100.0
Table 4.5: Functional departments of the respondents
Source: SPSS output of the survey,2021
4.2.6. Respondent’s Position
As it can be seen from the Table 4.6, 38.5 % of the respondents are experts (staff), 19.2%
of the respondents were officers, 11.5 % of the respondents were (chef officers,
Supervisors and domain experts) and 7.7% of the respondents were supervisors since
these are the peoples who actually involved in ERP project implementation, they are
expected to give the reality in the current status of ERP project. So, we can conclude that
the responses were worthwhile since all the relevant respondents are involved in this
study.
Position
pJunior officer
eSenior officer
rChief officer
V
Director
aManager
lProject
imanager
dstaff
Total
Frequency
94
21
13
8
13
13
percentage
28
190
49.5
11.1
6.8
4.2
6.8
6.8
14.7
100.0
Table 4.5: Positions of the respondents
Source: SPSS output of the survey,2021
4.3
Reliability and Validity of the Research Instrument
4.3.1 Reliability Analysis
Cronbach’s coefficient alpha was used to determine the internal reliability of the
questionnaire employed in this study. The alpha values range between 0 and 1.0 whereby
while 1.0 indicates perfect reliability, the value 0.70 is deemed to be the lower level of
acceptability (Tavakol et al., 2014). The reliability values for each of the variables are
presented in Table 4.4 where it is evident that Cronbach’s alpha values for each of the
variables were well above the lower limit of acceptability of 0.70. The findings indicated
that executive commitment had a coefficient of 0.730, user involvement had a coefficient
of 0.777, project team capability had a coefficient of 0.717, project management approach
had a coefficient of 0.773, project risk had a coefficient of 0.707 and implementation of
information technology projects garnered a coefficient of 0.756. These results therefore
indicated that the questionnaire used in this study had a high level of reliability.
No
Variable
1
2
PPM
3
PSM
PMM
4
5
6
NO of
Items
9
9
Alpha
Value
.827
.805
Very good
Very good
9
.787
Good
EC
PR
ITPI
8
6
9
.901
.814
.914
Excellent
Very good
Excellent
Total Cronbach's alpha
50
.899
Very good
Table 3 4: Reliability Statistics/Cronbach's Alpha
Source: Respondents Survey Test Result, 2021.
4.3.2 Validity
Validity is the degree to which the results obtained from the analysis of the data actually
represents the phenomenon under study (Mugenda et al., 2012). Face validity was carried
out through relevant literature review and peer review, which included use of accepted
methods and standards that were adopted in other relevant studies. To ensure content and
construct validity, the preliminary questionnaire was pre-tested with a sample of
respondents drawn from the relevant cadres of staff who were well versed with
information technology projects, but who would eventually not be part of the sample.
Also, survey items were extracted from existing project management theory and use of
tested instruments where available. A 95% response rate was realized in the pilot data
collection and feedback received was incorporated in the final questionnaire hence
improving it and was found to be adequate for final data collection.
4.4
Descriptive Statistics
The purpose of this study was to analyze the factors of implementation of information
technology projects by commercial banks in Ethiopia. This section tries to assess the
relationship between each of the independent variables (project procurement management,
project scope management, project management methodology and executive/top
management commitment) to the dependent variable of implementation of IT projects.
Frequencies and descriptive statistics are presented first, followed by qualitative analysis
then inferential statistics. Questionnaire responses were based on a Likert scale which was
aptly coded with numerical values for ease of data analysis. The values assigned were 1 =
strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, then 5 = strongly
agree.
4.4.1 Project procurement management and Implementation of IT projects
Procurement management is a way to more efficiently and productively handle the process
of sourcing, requisitioning, ordering, expediting, inspecting and reconciliating of
procurement for successful implementation of projects. Project procurement management
is one of the factors that expected to be a major factor to examine the implementation of
IT projects. Nine statements were presented for respondents to rate them on a Likert scale.
The following table presents the responses in each statement and the average reaction of
respondents in all of the statements. The average result is found by computing the
responses in each statement.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 7 Responses Related to Project procurement management
N
1.1 The procedure for obtaining the goods or services
required is well defined.
20
%
66 12
38.4 10
34.7 6.3
17
76
16
72
8.9
40
8.4 37.9 4.7
17
68
25
8.9
35.8 13.2 36.3 5.8
10
79
5.3
41.6 19.5 28.4 5.3
17
76
23
8.9
40
12.1 34.7 4.2
16
76
27
8.4
40
14.2 33.7 3.7
N
29
60
26
%
15.3
31.6 13.7 34.7 4.7
N
17
47
%
1.3 Payment to the supplier is made once the contract's N
payment criteria have been met and the work has
progressed.
%
1.4 Inspection and audit are performed to ensure that
N
the seller's work processes or deliverables are in
conformity.
%
1.5 For project procurements, a procurement strategy N
is produced that specifies what will be procured, the
specifications required, and the acquisition timeframe. %
1.6 The make or buy analysis is used to decide if a
N
project's work should be completed by the project
team or purchased from a third party.
%
1.7 Market research is conducted and used to inform
procurement decisions.
19
10.5
1.2 In the procurement policy, there is a well-defined N
formal evaluation review process.
73
37
43
69
54
66
64
66
73
9
11
10
8
7
9
10
2.8789 1.18236
2.8947 1.14987
190
190
2.9421 1.14634
190
2.8684 1.05337
190
2.8526 1.12187
190
2.8421 1.09656
2.8211 1.19946
3.0632 1.09651
190
190
190
1.8 By mutual consent and negotiation, the buyer and
seller can alter the procurement contract at any
moment.
%
1.9 A uniform procurement contract close-out process N
is used on all projects.
%
* Source: SPSS output of the survey, 2021
8.9
31
16.3
24.7 22.6 38.4 5.3
51 40 65 3
26.8 21.1 34.2 1.6
2.7789 1.13323
190
As we can see from table 4.7, all of the respondents were disagreeing responses for the
nine different questions in relation to project procurement management. The respondents
were asked about the procedure for obtaining the goods or services required is well defined to
implement the projects 38.4% of the respondents with the mean average result of 2.8 were
disagree, when asked in the procurement policy, there is a well-defined formal evaluation review
process 40% of the respondents with the mean average result of 2.9 were disagree, when
asked Payment to the supplier is made once the contract's
payment criteria have been met and the work has progressed, 36.3% of the respondents
with the mean average result of 2.94 were agree, when asked inspection and audit are
performed to ensure that the seller's work processes or deliverables are in conformity
41.6% of the respondents with the mean average result of 2.86 were disagree, when asked
for project procurements, a procurement strategy is produced that specifies what will be
procured, the specifications required, and the acquisition timeframe 40% of the
respondents with the mean average result of 2.85 were disagree, when asked the make or
buy analysis is used to decide if a project's work should be completed by the project team
or purchased from a third party 40% of the respondents with the mean average result of
2.84 were disagree, when asked market research is conducted and used to inform
procurement decisions, 34.7% of the respondents with the mean average result of 2.82
were agree, when asked by mutual consent and negotiation, the buyer and seller can alter
the procurement contract at any moment, 38.4% of the respondents with the mean average
result of 3.06 were agree, when finally the respondent asked that a uniform procurement
contract close-out process is used on all projects 34.2% of the respondents with the mean
average result of 2.86 agree. Even though the project procurement management is
important in implementation of IT projects, the average responses of the respondent show
disagree level of agreement, this implies that the majority of the respondent indifference
on project procurement management. This means that the selected commercial banks are
facing project procurement challenges during implementation process. Thus, the selected
commercial banks should pay more attention to project procurement management in order
to realize the benefits after the implementation of the IT project.
4.4.2 Project scope management and Implementation of IT projects
Project scope management is a process that helps in determining and documenting the list
of all the project goals, tasks, deliverables, deadlines, and budgets as a part of the planning
process. The same way as it is done to project scope management nine statements that can
measure project management issues were posed to the respondents. Almost all statements
provided to each respondent being responded as a disagree level of agreement, but it was
expected that this variable as being a major factor in examining the implementation of IT
projects. The results of the respondents’ responses were compiled and presented in the
following table. The table presents the responses in each statement and the average
reaction of respondents in all of the statements.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 8 Responses Related to Project scope management
N
2.1There were tools and procedures for establishing
14
scope that were well-defined.
%
2.2 The objectives are set in accordance with the
N
customer's requirements.
%
2.3 In order to manage scope, the necessary tools and
procedures were used.
2.4 The projects were managed using the typical
project management process of initiating, planning,
executing, monitoring, and evaluating and closing.
81
17
67 11
2.8947 1.14063
190
7.4
42.6 8.9 35.3 5.8
26
74
13.7
38.9 11.1 31.6 4.7
N
%
26
13.7
72 18 61 13
37.9 9.5 32.1 6.8
2.8053 1.22106
190
N
28
51
2.9789 1.24279
190
%
14.7
26.8 11.1 40.5
2.5 Stakeholder conflict over scope was avoided using N
several ways.
%
35
67 35 45 8
35.3 18.4 23.7 4.2
2.6000 1.15836
190
N
%
N
%
21
11
5.8
9
4.7
2.8895 1.18786
13.2
71 17
37.4 8.9
69 26
36.2 13.7
29
15.3
23
40 30 74 17
21.1 15.8 38.9 8.9
77 34 54 2
3.0526 1.25457
190
2.6579 1.05098
190
12.1
40.5 17.9 28.4 1.1
2.6 The primary operations that must be completed
and the intended end result are clearly specified.
2.7 The project had clear goals
2.8 There is a custom of confronting difficulties with a N
skewed scope.
%
2.9 The PMBOK (project management body of
N
knowledge) and CPM (project management
methodology) were used in this project (critical path
method)
%
* Source: SPSS output of the survey, 2021
18.4
11.1
25
21
21
60
77
70
36.8
61
32.1
9
13
2.7474 1.17710
190
6.8
2.7895 1.16741
190
190
The respondents were asked about if there were tools and procedures for establishing
scope that were well-defined 42.6% of the respondents with the mean average result
of 2.89 were disagree, when asked if the objectives are set in accordance with the
customer's requirements 38.9% of the respondents with the mean average result of
2.74 were disagree, when asked in order to manage scope, the necessary tools and
procedures were used 37.9% of the respondents with the mean average result of 2.80
were disagree, when asked if the projects were managed using the typical project
management process of initiating, planning, executing, monitoring, and evaluating and
closing 40.5% of the respondents with the mean average result of 2.97 were agree,
when asked if Stakeholder conflict over scope was avoided using several ways 35.3% of
the respondents with the mean average result of 2.60 were disagree, when asked if
the primary operations that must be completed and the intended end result are clearly
specified 37.4% of the respondents with the mean average result of 2.88 were
disagree, when asked if the project had clear goals 36.2% of the respondents with the
mean average result of 2.78 were disagree, when asked if there is a custom of
confronting difficulties with a skewed scope 38.9% of the respondents with the mean
average result of 3.05 were agree when finally the respondent asked that the PMBOK
and CPM were used in this project 40.5% of the respondents with the mean average
result of 2.65 disagree. Even though the execution of project scope management was
important in implementation of IT projects, the average responses of the respondents
were disagreed level of agreement, this implies that majority of the respondent’s
indifference with project scope management. Thus, the selected banks should pay
more attention to project PSM in order to realize the benefits after the
implementation of the IT projects.
4.4.3 Project management methodology and Implementation of IT projects
Nine questions were presented to the respondents to assess their level of agreement
towards the project management methodology. The following table presents the
summarized result of the questions.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 8 Responses Related to Project management methodology
N
3.1 When it comes to project implementation, the bank
26
frequently uses project management technique.
%
3.2 The bank employs a project management
N
technique that it created in-house.
%
3.3 The bank uses a defined project management
technique to tackle projects.
N
%
63
27
62 12
2.8474 1.20091
190
13.7
33.2 14.2 32.6 6.3
23
74
12.1
38.9 16.8 27.9 4.2
26
13.7
78 32 50 4
41.1 16.8 26.3 2.1
2.6211 1.08065
190
3.4 The bank employs a bespoke project management N
technique at all times.
%
24
67 36 55 8
35.3 18.9 28.9 4.2
2.7684 1.12643
190
N
%
24
71 22 65 8
37.4 11.6 34.2 4.2
2.8000 1.16474
190
N
3.6 Throughout the lifecycle of a project, the bank
uses agile project management technique.
%
3.7 In most cases, the bank follows a well-documented N
traditional project management technique.
%
3.8 Project managers are expected to adapt the project N
management approach to the unique characteristics of
each project and the surrounding environment.
%
3.9 The tools and approaches advocated in your bank's N
IT strategic emphasis are employed to complete IT
initiatives
%
* Source: SPSS output of the survey, 2021
33
8
4.2
20
10.5
2.5842 1.16871
14
7.4
78 22 49
41.1 11.6 25.8
63 27 66
33.2 14.2 34.7
13
48
29
3.2316 1.18592
190
6.8
41
25.3 22.1 31.6 15.3
74 9
49 17
2.6158 1.31526
190
21.6
38.9 4.7 25.8 8.9
3.5 Processes linked with agile project management
approach are always embraced by the bank.
12.6
12.6
17.4
32
40
53
60
8
190
2.7316 1.12054
3.0789 1.18146
190
190
As we can see from table 4.3, the respondents were asked about when it comes to project
implementation, the bank frequently uses project management technique, 33.2% disagreed,
when asked if the bank employs a project management technique that it created in-house 38.9%
disagreed, when asked if the bank uses a defined project management technique to tackle
projects, 41.1% disagreed, when asked if the bank employs a bespoke project management
technique at all times, 35.3 disagreed, when asked if Processes were linked with agile project
management approach are always embraced by the bank, 37.4 % disagreed, when asked if
throughout the lifecycle of a project, the bank uses agile project management technique, 41.1%
disagreed, when asked if in most cases, the bank follows a well-documented traditional project
management technique, 34.7% agreed, when asked if project managers are expected to adapt the
project management approach to the unique characteristics of each project and the surrounding
environment 31.6% agreed, when finally the respondent were asked the tools and
approaches advocated in your bank's IT strategic emphasis are employed to complete IT
initiatives 38.9% disagreed. From the response, a number of respondents seemed unsure
of the project management methodologies used in their respective banks since there are
many methodologies and the respondents were not in a position to comfortably delineate
the particular methodologies that were in use.
Table 4. 8 Responses Related to Executive/Top management commitment and
implementation of IT projects
For the aim of this research, the executive commitment refers to the provision of necessary
financial and other resources, establishing policies and procedures, delegating implementation
authority, taking risk and responsibilities during implementation of IT projects. The following table
presents the responses in each statement and the average reaction of respondents in all of the
statements.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 8 Responses Related to Executive commitment
N
4.1 Upper-level management of the bank routinely
38
backs projects and provides resources.
%
4.2 When it comes to participation in industry
N
71
11
57 13
20
37.4 5.8 30
6.8
34
73
12
17.9
38.4 4.7 32.6 6.3
9
62
2.6632 1.28162
2.7105 1.26633
190
190
regulator efforts, the bank has a flexible corporate
culture.
%
4.3 When new projects need to be implemented, the
bank always welcomes change management.
4.4 The executive management of the organization
creates policies and processes to monitor project
implementation.
4.5 The bank's planning, monitoring, and controlling
methods have consistently ensured project efficiency
and effectiveness.
4.6 The bank's leadership has a reputation for being
willing to take calculated risks while implementing
projects.
4.7 For projects, the bank regularly provides a defined
vision, goal, and objectives.
N
%
40
21.1
70 20 46 14
36.8 10.5 24.2 7.4
2.6000 1.26324
190
N
33
74
2.6105 1.18003
190
%
17.4
38.9 14.2 24.2 5.3
N
32
73
2.6895 1.23588
190
%
16.8
38.4 11.1 26.3 7.4
N
29
80
%
N
%
15.3
42.1 15.3 23.7 4.2
81 25 56 6
42.6 13.2 29.5 3.2
22
11.6
27
21
29
46
50
45
10
14
7
2.5842 1.11780
2.7000 1.10769
190
190
4.8 The team members are rewarded by the executive N
management.
%
* Source: SPSS output of the survey, 2021
44
23.2
74 16 45 11
38.9 8.4 23.7 5.8
2.5000 1.24191
190
The respondents were asked if upper-level management of the bank routinely backs projects and provides
resources, 37.4% of the respondents with the mean average result of 2.66 were disagree, when asked
when it comes to participation in industry regulator efforts, the bank has a flexible corporate culture
38.4% of the respondents with the mean average result of 2.71 were disagree, when asked when
new projects need to be implemented, the bank always welcomes change management 36.8% of
the respondents with the mean average result of 2.6 were disagree, when asked the executive
management of the organization creates policies and processes to monitor project implementation
38.9% of the respondents with the mean average result of 2.61 disagree, when asked the bank's
planning, monitoring, and controlling methods have consistently ensured project efficiency and
effectiveness 38.4% of the respondents with the mean average result of 2.68 were disagree, when
asked the bank's leadership has a reputation for being willing to take calculated risks while
implementing projects 42.1% of the respondents with the mean average result of 2.58 were
disagree, when asked for projects, the bank regularly provides a defined vision, goal, and objectives
42.6% of the respondents with the mean average result of 2.7 were disagree when finally the
respondent asked that the team members are rewarded by the executive management 38.6% of the
respondents with the mean average result of 2.5 were disagree. Even though the executive
commitment is important in implementation of IT projects, the average responses of the respondent
show disagreed level of agreement, this implies that the majority of the respondent indifference on
top executive commitment. This means that the executive management has not played an
instrumental role in the implementation process. Thus, the organization should pay more attention
to executive commitment in order to realize the benefits after the implementation of IT projects.
4.4.5 Project risk and Implementation of IT projects
The study sought to determine the moderating effect of project risk on the determinants of
implementation of information technology projects by commercial banks in Ethiopia. The
following table presents the responses in each statement and the average reaction of respondents
in all of the statements.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 8 Responses Related to project risk
N
5.1 Technical complexity has a significant impact on
13
project implementation
%
5.2 The relative project size has a significant impact
N
on project implementation.
%
5.3 The urgency with which a project must be
completed has a significant impact on project
implementation.
5.4 The degree to which a project is vital has a
significant impact on its execution.
30
22 103 22
6.8
15.8 11.6 54.2 11.6
8
36
4.2
18.9 8.4 57.4 11.1
N
8
37
%
4.2
19.5 7.9 60.5 7.9
N
%
14
16
15
109 21
115 15
3.4789 1.10174
3.5211 1.05263
190
190
3.4842 1.02728
190
3.3895 1.15282
190
7.4
41 12 103 20
21.6 6.3 54.2 10.5
5.5 Project implementation is frequently hampered by N
specification revisions.
%
8
4.2
35 20 117 10
18.4 10.5 61.6 5.3
3.4526 .98956
190
5.6 Project implementation is frequently hampered by N
technological uncertainty.
%
15
7.9
29 17 117 13
15.3 8.9 61.1 6.1
3.4368 1.08072
190
* Source: SPSS output of the survey, 2021
Among the questions posed to the respondents on the subsets of project risk was whether
technical complexity of IT projects had a marked effect on their implementation and
54.2% representing 103 out of 190 respondents agreed. This question returned a mean
score of 3.47. 57.4% representing 109 out of 190 agreed on relative project size has a
significant impact on project implementation. This question returned a mean score of 3.52.
60.5% representing 115 out of 190 agreed on the urgency with which a project must be
completed has a significant impact on project implementation. This question returned a
mean score of 3.48. 54.2% representing 103 out of 190 agreed on the degree to which a
project is vital has a significant impact on its execution. This question returned a mean
score of 3.38. 61.6 % representing 117 out of 19 agreed on the project implementation is
frequently hampered by specification revisions. This question returned a mean score of
3.45. 61.1% representing 117 out of 190 agreed on the project implementation is
frequently hampered by technological uncertainty. This question returned a mean score of
3.43.
4.4.6 Summary of survey results for the dependent variable
Nine statements were presented to respondents to look at different issues that can
measure the dependent variable (implementation of IT projects). The following
table summarizes the views of respondents with regard to implementation of IT
projects.
N
St.
Deviation
Mean
Strongly
Agree
Agree
Statements
Neutral
Strongly
disagree
Disagree
Table 4. 8 Responses Related to Implementation of IT projects
N
6.1 The bank’s IT initiatives are completed on
37
66
25
53 19
2.6368 1.21268
190
schedule and within the budget.
%
19.5
34.7 13.2 27.9 4.9
6.2 The projects were finished on time and on
N
67
77
budget.
%
35.3
40.5 4.7 27.9 4.2
6.3 The projects were finished on schedule and under N
budget.
%
56
29.5
70 7
41 16
36.8 3.7 21.6 8.4
2.4263 1.33426
190
6.4 The bank's project deployment yielded the
anticipated commercial benefits.
43
22.6
72 4
58 13
37.9 2.1 30.5 6.8
2.6105 1.31169
190
29
67 10 68 16
35.3 5.3 35.8 8.4
2.8684 1.28424
190
8
4.2
3
1.6
2.5842 1.16871
6
3.2
9
4.7
2.5053 1.13970
190
2.1421 1.16647
190
N
%
6.5 During the project life cycle, the scope and goals N
of the project are not changed needlessly.
%
15.3
9
13.5 5.8
6.6 Project deliverables usually satisfy key
stakeholders in the bank.
6.7 The bank anticipated the project to meet all of its
quality criteria, which it did
N
%
N
%
33
39
20.5
78 22 49
41.1 11.6 25.8
86 14 48
45.3 7.4 25.3
6.8 The initiatives were done in a way that will help
them achieve their intended aim
6.9 The initiatives' planned objectives were met with
great success.
* Source: SPSS output of the survey, 2021
N
%
N
%
34
17.9
64
33.7
85 18
44.7 9.5
79 12
41.6 6.3
17.4
47
24.7
26
13.7
2.1421 1.20660
2.4211 1.12291
190
190
190
As Table 4.8 shows the majority of the respondents were unsatisfied with questions
related to implementation of IT projects. When the respondent asked that the bank’s IT
initiatives are completed on schedule and within the budget 34.7% with an average mean
of 2.63 were in disagreed level, when asked the projects were finished on time and on
budget,35.3% of the respondent with an average mean of 2.14 were in agreement level
of strongly disagree, when asked the projects were finished on schedule and under budget
36.8% the respondent with an average mean of 2.42 were in agreement level of disagree,
when asked the bank's project deployment yielded the anticipated commercial benefits 37.9%
and 22.6% of the respondent with an average mean of 2.61 were in agreement level of
disagree and strongly disagree respectively , when asked during the project life cycle, the
scope and goals of the project are not changed needlessly 35.8% of the respondent with
an average mean of 2.86 were in agreement level of agree, when asked if project
deliverables usually satisfy key stakeholders in the bank 41.1% of the respondents with
an average mean of 2.58 were in agreement level of disagree , when asked the bank
anticipated the project to meet all of its quality criteria, which it did 45.3% of the
respondents with an average mean of 2.42 were in agreement level of disagreement, when
asked if the initiatives were done in a way that will help them achieve their intended aim
44.7 of the respondents with an average mean of 2.50 were in agreement level of
disagreement, when finally asked if the initiatives' planned objectives were met with great
success 41.6% of the respondents with an average mean of 2.14 were in agreement level
of disagree. From the responses, it can be seen that the respondent disagreed that the
overall implementation of IT projects was successful and effectively implemented. From
all response we can conclude that majority of the respondent believe the IT projects are
facing problems during implementation. Thus, the selected commercial banks should pay
more attention to the project in order to realize the benefits after the implementation of
the project.
4.2.9. Summary of frequency results of each variable and Mean Score for dependent
and independent variables
As it is known, the mean value or score of a certain set of data is equal to the sum of all
the values in the data set divided by the total number of values. In this context, the mean
of the response and score of all statements for each independent variables and dependent
variable is calculated and the meaning of the score is interpreted accordingly.
In this research project procurement management, project scope management, project
management methodology and executive commitment, were the independent variables
and project risk was moderator variable and implementation of IT projects as dependent
variable. Statements under this variable are scaled 1 to 5 with a meaning of 1= strongly
disagree, 2=disagree, 3=neutral, 4=agree, and 5=strongly agree. To match the result of
the mean score of each variable with the respondent level agreement in Likert scale and
to summarize the narrative outcomes; the researcher used criterion-referenced definitions
for rating scales to describe the collected data. Unlike norm-referenced scales, criterionreferenced scale measure response scores against a fixed set of criteria.
4.4.6 Aggregation of Independent Variables
With independent variables having met the reliability test, items under each variable were
aggregated and the average shown (mean and standard deviation). From the descriptive
statistics, project procurement management had the highest rating but the second lowest
variation in responses (M = 2.8825, S.D = 0.74155). Project scope management had the
second highest rating with the second highest variation in responses (M = 2.8240, S.D
= 0.77243). Project management methodology had the third highest rating but lowest
variation in responses (M = 2.8088, S.D = 0.71381). The variable which recorded the
lowest rating was executive commitment but had the highest variation in responses (M =
2.6322, S.D = 0.93357). From the given scores, executive commitment became the worst
predictor followed by project management methodology. The second-best predictor was
project scope management with the best predictor being project procurement
management. The aggregation is shown in Table 4.10;
Table 4.10: Summary of Means and Standard Deviations
Variable
Mean
Std. Dev.
Min
Max
Project procurement management – X1
3.2433
0.65196
1.67 5.00
Project scope management – X2
3.2649
0.73004
1.56 5.00
Project management methodology – X3
3.0667
0.64361
1.33 5.00
Executive commitment – X4
3.0461
0.76637
1
5.00
Ranked on scale: (Strongly Disagree = 0 – 1.8; Disagree = 1.8 – 2.6; Neutral = 2.6 – 3.4;
Agree = 3.4 – 4.2; Strongly Agree = 4.2 – 5.0). Source; (MacEachern, 1982)
Based on the Likert scale “3" means “neither agree nor disagree, while value “4” means
“agree”, etc. Here in criterion-referenced scale, if value of 3 is recorded as any of the
subsequent measurement, it means that level is neither high nor low, or in other words, it
is in “average or medium level”. If a value of (4) is obtained, it means s “high” level.
Similarly, value one (1) and five (5) mean “very low” level and “very high” level
respectively while value two (2) means “low” level.
Table 4. 11: Mean score result summary according to Criterion–referenced scale
Variables
PPM
PSM
Mean Score
3.24
3.26
Degree/level of
agreement
N
N
Description
M
PMM EC
3.06
M
3.04
PR
IITP
3.54
2.47
N
N
A
N
M
M
H
M
*Source: SPSS output of the survey, 2021
As it is shown in the Table 4.11, the average mean score of the respondents for PPM,
PSM, PMM, EC, PR, is 2.88, 2.82, 2.80, 2.63 and 3.46respectively towards
“implementation of IT projects.”
Based on criterion- referred definitions of Table 4.10, the mean of PPM, PSM PMM and
EC, shown as medium, this implies that the response for those individual question to those
independent variables were neutral level of agreement. Respondents’ opinion on project
risk shown as high; this implies that the response for individual questions to this
independent variable was respond to an agreed level and those respondents have high
scored relative to this moderator variable.
The grand or cumulative mean score of the dependent variable implementation of IT
projects is 2.47. This implies that all and nearly all have got a neutral level of agreement
which indicates that the status of implementation of IT projects in mind of employees has
moderate successful implementation.
4.4.7 Qualitative Analysis
This study employed both qualitative and quantitative means in obtaining data. Arising
from the concept of triangulation, data was obtained from respondents through open and
closed ended questions. Methodological triangulation entails combining both quantitative
and qualitative data collection methods (Creswell & Creswell, 2017), based on the
rationale that a single data collection method is insufficient to provide adequate and
accurate research results. A wide array of open-ended questions related to the study
objectives were included in the questionnaire and content analysis done by use of SPSS
version 25.
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4.5
Tests of Assumptions
Having explored the independent variables through descriptive statistical analysis, the
study sought to establish the relationship between these independent variables with the
dependent variable. This meant a bivariate nature of the relationship between the variables
had to be established. Correlation analysis was used to evaluate the strength and direction
of the relationship among the variables and linear regression used to determine the nature
of the relationships. The researcher applied inferential statistics to test the study
hypotheses and reject or fail to reject the null hypotheses. At 5% level of significance, the
null hypothesis was rejected if the p value was < 0.05.
4.5.1 Normality Tests for Variables
Assumption 1 - Normality Distribution Test
Multiple regressions require the independent variables to be normally distributed.
Skewness and kurtosis are statistical tools that enable the researcher to check if the data is
normally distributed or not. According to Smith and Wells (2006), kurtosis is defined as
“property of a distribution that describes the thickness of the tails. The thickness of the tail
comes from the number of scores falling at the extremes relative to the Gaussian/normal
distribution” Skewness is a measure of symmetry. A distribution or data set is symmetric
if it looks the same to the left and right of the center point.
If the skewness and kurtosis test results of the data are within the acceptable range (-1.0
to +1.0), it can be concluded that the data is normally distributed. For this purpose and
taste of normal distribution, the kurtosis and skewness results are shown in table 4.15
Table 4. 15: Normality of data distribution Descriptive Statistics
N
Mean
Std.
Deviation
Skewness
Kurtosis
Statistic
Statistic
Std. Error
Statistic Std. Error
.466
-.060
.176
-.515
.351
PPM
Statistic Statistic
190 3.26466
PSM
190 3.3023
.71523
-.306
.176
-.315
.351
PMM
EC
PR
CEB
190
190
190
190
3.0774
3.0658
3.5884
3.1947
.65235
.65235
.71245
.64355
-.026
.090
-.805
-.266
.176
.176
.176
.176
-.431
-.644
1.015
-.610
.351
.351
.351
.351
PMPCC
IITP
Valid N
(Listwis
e)
190 3.0314
190 2.7180
190
.69275
.92283
.057
.126
.176
.176
-.511
-1.050
.351
.351
Source: SPSS output of survey questionnaire, 2021
The acceptable range for normality for both statistics is between -1.0 and + 1.0. as shown
in table 4.15, all variables for both skewness and kurtosis statistics are fall in the acceptable
standard of normality (-1.0 -, +01.0). Graphically this normality assumption distribution
is shown below.
Also, it is recommended that normality be assessed both visually using instruments like
Quantile – Quantile (Q-Q) plots and through other normality tests such as Shapiro-Wilk.
To test the significance of departure from normality, Q-Q plots were done and the results
shown in figures 4.2, 4.3, 4.4, 4.5 and 4.6.
4.5.1 (a) Normal Q-Q plot of project procurement management
Departure from normality for project procurement management was not so pronounced
and the same was confirmed from the approximation of line of fit in Figure 4.2. Data was
therefore near to normal distribution and could be used in regression analysis.
Figure 4.2: Normal Q-Q plot of project procurement management
4.5.1 (b) Normal Q-Q plot of project scope management
For the independent variable project scope management, departure from normality was
also not so much as can be seen from the approximation line of fit. This was a confirmation
that data was near normal in its distribution and could therefore be used in regression
analysis. This is shown in Figure 4.3;
Figure 4.3: Normal Q-Q plot of project scope management
4.5.1 (c) Normal Q-Q plot of Project management methodology
The departure from normality for independent variable project management methodology
was also not very far off the line of approximation of fit. That was a confirmation that the
data was almost normally distributed and hence could be used for regression analysis. This
is illustrated in Figure 4.4;
Figure 4.4: Normal Q-Q plot of Project management methodology
4.5.1 (d) Normal Q-Q plot of Executive commitment
For the independent variable executive commitment, departure from normality was also
not so much as shown by the approximated line of fit in Figure 4.5. This confirmed that
the data was near normal distribution and could therefore be used in regression analysis.
Figure 4.5: Normal Q-Q plot of Executive commitment
4.5.1 (e)
Normal Q-Q plot of Implementation of IT projects (Dependent variable)
Data for the dependent variable, implementation of IT projects, was not far off the
approximation line of fit and could therefore be used in regression analysis. This is shown
in Figure 4.6;
Figure 4.6: Normal Q-Q plot of Implementation of IT projects
4.5. Multicollinearity
Multicollinearity refers to the situation in which the independent/predictor variables are
highly correlated. When independent variables are multicollinear, there is “overlap” or
sharing of predictive power. This may lead to the paradoxical effect, whereby the
regression model fits the data well, but none of the predictor variables has a significant
impact in predicting the dependent variable. This is because when the predictor variables
are highly correlated, they share essentially the same information. Thus, together, they
may explain a great deal of the dependent variables, but may not individually contribute
significantly to the model. Meaning, they can be considered as one variable than two
separate variables. The existence of multicollinearity can be checked using “Tolerance”
and “VIF” values for each predictor variable. Tolerance values less than 0.10 and VIF
(variance inflation factor) greater than 10 indicates the existence of multicollinearity
(Robert, 2006). As can be seen from the table below, multicollinearity is not an issue for
this current data.
Table 4. 16: Multicollinearity Test Table
Coefficientsa
Model
1 (Constant)
Collinearity
Statistics
Tolerance VIF
PPM
.524
1.910
PSM
.485
2.076
PMM
EC
PR
CEB
PMPCC
.487
.434
.867
.405
.316
2.055
2.305
1.154
2.469
3.163
Source: SPSS output of survey questionnaire, 2021
As it is stated above for the assumption to be met values of Variance Inflation Factor
(VIF) scores must be below 10, and tolerance scores to be above 0.1; which is the case in
as shown in table 4.16, the tolerance and VIF of project procurement management,
project scope management, project management methodology, executive commitment,
cost estimating and budget, project management and controlling capability
0.524,0.485,0.487,0.434,0.867,0.405, and 0.316 respectively. For this reason, this
research model fits the requirement and collinearity is not a problem.
4.5.1 Correlation Analysis for the Linear Relationship between the Study Variables
The researcher ran a correlation matrix to establish if there existed a relationship between
the variables. Pearson Product Moment Correlation was used for the correlation analysis,
with (r) being used to determine the linear relationship between the study variables.
According to Mugenda et al., (2012), the correlation coefficient yields a statistic that
ranges between -1.0 (perfect negative correlation) to 1.0 (perfect positive correlation) and
it shows the magnitude of the relationship between two variables. How big the correlation
coefficient value is points to a stronger association between two variables. A zero value
of (r) shows that there is no association between two variables. The correlation coefficients
were computed for each pair of the variables and the results shown in the correlation
matrix (Table 4.12).
Table 4.12: Correlation Analysis Results for Study Variables
Project
Pearson
1
procurement Correlation
management Sig. (2-tailed)
N
190
Project scope Pearson
.567**
1
management Correlation
Sig. (2-tailed)
.000
N
190
190
Project
Pearson
.480** .554**
1
management Correlation
methodology Sig. (2-tailed)
.000
.000
N
190
190
190
**
**
Executive
Pearson
.516
.519
.550**
1
commitment Correlation
Sig. (2-tailed)
.000
.000
.000
N
190
190
190 190
**
*
Project risk Pearson
.205
.179
.181* .194
1
**
Correlation
Sig. (2-tailed)
.004
.014
.012 .007
N
190
190
190
**
**
Cost
Pearson
.633
.634
.607**
estimating
Correlation
and budget
Sig. (2-tailed)
.000
.000
.000
N
190
190
190
**
**
Project
Pearson
.491
.613
.659**
management Correlation
and Project Sig. (2-tailed)
.000
.000
.000
control
N
190
190
190
capability
Implementati Pearson
.512** .429** .563**
on of IT
Correlation
projects
Sig. (2-tailed)
.000
.000
.000
N
190
190
190
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
190
190
.528 .289**
1
**
.000
.000
190
190
190
**
.726 .323
.626**
1
**
.000
190
.000
190
.000
190
190
.665 .197** .643**
.677**
**
.000
190
.007
190
.000
190
.000
190
The findings showed that implementation of IT projects had a high correlation with cost
estimating and budgeting (r = .633, p-value < 0.001). That meant that a positive change in
cost estimating and budgeting resulted in effective implementation of IT projects. Also,
commercial banks that focused on executive commitment in projects recorded improved
effectiveness in implementation of IT projects as indicated by a significant correlation
value (r = .516, p-value < 0.001). The study findings also showed that implementation of
IT projects and adoption of project management and control had a relatively significant
relationship (r = .491, p-value < 0.001). It showed that banks that embraced project
management and control achieved effective implementation of IT projects.
Project risk being the moderating variable also showed a weak positive correlation with
implementation of IT projects (r = .205, p-value <0.001). The p-values for project risk
were above the criteria of α < 0.05 and therefore they were not statistically significant.
Since all variables returned a positive correlation as shown in Table 4.12, they were
therefore subjected to further regression analysis to determine their individual
contributions.
1
4.6
Regression Analysis Results
The researcher used multiple regression analysis to determine the linear statistical
relationship between the independent, moderating and dependent variables of the study.
The null hypotheses of the study were tested using linear regression models. F- test was
used to test the validity of the model while (r2) was used to measure the model’s goodness
of fit. The regression coefficient was used to describe the results of regression analysis
and outline the nature and intensity of the relationships between the study variables.
4.6.1 Influence of project procurement management on Implementation of IT
projects
To find out the influence of project procurement management (X1) on implementation of
IT projects (Y), a regression model was fitted to the data and it was found to be statistically
significant (F (1, 188) = 66.797, p = .000). The co-efficient of determination (R2) of .262
was an indicator that project procurement management explained a paltry 26.2% variation
in improvement of implementation of IT projects. The adjusted R2 explained 25.8%
variation while the remainder could be explained by other factors not included in the
model. R value of .512 indicated a strong positive correlation between project
procurement management and implementation of IT projects.
The hypothesis to be tested was H01: Project procurement has no significant influence on
implementation of IT projects by commercial banks in Ethiopia.
The survey results showed that there was a strong positive relationship between project
procurement management and implementation of IT projects by commercial banks in
Ethiopia (β1= .708, t = 8.173, p-value < .001). The regression model fitted to test the
relationship was;
Y = β0 + β1X1 + ε;
where;
Y = Implementation of IT projects, β0 = Constant, X1 = project procurement
management,
ε = Error term
The null hypothesis stating that project procurement management has no significant
influence on implementation of IT projects by commercial banks in Ethiopia (H01: β1= 0)
was therefore rejected (β1= .708, t = 8.173, p-value <.001) and a conclusion drawn that
project procurement management (X1) has a marginal influence on implementation of
information technology projects by commercial banks in Ethiopia (Y). The resulting
regression model was;
Y = 0.407 + 0.708X1
The model equation shows that standardized implementation index of information
technology projects will increase by .708 units with one unit increase in the index of
project procurement management.
Table 4.13: Regression analysis results on executive commitment and
implementation of IT projects
a) Model Summary
Change Statistics
Std.
Error of
R
R
Adjusted
the
Square
Model
R
Square R Square Estimate Change F Change
df1
df2 Sig. F Change
a
1
.512
.262
.258 .79480
.262
66.797
1 188
.000
a. Predictors: (Constant), Project procurement management
b)ANOVAa
Sum of
Model
Squares
df
Mean Square
1
Regression
42.196
1
42.196
Residual
118.760
188
.632
Total
160.956
189
a. Dependent Variable: implementation of IT projects
b. Predictors: (Constant), Project procurement management
F
66.797
Sig.
.000b
c)Coefficientsa
Standardize
Unstandardized
d
95.0% Confidence Interval
Coefficients
Coefficients
for B
Correlations
Partia Par
Model
1
B
Std. Error
(Constant)
.407
.289
Project
.708
.087
Beta
.512
t
Sig. Lower Bound Upper Bound Zero-order
1.410 .160
-.162
.976
8.173 .000
.537
.879
.512
procurement
management
a. Dependent Variable: implementation of IT projects
Pearson product-moment correlation coefficient for project procurement management and
implementation of IT projects (r =.512, p-value = .000) was significant at .05 level of
significance. Regression analysis carried out showed that project procurement
management had a marginal influence on implementation of information technology
projects by commercial banks in Ethiopia. These results on the influence of project
procurement management on implementation of information technology projects were
consistent with earlier studies conducted by Cooper, Schindler, and Sun (2006) asserts
that procurement suppliers, subsidiaries, logistics and manufacturing companies that
affect the exchange of goods and services also face procurement challenges and this also
affects effective implementation of projects.
4.6.2 Influence of Project scope management on Implementation of IT Projects
In establishing the influence of project scope management (X2) on implementation of IT
projects (Y), the regression model was found to be significant (F (1, 188) = 42.448, p –
value 0.000), indicating that user project scope management was a valid predictor in the
model. The coefficient of determination (R2) value of .184 implied that project scope
management independently explained 18.4% variation in effective implementation of
information technology projects. The adjusted R2 explained 18% and therefore the
remainder could be explained by other factors not included in the model. The R value of
.429 indicated a moderate positive correlation between project scope management and
implementation of IT projects.
The hypothesis to be tested was H02: project scope management has no significant
influence on implementation of information technology projects by commercial banks in
Ethiopia.
l
.512
t
.51
2
The survey results showed that there was a positive relationship between project scope
management and implementation of information technology projects by commercial
banks in Ethiopia (β2 = .554, t = 6.515, p-value < 0.001). The regression model fitted to
test the relationship was;
Y = β0+ β2X2 + ε.
The null hypothesis H02: project scope management has no significant influence on
implementation of information technology projects by commercial banks in Ethiopia was
therefore rejected (β2 = .554, t = 6.515, p-value < 0.001) and a conclusion drawn that:
project scope management (X2) moderately influenced implementation of information
technology projects (Y).
The model equation was;
Y = .889 + .554X2 where; Y = Implementation of IT projects and X2 = project
scope management. The beta coefficient for project scope management was
significant (β2 = .554, t = 6.515, p-value < 0.001), implying that for every
single unit increase in the index of project scope management, there is an
improvement index of .554 in effectiveness of information technology project
implementation as shown in Table 4.14.
Table 4.14: Regression results for the relationship between user involvement and
implementation of IT projects.
a) Model Summary
Change Statistics
Std. Error
Mod
R
Adjusted
of the
R Square
F
el
R
Square R Square Estimate
Change Change
df1
df2
a
1
.429
.184
.180
.83573
.184 42.448
1
188
a. Predictors: (Constant), Project scope management
b) ANOVAa
Sum of
Squares
Model
1
Regression
df
Mean Square
29.648
1
29.648
Residual
131.309
188
.698
Total
160.956
189
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Project scope management
F
42.448
Sig.
.000b
Sig. F
Change
.000
C)Coefficientsa
Unstandardized
Standardized
Coefficients
Coefficients
Model
B
Std. Error
1
(Constant)
.889
.287
Project scope
.554
.085
t
Sig.
Beta
95.0% Confidence Interval for B
Lower Bound
.429
Upper Bound
3.097
.002
.323
1.456
6.515
.000
.386
.721
Correlations
Zero-
Part
order
ial
Part
.42
.429
.429
management
a. Dependent Variable: Implementation of IT projects
Similarly, findings of this study are consistent with Dekkers & Forselius (2007), Increase
IT project success with concrete scope management, presents that through concrete scope
management processes, IT project managers can learn and embrace proven approaches
that measure the size of software projects, streamline the requirements articulation and
management, and impose solid change management controls, to keep projects on time and
on budget.
4.6.3 Influence of Project Management methodology on Implementation of IT
projects
As shown in Table 4.16, the regression model of project management methodology (X3)
and implementation of information technology projects was significant (F (1, 188) =
87.034, p-value < 0.001), confirming that project management methodology was a valid
predictor in the model. The coefficient of determination (R2) was .316 implying that
31.6% improvement in effective implementation of information technology projects could
be explained by project management methodology adopted. Adjusted R2 was .313
meaning 31.3% was explained by project management methodology and the rest could be
attributed to other factors not captured in the model. R score of .563 indicated a moderate
positive correlation between project management methodology and implementation of
information technology projects. The standard error of .765 indicated the deviation from
the line of best fit.
The hypothesis to be tested was H03: Project management methodology has no significant
influence on implementation of information technology projects by Ethiopian commercial
banks.
The survey results showed that there was a positive relationship between project
management approach and implementation of information technology projects (β3 =
9
.796, t = 9.329, p-value < 0.001). In order to test the relationship, the regression model
fitted was Y = β0 + β4X4+ ε;
The null hypothesis H03 stating that project management methodology has no significant
influence on implementation of information technology projects by commercial banks in
Ethiopia (H03: β3= 0) was therefore rejected (β3 =.796, t = 9.329, p-value < 0.001) and a
conclusion drawn that indeed project management methodology (X3) influences
implementation of IT projects (Y). The resulting regression model was;
Y = 2.69 + .796X3;
where; Y = Implementation of IT projects and X3 = Project management approach
The beta coefficient for project management approach was significant (β4 = .284, t =
4.410, p-value < 0.001), implying that for every one unit varying in the index of project
management methodology, there was a .796 index improvement in effectiveness of
implementation of IT projects.
Table 4.16: Regression results for the relationship between project management
methodology and implementation of IT projects
Model Summary
Mod
el
R
Adjusted
Square R Square
R
a
1
.563
.316
Std. Error
of the
Estimate
.313
Change Statistics
R Square
Change
.76500
.316
F Change
df1
87.034
df2
1
188
a. Predictors: (Constant), Project management methodology
ANOVAa
Sum of
Squares
Model
1
Regression
df
Mean Square
50.934
1
50.934
Residual
110.022
188
.585
Total
160.956
189
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Project management methodology
F
87.034
Sig.
.000b
Sig. F Change
.000
Coefficientsa
Unstandardize
Standardized
95.0% Confidence
d Coefficients
Coefficients
Interval for B
Std.
Model
1
B
Error
Correlations
Lower
Beta
t
Sig.
Bound
Par
Upper Bound
(Constant)
.269
.268
1.003 .317
-.260
.798
Project
.796
.085
.563 9.329 .000
.628
.964
Zero-order
.563
Partial
.563 .56
management
3
methodology
a. Dependent Variable: Implementation of IT projects
Similarly, this study’s findings concur with Mohabuth’s (2017) study of 720 information
technology projects that found that the use of an inappropriate methodology is actually
the most critical risk driver for sub-optimal project implementation. Therefore, matching
the project type and information technology project methodology would be expected to
enhance chances of effective implementation of information technology projects by
Ethiopian commercial banks. Špundak (2014) researched on the issue of introducing agile
methods to traditional environments and noted that larger organizations contained some
unique management challenges.
4.6.4 Influence of Executive Commitment on Implementation of IT projects
To find out the influence of executive commitment (X4) on implementation of IT projects
(Y), a regression model was fitted to the data and it was found not to be statistically
significant (F (1, 188) = 149.102, p-value< .001). The co-efficient of determination (R2)
of .0442was an indicator that executive commitment explained a paltry 44.2% variation
in improvement of implementation of IT projects. The adjusted R2 explained 43.9%
variation while the remainder could be explained by other factors not included in the
model. R value of .665 indicated a weak positive correlation between executive
commitment and implementation of IT projects.
The hypothesis to be tested was H04: Executive commitment has no significant influence
on implementation of IT projects by commercial banks in Ethiopia.
The survey results showed that there was a moderate positive relationship between
executive commitment and implementation of IT projects by commercial banks in
Ethiopia (β4=.771, t = 12.211, p-value <0.001). The regression model fitted to test the
relationship was;
Y = β0 + β4X4+ ε;
t
where;
Y = Implementation of IT projects, β0 = Constant, X4 = Executive commitment,
ε = Error term
The null hypothesis stating that executive commitment has no significant influence on
implementation of IT projects by commercial banks in Ethiopia (H04: β4= 0) was therefore
rejected (β4= .771, t = 12.211, p-value < .001) and a conclusion drawn that executive
commitment (X4) has a moderate influence on implementation of information technology
projects by commercial banks in Ethiopia (Y). The resulting regression model was;
Y = 0.355 + 0.771X4
The model equation shows that standardized implementation index of information
technology projects will increase by .771 units with one unit increase in the index of
executive commitment.
Table 4.13: Regression analysis results on executive commitment and
implementation of IT projects
a) Model Summary
Change Statistics
Adjuste Std. Error
Mo
R
dR
of the R Square
del
R
Square Square Estimate Change F Change df1
df2
a
1
.665
.442
.439
.69099
.442 149.102
1
a. Predictors: (Constant), Executive commitment
Sig. F
Change
188
.000
b) ANOVAa
Sum of
Model
Squares
df
Mean Square
1
Regression
71.192
1
71.192
Residual
89.765
188
.477
Total
160.956
189
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Executive commitment
F
149.102
Sig.
.000b
Coefficientsa
Standardize
Unstandardized
d
95.0% Confidence
Coefficients
Coefficients
Interval for B
Model
B
1 (Constant)
.355
Std. Error
.200
Beta
t
Sig. Lower Bound
1.775 .078
-.040
Correlations
Upper
Zero- Partia
Bound
order
.749
l
Part
Executive
.771
.063
.665 12.211 .000
.646
.895
.665
.665
commitment
a. Dependent Variable: Implementation of IT projects
These results on the influence of executive commitment on implementation of information
technology projects were consistent with earlier studies by Yunis et al., (2013) who
indicated that among all the subsets of executive commitment, executive management
support has been suggested to be the primary determinant for effective implementation of
IT projects. This is probably because executive management support drives and influences
other executive commitment subsets. Imreh et al., (2011) and Mansor et al., (2011) also
emphasize that no project can be completed successfully unless the project manager
secures commitment from the senior management. This implies that for any effective
project implementation, there is necessity for sustained executive management
commitment to provide resources, authority and influence. Consistently, Dyck and
Majchrzak (2012) found that executive management commitment has a positive impact
on effective implementation of IT projects.
4.5.6. Project management and project control capability on implementation of IT
projects
To find out the influence of PMCC (X4) on implementation of IT projects (Y), a regression
model was fitted to the data and it was found not to be statistically significant (F (1, 188)
= 159.187, p-value< .001). The co-efficient of determination (R2) of .459was an indicator
that PMCC explained a paltry 45.9% variation in improvement of implementation of IT
projects. The adjusted R2 explained 45.6% variation while the remainder could be
explained by other factors not included in the model. R value of .677 indicated a weak
positive correlation between PMCC and implementation of IT projects.
The hypothesis to be tested was H07: PMCC has no significant influence on
implementation of IT projects by commercial banks in Ethiopia.
The survey results showed that there was a moderate positive relationship between PMCC
and implementation of IT projects by commercial banks in Ethiopia (β7=.902, t = 12.617,
p-value <0.001). The regression model fitted to test the relationship was;
Y = β0 + β7X7+ ε;
where;
Y = Implementation of IT projects, β0 = Constant, X7 = PMCC,
ε = Error term
.665
The null hypothesis stating that executive commitment has no significant influence on
implementation of IT projects by commercial banks in Ethiopia (H07: β7= 0) was therefore
rejected (β7=.902, t = 12.617, p-value <0.001) and a conclusion drawn that PMCC (X7)
has a moderate influence on implementation of information technology projects by
commercial banks in Ethiopia (Y). The resulting regression model was;
Y = -0.16 + 0.902X7
The model equation shows that standardized implementation index of information
technology projects will increase by .902 units with one unit increase in the index of
executive commitment.
Model Summary
Std.
Mo
del
R
.677a
1
a.
Change Statistics
Adjuste
Error of
R
dR
the
R Square
Square
Square
Estimate
Change
.459
.456
.68088
Sig. F
F Change
.459
df1
df2
159.187
Change
1
188
.000
Predictors: (Constant), Project management and Project control capability
ANOVAa
Sum of
Squares
Model
1
df
Mean Square
F
Regression
73.799
1
73.799
Residual
87.157
188
.464
160.956
189
Total
Sig.
.000b
159.187
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Project management and Project control capability
Coefficientsa
Unstandardize
Standardized
95.0% Confidence
d Coefficients
Coefficients
Interval for B
Std.
Model
1 (Constant)
Project management
B
Error
Beta
t
Lower
Upper
Zero-
Parti
Sig.
Bound
Bound
order
al
Part
.677
.677
-.016
.222
-.074
.941
-.455
.422
.902
.071
.677 12.617
.000
.761
1.043
and Project control
capability
a. Dependent Variable: Implementation of IT projects
Correlations
.677
This means there is a very high relationship between PMCC and implementation of IT
projects where the increase (good condition) in PMCC process could also result in an
increase in the success of implementation of IT projects.
This is also supported by literature and empirical evidence that confirms project
management and control capability is influential in the implementation of IT projects
(Ngai et al., 2008; Tsai et al, 2011; Zhang et al., 2003). This finding implies that PMCC
is one of the critical factors for the successful implementation of IT projects in Ethiopian
commercial banks. Having effective project management and controlling capability
enables organizations to plan, coordinate, and monitor various implementation activities.
In addition, it also monitors activities to ensure that the stated objectives during
implementation of IT projects.
4.6.3 Influence of Project cost estimation and budget on Implementation of IT
projects
As shown in Table 4.16, the regression model of project cost estimation and budget(X6)
and implementation of information technology projects was significant (F (1, 188) =
132.709, p-value < 0.001), confirming that CEB was a valid predictor in the model. The
coefficient of determination (R2) was .414 implying that 41.4% improvement in effective
implementation of information technology projects could be explained by CEB adopted.
Adjusted R2 was .414 meaning 41.4% was explained by CEB and the rest could be
attributed to other factors not captured in the model. R score of .643 indicated a moderate
positive correlation between project CEB and implementation of information technology
projects. The standard error of .708 indicated the deviation from the line of best fit.
The hypothesis to be tested was H06: Project CEB has no significant influence on
implementation of information technology projects by Ethiopian commercial banks.
The survey results showed that there was a positive relationship between project CEB and
implementation of information technology projects (β6 = .922, t = 11.520, p-value <
0.001). In order to test the relationship, the regression model fitted was Y = β0 + β6X6+ ε;
The null hypothesis H06 stating that project CEB has no significant influence on
implementation of information technology projects by commercial banks in Ethiopia (H06:
β6= 0) was therefore rejected (β6 = .922, t = 11.520, p-value < 0.001) and a conclusion
drawn that indeed project CEB (X6) influences implementation of IT projects (Y). The
resulting regression model was;
Y = -2.29 + .922X6;
where; Y = Implementation of IT projects and X6 = project cost estimation and budget
the beta coefficient for project CEB was significant (β6 = .922, t = 11.520, p-value <
0.001), implying that for every one unit varying in the index of project CEB, there was a
.922 index improvement in effectiveness of implementation of IT projects.
Table 4.16: Regression results for the relationship between project management
methodology and implementation of IT projects
Model Summary
Mo
del
R
.643a
1
Change Statistics
Adjuste Std. Error
R
dR
of the
R Square
Square Square Estimate Change F Change
df1
df2
.414
.411
.70843
.414
132.709
1
Sig. F
Change
188
.000
a. Predictors: (Constant), Cost estimation and budget
ANOVAa
Sum of
Squares
Model
1
df
Mean Square
Regression
66.604
1
66.604
Residual
94.353
188
.502
160.956
189
Total
F
Sig.
.000b
132.709
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Cost estimation and budget
Coefficientsa
Unstandardi
95.0%
zed
Standardized
Confidence
Coefficients
Coefficients
Interval for B
Correlations
Std.
Erro
Model
1 (Constant)
Cost estimation
B
r
Lower
Beta
t
Sig.
Upper
Zero-
Parti
Bound Bound
order
al
-.229 .261
-.877 .381
-.744
.286
.922 .080
.643 11.520 .000
.764
1.080
and budget
a. Dependent Variable: Implementation of IT projects
.643 .643
Part
.643
These results on the influence of project cost and budget on implementation of information
technology projects were consistent with earlier studies by Mir et al., (2014), for
organizations running several short-term customer projects, managing project cost and
budget is designed to control their resources in a given activity within the constraints of
time, cost, acceptable level of performance and good customer relations.
These results confirm that adequate project cost and budget have an inner feeling of
loyalty and responsibility to the project.
4.6.5 Joint Influence of Independent Variables on Implementation of IT projects
Multiple regression was carried out where all the variables were aggregated to assess their
collinearity with implementation of IT projects. A multiple regression model was fitted to
the data and it was found to be statistically significant (F (7,182) = 40.566, p-value <
0.001). The seven variables’ R2 was .609 meaning that they jointly explained 60.9%
variation in effective implementation of information technology projects.
The hypothesis to be tested was that the joint factors of implementation of projects have
no significant influence on implementation of information technology projects by
commercial banks in Ethiopia. To test the hypothesis, the following model was fitted;
Y = β0 + β1X1+ β2X2 + β3X3 + β4X4 + β5X5 + β6X6 ε; where;
Y = Implementation of IT projects
X1 = Project procurement management
X2 = Project scope management
X3 = Project management methodology
X4 = Executive commitment
X5 =Adequate project cost estimation and budget
X6 = Project management and project control capability
ε= Error term (referring to other factors not captured in the study but had influence).
The regression model fitted was given by;
Y= – 0.718 + 0.117X1 – 0.270X2 + 0.110X3 + 0.3506X4 + 0.499X5 + 0.389X6
Under joint influence of factors, the null hypothesis stated that factors of implementation
of projects have no significant influence on implementation of information technology
projects by commercial banks in Ethiopia. Regression analysis results revealed that
Project procurement management had a positive and significant relationship with
implementation of information technology projects. We therefore reject the null
hypothesis and conclude that Project procurement management (X1) has significant
influence on implementation of information technology projects (Y).
User involvement had a positive and significant effect on implementation of information
technology projects (H02: β2 = 0), since t = 2.210, p-value =.029). We therefore reject the
null hypothesis and conclude that user involvement (X2) has a significant influence on
implementation of information technology projects (Y). It means a unit increase in user
involvement causes 0.151 improvement in implementation of information technology
projects when all the other variables are held constant.
Project team capability had a positive and significant effect on implementation of
information technology projects (H03: β3 = 0), since t = 2.190, p-value =.030). We reject
the null hypothesis and conclude that project team capability (X3) has a significant
influence on implementation of IT projects (Y). It means a unit increase in project team
capability causes 0.187 improvement in implementation of information technology
projects when all the other variables are held constant.
Project management approach had a positive but insignificant effect on implementation
of information technology projects (H04: β4 = 0), since t = 1.926, p-value =.056). We fail
to reject the null hypothesis and conclude that project management approach (X4) has no
significant influence on implementation of information technology projects (Y). It means
a unit varying in project management approach causes 0.146 improvement in
implementation of information technology projects.
In the model equation, the significant terms were the constant (t = 5.913, p<0.001), user
involvement (t = 2.210, p = 0.029) and project team capability (t = 2.190, p = 0.030).
Therefore, the model equation implies that for one unit increase in the index of user
involvement, effective implementation of IT projects improves by an index of .151 when
all the other variables are held constant. Effective implementation of IT projects will
improve by an index of 0.187 with a unit increase in the index of project team capability,
holding other variables constant and lastly, for one unit increase in the index of project
management approach, there is a corresponding improvement in implementation of IT
projects by an index of 0.146, holding other variables constant.
Model Summary
Mo
del
R
Adjusted
Square R Square
R
1
.781
a
.609
Change Statistics
Std. Error
of the
Estimate
.594
R Square
Change
.58773
F Change
.609
df1
40.566
df2
7
Sig. F Change
182
.000
a. Predictors: (Constant), Project management and Project control capability, Project risk, Project
procurement management, Project management methodology, Project scope management, Executive
commitment, Adequate cost estimation and budget
ANOVAa
Sum of
Squares
Model
1
Mean
Square
df
F
Regression
98.089
7
14.013
Residual
62.868
182
.345
160.956
189
Total
Sig.
40.566 .000b
a. Dependent Variable: Implementation of IT projects
b. Predictors: (Constant), Project management and Project control
capability, Project risk, Project procurement management, Project
management methodology, Project scope management, Executive
commitment, Adequate cost estimation and budget
Coefficientsa
Unstandardized Standardized
Coefficients
95.0% Confidence
Coefficients
Interval for B
Std.
Model
1
(Constant)
Project procurement
B
Error
-.718
.299
.117
.089
-.270
Beta
t
Sig.
Correlations
Lower
Upper
Zero-
Bound
Bound
order
Partial
Part
-2.402
.017
-1.308
-.128
.085
1.321
.188
-.058
.292
.512
.097
.061
.086
-.209
-3.133
.002
-.440
-.100
.429
-.226
-.145
.110
.094
.077
1.167
.245
-.076
.295
.563
.086
.054
.350
.082
.302
4.288
.000
.189
.510
.665
.303
.199
-.066
.064
-.051
-1.029
.305
-.194
.061
.197
-.076
-.048
.499
.104
.348
4.781
.000
.293
.705
.643
.334
.221
.389
.110
.292
3.548
.000
.173
.606
.677
.254
.164
management
Project scope
management
Project management
methodology
Executive commitment
Project risk
Adequate cost estimation
and budget
Project management and
Project control capability
a. Dependent Variable: Implementation of IT projects