Syntax Literate: Jurnal Ilmiah Indonesia p–ISSN:
2541-0849 e-ISSN: 2548-1398
Vol. 9, No.
11, November 2024
FACTOR
AFFECTING STRATEGIC PLAN IMPLEMENTATION: A CASE STUDY OF COMMERCIAL BANK OF
ETHIOPIA HEAD OFFICE
Bekele Asamnew T1,
Getachew Tareke Abebe2, Waganeh Wassie Ayele3
Addis Ababa City Road
Authority (AACRA), Ethiopia1
Unity University and Ayertena Health Science and Business College, Ethiopia2
Private PLC, Ethiopia3
Email: [email protected]1, [email protected]2, [email protected]3
Abstract
This study
examines the factors affecting strategic plan implementation in case of
commercial bank of Ethiopia. The research design was an explanatory,
quantitative study that examined the factors associated with the successful
implementation of strategic plans within Commercial Bank of Ethiopia. Using a
judgmental sampling technique, the analysis was conducted on a sample size of
196. The findings revealed that several organizational enablers had a
significant positive relationship with strategic plan implementation (SPI). The
strongest predictor of SPI was effective communication, underscoring the
critical role of clear, consistent, and transparent communication from
leadership in driving strategy execution. Stakeholder engagement, leadership
support, and the alignment of human resources practices with strategic
priorities also emerged as key drivers of successful SPI. Interestingly, the
study did not find a significant relationship between resource availability and
SPI, challenging the common assumption that a lack of resources is a primary
barrier to strategy implementation. These findings emphasize the importance of
developing organizational capabilities, such as communication, stakeholder
management, and HR-strategy alignment, to enhance the effective translation of
strategic plans into tangible outcomes. The study provides valuable insights
for organizations seeking to bridge the strategy-to-execution gap and improve
the implementation of their strategic initiatives. By focusing on cultivating
these organizational enablers, particularly effective leadership communication,
organizations can more successfully drive the execution of their strategic
plans and achieve their desired outcomes.
Keywords: Communication, Strategic Plan Implementation,
Resource Allocation, Human Resource, Stake Holder Engagement
Introduction
The current business environment is characterized by
increasing unreliability and unpredictability for both for-profit and nonprofit
organizations. As a result, managers and leaders in various institutions must
adopt strategic thinking, learning, and action
An institution's strategy serves as its game plan for
achieving goals, managing operations, establishing market position, attracting
and retaining clients, and competing successfully in the marketplace
A review of the literature indicates that many
businesses fail to implement more than 80% of their new strategic plans, with
20% of them making no progress at all (Gizaw, 2020).
Consequently, the emphasis in the field of strategic management has shifted
from strategy formation to strategy implementation. Poor execution hinders the
sustainability of priorities and the achievement of organizational objectives
Furthermore, organizations may not be fully aware that
effective strategy implementation requires well-structured management processes
that go beyond routine business operations. It is crucial to go above and
beyond standard practices to increase the chances of successful strategy
implementation. Additionally, identifying and analyzing the key factors and
their interrelationships in strategy implementation is essential
Research Methods
The study was adopting a descriptive and explanatory
research deign to achieve the objective of the study and collect data from the
respondents. The choice of a descriptive survey approach was motivated by the
need for a complete description of the situation and to minimize bias in data
collection. Descriptive survey research is well-suited for providing an
accurate portrayal of individuals, events, or characteristics such as behavior,
ability, belief, opinion, and knowledge within a specific individual or group.
It focuses on describing the characteristics of a particular individual or
group, which aligns with the objectives of the study. By employing a
descriptive survey approach, the researcher aimed to collect information from
employees of the Commercial Bank of Ethiopia to gain a comprehensive
understanding of the factors influencing strategy implementation in the
organization. This approach allows for the collection of data that describes
the current state of affairs and provides insights into the employees'
perspectives, experiences, and opinions related to the topic of study.
Population refers to the
entire group of individuals, objects, or elements that meet the criteria of
interest for a particular study
The researcher intends to
use a judgmental sampling technique, selecting a sample size of 196 employees.
The decision to use judgmental sampling suggests that the researcher was
purposefully select individuals from the population based on their knowledge,
expertise, or relevance to the research topic. This approach allows for
efficient data collection from individuals who are considered to be
representative of the population and can provide valuable insights into the
research questions.
The researcher plans to use
a standard questionnaire to examine the factors affecting strategic
implementation in the case of the Commercial Bank of Ethiopia. The
questionnaire was adjusted to specifically reflect the scope of this study. The
questions were primarily close-ended, meaning that respondents were choose from
the alternatives provided by the researcher. The decision to use close-ended
questions is influenced by the busy schedules of the staff, making it difficult
for them to allocate time for lengthy questionnaires. Additionally, close-ended
questions make the coding of data easier for analysis purposes. The
questionnaire was asking respondents to rate aspects related to leadership
style, human resources, resource availability, and communication on a
five-point Likert scale, where 1 represents "strongly disagree" and 5
represents "strongly agree." This Likert scale was used for all
survey items. Before distributing the questionnaires, the researcher was
seeking permission from the various heads of departments at the Commercial Bank
of Ethiopia. Once permission is granted, the researcher was explaining the
purpose and content of the questionnaire to the employees. This step aims to
ensure that the respondents have a better understanding of the questions and
can provide their independent opinions. The researcher was personally collect all the data from the respondents and undertake the
analysis herself. By maintaining control over the data collection process, the
researcher can ensure the accuracy and reliability of the collected data for
further analysis.
Results and Discussion
Response
Rate
The study achieved a good response rate, with 190
questionnaires collected out of the 196 that were distributed, representing a
96.9% response rate. This high response rate suggests the study was able to
gather a comprehensive set of data from the target population, which
strengthens the reliability and validity of the findings
Demographic profile of the
respondents
Before start the analysis of the data some background
information’s i.e. Demographic Data, is useful in order to make the analysis
more meaning full for the readers. The purpose of the demographic analysis in
this research is to describe the characteristics of the sample such as the
number of respondents, proportion of males and females in the sample, range of
age, education level, and work experience etc.
Table 1. Demographic profile of the respondents
|
Gender |
Age |
Qualification |
Year of
experience |
|
Male =
60.5% Female
=39.5% |
22-25=
48.9% 26-35=27.9% 36-45=17.4% 46-55=5.8% |
Bachelor=
56.3% Master
=43.2% PhD = .5% CPA &
CA =1% |
<5
=20.5% 6-10 year
=61.6% 1-15 year
= 16.8% >15
year =1.1% |
Source: researcher survey, (2024)
Gender of the respondents
The figure shows that the sample had a higher
proportion of male respondents, with 60.5% being male and 39.5% being female.
Figure 1. Gender of the respondents
Age of the respondents
The result of the study reveals an age distribution
that is skewed towards younger adults. Nearly half of the respondents (48.9%)
fall within the 22-25 age range, indicating this demographic makes up the
majority of the sample. The second largest group is the 26-35 age range,
accounting for 27.9% of participants. This suggests the sample has good
representation from early-to-mid career individuals as well. However, the older
age groups are less prominent, with the 36-45 and 46-55 age ranges making up 17.4%
and 5.8% of the respondents respectively. Overall, the age breakdown points to
a sample that is predominantly composed of younger working professionals in
their 20s and 30s. So this is a god finding the
information is collected from experienced employee.
Figure 2. Age of the respondents
Educational Qualification
The data reveals that the sample is predominantly
composed of respondents with bachelor's degrees, who make up 56.3% of the
participants. The second largest group are those holding master's degrees,
accounting for 43.2% of the sample. However, only a very small percentage
(0.5%) of respondents have a PhD.
Figure 3. Educational Qualification of the respondents
Year of experience
The result of the study shows that the majority of
respondents (61.6%) having between 6 to 10 years of experience. There is also a
notable proportion of relatively junior individuals, as 20.5% of the
participants have less than 5 years of experience. However, the perspectives of
more seasoned professionals are likely underrepresented, as those with 11 to 15
years of experience make up only 16.8% of the sample, and those with over 15
years’ account for a mere 1.1%.
Figure 4. Year of experience of the respondents
Descriptive Statistics
The resource allocation (RA) indicator has a
(mean=3.1772, SD=0.81984), with values ranging from 2.00 to 5.00. This suggests
the organization places a strong emphasis on optimizing the allocation of
critical resources to enable the successful execution of strategic initiatives.
Stakeholder engagement (SHE) has a (mean=15.5789, SD=6.44567), with values
between 5.00 and 24.00, indicating that nurturing stakeholder relationships and
aligning diverse interests is a key priority. The leadership (LS) variable has
a (mean=16.8789, SD=6.31460), with a minimum of 6.00 and a maximum of 30.00,
highlighting the organization's focus on cultivating effective leadership
styles to empower teams and drive transformation. The human resources (HR)
indicator has a (mean=24.5421, SD=6.72481), with values ranging from 12.00 to
43.00, suggesting a strong emphasis on human capital management. The strategic
plan implementation (SPI) variable has a (mean=12.7579, SD=5.75738), with
values between 5.00 and 24.00, underscoring the importance of translating
strategic objectives into tangible actions. Finally, the communication (CO)
indicator has a (mean=14.8947, SD=5.75693), with a minimum of 6.00 and a
maximum of 24.00, indicating that effective communication is a key focus area
for the organization.
Table 2. Descriptive Statistics
|
Descriptive
Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
RA |
190 |
2.00 |
5.00 |
3.1772 |
.81984 |
|
SHE |
190 |
5.00 |
24.00 |
15.5789 |
6.44567 |
|
LS |
190 |
6.00 |
30.00 |
16.8789 |
6.31460 |
|
HR |
190 |
12.00 |
43.00 |
24.5421 |
6.72481 |
|
SPI |
190 |
5.00 |
24.00 |
12.7579 |
5.75738 |
|
CO |
190 |
6.00 |
24.00 |
14.8947 |
5.75693 |
|
Valid
N (listwise) |
190 |
|
|
|
|
Source: Researcher survey, (2024)
Assumption Tests of Regression
Analysis
a.
Normality Test
Assessing the normality of the data is a crucial
assumption that should be tested when conducting regression analysis. Normality
refers to the assumption that the residuals (the differences between the
observed and predicted values) in the regression model follow a normal
distribution
Generally, a skewness value between -1 and 1 and a
kurtosis value between -3 and 3 are considered acceptable for assuming
normality. Since all the variables have skewness and kurtosis values (See table
3) within the recommended ranges, the normality assumption can be considered
met.
Table 3. Normality Test
|
|
Skewness |
Kurtosis |
||
|
Statistic |
Std. Error |
Statistic |
Std. Error |
|
|
RA |
.345 |
.176 |
-.661 |
.351 |
|
CO |
-.035 |
.176 |
-1.394 |
.351 |
|
SHE |
-.473 |
.176 |
-1.339 |
.351 |
|
LS |
-.065 |
.176 |
-1.218 |
.351 |
|
HR |
.381 |
.176 |
-.223 |
.351 |
|
SPI |
.071 |
.176 |
-1.343 |
.351 |
Source: researcher survey, (2024)
b.
Multicollinearity Test
Multicollinearity is an important statistical
assumption that should be tested when conducting multiple regression analysis.
Multicollinearity refers to the situation where two or more predictor variables
in a regression model are highly correlated with each other, which can lead to
unstable and unreliable estimates of the regression coefficients
According to the guidelines in the literature, a
tolerance value less than 0.1 or a VIF value greater than 10 would indicate the
presence of multicollinearity
Table 4. Multicollinrarity test
|
Model |
Collinearity Statistics |
||
|
Tolerance |
VIF |
||
|
1 |
(Constant) |
|
|
|
CO |
.424 |
2.359 |
|
|
SHE |
.502 |
1.991 |
|
|
LS |
.404 |
2.478 |
|
|
HR |
.358 |
2.797 |
|
|
RA |
.464 |
2.156 |
|
Source: researcher survey, (2024)
c.
Homoscedasticity Test
The assumption of homoscedasticity, which states that
the variance of the residuals is constant across the range of predicted values,
is an important assumption that should be checked when conducting regression
analysis
d.
Correlation Analysis
Table 5. Correlation
Analysis
|
Correlations |
|||||||
|
|
CO |
SHE |
LS |
HR |
SPI |
RA |
|
|
CO |
Pearson
Correlation |
1 |
|
||||
|
Sig.
(2-tailed) |
|
|
|||||
|
N |
190 |
|
|||||
|
SHE |
Pearson
Correlation |
.685** |
|
|
|||
|
Sig.
(2-tailed) |
.000 |
|
|
||||
|
N |
190 |
190 |
|
||||
|
LS |
Pearson
Correlation |
.626** |
.550** |
|
|
||
|
Sig.
(2-tailed) |
.000 |
.000 |
|
|
|||
|
N |
190 |
190 |
|
|
|||
|
HR |
Pearson
Correlation |
.202** |
.182* |
.539** |
|
|
|
|
Sig.
(2-tailed) |
.005 |
.012 |
.000 |
|
|
||
|
N |
190 |
190 |
190 |
|
|
||
|
SPI |
Pearson
Correlation |
.759** |
.696** |
.648** |
.342** |
|
|
|
Sig.
(2-tailed) |
.000 |
.000 |
.000 |
.000 |
|
|
|
|
N |
190 |
190 |
190 |
190 |
|
|
|
|
RA |
Pearson
Correlation |
.170* |
.097 |
.335** |
.723** |
.215** |
|
|
Sig.
(2-tailed) |
.019 |
.185 |
.000 |
.000 |
.003 |
|
|
|
N |
190 |
190 |
190 |
190 |
190 |
|
|
|
**. Correlation
is significant at the 0.01 level (2-tailed). |
|||||||
|
*.
Correlation is significant at the 0.05 level (2-tailed). |
|||||||
Source: researcher survey, (2024)
CO (Communication) has a strong positive correlation
with SHE (Stake Holder Engagement) (r = 0.685, p < 0.01) and SPI (Strategic
Plan Implementation) (r = 0.759, p < 0.01). This suggests that higher levels
of organizational commitment are associated with stronger
supervisor-subordinate exchange relationships and improved safety performance.
SHE (Stake holder engagement) has a moderate positive
correlation with LS (Leadership Support) (r = 0.550, p < 0.01) and a strong
positive correlation with SPI (Strategic Plan Implementation) (r = 0.696, p
< 0.01). This indicates that better supervisor-subordinate exchange
relationships are related to stronger leadership and improved safety
performance. LS (Leadership Support) has a moderate positive correlation with
HR (Human Resource) (r = 0.539, p < 0.01) and a strong positive correlation
with SPI (Strategic Plan Implementation) (r = 0.648, p < 0.01). HR (Human
Resource) has a strong positive correlation with RA (Resource Availability) (r
= 0.723, p < 0.01). SPI (Strategic plan implementation) has a moderate
positive correlation with RA (Resource availability) (r = 0.215, p < 0.01).
Analysis of Variance
Table 6. Analysis of variance
|
ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
4236.557 |
5 |
847.311 |
76.865 |
.000b |
|
Residual |
2028.306 |
184 |
11.023 |
|
|
|
|
Total |
6264.863 |
189 |
|
|
|
|
|
a.
Dependent Variable: SPI |
||||||
|
b.
Predictors: (Constant), RA, SHE, LS, CO, HR Source: Researcher survey,
(2024) |
||||||
The ANOVA results indicate that the multiple linear regression
model with five predictors (RA, SHE, LS, CO, and HR) is statistically
significant in predicting the dependent variable SPI (F = 76.865, p <
0.001). The regression model explains a substantial portion of the variance in
SPI, as evidenced by the large F-statistic and the small p-value, which is less
than the typical significance level of 0.05. This suggests that the overall
regression model is a good fit for the data and that at least one of the
independent variables is significantly related to the dependent variable SPI.
Coefficient Table
Table 7. Coefficients Table
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
-2.317 |
1.127 |
|
-2.057 |
.041 |
|
CO |
.454 |
.064 |
.454 |
7.043 |
.000 |
|
|
SHE |
.256 |
.053 |
.287 |
4.841 |
.000 |
|
|
LS |
.124 |
.060 |
.136 |
2.053 |
.041 |
|
|
HR |
.141 |
.060 |
.164 |
2.340 |
.020 |
|
|
RA |
-.380 |
.433 |
-.054 |
-.879 |
.381 |
|
Source: Researcher survey, (2024)
Interpretation
of the model
Y=a0
+ ax1 + bx2 + cx3 + dx4
+ ex5 + e
Y=
Strategic Plan Implementation
Where
a0 = Constant
a,
b, c, and d = Regression coefficient
x1
= Leadership Style
x2 = Human Resource
x3
= Resource Availability
x4
= Communication
dx5
= Stakeholder engagement
e
= error term
Strategic Plan Implementation (Y)= -2.317+ 0.454
(Communication) + 0.256 (Stake Holder Engagement) + 0.124 (Leadership Support)
+ 0.141 (Human Resource) – 0.380 (Resource Availability)
The regression analysis results show that the multiple
linear regression model with five predictors (CO, SHE, LS, HR, and RA) is
effective in explaining the variation in the dependent variable, SPI. The
constant term in the model is -2.317, which represents the predicted value of
SPI when all the independent variables are equal to 0. Among the predictors, CO
has the strongest positive association with SPI, with an unstandardized
coefficient of 0.454 (p < 0.001), indicating that a one-unit increase in CO
is associated with a 0.454 unit increase in SPI, holding all other variables
constant. SHE also has a moderate positive relationship with SPI, with an
unstandardized coefficient of 0.256 (p < 0.001). LS and HR have weaker, but
still positive, associations with SPI, with unstandardized coefficients of
0.124 (p = 0.041) and 0.141 (p = 0.020), respectively. The only variable that
does not have a statistically significant relationship with SPI is RA, with an
unstandardized coefficient of -0.380 (p = 0.381), suggesting that RA is not a
significant predictor of SPI in this model. Overall, the results indicate that
CO, SHE, LS, and HR are important factors in predicting SPI, while RA does not
seem to have a significant influence on the dependent variable.
Table 8. Model Summary
|
Model Summaryb |
||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
|
1 |
.822a |
.676 |
.667 |
3.32015 |
|
a. Predictors: (Constant), RA, SHE, LS,
CO, HR |
||||
|
b. Dependent Variable: SPI |
||||
a.
Predictors: (Constant), RA, SHE, LS, CO, HR
b.
Dependent Variable: SPI
Source: Researcher survey, (2024)
Model Summary
The model summary indicates that this multiple linear
regression model is effective in explaining the variation in the dependent
variable, SPI. The R-squared value is 0.676, meaning the model explains 67.6%
of the variation in SPI. The adjusted R-squared value of 0.667 takes into
account the number of predictors and provides a better estimate of the true
population R-squared.
Summary of Hypothesis Test
Table 9. Summary of Hypothesis Test
|
Proposed hypotheses |
Significant level P< 0.05 |
Decision based on the finding |
|
H1:
There is a significance relationship between communication and Strategic plan
implementation. |
.000 |
Accepted |
|
H2:
There is a significance relationship between stake holder engagement and
Strategic plan implementation. |
.000 |
Accepted |
|
H3:
There is a significance relationship between leadership support and Strategic
plan implementation. |
.041 |
Accepted |
|
H4:
There is a significance relationship between human resource and Strategic
plan implementation. |
.020 |
Accepted |
|
H5:
There is a significance relationship between Resource availability and
Strategic plan implementation. |
.381 |
Rejected |
Source: Researcher survey, (2024)
Based on the p-values, the alternative hypothesis is
accepted for CO, SHE, LS, and HR, as they have p-values less than the
significance level of 0.05, suggesting their regression coefficients are
statistically significant. However, the alternative hypothesis is rejected for
RA, as its p-value of 0.381 is greater than 0.05, indicating the regression
coefficient for RA is not statistically significant.
Conclusion
Based on the key findings of the study, several
important conclusions can be drawn. The analysis revealed that several
organizational factors had a significant positive association with strategic
plan implementation (SPI), while resource availability did not show a
significant relationship. The strongest predictor of successful SPI was
effective communication (CO). This aligns with recent literature highlighting
the critical role of clear, consistent, and transparent communication from
leadership in driving strategy execution. By ensuring open communication of the
strategic vision and priorities, organizations can help align and engage
employees throughout the implementation process.
Another key driver of SPI identified in the analysis
was stakeholder engagement. The findings indicate that actively involving and
attending to the needs of key stakeholders, such as employees, customers, and
shareholders, can facilitate the successful execution of strategic plans. This
supports research emphasizing the importance of stakeholder management in
overcoming resistance to change and building commitment to strategic
initiatives. The analysis also revealed positive associations between SPI and
both leadership support and the human resources
function. These findings underscore the multidimensional nature of effective
strategy implementation, which requires not only communication and stakeholder
engagement, but also committed leadership and the alignment of HR practices
with strategic priorities. Interestingly, the study did not find a significant
relationship between resource availability and SPI. This challenges the common
assumption that a lack of resources is a primary barrier to strategy execution.
Instead, the literature suggests that factors such as organizational
capabilities, communication, and stakeholder management may be more critical in
determining the success of strategy implementation.
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