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CHAPTER FOUR

DATA PRESENTATION, ANALYSIS AND INTERPRETATION OF FINDINGS

4.1 Introduction

This chapter of dissertation comprises of the presentation, analysis, interpretation and discussion of the study findings according to the study objectives. Responses of the Study respondents from different areas were entered into a computer for analysis using SPSS Program. Frequencies, percentages and relationship tests were used to examine the relationship between Contractors Selection Criteria and project performance among the UPE Schools in Mbarara District.  Results on objectives and hypotheses are presented using descriptive and inferential statistics.

4.2 Response Rate

Thirty-six (36) questionnaires were distributed to respondents and were targeted. Out of the 36 questionnaires, were returned fully completed, giving a response rate of 100%. The details are shown in the table 2.

Table 1: Response Rate

Source: Researcher 2024

The findings from the table above indicate that the percentage of the returned questionnaires was 100%. This finding therefore according to Amin, (2005) indicate that the response rate was good and therefore the study could be conducted since the response rate was above 100%.

4.3 Demographic Information of Respondents

The researcher sought out to collect demographic information about the respondents. This information was about gender, age, department of employee, education level and duration one worked.

4.3.1 Gender of respondents

The researcher requested the respondents to indicate their gender, and this was intended to find out whether the sample size was a fair representation of the population. The response was presented in Table 4.3

Table 2: Gender of respondents

The study also investigated the gender of respondents in order to eliminate biased data.

Source: Primary Data 2024

From the table 2, it is observed that 95% of the respondents were male while 5% were females

4.3.2 Age range of respondents

The age range of the respondents was also investigated during the study

Table 3: Age category of respondents

Source: Primary Data

The data shows an equal distribution of individuals between the three age ranges, with both a frequency of 18, 17 and 5 and a percentage of 45.0%, 42.5% and 5% respectively. This could suggest a deliberate sampling strategy or a demographic characteristic of the studied population. Representativeness: The equal distribution might imply an effort to ensure a representative sample across different age groups. If the goal is to capture a diverse range of experiences and perspectives, this approach is beneficial. Understanding the age distribution is crucial for various demographic analyses. In this case, the focus is on individuals between 30 and 55 years old. It would be interesting to explore the reasons behind this specific age range selection and how it aligns with the objectives of the study or analysis.

4.3.3 Educational level of respondents

Educational level of respondents was also investigated to ensure that the respondents are able to understand the topic under investigation since it was a technical and complex study.

Table 4: Educational level of respondents

Source: Primary Data 2024

The majority of respondents, comprising 60.0%, hold a Bachelor’s degree. This suggests that the survey or study may be more accessible to individuals with undergraduate education. Despite the prevalence of Bachelor’s degrees, a noteworthy 40.0% of respondents possess Master’s degrees. This indicates a diverse and educated sample, reflecting a higher level of expertise or specialization among a substantial portion of the participants.

The presence of both Bachelor’s and Master’s degree holders highlights educational diversity within the respondent pool. This diversity could contribute to a richer and more comprehensive dataset, bringing different perspectives and knowledge levels to the study.

The distribution also raises questions about the respondents’ inclination towards pursuing higher education. For instance, are those with Bachelor’s degrees planning to pursue Master’s degrees, or do Master’s degree holders consider further specialization or doctoral studies. Researchers and policymakers should take note of the educational distribution to ensure that findings or interventions are applicable and relevant to individuals with varying educational backgrounds. Tailoring educational programs or initiatives to cater to both Bachelor’s and Master’s degree holders may be important.

4.4 Empirical Findings

This section analyses and presents information based on objectives of the study. Empirical findings as per objectives of the study were presented in tables 4.7, 4.8, 4.9. Respondents were presented with items and requested to either agree or disagree basing on a five Likert scale of; Strongly Agree (SA), Agree(A), Neutral (N), Disagree(D), Strongly Disagree (SD).

SA+A= Agree, SD+D= Disagree, N=Undecided/neutral.  The study grouped SA and A to mean agreed, SD and D to mean disagree, and N to mean respondents who were undecided.  Percentages, mean and standard deviation were used to interpret empirical results.  The mean above 3 implied that majority agreed, and that below 3 means disagreed while 3 imply undecided/neutral.  Also Pearson correlation was used in establishing the relationship between the independent variables and dependent variables, and regressions (model summary) were run to establish the variance (contribution) of the IV on the DV.

4.4.1 Objective one; Contractors’ financial capability

The findings of this objective were gathered from questionnaires administered to the respondents. The variables were measured using 5 items scored on a five-point Likert scale of 1=strongly disagree, 2= Disagree, 3=Uncertain, 4=Agree, 5= strongly agree.

Table 5: Contractors financial capability

Source: Researchers Primary Data (2024)

Capacity to honor short-term financial obligations; The majority of respondents (61.1%) agree that the contractor has the capacity to honor short-term financial obligations. This indicates a relatively strong positive sentiment toward the contractor’s ability to meet short-term financial commitments. The mean score of 3.81 suggests a generally positive perception, with a moderate level of agreement among respondents.

The findings also were in line with the response from the interview where one of the respondents indicated that;

“The financial capacity of the contractors to honor immediate needs of the contract, is very essential if in the contract a contractor cannot meet the payment of its employees without receiving the payments is very essential as this shows that the contractor will not run away and the contractor is stable to accomplish the task”. (Interview on December; 2023).

In the Mbarara district local government the issue of the contractor needing to have financial Capacity to honor short term financial obligations using the liquidity of current assets was quite evident in the contract of 2017/2018 construction of Multi-purpose Hall at a primary school due to the poor flow of funds the construction of blocks in primary schools among  the projects that were under construction were found to have dragged for 8 months, this information reveals how there is need for the contractor to have  a sound financial capacity to ensure the success of the contract.

Capacity to honor short-term financial obligations using the liquidity of current assets; Similar to the first statement, a majority (61.1%) believe that the contractor has the capacity to honor short-term financial obligations using the liquidity of current assets. The mean score of 3.75 is close to the mean of the first statement, reinforcing the positive sentiment regarding the contractor’s financial capability.

Immediate liquidity to cover short-term commitments; While the majority (55.6%) still agrees that the contractor has immediate liquidity, the percentage is slightly lower compared to the first two statements. The mean score of 3.61 suggests a slightly lower level of agreement compared to the previous statements, indicating some variation in opinions among respondents.

This view was also further in line with the response from the interview were one of the respondents asserted that;

The contractor must have the ability to meet his financial needs like buying fuel for the cars, buying raw materials and also to pay wages of casual workers, ability to accomplish these short term financial needs demonstrates that the contractor has good financial management and capacity to accomplish the different tasks. (Interview on December; 2023).

In the analysis of the documents in Mbarara district local government the documents further revealed that;

The 2017 Construction of science laboratory at St. Pauls Seed School Kagongi and Construction of Bukiiro Seed School, the construction dragged for over 8 months and the latter had a budget over run of 15% of the original contract sum, these results also further indicates that Immediate liquidity to cover short-term commitments by contractors is essential because incase where the contractor does not have immediate financial capacity the contract will stall for many months or even years sometimes even failing to honor contractual commitments.

In another document there was a budget overrun of 20% a primary school block this was due to a design review which resulted into an additional floor, Construction of Multi-purpose hall at St. Andrews in these circumstances where there is a design review before Mbarara district local government releases funds it is imperative for the contractor to have enough funds to avoid the project being stalled due to lack of funds.

Still during analysis of Projects in Financial Year 2014/2015 there was Completion of 12 classroom storied block at a primary school, the completion indicated that the contractor had a sound financial capacity and this propelled the success of the project.

The contract documents further indicated that; in Financial Year 2016/2017 there was Construction of 2 classroom block and completion of Administration block at St. Pauls Kagongi Seed School, Amonger Projects in Financial Year 2017/2018 there was Completion of vocational center at Mbarara Municipal School, Construction of a classroom block at St. Pauls Kagongi Seed School, Construction of Multi-purpose hall at a school in Mbarara the success of these projects points towards the fact that the contractor has a sound financial capacity.

Financial autonomy measures the company’s solvency; Again, a majority (55.6%) agrees that the contractor’s financial autonomy measures the company’s solvency. The mean score of 3.47 is the lowest among the first four statements, indicating a slightly lower level of agreement and a more diverse range of opinions among respondents.

This view was also further in line with the response with one of the respondents who asserted that;

A company should be able to do at least more than 30% of the work on its own money without first receiving financial payments from the government” (Interview on December; 2023).

Contractor does not use borrowed funds for the project; The responses are more evenly distributed for this statement. The mean score of 2.83 indicates a moderate level of agreement, but there is a notable percentage (30.6%) of respondents who disagree or are neutral about the contractor not using borrowed funds. The standard deviation of 0.878 suggests a higher degree of variability in opinions compared to the other statements.

Regarding the contractor using borrowed funds one of the respondents during the interview indicated that;

The contractor can use Borrowed funds during the contract because he must have good credit history to ensure that the banks can offer financial security to him in times of need, however if the banks can’t extend financial security to him it shows that the contractor is not good, (Interview on December; 2023).

Overall, the data suggests that respondents generally view the contractors positively in terms of their financial capabilities, with a slightly more mixed opinion on the specific measure of not using borrowed funds for the project. The standard deviations indicate varying degrees of consensus among respondents for each statement.

4.4.1.2 Correlation analysis between contractors’ financial capability and project performance.

Table 6: Correlation of contractors financial capacity and project performance

The correlation coefficient of 0.469 suggests a moderate positive linear relationship between financial capacity and project performance. As financial capacity increases, project performance tends to increase as well. The double asterisks (**) denote that the correlation is significant at the 0.01 level. Organizations with higher financial capacity may experience better project performance. Decision-makers can consider financial capacity as a potential predictor or factor influencing project success. This correlation does not imply causation, and other variables may also contribute to project performance. It’s important to note that correlation does not imply causation, while a significant correlation is observed; it does not necessarily mean that financial capacity directly affects improved project performance. Other factors not considered in this analysis may influence the relationship, this results further establishes the existence of the relationship between contractors’ financial capability and Project Performance Among UPE Schools in Mbarara District, this also further, this finding further confirms the findings in the agency theory developed by Eisenhardt (1989). The agency theory underpins that, a contract between the government and a contractor reflects a principal-agent relationship. The principal (government) contracts with the agent (contractor) to perform some level of effort, such as construction of schools on behalf of government which is eminent in this current study.  In this relationship, the government’s objectives include obtaining the product or service at the right quality, right quantity, right source, right time, and at the right price (Lee & Dobler, 1971), from this finding therefore it is evident that the principle must contract the agent with sound financial capability to enhance better project performance.

4.4.2.1 Regression Analysis

4.4.2.2 Model Summary

Table 7: Model summary of contactors financial capacity on project performance

The R-Square value of .220 indicates that approximately 22% of the variance in the dependent variable (Project performance) is explained by the predictor variable, “Financial capacity.” In other words, the model accounts for 22% of the variability in the response variable, this finding further indicates that 22% of the variances in project performance are explained by financial capacity it also further shows that financial performance is not the only factor that determined project performance as there are many other factors that determined project performance and not necessarily financial capacity.

Adjusted R Square, The Adjusted R Square takes into account the number of predictors in the model. In this case, it is .197. It provides a more accurate measure of the proportion of variance explained by the model when the number of predictors is considered. It is slightly lower than the R Square, which might indicate that there is a limited improvement in predictive power when considering the additional complexity of the model.

Std. Error of the Estimate: The Std. Error of the Estimate (4.416) is a measure of the average distance between the observed values and the values predicted by the model. Lower values indicate a better fit. In this case, a value of 4.416 suggests that, on average, the predicted values deviate from the actual values by approximately 4.416 units.

The R Square value of .220 indicates that the model explains a modest portion of the variability in the dependent variable. It suggests that factors other than “Financial capacity” may also influence the outcome of project performance. The Adjusted R Square being close to the R Square indicates that the model’s additional predictors (if any) do not contribute significantly to the explanatory power, this further affirms that though financial capacity affects project performance other factors also have an influence on project performance.

4.4.2.3 Coefficient of variable

Table 8: Coefficient of variable on financial capacity and project performance

According to the findings from the table above the P-value 0.004 <0.05 indicates that there is a statistically significant relationship between Financial capacity and project performance. This findings also further shows that the alternative hypothesis is accepted while the null hypothesis is rejected concluding that; there is a significant relationship between contractors’ financial capability and Project Performance Among UPE Schools in Mbarara District. This finding also correlated with the Agency theory by Eisenhardt (1989), which it proposes that, a contract between the government and a contractor reflects a principal-agent relationship. The principal (government) contracts with the agent (contractor) to perform some level of effort, such as construction of schools on behalf of government which is eminent in this current study.  In this relationship, the government’s objectives include obtaining the product or service at the right quality, right quantity, right source, right time, and at the right price, it further notes for the government the principal to achieve better project performance there is need to be cognizant of financial capability of the organization.

The coefficient for “Financial capacity” is 0.764, indicating that for a one-unit increase in financial capacity, the dependent variable (project performance) is expected to increase by 0.764 units. The standardized coefficient (Beta) is 0.469, representing the strength and direction of the relationship in standard deviation units. The t-value of 3.094 and a significance level of 0.004 suggest that the coefficient for “Financial capacity” is statistically significant. The positive Beta value indicates a positive relationship between financial capacity and project performance.  In summary, the regression model suggests that both the intercept and the coefficient for “Financial capacity” have statistically significant effects on the dependent variable “Project performance.” The positive coefficient for “Financial capacity” implies that higher financial capacity is associated with better project performance, and the model seems to fit the data well.

4.5.2 Contractors’ technical ability

The findings of this objective were gathered from questionnaires administered to the respondents. The variables were measured using 8 items scored on a five-point Likert scale of 1=strongly disagree, 2= Disagree, 3=Uncertain, 4=Agree, 5= strongly agree.

Table 9: Contractors’ technical ability and project performance

Source: Primary Data

Experience in the Construction Industry; A substantial majority (72.2%) strongly agreed that the contractor has wider experience in the construction industry. This is a positive sign as experience is often crucial in the construction sector for successful project execution.

This view was also in line with the response from one of the interviews were one of the respondents asserted that;

It is very risky to work with a contractor who does not have experience because construction is very complex most of all in the government setting we need a contractor who has at least done some work in the construction area. (Interview on December; 2023).

Technical Team Competence; A significant majority (72.2%) also agreed that the contractor has a well-trained technical team. This is a crucial factor as the expertise and competence of the technical team can directly impact project delivery.

This view was also shared by one of the respondents during the interview who asserted that;

The technical capacity of employees that the contractor has is very essential if the contractor does not have very qualified people that is a very risky and the chances are high that the contractor may not perform well however if the contractor has employees with the right technical capacity the contract is going to be accomplished easily something that will enable accomplishment of the government task. (Interview on December; 2023).

The influence of a contractor having a well-trained technical team is important as it is evidenced by the fact that there was successful completion of the projects of in Financial Year 2018/2019 there was Construction of Bukiro Seed School under UGIFT (Uganda Intergovernmental Fiscal Transfer Programme) and another in Financial Year 2020/2021 there was Construction of Dormitory block at Ntare School.

Quality Management System, while a considerable number (55.6%) agreed that the contractor has a quality management system, there is a significant portion (36.1%) that disagreed or strongly disagreed. This suggests some uncertainty or dissatisfaction with the quality-related processes.

Safety Mechanisms; The results show a mixed response regarding the contractor’s safety mechanisms, with a notable portion (38.9%) strongly disagreeing or disagreeing. Safety is a critical concern in construction, and these results indicate potential areas of improvement in safety practices.

Efficient Equipment; A substantial majority (69.4%) agreed that the contractor has efficient equipment suitable for project delivery. This is a positive aspect as having the right equipment is essential for successful and timely project completion.

This view was also further shared by one of the respondents from the interview who asserted that;

The contractor must have good construction equipment if he does not have then it is essential for him to borrow because contraction requires heavy and expensive equipment like rollers, tractors, tippers. This is to facilitate work. (Interview on December; 2023).

Clientele Relationship; The majority (50.0%) strongly agreed that the company has a well-established clientele relationship. This positive response indicates satisfaction among respondents regarding the contractor’s client interactions. These results were also further in line with one of the views from the interviews were one respondent asserted that;

The contractor must establish good relationship with suppliers so as to be in position to have raw materials in time however as a contract awarding body it has for us to establish that there is a relationship between the suppliers and contractors however we look at the details of previous experience for us to determine that relationship. (Interview on December; 2023).

Familiarity with Local Area and Resources; A significant majority (55.6%) agreed that the contractor is familiar with the local area and resources. Local knowledge is valuable in construction projects, and this positive perception can contribute to successful project planning and execution.

The study results are also in line with the response from one of the respondents during the interview who asserted that;

Depending on the value of contract however it is imperative that the contractor is conversant with the local area this is very essential as it helps the contractor to navigate local challenges as some locals are skeptical on the foreign owned companies. (Interview on December; 2023).

In summary, while there are positive perceptions in areas such as experience, technical team competence, and equipment, there are also areas, particularly related to financial transparency, quality management, and safety mechanisms, where improvements or further communication may be needed. The contractor should take these results as valuable feedback for potential areas of enhancement in their operations.

4.5.2 Correlation analysis

Table 10: correlation analysis of technical capacity on project performance

The correlation coefficient between technical capacity and project performance is reported as 0.356, with a significance level (p-value) of 0.033. This finding shows that the P-value 0.033<0.05, indicates that there is a statistically significant relationship between technical capacity and project performance. The Pearson correlation coefficient 0.356 further indicates that there is a moderate positive significant relationship between technical capacity and project performance.

The asterisk (*) indicates that the correlation is statistically significant at the 0.05 level (2-tailed). Let’s break down and discuss the key elements of this correlation analysis, Strength of the correlation (0.356); The correlation coefficient ranges from -1 to 1, with 0 indicating no correlation, 1 indicating a perfect positive correlation, and -1 indicating a perfect negative correlation. In this case, a positive correlation of 0.356 suggests a moderate positive relationship between technical capacity and project performance. Significance level (0.033), The significance level (p-value) is crucial in determining whether the observed correlation is statistically significant or if it could have occurred by random chance. In this analysis, the p-value of 0.033 is less than the conventional threshold of 0.05, indicating that the correlation is statistically significant. Interpretation of significance (0.05 level, 2-tailed); The asterisk (*) next to the correlation coefficient indicates that the correlation is statistically significant at the 0.05 level with a two-tailed test. This means that there is less than a 5% probability that the observed correlation occurred by chance.

The sample size is crucial in interpreting the reliability of the correlation. In this case, the analysis is based on a sample of 36 data points for both technical capacity and project performance. In practical terms, this correlation suggests that as technical capacity increases, project performance tends to improve, and this relationship is statistically significant. However, it’s important to note that correlation does not imply causation. Other factors may also influence project performance, and further research or analysis may be needed to understand the underlying dynamics of this relationship and to establish causation if applicable.

4.5.3 Regression Analysis

Table 11: Model summary on contractor’s technical capacity on project performance

R (Multiple Correlation Coefficient): The value is 0.356 (a). This represents the strength and direction of the linear relationship between the predictor variable(s) and the response variable. In this case, the relationship is relatively weak. R Square (Coefficient of Determination): The R-squared value is 0.127. This represents the proportion of the variance in the dependent variable (response variable) that can be explained by the independent variable(s) (predictor variable(s)). In this case, about 12.7% of the variance is explained by the model. Adjusted R Square: The adjusted R-squared value is 0.101. This is a modified version of the R-squared that adjusts for the number of predictors in the model. It helps account for the possibility of over fitting. In this case, the adjusted R-squared is slightly lower than the R-squared, indicating that the model may not be a perfect fit. Std. Error of the Estimate: The standard error of the estimate is 4.671. This measures the accuracy of the predictions made by the model. Lower values indicate better predictive accuracy. The only predictor variable in this model is “Technical capacity.” The R-squared value of 0.127 suggests that the model explains only a small portion of the variability in the dependent variable. This could indicate that the chosen predictor variable (Technical capacity) may not be sufficient to capture the complexity of the relationship.

The adjusted R-squared being slightly lower than the R-squared suggests that the model may not be significantly improved by the inclusion of the Technical capacity predictor. The relatively high standard error of the estimate (4.671) indicates that there is a considerable amount of variability in the data that is not explained by the model.

4.5.4 Coefficient of Variables

Table 12: Coefficient of variables of contractors technical capacity on project performance

The study results further indicate that P-value 0.033 <0.05indicates that there is a statistically significant relationship between technical capacity and project performance. this al so indicates that the null hypothesis is rejected and alternative hypothesis is accepted indicating that there is a significant relationship between technical capacity and project performance. This finding also further shows that the study will be grounded in agency theory developed by Eisenhardt’’ (1989). The agency theory underpins that, a contract between the government and a contractor reflects a principal-agent relationship. The principal (government) contracts with the agent (contractor) to perform some level of effort, such as construction of schools on behalf of government which is eminent in this current study.  In this relationship, the government’s objectives include obtaining the product or service at the right quality, right quantity, right source, right time, and at the right price, this finding further show that the government being the principle is mandated to choose an agent with a sound technical capacity to carry out the project. The constant term represents the expected value of the dependent variable (Project performance) when all independent variables are zero. In this case, the intercept is 11.682, but it is not statistically significant at conventional levels (p-value = 0.075). The coefficient for Technical Capacity is 0.618, suggesting that for a one-unit increase in Technical Capacity, the expected change in Project performance is 0.618 units. The standardized coefficient (Beta) is 0.356, indicating the relative importance of Technical Capacity in explaining the variation in Project performance. The t-values for both the constant term and Technical Capacity are provided. The t-value is a measure of how many standard errors the coefficient estimate is away from zero. A larger absolute t-value indicates a more statistically significant result. In this case, the Technical Capacity variable has a t-value of 2.222, which, along with a significance level of 0.033, suggests that Technical Capacity is statistically significant in predicting Project performance, the overall model fit is not discussed in the provided output. It would be beneficial to know additional information such as the R-squared value, which indicates the proportion of variance in the dependent variable explained by the independent variables, In summary, the results suggest that Technical Capacity has a statistically significant impact on Project performance, as evidenced by its significant standardized coefficient (Beta) and the t-value. However, further analysis and consideration of other factors, along with the overall model fit, are necessary to draw comprehensive conclusions about the relationship between Technical Capacity and Project performance.

4.6 Contractors’ managerial capability

The findings of this objective were gathered from questionnaires administered to the respondents. The variables were measured using 5 items scored on a five-point Likert scale of 1=strongly disagree, 2= Disagree, 3=Uncertain, 4=Agree, 5= strongly agree.

Table 13: Contractors’ managerial capability

Source: Primary Data

The contractor is able to plan for activities before implementation; Majority agree (80.6%). Strong planning is a positive sign for project success. The high agreement suggests a commendable capability in pre-implementation planning. The contractor implements the projects in line with goals and objectives:

This view was also in line with one of the respondents who asserted that;

“It is essential for the contractor to make proper plans before starting any contract since it helps the contractor to make proper budget estimates and is also able to determine his resource envelope”. (Interview on December; 2023).

The findings in the study shows that Strong agreement (72.2%), Aligning project implementation with goals is crucial. The high agreement indicates that the contractor is effective in maintaining this alignment.

On findings out if the Risk management is done and factored throughout the implementation process, the results indicated that Mixed responses, with a significant proportion disagreeing or strongly disagreeing (41.7%). The lower agreement on risk management suggests a potential area for improvement. Addressing risk factors is crucial for project success, and the contractor may need to enhance their approach to risk management.

The study results further were also in line with the response from one of the respondents who asserted that;

The contractor should be able to bear the risks and should not be allowed the money from the government to start a project with as this can cause a loss on the side of the government”. (Interview on December; 2023).

The contractor works in teams with the workers; Strong agreement that the contractor works in teams (69.4%).  Effective teamwork is vital for project success. The high agreement indicates a positive team-oriented approach by the contractor.

The contractor has good record systems for project implementation, Mixed responses, with a significant percentage neutral (61.1%). The neutral responses suggest uncertainty or a lack of consensus regarding the contractor’s record-keeping. Establishing clear and robust record systems might be an area for improvement.

This view was also further highlighted by one of the respondents during the interviews who asserted that;

The contractor should be able to have key specific records like company resolutions, taxes paid, names and the academic qualification of employees. (Interview on December; 2023).

There is effective and efficient communication from the contracted team; Strong agreement (55.6%), but with a significant portion in neutral or disagree, while there is general agreement, the presence of neutral and disagree responses suggests potential variations in communication effectiveness. Investigating and addressing communication concerns may be beneficial.

The contractor has the ability to control activities to ensure quality compliance, Strong agreement (61.1%), Effective control for quality compliance is essential. The high agreement indicates that the contractor is perceived to have this ability.

This view was also in line with the response from one of the respondents who asserted that;

“Contractors should ensure that whatever they supply complies in terms of quality of the product and meets the tastes and preference of the clients”.

The contractor appears to excel in certain areas, such as planning, goal alignment, teamwork, and quality control. However, there are areas, notably risk management and record-keeping, where improvements may be considered. Additionally, addressing communication concerns could further enhance the overall effectiveness of the contractor’s managerial capabilities.

4.6.1 correlation analysis between managerial capability and project performance.

A positive correlation coefficient (0.311) suggests a positive linear relationship between managerial capability and project performance. This means that as one variable increases, the other tends to increase as well, and vice versa. The strength of the correlation is moderate, as the coefficient is between 0.3 and 0.5. The significance level (p-value) is 0.065, which is slightly above the conventional threshold of 0.05. A p-value less than 0.05 is often considered statistically significant, indicating that the observed correlation is unlikely to be due to random chance. In this case, the p-value is marginally above 0.05, suggesting that the correlation is not quite statistically significant at the 0.05 level. The sample size for both managerial capability and project performance is 36. Positive Correlation: The positive correlation coefficient implies that there is a tendency for higher levels of managerial capability to be associated with better project performance. This is an encouraging finding, as it suggests that investing in and improving managerial capability may contribute positively to project outcomes. The moderate strength of the correlation indicates that while there is a discernible relationship between managerial capability and project performance, other factors may also influence project outcomes. Although the p-value is slightly above the conventional significance level of 0.05, it is still relatively close. It is essential to interpret this with caution. The relationship may still be practically significant, but additional data or further analysis may be needed to strengthen the statistical significance. Organizations may consider focusing on enhancing managerial capabilities, as suggested by the positive correlation. This could involve providing training, improving leadership skills, or implementing effective project management practices. Correlation does not imply causation. While the correlation suggests an association between managerial capability and project performance, it does not establish a cause-and-effect relationship. Other unexamined factors may also contribute to project outcomes.

4.6.2 Model Summary

The findings from the model summary suggests that 9.7% of the changes in project performance is determined by the managerial capability of the contractors this finding also further shows that there are many other factors that determine the changes in project performance and not only managerial capability. The results further show that managerial capability is weak at determining the project performance. The statistics further shows that R (Multiple Correlation Coefficient): This value, denoted as ‘a’ in the table, is 0.311. This suggests a weak positive linear relationship between the predictor variable (Managerial capability) and project performance. R Square (Coefficient of Determination): The square of the multiple correlation coefficient. It represents the proportion of the variance in the dependent variable (response) that can be explained by the independent variable (predictor) in the model. Here, it is 0.097, indicating that only 9.7% of the variability in the response variable is explained by the predictor. Adjusted R Square: This is a modification of R Square that adjusts for the number of predictors in the model. It is often considered a more reliable measure in multiple regression. Here, it is 0.070. Std. Error of the Estimate: This represents the standard deviation of the residuals (the differences between observed and predicted values). In this case, it is 4.751. The R Square value of 0.097 suggests that the model has a limited ability to explain the variability in the response variable based on the provided predictor (Managerial capability). The Adjusted R Square is lower than the R Square, which might indicate that adding “Managerial capability” to the model did not significantly improve its explanatory power. The low R Square value could be indicative of other unaccounted factors influencing the response variable, or it might suggest that “Managerial capability” alone may not be a strong predictor.

4.6.3 Coefficient of variables

It’s important to note that the statistical significance of this relationship is borderline (p-value = 0.065), and it does not reach the conventional threshold of 0.05. this study results further shows that P-Value 0.065 >0.05 indicating that there is no statistical relationship between managerial capability and project performance. the results also indicate that accept the null hypothesis and reject the alternative hypothesis stating that there is no significant relationship between managerial capability and project performance.

The constant term (11.015) is the predicted value of the dependent variable when all independent variables are zero. However, its statistical significance is not established as the p-value is 0.164, which is greater than the commonly used threshold of 0.05. The managerial capability variable has a positive unstandardized coefficient (0.475), suggesting that an increase in managerial capability is associated with an increase in project performance. The standardized coefficient (Beta) provides a measure of the strength of the relationship, taking into account the different scales of the variables. A Beta of 0.311 indicates a moderate positive relationship between managerial capability and project performance. The t-statistic (1.907) is less than 2 in absolute value, suggesting that the coefficient is not very robust, and there might be some uncertainty associated with it.

4.7 Project Performance

The findings of this finding were gathered from questionnaires administered to the respondents. The variables were measured using 7 items scored on a five-point Likert scale of 1=strongly disagree, 2= Disagree, 3=Uncertain, 4=Agree, 5= strongly agree.

Source: Primary Data

The findings in the study indicates that The project is cost-effective as indicated by the Mean value of 3.97 and Std. Deviation: 0.810, The majority of respondents agree that the project is cost-effective, as indicated by the mean score. The low standard deviation suggests that there is a relatively high level of agreement among respondents.

The findings in the study further reveals that, the project takes a short time to complete, the mean value of 3.75 and Std. Deviation: 1.228, While there is a generally positive sentiment towards the project completion time, the higher standard deviation indicates some variability in opinions. This could imply that there is a diversity of perspectives on the time efficiency of the project.

The rate of health and safety incidents recorded is low, Mean: 3.36 and Std. Deviation: 0.867, The mean suggests a moderate level of agreement that health and safety incidents are low. The standard deviation indicates some dispersion in responses, suggesting differing views on the safety performance of the project.

Project outputs are high compared to costs; Mean, 3.78, Std. Deviation: 0.637 and Respondents generally agree that project outputs are high relative to costs, and the low standard deviation indicates a more consistent opinion among participants.

Clients are highly satisfied with the contractor’s performance, Mean: 3.61 and Std. Deviation: 0.871, this results further shows that There is a moderate level of agreement that clients are satisfied with the contractor’s performance. The standard deviation suggests some diversity in opinions, indicating varying levels of satisfaction among respondents.

There is high-quality work done by the contractor, the Mean: 3.69 and Std. Deviation: 0.951, The mean suggests a generally positive perception of the contractor’s work quality. However, the higher standard deviation indicates some variability in opinions, reflecting differing views on the quality of work.

The projects are environmentally sustainable, Mean: 3.53 and Std. Deviation: 1.158, The mean indicates a moderate level of agreement regarding the environmental sustainability of the projects. The higher standard deviation suggests a diversity of opinions on the sustainability aspect, while there are generally positive perceptions of various project performance aspects, the standard deviations reveal some diversity in opinions among the respondents. It would be valuable for project managers or stakeholders to further investigate and understand the underlying reasons for the variability in opinions to address any potential concerns and enhance overall project performance.

4.8 Correlation results for different objectives

The correlation coefficient of 0.469 suggests a moderate positive linear relationship between financial capacity and project performance. As financial capacity increases, project performance tends to increase as well. The significance level of 0.004 indicates that there is a 0.4% probability of observing such a strong correlation by random chance. With a significance level of 0.01, the result is considered statistically significant. The double asterisks (**) denote that the correlation is significant at the 0.01 level. Organizations with higher financial capacity may experience better project performance. Decision-makers can consider financial capacity as a potential predictor or factor influencing project success. This correlation does not imply causation, and other variables may also contribute to project performance. It’s important to note that correlation does not imply causation, while a significant correlation is observed, it does not necessarily mean that financial capacity directly affects improved project performance. Other factors not considered in this analysis may influence the relationship.

The correlation coefficient between technical capacity and project performance is reported as 0.356, with a significance level (p-value) of 0.033. The asterisk (*) indicates that the correlation is statistically significant at the 0.05 level (2-tailed). Let’s break down and discuss the key elements of this correlation analysis, Strength of the correlation (0.356); The correlation coefficient ranges from -1 to 1, with 0 indicating no correlation, 1 indicating a perfect positive correlation, and -1 indicating a perfect negative correlation. In this case, a positive correlation of 0.356 suggests a moderate positive relationship between technical capacity and project performance. Significance level (0.033), The significance level (p-value) is crucial in determining whether the observed correlation is statistically significant or if it could have occurred by random chance. In this analysis, the p-value of 0.033 is less than the conventional threshold of 0.05, indicating that the correlation is statistically significant. Interpretation of significance (0.05 level, 2-tailed); The asterisk (*) next to the correlation coefficient indicates that the correlation is statistically significant at the 0.05 level with a two-tailed test. This means that there is less than a 5% probability that the observed correlation occurred by chance.

The sample size is crucial in interpreting the reliability of the correlation. In this case, the analysis is based on a sample of 36 data points for both technical capacity and project performance. In practical terms, this correlation suggests that as technical capacity increases, project performance tends to improve, and this relationship is statistically significant. However, it’s important to note that correlation does not imply causation. Other factors may also influence project performance, and further research or analysis may be needed to understand the underlying dynamics of this relationship and to establish causation if applicable.

A positive correlation coefficient (0.311) suggests a positive linear relationship between managerial capability and project performance. This means that as one variable increases, the other tends to increase as well, and vice versa. The strength of the correlation is moderate, as the coefficient is between 0.3 and 0.5. The significance level (p-value) is 0.065, which is slightly above the conventional threshold of 0.05. A p-value less than 0.05 is often considered statistically significant, indicating that the observed correlation is unlikely to be due to random chance. In this case, the p-value is marginally above 0.05, suggesting that the correlation is not quite statistically significant at the 0.05 level. The sample size for both managerial capability and project performance is 36. Positive Correlation: The positive correlation coefficient implies that there is a tendency for higher levels of managerial capability to be associated with better project performance. This is an encouraging finding, as it suggests that investing in and improving managerial capability may contribute positively to project outcomes. The moderate strength of the correlation indicates that while there is a discernible relationship between managerial capability and project performance, other factors may also influence project outcomes. Although the p-value is slightly above the conventional significance level of 0.05, it is still relatively close. It is essential to interpret this with caution. The relationship may still be practically significant, but additional data or further analysis may be needed to strengthen the statistical significance. Organizations may consider focusing on enhancing managerial capabilities, as suggested by the positive correlation. This could involve providing training, improving leadership skills, or implementing effective project management practices. Correlation does not imply causation. While the correlation suggests an association between managerial capability and project performance, it does not establish a cause-and-effect relationship. Other unexamined factors may also contribute to project outcomes

4.9 Model Summary

1. Table 7: Model summary of contactors financial capacity, Technical Ability
2. Management Capabilities on project performance

The finding from the study indicates that the R-square values of 0.22 shows that financial capacity affects project performance by 22%, indicating that though financial capacity affects projects performance, there are other factors that affects project performance.

The table results show that R-squared value of 0.127 indicates that 12.7% of the project performance is affected by technical capacity, this shows that there are other factors that affect project performance rather than Technical capacity only.

The R-squared value of 0.097 indicates that project performance affects managerial capability by 9.7%, indicating that there are other factors that affect project performance and not only project managerial capability

4.10 Regression Results

The coefficient for “Financial capacity” is 0.764, indicating that for a one-unit increase in financial capacity, the dependent variable (project performance) is expected to increase by 0.764 units. The standardized coefficient (Beta) is 0.469, representing the strength and direction of the relationship in standard deviation units. The t-value of 3.094 and a significance level of 0.004 suggest that the coefficient for “Financial capacity” is statistically significant. The positive Beta value indicates a positive relationship between financial capacity and project performance.  In summary, the regression model suggests that both the intercept and the coefficient for “Financial capacity” have statistically significant effects on the dependent variable “Project performance.” The positive coefficient for “Financial capacity” implies that higher financial capacity is associated with better project performance, and the model seems to fit the data well.

The constant term represents the expected value of the dependent variable (Project performance) when all independent variables are zero. In this case, the intercept is 11.682, but it is not statistically significant at conventional levels (p-value = 0.075). The coefficient for Technical Capacity is 0.618, suggesting that for a one-unit increase in Technical Capacity, the expected change in Project performance is 0.618 units. The standardized coefficient (Beta) is 0.356, indicating the relative importance of Technical Capacity in explaining the variation in Project performance. The t-values for both the constant term and Technical Capacity are provided. The t-value is a measure of how many standard errors the coefficient estimate is away from zero. A larger absolute t-value indicates a more statistically significant result. In this case, the Technical Capacity variable has a t-value of 2.222, which, along with a significance level of 0.033, suggests that Technical Capacity is statistically significant in predicting Project performance, the overall model fit is not discussed in the provided output. It would be beneficial to know additional information such as the R-squared value, which indicates the proportion of variance in the dependent variable explained by the independent variables, In summary, the results suggest that Technical Capacity has a statistically significant impact on Project performance, as evidenced by its significant standardized coefficient (Beta) and the t-value. However, further analysis and consideration of other factors, along with the overall model fit, are necessary to draw comprehensive conclusions about the relationship between Technical Capacity and Project performance.

The constant term (11.015) is the predicted value of the dependent variable when all independent variables are zero. However, its statistical significance is not established as the p-value is 0.164, which is greater than the commonly used threshold of 0.05. The managerial capability variable has a positive unstandardized coefficient (0.475), suggesting that an increase in managerial capability is associated with an increase in project performance. However, it’s important to note that the statistical significance of this relationship is borderline (p-value = 0.065), and it does not reach the conventional threshold of 0.05. The P-value of 0.065 suggest that there is no significant relationship between managerial capability and project performance, this further shows that the null hypothesis is accepted and the alternative hypothesis is rejected therefore concluding that there is no significant relationship between managerial capacity and project performance.