Research proposal sample

CHAPTER FIVE

SUMMARY, DISCUSSION OF FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS

5.0 Introduction

This chapter presents a summary of the study findings, their discussion, conclusions drawn, and recommendations for improvement.

5.1 Discussion of Findings

This section discusses the study findings in relation to the research objectives.

5.1.1 Contractors’ Financial Capability and Project Performance

The correlation coefficient of 0.469 indicates a moderate positive relationship between financial capacity and project performance. A significance level of 0.004 (p < 0.01) confirms that this relationship is statistically significant. Regression analysis further supports this, with a p-value of 0.004 (< 0.05), leading to the rejection of the null hypothesis. Thus, contractors with strong financial capabilities tend to perform better in UPE school projects in Mbarara District.

These findings align with Agency Theory (Eisenhardt, 1989), which posits that the government (principal) must ensure contractors (agents) possess adequate financial resources to deliver quality projects efficiently. Additionally, Ghalayini & Noble (2011) emphasize that financial capacity encompasses liquidity, credit access, and efficient resource allocation, which are crucial for project success.

The study also highlights that experience in the construction industry enhances performance, as contractors with industry knowledge navigate challenges more effectively (Obelle, 2012). Furthermore, technical team competence and quality management systems significantly influence project outcomes, corroborating Kerzner’s (2015) assertion that project success depends on timely, cost-effective, and specification-compliant delivery.

5.1.2 Technical Capability and Project Performance

A correlation coefficient of 0.356 (p = 0.033) suggests a moderate positive relationship between technical capacity and project performance. The regression analysis (p = 0.033 < 0.05) confirms statistical significance, supporting the alternative hypothesis.

Key findings include:

Experience in Construction: Contractors with extensive industry experience perform better (Lim & Mohammed, 1999).
Technical Team Competence: A well-trained team improves project execution (Brown & Adams, 2008).
Safety and Equipment Efficiency: Efficient machinery enhances productivity, though safety mechanisms need improvement.
Local Knowledge: Familiarity with local resources facilitates smoother project execution.

5.1.3 Contractors’ Managerial Capability and Project Performance

A positive correlation (0.311) indicates that stronger managerial capability improves project performance. Key observations include:

Planning and Goal Alignment: Effective planning ensures project objectives are met (Carvalho et al., 2015).
Teamwork and Leadership: Collaborative work environments enhance productivity (Howell, 2018).
Risk Management and Record-Keeping: These areas require improvement to minimize project disruptions (Nixon et al., 2012).

5.2 Conclusions

Financial Capability: Contractors with sound financial management perform better, though transparency in financial autonomy needs enhancement.
Technical Capability: Experience, skilled teams, and efficient equipment drive success, but safety protocols require strengthening.
Managerial Capability: Strong planning and teamwork are strengths, while risk management and communication need refinement.
Regression Analysis: Financial and technical capacities significantly influence performance, whereas managerial capability shows borderline significance.

5.3 Recommendations

Financial Capability:

Improve transparency in financial decision-making.
Engage stakeholders in financial planning to align expectations.

Technical Capability:

Strengthen safety protocols and quality management systems.
Invest in advanced equipment and continuous technical training.

Managerial Capability:

Enhance risk management strategies and documentation practices.
Conduct leadership training to improve communication and decision-making.

Further Research:

Expand variables in regression models for deeper insights.
Incorporate qualitative studies to complement quantitative findings.