Risk Assessment and Operational Efficiency of Companies in the Road Construction Industry in Kenya

Authors

Elson Kiplangat Kirui

Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000-00200, Nairobi (Kenya)

Josephat Oluoch Oluoch

Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000-00200, Nairobi (Kenya)

Elijah Maina Kimani

Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000-00200, Nairobi (Kenya)

David Kimani Nduruhu

Jomo Kenyatta University of Agriculture and Technology, P. O. Box 62000-00200, Nairobi (Kenya)

Article Information

DOI: 10.47772/IJRISS.2026.100900165

Subject Category: Civil Engineering

Volume/Issue: 10/9 | Page No: 2330-2338

Publication Timeline

Submitted: 2026-09-20

Accepted: 2026-09-25

Published: 2026-10-05

Abstract

Operational efficiency is important to road construction companies because it reflects firms’ capacity to use resources economically, complete projects promptly, deliver services effectively and meet client quality requirements. This study examined the relationship between risk assessment and operational efficiency of companies in the road construction industry in Kenya. The study adopted a descriptive research design. The target population comprised 16,684 road works contractors licensed by the National Construction Authority as of 1 July 2025. A sample of 391 was determined, and 288 usable responses were obtained, representing a 74% response rate. Risk assessment was measured using four items covering risk identification, risk sensitization, risk evaluation and risk response. The risk-assessment scale recorded Cronbach’s alpha of .752, KMO of .597 and a significant Bartlett’s test (χ² = 475.246, df = 6, p < .001). Operational efficiency was measured perceptually using four questionnaire items assessing respondents’ views on the contribution of internal controls to cost effectiveness, prompt project completion, service delivery and meeting client quality demands. The operational-efficiency scale recorded Cronbach’s alpha of .856. Pearson correlation analysis showed a positive and statistically significant relationship between risk assessment and operational efficiency (r = .433, p < .001, N = 288). Simple linear regression analysis further established that risk assessment significantly predicted operational efficiency, F (1, 286) = 66.01, p < .001, accounting for approximately 18.7% of the variation in operational efficiency (R² = .187). The standardized regression coefficient was positive (β = .433). The null hypothesis that risk assessment has no statistically significant relationship with operational efficiency was therefore rejected. The study concludes that stronger risk-assessment practices are associated with higher perceived operational efficiency among road construction companies in Kenya. It recommends strengthening systematic risk identification, risk sensitization, risk evaluation and timely response to risk mitigation measures to support operational efficiency.

Keywords

Risk assessment, operational efficiency, internal controls, road construction industry, Kenya

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References

1. Adeyemo, K. A., & Adebayo, G. A. (2021). Risk management practices and firm performance: Evidence from non-financial listed companies in Nigeria. International Journal of Advances in Engineering and Management, 3(11), 452–464. [Google Scholar] [Crossref]

2. Gerami, S., & Gerami, H. (2025). Improving operational efficiency in construction businesses. International Journal of Civil Infrastructure, 8, 68–83. https://doi.org/10.11159/ijci.2025.008 [Google Scholar] [Crossref]

3. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X [Google Scholar] [Crossref]

4. Machira, A. M., & Kising’u, T. M. (2024). Risk management practices and performance of road construction projects in Kilifi County, Kenya. The Strategic Journal of Business & Change Management, 11(1), 796–822. [Google Scholar] [Crossref]

5. Mberia, J., Omare, M., & Ronoh, E. (2025). Effect of risk evaluation practices on the performance of construction firms in Nairobi County, Kenya. European Journal of Management and Marketing Studies, 10(1). [Google Scholar] [Crossref]

6. Obayagbona, J., & Osagiende, M. (2023). Risk management and performance of the Nigerian banking industry. Journal of Business Studies and Management Review, 6(2), 118–127. [Google Scholar] [Crossref]

7. Oluwaleye, T. O., Kolapo, F. T., & Adejayan, A. O. (2023). Risk management and profitability of quoted banks in Nigeria. European Journal of Economic and Financial Research, 7(1), 208–222. [Google Scholar] [Crossref]

8. Park, J. H., & Kim, J. I. (2021). Practical consideration of factor analysis for the assessment of construct validity. Journal of Korean Academy of Nursing, 51(6), 643–647. [Google Scholar] [Crossref]

9. Rasheed, M. Y., Saeed, A., & Gull, A. A. (2018). The role of operational risk management in performance of banking sector: A study on conventional & Islamic banks of Pakistan. Pakistan Journal of Humanities and Social Sciences, 6(1), 1–16. https://doi.org/10.52131/pjhss.2018.0601.0029 [Google Scholar] [Crossref]

10. Surucu, L., & Maslakci, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), 2694–2726. [Google Scholar] [Crossref]

11. Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper & Row. [Google Scholar] [Crossref]

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