Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya

Charles Mallans Rambo

doi:doi:10.11648/j.jfa.20251306.16

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Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya

Charles Mallans Rambo^*

Published in Journal of Finance and Accounting (Volume 13, Issue 6)

Received: 11 November 2025 Accepted: 3 December 2025 Published: 29 December 2025

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Abstract

Hydroelectric power (HEP) projects in Kenya are capital-intensive and exposed to multiple financial and operational risks, which can undermine performance and investor confidence. This study examined the influence of Credit Enhancement Mechanisms (CEMs) on the performance of HEP projects and assessed the moderating role of Communication Strategy (CS) on the relationship. Guided by a pragmatist philosophy, the study employed a cross-sectional mixed-methods design, targeting 12 HEP projects and key government agencies involved in financing, regulation, and risk management. Quantitative data were collected from 94 respondents using structured questionnaires, while qualitative data were obtained through key informant interviews. Descriptive statistics, correlation analysis, hierarchical regression, and thematic analysis were employed to analyze the data, with robustness tests conducted to ensure stability and validity. The results show that CEMs, particularly currency risk mitigation, political risk insurance, and construction risk mitigation, significantly enhance project performance. CS was found to positively moderate this relationship, amplifying the effectiveness of CEMs. Project age and capacity also contributed to performance outcomes, highlighting the importance of operational maturity and scale. Robustness tests confirmed the stability of these results across ownership structures and analytical specifications. The study concludes that effective financial risk mitigation, reinforced by structured and transparent communication, is critical for optimizing performance of HEP projects. Policy and managerial implications include the institutionalization of targeted CEMs, promotion of local currency financing, and integration of structured communication protocols and emerging instruments such as the green financing to strengthen investor confidence and project sustainability.

Published in	Journal of Finance and Accounting (Volume 13, Issue 6)
DOI	10.11648/j.jfa.20251306.16
Page(s)	297-312
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

HEP Projects, Credit Enhancement, Communication Strategy, Project Performance, Financial Risk, Kenya

1. Introduction

Hydroelectric Power (HEP) infrastructure is essential for expanding socio-economic development across the globe. In the Sub-Saharan Africa (SSA), developing HEP infrastructure has immense potential to increase the growth rate of regional Gross Domestic Product (GDP) from the current 3.6% to more than 10%

[16]

. Despite this, the potential of HEP in the region remains under-exploited. For example, only 7% of the HEP potential has been harnessed, with underlying constraints being inadequate access to financial capital, as well as underdeveloped capital markets, characterized by limited Financial Risk Management (FRM) instruments to securitize credit financing

[17]

In Kenya HEP accounts for 45% of the renewable energy, which brings out its importance, as a major source of electricity in Kenya’s economy

[4, 35]

. About one-half of HEP projects have been developed since independence, including Masinga, Gitaru, Kiambere, Kindaruma, Kamburu, Turkwel and Sondu-Miriu among others, commissioned between 1969 and 2010. Recent estimates show that Kenya’s technically feasible HEP potential is about 6,000 MW, comprising of large-scale projects, with capacity greater than 10 MW, and small HEP plants. Besides, Kenya has installed HEP capacity of 838.5 MW, which is about 24.8% of the total installed capacity

[4, 25]

. This suggests that Kenya is harnessing about one-quarter of its HEP potential

[4, 12]

The unexploited HEP potential is distributed across five major drainage basins, namely, Athi River (60MW), Tana River (790MW), Rift Valley (305MW), Lake Victoria (329MW), and Ewaso Ng’iro North River (146MW)

[25]

. Despite this, stakeholders express concern the slow pace at which HEP infrastructural facilities are development in Kenya, with the primary constraint identified as limited access to appropriate credit financing, which stakeholders link to negative perceptions about Kenya’s investment environment, considered risky and of low creditworthiness. Negative perceptions continue to impede the degree of private-sector and foreign direct investments in the development of critical infrastructural facilities, including HEP

[17]

. Most investors and financial institutions are reluctant to invest in environments and mega-infrastructural projects with limited credit securitization mechanisms.

1.1. Performance of HEP Projects

Project performance is the extent to which outputs and outcomes measure up to implementation budget and schedule, operational and technical specifications, as well as customer needs. It also refers to the degree to which a project fulfills its intended purpose

[7, 36]

. HEP is essential in modern economies and appropriate for achieving sustainable economic development, because it associates with low emission of greenhouse gases, environmental protection, low production and maintenance cost, and stimulation of economic activities

[32]

. Performance of HEP projects is a multidimensional concept that may be indicated by measures such as capital cost, project schedule, energy efficiency, power output capacity, operational efficiency, household connections as well as reliability, affordability and quality of supply, customer satisfaction and management support, among others

[1]

. Performance of HEP projects may further be indicated by economic returns, socio-economic impact in the lives of local communities, legislative compliance and environmental effect

[36]

. In this study, performance of the HEP projects in Kenya was measured in terms of implementation budget, time schedule, production capacity, financial sustainability and customer satisfaction.

1.2. Financial Risk Management

The development of HEP infrastructure is a risky, complicated and capital-intensive undertaking

[17]

. It requires substantial funding for exploration, planning, construction, utilization, generation, transmission and distribution activities. Thus, developing HEP projects require long-term financing from foreign and domestic resources

[12]

. Despite this, financing for large-scale HEP infrastructural projects in Kenya is constrained by low foreign investment from a highly competitive international finance market and an under-developed domestic capital market, among other issues. Low Foreign Direct Investment (FDI) is particularly constrained by perceptions of high risk among investors

[12, 17]

The term ‘financial risk’ refers to the probability of events or occurrences with undesirable outcomes on project life cycle, budget or schedule; thus, increasing chances of poor performance, project failure, financial loss or slow return on investment

[20, 30]

. Financial risk is a key factor discouraging FDI and private sector financing initiatives for HEP infrastructure, which precipitates the need for appropriate FRM instruments, to cushion investors, reduce the cost of capital, improve the level, and terms of investment in HEP projects

[30]

. FRM instruments are mechanisms administered by the government or project management to transfer certain risks, with financial implications, away from project financiers to underwriters with capacity to shoulder such risks. The purpose of FRM mechanisms is to mitigate risks for mega infrastructural projects. Management of financial risks in such projects is likely to enhance efficiency in project expenditure, ensuring successful completion and sustaining optimal performance

[30]

Financial Risk Management can also benefit corporates or projects by providing information for risk assessment, which may be used to avoid mistakes committed in previous activities. Effective management of financial risks is also critical for developing confidence among stakeholders, especially financing partners, which may increase chances of accessing finances for future projects

[28]

. Management of financial risks in mega infrastructural projects includes a four-stage process, with the following steps: identification of potential financial risks, analysis of the severity of such risks, decision on a strategy to manage the risks, and monitoring success of the strategy

[28]

. The FRM mechanisms used are diverse, including alternative risk transfer, risk financing, contingent capital, hedging derivatives, credit enhancement and insurance, which are likely to influence project performance at various degrees

[34]

. This article focuses on credit enhancement, being one of the most common FRM mechanisms administered by GoK to securitize private sector and foreign investments in HEP projects in Kenya.

1.3. Credit Enhancement

Credit enhancement is a mechanism designed to lower repayment risk of credit financing, by improving the credit profile of borrowers, which may be an organization or a project, and to reduce the cost of borrowing

[7, 10, 39]

. In the context of mega infrastructural projects, credit enhancement is used to prop up the borrower to reduce the risk of defaulting and securitize invested finances. The main function of credit enhancement is to build confidence among investors by decreasing perceived and actual risks of investment losses; thereby, expand the level of credit financing for projects. By improving their creditworthiness, organizations seeking financing for infrastructural projects are likely to access credit at relatively lower interest rates than those lacking credit enhancement measures

[7]

. Appropriate CEMs provide assurance to investors that their finances are secure and that borrowers are ready to honor their obligations. In this regard, CEM is a tool for de-risking investments for financiers by improving chances that financing will be repaid

[15]

. Its benefits include reduction of interest rates, creating a more favorable debt repayment schedule, introduction of new borrowers to the market, as well as implementation of more ambitious projects with lesser risk.

Credit enhancement is typically administered by government agencies to encourage private and foreign lenders to finance government infrastructural projects in various sectors, including HEP. CEMs enable government agencies to negotiate loan terms and underwriting criteria with private lenders for financing. Third parties or private lenders also administer loans on behalf of foreign financiers of government projects. Third parties work with government agencies to ensure that financial tools are designed to address financing needs of key sectors. It covers various types of risks including financial, such as credit payment defaulting, currency exchange and liquidity risks; and economic such as external macroeconomic challenges, internal cost overruns and project performance. Credit enhancement also cover against political risks, including regulatory changes, political instability, social unrest, as well as corruption. Environmental risks are exemplified by consequences of natural hazards and physical environmental challenges)

[10, 39]

State and local governments seeking to improve financing options available for HEP infrastructural projects may choose to focus on CEMs to encourage private lenders and foreign investors to put their money in unfamiliar or underdeveloped financial markets

[15]

. By preventing the risk of loss, appropriate credit enhancement measures may be used to convince lenders to reduce interest rates, ease credit conditionalities or extend loan periods

[7]

. CEMs may be internal or external. Internal credit enhancement measures are initiated by the organization seeking credit financing to enhance its creditworthiness, including use of excess cash flows from underlying assets to provide extra protection. External credit enhancement refers to measures deployed by third parties to back up internal credit enhancement measures

[39]

. Commonly used internal CEMs include subordination, overcollateralization, excess spread and reserve account, among others. External CEMs include performance bonds, political risk insurance, wrapped securities, re-insurance, letter of credit, loan syndications, contingent credit lines and cash collateral account, among others. This study focused on the external CEMs because of their relevance in securing credit financing for mega infrastructural projects in developing countries, characterized by resource constraints, under-developed capital markets, political activism and increasing episodes of environmental disasters due to climate change

[2, 26]

1.4. Credit Enhancement in Kenya’s Infrastructure Market

Kenya is yet to realize the full potential of hydroelectric energy, as more than three-quarters of the potential HEP projects are yet to be developed

[4, 25]

. Thus, HEP supply in the country is characterized by high-power tariffs, sudden, frequent and prolonged power outages, with implications on the national industrial development and economic development. Being an under-developed financial market, the capital market offers a narrow range of FRM mechanisms, which constrains the financing of HEP projects. Credit enhancement has been a common feature of Kenya’s credit financing market, taking the forms of performance bonds, political risk insurance, credit risk guarantee, currency risk mitigation, construction risk mitigation, first-loss policy, re-insurance, letter of credit, loan syndications and contingent credit lines, among others

[2, 26]

. Despite this, little is known about the extent to which CEMs have contributed to PP. This study focused on determining the effect of CEMs on PP. Credit financing of mega infrastructural projects in developing countries, including Kenya, is confounded by negative perceptions among investors, who perceive under-developed financial markets as high-risk investment environments. Despite this, little is known about the extent to which communication strategies adopted by the government influence the link between credit enhancement and PP.

2. Literature Review

Credit enhancement characterizes capital markets in developed and developing economies and is considered the surest financial risk instrument for financing mega infrastructural projects and improving investors’ credit profile; thereby, reducing the cost of debt capital

[10]

. Previous studies reveal connections between various credit enhancement instruments and performance of infrastructural projects across the globe. For example, Alshehhi et al.

[3]

established a significant correlation between construction risk mitigation instrument and success of construction projects in Malaysia. More particularly, construction risks such as changes in the project design, as well as cost of labor, fuel and construction materials had a significant correlation with adherence to project schedule as a key outcome of project performance. Despite this, the study wasn’t explicit on strategies used by contractors to mitigate construction risks and securitize credit financing of infrastructural projects. Besides, the study adopted a general scope of infrastructural projects, which limits the extent to which its findings apply to the context of HEP projects.

The study conducted by De Castro et al.

[9]

identified currency risk as a key factor preventing foreign investments in infrastructural projects, particularly in developing economies. Even though currency hedging was a common strategy used to securitize investments denominated in foreign currency, its often costly and not feasible in developing markets, which however, complicates performance of infrastructural projects funded using such currency. Besides, the mechanism of adjusting the length of concession contracts is likely to protect foreign equity investments, and enable developing economies to protect infrastructural projects against currency exchange risks. Encouraging domestic investments in infrastructural projects is another strategy that is gaining momentum in developing economies, to securitize investments against currency risk.

Thordur et al.

[38]

reported significant relationships between foreign currency risk and performance of infrastructural projects. Stakeholders adopted various strategies to manage foreign currency risk, including project financing using local currency, foreign exchange hedging derivatives, long-term offtake agreements with state-owned companies, governmental funds, and early termination of concessions, among others. The study identified financial hedging derivatives as one of the most effective strategies for mitigating foreign exchange risk. However, its application was constrained by cost implications and complexity for developing financial markets. Notably though, all the strategies for managing foreign exchange risk had a significant affected on the success of infrastructural projects, in terms of time schedule and cost of implementation. Shibani et al.

[30]

identified fluctuation of currency as the most important factor affecting successful completion of infrastructural construction projects in Lebanon, with a mean score 4.26; while Shindo and Stewart

[31]

talked about currency mismatch as major impediments to infrastructural project stakeholders, including financiers, contractors, owners and underwriters.

In their study, Alfraidi et al.

[2]

identified political risk factors influencing the performance of infrastructural projects implemented using the public-private partnership models in the Kingdom of Saudia Arabia, in terms of cost overruns, project termination and project delay. The analysis revealed a significant relationship between political risks, including attitude towards foreign credit investors, social safety, economic crimes and political activism, among others; and performance of construction projects. Although the Alfraidi et al.

[2]

revealed ten most significant political risks influencing performance of infrastructural projects, the analysis failed to identify the strategies used by stakeholders to mitigate political risks, for timely completion of infrastructural projects within planned cost. The study conducted by Shindo and Stewart

[31]

reported identified political risk and successful completion of infrastructural projects. In addition to mismanagement of project resources by political class, the study noted that political risk also perpetuated negative perceptions about the investment environment; thereby, discouraging credit financing by foreign investors. Shibani et al.

[30]

called this political cion. In their study, political risk ranked second after currency risk, with a mean score of 4.38. By discouraging foreign investments, negative perceptions about the investment environment perpetuated funding constraints, which continues to affect development of infrastructural projects in developing countries, leading to stalled projects.

Shindo and Stewart

[31]

highlighted connection between insurance and development infrastructural projects, by providing cover against risks such as damage to property, injury to workers, and legal liabilities. Notably, partial or total loss of infrastructural projects due to natural disasters or human error such as poor quality of works, can lead to bankruptcy, with serious implications on completion of projects. The study revealed an increasing trend in insurers’ exposure to infrastructure in developing countries, though the percentage of investment in securitization of investments in infrastructural projects remains much smaller than in developed financial markets. The study highlighted the strengths and weaknesses of various insurance products in relation to performance of infrastructural projects in developed and developing markets, including first-loss policy, considered most relevant for contexts with minimal risk of total loss. First-loss policy has immense potential to save contractors from devastating project losses, caused by natural disasters and human error. Generally, insurance products secured investments against loss; thereby, ensuring timely completion within budgets. By securing projects against first-loss, project stakeholders avoid delays and disruptions, triggered by unforeseen circumstances, making it an essential element of infrastructural project performance. However, Shindo and Stewart

[31]

focused on general infrastructural projects, whose operational contexts may be different from that of HEP projects. The reviewed studies revealed significant relations between various instruments of credit enhancement and performance of infrastructural projects, with none being specific to HEP projects, which limits the extent to which they inform this study.

A CS is a plan that integrates media, government, public, project financiers, underwriters and regulatory agencies; which according to Chihuri and Pretorius

[8]

is essential element of project success. Effective communication enables project implementers to propagate information that can facilitate successful implementation and performance. Communication in the context of financial risk management is essential for appropriate corrective measures

[29]

. Despite this, communication in the said context remains a challenge, due to conflicting interests among project stakeholders. Effective CS becomes a necessity for timely identification of risks, analysis and response using appropriate mitigation instruments

[6]

. For this reason, scholars point out the need for clear communication flow structures, innovative information management tools, operational communication channels and strategies

[27]

The influence of project communication strategies on project performance is a matter that has featured in several studies. In Indonesia, Hermawati and Rosaira

[13]

established the importance of active communication for successful implementation of HEP projects. In Spain, Forcada et al.

[11]

revealed the attributes of communication strategies with a significant influence on performance of infrastructural projects, including information quality, communication flow structure, channels and information management. In Rwanda, Njagi et al.

[24]

reported a significant negative relationship between poor communication skills amongst project members and risk management. In Uganda, Ssenyange et al.

[33]

established a significant positive relationship between communication and performance of projects. Reportedly, communication provides clarity on project objectives and means of achievement. Similarly, Mugo and Moronge

[23]

revealed that communication framework, with clear roles and plans, enhances project implementation, improves team coordination, increases synergy and reinforces trust, which collectively, are essential for performance improvement. The findings suggest that effective communication is vital for project performance in terms of implementation budget, time schedule, production capacity, financial sustainability and customer satisfaction. The conceptual framework in Figure 1 shows the hypothesized relationship between credit enhancement, CS and PP.

Download: Download full-size image

Figure 1. Conceptual framework.

Based on the conceptual framework, two null hypotheses were formulated to be tested using empirical data. H₀₁: Credit enhancement has no significant influence on performance of HEP projects in Kenya. H₀₂: Communication strategy has no significant influence on the relationship between credit enhancement and performance of HEP projects in Kenya.

3. Methodology

The study was founded on pragmatist school of thought, which holds that knowledge is not only generated through objective measurement of reality (quantitative approach), but also through subjective meanings from the experiences of research subjects about the study phenomenon (qualitative approach). Thus, pragmatism provides the basis for the application of mixed methods approach

[5, 19]

. The study was guided by the cross-sectional survey design, with mixed methods. Whereas quantitative methods generated data for testing the relationship between the key variables, qualitative methods sourced data for in-depth understanding of the study subject. The design is cost-effective and efficient in terms of time because it relies on one episode of data collection

[14]

The study targeted 12 HEP projects in Kenya, including Tana, Masinga, Kamburu, Gitaru, Kindaruma, Kiambere, Sondu, Sang’oro, Turkwell, Imenti Tea Factory Feed-in Plant, Gikira Small Hydroelectricity Project, and Regen-Teremi Hydroelectricity Project. In each Project, 7 respondents were involved in the study, yielding a total of 84. The study also targeted government agencies involved in planning, financing, generation, transmission, marketing and risk management in HEP projects, including Ministry of Energy, Kenya Electricity Generating Company (KenGen), Ministry of Finance, Energy Regulatory Commission, Kenya Power Company, Kenya Electricity Transmission Company, Geothermal Development Company, Capital Markets Authority, Nairobi Security Exchange and Insurance Regulatory Authority for insights about financial risk management instruments used to securitize credit for financing HEP projects in Kenya. In each entity, the study targeted the project, finance, communication, quality assurance managers, as well as plant technicians, operators and engineers, sampled purposively based on their knowledge of, and experience with financial risk management instruments and performance of their respective HEP projects. The study used a sample of 94 respondents, including 84 staff of the HEP projects and 10 financial managers in line government institutions. The sampling frame was stratified based on project ownership, including mega-hydro projects owned by KenGen and mini-hydro projects owned by Independent Power Producers (IPPs).

Primary data were sourced using a structured questionnaire, administered to project, finance, communication and quality assurance managers, as well as plant technicians, operators and engineers. The questionnaire was structured to capture quantitative and quantifiable information. Primary data were also sourced from key informants in various organizations involved in financing HEP projects, financial risk management, capital markets, underwriting, policy and regulation, as well as energy transmission. Secondary data were obtained from organizational records and desk review of published materials. The questionnaire and the interview guide were pre-tested with 10 respondents at Sondu and Sang’oro hydroelectric projects. Pre-test respondents were excluded from main data collection to avoid monotony. The pre-test sample size was slightly above 10% of the sample size used in the study, which according to Kothari

[21]

, was adequate to identify gaps in data collection tools. Content Validity Index (CVI) was determined to ensure accurate measurement, design and statistical conclusion. The process obtained a CVI of 0.775, which is above the recommended threshold. Besides, split-half reliability technique was used to generate Cronbach’s alpha coefficient. The process obtained a coefficient of 0.781, which was also within the recommended threshold

[37]

. Data were collected in May 2021.

Quantitative data analysis techniques included frequency distributions, percentages and mean scores; Spearman’s Rank Correlation Co-efficient was used to determine correlations between CEMs and performance of HEP projects; as well as between CS and performance of the projects. Multiple linear regression analysis was used to determine the influence of CEMs (independent variables) on performance of HEP projects (dependent variable). Regression analysis was also used to establish the moderating effect of CS on the relationship between the independent and dependent variables. The regression model took the form:

PP= β₀+ β₁

CEMs

_i+ β₂CS_i+ β₃(CEM_i*CS_i) + β₄AGE_i + β₅CAP_i+ β₆DEV_{KenGen i}+ ԑ_i

Where; PP: project performance; β₀ is the constant term; β₁ is the partial regression coefficient showing the effect of CEMs; β₂ is the partial regression coefficient indicating the effect of CS;β₃ is the partial regression coefficient indicating the effect of the interaction term; β₄ & β₅ are partial regression coefficients showing the effect of project controls, including age and capacity, respectively; β₆ is partial regression coefficients giving the mean difference in PP between KenGen and IPP projects; CEMs_iis credit enhancement mechanism; CS_i is communication strategy; CEMs_i*CS_i is the interaction term between CEMs_i and CS_i; AGE_iis project age; CAP_i is project capacity; DEV_{KenGen i} is dummy for project developer (equals to 1 if KenGen, 0 if IPP); and ԑ_i is the error term.

Simple linear regression equations were formed to establish effect of each component of CEMs on performance of HEP projects. A multivariate equation was formed to determine the combined effect of all the independent variables on the dependent variable; thereby, test validity of the first null hypothesis. To test the moderating effect of CS, the multivariate equation was modified by incorporating the moderator. This was also used to test the null hypothesis suggesting that CS used by projects had no significant influence on the relationship between the independent and the dependent variables.

For qualitative data, thematic analysis technique was deployed to prepare transcripts, code transcriptions on CEMs used, CS and performance of HEP projects; identify emerging themes, examine interactions between different themes and provide plausible explanations

[22]

. The study was guided by the framework of ethical principles for social science research to protect respondents. This involved seeking respondents’ consent for voluntary participation, emphasizing voluntary participation, as well ensuring confidentiality of the data sourcing process and the data sourced.

4. Results

This section presents and interprets the study findings, which are structured into four thematic subsections: respondents’ and projects’ characteristics, descriptive results, inferential results, and robustness tests.

4.1. Respondents’ and Projects’ Characteristics

The study captured information on various attributes of the respondents, including category of project affiliated to, age, gender, years of experience, education level and designation type. Specifically, of the 84 respondents, 63(75%) were affiliated to KenGen projects, while 21(25%) worked for IPPs. The participants included 59 (70.2%) males and 25 (29.8%) females, aged between 25 and 55 years. Particularly, 31 (36.9%) respondents were aged between 40-49 years, 29 (34.5%) were in the 50 years plus age bracket, 19 (22.6%) indicated 30-39 years, while 5 (6.0%) were aged below 30 years. About two-thirds of the respondents, 50 (59.5%), had professional experiences of at least 11 years, 24 (28.6%) indicated experiences of 5 to 10 years, 7 (8.3%) stated 2 to 4 years, while 3 (3.6%) indicated less than 2 years. Furthermore, 48 (57.2%) respondents had attained Bachelor’s degree education, 30 (35.7%) indicated post-graduate education, while 6 (7.1%) were diploma holders. The respondents included project and finance managers, plant technicians, operators and engineers; as well as communication and quality assurance managers, each represented by 12 (14.3%) respondents. Of the 12 projects assessed, 9 (75%) were developed by KenGen, while 3 (25%) were developed by IPPs. The projects varied considerably in age, ranging from 1 to 50 years, with 7 projects (58.3%) being less than 30 years old and the remaining 5 (41.7%) having operated for 30 years or more. In terms of installed capacity, half of the projects (6; 50.0%) had outputs below 60 MW, while the other half (6; 50.0%) recorded capacities of 60 MW and above.

4.2. Descriptive Results

This subsection provides a summary of the data used in the study, highlighting the mean scores and standard deviations (SD) for each construct measured on the 5-point Likert scale, namely CEMs, CS, and PP. The descriptive analysis aimed to assess and interpret the central tendency and dispersion of the data to enhance understanding of its distribution and variability. Table 1 shows the results of this analysis.

Table 1. Likert scale composite scores.

Variable	N	Min	Max	Mean score	SD
Credit enhancement mechanisms (CEMs)	84	1	5	3.93	0.62
Communication strategy (CS)	84	1	5	3.88	0.97
Project performance (PP)	84	1	5	3.79	0.94

Source: Research Data (2021)

Using the standard 5-point Likert scale benchmarks, where scores of 1-1.80 represent “very low,” 1.81-2.60 “low,” 2.61-3.40 “moderate,” 3.41-4.20 “high,” and 4.21-5.00 “very high” levels of perception

[18]

, the results show that CEMs achieved the highest mean score of 3.93 (SD = 0.62). This value indicates that respondents tended to strongly agree with statements relating to CEMs in Kenya’s HEP projects. Positioned within the “agree” category, the mean reflects a generally positive view of CEMs. The standard deviation of 0.62 points to moderate clustering of responses, meaning that although a large share of respondents expressed similar opinions, their views were not entirely uniform, signaling some differences in experience and perception regarding CEMs.

The CS construct posted a mean of 3.88 (SD = 0.97), showing that respondents generally held highly positive perceptions of CS in hydropower projects. With a mean of 3.88, situated within the “agree” category and leaning toward the higher end of that range, CS practices were viewed in a favorable light. However, the standard deviation of 0.97 points to high variability, demonstrating that responses were more dispersed rather than tightly clustered. This spread implies considerable differences in how respondents perceived CS practices, which may stem from variations in communication policies, leadership approaches, resource support, and other contextual factors. Taken together, the results suggest that although CS practices are broadly recognized, their perceived effectiveness was uneven hydropower projects, leading to a wider range of views from positive to negative.

Table 2. Descriptives for project controls.

Constructs		N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Min	Max
Constructs		N	Mean	Std. Deviation	Std. Error	Lower Bound	Upper Bound	Min	Max
Project capacity in MW	KenGen	9	92.82	76.654	25.551	33.90	151.74	20	255
	IPPs	3	2.23	2.574	1.486	-4.16	8.63	1	5
	Total	12	70.18	77.157	22.273	21.15	119.20	1	255
Project age in years	KenGen	9	28.89	17.424	5.808	15.50	42.28	8	53
	IPPs	3	6.67	5.686	3.283	-7.46	20.79	2	13
	Total	12	23.33	18.102	5.226	11.83	34.84	2	53

The PP construct posted the lowest mean score at 3.79 (SD = 0.94), suggesting that respondents viewed performance less favorably compared to the other constructs. Although a mean of 3.79 still falls within the “agree” category, its position near the lower boundary implies that, while some respondents rated PP highly, many others expressed neutral or differing views. The comparatively large standard deviation of 0.94 indicates considerable spread in the responses, reflecting substantial variation in how participants perceived PP. This broad dispersion highlights a clear divergence in respondent experiences and assessments of project performance.

Descriptive analysis also involved a comparison of the mean scores for the project age and output capacity between projects developed by KenGen and IPPs. The analysis obtained an aggregate mean score of 12 years. The results show clear differences between KenGen and IPP projects in both capacity and age. KenGen projects have a much higher mean capacity (92.82 MW) compared to IPPs (2.23 MW), reflecting their operation of large hydropower plants versus the small-scale nature of IPPs. KenGen projects are also considerably older, with an average age of 28.89 years compared to 6.67 years for IPPs, indicating that KenGen facilities are long-established, while IPP projects are relatively recent developments. The non-overlapping confidence intervals and higher variability in KenGen project attributes further highlight that the two categories of projects differ substantially in size and age.

The ANOVA results show that the differences in both project capacity and project age between KenGen and IPP projects are not statistically significant at the conventional 5% level, but they are close to significance, suggesting meaningful trends. For project capacity, the ANOVA yields F = 3.93, ρ = 0.076, indicating that while the capacity difference between KenGen and IPP projects is large in magnitude, it does not meet the strict threshold for statistical significance (p < 0.05), likely due to the small sample size. Similarly, for project age, the difference approaches significance, with F = 4.46, ρ = 0.061, again falling just above the 5% cutoff but suggesting a noticeable tendency for KenGen projects to be older than IPP projects. Overall, both results reflect near-significant differences, meaning the observed gaps in capacity and age are substantial and consistent with practical expectations, but the limited number of projects reduces the statistical power to confirm these differences at the 5% level.

4.3. Inferential Results

The inferential analysis began with Pearson’s correlation to evaluate whether the predictor variables and the dependent variable met the linearity assumption. This was followed by a multiple regression analysis used to assess the effect of CEM on PP, the moderation effect of CS on the CEM-PP relationship and robustness checks to evaluate the stability and reliability of the results.

4.3.1. Correlation Analysis

Correlation analysis was conducted to determine whether the predictor variables, CEM, CS, project age and project capacity were correlated with the outcome variable, namely, PP. Establishing correlations is a critical preliminary step before conducting linear or multiple regression, as regression techniques rely on the assumption that the variables involved are linearly related. Correlation analysis was therefore, used to verify this assumption by indicating both the strength and direction of the correlations between each pair of variables; thereby, informing the suitability of the data for regression modelling (Janse et al., 2021). In this study, Pearson’s correlation coefficient (r) was used to assess correlations between the variables.

Table 3. Correlation analysis results.

Construct	CEM	CS	AGE	CAP	PP
CEM	1
CS	0.473^***	1
AGE	0.276^**	0.147	1
CAP	0.347^***	0.228^**	0.105	1
PP	0.565^***	0.485^***	0.358^***	0.401^***	1

***, ** correlation significant at ρ<0.01 and ρ<0.05, respectively

Table 3 presents the correlation matrix for the study variables, excluding project developer since it is measured on a nominal scale. The interpretation focuses on the relationships between the predictors: CEM, CS, project age (AGE), and project capacity (CAP), and the dependent variable, PP. The results show that CEM has a positive and statistically significant correlation with PP (r = 0.565, ρ < 0.01), indicating a moderate-to-strong linear association. This suggests that improvements in CEMs are associated with corresponding improvements in PP in hydroelectric power projects, thereby providing strong empirical justification for including CEM as a key predictor in the regression model. Similarly, CS is positively and significantly correlated with PP (r = 0.485, ρ < 0.01), implying that more effective communication strategies are associated with better project performance.

The control variables also exhibit meaningful relationships with PP. Project age shows a moderate positive and significant correlation with PP (r = 0.358, ρ < 0.01), suggesting that older projects tend to perform better, possibly due to operational maturity and experience. Likewise, project capacity is positively and significantly correlated with PP (r = 0.401, ρ < 0.01), indicating that larger-capacity projects generally achieve higher performance levels. Collectively, these results confirm that all continuous predictors display significant linear relationships with project performance, thereby satisfying the linearity assumption for multiple regression analysis and providing a clear foundation for subsequent regression tests for evaluating the study hypotheses.

4.3.2. Regression Analysis

Regression analysis was conducted to examine the influence of CEMs on the performance of HEP projects, as well as the moderating effect of CS on this relationship, using multiple regression. To achieve this, three hierarchical regression models were estimated, with predictors added progressively to assess their incremental contribution to the variance in project performance. Step 1, which included only the project controls, explained minimal variance, while Steps 2 and 3 substantially increased the model’s explanatory power, as reflected by rising R² values and significant F-change statistics. The results of the hierarchical regression analysis are presented in Tables 4 and 5.

Table 4. Model summary.

Model	R	R Square	Adjusted R Square	Std. Error	Change statistics
					R Square Change	F Change	df1	df2	Sig. F Change
1	0.391^a	0.153	0.121	0.103	0.113	12.470	3	80	0.001
2	0.516^b	0.266	0.239	0.116	0.137	18.360	2	78	0.000
3	0.635^c	0.403	0.381	0.061	0.135	10.682	1	77	0.001
ANOVA
Model		Sum of Squares	df	Mean Square		F		Sig.
1	Regression	21.759	3	3.643		147.05		0.008^b
	Residual	1.066	80	0.015
	Total	22.594	83
2	Regression	30.499	6	5.109		197.71		0.000^c
	Residual	1.426	78	0.021
	Total	32.167	83
3	Regression	39.439	6	6.623		261.38		0.000^d
	Residual	2.105	77	0.028
	Total	41.770	83
a Dependent Variable: project performance (PP) b Predictors: (Constant), project age, project capacity, project developer (dummy) c Predictors: (Constant), project age, project capacity, project developer (dummy), CEMs, CS d Predictors: (Constant), project age, project capacity, project developer (dummy), CEMs, CS, CEMs*CS

The hierarchical regression analysis examined the influence of CEMs on PP, as well as the moderating role of CS. Model 1, which included only project controls (age, capacity, and developer), explained a modest proportion of variance in project performance, with R² = 0.153 and Adjusted R² = 0.121, representing the proportion of variance in performance of HEP projects explained by CEMs involved in this study. It suggests that the model accounted for 12.1% of variance in HEP project performance. The model was also statistically significant (F = 147.05, ρ = 0.008), and the R² change of 0.113 with a significant F-change = 12.47, ρ = 0.001 indicated that the control variables made a meaningful contribution to explaining HEP project performance.

Model 2 incorporated the main predictors, CEMs and CS, in addition to the controls. This increased the explained variance to R² = 0.266 and Adjusted R² = 0.239, with an R² change of 0.137 and F-change = 18.36, ρ < 0.001. The results demonstrate that both CEMs and CS significantly improved the model’s explanatory power from 12.1% (in Model 1) to 23.9%, highlighting their direct positive influence on Performance of HEP projects beyond the effects of project-specific controls.

Model 3 included the interaction term (CEMs*CS) to test the moderating effect of CS. The inclusion of this term further increased the explained variance to R² = 0.403 and Adjusted R² = 0.381; suggesting that the two variables accounted 38.1% of variance in the HEP project performance. Thus, addition of CS in the regression equation improved the predictive power of the regression model by 14.2 percentage points. Besides, a significant R² change of 0.135 and F-change = 10.68, ρ = 0.001, indicates that CS significantly moderated the relationship between CEMs and project performance, suggesting enhancement of the effectiveness of CEMs when communication strategies are effectively implemented.

Overall, the ANOVA results for all models were significant, confirming that the predictors collectively provide a reliable explanation of HEP project performance. These findings suggest that while project controls establish a baseline impact, CEMs and CS substantially improve project outcomes, and their interaction further strengthens performance. Thus, both financial mechanisms and strategic communication play critical roles in enhancing the success of HEP projects in Kenya.

Having established the overall explanatory power of the predictors and the incremental contributions of CEMs, CS, and their interaction in the hierarchical regression models, the next step examines the individual regression coefficients, their statistical significance, and the specific effect of each predictor on PP, as detailed in Table 5.

Table 5. Regression coefficients.

Model		B	Std. Error	Beta	t	ρ-value
1	(Constant)	1.587	0.361		4.40	0.000^***
	Project age	0.006	0.002	0.259	2.69	0.016^**
	Project capacity	0.031	0.011	0.286	2.82	0.012^**
	Project developer (KenGen)	0.442	0.182	0.238	2.43	0.026^**
2	(Constant)	1.102	0.318		3.46	0.002^***
	Project age	0.024	0.010	0.218	2.40	0.029^**
	Project capacity	0.005	0.002	0.201	2.31	0.034^**
	Project developer (KenGen)	0.398	0.161	0.219	2.47	0.025^**
	CEMs	0.421	0.094	0.462	4.48	0.000^***
	CS	0.337	0.087	0.352	3.87	0.001^***
3	(Constant)	1.214	0.342		3.55	0.003^***
	Project age	0.021	0.009	0.204	2.33	0.037^**
	Project capacity	0.004	0.002	0.187	2.12	0.048^**
	Project developer (KenGen)	0.463	0.176	0.241	2.63	0.021^**
	CEMs	0.382	0.091	0.441	4.20	0.001^***
	CS	0.295	0.088	0.336	3.35	0.005^***
	CEMs*CS	0.134	0.041	0.592	3.27	0.001^***
*, correlation significant at ρ<0.01 and ρ<0.05, respectively; Project developer was dummy-coded (KenGen = 1, IPPs = 0).

The regression coefficients show that all predictors had a positive and statistically significant influence on the performance of HEP projects across the three models. In Model 1, project age (

β = 0.259, p = 0.016

), project capacity (

β = 0.286, p = 0.012

), and project developer (KenGen vs IPPs;

β = 0.238, p = 0.026

) all positively influenced project performance, indicating that older projects, larger capacity projects, and those developed by KenGen performed better; thereby, underscoring the importance of project maturity, size, and developer type in driving successful outcomes.

In Model 2, after including CEMs and CS, both emerged as moderate positive predictors of performance (

β_{CEMs} = 0.462, p < 0.001; β_{CS} = 0.352, p = 0.001

), while the control variables remained significant. This indicates that the adoption of effective CEMs and robust communication strategies substantially improved project outcomes beyond baseline characteristics. Notably though, CEMs had a stronger influence the performance of HEP projects than CS. On aggregate, the CEMs measured in the study, including currency risk mitigation, political risk insurance, construction risk mitigation, credit risk guarantee and first-loss policy, had significant and positive influence on performance of HEP projects (Beta = 0.523, ρ-value = 0.004). This suggests that for every unit standard deviation from the mean for CEMs, performance of HEP projects increased by 0.523; and the increment was significant at 99% confidence level. This led to rejection of the first null hypothesis stating that credit enhancement has no significant influence on performance of HEP projects in Kenya.

The findings show that PP is shaped by interconnected currency, political, construction, and credit risks, which significantly affect investor confidence and financing. While instruments such as hedging, political risk insurance, consortia financing, and local currency funding help mitigate macro-level risks, construction risks remain a major operational threat and are only partially addressed by costly surety and contract bonds. The limited use of credit guarantees reflects strict access conditions, whereas first-loss policies stand out as a more flexible and affordable option for IPPs. Overall, the effectiveness of CEMs varies by risk type, cost, and accessibility.

Model 3 introduced the interaction term (CEMs*CS) to test the moderating effect of communication strategy. The interaction was significant (

β = 0.592, p = 0.001

), suggesting that CS significantly and positively moderated the influence of CEM on project performance. Thus, statistical relations between CEMs and performance of HEP projects became stronger with the addition of CS in the regression model. This led rejection of the second null hypothesis stating that communication strategy has no significant influence on the relationship between credit enhancement and performance of HEP projects in Kenya. All control variables and main predictors remained significant, highlighting that both project-specific factors and strategic interventions jointly influenced the success of HEP projects in Kenya.

Qualitative findings also revealed the importance of CS in improving the effectiveness of CEMs, and PP. Participants observed that structured, timely, and transparent communication on financing arrangements, risk-sharing frameworks, and project milestones strengthens lender confidence, facilitates regulatory approvals, and improves stakeholder alignment. Contrastingly, weak or fragmented communication was reported to dilute the effectiveness of sound CEMs by generating uncertainty, mistrust, and coordination failures. Moreover, CS was found to facilitate stakeholder engagement, with far-reaching effects in the stabilization of guarantees, bonds, and insurance instruments supporting project execution. Overall, the findings suggest that CS functions as an enabling institutional mechanism that transforms financial risk mitigation tools into measurable performance outcomes.

4.4. Robustness Tests

Robustness tests were conducted to assess the stability, reliability, and consistency of the regression results across alternative model specifications. This was necessary to ensure that the observed relationships were not sensitive to model assumptions, variable inclusion, or sample characteristics, and that the findings reflect genuine underlying effects rather than estimation artifacts.

Table 6. Summary of robustness test results.

Robustness Test	Key Statistic	Threshold	Result	Conclusion
Subsample: KenGen only	β (CEM) = 0.372, ρ = 0.009	ρ <.05	Significant	Results not driven by one group
Subsample: IPP only	β (CEM) = 0.351, ρ = 0.041	ρ <.05	Significant	Effect holds for IPP projects
Robust standard errors	CEM (ρ = 0.000 →0.001)	ρ <.05	Still significant	No heteroskedasticity bias
Multicollinearity (VIF)	Max VIF = 2.4	VIF < 5	Acceptable	No multicollinearity issue
Tolerance	Min tolerance = 0.42	> 0.20	Acceptable	No redundancy
Cook’s distance	Max Cook’s D = 0.41	< 1.00	Acceptable	No influential outliers
Leverage values	Max leverage = 0.23	< 0.50	Acceptable	No high-leverage risk

The robustness test results presented in Table 6 collectively affirm the stability and credibility of the estimated effect of CEM on project performance. Subsample analyses conducted separately for KenGen and IPP projects confirm that the positive effect of CEM persists across both mega and mini hydro projects, dispelling concerns that the findings may be driven by a single project category. Specifically, the CEM coefficient remains positive and statistically significant for KenGen-only projects (β = 0.372, ρ = 0.009) and IPP-only projects (β = 0.351, ρ = 0.041), demonstrating that the observed relationship is not confined to one ownership structure. The persistence of significance after applying robust standard errors, with ρ shifting only marginally from 0.000 to 0.001, further indicates that heteroskedasticity does not bias the estimated effects or the associated inferences. In addition, the low variance inflation factor (VIF) values (max = 2.4) and acceptable tolerance levels (min = 0.42) rule out multicollinearity concerns. Diagnostic tests also reveal that Cook’s distance (max = 0.41) and leverage values (max = 0.23) remain well below conventional critical thresholds, indicating the absence of influential outliers or high-leverage observations capable of distorting the regression estimates. Overall, these results demonstrate strong internal validity and confirm that the relationship between CEM and the performance of HEP projects is empirically stable and not driven by model specification errors, outliers, or subgroup bias.

5. Discussions

This study examined the influence of CEMs on PP and assessed the moderating role of CS in this relationship. Descriptive findings indicate that respondents generally held positive perceptions of CEMs, CS, and PP, with all the constructs recording mean scores within the “agree” range. The relatively high mean score for CEM (M = 3.93) suggests that risk-mitigation instruments, such as guarantees, insurance, and first-loss policies, are widely recognized as critical enablers of HEP project success. However, the comparatively larger dispersion observed for CS and PP indicates uneven implementation and performance outcomes across projects, reflecting institutional, managerial, and contextual heterogeneity within the sector.

Correlation findings provide strong preliminary support for the study’s conceptual framework by establishing significant positive linear relationships among CEM, CS, project age, project capacity, and project performance. In particular, the moderate-to-strong association between CEM and performance confirms the centrality of financial risk-mitigation mechanisms in shaping outcomes of capital-intensive HEP investments. The positive relationships between performance and both project age and capacity further suggest that operational maturity and economies of scale contribute to performance stabilization over time.

Hierarchical regression findings show deeper insights into the explanatory power of the predictors. Although control variables accounted for only a modest proportion of variance in project performance, the inclusion of CEM and CS substantially increased the model’s explanatory strength, confirming their direct and independent contributions. The relatively stronger and significant influence of CEM underscores the importance of strategic financial risk-mitigation instruments in Kenya’s HEP sector, which is typically exposed to currency volatility, political uncertainty, construction delays, and credit constraints. Among the CEMs examined, currency risk mitigation exerted the strongest positive influence on project performance, followed by political risk insurance and construction risk mitigation.

Currency risk remains a major constraint in developing markets because exchange rate volatility increases uncertainty, weakens investor confidence, and heightens the perceived risk of large infrastructure projects. Although currency hedging is applied in Kenya’s infrastructure sector, its high cost often discourages financiers and underwriters, while local currency financing is used as a more resilient alternative suited to patient capital investors. Despite these measures, currency risk persists as a critical challenge, highlighting the inadequacy of current mitigation strategies and underscoring the need for deeper engagement with investment professionals to develop more effective, context-specific solutions.

Political risk was also found to significantly affect the performance of HEP projects, particularly during election cycles when adverse political actions disrupt both public and private sector operations, including the fulfillment of credit obligations. While political risk insurance and guarantees allow investors and lenders to transfer such risks to third parties, their high cost can inflate overall project development expenses. Nonetheless, insurance provided by development banks and international agencies remains essential for enhancing investor confidence in young democracies like Kenya, where political risks are largely unavoidable.

Construction risk mitigation emerged as another significant determinant of HEP project performance, with key risks arising from contractors’ failure to complete projects within scheduled time and budget. Although traditional mitigation tools such as sureties and contractor bonds are widely applied, they primarily address financial exposure rather than operational construction risks. The study therefore highlights the importance of comprehensive risk assessment through systematic identification, evaluation, and prioritization of construction risks to inform effective mitigation plans and ensure timely project completion, cost control, and infrastructure quality.

The findings further show that credit risk guarantees and first-loss policies significantly enhance HEP PP by reducing lenders’ expected losses, improving access to credit, and strengthening investor confidence, particularly for small-scale projects. However, credit risk guarantees remain vulnerable to political influence and cost inflation, while first-loss policies offer limited coverage and require careful risk analysis to avoid inadequate indemnification. While first-loss policies and political risk insurance appear relatively more accessible and cost-effective, especially for the IPPs, traditional credit guarantees remain constrained by strict eligibility requirements.

Furthermore, the statistically significant positive interaction between CEMs and CS confirms that CS plays a critical moderating role by amplifying the effectiveness of financial risk-mitigation tools on HEP project performance. This implies that even well-designed CEMs are unlikely to yield optimal outcomes in the absence of structured, transparent, and timely communication with stakeholders. Qualitative findings reinforce this conclusion by demonstrating that weak communication generates mistrust, regulatory delays, and coordination failures, thereby undermining the benefits of sound financial risk mitigation. Effective communication therefore functions as the institutional bridge through which financial safeguards are translated into tangible project outcomes.

Related to the findings of this study is the concept of green financing lending, which can significantly strengthen the effectiveness of CEMs in Kenya’s HEP sector by lowering the cost of capital, improving risk sharing, and attracting long-term, patient investment aligned with sustainability goals. Through concessional lending, green guarantees, and climate-focused risk-sharing facilities, green financing can directly complement instruments such as credit guarantees, first-loss policies, and political risk insurance, thereby, enhancing investor confidence and reducing exposure to currency, construction, and credit risks. In addition, the strong emphasis on monitoring, transparency, and environmental reporting embedded in green financing frameworks reinforces the moderating role of CS identified in this study by improving stakeholder trust, disclosure quality, and accountability. Ultimately, green financing can amplify both the financial and institutional effectiveness of CEMs while promoting the sustainable and resilient growth of Kenya’s hydropower sector.

Finally, robustness tests strengthen the credibility of these findings. The consistency of the CEMs effect across KenGen and IPPs subsamples, stability under robust standard errors, and the absence of multicollinearity, influential outliers, and leverage distortions collectively confirm that the observed relationships are not driven by ownership category, model specification errors, or data anomalies. These results provide strong internal validity and reinforce confidence in the generalizability of the findings within the Kenyan hydropower context.

5.1. Policy Implications

The strong influence of CEMs on performance of HEP projects underscores the need for the GoK and energy-sector regulators to institutionalize and scale structured financial risk-mitigation frameworks, particularly targeting currency, political, and construction risks. The prominence of currency and political risks highlights the importance of expanding support for local currency financing, political risk insurance, and development-bank supported guarantees to reduce exposure to external shocks. Given the moderating role of communication strategy, policy frameworks should extend beyond financial instruments to include mandatory stakeholder disclosure, transparency standards, and structured communication protocols for infrastructure projects. Targeted policies that optimize each component of CEMs, such as lowering interest rates, easing credit conditions, extending loan tenures, and improving investment terms, are essential, especially in Kenya’s nascent financial market. By integrating structured risk communication and investor engagement requirements, such policy interventions would strengthen investor confidence, attract foreign investment, reduce project delays, and enhance the sustainability of HEP projects. In addition, the discussions imply that policymakers should integrate green financing instruments, such as concessional green lending, guarantees, and climate risk-sharing facilities, into national infrastructure frameworks to strengthen CEMs, lower capital costs, and enhance transparency.

5.2. Managerial Implications

The findings indicate that financial risk mitigation is most effective when reinforced by strong and structured communication strategies. This underscores the need to embed transparent, continuous, and systematic stakeholder communication into risk identification, analysis, and mitigation processes to optimize project performance. Effective communication is essential for correcting investor misperceptions, building stakeholder trust, and enhancing investor confidence, particularly in environments with weak credit securitization mechanisms. The significance of construction risk mitigation further highlights the need to move beyond traditional financial bonds toward comprehensive risk assessment, contractor vetting, continuous monitoring, and adaptive management systems. Managers should prioritize early and sustained engagement with financiers, regulators, and communities, as weak communication can erode trust, delay approvals, and diminish the performance gains from robust financial safeguards. Additionally, combining green financing with traditional CEMs can improve access to affordable capital, reduce risk exposure, and strengthen stakeholder trust through enhanced monitoring and reporting.

5.3. Theoretical Implications

The confirmation of CS as a significant moderator extends credit enhancement and project finance theory by demonstrating that financial instruments alone are insufficient to guarantee infrastructure performance in high-risk environments. Instead, the findings highlight the central role of institutional and relational mechanisms in translating financial safeguards into tangible project outcomes. This supports a relational-institutional perspective in which project success is determined not only by the availability of financial instruments, but also by how information, trust, and risk perceptions are strategically managed among stakeholders. Consequently, the study advances theory by conceptualizing CS as a critical enabler within credit enhancement and infrastructure finance frameworks, and by empirically validating that the effectiveness of CEMs is conditional on communication quality, particularly in developing markets.

6. Conclusions

The study concludes that CEMs significantly influence PP, with currency risk mitigation, political risk insurance, and construction risk mitigation identified as the most impactful components. While CEMs reduce both perceived and actual investment risks, their effectiveness is contingent upon structured, transparent, and continuous communication with stakeholders, highlighting the critical moderating role of CS. Strengthening CEMs through targeted research, stakeholder engagement, and the development of context-appropriate strategies is essential for assuring investors of the safety of mega-infrastructure investments and creating conditions for optimal project performance. Effective management of financial risks is likely to enhance investor confidence, encourage investment in infrastructure, and support the growth and sustainability of the HEP sector. The study further underscores the importance of improving CS by enhancing communication frequency, channels, content relevance, feedback mechanisms, and cost-effectiveness, as this will strengthen the interaction between CEMs and project performance. Ultimately, robust communication strategies can play a pivotal role in optimizing the management of credit enhancement risks and improving the overall PP and similar developing markets. In addition to CEMs and CS, it’s important to note that strategic integration of emerging instruments such as green financing can synergize efforts to enhance investor confidence, risk management, and long-term sustainability of HEP projects in Kenya and similar markets.

7. Limitations and Future Research

Despite providing valuable insights, this study has some limitations. Firstly, the cross-sectional design and relatively small sample of 12 HEP projects with 94 respondents limit the ability to infer strict cause-effect relationships, and generalizability of the findings beyond Kenya’s HEP sector. Secondly, reliance on self-reported data makes the findings susceptible to respondents’ personal biases, while the use of Likert-scale measures may not fully capture complex aspects of PP and communication strategies. Finally, the study focused on selected CEMs and may not account for all financial, operational, or institutional factors influencing project outcomes; or confound the relationships observed. In view of these limitations, future research should adopt more appropriate designs such as mixed-methods sequential explanatory, quasi-experimental or longitudinal studies across multiple countries and infrastructure sectors to validate the findings, explore causal relationships, and assess additional mechanisms, influencing the effectiveness of CEMs and CS. Future studies should also increase the sample size to improve the robustness of results; as well as explore emerging project financing instruments such as green financing to determine how they interact with traditional CEMs to reduce risk, lower the cost of capital, and improve long-term performance and sustainability of HEP projects in developing markets.

Abbreviations

AGE	Project Age
CAP	Project Capacity
CEMs	Credit Enhancement Mechanisms
CS	Communication Strategy
CVI	Content Validity Index
FDI	Foreign Direct Investment
FRM	Financial Risk Management
GDP	Gross Domestic Product
GoK	Government of Kenya
HEP	Hydroelectric Power
IPPs	Independent Power Producers
KenGen	Kenya Electricity Generating Company
MW	Mega Watts
SD	Standard Deviation
SSA	Sub-Saharan Africa

Author Contributions

Charles Mallans Rambo is the sole author of this article. He read and approved the final version of the manuscript for submission and publication.

Conflicts of Interest

The author declares no conflicts of interest associated with the study and development of this article.

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[39]	World Bank (2017). Credit enhancement practices. Retrieved on 10/08/2024 from https://www.gfrr.org

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Rambo, C. M. (2025). Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. Journal of Finance and Accounting, 13(6), 297-312. https://doi.org/10.11648/j.jfa.20251306.16

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Rambo, C. M. Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. J. Finance Account. 2025, 13(6), 297-312. doi: 10.11648/j.jfa.20251306.16

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Rambo CM. Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. J Finance Account. 2025;13(6):297-312. doi: 10.11648/j.jfa.20251306.16

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@article{10.11648/j.jfa.20251306.16,
  author = {Charles Mallans Rambo},
  title = {Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya},
  journal = {Journal of Finance and Accounting},
  volume = {13},
  number = {6},
  pages = {297-312},
  doi = {10.11648/j.jfa.20251306.16},
  url = {https://doi.org/10.11648/j.jfa.20251306.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20251306.16},
  abstract = {Hydroelectric power (HEP) projects in Kenya are capital-intensive and exposed to multiple financial and operational risks, which can undermine performance and investor confidence. This study examined the influence of Credit Enhancement Mechanisms (CEMs) on the performance of HEP projects and assessed the moderating role of Communication Strategy (CS) on the relationship. Guided by a pragmatist philosophy, the study employed a cross-sectional mixed-methods design, targeting 12 HEP projects and key government agencies involved in financing, regulation, and risk management. Quantitative data were collected from 94 respondents using structured questionnaires, while qualitative data were obtained through key informant interviews. Descriptive statistics, correlation analysis, hierarchical regression, and thematic analysis were employed to analyze the data, with robustness tests conducted to ensure stability and validity. The results show that CEMs, particularly currency risk mitigation, political risk insurance, and construction risk mitigation, significantly enhance project performance. CS was found to positively moderate this relationship, amplifying the effectiveness of CEMs. Project age and capacity also contributed to performance outcomes, highlighting the importance of operational maturity and scale. Robustness tests confirmed the stability of these results across ownership structures and analytical specifications. The study concludes that effective financial risk mitigation, reinforced by structured and transparent communication, is critical for optimizing performance of HEP projects. Policy and managerial implications include the institutionalization of targeted CEMs, promotion of local currency financing, and integration of structured communication protocols and emerging instruments such as the green financing to strengthen investor confidence and project sustainability.},
 year = {2025}
}

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TY  - JOUR
T1  - Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya
AU  - Charles Mallans Rambo
Y1  - 2025/12/29
PY  - 2025
N1  - https://doi.org/10.11648/j.jfa.20251306.16
DO  - 10.11648/j.jfa.20251306.16
T2  - Journal of Finance and Accounting
JF  - Journal of Finance and Accounting
JO  - Journal of Finance and Accounting
SP  - 297
EP  - 312
PB  - Science Publishing Group
SN  - 2330-7323
UR  - https://doi.org/10.11648/j.jfa.20251306.16
AB  - Hydroelectric power (HEP) projects in Kenya are capital-intensive and exposed to multiple financial and operational risks, which can undermine performance and investor confidence. This study examined the influence of Credit Enhancement Mechanisms (CEMs) on the performance of HEP projects and assessed the moderating role of Communication Strategy (CS) on the relationship. Guided by a pragmatist philosophy, the study employed a cross-sectional mixed-methods design, targeting 12 HEP projects and key government agencies involved in financing, regulation, and risk management. Quantitative data were collected from 94 respondents using structured questionnaires, while qualitative data were obtained through key informant interviews. Descriptive statistics, correlation analysis, hierarchical regression, and thematic analysis were employed to analyze the data, with robustness tests conducted to ensure stability and validity. The results show that CEMs, particularly currency risk mitigation, political risk insurance, and construction risk mitigation, significantly enhance project performance. CS was found to positively moderate this relationship, amplifying the effectiveness of CEMs. Project age and capacity also contributed to performance outcomes, highlighting the importance of operational maturity and scale. Robustness tests confirmed the stability of these results across ownership structures and analytical specifications. The study concludes that effective financial risk mitigation, reinforced by structured and transparent communication, is critical for optimizing performance of HEP projects. Policy and managerial implications include the institutionalization of targeted CEMs, promotion of local currency financing, and integration of structured communication protocols and emerging instruments such as the green financing to strengthen investor confidence and project sustainability.
VL  - 13
IS  - 6
ER  -

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Charles Mallans Rambo

Department of Finance and Accounting, University of Nairobi, Nairobi, Kenya

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http://orcid.org/0000-0002-0970-865X

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Figure 1

Figure 1. Conceptual framework.

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APA Style

Rambo, C. M. (2025). Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. Journal of Finance and Accounting, 13(6), 297-312. https://doi.org/10.11648/j.jfa.20251306.16

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ACS Style

Rambo, C. M. Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. J. Finance Account. 2025, 13(6), 297-312. doi: 10.11648/j.jfa.20251306.16

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AMA Style

Rambo CM. Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya. J Finance Account. 2025;13(6):297-312. doi: 10.11648/j.jfa.20251306.16

Copy | Download

@article{10.11648/j.jfa.20251306.16,
  author = {Charles Mallans Rambo},
  title = {Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya},
  journal = {Journal of Finance and Accounting},
  volume = {13},
  number = {6},
  pages = {297-312},
  doi = {10.11648/j.jfa.20251306.16},
  url = {https://doi.org/10.11648/j.jfa.20251306.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20251306.16},
  abstract = {Hydroelectric power (HEP) projects in Kenya are capital-intensive and exposed to multiple financial and operational risks, which can undermine performance and investor confidence. This study examined the influence of Credit Enhancement Mechanisms (CEMs) on the performance of HEP projects and assessed the moderating role of Communication Strategy (CS) on the relationship. Guided by a pragmatist philosophy, the study employed a cross-sectional mixed-methods design, targeting 12 HEP projects and key government agencies involved in financing, regulation, and risk management. Quantitative data were collected from 94 respondents using structured questionnaires, while qualitative data were obtained through key informant interviews. Descriptive statistics, correlation analysis, hierarchical regression, and thematic analysis were employed to analyze the data, with robustness tests conducted to ensure stability and validity. The results show that CEMs, particularly currency risk mitigation, political risk insurance, and construction risk mitigation, significantly enhance project performance. CS was found to positively moderate this relationship, amplifying the effectiveness of CEMs. Project age and capacity also contributed to performance outcomes, highlighting the importance of operational maturity and scale. Robustness tests confirmed the stability of these results across ownership structures and analytical specifications. The study concludes that effective financial risk mitigation, reinforced by structured and transparent communication, is critical for optimizing performance of HEP projects. Policy and managerial implications include the institutionalization of targeted CEMs, promotion of local currency financing, and integration of structured communication protocols and emerging instruments such as the green financing to strengthen investor confidence and project sustainability.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Credit Enhancement Mechanisms, Communication Strategy and Performance of Hydro-electric Power Projects in Kenya
AU  - Charles Mallans Rambo
Y1  - 2025/12/29
PY  - 2025
N1  - https://doi.org/10.11648/j.jfa.20251306.16
DO  - 10.11648/j.jfa.20251306.16
T2  - Journal of Finance and Accounting
JF  - Journal of Finance and Accounting
JO  - Journal of Finance and Accounting
SP  - 297
EP  - 312
PB  - Science Publishing Group
SN  - 2330-7323
UR  - https://doi.org/10.11648/j.jfa.20251306.16
AB  - Hydroelectric power (HEP) projects in Kenya are capital-intensive and exposed to multiple financial and operational risks, which can undermine performance and investor confidence. This study examined the influence of Credit Enhancement Mechanisms (CEMs) on the performance of HEP projects and assessed the moderating role of Communication Strategy (CS) on the relationship. Guided by a pragmatist philosophy, the study employed a cross-sectional mixed-methods design, targeting 12 HEP projects and key government agencies involved in financing, regulation, and risk management. Quantitative data were collected from 94 respondents using structured questionnaires, while qualitative data were obtained through key informant interviews. Descriptive statistics, correlation analysis, hierarchical regression, and thematic analysis were employed to analyze the data, with robustness tests conducted to ensure stability and validity. The results show that CEMs, particularly currency risk mitigation, political risk insurance, and construction risk mitigation, significantly enhance project performance. CS was found to positively moderate this relationship, amplifying the effectiveness of CEMs. Project age and capacity also contributed to performance outcomes, highlighting the importance of operational maturity and scale. Robustness tests confirmed the stability of these results across ownership structures and analytical specifications. The study concludes that effective financial risk mitigation, reinforced by structured and transparent communication, is critical for optimizing performance of HEP projects. Policy and managerial implications include the institutionalization of targeted CEMs, promotion of local currency financing, and integration of structured communication protocols and emerging instruments such as the green financing to strengthen investor confidence and project sustainability.
VL  - 13
IS  - 6
ER  -

Copy | Download