Factors influencing Somalia households’ willingness to pay renewable energy: Employing structural equation modeling

Factors influencing Somalia households’ willingness to pay renewable energy: Employing structural equation modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Factors influencing Somalia households’ willingness to pay renewable energy: Employing structural equation modeling Galad Mohamed Barre, Ahmed Hassan Mohamud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8567172/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study uses PLS path analysis and structural equation modelling (SEM) to examine the factors influencing Somali households' willingness to pay for renewable energy. Nonprobability purposive sampling was used in a quantitative survey to select respondents from Mogadishu, the capital city of Somalia. 300 home power bill payers who were informed about energy costs and renewable energy requirements were given a standardized closed-ended questionnaire. Following data cleaning, SPSS version 25 and SmartPLS-4 were used to analyze 255 valid replies using descriptive and inferential statistics. With an R2 of 0.428, the structural model has moderate explanatory power, accounting for 42.8% of the variance in willingness to pay for renewable energy. The model's robustness is confirmed by an adjusted R2 of 0.402. The findings show that consumer intention, environmental concern, perceived behavioral control, Subjective norms, and Attitude have a positive and significant impact on willingness to pay for renewable energy. Belief about the cost of renewable energy shows no significant relationship with willingness to pay for renewable energy. The results of the moderation analysis indicate that the relationships between environmental concern, subjective norms, and attitude with willingness to pay for renewable energy are considerably moderated by customer intention to use renewable energy. However, the relationship between perceived behavioral control and belief about the cost of renewable energy with willingness to pay for renewable energy is not moderated by consumer intention. The findings offer policymakers and renewable energy stakeholders insights to increase adoption rates. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies willingness to pay Renewable energy structural equation modeling Somalia Figures Figure 1 Figure 2 1. Introduction The rising demand for energy and the risk of fossil fuel depletion have driven the rapid development of renewable energy sources to meet clients’ needs (Ashinze, Tian, Ashinze, Nazir, & Shaheen, 2021 ). Renewable energies entail the exploitation of natural energy flows (e.g., wings, sunlight, falling water, waves, tides, and ocean currents) or the exploitation of natural resources at a level equivalent to or higher than the human utilization rate (e.g., biofuels, ocean thermal gradients, and hydroelectric reservoirs)(Chen, He, Li, & Zhang, 2018 ; Holdren, Morris, & Mintzer, 1980 ). Thus, environmentally-friendly products are demanded by consumers as a new segment to protect against climate change (Amberg & Fogarassy, 2019 ). One of the most important initiatives to combat climate change is to reduce energy consumption (Pelau & Acatrinei, 2019 ; Tan, Ooi, & Goh, 2017 ). During the last few decades, electricity consumption has grown rapidly, mainly in the residential and service sectors (Ashinze et al., 2021 ). This rapid electricity consumption has led to increasing CO2 emissions and ultimately impacts global warming (Gaspar & Antunes, 2011 ; Tan et al., 2017 ). The demand for energy is at its peak, driven by population growth and economic development (Etokakpan, Solarin, Yorucu, Bekun, & Sarkodie, 2020 ). Therefore, policymakers are considering alternative energy sources and ways to reduce carbon footprints by relying less on fossil fuels (Ahmad, Zhao, Irfan, & Mukeshimana, 2019 ). In turn, lower fossil fuel use and fewer greenhouse gas emissions can be achieved (Mills & Schleich, 2012 ). The installation of energy-efficient appliances (EEAs) plays a significant role in reducing household energy consumption (Tan et al., 2017 ). Moreover, consumers who engage in pro-environmental behavior (PEB) have a lower negative environmental impact (Kollmuss & Agyeman, 2002 ; Nekmahmud & Fekete-Farkas, 2020 ). Pro-environmental consumer behavior research is conducted in developed markets but is still in its early stages in several emerging markets, including Asia and Africa (Hossain, Fekete-Farkas, & Nekmahmud, 2022 ). However, Africa still struggles with a persistent energy crisis despite its abundant energy sources and potential for RE growth, with more than $ 10 billion being spent each year on kerosene lighting in Sub-Saharan Africa (Wilson, Rai, & Best, 2014 ). In 2019, the overall energy produced in all varieties amounted to 245 GW (Okesiji, Olaniyi, & Okorie, 2025 ). However, interest in renewable energy (RE) options is rising due to their ability to serve as a cleaner alternative to fossil fuels, which come with high production costs, contribute to environmental damage, and pose health risks to humans and animals (Oyedun et al., 2025 ). 1.2 Renewable energy in Somalia The momentum of Somalia's transition to renewable energy is growing, driven by the need to shift from expensive, inefficient diesel generators to more sustainable alternatives. The nation lacks a unified national grid, and a significant portion of its energy consumption depends on firewood and charcoal, which exacerbate environmental degradation. Given its prevalence in Somalia, solar energy is emerging as a feasible alternative. A multitude of studies have investigated the potential and economic viability of solar power systems, both off-grid and hybrid, for meeting the nation's energy requirements. These initiatives constitute an integral part of a comprehensive strategy to enhance the electrification rate while simultaneously reducing dependence on imported diesel. Somalia is characterized by an annual solar insolation of approximately 3,000 hours, with daily solar radiation metrics fluctuating between 5 and 7 kWh/m², thereby rendering it exceptionally suitable for the generation of solar energy (Yusuf & Ahmed, 2024 ). A case study in Baidoa demonstrated that a 1.98 kW off-grid solar PV system could produce 7,400 kWh/year, with a payback period of approximately 2 years and 7 months (A. A. Ahmed et al., 2024 ). Hybrid systems combining solar PV, diesel generators, and battery storage have been explored to optimize costs and reduce environmental impacts. The hybrid PV-DG-grid system without battery storage was found to be the most cost-effective (Elmi, Jazayeri, & Salman, 2022 ). In Beledweyne, a hybrid system including solar, wind, and hydropower was analyzed, highlighting the potential for diverse renewable energy solutions (Coban, 2023 ). Rural electrification remains a challenge due to low population density and economic constraints, but solar-powered mini-grids offer a promising solution (Abdi & Zorlu, 2021 ). The transition to renewable energy is crucial for reducing Somalia's dependence on imported diesel and mitigating environmental impacts (Elmi et al., 2022 ). Although the integration of renewable energy sources in Somalia is advancing, persistent obstacles, including financial constraints and the need for infrastructure development, persist. Nevertheless, the country’s abundant solar energy potential and the continuation of related initiatives signal an optimistic pathway towards sustainable energy solutions. Somalia faces significant energy challenges, with limited grid infrastructure and widespread reliance on expensive, polluting energy sources. Understanding consumer behavior and willingness to adopt renewable energy is crucial for developing effective energy policies and deployment strategies. While existing studies have examined various aspects of renewable energy investment and adoption, there are specific areas that remain underexplored, particularly in the context of Somalia (Nor, 2025 ; Sabroso, Suaner, Lucmayon, & Asio, 2024 ). A study on the adoption of renewable energy in Somalia reveals several gaps that need to be addressed to enhance understanding and facilitate the transition to sustainable energy sources. 1.3 Theory of Planned Behavior (TPB) and hypothesis development The Theory of Planned Behavior (TPB) is used to explain behaviors in which individuals can exercise self-control (Wall, Khalid, Urbański, & Kot, 2021 ). The theory uses various constructs to predict individuals’ control over their behavior, including attitude, subjective norms, and perceived behavioral control. The TPB is a widely used model to predict consumer intentions, including the adoption of renewable energy. In the context of Somalia, the model has been extended to incorporate two additional variables: beliefs about the costs of renewable energy and environmental concern. These additions aim to provide a more comprehensive understanding of consumer intentions in adopting renewable energy. No study in Africa has combined beliefs about renewable energy costs and environmental concern within a Theory of Planned Behavior (TPB) model tailored to the African context. Research in Pakistan includes both factors, but it is not set in Africa. Nigerian studies address costs but do not explicitly add environmental concerns to their models. Moreover, previous studies have not modeled consumer intention as a moderating variable in Theory of Planned Behavior (TPB) research on renewable energy; instead, intention is typically treated as an outcome or a proximal predictor (Ashinze et al., 2021 ; Mustafa, Zhang, Sohail, Rana, & Long, 2023 ; Nor, 2025 ; Zulu, Zulu, & Chabala, 2022 ). However, this study uses consumer intentions as a moderating variable between attitude, subjective norms, perceived behavioral control, belief about the costs of renewable energy, environmental concern, and willingness to pay for renewable energy. The attitude in TPB is defined as an individual’s evaluation of a particular behavior as favorable or unfavorable (Icek Ajzen, 1985 ). Consumers with favorable views on renewable energy's benefits are more likely to support and adopt it (Irfan, Zhao, Li, & Rehman, 2020a ). This study reports a positive correlation between attitude and consumers’ intention to purchase environmentally friendly vehicles (Afroz, Rahman, Masud, Akhtar, & Duasa, 2015 ). Similarly, this study exposed that attitude is positively related to consumers’ intentions to utilize less energy. Attitude is a dominant predictor of residential energy consumption (Tan et al., 2017 ). Attitudes are shaped by perceived utility and social influences, which can enhance consumer commitment to renewable energy adoption (Gârdan, Micu, Paștiu, Micu, & Gârdan, 2023 ). Positive attitudes, shaped by beliefs about the benefits of renewable energy, lead to a higher likelihood of consumers being willing to adopt for green energy options (Clark & Doll, 2024 ). Although, attitudes towards renewable energy investments are identified as a determinant of investment intentions, there is limited research on how these attitudes specifically influence consumer willingness to adopt renewable energy in Somalia (Nor, 2025 ). The relationship between attitude and behavioral intention is strong, but further exploration is needed to understand the nuances of this relationship in the Somali context (Y. A. Ahmed, Rashid, & Khurshid, 2022 ). H1: Attitude positively influences consumer willingness to pay for renewable energy in Somalia. H10: Consumer intention to use renewable energy positively moderates between attitude and willingness to pay for renewable energy. Subjective Norms are perceived social stress of performing or not performing a particular behavior is termed the subjective norm (SBN) (I Ajzen, 1991 ). Subjective norms —the perceived social pressure to engage in renewable energy consumption — positively influence attitudes and WTP. Consumers are more likely to invest in renewable energy if they believe their peers support such actions (Clark & Doll, 2024 ; Jabbour Al Maalouf, Sayegh, Inati, & Sarkis, 2024 ). Generally, the actions and opinions of other people have a great impact on consumers’ buying intentions (Irfan et al., 2020a ). The Subjective Norms are recognized as influencing investment intentions, yet there is a lack of detailed analysis on how societal pressures and cultural expectations impact individual willingness to adopt renewable energy (Nor, 2025 ). H6: Subjective Norms positively influence consumer willingness to pay for renewable energy in Somalia. H9: Consumer intention to use renewable energy positively moderates between Subjective Norms and willingness to pay for renewable energy. Perceived Behavioral Control (PBC) refers to an individual's belief in their ability to perform a specific behavior, which directly influences their intentions and indirectly affects their actual behavior (Icek Ajzen, 1985 ). These studies found that PBC positively impacts consumers' intentions to conserve energy (Alam et al., 2014 ; Wang, Zhang, & Li, 2014 ). Moreover, It found that perceived behavioral control significantly influenced intentions, more than attitude and subjective norms (Gobel, Ramadhan, Ratama, & Hendriana, 2024 ). The PBC plays a critical role in influencing consumers' decisions to purchase energy-efficient vehicles, highlighting its importance in broader sustainable technology adoption (Wang, Zhao, Yin, & Zhang, 2017 ). Although perceived behavioral control is acknowledged as a factor in renewable energy investment, its specific impact on consumer adoption intentions in Somalia remains underexplored (Y. A. Ahmed et al., 2022 ). H5: Perceived Behavioral Control positively influences consumer willingness to pay for renewable energy in Somalia. H11: Consumer intention to use renewable energy positively moderates between Perceived Behavioral Control and willingness to pay for renewable energy. Belief about Renewable Energy Cost (BREC) refers to consumers' perception of the financial sacrifices associated with adopting renewable power technologies (RPTs). This variable plays a critical role in shaping consumer behavior, as the high upfront costs and maintenance expenses of renewable energy systems often act as barriers to adoption. Perceptions of renewable energy costs significantly influence consumer adoption intentions. High perceived costs can deter adoption, while perceived affordability can encourage it (Vu, Nguyen, & Nguyen, 2023 ). When consumers hold strong beliefs about the advantages of renewable energy, they are more inclined to develop favorable attitudes towards it, which in turn enhances their willingness to pay (Clark & Doll, 2024 ). However, beliefs about the costs of renewable energy can negatively impact the adoption of renewable energy. If consumers perceive renewable energy as expensive, their willingness to pay decreases (Irfan et al., 2020a ). Belief About Costs The economic analysis of solar systems in Somalia highlights cost-effectiveness, but there is insufficient research on how consumer beliefs about these costs affect their adoption decisions (A. A. Ahmed et al., 2024 ). The financial implications of renewable energy adoption, such as initial investment and payback periods, need further investigation to understand consumer perceptions (A. A. Ahmed et al., 2024 ). H2: Belief about the cost of renewable energy positively influences consumer willingness to pay for renewable energy in Somalia. H7: Consumer intention to use renewable energy positively moderates between Belief about Renewable Energy Cost and willingness to pay for renewable energy. Environmental Concern (ECN) refers to the level of awareness individuals have of environmental problems and their motivation to address them. It is often considered a key factor influencing consumers' adoption of renewable energy (RE) technologies. While environmental awareness is generally associated with positive attitudes towards renewables, it does not consistently predict the adoption of renewable energy (Karasmanaki, 2021 ). Environmental awareness and concern are crucial in shaping attitudes towards renewable energy, influencing both the intention and actual adoption of green technologies (Ben Saad, 2021 ). The lack of government-led awareness campaigns and education on ecological issues may further diminish ECN's role in purchase decisions. Similarly, the environmental benefits of renewable energy are noted, but there is a gap in understanding how environmental concerns drive consumer adoption in Somalia (Abdi & Zorlu, 2021 ). However, the willingness to pay for renewable energy varies by type, indicating that consumer preferences are not uniform across different renewable sources (Katare, Wang, Wetzstein, Jiang, & Weiland, 2024 ). H4: Environmental Concern positively influences consumer willingness to pay for renewable energy in Somalia. H8: Consumer intention to use renewable energy positively moderates the relationship between Environmental Concern and willingness to pay for renewable energy. H3: Consumer intention to use renewable energy positively influences consumer willingness to pay for renewable energy in Somalia. 2. Research Methods 2.1 Research Instrument The method utilized for this study is a survey and regression research design based on quantitative methodologies. The researchers chose the sample using a nonprobability sampling approach, specifically purposive sampling. A structured, closed-ended questionnaire was used to collect data from residents of Mogadishu, Somalia's capital city. Mogadishu was chosen since it is the most populous city in Somalia, and people pay energy bills. The study focused on persons who pay household power bills and are knowledgeable about the cost of electricity and the need for renewable energy. This study's sample size was 300 respondents; however, after data cleaning, only 255 were considered credible for analysis. Researchers used structured questionnaires. Data generated for the study were analyzed using descriptive and inferential statistics via the Statistical Package for the Social Sciences (SPSS) version 25 and SMART PLS-4. The study uses descriptive statistics to summarize the collected data and regression analysis to show the impact of the independent variable on the dependent variable. 2.2 Measurement Scale and SEM model To develop the research constructs used in the study, various previous works were consulted. All scale items are adopted from (Irfan, Zhao, Li, & Rehman, 2020b ). All item scales were measured using the 5-point Likert scale (1 = strongly agree to 5 = strongly disagree) in the descriptive section. Structural equation modeling (SEM) was applied to test the study's hypothesis, which depicted the relationships among the study's variables. SEM was considered a suitable model for the study because it provides accurate and meaningful outcomes regarding the study constructs (Steenkamp & Baumgartner, 2000 ). 3. Results and Analysis 3.1 descriptive section Table 1 Respondents profile Variable Frequency Percent (%) Gender Male 124 48.6 Female 131 51.4 Total 255 100.0 Age 18–20 years 82 32.2 21–25 years 118 46.3 26–30 years 15 5.9 31–35 years 8 3.1 above 35 years 32 12.5 Total 255 100.0 Level of education Graduate 83 32.5 Undergraduate 139 54.5 High school 17 6.7 other 16 6.3 Total 255 100.0 Marital status Married 60 23.5 single 195 76.5 Total 255 100.0 Household size 1–3 members 43 16.9 4–6 members 65 25.5 7 or more members 147 57.6 Total 255 100.0 primary source of income agriculture 12 4.7 livestock 22 8.6 business 130 51.0 employment 91 35.7 Total 255 100.0 Cost of electricity per month 1–10 $ 81 31.8 11–20 $ 121 47.5 21–30 $ 37 14.5 31–40 $ 12 4.7 Above 40 $ 4 1.6 Total 255 100.0 Table 1 shows the demographics of this research. 48.6 percent of the participants were men, while 51.4 percent were women. The bulk of respondents (46.3 percent) were between the ages of 21 and 25, while the next-largest group was 18- to 20-year-olds, accounting for 32.2 percent. Regarding education, 54.5 percent of respondents were undergraduate students. The bulk of respondents (76.5 percent) reported being single. The majority of respondents' households had seven or more individuals, accounting for 56.6 percent of the total, indicating that Somalis had high household sizes. In terms of income source, the majority of families earned 51.0 percent from business, with employment accounting for 35.7 percent. Finally, the monthly energy cost for the majority of Somali families (47.5 percent) is between $ 11 and $ 20. 3.2 Measurement Model Table 2 Loading and reliability factors. Latent Variables Indicators Loading CR AVE VIF Attitude 0.791 0.654 It is wise to utilize renewable power generation technologies (RPTs) at my home A3 0.817 1.106 It would be pleasant to utilize renewable power generation technologies (RPTs) at my home A4 0.801 1.106 Subjective norms 0.767 0.524 People who are valuable for me have the opinion that I should use renewable power generation technologies (RPTs) at my home S1 0.736 1.095 I will use renewable power generation technologies (RPTs) if my friends also use it S4 0.683 1.202 I will use renewable power generation technologies (RPTs) if my relatives advise me to use S5 0.750 1.209 Perceived behavioral control 0.807 0.583 I have the required knowledge to utilize renewable power generation technologies (RPTs) P2 0.795 1.326 I have the resources to utilize renewable power generation technologies (RPTs) P3 0.772 1.341 I have full control of utilizing renewable power generation technologies (RPTs) P4 0.721 1.173 Belief about costs of renewable energy utilization 0.773 0.532 Electricity price will be increased, as renewable energy (RE) projects require a high initial capital B2 0.708 1.144 The utilization of renewable energy (RE) requires high installation costs B4 0.787 1.223 Battery replacement in case of using solar PV requires additional costs B5 0.689 1.143 Environmental concern 0.678 0.525 I am worried about climate change E2 0.545 1.003 Renewable power generation technologies (RPTs) would improve the environment E4 0.868 1.003 Willingness to Pay (WTP) for Renewable Energy 0.787 0.554 Energy-saving behavior motivate me to pay for renewable energy (RE) W2 0.704 1.212 Environmental friendliness nature of renewable energy (RE) motivate me to pay for renewable energy (RE) W3 0.829 1.317 Reliability of renewable power generation technologies (RPTs) motivate me to pay for renewable energy (RE) W4 0.691 1.152 Consumer's Intention to Use Renewable Energy 0.792 0.561 I intend to use renewable power generation technologies (RPTs) if banks provide loan and financial assistance C4 0.810 1.286 I am planning to spend more money on renewable power generation technologies (RPTs) than traditional electricity C5 0.746 1.216 I have a positive intention that other people should utilize renewable power generation technologies (RPTs) at their homes C6 0.685 1.168 This study adopts a quantitative approach and uses structural equation modelling. The measurement model was assessed using factor loadings, composite reliability, average variance extraction, and the Variance Inflation Factor. According to (Joseph F Hair, 2014 ), to achieve convergent validity, published recommendations require factor loadings of 0.70 or higher, composite reliability of 0.70 or higher, and an average variance extracted of 0.50 or higher. Using the Smart PLS-4 technique, researchers found that all factor loadings were above the suggested 0.70, except for S4, B5, E2, W4, and C6, as indicated in Table 2 . However, factor loadings between 0.40 and 0.70 are regarded as acceptable if the average variance extracted is more than 0.50, as stated by (Byrne, 2016). The average variance extracted in this study exceeds 0.50. Furthermore, the composite reliability (CR) values in this study are above the required level of 0.70, as stated by prior studies like (Fornell & Larcker, 1981 ; Sarstedt, 2008 ). Regarding the Variance Inflation Factor (VIF), all indicators have values below 5, indicating strong multicollinearity in this model. Overall, the model exhibits high explanatory power and predictive relevance, with P2 standing out as the most relevant predictor (see in Table 2 ). Table 3 Fornell-Larcher Criterion Analysis for Checking Discriminant validity A B C E P S W A 0.809 B 0.253 0.729 C 0.340 0.293 0.749 E 0.250 0.285 0.472 0.725 P 0.249 0.185 0.256 0.136 0.763 S 0.397 0.267 0.322 0.289 0.269 0.724 W 0.340 0.296 0.445 0.458 0.376 0.421 0.744 Discriminant validity was assessed using the Fornell-Larcker criterion. Discriminant validity is achieved when the square root of each construct's average extracted variance is greater than the correlations between that construct and other constructs (Fornell & Larcker, 1981 ). Table 3 shows that the diagonal components correspond to the square roots of each construct's average retrieved variance. These findings were much higher than the previously reported correlations, demonstrating that the assessments exhibit discriminant validity. Table 4 Heterotrait-monotrait (HTMT) ratio A B C E P S W A B 0.648 C 1.115 0.504 E 0.450 1.242 0.743 P 0.769 0.314 0.417 0.570 S 0.641 0.475 0.576 1.220 0.451 W 0.493 0.511 0.727 1.735 0.609 0.717 Discriminant validity testing has become a commonly accepted prerequisite for studying links between reflectively rated constructs. The HTMT ratio criteria were used to assess discriminant validity. Such assessments analyse the distinctness of different concepts within the measuring model (Joe F Hair, Ringle, & Sarstedt, 2011 ). The proposed measuring the HTMT correlation ratio to a threshold of less than 0.85 to determine discriminant validity (Henseler, Ringle, & Sarstedt, 2015 ). As seen in Table 4 , all HTMT values are less than 0.85, showing high discriminant validity. 3.3 Structural Model Table 5 presents the structural model assessment using key indicators, including R² and Adjusted R². The R² value for willingness to pay for renewable energy is 0.428, suggesting that 42.8% of the variation is explained by independent variables. The modified R² of 0.402 shows the model's robustness given the number of predictors. The findings reported in Table 7 investigate the elements that influence consumers' decisions to use renewable energy. The findings are based on hypothesis testing, most often using structural equation modelling (SEM) or regression analysis. This study proposed 11 hypotheses to investigate the correlations between the variables under examination. The structural model results illustrated in Fig. 2 and documented in Table 7 provide useful insights into the linkages between the constructs, offering light on the underlying dynamics driving the events being studied. The PLS path analysis examines direct and indirect effects equally and is the only approach suitable for mediation research. In the structural model, the researchers used p-value and t-value to assess the proposed hypotheses. If the t-value exceeds 1.96 or the p-value is less than 0.10, the hypotheses can be accepted. Table 7 summarizes the hypothesized relationships between the constructs and the dependent variable. H1 supports a relationship between attitude and willingness to pay for renewable energy, with β = 0.064, p < 0.066, and a t-statistic of 1.510. The mindset has a favorable impact on Somali households' willingness to pay for renewable energy. Therefore, customers' positive attitudes towards renewable energy increase the likelihood that they will pay for it. H2 supports the relationship between beliefs about renewable energy costs and willingness to pay for renewable energy (β = 0.058, p < 0.109, and statistics at 1.231). The willingness of Somali households to pay for renewable energy is not significantly influenced by their beliefs about its cost. H3 demonstrates a correlation between consumer intention and willingness to pay for renewable energy, with β = 0.069, p < 0.001, and a t-statistic of 3.001. Consumer intention has a favorable impact on Somali households' willingness to pay for renewable energy. Thus, customers plan to spend more on renewable energy than on traditional electricity. Furthermore, customers encourage others to use renewable power-generating technologies (RPTs) at home. H4 demonstrates a link between environmental concern and willingness to pay for renewable energy, supported by β = 0.068, p < 0.000, and a t-statistic of 4.102. Environmental concerns positively affect Somali households' willingness to pay for renewable energy. Thus, if people are concerned about climate change, they will choose renewable energy over conventional power, thereby benefiting the environment. H5 supports the association between Perceived Behavioral Control and willingness to pay for renewable energy, with β = 0.056, p < 0.000, and a t-statistic of 3.662. Perceived Behavioral Control positively affects Somali households' willingness to pay for renewable energy. Thus, if customers have the necessary knowledge and resources, they will use renewable energy, which encourages them to pay for it and gives them complete control over the use of renewable power production technologies (RPTs). H6 demonstrates a correlation between Subjective Norms and willingness to pay for renewable energy (β = 0.064, p < 0.007, t-statistic = 2.448). Subjective norms favorably impact Somali households' willingness to pay for renewable energy. Thus, if those important to customers, such as friends and family, have a positive attitude towards renewable energy, consumers will adopt renewable power production technologies (RPTs) in their homes. In terms of moderation, consumer intention moderates the relationship between the findings of H8, H9, and H10 (environmental concern, subjective norms, and attitude) and Somali households' willingness to pay for renewable energy. While consumer desire to utilize renewable energy did not mediate a correlation between the findings of H7 and H11 (belief about renewable energy cost and perceived behavioral control) and Somali households' willingness to pay for renewable energy (see in Table 7 ). 3.4 Model Fit Indicators The standard root mean square (SRMR) is a measure use to assess the goodness of fit (GoF) in PLS models (Henseler, Ringle, & Sinkovics, 2009 ). SRMR refers to the discrepancies that remain between the correlated data of the samples and the expected correlated model, as described by (Hooper, Coughlan, & Mullen, 2008 ). The SRMR scale ranges from 0 to 1.0, with values close to zero suggesting an ideal fit for the model. A well-fitting model should have an SRMR value that is less than or equal to 0.05, as stated by (Hooper et al., 2008 ). The SRMR value of the current study is 0.091, suggesting that the findings align with the expected model (see in Table 6 ). The Goodness of fit, Model fit indicators, empirical results, and structural model are displayed in Table 5 , 6 , 7 , and Fig. 2 respectively. Table 5 Goodness of fit. Constructs R-square R-square adjusted W 0.428 0.402 Table 6 Model fit indicators Particulars Saturated model Estimated model SRMR 0.091 0.091 d_ULS 1.567 1.565 d_G 0.451 0.449 Chi-square 685.114 683.523 NFI 0.307 0.308 Table 7 Empirical results of structural model analysis (hypothesis testing) Hypothesized path Standardized coefficients T-value P-value Decision H1. A -> W 0.064 1.510 0.066 Accept H2. B -> W 0.058 1.231 0.109 Reject H3. C -> W 0.069 3.001 0.001 Accept H4. E -> W 0.068 4.102 0.000 Accept H5. P -> W 0.056 3.662 0.000 Accept H6. S -> W 0.064 2.448 0.007 Accept H7. B x C -> W 0.048 0.025 0.490 Reject H8. E x C -> W 0.062 1.937 0.026 Accept H9. S x C -> W 0.066 1.767 0.039 Accept H10. A x C -> W 0.052 1.379 0.084 Accept H11. P x C -> W 0.068 1.041 0.149 Reject 4. Discussion In this world, which is addicted to fossil fuels, particularly lighting in homes, the energy we use daily is primarily powered by fossil fuels, the main drivers of climate change (Nor, 2025 ). Thus, many nations, including Somalia, are over-reliant on fossil fuels. Burning fossil fuels releases large amounts of carbon dioxide, a greenhouse gas, into the air. In turn, greenhouse gases blanket the Earth and trap the sun’s heat, warming the planet with far-reaching consequences for people and the planet. However, renewable energy options are rising due to their ability to serve as a cleaner alternative to fossil fuels, which come with high production costs, contribute to environmental damage, and pose health risks to humans and animals (Oyedun et al., 2025 ). This study investigates the factors that influence consumers' intention and desire to utilise renewable energy, both directly and in moderation. TPB was used as a theoretical framework for this study, with its original constructs Attitude, Subjective Norms, and Perceived Behavioral Control, focusing on consumer intention to use renewable energy, which has not been thoroughly studied in previous studies, as well as extending the theory to include two additional variables: belief about the costs of renewable energy utilization and environmental concern. These enhancements aim to provide a more thorough understanding of customers' intentions and willingness to adopt renewable energy. The independent factors explained 42.8% of the variance in consumer willingness to pay for renewable energy (R² = 0.428). The corrected R² of 0.402 verifies the model's robustness, indicating that the predictors significantly help to understand consumer behavior regarding renewable energy uptake in Somalia. Consumer intention to use renewable energy had the greatest impact on willingness to pay for renewable energy (β = 0.069, p < 0.001, t = 3.001). This conclusion confirms the core assumption of the Theory of Planned Behavior, which states that behavioral intentions are the most proximal predictor of actual behavior (I Ajzen, 1991 ). Similarly, the result aligns with (Bamberg & Möser, 2007 ; Irfan et al., 2020b ; Ud Din et al., 2023 ), consumer intention has continuously been highlighted as an important connection between attitudes, norms, control perceptions, and actual behavior. Thus, Somali families who express a desire to adopt renewable energy are significantly more likely to be prepared to pay for it. Environmental concern has a significant impact on Somali households' willingness to pay for renewable energy (β = 0.068, p < 0.000, t = 4.102). The results are consistent with earlier research that has established the relevance of environmental protection in influencing pro-environmental behavior internationally (Ali, Irfan, Ozturk, & Rauf, 2023 ; Dunlap, Van Liere, Mertig, & Jones, 2000 ; Irfan et al., 2020b ; Srivastava & Mahendar, 2018 ; Stern, 2000 ). Therefore, the findings suggest that environmental awareness and education may be particularly beneficial for boosting renewable energy use in Somalia and other comparable environments. Third, perceived behavioral control has a positive and substantial association with willingness to pay for renewable energy (β = 0.056, p < 0.000, t = 3.662). This result aligns with the Theory of Planned Behavior's emphasis on the role of control perceptions in shaping behavior (Bandura, 1997 ). This study also aligns with (Irfan et al., 2020b ; Kuruppu & Ketakumbura, 2019; Ud Din et al., 2023 ). Thus, the findings show that when Somali households think they have the knowledge, skills, and resources to use renewable energy, they are more prepared to engage financially in these solutions. Subjective Norms significantly increase willingness to pay for green energy (β = 0.064, p < 0.007, t = 2.448). Previous research has validated these conclusions (Irfan et al., 2020b ; Venkatesh & Davis, 2000 ; 피터, 2025 ). This finding demonstrates that the attitudes and behaviors of significant individuals, such as friends, family, and community members, strongly impact Somali families' decisions to use renewable energy. The study found a marginally significant positive connection (β = 0.064, p < 0.066, t = 1.510) between attitude and willingness to pay for renewable energy. This supports the Theory of Planned Behavior, which states that attitudes impact behavioral intentions and subsequent behavior (Fishbein & Ajzen, 2011 ). This also aligns with (Ali et al., 2023 ; Irfan et al., 2020b ; Ud Din et al., 2023 ). Hence, positive attitudes towards renewable energy—including perceptions of its benefits, advantages, and attractiveness—tend to enhance willingness to pay. However, Belief About Renewable Energy Costs appears to have a negligible effect. Surprisingly, views regarding renewable energy prices had no significant impact on willingness to pay (β = 0.058, p < 0.109, t = 1.231). This finding is somewhat unexpected, as cost factors are commonly significant in technology adoption decisions, particularly in resource-constrained environments; moreover, most prior research suggested a negative influence of renewable energy costs on willingness to pay for it (Ali et al., 2023 ; Irfan et al., 2020b ). However, the insignificance of cost beliefs might indicate that perceived benefits, environmental, social, and practical, outweigh cost considerations for those predisposed to adopt renewable energy. In terms of moderation, the findings show that consumer intention strongly regulates the correlations between environmental concern (H8), subjective norms (H9), attitude (H10), and willingness to pay for renewable energy. These moderating effects show that the impact of environmental concern, societal norms, and attitudes on willingness to pay is proportional to the strength of consumer intentions to utilise renewable energy. These variables impact not just willingness to pay for renewable energy, but also function in tandem with consumer goals. For example, a consumer who is concerned about the environment may be prepared to pay more for renewable energy if they have strong plans to use such technology. Similarly, positive social effects (subjective norms) may be most successful in increasing willingness to pay for renewable energy among people who have already made tentative plans to utilise it. Consumer intention did not significantly moderate the relationships among perceptions of renewable energy prices (H7), perceived behavioural control (H11), and willingness to pay for renewable energy. The lack of moderation in cost views is consistent with the discovery that cost beliefs did not significantly affect willingness to pay directly. This trend suggests that cost factors are independent of intention strength, neither amplifying nor diminishing customers' intent to use renewable energy. The lack of moderation in perceived behavioural control is potentially fascinating. It suggests that the empowering effect of perceived control on willingness to pay operates consistently regardless of intention strength. 4.1 Theoretical and policy implication This work provides numerous significant theoretical advances in understanding renewable energy uptake in underdeveloped countries. First, it establishes the TPB theory's applicability and significance in the Somali context, where it has previously been underutilized. Attitudes, subjective norms, and perceived behavioral control have substantial influence on renewable energy use decisions across a variety of cultural and economic contexts. Second, the study broadens TPB Theory by including Belief about Renewable Energy Cost and environmental concern as predictors, suggesting that environmental values play an important role beyond typical TPB concepts. Given the considerable impact of environmental concern, models of pro-environmental behaviour in energy contexts should consistently incorporate environmental values and concerns as crucial predictors. Third, investigating moderating effects yields detailed insights into how many factors interact to impact willingness to pay for renewables. The discovery that consumer intention modifies certain associations but not others implies that the paths from psychological antecedents to behaviour are complicated and conditional, necessitating more sophisticated theoretical models that account for interaction effects. Somalia confronts substantial energy access issues, with little grid infrastructure and a high dependence on diesel generators and traditional biomass for electricity (IRENA, 2022 ). In this context, renewable energy particularly solar power represents not merely an environmental choice but a practical solution to energy access problems. The findings of this provide some useful insights for politicians, energy planners, and renewable energy supporters. First, launch extensive public awareness campaigns to educate families about the advantages, viability, and accessibility of renewable energy technologies. Second, encourage the development and deployment of renewable energy technologies. Third, provide accurate, balanced information on renewable energy technology that honestly discusses both its benefits and limits. Fourth, introduce direct subsidies, tax breaks, or rebate schemes to reduce the upfront costs of renewable energy installations. 4.2 Limitations and Future studies First, the cross-sectional design precludes causal conclusions because all variables were assessed at a single time point. Second, the analysis is based on self-reported willingness to pay for renewable energy, rather than actual adoption behavior or payment statistics. Third, the analysis explains 42.8% of the variation in willingness to pay for renewable energy, suggesting that additional key aspects remain to be investigated. Future studies should examine additional determinants, such as economic factors like income and access to funding. Finally, because the study was conducted in Somalia, generalizability to other contexts should be assessed through replication studies across nations and cultural contexts. While the findings are likely applicable to other developing countries facing comparable energy issues, local variables may change the relative impact of several predictors. Declarations Data availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Author contributions Galad Mohamed Barre wrote the introduction, literature review, econometric methodology, data collection, analy­sis, interpretation of results, discussion section, manuscript writing, and Ahmed Hassan Mohamud reviewed the manuscript. Funding This research was supported by Simad University. Ethics approval and consent to participate All methods were conducted according to relevant guidelines and regulations. The research protocol, survey tools, and consent process were approved by the Ethics Committee of the Centre for Research and Development at SIMAD University (Approval Number: EC000138). Informed consent was obtained from all respondents before data collection. Participation was voluntary, and all respondents were assured of the confidentiality and anonymity of their responses. Competing interests No potential conflict of interest was reported by the author(s). References Abdi, A. H., & Zorlu, H. (2021). Rural electrification with solar powered mini-grids and stand-alone solar system installations: case of Somalia. Paper presented at the Proceedings of the 5th International Students Science Congress, Izmir, Turkey. Afroz, R., Rahman, A., Masud, M. M., Akhtar, R., & Duasa, J. B. (2015). How individual values and attitude influence consumers’ purchase intention of electric vehicles—Some insights from Kuala Lumpur, Malaysia. Environment and Urbanization ASIA, 6 (2), 193-211. 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Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries. Energy Policy, 49 , 616-628. Mustafa, S., Zhang, W., Sohail, M. T., Rana, S., & Long, Y. (2023). A moderated mediation model to predict the adoption intention of renewable wind energy in developing countries. Plos one, 18 (3), e0281963. Nekmahmud, M., & Fekete-Farkas, M. (2020). Why not green marketing? Determinates of consumers’ intention to green purchase decision in a new developing nation. Sustainability, 12 (19), 7880. Nor, B. A. (2025). Individual perceptions of renewable energy investment in Somali firms. Scientific Reports, 15 (1), 31400. Okesiji, S. O., Olaniyi, A. M., & Okorie, V. O. (2025). Innovations in Hydroelectric Power for Sustainable Development in Africa. Sustainability and Climate Change, 18 (1), 54-67. Oyedun, A. O., Owolabi, A. B., Yakub, A. O., Suh, D., Akamo, D. O., Bala, A., & Komolafe, D. (2025). Renewable energy adoption with business-to-consumer approach in Africa. Heliyon, 11 (13). Pelau, C., & Acatrinei, C. (2019). The paradox of energy consumption decrease in the transition period towards a digital society. Energies, 12 (8), 1428. Sabroso, L., Suaner, M. N. K., Lucmayon, E., & Asio, J. R. (2024). Household awareness, acceptance, and willingness to pay for renewable energy. Diversitas Journal, 9 (1_Special). Sarstedt, M. (2008). A review of recent approaches for capturing heterogeneity in partial least squares path modelling. Journal of modelling in Management, 3 (2), 140-161. Srivastava, C., & Mahendar, G. (2018). Intention to adopt sustainable energy: Applying the theory of planned behaviour framework. Indian Journal of Marketing, 48 (10), 20-33. Steenkamp, J.-B. E., & Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International journal of research in marketing, 17 (2-3), 195-202. Stern, P. (2000). Toward a Coherent Theory of Environmentally-Significant Behaviour'Journal of Social Issues 56 (3): 407-424. Přejít k původnímu zdroji . Tan, C.-S., Ooi, H.-Y., & Goh, Y.-N. (2017). A moral extension of the theory of planned behavior to predict consumers’ purchase intention for energy-efficient household appliances in Malaysia. Energy Policy, 107 , 459-471. Ud Din, S., Wimalasiri, R., Ehsan, M., Liang, X., Ning, F., Guo, D., . . . Abioui, M. (2023). Assessing public perception and willingness to pay for renewable energy in Pakistan through the theory of planned behavior. Frontiers in Energy Research, 11 , 1088297. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46 (2), 186-204. Vu, T. D., Nguyen, H. V., & Nguyen, T. M. N. (2023). Extend theory of planned behaviour model to explain rooftop solar energy adoption in emerging market. Moderating mechanism of personal innovativeness. Journal of Open Innovation: Technology, Market, and Complexity, 9 (2), 100078. Wall, W. P., Khalid, B., Urbański, M., & Kot, M. (2021). Factors influencing consumer’s adoption of renewable energy. Energies, 14 (17), 5420. Wang, Z., Zhang, B., & Li, G. (2014). Determinants of energy-saving behavioral intention among residents in Beijing: Extending the theory of planned behavior. Journal of Renewable and Sustainable Energy, 6 (5). Wang, Z., Zhao, C., Yin, J., & Zhang, B. (2017). Purchasing intentions of Chinese citizens on new energy vehicles: How should one respond to current preferential policy? Journal of cleaner production, 161 , 1000-1010. Wilson, E., Rai, N., & Best, S. (2014). Sharing the Load: Public and private sector roles in financing pro-poor energy access. IIED, London . Yusuf, A. Y., & Ahmed, M. A. S. (2024). Designing a 10 MW peak solar power plant using a system advisor model (SAM software). Case study: Somalia, Mogadishu Region. World Journal of Advanced Research and Reviews, 22 (2), 1812-1824. Zulu, S., Zulu, E., & Chabala, M. (2022). Factors influencing households' intention to adopt solar energy solutions in Zambia: insights from the theory of planned behaviour. Smart and Sustainable Built Environment, 11 (4), 951-971. 피터. (2025). Analyzing Consumer Preference of Residential Solar Photovoltaic Adoption Behavior in Zimbabwe. 서울대학교 대학원, Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8567172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":586443239,"identity":"83e1fff8-7fba-4f84-af76-19f9aa0814b3","order_by":0,"name":"Galad Mohamed Barre","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYDCCAyCC7X894/zDIKaEDLFamBOYZ7AlgLTwEK+FfQaPAYhJWAvf8cMPP/woY8vjnd3z+dWNGgseBvbDRzfg0yJ5Js1YsuccT7HknLPbrHOOAR3Gk5Z2A58WgwMJZgy8bRKMGxtytxnnsAG1SPCY4ddy/vk3xr9tBoz7D+Q8M875R4yWGzlmzLxtCYmNM3KYH+e2EaFF8sabYmmZcweMGXuOmTHn9knwsBHyC9/59I0f35QdkGNsb378OedbnRw/++FjeLUgAzYJMEmschBg/kCK6lEwCkbBKBg5AABFJ0zdKvtUYQAAAABJRU5ErkJggg==","orcid":"","institution":"Simad University","correspondingAuthor":true,"prefix":"","firstName":"Galad","middleName":"Mohamed","lastName":"Barre","suffix":""},{"id":586443240,"identity":"66baaa23-5ef1-4a9d-8406-8ff97af91154","order_by":1,"name":"Ahmed Hassan Mohamud","email":"","orcid":"","institution":"Simad University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Hassan","lastName":"Mohamud","suffix":""}],"badges":[],"createdAt":"2026-01-10 09:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8567172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8567172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102219550,"identity":"f2860106-2ff7-4b82-9861-4212bf70b1ac","added_by":"auto","created_at":"2026-02-09 13:37:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104577,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8567172/v1/1ced9c736efe8ffac60531d9.png"},{"id":102219552,"identity":"8ed32077-b8c2-445b-9d39-59a75d1a4afd","added_by":"auto","created_at":"2026-02-09 13:37:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated relationships of structural model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8567172/v1/4973a54c4c409a804834ce18.png"},{"id":102296992,"identity":"60e21534-eeec-4171-b732-2deb06d37737","added_by":"auto","created_at":"2026-02-10 10:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1491134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8567172/v1/cce69606-879e-4d53-8140-9fa17aa9dd3a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors influencing Somalia households’ willingness to pay renewable energy: Employing structural equation modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe rising demand for energy and the risk of fossil fuel depletion have driven the rapid development of renewable energy sources to meet clients\u0026rsquo; needs (Ashinze, Tian, Ashinze, Nazir, \u0026amp; Shaheen, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Renewable energies entail the exploitation of natural energy flows (e.g., wings, sunlight, falling water, waves, tides, and ocean currents) or the exploitation of natural resources at a level equivalent to or higher than the human utilization rate (e.g., biofuels, ocean thermal gradients, and hydroelectric reservoirs)(Chen, He, Li, \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Holdren, Morris, \u0026amp; Mintzer, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e). Thus, environmentally-friendly products are demanded by consumers as a new segment to protect against climate change (Amberg \u0026amp; Fogarassy, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). One of the most important initiatives to combat climate change is to reduce energy consumption (Pelau \u0026amp; Acatrinei, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tan, Ooi, \u0026amp; Goh, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). During the last few decades, electricity consumption has grown rapidly, mainly in the residential and service sectors (Ashinze et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This rapid electricity consumption has led to increasing CO2 emissions and ultimately impacts global warming (Gaspar \u0026amp; Antunes, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The demand for energy is at its peak, driven by population growth and economic development (Etokakpan, Solarin, Yorucu, Bekun, \u0026amp; Sarkodie, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, policymakers are considering alternative energy sources and ways to reduce carbon footprints by relying less on fossil fuels (Ahmad, Zhao, Irfan, \u0026amp; Mukeshimana, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In turn, lower fossil fuel use and fewer greenhouse gas emissions can be achieved (Mills \u0026amp; Schleich, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The installation of energy-efficient appliances (EEAs) plays a significant role in reducing household energy consumption (Tan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, consumers who engage in pro-environmental behavior (PEB) have a lower negative environmental impact (Kollmuss \u0026amp; Agyeman, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Nekmahmud \u0026amp; Fekete-Farkas, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pro-environmental consumer behavior research is conducted in developed markets but is still in its early stages in several emerging markets, including Asia and Africa (Hossain, Fekete-Farkas, \u0026amp; Nekmahmud, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHowever, Africa still struggles with a persistent energy crisis despite its abundant energy sources and potential for RE growth, with more than \u003cspan\u003e$\u003c/span\u003e10 billion being spent each year on kerosene lighting in Sub-Saharan Africa (Wilson, Rai, \u0026amp; Best, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). In 2019, the overall energy produced in all varieties amounted to 245 GW (Okesiji, Olaniyi, \u0026amp; Okorie, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, interest in renewable energy (RE) options is rising due to their ability to serve as a cleaner alternative to fossil fuels, which come with high production costs, contribute to environmental damage, and pose health risks to humans and animals (Oyedun et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e1.2 Renewable energy in Somalia\u003c/h2\u003e\n \u003cp\u003eThe momentum of Somalia\u0026apos;s transition to renewable energy is growing, driven by the need to shift from expensive, inefficient diesel generators to more sustainable alternatives. The nation lacks a unified national grid, and a significant portion of its energy consumption depends on firewood and charcoal, which exacerbate environmental degradation. Given its prevalence in Somalia, solar energy is emerging as a feasible alternative. A multitude of studies have investigated the potential and economic viability of solar power systems, both off-grid and hybrid, for meeting the nation\u0026apos;s energy requirements. These initiatives constitute an integral part of a comprehensive strategy to enhance the electrification rate while simultaneously reducing dependence on imported diesel. Somalia is characterized by an annual solar insolation of approximately 3,000 hours, with daily solar radiation metrics fluctuating between 5 and 7 kWh/m\u0026sup2;, thereby rendering it exceptionally suitable for the generation of solar energy (Yusuf \u0026amp; Ahmed, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). A case study in Baidoa demonstrated that a 1.98 kW off-grid solar PV system could produce 7,400 kWh/year, with a payback period of approximately 2 years and 7 months (A. A. Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHybrid systems combining solar PV, diesel generators, and battery storage have been explored to optimize costs and reduce environmental impacts. The hybrid PV-DG-grid system without battery storage was found to be the most cost-effective (Elmi, Jazayeri, \u0026amp; Salman, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Beledweyne, a hybrid system including solar, wind, and hydropower was analyzed, highlighting the potential for diverse renewable energy solutions (Coban, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRural electrification remains a challenge due to low population density and economic constraints, but solar-powered mini-grids offer a promising solution (Abdi \u0026amp; Zorlu, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The transition to renewable energy is crucial for reducing Somalia\u0026apos;s dependence on imported diesel and mitigating environmental impacts (Elmi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the integration of renewable energy sources in Somalia is advancing, persistent obstacles, including financial constraints and the need for infrastructure development, persist. Nevertheless, the country\u0026rsquo;s abundant solar energy potential and the continuation of related initiatives signal an optimistic pathway towards sustainable energy solutions.\u003c/p\u003e\n \u003cp\u003eSomalia faces significant energy challenges, with limited grid infrastructure and widespread reliance on expensive, polluting energy sources. Understanding consumer behavior and willingness to adopt renewable energy is crucial for developing effective energy policies and deployment strategies. While existing studies have examined various aspects of renewable energy investment and adoption, there are specific areas that remain underexplored, particularly in the context of Somalia (Nor, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sabroso, Suaner, Lucmayon, \u0026amp; Asio, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). A study on the adoption of renewable energy in Somalia reveals several gaps that need to be addressed to enhance understanding and facilitate the transition to sustainable energy sources.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e1.3 Theory of Planned Behavior (TPB) and hypothesis development\u003c/h2\u003e\n \u003cp\u003eThe Theory of Planned Behavior (TPB) is used to explain behaviors in which individuals can exercise self-control (Wall, Khalid, Urbański, \u0026amp; Kot, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The theory uses various constructs to predict individuals\u0026rsquo; control over their behavior, including attitude, subjective norms, and perceived behavioral control. The TPB is a widely used model to predict consumer intentions, including the adoption of renewable energy. In the context of Somalia, the model has been extended to incorporate two additional variables: beliefs about the costs of renewable energy and environmental concern. These additions aim to provide a more comprehensive understanding of consumer intentions in adopting renewable energy. No study in Africa has combined beliefs about renewable energy costs and environmental concern within a Theory of Planned Behavior (TPB) model tailored to the African context. Research in Pakistan includes both factors, but it is not set in Africa. Nigerian studies address costs but do not explicitly add environmental concerns to their models. Moreover, previous studies have not modeled consumer intention as a moderating variable in Theory of Planned Behavior (TPB) research on renewable energy; instead, intention is typically treated as an outcome or a proximal predictor (Ashinze et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mustafa, Zhang, Sohail, Rana, \u0026amp; Long, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nor, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zulu, Zulu, \u0026amp; Chabala, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, this study uses consumer intentions as a moderating variable between attitude, subjective norms, perceived behavioral control, belief about the costs of renewable energy, environmental concern, and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003eThe attitude in TPB is defined as an individual\u0026rsquo;s evaluation of a particular behavior as favorable or unfavorable (Icek Ajzen, \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e). Consumers with favorable views on renewable energy\u0026apos;s benefits are more likely to support and adopt it (Irfan, Zhao, Li, \u0026amp; Rehman, \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e). This study reports a positive correlation between attitude and consumers\u0026rsquo; intention to purchase environmentally friendly vehicles (Afroz, Rahman, Masud, Akhtar, \u0026amp; Duasa, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, this study exposed that attitude is positively related to consumers\u0026rsquo; intentions to utilize less energy. Attitude is a dominant predictor of residential energy consumption (Tan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Attitudes are shaped by perceived utility and social influences, which can enhance consumer commitment to renewable energy adoption (G\u0026acirc;rdan, Micu, Paștiu, Micu, \u0026amp; G\u0026acirc;rdan, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Positive attitudes, shaped by beliefs about the benefits of renewable energy, lead to a higher likelihood of consumers being willing to adopt for green energy options (Clark \u0026amp; Doll, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although, attitudes towards renewable energy investments are identified as a determinant of investment intentions, there is limited research on how these attitudes specifically influence consumer willingness to adopt renewable energy in Somalia (Nor, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The relationship between attitude and behavioral intention is strong, but further exploration is needed to understand the nuances of this relationship in the Somali context (Y. A. Ahmed, Rashid, \u0026amp; Khurshid, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eH1: Attitude positively influences consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n \u003cp\u003eH10: Consumer intention to use renewable energy positively moderates between attitude and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003eSubjective Norms are perceived social stress of performing or not performing a particular behavior is termed the subjective norm (SBN) (I Ajzen, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). Subjective norms \u0026mdash;the perceived social pressure to engage in renewable energy consumption \u0026mdash; positively influence attitudes and WTP. Consumers are more likely to invest in renewable energy if they believe their peers support such actions (Clark \u0026amp; Doll, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jabbour Al Maalouf, Sayegh, Inati, \u0026amp; Sarkis, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Generally, the actions and opinions of other people have a great impact on consumers\u0026rsquo; buying intentions (Irfan et al., \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e). The Subjective Norms are recognized as influencing investment intentions, yet there is a lack of detailed analysis on how societal pressures and cultural expectations impact individual willingness to adopt renewable energy (Nor, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eH6: Subjective Norms positively influence consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n \u003cp\u003eH9: Consumer intention to use renewable energy positively moderates between Subjective Norms and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003ePerceived Behavioral Control (PBC) refers to an individual\u0026apos;s belief in their ability to perform a specific behavior, which directly influences their intentions and indirectly affects their actual behavior (Icek Ajzen, \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e). These studies found that PBC positively impacts consumers\u0026apos; intentions to conserve energy (Alam et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang, Zhang, \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, It found that perceived behavioral control significantly influenced intentions, more than attitude and subjective norms (Gobel, Ramadhan, Ratama, \u0026amp; Hendriana, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The PBC plays a critical role in influencing consumers\u0026apos; decisions to purchase energy-efficient vehicles, highlighting its importance in broader sustainable technology adoption (Wang, Zhao, Yin, \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although perceived behavioral control is acknowledged as a factor in renewable energy investment, its specific impact on consumer adoption intentions in Somalia remains underexplored (Y. A. Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eH5: Perceived Behavioral Control positively influences consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n \u003cp\u003eH11: Consumer intention to use renewable energy positively moderates between Perceived Behavioral Control and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003eBelief about Renewable Energy Cost (BREC) refers to consumers\u0026apos; perception of the financial sacrifices associated with adopting renewable power technologies (RPTs). This variable plays a critical role in shaping consumer behavior, as the high upfront costs and maintenance expenses of renewable energy systems often act as barriers to adoption. Perceptions of renewable energy costs significantly influence consumer adoption intentions. High perceived costs can deter adoption, while perceived affordability can encourage it (Vu, Nguyen, \u0026amp; Nguyen, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWhen consumers hold strong beliefs about the advantages of renewable energy, they are more inclined to develop favorable attitudes towards it, which in turn enhances their willingness to pay (Clark \u0026amp; Doll, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, beliefs about the costs of renewable energy can negatively impact the adoption of renewable energy. If consumers perceive renewable energy as expensive, their willingness to pay decreases (Irfan et al., \u003cspan class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Belief About Costs The economic analysis of solar systems in Somalia highlights cost-effectiveness, but there is insufficient research on how consumer beliefs about these costs affect their adoption decisions (A. A. Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The financial implications of renewable energy adoption, such as initial investment and payback periods, need further investigation to understand consumer perceptions (A. A. Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eH2: Belief about the cost of renewable energy positively influences consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n \u003cp\u003eH7: Consumer intention to use renewable energy positively moderates between Belief about Renewable Energy Cost and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003eEnvironmental Concern (ECN) refers to the level of awareness individuals have of environmental problems and their motivation to address them. It is often considered a key factor influencing consumers\u0026apos; adoption of renewable energy (RE) technologies. While environmental awareness is generally associated with positive attitudes towards renewables, it does not consistently predict the adoption of renewable energy (Karasmanaki, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Environmental awareness and concern are crucial in shaping attitudes towards renewable energy, influencing both the intention and actual adoption of green technologies (Ben Saad, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The lack of government-led awareness campaigns and education on ecological issues may further diminish ECN\u0026apos;s role in purchase decisions. Similarly, the environmental benefits of renewable energy are noted, but there is a gap in understanding how environmental concerns drive consumer adoption in Somalia (Abdi \u0026amp; Zorlu, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the willingness to pay for renewable energy varies by type, indicating that consumer preferences are not uniform across different renewable sources (Katare, Wang, Wetzstein, Jiang, \u0026amp; Weiland, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eH4: Environmental Concern positively influences consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n \u003cp\u003eH8: Consumer intention to use renewable energy positively moderates the relationship between Environmental Concern and willingness to pay for renewable energy.\u003c/p\u003e\n \u003cp\u003eH3: Consumer intention to use renewable energy positively influences consumer willingness to pay for renewable energy in Somalia.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Research Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Instrument\u003c/h2\u003e \u003cp\u003eThe method utilized for this study is a survey and regression research design based on quantitative methodologies. The researchers chose the sample using a nonprobability sampling approach, specifically purposive sampling. A structured, closed-ended questionnaire was used to collect data from residents of Mogadishu, Somalia's capital city. Mogadishu was chosen since it is the most populous city in Somalia, and people pay energy bills. The study focused on persons who pay household power bills and are knowledgeable about the cost of electricity and the need for renewable energy. This study's sample size was 300 respondents; however, after data cleaning, only 255 were considered credible for analysis. Researchers used structured questionnaires. Data generated for the study were analyzed using descriptive and inferential statistics via the Statistical Package for the Social Sciences (SPSS) version 25 and SMART PLS-4. The study uses descriptive statistics to summarize the collected data and regression analysis to show the impact of the independent variable on the dependent variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cb\u003eMeasurement Scale and SEM model\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo develop the research constructs used in the study, various previous works were consulted. All scale items are adopted from (Irfan, Zhao, Li, \u0026amp; Rehman, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). All item scales were measured using the 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly agree to 5\u0026thinsp;=\u0026thinsp;strongly disagree) in the descriptive section. Structural equation modeling (SEM) was applied to test the study's hypothesis, which depicted the relationships among the study's variables. SEM was considered a suitable model for the study because it provides accurate and meaningful outcomes regarding the study constructs (Steenkamp \u0026amp; Baumgartner, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Analysis","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 descriptive section\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespondents profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eabove 35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;6 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 or more members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eprimary source of income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eagriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elivestock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebusiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCost of electricity per month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;10 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;30 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove 40 \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the demographics of this research. 48.6 percent of the participants were men, while 51.4 percent were women. The bulk of respondents (46.3 percent) were between the ages of 21 and 25, while the next-largest group was 18- to 20-year-olds, accounting for 32.2 percent. Regarding education, 54.5 percent of respondents were undergraduate students. The bulk of respondents (76.5 percent) reported being single. The majority of respondents' households had seven or more individuals, accounting for 56.6 percent of the total, indicating that Somalis had high household sizes. In terms of income source, the majority of families earned 51.0 percent from business, with employment accounting for 35.7 percent. Finally, the monthly energy cost for the majority of Somali families (47.5 percent) is between \u003cspan\u003e$\u003c/span\u003e11 and \u003cspan\u003e$\u003c/span\u003e20.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measurement Model\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLoading and reliability factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAttitude\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIt is wise to utilize renewable power generation technologies (RPTs) at my home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIt would be pleasant to utilize renewable power generation technologies (RPTs) at my home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective norms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeople who are valuable for me have the opinion that I should use\u0026nbsp;\u0026nbsp;renewable power generation technologies (RPTs) at my home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI will use renewable power generation technologies (RPTs) if my friends also use it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI will use renewable power generation technologies (RPTs) if my relatives advise me to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived behavioral control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have the required knowledge to utilize renewable power generation technologies (RPTs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have the resources to utilize renewable power generation technologies (RPTs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have full control of utilizing renewable power generation technologies (RPTs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBelief about costs of renewable energy utilization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity price will be increased, as renewable energy (RE) projects require a high initial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe utilization of renewable energy (RE) requires high installation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBattery replacement in case of using solar PV requires additional costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental concern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am worried about climate change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenewable power generation technologies (RPTs) would improve the environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWillingness to Pay (WTP) for Renewable Energy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy-saving behavior motivate me to pay for renewable energy (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental friendliness nature of renewable energy (RE) motivate me to pay for renewable energy (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliability of renewable power generation technologies (RPTs) motivate me to pay for renewable energy (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumer's Intention to Use Renewable Energy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI intend to use\u0026nbsp; renewable power generation technologies (RPTs)\u0026nbsp;\u0026nbsp;if banks provide loan and financial assistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI am planning to spend more money on\u0026nbsp; renewable power generation technologies (RPTs)\u0026nbsp;\u0026nbsp;than traditional electricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have a positive intention that other people should utilize\u0026nbsp; renewable power generation technologies (RPTs)\u0026nbsp;\u0026nbsp;at their homes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study adopts a quantitative approach and uses structural equation modelling. The measurement model was assessed using factor loadings, composite reliability, average variance extraction, and the Variance Inflation Factor. According to (Joseph F Hair, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to achieve convergent validity, published recommendations require factor loadings of 0.70 or higher, composite reliability of 0.70 or higher, and an average variance extracted of 0.50 or higher. Using the Smart PLS-4 technique, researchers found that all factor loadings were above the suggested 0.70, except for S4, B5, E2, W4, and C6, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, factor loadings between 0.40 and 0.70 are regarded as acceptable if the average variance extracted is more than 0.50, as stated by (Byrne, 2016). The average variance extracted in this study exceeds 0.50. Furthermore, the composite reliability (CR) values in this study are above the required level of 0.70, as stated by prior studies like (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Sarstedt, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Regarding the Variance Inflation Factor (VIF), all indicators have values below 5, indicating strong multicollinearity in this model. Overall, the model exhibits high explanatory power and predictive relevance, with P2 standing out as the most relevant predictor (see in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFornell-Larcher Criterion Analysis for Checking Discriminant validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was assessed using the Fornell-Larcker criterion. Discriminant validity is achieved when the square root of each construct's average extracted variance is greater than the correlations between that construct and other constructs (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the diagonal components correspond to the square roots of each construct's average retrieved variance. These findings were much higher than the previously reported correlations, demonstrating that the assessments exhibit discriminant validity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterotrait-monotrait (HTMT) ratio\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity testing has become a commonly accepted prerequisite for studying links between reflectively rated constructs. The HTMT ratio criteria were used to assess discriminant validity. Such assessments analyse the distinctness of different concepts within the measuring model (Joe F Hair, Ringle, \u0026amp; Sarstedt, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The proposed measuring the HTMT correlation ratio to a threshold of less than 0.85 to determine discriminant validity (Henseler, Ringle, \u0026amp; Sarstedt, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, all HTMT values are less than 0.85, showing high discriminant validity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Structural Model\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the structural model assessment using key indicators, including R\u0026sup2; and Adjusted R\u0026sup2;. The R\u0026sup2; value for willingness to pay for renewable energy is 0.428, suggesting that 42.8% of the variation is explained by independent variables. The modified R\u0026sup2; of 0.402 shows the model's robustness given the number of predictors. The findings reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e investigate the elements that influence consumers' decisions to use renewable energy. The findings are based on hypothesis testing, most often using structural equation modelling (SEM) or regression analysis. This study proposed 11 hypotheses to investigate the correlations between the variables under examination. The structural model results illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and documented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provide useful insights into the linkages between the constructs, offering light on the underlying dynamics driving the events being studied. The PLS path analysis examines direct and indirect effects equally and is the only approach suitable for mediation research. In the structural model, the researchers used p-value and t-value to assess the proposed hypotheses. If the t-value exceeds 1.96 or the p-value is less than 0.10, the hypotheses can be accepted.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the hypothesized relationships between the constructs and the dependent variable. H1 supports a relationship between attitude and willingness to pay for renewable energy, with β\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.066, and a t-statistic of 1.510. The mindset has a favorable impact on Somali households' willingness to pay for renewable energy. Therefore, customers' positive attitudes towards renewable energy increase the likelihood that they will pay for it. H2 supports the relationship between beliefs about renewable energy costs and willingness to pay for renewable energy (β\u0026thinsp;=\u0026thinsp;0.058, p\u0026thinsp;\u0026lt;\u0026thinsp;0.109, and statistics at 1.231). The willingness of Somali households to pay for renewable energy is not significantly influenced by their beliefs about its cost. H3 demonstrates a correlation between consumer intention and willingness to pay for renewable energy, with β\u0026thinsp;=\u0026thinsp;0.069, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and a t-statistic of 3.001. Consumer intention has a favorable impact on Somali households' willingness to pay for renewable energy. Thus, customers plan to spend more on renewable energy than on traditional electricity. Furthermore, customers encourage others to use renewable power-generating technologies (RPTs) at home. H4 demonstrates a link between environmental concern and willingness to pay for renewable energy, supported by β\u0026thinsp;=\u0026thinsp;0.068, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000, and a t-statistic of 4.102. Environmental concerns positively affect Somali households' willingness to pay for renewable energy. Thus, if people are concerned about climate change, they will choose renewable energy over conventional power, thereby benefiting the environment. H5 supports the association between Perceived Behavioral Control and willingness to pay for renewable energy, with β\u0026thinsp;=\u0026thinsp;0.056, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000, and a t-statistic of 3.662. Perceived Behavioral Control positively affects Somali households' willingness to pay for renewable energy. Thus, if customers have the necessary knowledge and resources, they will use renewable energy, which encourages them to pay for it and gives them complete control over the use of renewable power production technologies (RPTs). H6 demonstrates a correlation between Subjective Norms and willingness to pay for renewable energy (β\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.007, t-statistic\u0026thinsp;=\u0026thinsp;2.448). Subjective norms favorably impact Somali households' willingness to pay for renewable energy. Thus, if those important to customers, such as friends and family, have a positive attitude towards renewable energy, consumers will adopt renewable power production technologies (RPTs) in their homes.\u003c/p\u003e \u003cp\u003eIn terms of moderation, consumer intention moderates the relationship between the findings of H8, H9, and H10 (environmental concern, subjective norms, and attitude) and Somali households' willingness to pay for renewable energy. While consumer desire to utilize renewable energy did not mediate a correlation between the findings of H7 and H11 (belief about renewable energy cost and perceived behavioral control) and Somali households' willingness to pay for renewable energy (see in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Model Fit Indicators\u003c/h2\u003e \u003cp\u003eThe standard root mean square (SRMR) is a measure use to assess the goodness of fit (GoF) in PLS models (Henseler, Ringle, \u0026amp; Sinkovics, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). SRMR refers to the discrepancies that remain between the correlated data of the samples and the expected correlated model, as described by (Hooper, Coughlan, \u0026amp; Mullen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The SRMR scale ranges from 0 to 1.0, with values close to zero suggesting an ideal fit for the model. A well-fitting model should have an SRMR value that is less than or equal to 0.05, as stated by (Hooper et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The SRMR value of the current study is 0.091, suggesting that the findings align with the expected model (see in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Goodness of fit, Model fit indicators, empirical results, and structural model are displayed in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e,\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness of fit.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR-square adjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticulars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaturated model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimated model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_ULS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed_G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e685.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e683.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEmpirical results of structural model analysis (hypothesis testing)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1. A -\u0026gt; W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccept\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2. B -\u0026gt; W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReject\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3. C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.069\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4. E -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.068\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.102\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5. P -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.056\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.662\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6. S -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.448\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH7. B x C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.490\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eReject\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH8. E x C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.062\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.937\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH9. S x C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.066\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.767\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH10. A x C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.052\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.379\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.084\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAccept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH11. P x C -\u0026gt; W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.068\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.149\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eReject\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this world, which is addicted to fossil fuels, particularly lighting in homes, the energy we use daily is primarily powered by fossil fuels, the main drivers of climate change (Nor, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, many nations, including Somalia, are over-reliant on fossil fuels. Burning fossil fuels releases large amounts of carbon dioxide, a greenhouse gas, into the air. In turn, greenhouse gases blanket the Earth and trap the sun\u0026rsquo;s heat, warming the planet with far-reaching consequences for people and the planet. However, renewable energy options are rising due to their ability to serve as a cleaner alternative to fossil fuels, which come with high production costs, contribute to environmental damage, and pose health risks to humans and animals (Oyedun et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates the factors that influence consumers' intention and desire to utilise renewable energy, both directly and in moderation. TPB was used as a theoretical framework for this study, with its original constructs Attitude, Subjective Norms, and Perceived Behavioral Control, focusing on consumer intention to use renewable energy, which has not been thoroughly studied in previous studies, as well as extending the theory to include two additional variables: belief about the costs of renewable energy utilization and environmental concern. These enhancements aim to provide a more thorough understanding of customers' intentions and willingness to adopt renewable energy. The independent factors explained 42.8% of the variance in consumer willingness to pay for renewable energy (R\u0026sup2; = 0.428). The corrected R\u0026sup2; of 0.402 verifies the model's robustness, indicating that the predictors significantly help to understand consumer behavior regarding renewable energy uptake in Somalia.\u003c/p\u003e \u003cp\u003eConsumer intention to use renewable energy had the greatest impact on willingness to pay for renewable energy (β\u0026thinsp;=\u0026thinsp;0.069, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, t\u0026thinsp;=\u0026thinsp;3.001). This conclusion confirms the core assumption of the Theory of Planned Behavior, which states that behavioral intentions are the most proximal predictor of actual behavior (I Ajzen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Similarly, the result aligns with (Bamberg \u0026amp; M\u0026ouml;ser, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Ud Din et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), consumer intention has continuously been highlighted as an important connection between attitudes, norms, control perceptions, and actual behavior. Thus, Somali families who express a desire to adopt renewable energy are significantly more likely to be prepared to pay for it. Environmental concern has a significant impact on Somali households' willingness to pay for renewable energy (β\u0026thinsp;=\u0026thinsp;0.068, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000, t\u0026thinsp;=\u0026thinsp;4.102). The results are consistent with earlier research that has established the relevance of environmental protection in influencing pro-environmental behavior internationally (Ali, Irfan, Ozturk, \u0026amp; Rauf, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dunlap, Van Liere, Mertig, \u0026amp; Jones, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Srivastava \u0026amp; Mahendar, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stern, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Therefore, the findings suggest that environmental awareness and education may be particularly beneficial for boosting renewable energy use in Somalia and other comparable environments. Third, perceived behavioral control has a positive and substantial association with willingness to pay for renewable energy (β\u0026thinsp;=\u0026thinsp;0.056, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000, t\u0026thinsp;=\u0026thinsp;3.662). This result aligns with the Theory of Planned Behavior's emphasis on the role of control perceptions in shaping behavior (Bandura, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This study also aligns with (Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Kuruppu \u0026amp; Ketakumbura, 2019; Ud Din et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, the findings show that when Somali households think they have the knowledge, skills, and resources to use renewable energy, they are more prepared to engage financially in these solutions. Subjective Norms significantly increase willingness to pay for green energy (β\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.007, t\u0026thinsp;=\u0026thinsp;2.448). Previous research has validated these conclusions (Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; 피터, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This finding demonstrates that the attitudes and behaviors of significant individuals, such as friends, family, and community members, strongly impact Somali families' decisions to use renewable energy. The study found a marginally significant positive connection (β\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.066, t\u0026thinsp;=\u0026thinsp;1.510) between attitude and willingness to pay for renewable energy. This supports the Theory of Planned Behavior, which states that attitudes impact behavioral intentions and subsequent behavior (Fishbein \u0026amp; Ajzen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This also aligns with (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Ud Din et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hence, positive attitudes towards renewable energy\u0026mdash;including perceptions of its benefits, advantages, and attractiveness\u0026mdash;tend to enhance willingness to pay. However, Belief About Renewable Energy Costs appears to have a negligible effect. Surprisingly, views regarding renewable energy prices had no significant impact on willingness to pay (β\u0026thinsp;=\u0026thinsp;0.058, p\u0026thinsp;\u0026lt;\u0026thinsp;0.109, t\u0026thinsp;=\u0026thinsp;1.231). This finding is somewhat unexpected, as cost factors are commonly significant in technology adoption decisions, particularly in resource-constrained environments; moreover, most prior research suggested a negative influence of renewable energy costs on willingness to pay for it (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Irfan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). However, the insignificance of cost beliefs might indicate that perceived benefits, environmental, social, and practical, outweigh cost considerations for those predisposed to adopt renewable energy.\u003c/p\u003e \u003cp\u003eIn terms of moderation, the findings show that consumer intention strongly regulates the correlations between environmental concern (H8), subjective norms (H9), attitude (H10), and willingness to pay for renewable energy. These moderating effects show that the impact of environmental concern, societal norms, and attitudes on willingness to pay is proportional to the strength of consumer intentions to utilise renewable energy. These variables impact not just willingness to pay for renewable energy, but also function in tandem with consumer goals. For example, a consumer who is concerned about the environment may be prepared to pay more for renewable energy if they have strong plans to use such technology. Similarly, positive social effects (subjective norms) may be most successful in increasing willingness to pay for renewable energy among people who have already made tentative plans to utilise it. Consumer intention did not significantly moderate the relationships among perceptions of renewable energy prices (H7), perceived behavioural control (H11), and willingness to pay for renewable energy. The lack of moderation in cost views is consistent with the discovery that cost beliefs did not significantly affect willingness to pay directly. This trend suggests that cost factors are independent of intention strength, neither amplifying nor diminishing customers' intent to use renewable energy. The lack of moderation in perceived behavioural control is potentially fascinating. It suggests that the empowering effect of perceived control on willingness to pay operates consistently regardless of intention strength.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Theoretical and policy implication\u003c/h2\u003e \u003cp\u003eThis work provides numerous significant theoretical advances in understanding renewable energy uptake in underdeveloped countries. First, it establishes the TPB theory's applicability and significance in the Somali context, where it has previously been underutilized. Attitudes, subjective norms, and perceived behavioral control have substantial influence on renewable energy use decisions across a variety of cultural and economic contexts. Second, the study broadens TPB Theory by including Belief about Renewable Energy Cost and environmental concern as predictors, suggesting that environmental values play an important role beyond typical TPB concepts. Given the considerable impact of environmental concern, models of pro-environmental behaviour in energy contexts should consistently incorporate environmental values and concerns as crucial predictors. Third, investigating moderating effects yields detailed insights into how many factors interact to impact willingness to pay for renewables. The discovery that consumer intention modifies certain associations but not others implies that the paths from psychological antecedents to behaviour are complicated and conditional, necessitating more sophisticated theoretical models that account for interaction effects. Somalia confronts substantial energy access issues, with little grid infrastructure and a high dependence on diesel generators and traditional biomass for electricity (IRENA, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, renewable energy particularly solar power represents not merely an environmental choice but a practical solution to energy access problems.\u003c/p\u003e \u003cp\u003eThe findings of this provide some useful insights for politicians, energy planners, and renewable energy supporters. First, launch extensive public awareness campaigns to educate families about the advantages, viability, and accessibility of renewable energy technologies. Second, encourage the development and deployment of renewable energy technologies. Third, provide accurate, balanced information on renewable energy technology that honestly discusses both its benefits and limits. Fourth, introduce direct subsidies, tax breaks, or rebate schemes to reduce the upfront costs of renewable energy installations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations and Future studies\u003c/h2\u003e \u003cp\u003eFirst, the cross-sectional design precludes causal conclusions because all variables were assessed at a single time point. Second, the analysis is based on self-reported willingness to pay for renewable energy, rather than actual adoption behavior or payment statistics. Third, the analysis explains 42.8% of the variation in willingness to pay for renewable energy, suggesting that additional key aspects remain to be investigated. Future studies should examine additional determinants, such as economic factors like income and access to funding. Finally, because the study was conducted in Somalia, generalizability to other contexts should be assessed through replication studies across nations and cultural contexts. While the findings are likely applicable to other developing countries facing comparable energy issues, local variables may change the relative impact of several predictors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGalad Mohamed Barre wrote the introduction, literature review, econometric methodology, data collection, analy\u0026shy;sis, interpretation of results, discussion section, manuscript writing, and Ahmed Hassan Mohamud reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Simad University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were conducted according to relevant guidelines and regulations. The research protocol, survey tools, and consent process were approved by the Ethics Committee of the Centre for Research and Development at SIMAD University (Approval Number: EC000138). Informed consent was obtained from all respondents before data collection. Participation was voluntary, and all respondents were assured of the confidentiality and anonymity of their responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi, A. H., \u0026amp; Zorlu, H. (2021). \u003cem\u003eRural electrification with solar powered mini-grids and stand-alone solar system installations: case of Somalia.\u003c/em\u003e Paper presented at the Proceedings of the 5th International Students Science Congress, Izmir, Turkey.\u003c/li\u003e\n\u003cli\u003eAfroz, R., Rahman, A., Masud, M. 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Factors influencing households\u0026apos; intention to adopt solar energy solutions in Zambia: insights from the theory of planned behaviour. \u003cem\u003eSmart and Sustainable Built Environment, 11\u003c/em\u003e(4), 951-971. \u003c/li\u003e\n\u003cli\u003e피터. (2025). \u003cem\u003eAnalyzing Consumer Preference of Residential Solar Photovoltaic Adoption Behavior in Zimbabwe.\u003c/em\u003e 서울대학교 대학원, \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"willingness to pay Renewable energy, structural equation modeling, Somalia","lastPublishedDoi":"10.21203/rs.3.rs-8567172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8567172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study uses PLS path analysis and structural equation modelling (SEM) to examine the factors influencing Somali households' willingness to pay for renewable energy. Nonprobability purposive sampling was used in a quantitative survey to select respondents from Mogadishu, the capital city of Somalia. 300 home power bill payers who were informed about energy costs and renewable energy requirements were given a standardized closed-ended questionnaire. Following data cleaning, SPSS version 25 and SmartPLS-4 were used to analyze 255 valid replies using descriptive and inferential statistics.\u003c/p\u003e \u003cp\u003eWith an R2 of 0.428, the structural model has moderate explanatory power, accounting for 42.8% of the variance in willingness to pay for renewable energy. The model's robustness is confirmed by an adjusted R2 of 0.402. The findings show that consumer intention, environmental concern, perceived behavioral control, Subjective norms, and Attitude have a positive and significant impact on willingness to pay for renewable energy. Belief about the cost of renewable energy shows no significant relationship with willingness to pay for renewable energy. The results of the moderation analysis indicate that the relationships between environmental concern, subjective norms, and attitude with willingness to pay for renewable energy are considerably moderated by customer intention to use renewable energy. However, the relationship between perceived behavioral control and belief about the cost of renewable energy with willingness to pay for renewable energy is not moderated by consumer intention. The findings offer policymakers and renewable energy stakeholders insights to increase adoption rates.\u003c/p\u003e","manuscriptTitle":"Factors influencing Somalia households’ willingness to pay renewable energy: Employing structural equation modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 13:36:59","doi":"10.21203/rs.3.rs-8567172/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T04:53:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T19:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65248801644397188436851213805919860663","date":"2026-03-14T06:30:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T10:11:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47536709908615024106496546149692173378","date":"2026-02-26T08:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165716347400278501714804565725175437506","date":"2026-02-16T14:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T18:39:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-29T01:48:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T14:02:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T14:02:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-10T09:15:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f7ea6699-9005-4528-a0a0-7de6b996c623","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62395308,"name":"Earth and environmental sciences/Environmental sciences"},{"id":62395309,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":62395310,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2026-05-13T08:56:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 13:36:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8567172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8567172","identity":"rs-8567172","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}