SU+ @ Strathmore University Library Electronic Theses and Dissertations This work is availed for free and open access by Strathmore University Library. It has been accepted for digital distribution by an authorized administrator of SU+ @Strathmore University. For more information, please contact library@strathmore.edu 2023 Socio-economic factors influencing adoption of solar energy technologies: a case of households in Narok County. Agandi, Victor Omondi Strathmore Business School Strathmore University Recommended Citation Agandi, V. O. (2023). Socio-economic factors influencing adoption of solar energy technologies: A case of households in Narok County [Strathmore University]. http://hdl.handle.net/11071/13362 Follow this and additional works at: http://hdl.handle.net/11071/13362 https://su-plus.strathmore.edu/ https://su-plus.strathmore.edu/ http://hdl.handle.net/11071/2474 mailto:library@strathmore.edu http://hdl.handle.net/11071/13362 http://hdl.handle.net/11071/13362 SOCIO-ECONOMIC FACTORS INFLUENCING ADOPTION OF SOLAR ENERGY TECHNOLOGIES: A CASE OF HOUSEHOLDS IN NAROK COUNTY VICTOR OMONDI AGANDI A RESEARCH DISSERTATION SUBMITED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION AT STRATHMORE UNIVERSITY JUNE 2023 ii DECLARATION I declare that this work has not been previously submitted and approved for the award of a degree by this or any other University. To the best of my knowledge and belief, the dissertation contains no material previously published or written by another person except where due reference is made in the dissertation itself. © No part of this dissertation may be reproduced without the permission of the author and Strathmore University Name of Candidate: Victor Omondi Agandi Approval The dissertation of Victor Omondi Agandi was approved by the following: Name of Supervisor: Dr. Vincent Omwenga School/Institute/Faculty: Strathmore University Business School Dr. Ceaser Mwangi Executive Dean Strathmore University Business School. Dr. Bernard Shibwabo Director, Office of Graduate Studies iii ACKNOWLEDGEMENTS I would like to acknowledge and sincerely express my deepest appreciation to my supervisor Dr. Vincent Omwenga for his assistance, support, and inspiration in writing this project. His guidance carried me through all stages of writing this project. From the whole Strathmore University community who were involved in this scholarly journey, I equally applaud your professionalism. I would also like to give a special thanks to my wife Geraldine Sande-Agandi and my entire family for their continuous encouragement and support while undertaking my research. Your prayers sustained me through this journey. Finally, I would like to thank God, for giving me strength and courage through the and guiding me daily through huddles that came by. I will keep on believing and trusting you for next. iv ABSTRACT The energy sector in Kenya contributes significantly to the country's economic development by creating employment opportunities and raising people's living standards. However, there is slow adoption of renewable energy, especially solar energy in the country due to various socioeconomic challenges, which is now a major concern to the government because if this remains unchecked, it may derail the country in achieving vision 2030 and other development goals. The purpose of this study, therefore, was to examine the influence of socioeconomic factors (household income, education of household head, dwelling characteristics, and household demographic characteristics) influencing adoption of solar energy in Narok County. The specific objectives of the study include: to evaluate the influence of household income, education of household head, household dwelling characteristics, and household demographic characteristics on the adoption of solar energy technologies in Narok County. The study is anchored on Technology Acceptance Model, the Unified Theory of Adoption and Use of Technology (UTAUT), and the Energy Ladder & Stacking hypothesis. Descriptive research design was adopted with a target population of households in Narok County. Random sampling was applied in this study where 400 respondents were proportionally distributed across the 6 counties respondents randomly selected to participate in this study. Semi-structured questionnaires were used to collect the primary data. The data was analyzed into both descriptive and inferential statistics. Descriptive research design was used to determine socio economic factors that influence adoption of solar energy. Regression analysis was also used to identify which factors played a significant role in explaining adoption of solar energy technology in Narok County. The study findings show that there was significant positive correlation between all the socioeconomic factors of household income, education of household head, household dwelling characteristics and household demographics characteristics and the adoption of solar energy technology adoption in Narok County. The findings play a great role in not only extending frontiers of knowledge in green energy strategic business management and regulations research, but also in informing key players in making sound solar energy technology business environment decisions for better green energy adoption. v TABLE OF CONTENTS DECLARATION ........................................................................................................................ ii ACKNOWLEDGEMENTS ....................................................................................................... ii ABSTRACT ............................................................................................................................... iv TABLE OF CONTENTS ........................................................................................................... v LIST OF TABLES .................................................................................................................... vii LIST OF ABRREVIATIONS AND ACRONYMS ................................................................ ix CHAPTER ONE: ........................................................................................................................ 1 INTRODUCTION TO THE STUDY ....................................................................................... 1 1.1. Background of the study ................................................................................................. 1 1.1.1 Adoption to Solar Energy Technology ................................................................................ 2 1.1.1.1 Adoption of Solar Energy Technology in Kenya ...................................................... 3 1.1.2 Socioeconomic Factors Affecting Adoption of Solar Energy ............................................. 5 1.1.3 The Socioeconomic Status of Narok County and Adoption to Power Energy .................... 7 1.2. Problem Statement .......................................................................................................... 9 1.3 Research Objectives ...................................................................................................... 10 1.3.1 General Objective .............................................................................................................. 10 1.3.2 Specific Objectives ............................................................................................................ 10 1.3.3 Research Questions ........................................................................................................... 11 1.4 Significance of the study ............................................................................................... 11 1.5 The scope of study ......................................................................................................... 12 CHAPTER TWO ...................................................................................................................... 13 LITERATURE REVIEW ........................................................................................................ 13 2.1 Introduction ................................................................................................................... 13 2.2 Theoretical Review ........................................................................................................ 13 2.2.1 Technology Acceptance Model ......................................................................................... 13 2.2.2 Unified Theory of Adoption and Use of Technology (UTAUT) ...................................... 14 2.2.3 Energy Ladder and Fuel Stacking Hypothesis .................................................................. 15 2.3 Empirical Review ........................................................................................................... 17 2.3.1 Effect of Household Income on Adoption of Solar Energy Technology .......................... 18 2.3.2 Effect of Education of Household Head on the Adoption of Solar Energy Technology .. 19 2.3.3 Effect of Dwelling Characteristics on the Adoption of Solar Energy Technologies ......... 20 2.3.4 Effect of Household demographic characteristics on the Adoption of Solar Energy Technologies ............................................................................................................................... 22 2.4 Overview of Literature and Research Gaps ............................................................... 23 2.4.1. Summary of Research Gap ............................................................................................... 23 2.5 Conceptual framework ................................................................................................. 25 2.5.1 Operationalization of Variables ................................................................................... 26 CHAPTER THREE .................................................................................................................. 27 RESEARCH METHODOLOGY ............................................................................................ 27 3.1 Introduction ................................................................................................................... 27 3.2 Research Philosophy ..................................................................................................... 27 3.3 Research Design ............................................................................................................. 28 3.4 Location of the Study .................................................................................................... 28 3.5 Target Population and Sampling Frame ..................................................................... 28 3.6 Sample size and sampling Technique .......................................................................... 29 vi 3.6 Research Instruments ........................................................................................................ 30 3.6.1 Data collection Procedures .......................................................................................... 31 3.7 Research Quality ........................................................................................................... 31 3.7.1 Reliability ..................................................................................................................... 31 3.7.2 Internal validity ............................................................................................................ 32 3.8 Data Analysis and Presentation ................................................................................... 33 3.8.1 Data analysis Model .......................................................................................................... 33 3.10 Ethical Considerations ..................................................................................................... 34 CHAPTER FOUR .................................................................................................................... 35 RESULTS AND FINDINGS .................................................................................................... 35 4.0 Introduction ........................................................................................................................ 35 4.1 Response rate ...................................................................................................................... 35 4.2 Major Source of Energy in Narok County ....................................................................... 36 4.3 Adoption of Solar Energy Technology in Narok County ................................................ 36 4.4 Socio-Economic Factors Affecting Adoption of Solar Energy Technology .................. 38 4.4.1 Household Income ............................................................................................................. 38 4.4.2 Education of Household Head ........................................................................................... 40 4.4.3 Dwelling Characteristics ................................................................................................... 42 4.4.4 Household Demographic Characteristics .......................................................................... 43 4.5 Correlation and Regression Analysis ................................................................................ 46 4.5.1 Effect of Household Income on the Adoption of Solar Energy Technology .................... 46 4.5.2 Effect of Education of Household Head on the Adoption of Solar Energy Technology .. 47 4.5.3 Effect of Dwelling Characteristics on the Adoption of Solar Energy Technology ........... 47 4.5.4 Effect of Household demographic characteristics on the Adoption of Solar Energy Technology ................................................................................................................................. 48 4.6 Inferential Statistics ............................................................................................................ 49 4.6.1 Logistic Regression ........................................................................................................... 49 CHAPTER FIVE ...................................................................................................................... 51 DISCUSSIONS, CONCLUSIONS AND RECOMMENDATIONS ..................................... 51 5.0 Introduction ........................................................................................................................ 51 5.1 Summary of Study .............................................................................................................. 51 5.2 Discussion of the Findings ................................................................................................... 52 5.2.1 Adoption of Solar Energy Technology .............................................................................. 52 5.2.2 Effect of Household Income on the Adoption of Solar Energy Technology .................... 53 5.2.3 Effect of Education of Household Head on the Adoption of Solar Energy Technology .. 53 5.2.4 Effect of Dwelling Characteristics on the Adoption of Solar Energy Technology ........... 54 5.2.4 Effect of Household Demographic Characteristics on the Adoption of Solar Energy Technology ................................................................................................................................. 54 5.3 Conclusion ........................................................................................................................... 55 5.4 Recommendation ................................................................................................................ 56 REFERENCES ......................................................................................................................... 57 APPENDICES ........................................................................................................................... 72 APPENDIX I: LETTER OF INTRODUCTION ................................................................... 72 APPENDIX II: QUESTIONNAIRE .......................................................................................... 73 APPENDIX III: CLEARANCE FROM ETHICAL APPROVAL ............................................... 1 APPENDIX IV: NACOSTI .......................................................................................................... 2 vii LIST OF TABLES Table 2.1: Operationalization of Variables ……………………………………………………24 Table 3.1: Population of Study………………………………………………………………...27 Table 3.2 Sample Distribution…………………………………………………………………28 Table 4.1: Response Rate………………………………………………………………………33 Table 4.2: Major Source of Power……………………………………………………………..34 Table 4.3: Adoption of Solar Energy Technology in Narok County…………………………..35 Table 4.4 Occupation, Income level and Frequency…………………………………………..37 Table 4.5 Type of education, Level of education and Training on Solar Technology………..39 Table 4.6 Household Dwelling Characteristics……….………………………………………40 Table 4.7: Household Demographic Characteristics of household head……………………..42 Table 4.8 Household Demographic Characteristics……………………………………………43 Table 4.9: Effect of Household Income on the Adoption of Solar Energy Technology…………………………………………………………………………………….44 Table 4.10: Effect of Education of Household Head on the Adoption of Solar Energy Technology…………………………………………………………………………………….45 Table 4.11: Effect of Household Dwelling Characteristics on the Adoption of Solar Energy Technology……………………………………………………………………………………46 Table 4.12: Effect of Household demographic characteristics on the Adoption of Solar Energy Technology……………………………………………………………………………………47 Table 4.13: Logistic Regression………………………………………………………………48 viii LIST OF FIGURES FIGURE 2. 1: CONCEPTUAL FRAMEWORK (SOURCE: RESEARCHER, 2022) .................................... 23 ix LIST OF ABRREVIATIONS AND ACRONYMS FDI Foreign direct investment IDAE Institute for the Diversification and Saving of Energy IEA International Energy Agency IRENA International Renewable Energy Agency MW Megawatt MWh Megawatt-hour OECD Organization for Economic Co-operation and Development PHS Pumped Hydro Storage PPP Public Private Partnerships PV Photovoltaic R&D Research and Development RE Renewable Energy RES Renewable Energy Sources RET Renewable Energy Technology UTAUT Unified Theory of Adoption and Use of Technology SET Solar Energy Technologies x DEFINITION OF TERMS Adoption of Technology: Refers to the process of accepting, integrating, and using new technology. Customer satisfaction: Is a measure of how products and services offered by the organization meet or surpass customer expectations. Households: These are defined and independent family units in a specified location. Lean Manufacturing: Is a methodology that focuses on minimizing waste in manufacturing setups while at the same maximizing productivity. Lead Time: Total time required to complete one unit of a product or service. Quality Products: Is incorporating features that have the capacity to meet customer needs and give customer satisfaction. Social-Economic Factors: These are factors such as education, religion, income, social support, gender, employment that shape an individual's consumption patterns. These factors can accelerate or decelerate the need to adopt solar energy by a household. . 1 CHAPTER ONE: INTRODUCTION TO THE STUDY 1.1. Background of the study International Energy Agency (IEA) defines access to energy as the ability of a household to obtain affordable, reliable, and clean energy for cooking and electrification (IEA, 2020). Access to electricity is a prerequisite for economic and sustainable development of any economy (World Bank, 2018). However, over 1 billion people globally live without electricity in their homes, 2.5 billion (about 40% of the world’s population) rely on traditional biomass to meet virtually all their domestic energy needs of which majority reside in Sub-Saharan Africa (IEA, 2019). Due to the high cost of energy involved in acquiring electricity in developing nations, many countries are embarking on alternative solutions such as solar energy but are faced with numerous socioeconomic challenges (Lin and Kaewkhunok, 2021). Electricity is considered a vital component in economic growth (Irfan et al, 2021). However, the continuous use of traditional sources in electricity generation is the primary cause of global climate change (Ahmad et al., 2020). As a result, to combat global warming, the world must transition to clean energy sources (Hussain et al, 2021; Ahmad et al, 2021). Solar Energy is the heat and light energy from the Sun, resulting from nuclear fusion at its core. In one day, the sun sends 10,000 to 15,000 times more energy to the earth than we can all collectively use (Msafiri, 2009). Solar power is the conversion of sunlight into electricity, either directly or using Photovoltaic (PV) technology, which captures solar energy for household electricity, and the solar thermal technology, which harnesses solar energy for home heating purposes (Schelly, 2010) by converting the suns radiation into direct current electricity using semiconductors. Solar energy, therefore, is considered the world’s most abundant energy form and source, which is corroborated by the constant emission of solar energy from the sun all year round (Păceşilă, 2015). While this is so, Pinner and Rogers (2015) note that the adoption of solar energy technology has been unhurried. In contrast to this, many studies have posited that the world’s current solar energy is enough to provide electricity for the whole world despite variations in production potentials (Johanson et al., 2004). 2 1.1.1 Adoption to Solar Energy Technology Solar PV, a form of clean energy, has become more common in recent times and reached a global installed capacity of 303 GW, with a healthy annual 33-percent growth rate (Pan et al., 2019). By 2025, solar PV is projected to meet 4% of the global electricity demand (IEA,2020). Solar PV, a novel energy green technology, effectively decreases the cost of imported oil and minimizes carbon dioxide (CO2) emissions (Rezaee, Yousefi and Hayati,2019). Different countries have taken steps to raise the proportion of solar energy in their portfolio structure (Merino, Herrera and Valdés, 2019; Valdés and Leon, 2019). According to the sustainable global progress report 2020, solar PV rose 12% and generated electricity of 115 GW in 2019. Until 2019, the estimated worldwide solar PV output reached 627 GW (REN21, 2021). The value of solar PV is highlighted by the fact that home appliances are one of the largest sources of CO2, accounting for 70% of global emissions (Ali et al., 2020). Therefore, IRENA (2020) reported that the share of solar PV to generate electricity globally has increased by 28.3%. Securing African benefits in a global green transition is the tenet of initiatives such as the 2021 African Union’s Green Recovery Action Plan (GRAP) (African Union 2021), and previous initiatives such as the 2016 Africa Renewable Energy Initiative (AREI). The African Development Bank (AfDB) has sought to grow finance for African renewable energy, launching its New Deal on Energy for Africa in 2016 with the aim to achieve universal adoption to energy by 2025 (AfDB,2021). In 2017, the Bank achieved 100% renewable energy in its energy portfolio approving power generation projects with a cumulative 1,400 MW from renewable energy in the same year (AfDB, 2021a). The African continent has a rich source of solar energy and in the recent years, solar PV has become a viable alternative source of electricity for both small and large-scale application in Africa (AU, 2021). With this realization, within the African domain, solar energy is now gaining prominence as a market commodity rather than a product of donor projects (Mutua and Kimuyu, 2015). Solar PV projects are believed to boost the quality of life for residents in numerous ways, such as providing job opportunities for people (Irfan et al, 2019), they can help to reduce CO2 emission (Sweerts et al,2019) and it is the cheapest source of renewable energy and is helpful to sustain the prices of electricity (Kabir et al, 2018). However, Africa currently has only 5 GW of 3 installed solar capacity (IRENA 2021a), compared to a total estimated potential of 315 GW by 2040, as envisioned by the African Union 2063 Agenda and the IEA African Case scenario (IEA 2019). Yet, integration of solar PV for on-grid storage increases the solar PV fraction which consequently leads to a 6% reduction in operation and investment costs by 2050 (Felix, 2021). Currently, Kenya is among the countries in the Sub-Saharan Africa (SSA) that are still in energy crisis due to various socioeconomic challenges (Takase, Kipkoech & Essandoh, 2021). According to George, et al. (2019), adoption to modern and renewable energy for a long time has been mainly centered around urban areas and hence considered to be a privilege in many countries, and it is same case in Kenya. Furthermore, in Kenya, access to electricity (% of population) was reported at 69.7 % in 2019 (Takase, Kipkoech & Essandoh, (2021). To address the electricity access gap, the government of Kenya has devised several policies and programs to increase adoption of alternative renewable energies, such as solar photovoltaic devices (Takase, Kipkoech & Essandoh, 2021; Energy Africa, 2018). However, various socioeconomic factors have been documented to affect households and institutions when making the decision to invest in generating energy through solar panel technologies (Klepacka et al, 2018). 1.1.1.1 Adoption of Solar Energy Technology in Kenya In Kenya, according to George, et al (2019), the Ministry of Energy and Petroleum manages energy adoption overall strategy and provides advice on the production and growth of energy sub-sector, including power, petroleum, and renewable energy. He continues to state that there still exist various challenges limiting adoption to the modern energy in Kenya. Indeed, due to some socioeconomic challenges, only 22.7 percent of households countrywide are connected to the national electricity grid (KNBS, 2019). According to Kiprop, Matsui, and Maundu (2019), renewable energy in Kenya was first introduced in the early 1970s by foreign investors. Today, the path to adopt clean and affordable energy is contained in the national strategy that aims to increase adoption to energy to all citizens by 2020 (World Bank, 2020). However, despite many challenges, there has been efforts to promote and accelerate adoption to solar energy in Kenya. For instance, to complement the last mile project in regions that do not have a grid, the Government launched the Kenya Off-grid Solar Adoption 4 Project (KOSAP) running between 2017 and 2022. The project leverages solar technology to provide electrification to 277,000 households, 1100 public facilities and community facilities (health facilities, education facilities, and administrative offices), 380 water pumps and enterprises in 14 underserved counties that collectively account for 20 per cent of the country’s population, and 72 per cent of the country’s total land area. With an estimated renewable energy generation capacity of 96 MW, the project targets to provide electricity through 120 solar hybrid mini-grids and off-grid standalone solar systems. The KOSAP is part of the Kenya National Electrification Strategy (KNES) that targets to scale up off-grid electricity adoption by undertaking 35,000 connections through 121 new mini-grids and 1.96 million connections through standalone solar home systems (Ministry of Energy, 2018a). Narok County is part of the KOSAP, which aims to provide electricity to parts of the country that are not served by the national grid (Mutuku and Mbatia, 2020). Another important strategy applied in solar energy adoption is Energy Adoption Explorer (EAE). EAE use geospatial data on demographics, socio-economic activities, energy resource availability, power infrastructure, environment, adoption to finance, and more in building more inclusive energy plans, while also accounting for critical development outcomes across Kenya. According to George et al (2019), the increased uptake of off-grid power in Kenya has been attributed to several factors, such as the availability and adoption of affordable solar panels, removal of tariffs on solar energy technologies among other government policy interventions. However, only around 1.2 percent of the households in Kenya had installed solar energy systems by the year 2019 due to various socioeconomic challenges which are yet to be explored and documented (Mburugu & Gikonyo, 2019). This study was therefore born focusing on Narok County as a case study because it is one among those categorized as energy marginalized in Kenya (Takase, Kipkoech & Essandoh, 2021; George, et al., 2019; KNBS, 2020b). Research has shown that Narok County residents have heavily relied on firewood as a source of energy for cooking (Ministry of Energy, 2018b; Mutuku and Mbatia, 2020; Ministry of Energy, 2018a). Since 2018, the various Government of Kenya intervention to increase energy adoption such as KOSAP and EAE initiatives have not born many fruits as evidenced by approximately 80 percent of the County households still depending on the usage of charcoal and firewood (Wood, 2018; KNBS, 2020b). Therefore, this study comes at the right time 5 to unravel the factors influencing adoption to solar energy technology despite the government of Kenya KOSAP, EAE, among other initiatives trying to ensure accelerated solar energy adoption among households in Narok County. 1.1.2 Socioeconomic Factors Affecting Adoption of Solar Energy Research has shown that solar energy technology adoption faces numerous challenges, with most in the Sub-Saharan Africa (SSA) rural settings (Takase, Kipkoech & Essandoh, 2021; George, et al., 2019; KNBS, 2020b; Ministry of Energy, 2018b; Mutuku and Mbatia, 2020; Ministry of Energy, 2018a). Only a few studies have attempted to highlight the specific socioeconomic factors and mostly in the developed world (e.g. Khan, 2020; Khan, 2020; Elliott and Lindley, 2017; IRENA, 2020; Bhamidipati, et al., 2021). About thirty years ago, the research community intensified its interest in the acceptability and deployment of technology in both private and organizational contexts (Guta, 2018). A sizable set of data on user traits and behavior associated with technology adoption had been produced through technology acceptance research by the year 2000 (Zeru & Guta, 2021). To comprehend the adoption of the technology, many models and theories have been developed, which collectively accounted for 40% of the variation in technology usage intention (Zeru & Guta, 2021). In this study, the four independent socio-economic variables of household income, education of household head, dwelling characteristics and demographics characteristics) were selected an mapped as applied in the original UTAUT model variables. Household income is the gain or recurring advantage that results from labour or capital and is typically quantified in money (Ekbring, 2022). Depending on its initial and ongoing expenditures, technology adoption is influenced by the amount and frequency of revenue. People weigh the costs and benefits of a suggested technology before deciding whether to embrace it (consciously or unconsciously) (Ekbring, 2022). For this study, the constructs on income will include Level and frequency of income, type and status of occupation and other competing costs. The education of household head, on the other hand, covers all disciplines and programmed categories that may be met at different stages of development, spanning the educational progression from the most fundamental to the most complex learning experience that can be both 6 formal and informal (Rothman, 2011). Informal education is that which is frequently provided outside of a formal educational setting, whereas formal education is that which is frequently provided in a classroom setting at an academic institution. Professional certifications in a variety of disciplines, including accounting, finance, marketing, human resources, and law, may also be included in the degree of education (Besley et al., 2011). Education advancement is essential for technological acceptance, according to Valero (2021), as it helps match a person's knowledge of the suggested technology and ease of its acceptance. The constructs of education in this study will include type of education, level of education and training on solar technology. According to Takase, Kipkoech, and Essandoh (2021), dwelling characteristics are one of the key factors influencing the adoption of technology since they serve as a platform for the expression of socioeconomic position. According to Mutuku and Mbatia (2020), dwelling features can be thought of as an intermediary structural factor connecting more general societal processes and impacts with a person's immediate social and physical environment. It creates a physical or social space in which social ties and healthy interpersonal relationships are nurtured and maintained while offering physical security, protection from the elements, and a key role in deciding an individual's source of identity and belonging. The three main housing features that are relevant to the adoption of technology are the material, meaningful, and geographical dimensions, according to a study on dwelling characteristics (George et al., 2019). The term "material dimension" refers to the immediate physical and structural elements of a home, such as its location, size, foundation, wall and floor structures, and roofing, that provide a protected area and amenities for preserving physical well-being. When it comes to an individual's experiences, the meaningful dimension of dwelling characteristics creates a sense of belonging and control in the home that supports ontological security, order, continuity, and meaning. This sense of personal and social identity may promote technology adoption. The spatial dimension deals with where housing is situated in relation to the facilities and services required to support the technology used (George, et al., 2019). The demographic characteristics of the household's members refers to the composition, number, and identity of members of the household as discussed by Etogo & Naidu (2022). This offers a summary of the population features and their development among household residents. The demographic context is important in determining how technology will be adopted since it gives a broad overview of the social and cultural aspects as well as the attitudes, values, and beliefs of the 7 family members regarding the adoption of new practices. For the purpose of this study, household demographic characteristics construct includes household members size, age, gender, occupation, and the household members energy use and needs. Globally, several studies have recently explored the relationships between adoption of solar energy and socioeconomic factors include: Moorthy, Patwa, and Gupta's (2019); Ba-kundukize’ et al. (2021); Irfan, Yadav and Shaw (2021); Blimpo et al. (2020), among others. They all indicate the importance of factors such as education, household composition, income, gender, residency among other socioeconomic factors in influencing adoption of solar energy technologies. Studies analyzing relationships between solar energy adoption and pure economic considerations include Thompson, Ajiboye, Oluwamide, and Oyenike (2021); Fleiß et al. (2017); and Jäger-Waldau (2020). They all demonstrate the importance of income factors and related cost-benefit considerations in adoption of solar energy technologies. Other related factors such as quality awareness, availability and adoption issues have also been recently explored by some authors such as Thompson, Ajiboye, Oluwamide, and Oyenike (2021); Sievert and Steinbuks, 2020); and Dagnachew et al. (2020), among others. 1.1.3 The Socioeconomic Status of Narok County and Adoption to Power Energy Narok County is one of the 47 counties created by the Constitution of Kenya 2010 (CoK, 2010) with the county headquarters being in Narok town, off Narok Nakuru road. The County is situated in the Great Rift Valley in the Southern part of the Country where it borders the Republic of Tanzania to the South, Kisii, Migori, Nyamira and Bomet counties to the West, Nakuru County to the North and Kajiado County to the East. Narok County covers an area of 17,933.1 square kilometers and shares an economic block with Kajiado County. Pastoralism, agricultural cultivation, tourism, trading, and other small-scale businesses are the county's main economic activity. Within the County is the renowned Maasai Mara Game Reserve, home to one of the "seven Wonders of the World"—the Great Wildebeest Migration. Residents of the county rely on a powerful ecological system for agriculture, tourism, water, and a variety of other advantages (NGEC, 2017). According to the latest census (KNBS, 2019), Narok County has a total population of 1,157,873 with equal 50% gender distribution. There are 241,125 households spread within an area of 17,932 8 square kilometers translating to 65 persons per square kilometer population density. The population is spread within the seven sub-counties as follows: Narok East (115,323), Narok North (251,862), Narok South (238,472), Narok West (195,287), Trans Mara East (111,183), Trans Mara West (245,714), and Mau Forest (32) according to the Kenya national bureau of statistic (KNBS, 2019). The annual population change of Narok County is estimated to be at 3.1% (2009-2019) which is higher than the national average of 1.9% (KNBS, 2019). Among the County’s estimated population, 49.3 per cent were male and 50.6 per cent female (KNBS, 2019). There were 9,046 (0.9%) people with impairments in the population. 33.0 percent of the population was under the age of 18, with 51.0% of them being female. There are 65 people per km2 in the County. 2.4% of the total population, 51.6% of which were women, were classified as elderly (age 65 and older). In 2019, 52.7% of the population (4 to 22 years old) was enrolled in school. Compared to the national poverty rate of 36.1% in 2015–2016, Narok County's overall poverty rate was 22.6% (KNBS, 2019). In addition, 22.4% of people lived in food poverty, and 70.8 percent of people experienced multidimensional poverty, which is defined as lacking access to information, housing, adequate food, clean water, sanitation, and hygiene, as well as education, knowledge of health and nutrition, and other basic necessities. As of 2017, the Gross County Product (GCP) of Narok County made up 2.4% of the total Gross Domestic Product (GDP). According to NGEC (2017), the GCP increased from Ksh. 92,987 million in 2013 to Ksh. 179,226 million in 2017. This is an annual average growth rate of 18.5%. 67.2% of GCP was given by the agriculture sector, while 30% and 2%, respectively, came from the service and other industries sectors (NGEC, 2017). Construction, wholesale and retail trade, transportation, and storage are all included in the services industry. With maize and wheat as the principal crops, agriculture is mostly dominated by livestock husbandry and both small- and large- scale agricultural cultivation (NGEC, 2017). Narok County is categorized as a marginalized county as nearly two-thirds of the County is classified as semi-arid (Narok DEAP 2009-2013). Communities in Narok face frequent drought occurring every four years, increasing the vulnerability of the inhabitants. (Takase, Kipkoech & Essandoh, 2021; George, et al., 2019; KNBS, 2019). Literature has also shown that residents of Narok County experience the challenge of adopting solar energy technology as most heavily relied on firewood (71%) as a source of energy for cooking (Ministry of Energy, 2018b; Mutuku and 9 Mbatia, 2020; Ministry of Energy, 2018a). Therefore, despite a 20 percent increase in households' electricity connectivity by 2019 through KOSAP and EAE initiatives, usage of charcoal and firewood is still high in the county (Wood, 2018). Therefore, accelerating solar energy technology adoption is critical in enhancing clean power in Narok County. Furthermore, the government KOSAP, EAE, among other initiatives are trying to ensure accelerated solar energy adoption among households in the marginalized areas in Kenya, Narok included. International Trade Administration (2022) reports that Kenya's power sector has grown steadily during the previous 20 years. Furthermore, Kenya possesses exceptional renewable resources, as shown by its position as one of the world's lowest cost geothermal power developers. Kenya has also made a concerted effort to widen access to the power grid, more than doubling that access from 32% of homes in 2013 to 75% of households in 2022. Rural Kenya has a 65% access rate compared to 100% in metropolitan areas. Kenya's Narok County, which is primarily a rural area, has a 65% average access rate to electricity. By the year 2022, the national electrification strategy target intended to attain universal access with a respectable level of service quality. However, the COVID-19 pandemic had a detrimental impact on the industry, as businesses reduced operations and power demand fell (International Trade Administration, 2022). 1.2. Problem Statement It has been established that studies dealing with the adoption of solar energy systems by rural households are rare and literature on the same has been scarce (Klepacka et al, 2018). In Kenya, for instance, only a few studies (EPRA, 2018; Elmer and Brix, 2014) concern themselves with solar energy advantages and disadvantages thereby losing in-depth analysis of the socio-economic aspects (Kiprop, Matsui & Maundu, 2019). Onsomu (2013) conducted a similar study which sought to examine the social-economic impacts of photovoltaic solar installation using the Borabu division in Kenya, his study seems to have been exploratory by nature, focusing on different aspects from the title and broad. The study specific objectives were to determine the impact of governmental organizations in Photovoltaic solar installation and usage; to evaluate the environmental impact of Photovoltaic solar installation and usage; to assess the climatic impact of Photovoltaic solar installation and usage, and to assess the impact of costs in the installation of PV panels in Borabu Division. These studies bring forth conceptual and contextual gaps compared to 10 the purpose that this study sought to address. Conceptually, the studies focused on advantages and disadvantages as well as the impact of installation of solar energy technologies that included environmental, climatic, and cost impact. Contextually, the scope in terms of location and time differences compared to the scope of this study were noted. The purpose of this study, therefore, is to assess the influence of socioeconomic factors; household income, education of household head, household dwelling characteristics, and household demographic characteristics affecting the adoption of solar energy technology in Narok County. The three studies (EPRA, 2018; Elmer & Brix, 2014; and Onsomu, 2013) are also very different in methodology and design aspects. Therefore, it is clear the study goes deeper and specific on socioeconomic aspects left out by previous studies hence would fill the gap in existing knowledge. The empirical literature review has shown that no similar study has been done focusing on current study’s socioeconomic factors influencing adoption of solar energy technology among a nomadic community with strong sociocultural traditions like it is in this case. Thus, leaving a research gap in this area. 1.3 Research Objectives The study was guided by general and specific objectives. 1.3.1 General Objective The main objective of the study was to examine socio-economic factors influencing adoption of solar energy technologies in Narok County because despite government and NGOs efforts, literature and empirical reviews stated earlier has also shown that residents of Narok County experience the challenge of adopting energy technology and are heavily relied on firewood as a source of energy. The study seeks to understand the factors that hinder or advance the use of solar energy technology. 1.3.2 Specific Objectives i. To determine the influence of household income in the adoption of solar energy technologies in Narok County 11 ii. To evaluate the influence of education of the household head on the adoption of solar energy technologies in Narok County iii. To determine the influence of household dwelling characteristics in the adoption of solar energy technologies in Narok County iv. To assess the influence of household demographic characteristics in the adoption of solar energy technologies in Narok County 1.3.3 Research Questions i. What is the influence of household income on the adoption of solar energy in Narok County? ii. What is the influence of education of the household head on adoption of solar energy in Narok County? iii. Do household dwelling characteristics influence the adoption of solar energy technologies in Narok County? iv. Does household demographic characteristics influence the adoption of solar energy technologies in Narok County? 1.4 Significance of the study This study focuses on assessing the influence of socioeconomic (household head income, education of household head, dwelling characteristics and household demographic characteristics) on adoption of solar energy. Reviewed previous literature is characterized by differences in theoretical, methodological, and practical applications, with some giving very contradictive findings (Bollinger and Gillingham, 2012; Müller and Rode,2013; Kiprop, Matsui, and Maundu (2019)). Theoretically, this study goes deeper and specific on socioeconomic issues (household head income, education of household head, dwelling characteristics, and household demographic characteristics) left out by previous studies hence would fill the gap in existing theoretical knowledge. Methodologically, the specific interest in Narok County is selected and sampled because it one of the counties under the government of Kenya KOSAP and EAE, initiatives that is seeking to accelerate solar energy adoption among households since 2018 but with little success so far. 12 Practically, the findings of this study are significant to several stakeholders, such as policymakers in both national and county governments, in developing frameworks and policies that will improve solar energy technologies adoption for sustained developments. These findings will be beneficial to the government by enhancing the understanding of the critical socio-economic and the extent to which each of the factors influence the adoption of solar energy technologies and further enhance structural and administrative actions. Through these findings, the government can develop appropriate strategies to close the solar energy technologies adoption gap in the rural Kenya and achieve vision 2030. Further, the findings are important for researchers and other scholars as theoretical and empirical reference material while carrying out similar studies in solar energy technology. 1.5 The scope of study The study only focused on socio-economic factors influencing adoption of solar energy in Narok County. Contextually, the study only focused on socio-economic factors including household head income, education of household head, household dwelling characteristics and household demographic characteristics influencing the adoption of solar energy in Narok County. The study adopted a descriptive research design and targeted households within Narok County. The unit of analysis for the study include households in Narok County. 13 CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This chapter presents pertinent literature review including a theoretical foundation of the study a discussion of the main solar energy socio-economic factors influencing adoption of solar energy. Further, a conceptual framework and a summary of research gaps to be addressed by the study are included in this chapter. 2.2 Theoretical Review A theoretical review is an examination of available models and theories from literature on which the conceptual framework was anchored and that subsequently informs the problem statement of the study (Mugenda and Mugenda, 2008). The study was anchored on Technology Acceptance Model (TAM) and the Unified Theory of Adoption and Use of Technology (UTAUT) was used to explain adoption of technology and the Energy Ladder and Fuel stacking hypothesis to explain the socio-economic factors that affect behavior. 2.2.1 Technology Acceptance Model In 1989, Fred Davis proposed the Technology Acceptance Model (TAM), which he later expanded with Vekantesh V (Lai, 2017). TAM has emerged as one of the most popular and reputable research models for predicting how individual users will react to and make use of information systems and technology. The model illustrates the factors influencing technology acceptance and how consumers behave and adopt new technologies across a wide range of user demographics (Lai, 2017). According to the notion, a user's actual use of a certain technology depended on their intention to do so (Lai, 2017). Perceived usefulness and perceived ease of use are the major internal characteristics of technology acceptance, according to the model. The likelihood that new technology will improve a potential user's experience is known as perceived usefulness, and it promotes the use of information technology to raise staff productivity. The degree to which potential users perceive something to be simple to use and require little effort is referred to as its ease of use (Mailu, 2019). Other external qualities, such as social, cultural, and political variables, have an impact on these two internal 14 characteristics. The user's appraisal of the technology's desirability, which results in a behavioral intention to use the application, determines whether they will utilize it. TAM has been used by academics all over the world to assess the adoption of various technology systems Shafeek (2011) used TAM to evaluate the acceptance and use of eLearning systems by teachers; Muller-Seitz et al. (2009) used TAM in the security sectors to evaluate the acceptance of the radio frequency identification (RFID); Ervasti & Helaakoski (2010) used a model founded on TAM to understand mobile service adoption; Portz et al. (2018) applied the TAM to explore a hospital patient portal user experience, intent to use, and use behavior by older adults with multiple. However, there have been some critiques of TAM, with Mathieson (1991) and Yi, Jackson, Park, and Probst (2006) making the case that human and social variables should be considered while developing the model to account for how technology is used and accepted (Lai, 2017). This model is relevant to the study as we seek to understand the adoption and intent of use of solar technology in Narok County. 2.2.2 Unified Theory of Adoption and Use of Technology (UTAUT) In "User Acceptance of Information Technology: Towards a Unified View" (Vankatesh et al., 2003), Venkatesh and colleagues developed the unified theory of acceptance and use of technology (UTAUT), a paradigm for technology adoption. The UTAUT seeks to explain users' initial technology system usage intentions and subsequent usage behavior. According to the theory, there are four main constructs: enabling factors, effort expectations, social influence, and performance expectations. The four main components' effects on usage intention and behavior are said to be moderated by factors such as gender, age, experience, and voluntariness of use. The theory was created by reviewing and combining the eight models that earlier research had used to explain how people used information systems: theory of reasoned action, technology acceptance model, motivational model, theory of planned behavior, combined theory of planned behavior and technology acceptance model, model of personal computer use, diffusion of innovations theory, and social cognitive theory. UTAUT was further validated by Venkatesh et al. (2003) in longitudinal 15 research, and they discovered that it accounted for around 50% of the variance in actual usage and 70% of the variance in behavioral intention to use (BI). Saleh, Haris and Ahmad (2014) have successfully applied the Unified Theory of Adoption and Use of Technology (UTAUT) model in their conceptualization of solar energy adoption study arguing that it is most suited for application in solar energy technological adoption studies because it unifies eight major previously used models in user technology acceptance and utilization studies. The eight models combined in UTAUT are namely the theory of reasoned action (TRA), the technology acceptance model (TAM), the theory of planned behavior (TOPB), the motivational model (MM), the innovation diffusion theory (IDT), the model of PC utilization (MPU), the social cognitive theory (SCT), and a model combining TAM and TOPB (C-TAM-TOPB). Through this combination, UTAUT sums up the main constructs from all eight models to five independent variables, which predict technology usage intention and technology usage overall behavior (Tan, 2013; Venkatesh et al., 2003). In this way, UTAUT sufficiently enabled capturing of all socioeconomic factors explain not only adoption, but also acceptance, availability, and utilization of technological innovations at individual, organizational and regional space scale (Saleh, Haris and Ahmad (2014). In this study, therefore, the four independent variables (household income, education of household head and household context that include dwelling and demographic characteristics) were well mapped into these original UTAUT variables. According to Saleh, Haris and Ahmad (2014). The UTAUT is considered the most powerful predictive model that relies on behavioral models from several acceptance theories which were developed to predict technology adoption. The key benefit is that the UTAUT offers a helpful means to measure households' behaviors towards the acceptance of a new technology to improve acceptance (Anderson et al., 2006). The UTAUT model will be therefore used in this study due to its strong theoretical foundation, comprehensiveness, and high explanatory power. Therefore, UTAUT application had a positive impact and an extremely immense contribution towards solar energy systems adoption. 2.2.3 Energy Ladder and Fuel Stacking Hypothesis Kirk Smith presented the energy ladder hypothesis to the World Health Organization (WHO) in the early 1990s, parallel to the fuel-wood issue that began to develop in the 1970s and 1980s 16 (Toole, 2015). The energy ladder was created to establish a hierarchical relationship between the type of fuel used for cooking and heating and the increase in household income. The customer typically chooses to buy more "superior" things and less "inferior" goods when wealth rises, according to consumer economic theory (Paunio, 2018). Consumer economic theory was subsequently connected to energy by researchers, who demonstrated that households behave similarly to consumers by attempting to maximize their use of energy utilities in accordance with their economic standing. Therefore, as a household's wealth increases, it begins consuming fuels that are found on higher ranks of the energy ladder and switches to cleaner and more expensive fuels (LPG, solar, and electricity), thereby moving up the ladder (Toole, 2015). The process of fully ascending the energy ladder can be described as a linear movement consisting of three distinct phases. The first phase is when a household achieves socioeconomic stability, which motivates it to give up inefficient, cheap, and polluting fuels; the second is when the household reduces its reliance on traditional fuels by switching to transition fuels like kerosene and coal; and finally, the third phase is when the household completely switches to LGP, solar, and electricity (Erdmann & Haigh, 2013). The Energy Transition refers to this transition from a less-than-optimal fuel to a more-than-optimal one. According to Paunio (2018), however, economic growth alone cannot be viewed as the primary and only driver for households to change their energy-use behavior as other significant drivers, such as: environmental and social pressure, technological advancement, resource availability, people's choices, levels of urbanization, and living standards, also play a significant role in forming these decisions. As a result, households are influenced by a complex and interactive web of influences when making decisions about replacing their fuel source. This switching process is interlaced and interrelated rather than occurring as a sequence of straightforward independent processes. As a result, the process of switching fuels is not one-way, and homes can utilize both more modern fuels for some reasons and more conventional ones for other household needs. Researchers found that the novel concept of "Fuel Stacking" has greater applicability for explaining home energy behavior after taking these factors into account (Erdmann & Haigh, 2013). According to the "Fuel-Stacking" concept, households do not completely switch to other fuel types as their income rises; instead, they employ an energy mix or as part of a menu. The households in 17 that situation use a variety of fuels to generate their energy, mixing superior fuels more than inferior ones (Erdmann & Haigh, 2013). The energy stacking opposes the idea that households should fully change the fuels they use as their wealth rises and instead proposes an alternate habit of using several fuels concurrently (Paunio, 2018). 2.3 Empirical Review The empirical review involved examination of the available and relevant empirical literature that relates to solar energy adoption and the socioeconomic factors to accelerating its adoption. Generally, existing literature clearly indicates that the energy demand of households forms a crucial component of the complete energy demand of nations, which shapes the paradigms of energy systems (Grunewald et al., 2012). The literature also points out that the socio-economic development and survival of individuals and society largely hinge on the availability of energy (Agyeman et al., 2020). Despite this assertion, majority of the world’s population does not have access to electricity (Taale & Kyeremeh, 2016), with the situation being more profound in the case of Africa where an estimated 635 million (57%) are without electricity (Metayer et al., 2015; REN21, 2019). In Kenya, only 50% have access to electricity where only 19.7% in Narok County accessing electricity. Parallel to the assertion of the lack of electricity in Africa, most households have been heavily reliant on energy sources such as cow dung, firewood, charcoal, and palm kernel among others, which have been identified to have adverse impacts on human health, and the environment because they trigger deforestation and the greenhouse emissions (Sovacool, 2012). However, the continent has a huge and abundant source of renewable energy. The capacity of the continent’s annual solar radiation ranges from 5 to 7 kWh/m2 (Brüderle, 2010). Solar PV deployment in most countries in Africa has mainly been driven by rural (off-grid) electrification (Samoita et al., 2020). According to Samoita et al. (2020), a key technical challenge for integration of PV technology is the temporal match with the demand; solar power is available during the day and there must be a viable way of harnessing it for use when the sun is not up. There also exist numerous socioeconomic challenges when it comes to solar energy deployment in rural households (Ashnani et al., 2014: Kruzner et al., 2013; Heng et al., 2020). The specific study socioeconomic variable relationships are as discussed below: 18 2.3.1 Effect of Household Income on Adoption of Solar Energy Technology Income, for the purpose of this study has been categorized as, Level and frequency of income, type and status of occupation and other competing costs. Income is one of the main factors likely to affect purchase of efficient appliances and influence energy consumption (Santin et al. (2009), Vassileva, Wallin and Dahlquist (2012) Ding et al. (2016) and Zhao et al. (2012)). In the context of the adoption of RSPVs; (Lam, (1998); Billino (2009), Ugulu (2019) and Qu, Hong & Jin (2019) found that household income levels propel the willingness to adopt solar PVs. Several researchers have since analyzed the role of income and found that it is positively and significantly related to the adoption of solar energy (Guta (2018); Legesse (2016). Another study conducted by Entele (2020) and in Ethiopia and Gyamfi et al (2015) and Abokyi et al (2018) in a Ghanaian sample found that consumers have a high willingness to pay for solar energy to generate electricity, and the tendency to pay is positively influenced by household income. These results are consistent with the study from rural Pakistan where the results show that households with high annual income can afford the solar PV system and prefer it to complement their energy needs (Ahmar et.al, 2022). While some studies have found a contrary finding that low-income households have a stronger tendency to install solar PVs (Bollinger and Gillingham, 2012; Müller and Rode,2013); the study by Vassileva et al. (2013) showed that it is more challenging to target high-income households for solar energy consumption, due to their low interest in energy efficient products and their “fear” of losing social status. Anteneh (2019) and Legesse (2016) revealed the effects of the occupation and business type on solar home system adoption. Legesse (2016) showed that consumers’ perceptions about solar PV benefits such as energy protection measures, combating climate change, and energy-saving is shaped by occupation (Komendantova and Yazdanpanah, 2017). One study found that farmers’ assumptions about perceived solar PV benefits had a major impact on their adoption in India (Kumar et al., 2020). 19 2.3.2 Effect of Education of Household Head on the Adoption of Solar Energy Technology In this study, education is looked at in three contexts: type of education, level of education and training on solar technology. A few scholars have conducted studies in developing countries as well to analyze the consumers’ level of education and its influence on their willingness to adopt solar PV. For instance, Guta (2018) investigated the determinants of household adoption of solar home system and found that education level of the head has a positive effect on solar home system adoption. Mensah and McWilson (2021), Gyamfi et al (2015) and Abokyi et al (2018) conducted studies in Ghana and found that level of education influenced adoption of solar home systems (SHSs) such that the higher the education level, the higher the adoption. Alrashoud and Tokimatsu (2019) examined considerations that may either empower or dissuade Saudi Arabian people from purchasing solar photovoltaic (PV) systems and found that education had the greatest positive effect. Ahmar et al. (2022) also found that education had the highest effect among all the demographic variables that were considered in the study. In their logistic regression study, they reported that a 1-year increase in education of the household head increases the odds ratio of PV system adoption by 1.42. Another study conducted by Entele (2020) in Ethiopia found that consumers have a high willingness to pay for solar energy to generate electricity, and the tendency to pay is positively influenced by education. Abera (2019) examined determinants of lighting Energy transitions in rural Ethiopia, who revealed level of education and adoption to modern communication technologies have a positive influence on the adoption of renewable energy resources including solar. Training delivered to households affects the adoption of solar home system. To be more confident about the innovations, training can help people towards the adoption and active usage of the technologies provided (Bizien, 2017), adds crucial value in the minds of trainees where they acquire this by performing practically the knowledge or the information they read and heard from different sources (Ali, 1997). A study conducted in Kenya found that there is a positive relationship between the individuals who had received informal or formal training on solar systems and use (Keriri, 2013). Thompson, Ajiboye, Oluwamide, and Oyenike (2021) established that adoption of solar energy in Nigeria was slow because consumers in the country were often unaware of the benefits associated with solar energy. 20 The adoption of solar home systems by households is significantly influenced by the amount of awareness and understanding (Naomi, 2014). Therefore, it is important to raise awareness and provide accurate information to help people better grasp the advantages and drawbacks of renewable technology (Rashid, 2012). The rise of the market demand for clean energy is hampered by households' lack of access to relevant knowledge about the adverse health effects linked to the inefficient combustion of solid fuels (Beyene, 2018). The adoption of solar home systems is influenced by the training provided to households. Training can assist people in adopting and actively using the supplied technologies, so they feel more confident about the advances (Bizien, 2017). Other researchers have analyzed the role of general social factors in the adoption of solar energy (e.g., Bollinger and Gillingham, 2012; Noll et al., 2014; Graziano and Gillingham, 2015; Rode and Weber, 2016). This literature concludes that learning from other adopters influences the probability that one adopts as well, hence word-of-mouth is considered an important channel for accelerated adoption. The studies also concluded that adopting solar energy may be considered as a very visible form of climate-friendly behavior, where people are more likely to go green when they see others, locally, going green (Carattini et al., 2017). Therefore, imitation is considered another plausible channel for accelerated adoption. 2.3.3 Effect of Household Dwelling Characteristics on the Adoption of Solar Energy Technologies Household dwelling characteristics that include the household capital available, household location (rural/urban), type of dwelling (permanent/semipermanent), and household energy use and needs have been shown to be positively and significantly associated with the adoption of solar energy (Ahmed et al., 2022). The place of residency, either rural of urban, and type of housing (roof, wall and floor type) have been used in a study in rural Bangladesh Asaduzzaman et al. (2010) who found that cost of the technology and adoption due to rural divide influence adoption of solar energy. Similarly, Hillerbrand and Goldammer (2018) used similar measures and established that low-income rural households were increasingly adopting or tapping solar energy to bring the desired change into their livelihoods and protect the environment. Furthermore, Thompson, Ajiboye, Oluwamide, and Oyenike (2021) used same indicators in conducting a study in rural 21 Nigeria to determine factors affecting households' preferences levels for solar energy and revealed that dwelling characteristics significantly contributed to the preference for solar energy. Similarly, Guta (2018) used same indicators when seeking to determine the factors contributing to households' adoption of solar energy in the rural Kebeles, Ethiopia. The study concluded that types of housing particularly living in tenements or huts were found to be negatively associated with adoption of solar PV. Scholars have distinguished between traditional and renewable energy sources, making purchasing decisions based on their occupation and socio-economic status (Colmenares-Quintero,2020). As a result, potential solar energy consumers were found to be more of professional caliber who appreciate green energy initiatives and act as promotors to increase residents’ understanding of the advantages of solar power use for improved air quality and lower carbon emissions (Madurai Elavarasan and Pugazhendhi, 2020). Further research carried out by Palm, Eidenskog and Luthander (2018) in Sweden found that people who are constantly aware of the benefits of solar PV and how it helps to alleviate the burden of electricity are highly motivated to adopt solar PV. According to Chamberlain (2018), people in green energy promotion occupation were more likely to adopt the solar PV. In rural Kenya, Onsomu (2013) sought to examine the social-economic impacts of photovoltaic solar installation using the Borabu division in Kenya as a case study. The specific objective of the study was to assess the influence of environmental factors, climatic factors, and cost of installation on the installation of solar energy. The findings established those existing environmental policies affected the installation of solar systems. Further, the study established that the high cost of installation and prevailing climatic conditions positively influenced installations and use of PV solar systems. However, Kiprop, Matsui, and Maundu (2019) carried out a study to determine the effect of household consumers' adoption of solar energy technology in Nairobi and Uasin Gishu Counties and revealed that income or other social-economic factors did not correlate with the adoption of renewable energy. 22 2.3.4 Effect of Household demographic characteristics on the Adoption of Solar Energy Technologies Household demographic characteristics have been shown to be significantly associated with the adoption of solar energy technology (Ahmed et al., 2022). The findings of the study show that a unit increase in the family size increases the probability of solar energy technology adoption by 2.16 percent. However, in their study of solar adoption in Seychelles in the context of 100 percent access to electricity, Etogo and Naidu (2022) did not find significance of family size, gender and age. Gender of household head was also found to be insignificant for the type of solar PV adopted (Briguglio and Formosa, 2017) Chodkowska-Miszczuk and Szyman´ska (2011) emphasizes the age of the farm head, referring to it as one of the most important influencers of solar energy adoption. The socio-demographic characteristics of farm heads were also the main subject of research by the team led by Brudermann, et al., (2013). The factors influencing the adoption of solar home systems by households were studied by Anteneh (2019). The author demonstrated that age and family size had a negative impact on willingness to pay for a solar home system, but that marriage, the gender of the household head, and the educational level of the children have beneficial effects. Additionally, he stated that it is statistically significant that families headed by women are less likely to acquire solar home systems than are households headed by men. In contrast, Guta (2018) also looked into the factors that influence whether or not a household adopts a solar home system. The results demonstrated that the adoption of solar home systems is positively influenced by the household head's age, family size, and degree of education. In addition, the author discovered that homes headed by men are less likely to install solar panels than their female counterparts. This is consistent with Partick's (2009) finding that households headed by women are more likely to install solar home systems than their male counterparts. According to Abera (2019), who looked at the factors influencing lighting energy transitions in rural Ethiopia, marriage, educational attainment, the gender of the household head, and contemporary communication technology all have a favorable impact on the uptake of renewable energy sources like solar. Family size, however, has a detrimental impact on the uptake of solar household systems. 23 2.4 Overview of Literature and Research Gaps In this chapter, the study appraised the relevant theoretical and empirical literature that formed the basis of this research work. The literature review has covered socioeconomic factors that are associated with the adoption of solar power technology in diverse contexts. While some studies have found significant and the largest effect of education on adoption of solar technology, others have found no significance. Higher household incomes have also been linked to a high adoptability of solar energy; given that higher incomes eliminate the cost barrier towards adopting solar technology. The results on the gender of the household head have shown mixed results. While limited studies exist within the Kenyan context, the focus on dwelling characteristics has been scarcely explored, with this study having a broader definition of household context. This study therefore hopes to contribute to this literature by analyzing the socioeconomic factors that determine households’ adoption of Solar Technology in Narok; an area that is largely marginalized and nomadic therefore less likely to adopt solar power technology as demonstrated by Lin and Kaewkhunok (2021). 2.4.1. Summary of Research Gap Variable Authors Purpose of Study Findings Research Gap Effect of income on adoption of solar energy adoption. Lam (1998); Billino (2009), Ugulu (2019) and Qu, Hong & Jin (2019) Factors likely to affect purchase of efficient appliances and influence energy consumption Household income levels propel the willingness to adopt solar PVs Studies were conducted in Asia and other developing economies. This study fills the geographical gap in Kenya Guta (2018) and Legesse (2016 Role of income in adoption of solar PVs Income is positively and significantly related to adoption of solar PVs The methodology was quantitative and there were little descriptive values for objectivity. This study applies a mixed methodology to cater for both objective and subjective data. Entele (2020), Gyamfi et al (2015) and Abokyi et al Income and adoption of solar technology Tendency to pay is positively influenced by household income Studies were conducted in several African economies. 24 (2018), Ahmar et.al, (2022) This study fills the geographical gap in Kenya. Bollinger and Gillingham, (2012), Müller and Rode, (2013), Vassileva et al. (2013) Factors affecting purchase of solar PVs Low-income households have a stronger tendency to install solar PVs Studies were conducted in developed economies. This study fills the geographical gap in Kenya Effect of Education on the Adoption of Solar Energy Technology Mensah and McWilson (2021), Gyamfi et al (2015) and Abokyi et al (2018) Determinants of household adoption of solar home system Education level of the head has a positive effect on solar home system adoption Studies were conducted in several African economies. This study fills the geographical gap in Kenya. Alrashoud and Tokimatsu (2019), Ahmar et al. (2022), Entele (2020) Considerations that may either empower or dissuade people from purchasing solar photovoltaic (PV) systems Consumers have a high willingness to pay for solar energy to generate electricity, and the tendency to pay is positively influenced by education Studies were conducted in several African economies. This study fills the geographical gap in Kenya. Bizien, 2017, Keriri, 2013, Thompson, Ajiboye, Oluwamide, and Oyenike (2021) Training delivered to households affects the adoption of solar home system. There is a positive relationship between the individuals who had received informal or formal training on solar systems and use The studies used a desktop review with secondary data. This study uses primary data. Effect of Household dwelling Characteristics on the Adoption of Solar Energy Technologies Ajiboye, Oluwamide, and Oyenike (2021), Hillerbrand and Goldammer (2018) Determine factors affecting households' preferences levels for solar energy Household dwelling characteristics significantly contributed to the preference for solar energy Studies were conducted in several African economies. This study fills the geographical gap in Kenya. Effect of Household demographic characteristics on the Adoption of Solar Energy Technologies Ahmed et al., 2022, Effect of family size in acceptance of solar technology A unit increase in the family size increases the probability of solar energy technology adoption by 2.16 percent The study specifically focused on general household factors. This study includes other socioeconomic factors. 25 Etogo and Naidu (2022), Briguglio and Formosa, 2017, Effect of household characteristics in adoption of solar PVs No significant relationship between of family size, gender, and age Studies were conducted in several African economies. This study fills the geographical gap in Kenya. 2.5 Conceptual framework Conceptual framework is a conjectured model that depicts the variables in the study as well as the correlation amongst the dependent, intervening (moderating) and independent variables (Kothari, 2014). This study postulates the interaction between the three socioeconomic factors (independent variables) and their influences in accelerating the adoption of solar energy technologies (Dependent variable) as discussed in section 2.3 above. The conceptual framework is as depicted below. INDEPENDENT VARIABLES DEPENDENT VARIABLE SOCIO-ECONOMIC FACTORS: Household Income • Level & Frequency of Income • Type and Status of Occupation • Competing costs Education of Household Head • Type of Education • Level of Education • Solar energy technology training Household Dwelling Characteristics • Location - Rural/Urban • Type of Dwelling – permanent/semipermanent Household Demographic Characteristics • Household size and composition • Household energy use & needs ADOPTION OF SOLAR ENERGY TECHNOLOGIES • Proportion of SET household Usage • Household length of SET Usage • Household uses of SET • Benefits and challenges of SET usage Figure 2.1: Conceptual framework (Source: Researcher, 2022) 26 2.5.1 Operationalization of Variables Fig 2.1 shows that the study considered the Dependent variable to be the adoption of solar technology. This was influenced by the independent variables are the household income level, level of education, dwelling characteristics, and the household demographic characteristics. It was expected that the variables greatly affected the adoption or lack of it in households in Narok County. The various variables under study were measured as indicated below: Table 2.1: Operationalization of Variables Objective Indicators Measurement Data Analysis To determine the influence of household income in the adoption of solar energy technologies in Narok County • Level & Frequency of Income • Type and Status of Occupation • Competing costs Qualitative & Quantitative Descriptive & correlation analysis To evaluate the influence of education of household head on the adoption of solar energy technologies in Narok County • Type of Education • Level of Education • Solar Energy Technology Training Qualitative & Quantitative Descriptive & correlation analysis To determine whether dwelling characteristics influences the adoption of solar energy technologies in Narok County • Location – Rural/Urban • Type of dwelling – permanent / semipermanent • Household Ownership Qualitative & Quantitative Descriptive & correlation analysis To determine whether household demographic characteristics influences the adoption of solar energy technologies in Narok County • Household size and composition • Household energy use and needs Qualitative & Quantitative Descriptive & correlation analysis Assessment of Adoption of solar energy technology • Proportion of SET household Usage • Household length of SET Usage • Household uses of SET • Benefits and challenges of SET usage Qualitative & Quantitative Descriptive Source: Researcher (2022) 27 CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter presents the conceptual framework and the methods that were used to collect and analyze data for this study. This section also includes the research design, the population of the study, the sampling design and sample size to be used. It further provides a step-by-step process of the data collection techniques/methods and in addition data analysis and ethical considerations. 3.2 Research Philosophy A research philosophy describes how information should be obtained, examined, and used. Epistemology (what is known to be true) and doxology (what is thought to be true) are both parts of research philosophy. Positivism and interpretivism are the two main research philosophies that have been discovered and employed in the field of social science (Kothari and Garg 2016). Positivists hold that reality is constant and that it is possible to observe and describe it objectively without affecting the occurrences under investigation. To find symmetry in and establish links between some of the constituent pieces, this entails manipulating reality by changing just one independent variable (Cooper & Schindler, 2006). On the other hand, interpretivists contend that reality can only be fully comprehended by subjective interpretation and intervention. They assert that while there may be numerous ways to interpret reality, each of these ways is a component of the scientific information they are seeking (Cooper & Schindler, 2006). The study adopted a positivist research approach. The role of a researcher under the positivist approach or paradigm is the use of a clear qualitative and quantitative approach in investigating a phenomenon (Saunders, Lewis & Thornhill, 2009). This approach requires a thorough focus and examination of facts, establishes causality, and reduce the phenomenon to simple and comprehensible elements, formulate and test hypotheses and test them to arrive at informed conclusion (Kothari, 2004). This study adopted a positivist philosophy because it used quantitative tools when measuring the research variables. 28 3.3 Research Design Research design is an overall strategy that a researcher chooses to integrate the components of the research study more coherently and logically to ensure the research problem is effectively addressed (Thompson Burdine, Thorne & Sandhu, 2021). The current study employs both descriptive and inferential statistics. The inferential statistics method involved observations as well as describing the behavior of the study subject without manipulating or influencing it. Descriptive research design describes attitudes, characteristics, values, and behavior (Mugenda & Mugenda, 2003). Therefore, in this study, the descriptive correlational design enabled describing of the various socio-economic factors influencing adoption of solar energy technology in Narok County. 3.4 Location of the Study The study was conducted in Narok County, which is situated in the Great Rift Valley in the Southern part of the Country where it borders the Republic of Tanzania to the South, Kisii, Migori, Nyamira and Bomet Counties to the West, Nakuru County to the North and Kajiado County to the East. According to the latest census, Narok County had a total population of 1,157,873 with 241,125 households spread within an area of 17,932 square kilometers translating to 65 persons per square kilometer population density (KNBS, 2019). The population is spread within the seven sub-counties as follows: Narok East (115,323), Narok North (251,862), Narok South (238,472), Narok West (195,287), Trans Mara East (111,183), Trans Mara West (245,714), and Mau Forest (32) and 30 wards, according to the Kenya national bureau of statistic (KNBS, 2019). 3.5 Target Population and Sampling Frame Kothari (2004) defines a target population as that population that the researcher wants to generalize the study's findings. Therefore, the target population of this study involved all the 241,125 households in Narok County (KNBS, 2019). The average size of each household is 4.8 individuals (KNBS, 2019). A sampling frame as defined by Sekaran & Bougie (2016) as the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled, and may 29 include individuals, households, or institutions. The sample frame of this study consisted of a list of households within Narok County. Table 3.2: Population of Study Sub County Name Total Population Males Females No. of Households Persons/SqKms NAROK EAST 115,323 58,699 56,617 25,078 56 NAROK NORTH 251,862 128,024 123,829 59,996 117 NAROK SOUTH 238,472 118,441 120,029 46,723 52 NAROK WEST 195,287 97,085 98,198 38,658 35 TRANS MARA EAST 111,183 54,545 56,637 20,506 359 TRANS MARA WEST 245,714 122,220 123,491 50,132 97 MAU FOREST 32 28 4 32 .. Total 1,157,873 579,042 578,805 241,125 Source: KNBS, 2019 3.6 Sample size and sampling Technique A sample refers to the finite segment of the population that is chosen for investigation to generalize the entire population (Ragab & Arisha, 2018). In other words, a sample is a representative subset of the entire population that is systematically investigated to make inferences concerning the same population of interest. According to Creswell (2012) sampling refers to the systematic process of selecting representative elements of the population to allow investigators to predict and make conclusions about the population using inferential statistics. Sample precision, level of significance, margin of error and confidence levels are the key reasons as to why researchers may opt to undertake sampling of populations in research investigations as opposed to undertaking a census (Blumberg, Cooper & Schindler, 2014). Sample size is defined as the total number of elements in a population of interest to an investigator that is precisely and objectively chosen to represent the characteristics of the population (Babbie, 2016). Furthermore, they observe that an optimum sample exhibits and meets the characteristics 30 and requirements of reliability, accuracy, precision, efficiency, representativeness, has acceptable confidence levels and flexibility. Random sampling was applied in this study. It involves giving all households an equal opportunity to be selected and participate in the study. (Kombo & Tromp, 2006). Following the proposed sampling design, the study selected the sample size using Slovin’s formula. Sample Size (n) = N / (1 + Ne2) where: n = Sample size of the Households to be studied N = The population of Household in Narok County which is 241,125 e = Margin of error which is 5% n = 241,125/ (1 + 241,125 (0.05)2) n = 399.3 Based on the sampling size of 400 the sampling frame was distributed proportionally across the sub counties and randomly selected as indicated in the table 3.2 below. Table 3.2 Sample Distribution Sub County Name No. of Households Distribution of Questionnaire NAROK EAST 25,078 42 NAROK NORTH 59,996 100 NAROK SOUTH 46,723 78 NAROK WEST 38,658 64 TRANS MARA EAST 20,506 34 TRANS MARA WEST 50,132 83 MAU FOREST 32 0 Total 241,125 400 3.6 Research Instruments The study collected both secondary and primary data. The primary data was collected using questionnaires bearing both open-ended and closed-ended questions to collect both quantitative and qualitative data required for analysis in the study. The use of a questionnaire also made it easy 31 to analyze data in a standardized manner. The questionnaire was divided into sections to cover all the variables in the study. Most of the questions used qualitative and quantitative options for the respondents to select agreement to the provided statements regarding various socio-economic factors influencing adoption of solar energy in the county. 3.6.1 Data collection Procedures The primary data was collected using researcher-administered questionnaires procedures because of the level of education of people in Narok County and the technical nature of issues under study. Data was collected through administration of questionnaires for the respondents. However, an introductory letter from Strathmore University and authorization from the National Commission for Science and Technology and Innovation was first obtained before undertaking the study. An introduction letter with an explanation of the purpose of the study was also attached by the researcher to enhance trust and assure privacy and observation of ethical standards throughout the study. An opening remark offering guidance to the respondents on how the questionnaire should be filled and seeking personal consents was also done. The researcher also enlisted research assistants who were thoroughly trained on the data collection procedures and ethical considerations. After gathering data, editing was conducted to ensure the accuracy of the data collection instrument. The compiled data was then coded, organized, and encoded into SPSS for analysis. 3.7 Research Quality In this study, the internal reliability and validity was determined through proper designing of the questions and adoption from previous similar studies where possible, while careful construction of the questionnaire and pilot testing was also considered (Saunders, Thornhill, et al., 2019). 3.7.1 Reliability Reliability refers to consistency, verification and stability of the data collection instrument used in the study. This means that the questionnaire is reliable if in its repeated use shows consistent and stable measures of the variables (Sekaran and Bougie, 2016). The internal consistency of the 32 distinct parts of the questionnaire are tested using Cronbach’s alpha. According to Sekaran and Bougie (2016), Cronbach’s alpha is an internal test of reliability undertaken through calculation of the averages of all possible split-half reliability coefficients. Existing literature concludes that the higher the reliability coefficients after consistent testing and verification of the research instruments, the better the reliability of the data collection tool. Bryman (2012) argues that Cronbach’s alpha values greater than or equal to 0.70 are treated as satisfactory levels of attainment of reliability test. Therefore, for the purpose of this study, all constructs with Cronbach’s alpha values greater than or equal to 0.70 were accepted. The pilot study was conducted with a sample of 15 household-heads but who were not included in the actual study. The goal of pilot testing was to improve the data collection instruments and enhance their reliability and validity, as well as provide insight on the planned data analysis techniques effectiveness as well as spotlighting the financial and human resource requirements (Doody & Doody, 2015). 3.7.2 Internal validity Validity refers to the degree to which a research data collection instrument accurately measures what it intends to measure to enhance the integrity of inferences arising to a research study (Babbie, 2016). This study utilized content validity and construct validity to test the validity of the questionnaire during the pilot testing phase. Construct validity refers to the extent to which a concept or behavior is translated into accurate operational and functioning indicators (Taherdoost, 2016). Construct validity was determined by establishing the contribution of each construct to the total variance generated in a phenomenon Kombo and Tromp (2006). Construct validity of the questionnaire was tested by Analysis of Moment structures (AMOS) version 21 software using the confirmatory factor analysis technique. This was statistically tested through convergent validity and discriminate validity, which are subsets of construct validity. This was guided in making inferences from the study results to predict the theories and hypothesis that are used to anchor the research study. Content validity refers to the extent to which questions in the data collection tool accurately reflect the concepts being measured (Rusticus, et al., 2014). The questionnaire was tested for content validity during the pilot testing stage to determine the representativeness of the variable indicators. 33 3.8 Data Analysis and Presentation Data analysis was undertaken using SPSS version 21 for multiplicity of different methods. The first method was to undertake diagnostics tests. Secondly, descriptive statistical analysis conducted in order guide in making statistical decisions using as mean and standard deviation. Tables were used to present the descriptive data. Thirdly, regression analysis was also employed to estimate the relationship existing between the dependent and independent variables. Factor analysis technique was specifically used to transform any possible set of correlated variables into observations comprising of linearly non correlated explanatory variables (Kothari & Garg, 2014). Analysis of qualitative data was specifically done through content analysis. Hsieh and Shannon (2005) defined qualitative content analysis as a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns. This research used Pearson correlation to analyze the relationship between the dependent and independent variables. The study hypothesis was then tested using the analysis of variance (ANOVA) F-test statistic to determine the goodness of fit of the model. Inferential statistics derived from multiple linear regression (MLR) analysis was utilized to predict the regress and through execution of rigorous and robust tests of statistical significance as well as ANOVA from the data collected using SPSS version 21. 3.8.1 Data analysis Model The logistic regression model was employed in determining the socioeconomic factors that affect the adoption of solar energy technology. The analysis was conducted using descriptive statistics because the feedback was majorly quantitative in nature. This study was guided by several researcher papers that had applied logistic regression in determining socio economic factors influencing adoption of solar energy technologies (Ahmar et al., 2022, Ahmed et al., 2022, Gitone 2010, Etongo and Naidu, 2022). 𝑌 = 𝛽! + 𝛽"𝑋" + 𝛽#𝑋# + 𝛽$𝑋$ + 𝛽&𝑋& + 𝜀 34 Where: Y= Represents the dependent variable (Adoption of solar Energy) 𝛽!= Constant 𝛽"............𝛽'=Represents the regression coefficients 𝑋"= Household income 𝑋#= Education of household head 𝑋$ = Household dwelling characteristics 𝑋& = Household demographic characteristics ε = represents the error term The coefficient of determination (R2) was adopted in assessing the goodness of fit of the regression model because it best reflects the study sample size and the number of explanatory variables in the model (Zikmund et al, 2013). Using SPSS, the regression model was tested on how well it fits the data. The significance of each independent variable was tested. The study used the F-test to establish the significance of the overall model at a 95% confidence level. The p-value for the F- statistic applies in determining the robustness of the model. The conclusion was based on p-value where if the p-value is less than 0.05 then it is concluded that the variable is significant and is a good predictor of the dependent variable and that the results are not based on chance. If the p-value is greater than 0.05 then the variable is not significant in explaining variations in the dependent variable. 3.10 Ethical Considerations The right code of behaviour regarding the rights of the respondents is referred to as research ethics. To collect data, the researcher had to obtain permission from Rwandan insurance firms. This was made possible by a letter from Strathmore Business School identifying the researcher as a student at the university. To avoid a breach of confidence, the researcher further said that the information gathered will not be disclosed to any unapproved parties. The responders' privacy was guaranteed. The respondents were provided with information on the nature and goals of the study to give them enough knowledge before they chose to participate. All information gathered is kept secret and handled with the highest