SOLAR ENERGY TECHNOLOGY ADOPTION AT HOUSEHOLD LEVEL

BY

GICHUHI REGINA MUKAMI

UNITED STATES INTERNATIONAL UNIVERSITY- AFRICA

SUMMER, 2016

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SOLAR ENERGY TECHNOLOGY ADOPTION AT HOUSEHOLD

LEVEL

BY

GICHUHI REGINA MUKAMI

A Research Project Report Submitted to the Chandaria School of Business in Partial

Fulfillment of the Requirement for the Degree of Masters in Business Administration

(MBA)

UNITED STATES INTERNATIONAL UNIVERSITY - AFRICA

SUMMER, 2016

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STUDENT’S DECLARATION

I, the undersigned, declare that this is my original work and has not been submitted to any

other college, institution or university other than the United States International University –

Africa in Nairobi for academic credit.

SIGNED: ______________________________ DATE: _______________________

GICHUHI REGINA MUKAMI

ID NO: 639126

This research project has been presented for examination with my approval as the appointed

supervisor.

SIGNED: ______________________________ DATE: ______________________

DR. JOSEPH NGUGI

SIGNED: ______________________________ DATE: _______________________

DEAN, CHANDARIA SCHOOL OF BUSINESS

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ABSTRACT

This study aimed to establish the level of solar energy technology adoption within Kiambu

County in Kenya. It also tried to establish what gaps exist and why, provided for the factors

influencing the solar energy technology adoption , socio economic implications of solar

energy technology and the effective strategies being used from across the globe for

increasing awareness.

The study adopted a stratified random sampling method. The reason for the choice of this

method was because the target population is divided into 12 Constituencies namely Gatundu

South, Gatundu North, Juja, Thika Town, Ruiru, Githunguri, Kiambu, Kiambu, Kabete,

Kikuyu, Limuru and Lari, whereby a sample size of 500 households was used. The study also

adopted a descriptive survey design as it allowed the researcher to describe record, analyse

and report conditions which existed. Both qualitative and quantitative approaches were used

to analyse the data collected and this approach helped to understand factors which influence

adoption of solar energy, the role of the level of household income in influencing acceptance

of solar energy use and also the role of relative advantage in solar energy adoption.

From the study findings, the researcher concluded that the people of Kiambu County have

not adopted much to Solar Energy Technology, a factor that can be attributed to the fact that

there has not been any formal or informal training on solar energy technology use which

resulted to the level of knowledge and awareness of solar energy and its use being relatively

low.The level of knowledge and awareness from the individuals who had installed solar

system in their household, had seen a solar lamp in use, had seen solar power in use, were

aware of solar technology providers and had received informal training which influenced the

adoption of the technology. The study also concludes that lack of information on financing

opportunities influenced the adoption of solar technology as it was perceived to be expensive

by most respondents.

Majority of the more educated people tended to adopt the use of solar technology and the

higher their education level the more the adopted to the use of solar energy. The level of

education is relatively low given that only a few people have received college education.

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The study concluded that the presence of substitute sources of energy that may be perceived

as cheaper and also the availability of hydro power in some of the households might have

deterred the respondents from adoption of solar technology. The study showed that there was

hardly any support provided by the County government in regard to solar energy technology

adoption. Since the majority of the respondents expressed willingness to invest in solar

energy technology, this provides a business opportunity for investor. For this to have long

term impact there is need to government intervention by way of awareness creation,

favorable financing opportunities as well incentives to attract investors. This will especially

be helpful to the Kiambu residents who appreciate that they are aware of the long-run savings

if they invested in use of solar energy technology.

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DEDICATION

This work is dedicated to my husband Simon Mwangi, my Children Steve, Silvin and Triza

whom I will have to deny sufficient quality time in order for me emerge a winner in this

project.

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ACKNOWLEDGEMENT

My deepest gratitude is to God for enabling me to undertake this project. My

acknowledgement also goes to Dr. Joseph Ngugi and Prof. Paul Katuse for their great

teaching and guidance in this undertaking. Their selfless guidance kept me motivated to

complete the project.

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TABLE OF CONTENTS

STUDENT’S DECLARATION ................................................................................................... ii

ABSTRACT .................................................................................................................................. iii

DEDICATION............................................................................................................................... v

ACKNOWLEDGEMENT ........................................................................................................... vi

TABLE OF CONTENTS ........................................................................................................... vii

LIST OF TABLES ....................................................................................................................... ix

LIST OF FIGURES ...................................................................................................................... x

LIST OF ACRONYMS ............................................................................................................... xi

CHAPTER ONE ........................................................................................................................... 1

1.0 INTRODUCTION................................................................................................................... 1

1.1 Background of the Study .......................................................................................................... 1

1.2 Problem Statement .................................................................................................................... 8

1.3 General objective of the study .................................................................................................. 9

1.4 Research Questions ................................................................................................................... 9

1.5 Significance of the Study .......................................................................................................... 9

1.5.1 Contributions of the Research ................................................................................................ 9

1.6 Scope of the Study .................................................................................................................... 9

1.7 Definition of Terms................................................................................................................. 10

CHAPTER TWO ........................................................................................................................ 11

2.0 LITERATURE REVIEW .................................................................................................... 11

2.1 Introduction ............................................................................................................................. 11

2.2 Factors Influencing Acceptance of Solar Energy ................................................................... 11

2.3 Role of Relative Advantage to Solar Energy Adoption .......................................................... 16

2.4 Role of Household Income Influence on Solar Energy Acceptance ....................................... 20

2.5 Chapter Summary ................................................................................................................... 24

CHAPTER THREE .................................................................................................................... 25

3.0 METHODOLOGY ............................................................................................................... 25

3.1 Introduction ............................................................................................................................. 25

3.2 Research Design...................................................................................................................... 25

3.3 Target Population .................................................................................................................... 25

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3.4 Sample and Sampling Procedure ............................................................................................ 25

3.5 Data Collection Method .......................................................................................................... 26

3.6 Research Procedure ................................................................................................................. 27

3.7 Data Analysis Procedure ......................................................................................................... 27

3.8 Chapter Summary ................................................................................................................... 27

CHAPTER FOUR ....................................................................................................................... 29

4.0 RESEARCH FINDINGS AND DISCUSSION ................................................................... 29

4.1 Introduction ............................................................................................................................. 29

4.2 Demographic Data .................................................................................................................. 29

4.3 Role of the Level of Household Income in influencing acceptance of solar energy

acceptance ..................................................................................................................................... 29

4.4 Factors Influencing Adoption of Solar Energy ....................................................................... 33

4.5 Role of Relative Advantage in Solar Energy Adoption .......................................................... 35

4.6 Reliability ................................................................................................................................ 37

4.7 Correlation .............................................................................................................................. 37

4.8 Regression ............................................................................................................................... 38

4.8 Chapter Summary ................................................................................................................... 40

CHAPTER FIVE ........................................................................................................................ 41

5.0 SUMMARY, CONCLUSION AND RECCOMMENDATIONS ...................................... 41

5.1 Introduction ............................................................................................................................. 41

5.2 Summary of the Study ............................................................................................................ 41

5.3 Discussions ............................................................................................................................. 42

5.4 Conclusion .............................................................................................................................. 44

5.5 Recommendations ................................................................................................................... 45

REFERENCES ............................................................................................................................ 47

APPENDICES ............................................................................................................................. 51

APPENDIX I: LETTER OF REQUEST TO CONDUCT RESEARCH ...................................... 51

APPENDIX 2: LETTER OF TRANSMITTAL ........................................................................... 52

APPENDIX 3: QUESTIONNAIRE ............................................................................................. 53

APPENDIX 4: FACTOR DESCRIPTION ................................................................................... 58

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LIST OF TABLES

Table 3.1 Sampling Frame ............................................................................................................ 26

Table 4.2 Response Rate ............................................................................................................... 29

Table 4.3 Constituency ................................................................................................................. 32

Table 4.4 Technological Innovation ............................................................................................. 33

Table 4.5 Government /Industry Factors ...................................................................................... 34

Table 4.6 Individual Factors ......................................................................................................... 34

Table 4.7 Interviewees Willingness .............................................................................................. 35

Table 4.8 Perceived Advantage .................................................................................................... 36

Table 4.9 Social Influence ............................................................................................................ 37

Table 4.10 Correlation Analysis ................................................................................................... 38

Table 4.11 Model Summary ......................................................................................................... 38

Table 4.12 Analysis of ANOVAa ................................................................................................. 39

Table 4.13 Analysis of Coefficientsa ............................................................................................ 39

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LIST OF FIGURES

Figure 2.1 A Model of Five Stages in the Innovation-Decision Process ...................................... 12

Figure 2.2: Technology Acceptance Model (TAM) ..................................................................... 15

Figure 4.1 Gender of the Respondents .......................................................................................... 30

Figure 4.2 Marital Status of the Respondents ............................................................................... 30

Figure 4.3 Age of the Respondent ................................................................................................ 31

Figure 4.4 Highest level of Education .......................................................................................... 31

Figure 4.5 National Electricity Grid ............................................................................................. 32

Figure 4.6 Factors Affecting Adoption of Innovation Technologies ............................................ 35

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LIST OF ACRONYMS

BOP Bottom of the Pyramid

CO Carbon Dioxide

COMESA Common Market for Eastern and Southern Africa

ERC Energy Regulatory Commission

FiT Feed- in Tariff

GDC Geothermal Development Company

GDP Gross Domestic Product

GWH Giga Watt Hour

IFC International Finance Corporation

KETRACO Kenya Electricity Transmission Company

KIHBS Kenya Integrated Household Budget Survey

KNBS Kenya National Bureau of Statistics

LCPP Least Cost Power Plan

LPG Liquefied petroleum gas

MoE Ministry of Energy

MW Mega Watt

PV Photovoltaic

REA Rural Electrification Authority

RET Renewable Energy Technologies

SHS Solar Home Systems

ST Solar Thermal

TWh Tetra Watt hour

UK United Kingdom

UNEP United Nations Environments program

W Watts

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CHAPTER ONE

1.0 INTRODUCTION

1.1 Background of the Study

All over the world, Energy is the lifeblood of the economy as it interacts with all other

goods and services that are critical for economies. Global energy demand has been

estimated to grow by more than one-third until 2035, with China, India and the Middle

East accounting for 60 per cent of this demand increase (Kandeh, 2012). The Green

Economy Report (GER), (UNEP, 2011) provides that global community and national

governments are faced with various challenges regarding to the energy sector such as

access to energy.

Currently 1.3 billion people – one in five globally – lack any access to electricity. Twice

that number – nearly 40 per cent of the world’s population – relies on wood, coal,

charcoal, or animal waste to cook their food. (Kandeh, 2012) Climate change and

emissions: Energy-related greenhouse gas (GHG) emissions are the main drivers of

anthropogenic climate change, exacerbating patterns of global warming and

environmental degradation. Global carbon dioxide (CO2) emissions from fossil-fuel

combustion are reported to have reached a record high of 31.6 gigatonnes (Mahat, 2014).

Health and biodiversity is the processing and use of energy resources pose significant

health challenges, pertaining to increased local air pollution, a decrease in water quality

and availability, and increased introduction of hazardous substances into the biosphere

(UNEP, 2011a). For example, the inhalation of toxic smoke from biomass combustion

can cause lung disease and is estimated to kill nearly two million people a year (Kandeh,

2012).

Adverse health effects from energy use are aggravated by increasing instances of land

degradation and deforestation, leading to a simultaneous loss of biodiversity. Energy

security on the other hand is the growth in global population and rising incomes will

increase energy demand and result in upward pressures on energy prices and growing

risks of importer dependency on a limited range of energy suppliers (Hancock, 2015).

The UN Secretary-General launched in January 2012 the “Sustainable Energy for All”

initiative. The significance of the initiative was stressed in the Rio Outcome Document

(UNEP, 2011b). The objectives of the initiative included: i) ensuring of universal access

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to modern energy services, ii) doubling of the share of renewable energy in the global

energy mix (from 15 to 30 per cent) by 2030, and iii) reducing of global projected

electricity consumption from buildings and industry (energy efficiency) by 14 per cent.

To meet the set objectives as discussed in the meeting, it was agreed that there is need for

provision of more accessible, cleaner and more efficient energy. To this end, private-

public partnership was viewed as the best approach to address the issue. The initiative

was granted a budget of approximately $ 50 billion which was mobilised from the private

sector and investors (UNEP, 2011a).

Solar energy technology can be defined as electrical power generated through the

conversion of sunlight into electricity, either directly using photovoltaic (PV) arrays, or

indirectly using concentrated solar energy technology (CSP) systems. The solar

photovoltaic (PV) technology can be placed in or near settlements, is technically easy to

operate, scalable and can be dimensioned according to shifting demands (Martin et al.,

2009).

Concentrated solar energy technology systems make use of lenses and tracking systems to

focus a large area of sunlight into a much smaller beam. Photovoltaic cells and arrays

then convert light into electric current using the photoelectric effect. Traditionally,

Photovoltaic arrays were used to power small and medium-sized applications, from the

calculator powered by a single solar cell to off-grid homes powered by a photovoltaic

array. This has continued to be the case to date. They thus offer a sustainable solution of

electrical energy where grid power is inconvenient, unreasonably expensive to connect, or

simply unavailable (Jakobsson, Fujii & Garling, 2010). However, as the cost of solar

electricity continues to reduce, solar energy technology is increasingly being used even in

grid connected situations as a way to feed low-carbon energy into the grid. A lack of

reliable lighting access limits the productivity of nearly a quarter of the world’s

population, hindering their ability to carry out basic activities at night or in the early

morning, including household chores, reading and completing schoolwork, and

conducting business. Given the slow growth of electrification, the global lighting crisis

increasingly separates those with reliable lighting from those who lack it, further leaving

a substantial proportion of the world’s population further behind (Jakobson et al., 2010).

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According to (UNEP, 2011b), Global investments in renewable energy in 2010 reached

US$211 billion representing a year-on-year increase of 32%. The increase was mainly

because of wind farm development in China and small-scale solar PV installations in

Europe. Africa achieved the largest percentage increase in investment in renewable

energy among developing regions. Total investment on the continent rose from US$750

million to US$3.6 billion, largely, because of strong performance in Egypt and Kenya

(Kshetri, 2010).

Africa accounts for a major share of the un-electrified. According to a Research

undertaken by Lighting Africa (an innovation by International Finance Corporation – IFC

and World Bank), approximately 110 million off-grid households across Africa

(encompassing 580 million individuals), more than half employ kerosene lamps as their

primary light source, with many needing several sources to fill their lighting needs. Other

non-renewable off-grid alternatives include candles, biofuels like wood, animal dung, and

crop waste, battery powered light devices, and diesel generators for the very richest

households and small businesses. These traditional lighting alternatives are typically

expensive and often both dangerous and environmentally harmful. While there is need for

such communities to join the digital world, it is impossible without serious adoption to

technology which again can only be made possible through availability of power. Solar

Lighting for the Base of the Pyramid Overview of an Emerging Market (Moore, 2012).

Australia is one of the leading has been a key player in the global solar energy technology

revolution. After World War 2, 'diggers' who had experience in engineering put their

knowledge and experience into the solar energy technology industry. From this, Australia

was able to lead global research and fund ideas from US inventors that were not

necessarily supported in their home countries. Australia's renewable energy industry was

particularly productive during the 1970s and 80s. However, in the last ten years,

Australia's influence in world solar energy technology technologies has dwindled (Lund,

2008).

Despite this decline, Australian technology and expertise have been adopted in many

nations such as Japan, Germany, China and the United States. By August 2011, more than

half a million household PV solar systems were installed across Australia, representing an

incredible uptake of solar panels over recent years, 35 times greater than it was in 2007.

Tis trend was observed in 2012 whereby a reduction of the feed-in tariffs which

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contributed to an increase in national sales of solar energy technology systems was

observed. (Mwakubo, Ikiara & Abila, 2007).

Similarly, achievement of China’s renewable energy goals both formal and informal

through 2015 and 2020 were dependent on an aggressive and successful expansion of the

electricity transmission grid. Also, the wind power sector faces adverse forces that are

likely to slow near-term growth from now into the 2014 period. With 20 GW of new

installed capacity added in 2011 and only 16 GW incrementally connected to the grid, the

pace of additions to installed capacity has exceeded the rate at which the wind power

capacity can be connected to the grid. It was estimated that by the end of 2012, there

would be an approximate GW overhang of wind power installations awaiting connection

to the grid (over and above the estimate of an amount of installed capacity awaiting

interconnection at any point in time equal to approximately 75% of total new installations

built in any year), and that an overhang was expected to continue into 2014 (Fri & Savitz,

2014).

In China, solar energy informally aims for 30–50 GW of solar PV by 2020 at the country

aims to install 30 GW of grid-connected solar energy technology by 2020, compared with

less than 1 GW currently installed. China still remain the only country with the modest

adoption of solar PV compared with wind power stems from a combination of relatively

high costs, the geographic remoteness of resource-rich regions, and a lack of transmission

to those areas. With the recent introduction of a feed-in tariff, rooftop and grid-scale solar

now have clearer policy support than traditional capital cost subsidies offered, although

distributed rooftop is slow to grow (Bazilian, 2013). Additionally, emphasis on renewable

energy is also designed to promote China’s competitiveness as a leading global supplier

of clean, low cost renewable energy technologies (Fischbein & Ajzen, 2010).

Japan has too adopted targets for the deployment of renewable energy through 2020.

These targets are sizable both in terms of total installed capacity as well as the anticipated

contribution of renewable energy to total electricity generation. A critical objective of

renewable energy development in Japan is to reduce CO2 emissions and the reliance on

imported energy by decoupling rising fossil energy use from economic growth over the

next several decades. This decoupling is expected to have a positive impact on local air

and water quality as environmental pollution is estimated to cost over 4% of GDP each

year (Karplus, Paltsev & Reilly, 2010).

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There were about 82,200 domestic micro generation and renewable energy systems by

2005 in the UK, with solar thermal water heating (STWH) accounting over 95% of them.

Although a typical flat plate or evacuated tube STWH system can provide about half of a

household’s hot water, they are still rare in Britain compared to other European countries.

Even rarer are micro- CHP, ground source heat pumps, wood-pellet stoves and boilers,

solar PV and micro-wind. India’s installed solar energy technology capacity of 15.2 MW

at the end of June 2010 was based entirely on PV technology with approximately 20% of

the capacity being used for off-grid applications. Currently, more attention is being paid

to large-scale solar PV projects. In Phase 1 of Jawaharlal Nehru National Solar Mission

India aims to install 500 MW of grid-connected solar PV power. New PV projects are

also being registered under state programs such as in Punjab, m Gujarat, West Bengal,

Rajasthan, and Karnataka, though many of these are being migrated to JNNSM. The

creation of special economic zones that provide land, water, and power as well as

financial incentives has spurred growth in the domestic manufacturing sector.

International companies from all over the world are now lining up to get a share of India’s

solar market, which is estimated will be valued at USD 70 billion through the end of

JNNSM in 2022, (Karplus, Paltsev & Reilly, 2010).

In Egypt where solar availability is quite high, the total capacity of installed photovoltaic

systems is about 4.5 MWp. These are used in remote areas for water pumping,

desalination, lighting of rural clinics, powering telecommunications, rural village

electrification, etc. Egypt has seen investment in renewable energy rise by US$800

million to just over US$1.3 billion as a result of just two deals, a 100MW solar thermal

project in Kom Ombo and a 220MW onshore wind farm in the Gulf of El Zeit (Bower &

Christensen, 2012).

Kenya has vast renewable energy resources such as solar, wind, biomass, bio-fuel,

geothermal and hydropower although their exploitation has been limited (apart from

hydropower). Expansion of the renewable energy sector is being catalysed by the growing

demand for and cost of electricity, increasing global oil and gas prices and environmental

pressure. In Kenya, biomass accounts for over 70% of total consumption, mainly through

charcoal and firewood used in cooking and lighting. The other sources are petroleum and

electricity, which account for about 22% and 9% respectively. (Mwakubo et al., 2007).

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Currently, the Kenyan energy sector is characterized by the heavy reliance on

unsustainable biomass use, frequent power outages, low access to modern energy, over

reliance on hydroelectricity and high dependence on oil imports. Renewable energy is,

therefore, an important and timely means to meet the challenges of growing demand and

addressing the related environmental concerns (UNEP, 2011a).

Kenya’s Least Cost Power Plan (LCPP) aims to identify new generation sources to enable

the national electricity supply to respond to demand, taking into account the 15% margin

required to ensure its security. In the light of frequent droughts and the increase in oil

prices, there is an emphasis on developing alternative energy resources especially

geothermal, solar, wind and coal. Since power projects take time to construct, there will

be measures to fast-track implementation of the power projects in the master plan, to

ensure adequate energy supply to meet the demand over the MTP period (Ministry of

Finance, 2011a). As evidenced by good government policy and energy planning that aim

to ensure a sustainable energy mix, Kenya’s move towards renewable energy has been

broad-based. Investment has grown from virtually zero to more than US$1.3 billion

(including funding for wind, geothermal and small hydro). Geothermal power generation

was the highlight, with the local electricity generating company, KenGen, securing debt

finance for additional units at its Olkaria project (UNEP, 2011). With the new financing

arrangement, the company is expected to add 280MW of power to the grid in the next

three years. In all this it can be seen that at the household level the adoption of solar

technology is still relatively low. There has been little government initiative in promoting

the adoption of solar energy technology for domestic use, essentially leaving the

householder on his/her own.Grid expansion is a vital objective and will be a long-term

solution for many African households, but for the majority, grid growth will take decades

and hence the urgent need to result to technology that can provide lasting solutions e.g.

use of Solar energy to power lighting, fridges, radios and mobile phones. (Rogers &

Prahalad, 2009).

Kenya has high insolation rates with an average of 5-7 peak sunshine hours (The

equivalent number of hours per day when solar irradiance averages 1,000 W/m2), and

receives an average daily insolation of 4-6kWh/m2. Only 10-14% of this energy can be

converted into electricity due to the conversion efficiency of PV modules. On 12th June

2014 Kenya introduced a VAT on solar products totaling 16% in Q3 2013, but the

government has recently decided to dismiss this tax in a move to cut cost of renewable

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energy products. Stand-alone PV systems represent the least-cost option for electrifying

homes in many rural areas, especially the sparsely populated arid and semi-arid lands.

“Solar home systems” (SHSs) are practical for providing small amounts of electricity to

households beyond distribution networks. The systems typically consist of a 10 – 50 Watt

peak (Wp) PV module and a battery sometimes coupled with a charge controller, wiring,

lights, and connections to small appliances (such as a radio, television, or mobile phones).

Other PV applications include water pumping, telecommunications and cathodic

protection for pipelines, power supply to off-grid non-commercial establishments and off-

grid small commercial establishments (Moore, 2012).

Kenya has one of the most active commercial PV system market in the developing world,

with an installed PV capacity in the range of 4 MW. An estimated 200,000 rural

households in Kenya have solar home systems and annual PV sales in Kenya are between

25,000-30,000 PV modules. In 2002, total PV sales were estimated to have been 750 kWp

and have grown by 170% in 8 yrs., even without government intervention or policies to

promote the uptake of PV technology. In comparison, the Kenya’s Rural Electrification

Fund, which costs all electricity consumers 5% of the value of their monthly electricity

consumption (currently an estimated 16 million US$ annually), is responsible for 70,000

connections. With access to loans and fee-for-service arrangements, estimates suggest

that the SHS market could reach up to 50% or more of un-electrified rural homes

(Loewenstein & Lerner, 2013).

Since 2006-2007, the Ministry of Energy has been actively promoting use of solar energy

for off grid electrification. In particular, it has funded the solar for schools programme

and is targeting to extend this to off grid clinics and dispensaries. Grid connected PV

systems covering an area of 15-20 km2 (3% of the Nairobi area) could provide 3801 GWh

of electrical energy a year, equivalent to the total grid electricity sales for Kenya in 2002-

2003. The costs, however, are prohibitive: - there are about 4 million households in rural

Kenya alone which present a vast potential for this virtually untapped technology. The off

grid market is estimated to be over 40MW.Solar Lighting for the Base of the Pyramid

Overview of an Emerging Market (Reddy, 2011).

The energy sector in Kenya is largely dominated by petroleum and electricity, with wood

fuel providing the basic energy needs of the rural communities, urban poor, and the

informal sector. An analysis of the national energy shows heavy dependency on wood

8

fuel and other biomass that account for 68% of the total energy consumption (petroleum

22%, electricity 9%, others account for 1%). Electricity access in Kenya is low despite

the government’s ambitious target to increase electricity connectivity from the current

15% to at least 65% by the year 2020 (Okello et al., 2013).

1.2 Problem Statement

Approximately one fifth of the world’s final energy production is consumed by electrical

appliances, including lighting (UNEP, 2011b). Lighting alone accounts for 19% of global

electricity demand (Kandeh, 2012). In developing countries, lighting is generally thought

to rank among the top three uses of energy, with cooking and entertainment (mainly

television) and space heating being of even greater significance (UNEP, 2011a; Kandeh,

2012). While cooking fuel choices have been examined in a number of empirical studies,

lighting fuel choices have received less attention. In addition, the adoption of renewable

energy sources is typically not placed in the context of a specific fuel choice. Yet only in

this specific context can adoption of renewable fuel switching be adequately understood

(Hancock, 2015).

In Kenya, solar household systems seem to mainly be used to a significant extent for

lighting, (Jakobson et al., 2010). Most of the Rural Population use kerosene for lighting

and charcoal or firewood for cooking which are known to have caused many health

problems due to the carbon emitted as well as burns caused by the open flames, the huge

risks of house fires and suffocation from use of these traditional fuels. Less than 44% of

the general population and 5% of the rural population in Kenya have access to electricity

(UNEP, 2011a). Demand is fast growing for electricity from both on- and off-grid

consumers. Evidence of this includes frequent blackouts due to insufficient supply and

the growing popularity of off-grid solutions such as diesel-powered generators and small-

scale hydro generation units found both in Kisii and the Mount Kenya highlands that are

largely illegal and poorly regulated energy wise, (Kshetri, 2010).

Hence, adoption of Solar Technology would provide one solution to this evident energy

gap though this tends to be neglected in most developing countries. It is thus not

surprising that in Kenya, representative data on Solar Energy use in at household level is

virtually non-existent. There has also been no evident comprehensive research on the

factors that influence adoption of solar energy. This study therefore seeks to find out the

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factors affecting the adoption of solar energy technology for domestic usage, a case study

of Kiambu County.

1.3 General objective of the study

The broad objective of this study is to investigate the determinants of solar energy

technology acceptance.

1.4 Research Questions

1.4.1 What are the factors influencing adoption of Solar energy?

1.4.2 What is the role of the level of household income in influencing acceptance of solar

energy?

1.4.3 What is the role of Relative advantage in solar energy Adoption?

1.5 Significance of the Study

The findings from this research will be beneficial not only to residents of Kiambu

County, but also to the country as a whole because it will be used by Energy practitioners,

Policy makers, Academicians as well as for future Researchers who may wish to advance

the knowledge gathered.

1.5.1 Contributions of the Research

This research set out to make contributions to knowledge as follows: i) To establish the

factors that influence adoption of solar energy ii) To establish the role of social influence

in acceptance of solar energy iii) To establish the role of relative advantage in solar

energy adoption.

1.6 Scope of the Study

This study targets only residents of Kiambu County in Kenya whose total population is

1.6m people (469,244 households) out of which only 5% has access to the national

electricity grid. This study focuses on adoption behavior of Technology by Kiambu

residents together with their intention to use Solar lanterns as an alternative source of

lighting and powering some household gadgets e.g. mobile phones, radio and TVs. The

sampled study population was asked to assess their current source of lighting, their level

of education and their Constituency within the County.

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1.7 Definition of Terms

1.7.1 Solar Technology

Solar Technology is seen as a mature and proven technology and barriers to widespread

individual households (Troncoso et al., 2013).

1.7.2 Innovation Diffusuion

Rogers (2013) describes diffusion as “the process in which an innovation is

communicated through certain channels over time among the members of a social

system”.

1.7.3 Adoption Rate

This rate of adoption is defined by Rogers (2013) as “the relative speed with which an

innovation is adopted by members of a social system”.

1.7.4 Social System

Rogers (2013) defines a social system as “a set of interrelated units that are engaged in

joint problem-solving to accomplish a common goal”.

1.7.5 Compatibility

Rogers (2013) stated that “compatibility is the degree to which an innovation is perceived

as consistent with the existing values, past experiences, and needs of potential adopters”.

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CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

This study aimed to establish the theoretical complexity and mechanisms of diffusion

process based on three main themes: innovation in developing countries, diffusion

process, and the interplay within socio-technical systems as far as solar energy adoption is

concerned. PV systems are seen as an affordable technology at a commercial level but are

incompatible with personal priorities ad unfortunately ‘compatibility’ is a basic criterion

of a consumers ‘willingness to pay’ for the technology.

2.2 Factors Influencing Acceptance of Solar Energy

2.2.1 Diffusion of Innovation Theory (IDT) and its Role in Social Influence in

Acceptance of Solar Energy

Rogers (2013) describes diffusion as “the process in which an innovation is

communicated through certain channels over time among the members of a social

system”. This subsection discusses the theoretical foundations of diffusion theory based

on four elements: innovation, communication channels, time, and social systems; as well

as a potential chasm between the early adopters and the adopting majority.

Various individuals and population groups may perceive the same innovation differently,

depending on certain characteristics. The first and perhaps most obvious attribute that

adopters seek in new technology is relative advantage: “the degree to which an innovation

is perceived as better than the idea it supersedes” (Greenhalgh et al., 2004). Emphasizes

that potential users will likely not consider the innovation if they do not see relative

advantages, which are foremost measured in economic returns. Moreover, there are

decisive social factors such as user satisfaction and prestige that influence an individual’s

perception of the relative advantage of innovations (Greenhalgh et al., 2004).

Rogers (2013) discusses four additional aspects that affect adoption: compatibility,

complexity, trialiability, and observability. Communication is the process through which

information is created, received, and shared. A main goal is to achieve mutual

understanding among the participants. The two most powerful communication channels

are mass media and the interpersonal exchange of information. The latter is more

powerful in convincing a social system to accept a new innovation the time dimension

describes how an individual passes from first exposure to the innovation until its adoption

12

or rejection. This timeframe can differ and is referred to as an innovation’s rate of

adoption this process is shown in Figure 2.1.

Figure 2.1 A Model of Five Stages in the Innovation-Decision Process

This rate of adoption is defined by Rogers (2013) as “the relative speed with which an

innovation is adopted by members of a social system”. He further defines a social system

as “a set of interrelated units that are engaged in joint problem-solving to accomplish a

common goal”. Units in a system can differ in their behavior by means of homo- and

heterophily: homophily refers to the similarity between individuals, e.g., regarding

education level, beliefs, and social status; whereas heterophily is when individuals differ

on these attributes. One distinctive challenge is that individual adopters are often

heterophilous. This makes it difficult for an actor attempting to diffuse an innovation to

choose only one single approach (Duan, 2010).

For effective diffusion, firms must adapt their strategy to the local context of a given

social system. The distinction between the different adopter groups is based on behavioral

characteristics regarding innovativeness and corresponding to normal distribution statistic

percentages. Innovators are those who want a certain product as soon as it becomes

available, are willing to take risks, and have financial capabilities. They are cosmopolites

who act and have contacts regionally and globally. Early adopters are a larger group who

also seek new products but are less sensitive to “hype” for example, they look more into

13

functionality. The early majority is the first mass of people to adopt a product, and this is

where the curve reaches maturity. The late majority adopts when the majority of the

market is already familiar with the product. Sales tend to slow during this phase (De

Groot & Steg, 2010).

Finally, the laggards adopt when the product is soon to be removed from the market.

These adopters are more price-sensitive and skeptical. Typically, the adoption process

begins relatively slowly, but once a critical mass is reached, it becomes an automatic

mechanism that forms an S-shaped curve. This critical mass is one reason that, after a

relatively slow start, the rate of adoption can form an S-shaped curve. During the

diffusion process, influence can be exerted between adopter groups. Individuals who are

influential within the social system and who spread information about an innovation are

defined as opinion leaders. They are often local and generally to be found among the

early adopters who have the highest degree of opinion leadership. According to Moore,

there can be gaps between different adopter groups. One of the most important gaps is

between the early adopters and the early majority, defined as the chasm. This occurs

when a new product or service cannot be translated into a significant benefit. Early

adopters can create bad references for the early majority (Moore, 2012).

2.2.2 Compatibility attribute towards Solar Energy adoption

Rogers (2013) stated that “compatibility is the degree to which an innovation is perceived

as consistent with the existing values, past experiences, and needs of potential adopters”.

A lack of compatibility in IT with individual needs may negatively affect the individual’s

IT use (McKenzie, 2001). In her literature review, Hoerup (2001) describes that each

innovation influences teachers’ opinions, beliefs, values, and views about teaching. If an

innovation is compatible with an individual’s needs, then uncertainty will decrease and

the rate of adoption of the innovation will increase. Thus, even naming the innovation is

an important part of compatibility. What the innovation is called should be meaningful to

the potential adopter. What the innovation means also should be clear. This is part of the

complexity attribute.

Quality light is critical and often unrecognized tool in the Community development.

There are estimated billions of people in the world who rely on inferior lighting systems

(i.e. kerosene wick lamps) and pay far more per unit than those in the developed world.

Without light, rural development is inhibited as people spend their nights “in the dark”

14

and are unable to engage in many types of evening activities that those in the developed

world take for granted (Kanagawa & Nakata, 2008).

According to Schweizer-Ries (2008), today, high quality lighting technologies are

available at affordable prices for all types of lighting systems. Solar electricity is an ideal,

cost effective power source for many lighting energy requirements. There are several

general categories of lighting each requiring different type of light and often different

type of lamps also called luminaries. Lighting needs can be divided into three categories

broadly described by the amount of light provided: The “Ambient” lighting provides a

minimum amount of illumination for people to see each other and move about; “General

lighting” provides enough illumination for reading or viewing objects; and “Task

lighting” provides bright enough light for close work and viewing detail.

2.2.3: Technology Acceptance Theory (TAM) and Perceived Usefulness in Solar

Energy Technology Acceptance

The Technology Acceptance Model (TAM) was developed from TRA (Davis & Bagozzi,

1999). This model used TRA as a theoretical basis for specifying the causal linkages

between two key beliefs: perceived usefulness and perceived ease of use and users’

attitudes, intentions and actual computer usage behavior. Behavioral intention is jointly

determined by attitude and perceived usefulness. Attitude is determined by perceived

usefulness (PU) and perceived ease of use (PEOU). TAM replaces determinants of

attitude of TRA by perceived ease of use and perceived usefulness. Generally, TAM

specifies general determinants of individual technology acceptance and therefore can be

and has been applied to explain or predict individual behaviours across a broad range of

end user computing technologies and user groups (Davis & Bagozzi, 1992).

TAM’s goal is to provide an explanation of the determinants of computer/technology

acceptance that is in general capable of explaining user behavior across a broad range of

end-user computing technologies and user populations, while at the same time being both

parsimonious and theoretically justified (Davis & Bagozzi, 1999). According to Gross

(2010), Davis introduced the Technology Acceptance Model (TAM) as an adaptation of

TRA in 1986 in his dissertation at Slone School of Management, Massachusetts Institute

of Technology (Morris & Davis, 2000). His dissertation was titled “A Technology

Acceptance Model for Empirically Testing New End-User Information Systems: Theory

and Results”. He then published “Perceived Usefulness, Perceived Ease of Use, and User

15

Acceptance of Information Technology” in MIS Quarterly in 1999 (Davis & Bagozzi,

1999).

In addition, he published “User Acceptance of Computer Technology: A comparison of

Two Theoretical Models” with (Davis & Bagozzi, 1999). Both works introduced the

original work on TAM, which has become well-established robust, powerful, and

parsimonious model for predicting user acceptance. (Venkatesh et al., 2013). Davis and

Bagozzi (1999) developed and validated better measures for predicting and explaining

use which focused on two theoretical constructs: perceived usefulness and perceived ease

of use, which were theorized to be fundamental determinants of system use. Apart from

their theoretical values, better measures for predicting and explaining system use would

have great practical value, both for vendors who would like to assess user demand for

new design ideas, and for information systems managers within user organizations who

would like to evaluate these vendor offerings (Troncoso et al., 2013).

Figure 2.2: Technology Acceptance Model (TAM) (Davis & Bagozzi, 2009)

TAM theorized that the effects of external variables such as system characteristics,

development process and training on intention to use are mediated by perceived

usefulness and perceived ease of use. Perceived usefulness is also influenced by

perceived ease of use because if other things are equal, the easier the system or

technology, the more useful it can be (Morris & Davis, 2000).

Assumptions made by TAM are that usage of a particular technology is voluntary and that

given sufficient time and knowledge about a particular behavioral activity, an individual’s

16

stated preference to perform the activity will closely resemble the way they do behave

(Davis & Bagozzi, 1999). These assumptions only apply when the behavior is under a

person’s volitional control (Ajzen & Fischbein, 2010).

TAM has strong behavioral elements as it assumes that when someone forms an intention

to act, they will be free to act without limitation. However, in the real world there are

many constraints such as limited ability, time constraints, environmental or organizational

limits, or unconscious habits which will limit the freedom to act (Davis & Bagozzi,

1992).

2.3 Role of Relative Advantage to Solar Energy Adoption

Relative advantage according to Rogers and Prahalad (2009) is the degree to which an

innovation is perceived as being better than the idea it supersedes, the degree of relative

advantage is often expressed in economic profitability but the relative advantage

dimension may be measured in other ways (e.g. social).

Rogers and Prahalad (2009) defined relative advantage as the degree to which a

technological factor is perceived as providing greater benefit for firms or individuals. It is

reasonable that firms take into consideration the advantages that stem from adopting

innovations.

Rogers and Prahalad (2009) described the innovation-diffusion process as “an uncertainty

reduction process”, and he proposes attributes of innovations that help to decrease

uncertainty about the innovation. Rogers (2013) stated that “individuals’ perceptions of

these characteristics predict the rate of adoption of innovations”. He also noted that

although there is a lot of diffusion research on the characteristics of the adopter

categories, there lacks research on the effects of the perceived characteristics of

innovations on the rate of adoption. Rogers (2013) defined the rate of adoption as “the

relative speed with which an innovation is adopted by members of a social system” such

as the number of individuals who adopted the innovation for a period of time can be

measured as the rate of adoption of the innovation. The perceived attributes of an

innovation are significant predictors of the rate of adoption. Rogers reported that 49-87%

of the variance in the rate of adoption of innovations is explained by these five attributes.

Additionally, the innovation-decision type (optional, collective, or authority),

communication channels (mass media or interpersonal channels), social system (norms or

17

network interconnectedness), and change agents may increase the predictability of the

rate of adoption of innovations. For example, personal and optional innovations usually

are adopted faster than the innovations involving an organizational or collective

innovation-decision. Roger’s, relative advantage is the strongest predictor of the rate of

adoption of an innovation.

To increase the rate of adopting innovations and to make relative advantage more

effective, direct or indirect financial payment incentives may be used to support the

individuals of a social system in adopting an innovation. Incentives are part of support

and motivation factors. Kenya has one of the most developed energy sectors in East

Africa. The Ministry of Energy coordinates the overall energy policy and provides

guidance on investment and development of the energy sub-sectors including electricity,

petroleum and renewable energy. The country’s energy policy is guided by the 2004

Sessional Paper No. 4 on Energy and by the resulting Energy Act 2006. In August 2010,

Kenya promulgated a new constitution that further promotes sustainability and the

independence of the energy sector to secure supply and protect the environment. The

energy policy and Act are being streamlined to incorporate the aspirations of the

constitution, (Schwartz, 2011). The Energy Act 2006 brought the regulations affecting all

the energy sub-sectors under one umbrella body, the Energy Regulatory Commission

(ERC). The ERC is a single-sector regulator with the responsibility of economic and

technical regulation of the electric power, renewable energy, and downstream petroleum

sub-sectors, including tariff-setting and review, licensing, enforcement, dispute settlement

and approval of power purchase and network service contracts (Wolsink, 2013).

The Act also recognizes other institutions such as the Rural Electrification Authority

(REA) to oversee the implementation of the rural electrification programme (previously

the role of the MoE) and the energy tribunal, and also created other key institutions such

as the Geothermal Development Company (GDC) to oversee geothermal exploitation,

and the Kenya Electricity Transmission Company (KETRACO) to carry out electricity

transmission in addition to the existing institutions in power generation, supply and

distribution. The new constitution provides for some regulatory functions to go to north

constituency governments in electricity and gas networks. Nevertheless, national laws and

policies supersede north constituency laws to avoid duplication (Kanagawa & Nakata,

2008).

18

Traditionally, modern sources of energy have been promoted in order to meet growing

demand. However, poverty levels and the nature of human settlements and dispersed

populations mean that these have been unable to cope with the demand for clean energy

at the household level (Gichungi, 2006). Hence, the National Energy Policy appreciates

the broad advantages of renewable energy: potential for income and employment

generation, diversification of energy supply and environmental benefits. Subsequently,

the national energy policy currently incorporates strategies for promoting the contribution

of renewable energy to electricity generation. For example section 6.3.2 of the policy

shows the government’s commitment to promote co-generation in the sugar industry and

other establishments to meet a target of 300 MW by 2015. Section 6.4.1 requires the

government to undertake pre-feasibility and feasibility studies on the potential for

Renewable Energy Technologies (RET) and for packaging and dissemination of

information on these technologies to raise investor and consumer awareness (Schweizer-

Ries, 2008).

Due to the previously low uptake of RET, the government has developed additional

policies and incentives to promote these technologies. These include Feed-in Tariffs (FiT)

to promote the adoption of solar, wind, small hydro and biomass as well as fiscal

incentives to investors in these technologies (Gichungi, 2006). For example, the import

and production of solar panels are zero tax-rated. A FiT seeks to promote the generation

of electricity from renewable energy sources. It allows power producers to sell and

obliges distributors to prioritize the purchase of renewable energy sources for generating

electricity at a fixed tariff for a fixed period of time. Kenya’s FiT policy aims to achieve

two main objectives, namely;-facilitate resource mobilization by providing investment

security and market stability for investors using renewable energy sources to generate

electricity and secondly to reduce transaction and administrative costs by eliminating the

conventional bidding processes (Schweizer-Ries, 2008).

Countries like Kenya that are located near to the equator have great potential to harness

solar energy, estimated to be 4–6KWH/M2/day. Currently, about 1.2% of households in

Kenya use solar energy technology primarily for lighting and powering television sets.

Solar energy has not yet been exploited commercially. This trend is however likely to rise

due to rising oil prices coupled with increased public awareness of the carbon emissions.

Traditionally, solar energy has been used for drying animal skins and clothes, preserving

meat, drying crops and evaporating seawater to extract salt. A lot of research is

19

continuously being undertaken to study how to exploit this huge resource. Nowadays,

solar energy is used at the household level for lighting, cooking and heating water using

medium-scale applications such as water heating in hotels and irrigation (Molin, 2015).

At the community level, solar energy is used for vaccine refrigeration, water pumping and

purification and electrification of remote rural communities. Industries use solar energy

for preheating boiler water and power generation, detoxification, municipal water heating,

telecommunications, and, more recently, transport solar cars. However, in Kenya, some

of these uses are still a distant dream. Solar energy is provided mainly through PV

systems for drying and water heating. In Kenya such systems are used mainly for

telecommunications, cathodic protection of pipelines, lighting and water pumping. Kenya

is a market leader for solar energy in Eastern Africa, largely because of availability of a

supportive policy environment (Bowen, 2008).

2.3.1. Diffusion of Innovation Theory (IDT) and its Relative Advantage to Solar

Energy Adoption

Today, information technology (IT) is universally regarded as an essential tool in

enhancing the competitiveness of the economy of a country. There is consensus that IT

has significant effects on the productivity of firms. These effects will only be realized if,

and when IT is widely spread and used. It is essential to understand the determinants of

IT adoption. Consequently it is necessary to know the theoretical models applicable while

studying information technology. There are few reviews in the literature about the

comparison of IT adoption models at the individual level. In this study, the researcher

reviewed theories for adoption models at household level as used in information systems

literature and discussed three prominent models based on three contexts namely Relative

advantage, Compatibility and Complexity. The analysis of these models took into account

the empirical literature, and the difference between independent and dependent variables

(Siegrist, 2012).

Just like any other new technology entering a developing country with an innovation

which is perceived as new within the social system is always a process rather than a

product break through, solar energy technology adoption has not been exempted. This

process is not a single event that occurs at a specific point of time, but rather, it is

necessary to transfer knowledge and skills so that the innovation is successfully adopted

Other scholars such as Schumpeter state that to achieve overall increased value, a specific

20

new innovation must be perceived as significantly better than its preceding version

(Schwartz & Howard, 1981).

An innovation is defined by Rogers as “an idea, practice, or object that is perceived as

new by an individual or other unit of adoption. When an innovation becomes accessible

to people within a social system, it can potentially quickly sweep through and take over

competing solutions. If this occurs, the innovation is labelled disruptive. Initially, a

disruptive innovation must compete with existing innovations, but eventually it is able to

capture the entire market.

2.3.2 Availability of Good Infrastructure

A lack of logistical infrastructure slows the diffusion. Promoting innovation in developing

countries, confirms that the transfer of new energy technologies is bound to certain

infrastructures. Another impediment to technology transfer and diffusion is that the

innovation can be misinterpreted or incompatible with the values of a specific targeted

system. This observation is aligned with a study of Troncoso et al. (2013) on the social

perceptions of a technological innovation that was implemented in rural Mexico. The

study showed that adoption is restricted by potential adopters’ perceptions. They further

note that businesses need an implementation strategy that targets different adopter

behaviors, as well as a long-term vision. Additionally, in a study focusing on the

assessment of bioenergy alternatives in Uganda by Okello et al. (2014) found that the

adoption rate of bioenergy technologies can potentially increase if policies can make it

more affordable.

2.4 Role of Household Income Influence on Solar Energy Acceptance

2.4.1 Low Income Markets

In low-income markets, a space can be reached in which disruptive innovations and thus

new competition can arise. According to Prahalad (2012), targeting low-income

communities in developing countries should be a key to businesses’ central mission to

create sustainable energy, products, and innovations. The fortune at the bottom of the

pyramid, revised and updated 5th anniversary edition: eradicating poverty through profits.

To be successful in these communities, companies should activate, inform, and involve

low-income populations. Co-creating a market that fulfils the needs of this segment can

help to alleviate and overcome poverty (Christensen, 2013).

21

The mirage of marketing to the bottom of the pyramid. Kandeh (2012) suggests that

reducing poverty is only possible by increasing a community’s real income, which can be

achieved by either lowering prices or increasing its disposable income. Providing access

to renewable energy may be one of the keys to raising disposable income. Multiple

scholars argue that innovations in developing countries are exposed to local barriers and

conditions, especially in the areas of energy and business modelling. Ajzen (2011) states

that in developing countries, these barriers can be low incomes, low education levels, and

bureaucratic organizational structures that hinder the successful promotion of the

innovation.

2.4.2: Technology Acceptance Theory (TAM) and Perceived Usefulness in Solar

Energy Technology Acceptance in Relation to Household Income

The Technology Acceptance Model (TAM) was developed from TRA by Davis (Davis &

Bagozzi, 1999). This model used TRA as a theoretical basis for specifying the causal

linkages between two key beliefs: perceived usefulness and perceived ease of use and

users’ attitudes, intentions and actual computer usage behavior. Behavioral intention is

jointly determined by attitude and perceived usefulness.

Attitude is determined by perceived usefulness (PU) and perceived ease of use (PEOU).

TAM replaces determinants of attitude of TRA by perceived ease of use and perceived

usefulness. Generally, TAM specifies general determinants of individual technology

acceptance and therefore can be and has been applied to explain or predict individual

behaviours across a broad range of end user computing technologies and user groups

(Davis & Bagozzi, 1999).

According to Gross, (2010), Davis introduced the Technology Acceptance Model (TAM)

as an adaptation of TRA in 1986 in his dissertation at Slone School of Management,

Massachusetts Institute of Technology (Davis & Bagozzi, 1999). His dissertation was

titled “A Technology Acceptance Model for Empirically Testing New End-User

Information Systems: Theory and Results”. He then published “Perceived Usefulness,

Perceived Ease of Use, and User Acceptance of Information Technology” in MIS

Quarterly in 1989. In addition, he published “User Acceptance of Computer Technology:

A comparison of Two Theoretical Models” with (Davis & Bagozzi, 1999). Each of these

works introduced the original work on TAM. TAM has become well-established as a

robust, powerful, and parsimonious model for predicting user acceptance.

22

Davis (1999) developed and validated better measures for predicting and explaining use

which focused on two theoretical constructs: perceived usefulness and perceived ease of

use, which were theorized to be fundamental determinants of system use. Aside from

their theoretical values, better measures for predicting and explaining system use would

have great practical value, both for vendors who would like to assess user demand for

new design ideas, and for information systems managers within user organizations who

would like to evaluate these vendor offerings (Troncoso et al., 2013).

2.4.3: Technology Acceptance Theory (TAM) and Perceived Usefulness in Solar

Energy Technology Acceptance in Relation to Household Income

Several terms are frequently used in technology acceptance research, such as

acceptability, support, adoption and attitudes. In this paper, acceptance is defined as

behavior towards energy technologies and acceptability as an attitude towards new

technologies and attitude towards possible behaviors in response to the technology (Davis

& Bagozzi, 1999). Acceptance reflects behavior that enables or promotes the use of a

technology, rather than inhibits or demotes the use of it. Support can be expressed in

proclaiming the technology for example because of its environmental benefits, or

purchasing and using the technology.

Acceptance is motivated by different goals or end-states towards which people strive.

(Lindenberg & Steg, 2010), explain that goals influence decision making: “goals govern

or ‘frame’ what people attend to, what knowledge and attitudes become cognitively most

accessible, how people evaluate various aspects of the situation, and what alternatives are

being considered.” They distinguish three important motives or goals that influence

behavior: gain, normative, and hedonic goals.

When a gain goal is focal, individuals base their choice by weighing the costs, risks and

benefits of options, and will choose options with the highest gain against the lowest costs

or risks. When normative goals are focal, individual’s base their choice on moral

evaluations, that is, on what is deemed to be the most appropriate in that situation. When

hedonic goal are focal, individuals base their decision on what feels best. When it comes

to certain technologies, individuals can thus base their acceptance on the overall

evaluation of costs, risks and benefits, moral evaluations, depending on the extent to

which the technology has a more positive or negative effect on the environment or society

23

and on positive or negative feelings related to the technology, such as feelings of

satisfaction, joy, fear or anger (Loewenstein & Lerner, 2013).

These three goals coincide with different psychological theories. The theory of planned

behavior, for example, assumes that people make rational choices, evaluating and

weighing perceived positive and negative expected outcomes, and thus focuses on gain

goals. The norm activation theory assumes that people act on feelings of moral

obligations, based on values that they endorse, and thus focus on normative goals.

Finally, theories on affect focus on the role of feelings, and thus focus on hedonic goals.

In the next sections, we briefly explain these three theories and elaborate on relations

between the three types of motives, (Ajzen, 2011).

In today's increasingly global, digital, and net-worked economy (Reddy, 2011),

information technology (IT) represents a substantial investment for most corporations and

constitutes a significant aspect of organizational work. However, its value is realized only

when information systems are utilized by their intended users in a manner that contributes

to the strategic and operational goals of the firm or household. Not surprisingly,

researchers and practitioners alike are concerned with the issue of understanding and

managing user reactions to information technologies. In response to this concern, several

theoretical models have been proposed to better understand and explain individual

attitudes and behaviors towards new IT: innovation diffusion theory (Rogers & Prahalad,

2009; Hoerup, 2001), the technology acceptance model (Davis & Bagozzi, 1999) the

theory of reasoned action (Ajzen & Fischbein, 2010) and Attitudes and the prediction of

behavior (Ajzen & Cote, 2012; Crano & Prislin, 2012). Despite differences among these

models regarding the specific constructs and relationships posited, there is some

convergence among them that individual's beliefs about or perceptions of IT have a

significant influence on usage behaviour. In general, beliefs are important not only

because they influence subsequent behaviour, but also because they are amenable to

strategic managerial manipulation through appropriate interventions such as system

design (Davis 1999) and training (Venkatesh et al., 2013).

Researchers have different opinions on the factors that affect the ERP system acceptance.

It is said that a better understanding of these factors would enable more effective

organizational interventions that lead to increased acceptance and use of systems

(Venkatesh et al., 2013). Hence, there are a number of existing literatures that explains

24

factor of influencing on technology acceptance: Agarwal and Prasad (1999) and Rogers

(2013) proposed a factor called Technological innovativeness; it describes the extent to

which a person is willing to try a new information technology; Another factor that

proposed in 2001, named User manual helpfulness which explains the extent to which a

person believes that lacking of user manuals is the reason that lead to the failure of

technology performance (Kshetri, 2010); In 2004, a new factor called “adapt to the

business processes” was introduced and appeared to illustrates that to adapt the business

processes from an end-user’s perspective depends on the extent to which the end-users’ or

organizations’ requirement are by the technology being offered (Loewenstein & Lerner,

2013); Bazilian (2013) proposed lack or poor training as a factor that affect users

acceptance, it indicates whether the amount of formal and in-formal training a user think

he or she has received is enough; and “system data quality” has also been quoted as an

influencing factor by Greenhalgh et al. (2004), as it emphasis that it is important to

achieve accurate data in order to improve the task efficiency.

2.5 Chapter Summary

The literature concerning the adoption of household solar energy technology adoption is

limited and typically paints a pessimistic picture of the potential for solar energy

technology systems. In summary, most insights on fuel choice stem from the empirical

analysis of cooking and lighting fuel choices. In addition, the determinants for the

adoption of solar energy technologies are typically examined without putting them into

the context of a particular fuel choice and often based on non-representative samples and

case studies. As lighting fuel choices and the role of lighting in energy use in developing

countries have not been investigated as thoroughly as cooking fuel choices, the researcher

focused the analysis on the fraction of household energy consumption that goes to

lighting. This investigation was important not only due to the role of lighting in

household energy use, but also as increased access to lighting is expected to contribute to

better adoption of solar technology, the achievement of the UN’s Millennium

Development Goals (Kandeh, 2012) and to Kenya’s vision 2030 (Kshetri, 2010).

25

CHAPTER THREE

3.0 METHODOLOGY

3.1 Introduction

This Chapter outlines the type of research methodology that was applied. It covers the

type of research design, sample and sampling procedure method, target population,

accessibility population and sample size. It also shows data collection procedure and

analysis, research instruments which the study adopted. It also focused on validity and

reliability of the instruments and the ethical issues.

3.2 Research Design

The research adopted a descriptive survey design. According to Kothari, (1985),

descriptive design allows the researcher to describe record, analyze and report conditions

that exist or existed. The research study used both qualitative and quantitative approaches.

The data was collected to study the factors which influence the adoption of solar

technology at household level in Kiambu County. The quantitative approach was used in

this study because it provides in depth understanding of information while the qualitative

approach provides summary information on many characteristics: Hai, Money, Samuel

and Page (2007).

3.3 Target Population

The target population was that which researcher wanted to generalize the results of the

study (Mugenda & Mugenda, (2003) .The population for the study comprised of the

households in Kiambu County. The total Population was 1.6m while the total number of

Households was 469,244. This study was concerned with the adoption of solar energy

technology adoption at household level.

3.4 Sample and Sampling Procedure

The study adopted a stratified random sampling method. The reason for the choice of this

method was because the target population is divided into 12 Constituencies namely

Gatundu South, Gatundu North, Juja, Thika Town, Ruiru, Githunguri, Kiambu, Kiambu,

Kabete, Kikuyu, Limuru and Lari. In this study, the sample size was 500 households.

3.4.1 Sample Size

The decisions about sample size should take into consideration the size of the target

population being studied and the level of accuracy one requires from the study (Fleiss,

26

1981). Hence the consideration of all the households, whether headed by women or men

of any age group.

Table 3.1 Sampling Frame

Constituency Frequency Percent %

Gatundu south 22 5

Ruiru 48 10

Kabete 13 1

Gatundu North 15 1

Githunguri 35 7

Kikuyu 44 9

Juja 31 7

Kiambu 71 15

Limuru 63 14

Thika town 20 4

Kiambaa 48 10

Lari 90 18

Total 500 100

Majority of the respondents 18% were from Lari

3.5 Data Collection Method

A self-administered questionnaire was used as a data collection instrument. It comprised

of both open ended and closed ended questions. The use of questionnaire was to enable

the respondents to remain anonymous and be honest in their responses (Cooper &

Schdler, 2003). The choice of the questionnaire was based on the fact that it is easy to

analyze the collected data statistically. Also, it is not biased and the responses were

gathered in a standardized manner and thus would be more objective in their results.

Focused interview were used to explore and understand the beliefs and education levels

and subsequently availability of financing for solar technology adoption. The data was

non numerical to a great extent and allowed the interviewee to talk freely thus generating

a discussion that was valuable insights into the factors influencing Solar technology

adoption. The questionnaire as divided into sections that examined the different

dependent variables and different independent variables which assisted in discovery of

27

what the real factors are which influence the adoption or lack of adoption of the

technology by the residents of Kiambu County.

3.6 Research Procedure

Validity is the degree to which an instrument measures what is supposed to measure

(Kothari, 1998). It is the degree to which the results obtained from the analysis of the data

actually represent the phenomenon under study. The validity was enhanced through

appraisal of the tools and verification by the study supervisor who is an expert. The

questionnaire was also subjected to pre-test to detect any deficiencies in it and appropriate

improvements made.

Mugemda and Mugenda (2003), defines realibility as a measure of a research instrument

which yields consistent results or data after repeated trials. According to Joppe (2002),

reliability is the extent to which results are consistent over a period of time. To test

reliability, a test re-test method was employed to the same categories of respondents after

a period of three days to examine the consistency of responses between the two tests in a

pilot study. The test retest sample comprised of 7% of the intended sample. This was

done in the neighboring County of Nyandarua South where 10% of the intended sample

was submitted to the instrument. This was a sample of 50 households selected randomly.

3.7 Data Analysis Procedure

Data analysis comprises of categorizing, tabulating or otherwise recombine the evidence

to address the initial prepositions of the study (Yin, 1994). The data collected was cleaned

and coded. This was to enhance basic statistical analysis. The data involved quantitative

and qualitative (numerical and descriptive). Qualitative data was analyzed based on

content analysis while quantitative data was analyzed using descriptive and inferential

statistics. Data was analyzed with the help of electronic spreadsheet SPSS Program which

has analysis tools. The data collected is presented using statistical techniques which

included percentages and frequency distribution tables

3.8 Chapter Summary

This Chapter presents a detailed look at the research methodology whereby a descriptive

design was adopted. This allowed an in depth study into the way of life of the intended

target population. The methodology allowed recording, analysis and the ability to get a

wholesome picture of the knowledge, education level and the general attitude of the

28

Kiambu residents towards Solar Technology adoption for their household use. The target

population comprised of the heads of the households as they were considered to be the

decision makers in most households. They determine what technology will be used for the

different energy uses in their households.

29

CHAPTER FOUR

4.0 RESEARCH FINDINGS AND DISCUSSION

4.1 Introduction

The purpose for this study was to establish the level of Technology Adoption of clean

energy within Kiambu County in Kenya. This chapter presents the data analysis results,

interpretation and presentation.

Table 4.1 Cronbach’s Alpha Results

Construct Cronbach’s Alpha

Technological Innovation 0.785

Government factors 0.789

Individual factors 0.614

Willingness 0.746

Relative advantage 0.649

Social influence 0.689

4.2 Demographic Data

The response rate of the respondent is presented in table 4.1. From table, 500 self-

administered questionnaires were distributed to the sampled respondents, 461

questionnaires were returned and this represented a 92% response rate which was

sufficient to proceed with the data analysis. The higher response rate is attributed to the

fact that the researcher personally administered the questionnaires to the respondents.

Table 4.2 Response Rate

Category Frequency Percentage

Responded 461 92

Did not Respond 39 8

Total 500 100

4.3 Role of the Level of Household Income in influencing acceptance of solar energy

acceptance

4.3.1 Gender of the Respondent

The respondents were asked to indicate their gender, marital status, age group, if they

were the house head. Figure 4.1 shows that 65% of the respondents were male while 35%

30

of the respondents were females. This implies that there were more male respondents than

female. This might be so because most homes are dominated by males as the households.

Figure 4.1 Gender of the Respondents

4.3.2 Marital Status of the Households

Respondents were asked to give their marital status, their responses are indicted in figure

4.2. 52% of the respondents were married, 43% of the household head were single, 2%

were divorced, 2% were separated and 1% was widower. This implies that that most of

those who responded were single.

Figure 4.2 Marital Status of the Respondents

4.3.3 Age of the Household Head

The research sought to find out the age of the respondents. Their responses are

highlighted in figure 4.3. 39% of the respondents were aged between 26-35 years, 31% of

the household heads were less than 25 years, 14% of the respondents were aged between

36-45 years, 11% were aged between 46-55 years and 5 % were aged above 56 years.

31

This indicates that the largest population was the youths and as a result they were able to

understand issues related to solar technology.

Figure 4.3 Age of the Respondent

4.3.4 Highest Level of Education

The study sought to find out the respondent’s level of Education, Figure 4.4 shows that

65% of the respondents had certificate qualification, 25% of them had diplomas, 8% had

degrees 1% of the respondents had post graduate diploma and 1% had masters’

qualification. This Implies that majority of the respondents were knowledgeable.

Figure 4.4 Highest level of Education

4.3.5 National Electricity Grid

The study sought to find out houses of the respondents connected to the national

electricity grid. Figure 4.5 indicates that 80% of the houses were connected to electricity,

14% were not connected, 3% had connection in progress and another 3% had no hopes of

32

accessing electricity power soon. This implies that majority of the houses had been

connected to the national grid.

Figure 4.5 National Electricity Grid

4.3.6 Constituency

Table 4.3 Constituency

Constituency Frequency Percent %

Gatundu south 21 5

Ruiru 45 10

Kabete 5 1

Gatundu North 7 1

Githunguri 34 7

Kikuyu 41 9

Juja 30 7

Kiambu 69 15

Limuru 63 14

Thika town 19 4

Kiambaa 45 10

Lari 82 18

Total 461 100

Table 4.3 shows that 5% of the respondents were from Gatundu South, 10% were from

Ruiru, 1% were from Kabete, 1% were from Gatundu North, 7% were from Githunguri,

33

9% were from Kikuyu, another 7% were from Juja, 15% were from Kiambu, 14% were

from Limuru, 4% were from Thika Town, 10% were from Kiambaa, and 18% were from

Lari. The table shows that majority of the respondents were from Lari.

4.4 Factors Influencing Adoption of Solar Energy

4.4.1 Technological Innovation

The study sought to find out how technological Innovation influences the adoption of

solar energy. Table 4.4 shows that 49% of the respondents agreed that solar energy

technology was fully compatible with their household needs, 53% of the respondents

agreed that solar technology was easy to use, 48% agreed that positive results of using

solar energy technology are very clear .47% of the household head agreed that it was

more advantageous to use solar energy than kerosene and firewood for lighting, 37%

disagreed that Technical support for solar energy technology was easily available, 50%

agreed that solar energy technology was user friendly, 53% agreed that solar energy was

secure ,46% agreed that the use of solar technology was cost effective.

Table 4.4 Technological Innovation

SD D N A SA

A1 19(4%) 33(7%) 43(9%) 222(49%) 139(30%)

A2 9(2%) 21(5%) 37(8%) 242(53%) 147(32%)

A3 8(2%) 26(6%) 64(14%) 219(48%) 139(30%)

A4 5(1%) 15(3%) 19(4%) 213(47%) 204(45%)

A5 79(17%) 169(37%) 79(17%) 83(18%) 46(10%)

A6 7(2%) 15(3%) 48(11%) 226(50%) 160(35%)

A7 3(1%) 12(3%) 49(11%) 241(53%) 151(33%)

A8 13(3%) 31(7%) 57(13%) 208(46%) 147(32%)

4.4.2 Government /Industry Factors

The study sought to find out how government /individual factors influence adoption of

solar energy. Table 4.5 shows that majority of the respondents 54% strongly disagree that

in their county, the administration encouraged people to adopt the use of solar energy

technology, majority (42%) strongly disagreed that the government encouraged investors

to invest in solar energy technology. Majority (49%) disagreed that there are incentives

for the people who adopt use of solar energy technology, majority (34%) of the

34

respondents strongly disagreed that faulty solar energy were quickly replaced once

reported .

Table 4.5 Government /Industry Factors

SD D N A SA

B9 245(54%) 118(26%) 31(7%) 48(11%) 14(3%)

B10 193(42%) 136(30%) 49(11%) 57(13%) 21(5%)

B11 225(49%) 134(29%) 45(10%) 40(9%) 12(3%)

B12 156(34%) 120(26%) 122(27%) 33(7%) 25(5%)

4.4.3 Individual Factors

The study sought to find out how individual factors influence adoption of solar energy,

table 4.6 shows that majority of the respondents (54%) agreed that they consider Solar

energy technology to be very useful , majority (53%) disagree that they are sufficiently

trained on how to use solar energy technology , majority (46%) disagreed that their

friends encourage them to use solar energy technology , majority (52%) disagreed that

most of the families in their neighborhood use solar energy technology, majority (38%)

strongly agreed that they prefer use of solar energy technology more than the use of

kerosene and firewood for lighting.

Table 4.6 Individual Factors

SD D N A SA

C13 12(3%) 25(5%) 37(8%) 248(54%) 134(29%)

C14 90(20%) 241(53%) 40(9%) 47(10%) 38(8%)

C15 47(10%) 210(46%) 51(11%) 111(24%) 37(8%)

C16 76(17%) 238(52%) 45(10%) 67(15%) 30(7%)

C17 20(4%) 20(4%) 28(6%) 215(47%) 173(38%)

4.4.4 Factors Affecting Adoption of Innovation Technologies

The respondents were asked to give their opinions on factors that affect the adoption of

innovation technologies in their area, majority of the respondents (33%) felt that they

have electricity and did not see the need of Solar products, 32% said that it was lack of

information, 30% highlighted lack of funds /finances, 3% of the respondents thought that

it was lack of government support and 2% said that they already had solar products as

indicated in figure 4.6.

35

Figure 4.6 Factors Affecting Adoption of Innovation Technologies

4.5 Role of Relative Advantage in Solar Energy Adoption

4.5.1 Interviewees Willingness

The study sought to find out the interviewees willingness to adopt to solar technology

adoption. Table 4.7 shows that Majority (37%) of the respondents agreed that they were

fully aware of how climate change affects them, majority (48%) agreed that they knew a

variety of renewable energy sources , majority (41%) disagreed that there had been a lot

of public awareness created on solar energy technology in their region, majority (33%)

agreed that it was their responsibility to take interest in the use of solar energy , majority

(49%) agreed that solar energy was quite important to them , majority (48%) agreed that

they would very much like to use solar technology in their household.

Table 4.7 Interviewees Willingness

SD D N A SA

W1 13(3%) 98(21%) 98(21%) 170(37%) 77(17%)

W2 22(5%) 122(27%) 53(12%) 218(48%) 41(9%)

W3 160(35%) 187(41%) 41(9%) 51(11%) 17(4%)

W4 74(16%) 116(25%) 66(14%) 150(33%) 50(11%)

W5 11(2%) 39(9%) 55(12%) 225(49%) 126(28%)

W6 11(2%) 38(8%) 48(11%) 217(48%) 142(31%)

36

4.5.2 Interviewees Perceived Advantage for Solar Energy Technology Adoption

The research sought to find out how interviewee’s perceived Advantages in solar energy

technology adoption. Table 4.8 indicates that majority of the respondents (45%) agreed

that they thought that solar technology was affordable. Majority (47%) disagrees that they

knew how they would get help on solar energy, majority (47%) agreed that they are

willing to invest money and obtain some solar energy for their household. Majority (52%)

thought by adopting the use of solar energy , they would be saving a lot of money in the

long run , majority (54%) agreed that they were fully aware of the advantages of adopting

the use of Solar technology for their household use. Majority (49%) agreed that adoption

of solar energy technology could quickly improve the general security of their area.

Table 4.8 Perceived Advantage

SD D N A SA

RA7 48(11%) 62(14%) 78(17%) 204(45%) 64(14%)

RA8 71(16%) 215(47%) 61(13%) 72(16%) 37(8%)

RA9 28(6%) 57(13%) 66(14%) 213(47%) 92(20%)

RA10 8(2%) 18(4%) 42(9%) 235(52%) 153(34%)

RA11 10(2%) 34(7%) 42(9%) 235(52%) 135(30%)

RA12 5(1%) 16(4%) 24(5%) 245(54%) 166(36%)

RA13 10(2%) 32(7%) 35(8%) 222(49%) 157(34%)

4.5.3 Role of Social Influence in Adoption of Solar Energy Adoption

The study sought to find out the social influence on solar energy technology adoption.

Table 4.9 indicates that majority of the respondents (38%) disagreed that their peers think

that they should use solar energy in their household, majority (34%) agreed that their

family was very much interested in using solar energy technology , majority (39%)

disagreed that their friends thought that they should adopt the use of Solar technology.

Majority (35%) disagreed that they knew where they could source for financial support to

enable them access solar energy technology, majority (35%) disagreed that they knew

they could easily finance the purchasing of solar technology for their household use.

Majority (43%) strongly disagreed that the local government was willing to provide

support to people who were willing to adopt the use of solar energy technology.

37

Table 4.9 Social Influence

SD D N A SA

SI14 29(6%) 173(38%) 74(16%) 133(29%) 47(10%)

SI15 9(2%) 103(23%) 79(17%) 155(34%) 110(24%)

SI16 27(6%) 179(39%) 78(17%) 127(28%) 45(10%)

SI17 80(18%) 160(35%) 89(20%) 100(22%) 27(6%)

SI18 52(11%) 159(35%) 101(22%) 98(21%) 46(10%)

SI19 197(43%) 153(34%) 51(11%) 33(7%) 22(5%)

4.6 Reliability

Cronbach’s alpha type of reliability co-efficient value of .60 or higher is considered as

usually sufficient. The results in the tables below show Cronbach’s alpha of the

constructs was above 0.6 and most of implying that the instruments were sufficiently

reliable for measurement. As most item total correlations were reasonably high, the

construct validity of the instruments was considered reasonable. However a few items had

0.0 correlation (no correlation) and very low standard deviation implying that the sub-

variables were not valid and therefore omitted.

4.7 Correlation

The study sought to find out the correlation between adoption of solar power, government

factors, individual factors and technological factors .The results are indicated in table 4.7.

Technological Innovation was found to be positively related to adoption of solar energy

technology since the correlation coefficient between the two was significantly different

from zero (r = 0.553, p-value = 0.000) at 0.01 levels of significance. Individual factors

were found to be positively related to adoption of solar energy technology since the

correlation coefficient between the two was significantly different from zero (r = 0.489,

p-value = 0.000) at 0.01 levels of significance. Government factors were found to be

positively related to adoption of solar energy technology since the correlation coefficient

between the two was significantly different from zero (r = 0.108, p-value = 0.000) at 0.05

levels of significance.

38

Table 4.10 Correlation Analysis

adoption Government Individual Tech

Adoption

Pearson

Correlation

1 .108* .489** .553**

Sig. (2-tailed) .021 .000 .000

N 452 452 452 452

Government

Pearson

Correlation

.108* 1 .354** -.189**

Sig. (2-tailed) .021 .000 .000

N 452 452 452 452

Individual

Pearson

Correlation

.489** .354** 1 .398**

Sig. (2-tailed) .000 .000 .000

N 452 452 452 452

Tech

Pearson

Correlation

.553** -.189** .398** 1

Sig. (2-tailed) .000 .000 .000

N 452 452 452 452

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

4.8 Regression

Table 4.11 Model Summary

Mode

l

R R Square Adjusted R

Square

Std. Error of

the Estimate

1 .632a .399 .395 .76574

a. Predictors: (Constant), Tech, Government, Individual

b. Dependent Variable: adoption

The model analysis of regression is shown in the table above. Regression indicates the

strength of the relationship between the independent variables (Technological innovation,

Government factors and Individual factors) and the dependent variable (solar energy

technology adoption). The R square value in this case is 0.399 which clearly suggests that

39

there is a strong relationship between solar energy technology adoption and

Technological innovation, Government factors and Individual factors. This indicates that

the Technological innovation, Government factors and Individual factors share a variation

of 39.9 % of solar energy technology adoption.

Table 4.12 Analysis of ANOVAa

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regression 174.687 3 58.229 99.307 .000b

Residual 262.686 448 .586

Total 437.372 451

a. Dependent Variable: adoption

b. Predictors: (Constant), Tech, Government, Individual

The table indicates F-test (F= 99.307, p-value (sig)=0.000) is significant and therefore the

entire model fits well.

Table 4.13 Analysis of Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -4.021 .247 -16.256 .000

Individual .507 .086 .269 5.917 .000

Government .114 .048 .101 2.378 .018

Tech .853 .079 .465 10.757 .000

a. Dependent Variable: adoption

The established multiple linear regression equation becomes

For the constant, if all the independent variables are held constant then the solar energy

technology adoption will reduce by 4.021. The coefficient of the constant is significantly

since (p-value=0.000<0.05 level of significance).

40

The regression coefficient of technological innovation is 0.853 with a t-value =10.757 (p-

value=0.000<0.05 level of significance) .This shows that one unit change in technology

adoption results in 0.853 unit increase in solar energy technology adoption.

The regression coefficient of Government influence is 0.114 with a t-value =2.378 (p-

value=0.018<0.05 level of significance) .This shows that one unit change in Government

influence results in 0.114 unit increase in solar energy technology adoption.

The regression coefficient of individual influence is 0.507 with a t-value =5.917

(pvalue=0.000<0.05 level of significance) .This shows that one unit change in individual

influence results in 0.507 unit increase in solar energy technology adoption.

4.8 Chapter Summary

This Chapter provides for a detailed study data analysis and interpretation, of the

information gathered from the 12 Constituencies in Kiambu County and the how the

various factors affecting the rate of solar adoption have interplayed.

41

CHAPTER FIVE

5.0 SUMMARY, CONCLUSION AND RECCOMMENDATIONS

5.1 Introduction

This chapter presents the summary of findings, discussions and conclusions drawn from

the findings and recommendations made. The conclusions and recommendations drawn

were focused on addressing the purpose of the study which was to investigate the factors

influencing Solar Technology adoption in Kiambu County.

5.2 Summary of the Study

This study aimed to establish the level of solar energy technology adoption within

Kiambu County in Kenya. It also tried to establish what gaps exist and why, provided for

the factors influencing the solar energy technology adoption , socio economic

implications of solar energy technology and the effective strategies being used from

across the globe for increasing awareness.

The study adopted a stratified random sampling method. The reason for the choice of this

method was because the target population is divided into 12 Constituencies namely

Gatundu South, Gatundu North, Juja, Thika Town, Ruiru, Githunguri, Kiambu, Kiambu,

Kabete, Kikuyu, Limuru and Lari, whereby a sample size of 500 households was used.

The study also adopted a descriptive survey design as it allowed the researcher to describe

record, analyse and report conditions which existed. Both qualitative and quantitative

approaches were used to analyse the data collected and this approach helped to

understand factors which influence adoption of solar energy, the role of the level of

household income in influencing acceptance of solar energy use and also the role of

relative advantage in solar energy adoption.

From the study findings, the researcher concluded that the people of Kiambu County have

not adopted much to Solar Energy Technology, a factor that can be attributed to the fact

that there has not been any formal or informal training on solar energy technology use

which resulted to the level of knowledge and awareness of solar energy and its use being

relatively low.

The level of knowledge and awareness from the individuals who had installed solar

system in their household, had seen a solar lamp in use, had seen solar power in use, were

42

aware of solar technology providers and had received informal training which influenced

the adoption of the technology. The study also concludes that lack of information on

financing opportunities influenced the adoption of solar technology as it was perceived to

be expensive by most respondents.

Majority of the more educated people tended to adopt the use of solar technology and the

higher their education level the more the adopted to the use of solar energy. The level of

education is relatively low given that only a few people have received college education.

The study concluded that the presence of substitute sources of energy that may be

perceived as cheaper and also the availability of hydro power in some of the households

might have deterred the respondents from adoption of solar technology.

The study showed that there was hardly any support provided by the County government

in regard to solar energy technology adoption. Since the majority of the respondents

expressed willingness to invest in solar energy technology, this provides a business

opportunity for investor. For this to have long term impact there is need to government

intervention by way of awareness creation, favorable financing opportunities as well

incentives to attract investors. This will especially be helpful to the Kiambu residents who

appreciate that they are aware of the long-run savings if they invested in use of solar

energy technology.

5.3 Discussions

5.3.1: The study sought to establish the factors influencing Solar Energy Adoption.

The study sought to investigate factors which influence adoption of solar technology in

Kiambu County. 54% agreed that solar energy is very useful. 53% disagreed that there

was sufficient training on the use of solar energy use in the county. 46% disagreed that

friends were a source of encouragement in the solar energy technology adoption. 52%

with the argument that their neighborhood used Solar energy Technology. 38% strongly

agreed that they prefer use of solar energy technology more than use of kerosene and

firewood for lighting. Individual factors were found to be positively related to the

adoption of solar energy technology.

The study sought to the level of support granted by the local government in solar

technology adoption .54% strongly disagreed that the government encouraged the use of

43

solar energy in the County. 42% strongly disagreed that the government encourages

investors interested in solar energy technology. 49% disagreed there were incentives for

people to adopt solar energy technology. 34% strongly disagreed that faulty solar energy

lanterns were easily replaced in the County. Government support was found to be

positively related to solar energy adoption.

5.3.2 Role of the level of household income in influencing acceptance of solar energy

The study sought to establish the respondents’ level of household income and how it

influenced their willingness to adopt use of solar energy technology. 37% agreed that they

were fully aware of their influence of Climactic change. 48% were knowledgeable on

variety of renewable energy sources available in Kenya. 41% disagreed that there had

been a lot of public awareness on solar energy technology in their region. 33% agreed that

it was their responsibility to take interest in the use of solar energy. 49% agreed that solar

energy technology was important to them. 48% expressed interest to have solar energy

technology in their households.

5.3.3 Role of Relative advantage in solar energy adoption

The study sought to determine interviewees’ perceived relative advantage in solar energy

adoption. 45% felt that solar energy technology is affordable. 47% disagreed that they

aware of how they would get guidance on solar energy technology. 47% expressed

willingness to invest in solar energy technology for their households. 52% agreed that use

of solar energy technology results in long-run savings. 52% agreed that they were fully

aware of the advantages of adopting use of solar energy technology. 54% agreed that

solar energy technology was safe to use. 49% felt that adoption of solar energy

technology quickly improves the general security of their area.

The study sought to establish the social influence on solar energy technology adoption.

38% disagreed that their peers were of the opinion that they should adopt use of solar

energy technology. 34% agreed that their families would be interested in using solar

energy technology. 39% disagreed that their friends would expect them to adopt use of

solar energy technology. 35% disagreed that they were aware of where to source for

financial support to purchase soar energy technology. 35% disagreed that they can easily

finance the purchasing of solar energy technology. 43% strongly disagreed that the local

government would be willing to support constituents who may be willing to adopt use of

solar energy technology.

44

5.4 Conclusion

5.4.1 Factors Influencing Adoption of Solar Energy

The study showed that solar energy was very useful and there was sufficient training on

the use of solar energy use in Kiambu County. The study showed that friends were not a

source of encouragement in the solar energy technology adoption. The study revealed that

most neighbourhoods used solar energy Technology and preferred the use of solar energy

technology more than use of kerosene and firewood for lighting. Individual factors were

found to be positively related to the adoption of solar energy technology. The study

concludes that the level of support granted by the local government in solar technology

adoption and it encouraged the use of solar energy in the County.

5.4.2 Role of the Level of Household Income in Influencing Acceptance of Solar

Energy

The study established that the level of household income influenced their willingness to

adopt the use of solar energy technology. The study showed that households were fully

aware of their influence of Climactic change and were knowledgeable on variety of

renewable energy sources available in Kenya. The study concludes that there existed a lot

of public awareness on solar energy technology in Kiambu region. The study indicated

that it was the responsibility of users to take interest in the use of solar energy and that

solar energy technology was important to them. The study concludes that the residents of

Kiambu have expressed an interest to have solar energy technology in their households.

5.4.3 Role of Relative Advantage in Solar Energy

The study concludes that residents felt that solar energy technology was affordable. It also

concludes that residents were aware of how they would get guidance on solar energy

technology. The study concludes that residents had expressed a willingness to invest in

solar energy technology for their households and use the solar energy technology results

in long-run savings. The study showed that residents were fully aware of the advantages

of adopting use of solar energy technology and they agreed that solar energy technology

was safe to use. The study established that the residents’ peers were of the opinion that

they should adopt use of solar energy technology and that their families would be

interested in using solar energy technology. The study concludes that the residents’

friends would expect them to adopt use of solar energy technology and they were aware

of where to source for financial support to purchase soar energy technology.

45

5.5 Recommendations

5.5.1 Recommendation of the Study

5.5.1.1 Factors influencing Adoption of Solar Energy

A number of factors ranging from lack of awareness on the benefits of use of solar energy

to lack of government support were cited as some of the factors influencing solar energy

adoption.

With increased cost of living against incomes which are not rising proportionately, this

gives the government an opportunity to attract investors keen to venture into the solar

energy space as a way uplifting the lives of the residents as adoption of solar energy will

result in saving money currently used to buy kerosene. The health and education

standards will also improve as this will mean increased study time and reduced

respiratory health challenges.

5.5.1.2 Role of the Level of Household Income in Influencing Acceptance of Solar

Energy

Majority of the study respondents were women whose level of education was fairly low

and thus had low income. As a result, they perceived solar energy use as expensive. This

gives an opportunity for the Kiambu County government an opportunity to create

awareness of the benefits of use of solar energy which supersede the cost element.

The lack of information on financing opportunities influenced adoption rate of solar

technology as it was perceived to be expensive by most respondents. This thus gives the

financial institutions an opportunity to provide for favourable solutions which the

residents can use to enable them access use of solar energy technology.

5.5.1.3 Role of Relative advantage in solar energy

Due to lack of public awareness of the benefits of solar energy adoption, Kiambu

residents have continued to use Kerosene as a source of lighting. Availability of

information will help them to make informed choices of what source of energy is best

suited in the current times. The level awareness from individuals who had installed solar

system in their household, had seen a solar lamp or solar power in use, were largely the

solar technology providers, this gives an opportunity for investors to provide for

46

increased awareness on the benefits of solar energy adoption and how those people

interested in buying can easily access them.

5.5.2 Recommendations for Further Research

The researcher recommends that more research should be undertaken on: the relationship

between training and solar energy technology adoption. How gender affects the adoption

of solar energy technology. The relationship between the government support and

willingness of investors to venture into the solar energy technology business.

47

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51

APPENDICES

APPENDIX I: LETTER OF REQUEST TO CONDUCT RESEARCH

Gichuhi-Mwangi Regina Mukami

P.O Box 86

Miharati

Mobile No 0722 395505

15th June 2015

The Head of Department

Ministry of Energy

Kiambu County

Dear Sir/Madam,

RE: PERMISSION TO CONDUCT RESEARCH ON SOLAR TECHNOLOGY

ADOPTION IN KIAMBU COUNTY.

I am an MBA Entrepreneurship Student with United States International University

(USIU-Africa). I wish to undertake research on, “Factors influencing Adoption of Solar

Technology Kiambu County, Kenya. The research will be conducted in the month of June

2015.

I am kindly seeking your permission and assistance to conduct the research.

I look forward to your kind consideration.

Yours faithfully

Gichuhi-Mwangi Regina Mukami

Student ID No. 639126

52

APPENDIX 2: LETTER OF TRANSMITTAL

Gichuhi-Mwangi Regina Mukami

P.O. Box 86

Miharati

Date…………..2015

To: -------------------------------------------------------------------------------------------------

Dear respondent,

RE: Data collection

I am a Masters in Business Administration (MBA) in Entrepreneurship at United States

International University Africa (USIU) student, undertaking a Research study to

determine the factors influencing Adoption Solar Technology in Kiambu County. Your

assistance on data collection will be highly appreciated as the study will benefit Kiambu

Country residents and more importantly the County leadership in their Energy provision

planning.

The information will be treated with confidentiality and I therefore request you to answer

the questions as honestly as it can possibly be.

Attached please find the questionnaires which you are requested to fill and provide

information by answering the questions.

Kindly treat this request as urgent and important.

Kind regards.

Yours faithfully,

Gichuhi-Mwangi Regina Mukami

Student ID. No. 639126

53

APPENDIX 3: QUESTIONNAIRE

Answer the following questions by ticking or marking the boxes using X or √ or by filling

the empty boxes.

PART I: GENERAL DEMOGRAPHICS

1. What is your gender?

Male ☐ Female ☐

2. What is your age range

Less than 25 years ☐ 26-35 years ☐ 36-45 years ☐ 46-55 years ☐

56 years and above ☐

3. Marital status

Married ☐ Single ☐ Divorced ☐

Separated ☐ Widow ☐ Widower ☐

4. What is your highest education level

Certificate ☐ Diploma☐ Degree ☐ Post graduate Diploma ☐ Masters

☐

5. Is your house connected to the national electricity grid?

Yes ☐ No ☐ Connection in progress ☐

Not hope of accessing electricity power soon ☐

PART II: Independent Variables

Please indicate the degree to which you agree or disagree with the following statements.

Use a scale of 1-5 where; [1] is strongly disagree; [2] disagree; [3] neutral; [4] agree; and

[5] strongly agree.

A. Characteristics of Technological Innovation

Strongly

disagree

(1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

agree

(5)

1. The Solar energy technology

is fully compatible with my

household needs

( ) ( ) ( ) ( ) ( )

54

2. The Solar energy technology

is easy to use ( ) ( ) ( ) ( ) ( )

3. The positive results of using

Solar energy technology are

clearly visible

( ) ( ) ( ) ( ) ( )

4. It is more advantageous to

use Solar energy technology

than Kerosene and firewood

for lighting

( ) ( ) ( ) ( ) ( )

5. Technical support for Solar

energy technology is easily

available

( ) ( ) ( ) ( ) ( )

6. Solar energy technology is

user friendly ( ) ( ) ( ) ( ) ( )

7. Solar energy technology is

secure ( ) ( ) ( ) ( ) ( )

8. Use of Solar technology is

cost effective ( ) ( ) ( ) ( ) ( )

B. Government/Industry Factors

Strongly

disagree

(1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree

(5)

9. In my County the administration

encourages people to adopt use of

Solar energy technology

( ) ( ) ( ) ( ) ( )

10. The government encourages

investors to invest in Solar energy

technology

( ) ( ) ( ) ( ) ( )

11. In my county, there are incentives

for people who adopt use of Solar

energy technology.

( ) ( ) ( ) ( ) ( )

12. Faulty Solar energy lanterns are

quickly replaced once reported

55

C. Individual Factors

Strongly

disagree

(1)

Disagree

(2)

Neutral

(3)

Agree

(4)

Strongly

Agree

(5)

13. I consider Solar energy technology

to be very useful ( ) ( ) ( ) ( ) ( )

14. I am sufficiently trained on how to

use Solar energy technology ( ) ( ) ( ) ( ) ( )

15. My friends encourage me to use

Solar energy technology ( ) ( ) ( ) ( ) ( )

16. Most of the families in my

neighborhood use Solar energy

technology

( ) ( ) ( ) ( ) ( )

17. I prefer use of Solar energy

technology more than use of

kerosene and firewood for lighting

( ) ( ) ( ) ( ) ( )

18. In your opinion, what other factors affect the adoption of innovation technologies in

your area?

…......................................................................................................................................

..........................................................................................................................................

PART III: Dependent Variables:-

Indicate the degree to which you agree or disagree to the following statements regarding

the adoption of Solar Energy Technology in your Constituency. Use a scale of 1-5 where;

[1] is strongly disagrees; [2] disagree; [3] neutral; [4] agree; and [5] strongly agree

(a) Factors affecting interviewees willingness to adopt to Solar technology adoption

Strongl

y

disagre

e

(1)

Disagree

(2)

Neutra

l

(3)

Agree

(4)

Strongly

Agree

(5)

56

1. I am fully aware of how climate

change affects me ( ) ( ) ( ) ( ) ( )

2. I know a variety f renewable energy

sources/technologies available in

Kenya

( ) ( ) ( ) ( ) ( )

3. There has been a lot of public

awareness created on Solar energy

technology in my region

( ) ( ) ( ) ( ) ( )

4. It is my responsibility to take interest

in use of Solar energy technology ( ) ( ) ( ) ( ) ( )

5. Solar energy Technology is quite

important to me ( ) ( ) ( ) ( ) ( )

6. I would very much like to use Solar

technology in my household ( ) ( ) ( ) ( ) ( )

(b) Interviewee’s perceived Relative Advantage in Solar energy technology adoption

7. I think Solar technology is affordable ( ) ( ) ( ) ( ) ( )

8. I know how I can get help/guidance

on Solar energy technology ( ) ( ) ( ) ( ) ( )

9. I am willing to invest money and

obtain some Solar energy for my

household

( ) ( ) ( ) ( ) ( )

10. I think by adopting use of Solar

technology, I will be saving a lot of

money in the long run

( ) ( ) ( ) ( ) ( )

11. I am fully aware of the advantages of

adopting use of Solar technology for

my household use

( ) ( ) ( ) ( ) ( )

12. Solar energy technology is safe to use ( ) ( ) ( ) ( ) ( )

13. Adoption of Solar energy technology

can quickly improve the general

security of my area

( ) ( ) ( ) ( ) ( )

57

( c) Social influence on Solar energy technology adoption

14. My peers think that I should use Solar

energy in my household

( ) ( ) ( ) ( ) ( )

15. My family is very much interested in

using Solar energy technology

( ) ( ) ( ) ( ) ( )

16. My friends think that I should adopt

use of Solar technology

( ) ( ) ( ) ( ) ( )

17. I know where I can source for

financial support to enable me access

Solar energy technology

( ) ( ) ( ) ( ) ( )

18. I know I can easily finance the

purchasing of Solar technology for my

household use

( ) ( ) ( ) ( ) ( )

19. The local government is willing to

provide support to people who are

willing to adopt use of solar energy

technology

( ) ( ) ( ) ( ) ( )

THANK YOU

58

APPENDIX 4: FACTOR DESCRIPTION

4.1 Factor description

ITEM DESCRIPTION CONSTRUCT

A1

The Solar energy technology is fully compatible with my

household needs

A2 The Solar energy technology is easy to use

A3

The positive results of using Solar energy technology are clearly

visible

A4

It is more advantageous to use Solar energy technology than

Kerosene and firewood for lighting

Technological

Innovation

A5 Technical support for Solar energy technology is easily available

A6 Solar energy technology is user friendly

A7 Solar energy technology is secure

A8 Use of Solar technology is cost effective

B9

In my County the administration encourages people to adopt use of

Solar energy technology

B10

The government encourages investors to invest in Solar energy

technology

B11

In my county, there are incentives for people who adopt use of

Solar energy technology.

Government

factors

B12 Faulty Solar energy lanterns are quickly replaced once reported

C13 I consider Solar energy technology to be very useful

C14 I am sufficiently trained on how to use Solar energy technology

C15 My friends encourage me to use Solar energy technology Individual factors

C16

Most of the families in my neighborhood use Solar energy

technology

C17

I prefer use of Solar energy technology more than use of kerosene

and firewood for lighting

W1 I am fully aware of how climate change affects me

W2

I know a variety f renewable energy sources/technologies available

in Kenya

W3

There has been a lot of public awareness created on Solar energy

technology in my region

59

W4

It is my responsibility to take interest in use of Solar energy

technology willingness

W5 Solar energy Technology is quite important to me

W6 I would very much like to use Solar technology in my household

RA7 I think Solar technology is affordable

RA8 I know how I can get help/guidance on Solar energy technology

RA9

I am willing to invest money and obtain some Solar energy for my

household

Relative

advantage

RA10

I think by adopting use of Solar technology, I will be saving a lot of

money in the long run

RA11

I am fully aware of the advantages of adopting use of Solar

technology for my household use

RA12 Solar energy technology is safe to use

RA13

Adoption of Solar energy technology can quickly improve the

general security of my area

SI14 My peers think that I should use Solar energy in my household

SI15

My family is very much interested in using Solar energy

technology

SI16 My friends think that I should adopt use of Solar technology Social influence

SI17

I know where I can source for financial support to enable me access

Solar energy technology

SI18

I know I can easily finance the purchasing of Solar technology for

my household use

SI19 The local government is willing to provide support to people who

are willing to adopt use of solar energy technology