The Factors That Affect Kenyan Domestic Tourists from Visiting Tourism Facilities in Kenya: Case of Nairobi, Mombasa and Nakuru Towns

THE FACTORS THAT AFFECT KENYAN DOMESTIC TOURISTS FROM VISITING TOURISM FACILITIES IN KENYA: CASE OF NAIROBI, MOMBASA AND NAKURU TOWNS.

Mong'are Omare

PhD Thesis in Tourism Management

Submitted to

School of Business and Economics

Department of Tourism and Tour Operations Management

Moi University

March 2016

DECLARATION

I declare that this research project is my original work and has not been presented in any other University.

Sign: Date: Mong’areOmare SBE/DPHIL/05/06

This research project has been submitted for examination with our approval as University supervisors.

Sign: Date: Prof. John S. Akama Department of Tourism Moi University

Sign: Date: Prof. TimothySulo, Department of Agricultural economics and Resource Management Moi University

DEDICATION

I dedicate this research to my family, dear wife Priscilla, sons-Mato, John, daughters;Godwillie, Lolia, Joy and Sara for their for their support throughout the course of the study.

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ACKNOWLEDGEMENT

I would like to acknowledge the guidance received from my university Supervisors; Prof. Akama and Prof. Sulo; in the process of developing this research thesis. I also acknowledge the support of my fellow students for their companionship in this academic journey.

ABSTRACT I This study examined the factors that affect domestic tourism in Mombasa, Nairobi and Nakuru with a view of addressing the effects in order to improve domestic tourism in Kenya. The study investigated a sample of cross-section of the tourist stakeholders and through questionnaires and interview schedules obtained data from respondents consisting of managers of hotels and lodges, and tour operating firms, Government officials, Kenya Wildlife Service officials, resident tourists and local residents. The broad objective of the study was to establish the factors that affect Kenyan residents from visiting tourist facilities in the three Towns. The study aimed at determining the effect of; socio-economic factors on domestic tourism in Kenya, town of residence on domestic tourism, the economic contributions made by Kenyans participating in domestic tourism, individual‟s psycho-factors on domestic tourism, and sustainable performance framework indicators for domestic tourism in Kenya. A sample of 600 respondents from tourist industry players in the regions was selected using purposive and stratified random sampling methods. A structured questionnaire and interview schedule with both closed and open-ended questions were used to collect data from the respondent. A total of 558 respondents filled the instruments, a response rate of 93%, from which data was extracted for analysis and made statistical inferences, presentations and conclusions. The study findings indicated that the level of education was a major contributing factor in domestic tourism with 50% of participants having middle level education,36 % had university degree with 1% of the participants having no education. It also revealed that the biggest impediment to domestic tourism was costs with 48% of respondents saying they could not afford, 50% of the male respondents could not afford while only 47% of the female respondents could not afford. Nairobi had 62% of the respondents indicating that affordability was an issue and 31% from Nakuru. The second most significant impediment to domestic tourism was family commitment with 19% of the respondents saying this was an impediment, 15% being male while 23% were female. The study also found that about 15% of the respondents had participated in domestic tourism making an average of 5.35 visits per year and spending an average of Kshs: 9,965 per visit. The results further showed that the respondents perceived domestic tourism as being expensive (48%)and could not afford participating in domestic tourism activities. The study concluded that the government and tourist industry stakeholders need to address the hindrances pointed out in order to improve domestic tourism; these include price adjustments, construction and maintenance of roads to the parks and Provision of cheaper transport to the national park. it was recommended that Creation of awareness of the attractions and sensitizing the local communities on the value of tourism and the need to conserve national resources for posterity. If these measures are taken then domestic tourism participation would improve. Leading to creation of employment. And generation of' income for towns and improve Gross Domestic Product and Gross National Product notwithstanding national development. The study will assist the government and other industry stakeholders in coming up with practical measures to address the obstacles with a view to increasing Kenyan participation in domestic tourism.

TABLE OF CONTENTS

DECLARATION ...... ii DEDICATION ...... iii ACKNOWLEDGEMENT ...... iv ABSTRACT ...... v TABLE OF CONTENTS ...... vi LIST OF TABLES ...... x LIST OF FIGURES...... xii LIST OF ACRONYMS& ABBREVIATIONS...... xiii CHAPTER ONE ...... 1 INTRODUCTION ...... 1 1.0 Introduction ...... 1 1.1 Background to the Study ...... 1 1.2 Problem Statement ...... 7 1.3 The General Objective ...... 8 1.4 The Specific Objectives ...... 8 1.5 Hypotheses ...... 9 1.6 The Significance of the Study ...... 9 1.7 The Scope of the Study ...... 11 1.8 Assumptions ...... 11 1.9 Operational Definitions ...... 11 CHAPTER TWO ...... 13 LITERATURE REVIEW ...... 13 2.1.0 The Concept and Brief History of Tourism ...... 13 2.1.0.1. The History of Kenya ...... 15 2.1.0.2. The Economy of Kenya...... 17 2.1.0.3. Effect of tourism on the economy ...... 17 2.1.1. The Development of Tourism and Tourism Categories ...... 19

2.1.2. Tourism Products ...... 21 2.1.2.1. Cultural Tourism ...... 23 2.1.2.2. Sports Tourism ...... 24 2.1.2.3. Wildlife Tourism ...... 25 2.1.2.4. Ecotourism ...... 27 2.1.2.5. Other Forms of Tourism ...... 27 2.1.3.Theoretical review ...... 28 2.1.4.Growth Pole Theory ...... 31 2.2.0 EMPIRICAL REVIEW ...... 33 2.2.1 Domestic Tourism ...... 33 2.2.2.Tourism Industry in Kenya ...... 37 2.2.3.Domestic Tourism in Kenya ...... 41 2.2.4 Determinants of Participation in Tourism ...... 44 2.2.4.1. Income ...... 44 2.2.4.2.Education ...... 45 2.2.4.3. Place of residence...... 46 2.2.4.4. Gender ...... 47 2.2.4.5. Mobility ...... 47 2.2.4.6. Urban Form Factors ...... 48 2.2.4.7. Psycho-social Factors...... 53 2.2.4.8. Pricing ...... 54 2.2.4.9. Other determinants ...... 55 2.3 Conceptual Framework ...... 56 CHAPTER THREE ...... 59 RESEARCH METHODOLOGY...... 59 3.1 Research Design ...... 59 3.2 Sampling Frame and Size ...... 60 3.3 The Target Population ...... 62 3.4 Sampling Design ...... 62 3.5. Instrumentation ...... 62 3.5.1. Validity of Instruments ...... 63

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3.5.2 Reliability ...... 63 3.6. Data Collection ...... 63 3.7 Data Collection Monitoring and Evaluation ...... 64 3.8 Data Analysis ...... 64 3.9 Study Area ...... 65 3.9.1 Nairobi ...... 65 3.9.2 Mombasa ...... 67 3.9.3 Nakuru ...... 68 CHAPTER FOUR ...... 70 DATA ANALYSIS AND PRESENTATION ...... 70 4.2. Factors Affecting Participation in Domestic Tourism ...... 70 4.2.1. Effect of Location on Domestic Tourism Participation ...... 74 4.2.2. Gender on Domestic Tourism Participation ...... 76 4.2.3 Age on Domestic Tourism Participation...... 78 4.2.4 Effect of Marital Status on Domestic Tourism Participation ...... 81 4.2.5 Occupation on Domestic Tourism Participation ...... 83 4.2.6 Education on Domestic Tourism Participation ...... 86 4.2.7. Effect of Income on Domestic Tourism Participation ...... 88 4.3.1 Overall Economic contribution by Kenyans participation in Domestic Tourism ...... 90 4.3.2 Participation and Contribution to Domestic Tourism by Location ...... 92 4.3.3. Participation and Contribution to Domestic Tourism by Gender ...... 93 4.3.4. Participation in Domestic Tourism by Age ...... 95 4.3.5. Participation in Domestic Tourism by Marital Status...... 96 4.3.6. Participation in Domestic Tourism by Level of Education ...... 98 4.3.7. Participation in Domestic Tourism by Occupation ...... 99 4.3.8 Participation in Domestic Tourism by Income Level ...... 101 4.5.0 Sustainable performance in domestic tourism in Kenya ...... 103 4.5.2 Number of visits to tourist sites ...... 105 4.5.3 Expenditures on Domestic Tourism...... 107 4.5.4 Desirability of Current Number of Domestic Visits ...... 108

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CHAPTER FIVE ...... 110 5.3.0 RECOMMENDATIONS ...... 116 5.3.1 Suggestions for Further Research ...... 116 REFERENCES ...... 118

LIST OF TABLES

Table 1: Correlation Matrix ...... 72 Table 2: Factors Influencing Participation in Domestic Tourism ...... 73 Table 3: Distribution of Respondents by Location ...... 74 Table 4: Differences in Domestic Tourism Participation by Location ...... 75 Table 5: Distribution of Respondents by Gender and Location ...... 77 Table 6: Differences in Domestic Tourism Participation by Gender ...... 78 Table 7: Distribution of Respondents by Age and Town of Residence ...... 79 Table 8: Differences in Domestic Tourism Participation by Age ...... 80 Table 9: Distribution of Respondents by Marital Status and Location ...... 81 Table 10: Differences in Domestic Tourism Participation by Marital Status ...... 82 Table 11: Distribution of Respondents by Occupation and Location ...... 84 Table 12: Differences in Domestic Tourism Participation by Occupation ...... 85 Table 13: Distribution of Respondents by Level of Education and Location ...... 86 Table 14: Differences in Domestic Tourism Participation by Education ...... 87 Table 15: Distribution of Respondents by Monthly Income and Location ...... 89 Table 16: Differences in Domestic Tourism Participation by Income Levels ...... 90 Table 17: Number of Visits in Local Tourism Sites and Expenditure per Visit ...... 91 Table 18: Number of Visits to Domestic Tourism Sites by Location ...... 93 Table 19: Expenditures on Domestic Tourism by Location ...... 93 Table 20: Number of Visits to Domestic Tourism Sites by Gender ...... 94 Table 21: Expenditure on Domestic Tourism by Gender ...... 94 Table 22: Number of Visits to Domestic Tourism Sites by Age ...... 95 Table 23: Expenditures on Domestic Tourism by Age ...... 96 Table 24: Number of Visits to Domestic Tourism Sites by Marital Status ...... 97 Table 25: Expenditures on Domestic Tourism by Marital Status ...... 98 Table 26: Number of Visits to Domestic Tourism Sites by Education ...... 99 Table 27: Expenditure on Domestic Tourism by Education ...... 99 Table 28: Number of Visits to Domestic Tourism Sites by Occupation...... 100 Table 29: Expenditures on Domestic Tourism by Occupation ...... 101 Table 30: Number of Visits by to Domestic Tourism Sites Income Levels ...... 102

Table 31: Expenditures on Domestic Tourism by Income Levels ...... 102 Table 32: Expected Number of Visits to Domestic Tourism Sites per Year ...... 105 Table 33: Differences in Number of Visits per Year ...... 106 Table 34: Expected Number of Visits by Location ...... 106 Table 35: Expected Number of Visits by Gender ...... 106 Table 36: Expected Average Expenditures on Domestic Tourism per Visit ...... 107 Table 37: Differences in Average Expenditures on Domestic Tourism ...... 107 Table 38: Expected Average Expenditures on Domestic Tourism by Location ...... 108 Table 39: Expected Average Expenditures on Domestic Tourism by Gender ...... 108

LIST OF FIGURES.

Chart 1: Number of Respondents by Town of Residence ...... 75 Chart 2: Number of Respondents by Town of Residence and Sex ...... 77 Chart 3: Number of Respondents by Age and Town of Residence ...... 80 Chart 4: Number of Respondents by Town of Residence and Marital Status ...... 82 Chart 5: Number of Respondents by Town of Residence and Occupation ...... 84 Chart 6: Number of Respondents by Location and Level of Education ...... 87 Chart 7: Number of Respondents by Town of Residence and Monthly Income ...... 89 Chart 8: Have you visited any tourist attraction in Kenya? ...... 91 Chart 9: Have you ever participated in foreign tourism? ...... 92 Chart 10: Participation in domestic tourism by Location ...... 92 Chart 11: Participation in domestic tourism by gender ...... 94 Chart 12: Participation in domestic tourism by age ...... 95 Chart 13: Participation in Domestic Tourism by Marital Status...... 97 Chart 14: Participation in Domestic Tourism by Level of Education ...... 98 Chart 15: Participation in Domestic Tourism by Occupation ...... 100 Chart 16: Participation in Domestic Tourism by Income Level ...... 101 Chart 17: Constraints to Domestic Tourism ...... 103 Chart 18: Constraints to Domestic Tourism by Location ...... 104 Chart 19: Constraints to Domestic Tourism by Gender...... 105 Chart 20: Satisfaction with Current Tours in Kenya ...... 109

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LIST OF ACRONYMS& ABBREVIATIONS.

ANOVA - Analysis of Variance

DTCK - Domestic Tourism Council of Kenya

ELM - Elaboration Likelihood Model

KTB - Kenya Tourism Board

LEAD - Local Enterprise and Development

PCT - Personal Construct Theory

SPSS - Statistical Package for Social Sciences

USTTA - United States Travel and Tourism Administration

WTO - World Trade Organization

GNP - Gross Domestic Product

GDP - Gross National Product

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CHAPTER ONE

INTRODUCTION

1.0 Introduction

This chapter provides information on strategies that have been adopted by some selected countries in promotingsustainability of domestic tourism. Specific problems and challenges of interest to the research that have faced Kenya in her attempt to promote domestic tourism have been described. The chapter also outlines research objectives, research questions and hypotheses that guided the research work.

1.1 Background to the Study

The Global Development Research Centre (GDRC) defines sustainable tourism as "one that establishes a suitable balance between the environmental, economic and socio- cultural aspects of tourism development, and plays an important role in conserving biodiversity." Further, GDRC explains that sustainable tourism "attempts to minimize its impact on the environment and local culture so that it will be available for future generations, while contributing to generate income, employment, and the conservation of local ecosystems." (http://www.gdrc.org/uem/eco-tour/sustour-define.html. March 2,

2016).

In this study, sustainable tourism is viewed as the ability for a tourist to participate in tourism activities in order to experience and reap the full benefits of tourism products today and be able to do so in indefinite future.

The management of tourists‟ destinations for the purpose of achieving sustainability of the tourism industry has been on the general principles of planning which includes resource assessment, market analysis, developing strategy and marketing plans (Godfrey and Clarke, 2000).The strategic planning for the promotion of inbound tourism has been witnessed in countries such as Japan, New Zealand, China, Australia, USA and many others.

China is a country that had not given significant thoughts to tourism prior to 1978. By around that time, China embarked to the promotion of international tourism mainly by political invitation of dignitaries and guests from abroad while organizations and employers encouraged their employees to participate in domestic tourism activities (Wen and Tisdell, 2001).

In the wake of recent events including the 2001 September 11 bombing of the World

Trade Centre in New York and Pentagon in Washington, there has been decline in the number of arrivals across the globe prompting governments to adopt new approaches in the quest to regain and maintain the inbound tourists in their respective countries. Some of the promotional strategies being adopted include value addition in terms of tourism product offers, inclusive packages, reduced prices, and discounted tickets by airlines

(Buhalis and Eggar, 2008).

Africa has not been left behind when it comes to promoting both inbound and domestic tourism in particular. Between 1990 and 2000, both the number of tourists visiting Africa

South of Saharamore than doubled(Stock, 2004). Further, Stock (2004) provides that in

2000, 17.6 million visitors to Africa spent $6.6 billion, representing 8.4 percent export earnings.Africa‟s market share of global international tourism is projected to be five percent by 2020.

South Africa, in a move to strengthen her stake in the global tourism market, notably put together information and packages on a range of different “affordable” holiday options, including city breaks, bush retreats, coastal getaways, and so on to market her tourism industry. South Africa tourism stakeholders invented the “hotel voucher” scheme that is linked to the countrywide network of the post office. This scheme offers potential holiday makers the flexibility to purchase and accumulate hotel vouchers in small cash denominations with each voucher valid for a period of three years and accepted for “your dream holiday” at a network of 140 hotels, lodges, and game reserves across the country

(Rogerson and Visser, 2007).

Akama (1999) recognized that Kenya has invested massively in the promotion of inbound tourism. This has been prompted by the fact that over the years there has been decline in prices of agricultural commodities leading to shortfalls in foreign exchange earnings; and in an effort to correct this deficit, the government turned to its service sectors such as tourism as an alternative source of foreign exchange earnings. Over the last 40 years,

Kenya with its enormous diversity, its rich supply of natural resources and its wealth of cultural heritage, has increasingly become a popular international tourist destination

3 making tourism the largest foreign exchange earner with a relatively major ministerial portfolio (Sindiga, 2000, Sinclair, 1990).

The initial success of marketing Kenya as a suitable tourism destination led to a dependency on the tourism industry for foreign exchange earnings and this resulted in the entire tourism system, including tourism enterprises, becoming outward oriented; with its entire tourism structure specifically serving the international visitors (Bachmann, 1988).

The strategies for promoting tourism that have been used to promote Kenya as an international tourism destination include price adjustments aimed at attracting more international visitors, widening the tourism source markets in the wake of negative travel advisories from important tourism generating countries like Germany, UK, and US, and the strengthening of diplomatic relationships with potential tourism emerging markets such as China and North Korea. These, however, have not been successful ventures owing to the fact that in the Kenya coastal region, which usually receives over 60 percent of Kenya‟s international tourists, a major declined of over 50 percent was noted; from over 300,000 international tourist arrivals in the 1990‟s to less than 150,000 arrivals in the late 1990‟s (Economic Survey of Kenya, 1999).

As the number of inbound tourists continues to decline, the government has in the last few years attempted to promote domestic tourism as a viable sector for sustaining the tourism industry by establishing domestic tourism institutes such as Domestic Tourism

Council of Kenya that was launched in October 2007. Jones et al (2005) also recommended to the government of Kenya to broaden its tourism market base by

4 including the local and regional markets as viable source of tourists. The Kenya Tourism

Board which markets Kenya both locally and abroad has since launched its own magical

Kenya campaigns with an aim of making it possible for many Kenyans to afford at least a holiday a year at any of the many attractions. These promotions target to strengthen the domestic tourism which is geared towards improving the distribution of national resources, increase the levels of patriotism, and create a positive impact on national cohesion (Sindiga, 2000).

Although the government has put in an effort to target the locals to participate in domestic tourism, media reports and studies reveal that very little has been achieved in this regard (Jones et al, 2005; The Standard, 2007). Anecdotal reports found in various tourism websites claim that the marketing strategies that are being used by the Kenya

Tourism Board and the Domestic Tourism Council of Kenya target the elite, forgetting that the majority of Kenyans are the low and medium income earners who, if targeted properly, would come a long way in raising the levels of the domestic tourism industry.

This is supported by Jones et al (2005) observation that the government‟s tourism policy relies mainly on the traditional safari and coastal tourism products that were specifically meant for the inbound tourists. The Daily Nation‟s report (2008) that Kenyans were visiting the non-advertised sites such as Nakuru and Naivasha rather than the widely advertised Maasai Mara reveal how much the promotional strategies were off guard.

The end result of these strategies is that Kenyans have come to assume that the cost of tourism is too high –that it is cheaper driving to the rural areas than to the Kenyan coast

5 or to the lodge in Maasai Mara (Daily Nation, 2008). Akama (1999) suggested that to encourage many Kenyans to travel within their country, certain structural adjustment leading to the tourism product diversification are necessary.

In order for the government and the entire tourism industry to effectively strengthen the domestic tourism in the country, there is a need to thoroughly examine the nature of the promotional strategies that exist. Authors such Jones et al (2005), Sindiga (2000), and

Akama (1999) have seen the significance of promoting domestic tourism in line with the needs of the community. Jones et al (2005) identified the need to create awareness and put in place legislative measures not only for safeguarding the tourism industry but also for involving the local communities in the planning and promotion of tourism.

This study noted that there is an urgent need to promote domestic tourism in Kenya but more so found a greater need to research on factors that influence Kenyans' participation in domestic tourism in order to streamline the tourism products to sync with Kenyans abilities and desires to participate in the domestic tourism sector. This is in line with

Bulungula‟s (2001) point that it is very vital to understand the needs of the market and

Jones et al‟s (2005) recommendation that there should be a campaign to inform people about tourism and how tourism affects them in terms of available opportunities and as well as the positive and negative impacts of tourism.

It is in the background of researching on the factors that inform Kenya‟s participation in domestic tourism thatthis study was undertaken. This was in the realization that the

6 existing promotional strategies were either inadequate or do not seek to address the factors that define Kenyans world view on domestic tourism and participation therein.

1.2 Problem Statement

The global events including the recession of US and European economies from 2007 onwards together with the severallocal terrorist‟s attacks from August 1998 to the most recent Garissa attack that happened in April 2015,made Kenya to realize that over dependency on overseas visitors is rather risky. As a result,the government embarked on marketing domestic tourism as an alternative to international tourism sustain the overall tourism sector through campaign slogans such as “Tembea Kenya” and “Magical Kenya” that have the objective of encouraging Kenyans to travel within the country, however, despite of this noble efforts the results have been meagre.

For instance, immediately after travel advisories were issued following the Westgate attack in August 2013, Mpekoteni attacks in June 2014, and later the GarissaUniversity

College attack in April 2015, some hotels in Mombasa and Lamu closed down while several others downsized as bed occupancy grounded to zero. A year after the Mpeketoni attacks, hotels in Lamu and Mombasa were still closing down due to lack of inbound tourists (Daily Nation, May 26, 2015) and this could be attributed to the fact that participation in domestic tourism by Kenyans has not been sufficient to compensate for the decline in inbound tourism.

One major reason domestic tourism has not been able to effectively sustain tourism activities on its own is because Kenya Tourism Board (KTB) and other stakeholders have not been able to come up with promotional strategies that would effectively influence

Kenyans into participating in domestic tourism. The existing promotional strategies are the same ones employed in targeting visitors from abroad and as such do not put into considerationthe factors affecting Kenyans as far as participation in domestic tourism is concerned. Even so, the factors affecting Kenyans' participation in domestic tourism have not been studied and incorporated in the development of domestic tourism packages and effective promotional strategies.In light of these, the study exploredseveral factors that affect the participation of Kenyans in domestic tourism with a view to provide a framework intended to aid in developing appropriate marketing tools, plans, strategies and techniques useful in the development of sustainable domestic tourism industry in

Kenya.

1.3 The General Objective

In the backdrop of the economic, psychological, sociological and cultural foundations of

Kenyans, the study sought toestablish the factors that affect Kenyan residents from visiting tourist facilities in the three Towns.

1.4 The Specific Objectives

The study, specifically, sought to:

(a) Establish the effect of Socio-Economic factors on domestic tourism in Kenya.

(b) Determine the effect of town of residence on domestic tourism in Kenya.

tourism.

(d) Determine the effect of individual‟s attitude, perception and preference on

domestic tourism in Kenya.

1.5 Hypotheses

HO:Socio-economic factors have no effect on domestic tourism in Kenya.

Hi:socio economic factors have an effect on domestic tourism in Kenya.

HO:Town of residence or environment of residence has no effect on domestic tourism.

Hi:There is no effect on domestic tourism participation by town of residence.

HO:There is no economic contribution made by Kenyan participation in domestic tourism.

Hi:There is contribution made by Kenyans participating in domestic tourism.

HO:psycho-factors have no effect on domestic tourism in Kenya.

Hi:psycho-factors have influence on domestic tourism in Kenya.

1.6 The Significance of the Study

In May 2014, an online article by Capital FM quoted the Chairperson of Domestic

Tourism Association Ms AnastanziaWakesho as saying that the country lacked a national policy on domestic tourism, which she attributed as the reason the domestic tourism sector is unable to cushion the tourism industry in times of crisis affecting international travel (http://allafrica.com/stories/201405060849.html, March 3, 2016). This study not

9 only sought to explore the factors that hinder Kenyans from participating in domestic tourism but also intended to provide a framework on which domestic tourism policies and promotional tools can be founded. The findings of this study have been used to suggest appropriate avenues, through which domestic tourism can be developed to sustainability, provided knowledge on how several factors affect and influence Kenyans' participation in domestic tourism, and offer leads for future research works in the area of socio-economic factors that influence the development and growth of domestic tourism in Kenya.

Tourism stakeholders including the government through the ministry of Tourism, Kenya

Tourism Board, legislators, tour operators and agents, hoteliers and others will find recommendations in this research useful when formulating marketing and other promotional strategies intended to woo Kenyans into becoming consistent domestic tourists.

Insights on how factors influence decision making as far as participation in tourism activities is concerned will be useful for academic institutions and other researchers who would like to either gain knowledge or conduct further research in the area.

Lastly, the recommendations of this study are important to the overall economy as they attempt to provide a foundation with which domestic tourism can not only stand on its own feet to offer employment opportunities but also spur growth in other sectors such as agriculture and manufacturing and the entire hospitality industry.

1.7 The Scope of the Study

The study was limited to investigating a number offactors that affected the extent to which Kenyans living in Nairobi, Mombasa and Nakuru townsparticipate in domestic tourism activities. The study was carried out for a period of six months beginning March

2011 toDecember 2011 in Nairobi, Mombasa and Nakurutowns, targeting various stakeholders.

1.8 Assumptions

The study assumed the following:

i. That Kenyans living in Nairobi, Mombasa, and Nakuru represent the

characteristics of Kenya's domestic tourists.

ii. That factors influencing participation of residence of Nairobi, Mombasa and

Nakuru towns in domestic tourism affect other Kenyan domestic tourists in

similar fashion.

1.9 Operational Definitions

Domestic Tourism: The activities of tourists travelling within their country of residence.

Domestic Tourist: A person who engages in tourism activities within his/her country of

residence.

Personal Construct: A person‟s unique psychological process channelled by the way

he/she anticipates events.

Promotional Strategy: A strategy conceived and developed to create awareness to the

public about the existence and importance of a tourism

product.

Strategic Planning: A process by which an organisation defines its strategies.

Repertory Grid Technique: A technique used for eliciting information from a

respondent in line with Personal Construct Theory.

Tourist: A person who travels to pay for and experience a tourism product.

Tourism: “The activities of persons travelling to and staying in places outside their usual

environment for not more than one consecutive year for leisure, business

and other purposes not related to the exercise of an activity remunerated

from within the place visited” (WTO, 1993)

Tourism Product: The experience offered to an individual who participates in a tourism activity.

CHAPTER TWO

LITERATURE REVIEW

2.1.0 The Concept and Brief History of Tourism

Tourism is an industry that is widely considered as an effective contributor to socio- economic development particularly in less developed countries (Sharpley and Telfer

(2008). According to World Travel Tourism Council (WTTC), as quoted by Sharpley and

Telfer (2008), if domestic tourism is added, tourism as a whole constitutes ten percent of the global Gross Domestic Product and employment. Sharpley and Telfer (2008) also pointed out that most developing countries‟ foreign exchange earnings come from international tourism and that most economies view tourism as a driving force for development. Other authors have categorically identified social, cultural, and international political relationships as influencing and being influenced by tourism (Jack and Phipps, 2005). In trying to defend their views on the tourism intercultural exchange and why tourism matters, Jack and Phipps (2005) argued that tourism provides both a lens onto and an energy for relationships with everyday life and that, in a world of confusion, connections and disconnections between human beings, tourism provides a connect between lives. De Kadt (1984) stated that, “introduction of tourism often provides ample economic justification for introducing, expanding or upgrading the basic infrastructure such as airport, roads, electricity, drinking water, and sewage systems.”

13 major permanent or temporary remunerated activity.” Generally, this definition was the accepted definition in the 40s up until the early 70s when other authors and tourism organizations attempted to refine the definition to include other travellers restricted by the definition. The definition widely used by various scholars in the field of tourism is the

World Trade Organization‟s definition which recognizes tourism as comprising “the activities of persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes not related to the exercise of an activity remunerated from within the place visited” (WTO,

It is believed that modern tourism began with the advent of mass tourism in England during the industrial revolution. Also the rise of middle class and development of relatively speedy and inexpensive means of transportation contributed immensely to tourism development and growth. Commercial airlines such as jet aircraft and modern communication systems are some of the factors which have greatly contributed to the rapid growth of the tourism sector and the expansion of international travel. These among others have led to the rapid growth and development of tourism industry in the world today (Murphy, 1985). In the mid-80s, writers like Murphy (1985) claimed that tourism had become a powerful economic, environmental and social force of global proportions.

He stated that tourism had grown to be among the largest and most significant

14 movements of people in human history and has greater value than in iron and steel, petroleum and ornaments.

The development in definition and models for management and promotion of tourism has led to categorization of tourism into groups. Vanhove (2005) categorized tourism into domestic tourism, inbound tourism, outbound tourism, internal tourism (domestic tourism plus inbound tourism), and international tourism (inbound tourism plus outbound tourism). According to Swarbrooke and Horner (2007), tourism can also be classified according to reason of travel as business tourism where people travel due to occupational commitment, hedonistic tourism being seen as travel for pleasure and social life, educational tourism and lastly, religious tourism.

2.1.0.1. The History of Kenya

Kenya was founded in 1895 under the British Protectorate when Africa was being partitioned at the Berlin conference. As a process, two National languages, Kiswahili and

English, were identified out of the three ethnic categories of Bantus, Nilotes and Cushite.

Kenya is known as the cradle of mankind due to scientific findings of a prehistoric nature dating back 14 million years ago. The earliest settlements recorded 2000 years ago points to several African groups who left their homeland in search of pasture or escaping from conflict. Three of these groups became permanent settlers of Kenya. About the same time, people from the south east and Western Asia sailed to the Kenyan coast. These were groups of Arab and descent who began trading with the African peoples. They

15 established their cultural influence through intermarriages and the Islamic religion. From this mix emanated the Swahili speaking people.

Four centuries later the Portuguese also docked at the coast in search of gold and spices.

They destroyed many of the Arabic settlements gaining control of the Kenyan coast by building a military fort, fortJesus. They also brought with them a new religion –

Christianity. However, in 1729 the Arabs regain control of the fort until the 1800s. At this point in time, many European explorers and missionaries came to Kenya. They infiltrated the hinterland to eventually claim parts of Africa,Kenya included. The new rulers who were now the British began building the Mombasa – Uganda rail road in order to increase trade and accessibility. New centers of administration were formed along this rail road such as Nairobi, Mombasa and Kisumu. Most of the other towns were from settlement formed thousands of years ago and are now popular historical sites such as the Gede ruins. Along the rails road however towns like Nairobi being young cities lack historical buildings.

Kenya established. The British colonization of Kenya in 1892 began a process of

Europeans wrestling control of and access to natural resources such as land and wildlife from the native people. National parks began in the 1930s and intensified in the 1950s while the African settlements were relocated in reserved areas.The parks were designed to protect wildlife for expatriates and as such required the expulsion of residents and their exclusion from local land and wild life uses. The colonial state claimed ownership of all wildlife and issued hunting licenses at prices the African could not afford. This trend of excluding the African has kept up until today. Hotel owners are people of European and

Asian descent with a few African elitist groups who charge exorbitant prices for their services (Graham, 2003).

2.1.0.2. The Economy of Kenya.

Kenya‟s economy is market based and maintains a liberalized external trade system. The economy‟s heavy dependence on rain fed agriculture and the tourism sector makes it vulnerable to cycles of boom and bust. There has been a decline of the country‟s GDP up to a growth rate of only about 1.5 percent in the 90‟s. A myriad of factors have contributed to this down turn which has resulted in the levels of poverty, extreme class differentials and worsened employment rates.

2.1.0.3. Effect of tourism on the economy

The combination of tropical beaches and spectacular wildlife make Kenya a favourite tourist destination. Visitors‟ numbers have grown rapidly from just 61,000 per annum at independence to 925000 per annum in 1996. However security concerns such as the pre- election violence of 1996 and the twin bombings of the American embassy in Kenya and at a coastal resort, an aging infrastructure development, 6competition from new destinations such as South Africa and the recent economic melt have had an adverse effect on this industry. However it still generates 3 percent of Kenya‟s GDP and remains one of the largest foreign exchange earners and employs around 300,000 people directly or indirectly in hotels game parks curio stalls car hire and so on. The problem emanating from these combinations of factors is that of sustainability. Therefore although there has been a strong performance in tourism, risks to continuing robust growth remain.

Visitor

Cultural Domestic Taxes Government interaction tourism local

Park Access roads, water, power,

Tours Community telecommunications, health, security, and education

Breeding Handcrafts

Cultural

Game activities bird Maintenance

Vehicle hire Song/dance

Tradition food Transport

Supplies Fuel

Printing Business skills

Labour Better land usage

Canoe hire Good living

Tent repair Baker V

Furniture Fruit veg. eggs

Uniforms Grocery

Source: Owner

Fig 3: A representation of tourism‟s multiplier effect

As shown in fig 4 above, tourism is described as an industry without chimneys with the greatest multiplier effect. Studies have shown that the employment of one hotel manager has its equivalent to about 3,000 jobs within and in related sectors. In its simplest form it is how many times money spent by a tourist circulates through a country‟s economy.

2.1.1. The Development of Tourism and Tourism Categories

Tourism is an industry that is widely considered as an effective contributor to socio- economic development particularly in less developed countries (Sharpley and Telfer

(2008). According to World Travel Tourism Council (WTTC), as quoted by Sharpley and

19 provides ample economic justification for introducing, expanding or upgrading the basic infrastructure such as airport, roads, electricity, drinking water, and sewage systems.”

In an attempt to offer a technical definition of tourism, Hunziker and Krapf (1942), as translated by Williams (2004), defined tourism as “the totality of the relationships and phenomena linked with the stay of foreigners in a locality provided they do not occupy a major permanent or temporary remunerated activity.” Generally, this definition was the accepted definition in the 40s up until the early 70s when other authors and tourism organizations attempted to refine the definition to include other travelers restricted by the definition. The definition widely used by various scholars in the field of tourism is the

1993). Likewise, various tourism activities and the promotional and managerial activities have equally developed consideration being taken on the evolving political, economic, social, and cultural diversities both in time and place. The Kenya government's recognition of the importance of the environment to tourism is best captured in one government document: “the tourism sector, which provides around 11% of GDP, is largely dependenton healthy woody savannah, coastal, marine and forest ecosystems.

Safeguarding these ecosystems is also vital for maintaining adequate water supplies from the five main „water towers‟ Aberdares, Mt. Kenya, Mt. Elgon, Cherangani Hills and the

Mau escarpment). (GOK, 2008:1).

These among others have led to the rapid growth and development of tourism industry in the world today (Murphy, 1985). In the mid-80s, writers like Murphy (1985) claimed that tourism had become a powerful economic, environmental and social force of global proportions. He stated that tourism had grown to be among the largest and most significant movements of people in human history and has greater value than in iron and steel, petroleum and ornaments.

2.1.2. Tourism Products

There exists much confusion over the definition of a „tourism product‟. As such,

Jefferson and Lichorsh (1989) explained that “tourism product is not a sunny beach, grand hotel, or a flight or indeed any particular attraction, but a satisfying activity at a desired destination”. From this understanding, a tourism product can be described as a collection of physical products and desires that a tourist experiences. For example,

21 sightseeing tourism includes numerous component such as coach and the driver, guides commentary, view from the coach window, entrance to visitor attractions and refreshments. The quality of the experience is the product itself. The way in which the product is defined by the consumer is an important attribute. Armstrong (2004) as cited by Jones et al (2005) reasoned that the place the product occupies in the consumers‟ minds should be recognized. Thus, the correct positioning of a product means that the consumer can recognize it as being distinct from competitor‟s product (Jones et al, 2005).

Organizations offering tourism products have a task to understand consumer needs and wants in relation to competitor‟s products and the nature of the market to exploit. One way to achieve this is by employing the use of positioning maps that help in identifying opportunities in particular market places (Jones et al, 2005). Another useful tool in offering tourism products to the market is by using the technique of branding which includes application of brand names, logos or trademarks to particular tourism products.

The competitive nature of tourism products should take cognizance of the fact that tourism sector is highly fragmented and may consist of many stakeholders who are involved in the provision of diverse activities and services, which combine to form a tourism product, providing a satisfactory visitor experience (Laws, 1997).

The tourism products exist in different forms and categorizing them may not be easy.

However, the different types of tourism products have been grouped into cultural tourism, wildlife tourism, sports tourism, ecotourism, among many other forms of hedonistic, educational, religious and business tourism.

2.1.2.1. Cultural Tourism

Cultural tourism has been viewed to be the oldest form of the “new” tourism products dating back to the days of Romans (McKercher and Cros, 2002). McKercher and Cros

(2002) mention visiting historic sites, cultural landmarks, attending special events and festivals, or visiting museums as comprising the activity of cultural tourism. They also stated that a driving force for cultural tourism is the desire for people to travel to specifically gain a deeper understanding of the culture or heritage of the destination.

Lesoron (1997) argued that cultural tourism offers opportunities to portray the past in the present and thus provides infinite space through which the past can be experienced through endless possibilities of interpretation.

It might be expected that those who involve themselves in cultural tourism are the foreigners. But in some countries like the USA, only 20 percent or less of those who engage in cultural tourism are foreigners and over 46 percent of those who visit the cultural sites are domestic tourists (TIA, 2003), quoted by Richards (2006). Richards

(2006) also writes that the TIA document reported that over 40 percent of those who engage in cultural activities at the tourists‟ cultural sites lived in the local area. Another interesting aspect of cultural tourism is the fact that people working in jobs connected to the cultural sector tend to engage in cultural tourism more frequently (Richards, 2006).

There are different products in cultural tourism across the globe. The UK cultural tourism is predominant in visits to museums and such like galleries whereas in Africa, for example, the recent review undertaken by ATLAS Africa has underlined the

23 concentration of the product on traditional village life and natural attractions (Richards,

2006).

2.1.2.2. Sports Tourism

Many unique sports activities are associated with sports tourism where both the participant and the spectators are considered sports tourists (Jafari, 2003). One of the increasing common reasons why people travel is to participate in or experience sports in one way or another thus increasingly tourism destinations have been developed or rejuvenated through pursuit of sports related tourism development initiatives (Higham,

2005). The development of sports tourism was inspired by the father of modern Olympic,

Baron Pierre de Coubertin, in the belief that it would bring people together thus contributing to a better understanding among nations. This inspiration seems to have been well founded since sports tourism has been shown to provide significant economic impact not only from the hosting of major sports events but also from developed sports resorts and sports attraction (Jafari, 2003). According to reports by Sport Business Association as cited by Fuller (2008), sport tourism is one of the fastest growing areas of global tourism industry. According Higham (2005), the extent and volume of sport-related travel has grown exponentially and that both the tourism and sports sectors have undergone democratization by easing or removing the restriction that limited the access and participation in tourism sporting activities.

However, the findings in the research on sports tourism in Kenya noted that sports tourism is yet to be developed fully for the economic contributions realized elsewhere to be witnessed in the country (Cheruiyot, 1997). Though there is much that remains to be

24 done in this regard, efforts to market the country as a sports destination is ongoing. A simple Google search lists Kenya as one of the countries encouraging tourists to partake in various local sports activities (Fuller, 2008). It has also been recognized that sports tourism is the current vogue for internationally renowned athletes, Eldoret International

Airport and KipchogeKeino Stadium being major gateways for such sporting travels

(Finance, 1996). This can be reiterated by the fact that initially KTB was not of the idea of marketing golf tourism, but in the recent past, has been in the forefront advertising the country as one of the destinations for golf tourism (Hudson, 2002).

Both sport and tourism have diversified rapidly into a range of distinct forms with considerable expansion in the number of those interested in investing or managing the sectors. The diversification is noted when Murphy (2004) classifies sports tourism under adventure tourism and describes the participants of adventure tourism as those who seek some excitement, including the desire to seek for danger.

2.1.2.3. Wildlife Tourism

Wildlife tourism can be said to embrace all the three forms of natural area tourism because it is partly adventure travel, generally nature based, and involves ecotourism‟s key principles of being sustainable, educative as well as supporting conservation

(Newsome et al, 2005). An estimate by Ecotourism Society (1998) cited by Newsome et al (2005) was that 40 to 60 percent of international tourists were nature tourists of whom

20 to 40 percent preferred wildlife tourism.

Wildlife tourism has proved to be an important source of foreign exchange for most developing countries endowed with wildlife. In Kenya, for instance, wildlife alone was the second most important source of foreign exchange earnings in the early 1990s

(Swamson and Barbeir, 1992) and contributed a considerable 13 percent of gross tourist revenues in 1996 (Prins et al, 2000). Sindiga (2000) pointed out that a significant portion of Kenya‟s total tourism is wildlife based and yields substantial and increasing economic returns. However, he further noted that the local people do not benefit because major proportion of tourist expenditure remains with entrepreneurs elsewhere far removed from local communities.

The importance that has been given to wildlife tourism both in the global scale and national policies reveal how wildlife tourism is a source of government revenues.

Tourists themselves place value on protected areas and wildlife species in Africa; a phenomena that can be supported by the accruing of revenues to the governments of wildlife-rich countries (Pins et al, 2000). But just as any important activity, wildlife tourism has witnessed several challenges ranging from disturbances caused to the wildlife by the tourists, conflict between the community and the wildlife, and wildlife protected areas related technicalities (Sindiga, 2000). The dramatic growth of interest in nature tourism has also contributed to the increased dangers of stressing fragile environments and endangered species by overexposure.

2.1.2.4. Ecotourism

A recent development in tourism is the concept of eco-rating. The eco-rating scheme is designed to promote and increase awareness of environmental and socially sound business practices, conserve the natural resource base on which the Kenya‟s tourism depends and improve the overall standards of the tourism industry. According to Visser and Njuguna (1998), the unplanned haphazard tourist facilities at the Kenyan coastal and marine environments had led to problems of overcrowding, trampling and over exploitation of marine resources, such as coral reefs, mellus shells and marine turtles.

This overcrowding resulted to over harvesting of lobsters, prawns and crabs to meet the high demands of tourist consumption, with the fear that some were harvested to near extinction such as lobsters. The problem of congestion, particularly at peak seasons, had resulted to environmental pollution and marine resource degradation, impacting negatively to the quality of the coastal tourist attraction and hence reduced levels in visitor satisfaction.

2.1.2.5. Other Forms of Tourism

Tourism as an industry has variety of products some of which can be placed in more than one category. Beach tourism, for instance, has been classified partly as ecotourism by some and others have grouped it with adventure tourism activities. Most tourists visiting

Kenya prefer beach tourism due to leisure derived from it (Honey, 2008). The other form of a tourism product that has gained prominence in the recent past is sex tourism. Sex tourism has been described as a phenomenon where a tourists travel only to enjoy sex either with their partners or with partners bought or seduced at the destination (Ryan and

Hall, 2001) and there is also the organic tourism or natural area tourism which is a form of ecotourism (Newsome et al, 2002) that is being used to promote organic technologies e.g. organic fertilizers and other organic oriented farming activities

2.1.3. Theoretical review

The strategies for developing sustainable domestic tourism in Kenya within the boundaries of socio-economic factors determining participation in tourism by Kenyans was conceived and developed in the foundation of three important theoretical frameworks. The Personal Construct Theory by George Kelly was relied upon to provide insight on factors that stem from personalities, attitudes, motivation, perceptions, and lifestyles of the Kenyans as concerns domestic tourism. Promotion of the new and old tourism products and destinations within the country were based on the insight of

Elaboration Likelihood Model (ELM)by R. E and J. T. Cacioppo (1986) for behaviour change modelling. And finally the philosophy and principle of the Growth Pole Theory by Francois Perroux (Gove, 1984) wassuggested as an appropriate model for jump starting the domestic tourism industry in Kenya.

2.1.4. Personal Construct Theory (PCT)

Personal Construct Theory (PCT) is a social psychological theory of personality concerned with the way individuals construct meaning of events that was developed by

George Kelly (Dacko, 2008). The theory postulates that a “person‟s processes are psychologically channelized by the ways in which he anticipates events” (Partington,

2002). Frain (1999) quoted Kelly‟s (1955) definition of a construct as “a way in which two things are alike and by the same token different from a third”. At the level of structure, PCT suggests that meaning is a matter of construct; that “an individual attributes meaning to an event not only by constructing what it is but also by differentiating it from what it is not” (Craighead and Nemeroff, 2004). At a broader level,

Craighead and Nemeroff (2004) explained that individuals, social groups, and whole cultures orient themselves according to shared constructs which provide a basis for self- definition and social interaction.

Craig and Nemeroff (2004) mentioned the application of PCT in psychotherapy, the first area where PCT was applied by its originator. The tool that was developed by George

Kelly to solicit for information from his patients, the Repertory Grid Technique, has, since its inception, been applied in a wide variety of disciplines such as education, negotiation arbitration, interpersonal relationships, and business as a knowledge acquisition technique to elicit task information for experts (Jonasseset al, 1999). Personal

Construct Theory by George Kelly (Dacko, 2008) has also been of considerable use in the evaluation of consumer perceptions of travel destinations. It can provide a tourism marketer with both a theoretical lens and research methodology to understand better the nature of individual‟s meaning and perceptions of a marketer‟s offerings, including that of travel destinations (Dacko, 2008).

2.1.5. Elaboration Likelihood Model

Elaboration Likelihood Model (ELM) is a concept developed by R. E and J. T. Cacioppo in early 1980s for understanding how attitudes are formed and changed. As explained by

Hansen and Riss (2007), the model distinguishes between two routes of persuasion namely the central route and the peripheral route, each route being determined by motivation and level of elaboration employed when making choices. Witte et al (2001) showed that ELM focuses on how information is processed and the relationship between information processing and behaviour change, where, according to Larson (2009), elaboration in ELM refers to the conscience scrutiny of available information in making an evaluative judgment and required both the motivation and ability to process information.

When a person has sufficient information regarding available choices and is highly motivated to evaluate the information, this person opted for the central route (Larson

2009) whereas little interest, ability, and/or motivation to think about a message, the person tended to make choices peripherally (Witte et al, 2001). Central route has been shown to be associated with permanent changes in consumer behaviour because the final decision is based on informed choices. However, peripheral route has been widely used in advertising campaigns given a person‟s tendency to utilize mental shortcuts when making most of person buying decisions.

To attract citizens to domestic tourism, both central and peripheral routes to information processing and ultimate decision making must be utilized to both persuade the citizens to

30 participate in domestic tourism activities and ensure permanency of this participation.

ELM thus offered an important philosophy for modelling domestic tourism promotional strategies in this study.

2.1.4. Growth Pole Theory

Growth pole is a concept of economic growth that was conceived by a French economist,

Francois Perroux whose work was concerned with identifying the characteristics of propulsive units and their growth inducement mechanisms (Gove, 1984). Growth pole theory proposes a unit of economic growth; for instance an industry that has the capacity to induce the growth of another unit. As Wen and Tisdell (2001) explain, growth pole is a set that has the capacity to induce the growth of another set. That is, as Parroux stated in his work as cited by Gove (1984), growth pole is “a propulsive unit in a given environment”. In industrial terms, growth pole is the industry that propels the growth of other industries. The key industry that propels the growth of other industries has been identified as the lead industry, the propulsive industry, leading industry, or master industry. This industry creates poles of growth and centres of innovative change, which are called regions and a periphery, may be defined by its relationship of dependency to the core (Wen and Tisdell, 2001). House (1978) pointed out that economic growth does not appear everywhere at the same time, rather, it is manifest in points of growth poles and expands in different channels with variable intensity throughout the economy.

The concept of growth pole simply recognizes the contribution of propulsive units of economic growth to other units of growth. In this regards, tourism as an industry has been

31 considered an appropriate growth pole industry as an effective pole for economic growth given its multiplier effect (Wen and Tisdell, 2001; Gove, 1984). Strategies for funding particular products or areas can also be developed on the foundation of growth pole concept. Murphy (2004), when talking about „place based organisations‟ in her book

“Strategic management for tourism communities: bridging the gap”, singled out the

Convention and Visitor Bureau (CVB) as an organization that has identified the benefits of combining resources at key locations, particularly expertise and skills from a cross- section of industries and sectors. The resource allocation at key areas of development for

Local Enterprise and Development (LEAD) concept was based on the fundamental principle of growth pole, in that LEAD provides government grants and a supporting tourism framework for the development of „lead‟ businesses in „growth pole‟ areas, so that those areas attract additional investments and generate sustainable tourism growth.

2.2.0 EMPIRICAL REVIEW

2.2.1 Domestic Tourism

Domestic tourism is the tourism activities practiced by the residents of a particular country within their own country (Jafari, 2003). Domestic tourism is arguably one of the oldest forms of tourism on the planet given that international tourism could only be effective with the advent of transportation technologies for long distance travel.

Sharbrooke and Horner (2007) recognize that the desire to travel in the earlier times was particularly driven by the religious obligations e.g. the pilgrimage journeys in Judaism and Islam. Though Sharpley and Telfer (2008) state that “tourism can be described as the largest peaceful movement of people across cultural boundaries in the history of the world” by referring to Lett (1989), it is evident that such early forms of tourism could only be, in a large extent, domestic tourism.

Domestic tourism as an industry has for a long time been a spontaneous phenomena without proper planning and management. However many governments have come to recognize the significant contribution of domestic tourism in their national economies.

The recognition of domestic tourism in the United Nation‟s Manila Declaration of 1981 on world tourism helped in the acceleration of interest by world governments in this sector. In Australia, it is reported that over 75 percent of tourism activity is catered for by domestic tourists (Cooper and Hall, 2005). Cooper and Hall (2005) also give the figure of

4.3 percent as the contribution tourism gave to Australian‟s Gross Domestic Product in

2000/2001 of which 76 percent of the figure came from domestic tourism. Success of domestic tourism in Australia can been seen in the effective management of the sector by

33 the establishment of tourism institutes such as the Tourism Research Australia that provides research information to support improved decision making, marketing and tourism industry performance for the Australian community (www.tra.australia.com).

Becken and Hay (2007) provide insightful figures of the ratio of domestic tourism to total tourism activity in top ten countries. Listing USA, China, India, UK, Brazil, Germany,

Indonesia, Canada, France and Japan as the top ten countries in the world in total tourism activity, the percentage of domestic tourism out of the total tourism activity in these countries are all above 75 percent except for Germany that has 48 percent. Apart from

India, Brazil and Indonesia, the countries listed come from the developed world. These countries enjoy the benefits of mass domestic travel where people travel in millions. In

China, it is estimated that 644 million Chinese travel domestically. India enjoys 320 million, Brazil 176 million and UK 134 million.

The case in China is remarkable since domestic tourism wasn‟t given much attention at first. International tourism was on the other hand given priority for development in the

1980s due to limited infrastructure and domestic service industry still remained a low priority. This has been claimed to have been due to the perspective of China that domestic tourism was an unproductive. However, it is being reported that in 1992, the

Central Committee of Chinese Communist Party and the State Council made the important decision to increase the relative size of tertiary industry in the Chinese economy which helped to change the policy direction. A positive attitude thereafter

34 developed which was adopted towards developing the domestic tourism industry (Wen and Tisdel, 2001).

Though domestic tourism has taken a strong foundation in the developed world, the developing countries are yet to reap the benefits associated with investing in the industry

(Sharpley and Telfer, 2008). It is pointed out that domestic tourism can deflate the negative economics of international tourism that allow profits to be channelled back to the countries where the tourists come from (most tourism related businesses e.g. five star hotels and tour operator companies are owned by the foreign investors) by allowing locally owned tour businesses to be controlled locally. The fact that considerable amount of generated tourism revenue from tourism in underdeveloped countries are being returned directly to the tourist-generating countries has been supported by studies of national economies (Murphy, 1985).

Studies have acknowledged the importance of domestic tourism and have at the same time pointed out that international tourism has, over the years, taken precedence in terms of management and promotional strategies. Page (2002) refers to Pearce‟s (1995) acknowledgement that the scale and volume of domestic tourism worldwide exceeds that of international tourism though being viewed as a poorer partner in the compilation of statistics. Page (2002) argues that most statistics tend to underestimate the scale and volume of flows since certain aspects of domestic tourist movements are sometimes ignored in official sources. Nonetheless, domestic tourism has been considered as having the advantage of being less dependent on foreign political situations and that it provides a

35 rare area where nationals can exercise some control since it can be influenced by its own people and by its own leadership Walton (2005). Walton (2005) also views domestic tourism to be worth more than that of international tourism both in terms of volume and value. This is also mentioned by Sharpley and Tefler (2008) by stating that domestic tourism provides many of the benefits of international tourism such as employment, income, new business development and economic diversification.

From the foregoing, it can be seen that domestic tourism has transformed from a state of spontaneous travel to being marketed and organized by travel services (Wen and Tisdel,

2001). The free online encyclopaedia, Wikipedia, contains a tourism article that reports on the recent emphasis on domestic tourism. The article claims that domestic tourism has taken a new level wherein the tourism industry has shifted from the promotion of inbound tourism to the promotion of intra-bound tourism because many countries are experiencing tough competition for inbound tourists (www.wikipedia.org/wiki/tourism, March 4

2016).

The policy development and planning for the growth of domestic tourism have thus become issues of growing significance for many developing countries; Kenya included

(Sindiga, 2000; Rogerson and Visser, 2007). The promotion of domestic tourism will necessarily require some reversal of the established policy biased towards the attraction of international tourism, which is evident in national tourism planning as pursued by many African countries (Rogerson and Visser, 2007).

2.2.2.Tourism Industry in Kenya

Tourism has been Kenya‟s leading foreign exchange earner for decades. From 1993, tourism earnings have consistently surpassed the combined earnings from coffee and tea

(Kenya Economic Survey, 2002). The relative importance of tourism in Kenya‟s economy has also risen steadily over the last 40 years and due to this, the government has continued to spearhead tourism development as a source of foreign exchange earnings, job creation and economic growth. (Dieke, 1991; Sindiga, 2000).

Table 2.1: National earnings from coffee, tea and tourism in billions of Kenya shillings

YEAR 1997 1998 1999 2000 2001 Coffee 68 51 64 98 54 Tea 220 294 248 236 294 Tourism 22,640 17,509 21,367 21,553 24,239 Source: Kenya Economic Survey, 2002

Corresponding with the number of tourists is an increase in the country‟s total tourism receipt from Kshs 30 million in 1990 to Kshs 713 million in 1999 and it is being projected that the receipts will hit the Kshs 990 million mark by the year 2015 (Akama,

2002). But the impressive statistics and declared national goals on tourism development do not present a complete picture of Kenya‟s tourism industry. In recent years tourism researchers Bachmann (1988), Sinclair (1990), Dieke (1991) and Sindiga (2000) started to question the role played by tourism in Kenya‟s economic development and what effort the tourism industry is giving to improve the economy. Sindiga (1995) observed that more than 75 percent of the hotel guests in the major tourist areas were either resident

European or Asians who are known to spend their holidays at the beach, occupying tents and cottages or putting up with relatives (Bachamann 1988). Bachmann adjusted the extent of domestic tourism among Kenyan Africans to be “very rare”.

Table 2.2: Tourism visitors’ arrivals in Kenya by purpose of visit 1997 – 2001

Visitor types 1997 1998 1999 2000 2001 Holiday visitor 804,800 686,900 778,200 778,200 728,600 Business visitor 1,011,700 86,800 94,400 98,300 92,100 Visitors in transit 72,300 101,900 107,400 138,500 152,600 Other Visitors 21,800 18,700 20,600 21,500 20,100 TOTALS 1,000,600 894,300 9,693,00 1,036,500 99,3600 Source: Central Bureau of Statistics, 2002

The tourists visiting Kenya seem to be attracted by the country‟s enormous diversity and unique attractions on offer, enhancing hospitality of the people, political stability and its rich supply of cultural heritage (Mbova, 1996). There also exists abundant wildlife and birdlife, Kenya‟s diverse culture that is both complex and fascinating, the outstanding scenic beauty, the vast savannah grasslands, the desert regions of the north and their unique land forms and vegetation, the majesty of the highlands, the glaciated peaks of

Mount Kenya, the grandeur of the great Rift Valley, and the allure of the captivating

Indian Ocean (Akama, 2002).

As the industry in Kenya continued to mature over the years, there has been a corresponding growth in awareness as to its capacity for damaging the environment and the quality of life of those living in tourist destinations. This together with the fierce competition Kenya is currently facing from vast number of new destinations in other

38 parts of Africa for example Botswana, South Africa, Uganda, Tanzania among others that offer unspoiled environment and fresh new images, as well as obvious novelty value,

Kenya‟s actual share of total tourist arrivals has been on decline. This was attributed to global recession coupled with adverse publicity targeted at Africa and at Kenya in particular at the international media (Mbova, 1996). Reversing this declined will be the major challenge for Kenya‟s tourism industry during the years ahead.

During the last ten years, Kenya‟s tourism has been characterized by fluctuation of arrivals from 1 million in 1997 to 0.99 million in 2001. There was a marked decline to

0.89 million in 1998 before a gradual increase to 1.03 million in 2000. Tourist arrivals recorded wide fluctuations over the period 1997 to 2001. In the year 2001, there were substantial decline in visitor arrivals that resulted in an overall drop of 4.1 percent (CBS,

2002).

To allay the decline of tourism, the Kenya government has in the recent yearsincreasingly turned to the promotion of tourism. It was noted that the increasing importance of tourism is related to the increase in the number of international visitors to Kenya and as such

Kenya has fostered international tourism mainly on economic benefits platform. The government commitment to foster international tourism was renewed by the Minister of

Tourism during the takeover of K.I.C.C building from the former ruling party KANU

(Daily Nation, March 2003). It is evident that the Kenya government tourism policies, development, marketing and promotional programs are targeted to international market.

This was amplified further by an article by the Chairman of Kenya Tourism Board entitled “Tourism target foreign exchange earner” (Matiba, 2002).

Source: Kenyan National Bureau of Statistics 2007 f Fig 4: Hotel bed nights by zone, 2006 Fig 2.1 - Hotel bed nights by zone - Kenya Tourism Board, 2006

A recent report by the Ministry of Tourism posted in their website, www.tourism.go.ke, has this to say on the current nature and state of tourism industry in the country:

Currently, Tourism accounts for 10 percent of the Gross Domestic Product

(GDP), making it the third largest contributor to Kenya‟s GDP after agriculture

and manufacturing, and Kenya‟s third largest foreign exchange earner after tea

and horticulture. Tourism has been identified as one of the key drivers in

achieving the goals of the Vision 2030. Tourism plays a major role in the

economy of the country. In 2007, the tourism sector maintained an upward growth

momentum by recording approximately 2 million visitor arrivals, up from 1.6

million arrivals in 2006, reflecting a 12.5 percent growth.

Consolidated tourism earnings expanded from Kshs. 56.2 billion in 2006 to about

Kshs. 65.4 billion in 2007, reflecting an 11.6 percent growth… [Tourism] is now

the leading economic sector in Kenya recording the highest growth in the

economy at 13%. It contributes about 12% of the country‟s Gross Domestic

Product and accounts for over 9% of total wage employment, and is also a major

source of Government Revenue in the form of taxes, duties, licence fees, entry

fees, etc.

In addition, tourism through its multiplier effect has the capacity to promote

regional development, create new commercial and industrial enterprises, stimulate

demand for locally-produced goods and services and provide a market for

agricultural products. Tourism development in Kenya has therefore led to

economic growth and povertyeradication

(http://www.tourism.go.ke/ministry.nsf/doc/Facts%20&%20figures%202007.pdf/

$file/Facts%20&%20figures%202007.pdf)

2.2.3.Domestic Tourism in Kenya

In 2007, the promotion of tourism got a notch higher when the Domestic Tourism

Council of Kenya was launched. Its mandate was to create awareness and educate

Kenyans on the facilities available in the country and to counter negative impacts like poor booking habits by Kenyans, high airfares and accommodation rates especially for groups or families and to launch media campaigns to advertise other destinations other than Nakuru and Mombasa (Business Daily, 2007).

Domestic tourism offers a paradigm shift from the traditional safari and coast tourism products that were part of the anachronistic colonial model designed to meet the needs of an elite ex-metropolitan European clientele. Apart from a few snippet developments the identification of alternatives remains one of the key unexplored areas of Kenya‟s tourism industry (Jones, 2005) hence, the reason behind the failure of marketing campaigns strategy such as, “Magical Kenya” in bringing significant growth in domestic tourism.

The process is slow and gradual such as the use of e-tourism which was initiated in 2004 by the United Nations Trade and Development Agencies to help them sell tourism online.

Some agencies and tour firms have adopted this as an efficient marketing tool. An example is the leisure and travel guide which has a web site; www.goplaceskenya.comthat facilitates bookings and payments online. This development has been slow in catching on and quick study has revealed that most local tour agencies rely on brochures and seminars to market their product (The East African

Standard, 2009). Testimony has shown that if the needs of online travel agencies and online travel suppliers can find a way to meet the needs of customers exactly then the web can be a superior method for creating a base for lead generation and sales (Mills,

2005).

Another more popular strategy is the “Tembea Kenya” initiative that encourages conference tourism or incentive tourism for corporate or individuals respectively (Daily

Nation, 2009). This initiative has been implemented at some hotels like the White Sands beach hotel at the Kenyan northern coast. This niche market sub sector is also popular in

42 smaller hotels where it has shown to increase demand for domestic tourism. An example is the Tribe Village Hotel and the CrownPlaza in Nairobi central business district.

Closely related to conference tourism is the promotion of team building venues where times outside the conference room can be spent exploring various team building activities such as water rafting. Conference and exhibition tourism categorized more specifically under business tourism are a “special kind‟ of tourism as theoretically they represent the propelling factor of attendance rather than the characteristics of the destination itself. The bonus factor is space and a different environment from the office (Rogerson, 2007). Since this type of tourism is becoming more and more competitive more hotels are building conference facilities in order to capitalize on this new emerging tourism sector.

Ecotourism is a new concept that is being explored by local travellers. Nature lovers or scientist could visit the nature trail for example Haller Park in Mombasahas successfully contributed to the conservation of its environment without compromising its attraction.

This site is popular for educational trips and offers reasonable rates for group tours. In some areas some indigenous people are concerned about the encroachment of their natural habitats by tourism such as the Kayas, traditional shrines for the Mijikenda people. They usually want to see how the negative impacts of this process will be overcome by the positive ones before they welcome tourism (Blades, 2000). The prospects of alleviating poverty in local communities through tourism have been cited in

Kenya Economic Recovery Strategy for wealth and employment creation (Jones, 2005).

Besides the anticipation of revenue, Ecotourism can be used as a vehicle for wealth distribution and national integration in Kenya.

2.2.4 Determinants of Participation in Tourism

Page and Connell (2006) defined determinants of tourism participation as exogenous or external factors that shape the general demand for tourism within society or a specific population but referred to personal factors affecting an individual's desires and choices as motivations. In this study, both Determinants and Motivations are treated as

Determinants since bothdetermine whether an individual will participate in tourism or not, and when participation happens the very factors play significant roles in determining the extent to which participation happens (Vanhove, 2012). Common factors listed by these authors such as Cooper (1993), Richards (1996), and Vanhove (2012) as directly affecting participation in tourism include Gender, Education, Marital Status, Income,

Occupation, Place of Residence (either urban or rural setting), Access to Information

(marketing reach, government policies, etc), and Infrastructures.Vanhove (2012) further grouped the factors into categories where he had income, pricing and time grouped under economic factors, with the other determinants falling under demographic and geographic factors, socio-cultural factors, mobility factors, government regulatory factors among others.

2.2.4.1. Income

Vanhove (2012) states that the most important group of factors that drive participation in tourism is the economic factors, and key to these factors is the income and specifically the disposal income of a population. He furtherdiscussed the relationship between disposable income and outbound tourism demand as having an income-elasticity

44 relationship whereby when disposal income increases by 1 percent, demand for outbound tourism increases by more than 1 percent.

Earlier studies of visitors visiting museums in Europe concluded that tourism participation rates are much greater in high socio-economic groups and professionals with results from Merriman's analysis of 1991 indicating that whereas the proportion of

French population in the high socio-economic groups visiting museums had increased since early 1970s, the proportion of working class visitors had decreased (Richards,

1996).

The level of disposal income groups people into economic classes, which together with professional achievement of individuals, determine the type and nature of tourism products demanded. Richards (1996) reported that those likely to participate in cultural tourism are members of a population that are highly educated in arts and cultures and at the same time are high income earners.

2.2.4.2.Education

According to Richards (1996), studies have determined thatparticipation in cultural tourism largely depends on levels of education of the participants. Schuster (1993) is quoted by Richards (1996) as arguing that "the difference in participation rates across educational levels is greater than across income levels, indicating that education is a better predictor of an individual's probability of participation than income".

Torkildsen (2012) explained the importance of education on demand for tourism products thus; "education broadens horizons and stimulates the desire to travel. Also, the better educated the individual, the higher the awareness of travel opportunities, and exposure to information, media, advertising and sales promotion".

2.2.4.3. Place of residence

Place of residence determines demographic and geographic factors that affect a person and these factors were identified by Vanhove (2012) has being determinants of tourism participation. A number of literature reviewed on the subject of determinants of participation in tourism demarcates place of residence as either rural or urban, with authors such as Cooper (1993), Page and Connell (2006), and Vanhove (2012) agreeing that most tourists reside in urban areas. Specifically Vanhove (2012) noted that "In the

21st century cities and towns are the propellers for modern stay day visits" and at the same time the cities being the generators for attractions.

Place of residence has also been noted to influence people's attitude towards certain tourism products. For example as Vanhove (2012) exemplifies, people from northern climates believe that lying on beaches has therapeutic remedies whereas most tourists from other climatic regions believe that the ozone layer depletion has increased the incidences of skin cancer so they tend to avoid sun-bathing at the beaches.

2.2.4.4. Gender

Page and Connell (2006) acknowledged that even though gender is an important determinant of participation in tourism, it is a topic that has not been researched or discussed, citing that the only times gender feature in the tourism discussions is when products of tourism such as sex tourism are being examined. According to Page and

Connell (2006), the importance o gender as a determinant of participation in tourism goes back to the basic relations between individuals - which is primarily gender based. The next level they identify is in the role of sexes especially at household level where in most cases women have the responsibility of household organization and child care whereas the decision making on whether or not to travel, where to travel and the tourism product to buy tend to be the responsibility of men. As an example from motivational angle, Page and Connell (2006) contend that a woman may not be motivated to consider opting for self-catering holiday as the product may not provide a means of escape from the home environment.

2.2.4.5. Mobility

The ease with which an individual can move from the place of residence to travel destination impacts greatly on whether or not the individual will prefer to travel

(Torkildsen, 2012). Vanhove (2012) further explains that in areas with poor transport systems e.g. poor roads, inability to easily access airports and air strips, lack of modern rails greatly hinders the ability not only for foreign visitors to visit remote tourism sites but also discourages citizens from visiting their own country. Page and Connell (2006) explain that since a tourism product is the entire experience that begins at the onset of the

47 journey to the experience received from tour guides, mobility plays a central role in determining whether a tourist would visit a particular destination repeatedly.

As Cooper (1993) noted, tourists with ready access to cars either through rentals or family cars participate significantly higher in domestic tourismthan those who do not have immediate access to cars. This can be readily appreciated when requirements by most tourism destinations especially those of national parks and game reserves require visitors to be in cars before entering the parks.

2.2.4.6. Urban Form Factors

Boarnet& Crane (2001) studied the travel activities of 7469 households in Orange County and San Diego in 1993 and 1986. The two-day travel diary and telephone interview provided data for 7649 persons and 32,648 trips in total. The research aimed to determine what influence the landuse patterns of Orange County and San Diego has on travel behaviour. The authors found an extremely complex relationship indicating that land use and design proposals will influence the price of travel and hence the type of trip undertaken. Thus they indicate that urban form can influence travel behaviour. However, in another study by Boarnet& Sarmiento (1998) in Southern California the relationship between landuse variables and travel behaviour was found to be statistically insignificant.

It is interesting to note that this project studied non-work car trips over a two-day period only. The research found that the socio-demographic variables were more significant statistically. For example, women, and particularly women with children, were more likely to make non-work trips than men, and older people made fewer non-work trips.

Cervero (2002) studied travel behaviour in Montgomery County, Maryland using data from the Household Travel Survey of 1994. Cervero studied the impact of „new urbanist‟ areas on travel modes, more specifically whether compact, mixed-use and pedestrian- friendly developments could significantly influence travel modes. The author used a normative model which assessed the influence of three core dimensions of the built environment, namely density, diversity and design. The study found that the density and mixture of landuse was a significant influence in determining travel mode particularly in the decision to use public transport, share a car or drive alone. The study shows that higher gross densities lowered the occurrence of solo-commuting, ie driver-only car commuting. In addition, Cervero found that workplace destinations with a higher density of mixed land use produced a higher level of public transport use. The issues of congested ambient traffic conditions and greater provision of public transport options in these destination areas is discussed but their relative importance and influence is not resolved. However, the influence of urban design was relatively modest. The author does not discuss this in great detail but describes the impact of sidewalk ratio as the most important built environment variable. Thus, areas with a well-developed sidewalk or pavement infrastructure appear to encourage commuters to take the bus or, surprisingly, join a car-pooling or „vanpooling‟ initiative (Cervero, 2002).

Goudie (2002) studied the travel behaviours of 408 households in Townsville and Cairns in 1996 – 1997. The research found that location played a large part in fuel consumption and distances travelled. Thus participants located in the outer urban/suburban areas used

49 on average three times more fuel than the more centrally placed participants. Outer urban dwellers had the least sustainable travel behaviours which the author felt posed a challenge for policy-makers, developers and city planners. Guiliano& Narayan (2003) studied the travel behaviours of US and British populations and found that the US land use patterns reinforce vehicle dependence particularly in the sprawling suburbs of the major metropolitan regions. The authors suggest that the stronger urban planning and design controls in European countries have led to a more compact and higher density urban form and hence an increased use of public transport. The differences in travel behaviours between the American and British participants in this study were explained by the differences in urban form and household income. The study shows that in the UK the number of daily trip rates is not significantly influenced by income although the distances travelled do increase with income.

Soltani&Primerano (2005) studied 9000 randomly selected households in suburban

Adelaide to determine if urban form influenced travel behaviours. The main research question was whether pedestrian-orientated urban environments with high density mixed land use and high quality urban design would reduce car use and hence increase the market share of more sustainable modes of transport. The authors concluded that this was the case although warning of the limited range of suburbs used in the study. Participants in the study were located in only four suburbs of uniform size and density. Thus, the authors warn that it might be misleading to infer that the results would be applicable to the wider metropolitan regions of Adelaide, let alone other metropolitan centres. The study found that low-density, single use, large area zoning usually found in conventional suburbs limited the ability of participants to walk or cycle for their daily travel

50 requirements. Proximity to local shopping and service centres and local networks encouraged a wider choice of sustainable travel modes. Conversely, the location of suburban development away from major activity centres encouraged the use of the private car and decreased the use of other travel modes.

Naess (2003) and Naess& Jensen (2004) studied the influence of residential location on travel behaviour in Norway and Denmark. In particular they studied residential location and the distance from the city centre on travel behaviour and found a number of significant relationships. The closer the participants lived to the centre of the city then the more likely they were to walk or use a cycle to get to the facilities located there.

Srinivasan& Rogers (2005) also studied the impact of urban form on travel behaviour but within two suburbs of Chennai, India. Seventy households including 146 adults aged 16 years or more participated in the study using a one-day travel diary. This paper is of particular interest as little research work has been undertaken on travel behaviour and modal choice in developing countries and India in particular. Two significant variables were accessibility to transport modes and the location of employment opportunities.

Participants in the more densely populated areas of central Chennai were more likely to use non-motorised modes of travel (walking and cycle in particular) than those located in peripheral areas. The authors suggest that this is due to the location of employment opportunities located in central Chennai. The location of employment opportunities should be considered in the planning of new housing particularly for low-income households in order to reduce travel times and distances.

Few papers studied rural travel behaviour. One produced by Nutley (2005) studied the travel behaviours of two rural populations in Northern Ireland from 1979 to 2001, namely rural Antrim and North Sperrins. Surveys were undertaken in rural Antrim in 1979, 1989 and 2001 whereas the North Sperrins area was surveyed in 1988 and 2000. It is worth noting that the two areas provide geographical as well cultural differences in that the

Antrim area contains a well-developed network of service centres whereas North Sperrins is a marginal and remote area with poor infrastructure and economic opportunities. In addition, rural Antrim is a predominantly Protestant/Unionist area whereas the North

Sperrins area contains a predominantly Catholic/Nationalist population. The questionnaire was modified between surveys and the Antrim area was reduced in size in the 1989 and 2001 surveys. 595 households were interviewed in the Antrim area in 1979 and this reduced to 252 in 1989 and 226 in 2001. 194 households were interviewed in

North Sperrins in 1988 and this increased to 208 in 2000. It is unclear whether different households were interviewed in each survey or whether there was any consistency in participants throughout the research time-period. The author found significant changes in travel behaviour over this time-period with a rapid increase in car ownership throughout the 1990s and a substantial reduction in the provision and use of public transport. In addition, whilst trip duration and distances were predominantly local in 1979 this had changed significantly by 2001 to include longer commutes to regional town centres for employment and leisure facilities.

2.2.4.7. Psycho-social Factors

A smaller number of studies examined the influence of various psycho-social attributes on travel behaviour. Hiscock et al (2002) studied the perceived psycho-social benefits of car use and ownership. In particular the authors studied the significance of the car as providing protection, autonomy and prestige compared with public transport. This study took place in southern Scotland in early 1999 and included postal questionnaires and in- depth semi-structured interviews with car owners and non-car owners. The results found that there were some psycho-social benefits to car users. Car users felt that they gained protection, autonomy and prestige from their car and car-ownership gave them „street- cred‟. Their car provided them with protection from „undesirable‟ people, provided autonomy, convenience and greater access to a greater range of destinations than public transport. Socially desirable attributes such as competence, skill and „masculinity‟ were also perceived to be derived from car ownership. People who didn‟t own cars were felt to be eccentric particularly those who chose to travel by bicycle.

Cullinane (2002) found similar psycho-social perceptions amongst students attending five universities in Hong Kong. The findings are interesting in that car ownership was extremely low amongst the participants, less than 1% owned a car at the time of the study with the overall Hong Kong population having car ownership levels of 49 cars per 1000 population in 1999. Forty percent of participants felt that public transport was plentiful and low-cost in Hong Kong and suppressed their demand for a car. Few participants felt that they would own a car within the next ten years. However latent demand was high

53 particularly amongst male students. Male participants were more likely to feel that car ownership would improve their image and their life.

Anable (2005) used the Theory of Planned Behaviour (TPB), a psychological theory of attitude-behaviour relations, to study the behaviours of leisure travellers to a tourism location in England. The TPB assumes that behaviour is guided by behavioural beliefs, normative beliefs and control beliefs. Normative and control beliefs are important in this model as they include beliefs about the expectations of others and the motivations to comply with those expectations. Thus motivational beliefs may modify travel behaviour through perceived social or peer pressure. Control beliefs include the presence of factors which may facilitate or impede that travel behaviour. Anable included a number of other measures including environmental attitudes, efficacy and habit in this study. The study is limited to a particular type of travel behaviour namely a leisure trip to a tourism destination and it would have been interesting to apply this method to a larger sample of participants with a more comprehensive range of travel behaviours.

2.2.4.8. Pricing

Travel cost can also influence travel behaviours. Hensher& King (2001) studied the availability of parking spaces and the cost of parking on travel behaviours in Sydney.

Participants were required to consider six alternative scenarios for parking in the CBD, in a park and ride facility, switch to public transport or forego the trip to the CBD altogether. It was found that in 97% of the responses the cost of the parking option was the most significant factor which determined travel mode.

A study by Handy et al (2005) found similar outcomes despite a very different cohort of participants and methodology. This study used focus groups and face-to-face interviews with students and staff at the University of Austin, Texas to determine if Americans drive by choice or through necessity. Despite the relatively limited nature of the study, four neighbourhoods in the San Francisco Bay Area yielding 1682 participants, the data suggest that Americans drive due to the price of and lack of suitable modal alternatives.

Therefore, the authors suggest that a stronger policy agenda is required to reduce the need for driving through the provision of public transport infrastructure at a suitable cost.

2.2.4.9. Other determinants

There are several other socio-economic factors that influence whether or not individuals participate in tourism, and these include demographic factors such as age and population size, socio-cultural factors such as religious beliefs, geographic factors like the climatic conditions of an area of residence, and government policy issues like requiring employers to provide paid holidays to their employees (Cooper 1993; Page and Connell 2006;

Vanhove 2012; Torkildsen, 2012). The factors, as explained by the authors, contribute to the decision of individuals on whether or not they will travel to a tourism destination.

These factors when taken together, should inform tourism stakeholders in Kenya on the viable policies, legislative agenda and marketing approaches that can be used to develop and grow a sustainable domestic tourism industry in Kenya.

2.3 Conceptual Framework

The conceptual framework for the establishment of a sustainable domestic tourism in

Kenya utilized in this study is in line with new school of thought of a Steady State

Economy, that according to Daly (2014), is an economic state whereby the aggregate throughput of an economy is constant, but individual components of inputs and outputs are allowed to vary according to prevailing market conditions.The conceptual framework however recognizes the fact that the Steady State Economy is only pursued once the economic performance of a particular industry or nation has been optimized through

Growth Economy.

The framework considered the current needs of resource users (domestic tourists‟ constructs) and future generations‟ needs in order to maintain sustainability. Sustainable tourism has been identified as an industry committed to making a low impact on the environment and local culture while helping to generate income and employment for local people. Domestic tourism in Kenya is being seen has an effective tool to both sustain the tourism sector and provide the avenues for effective management of local resources, meeting the needs of the local tourists, and creating a stable source of national income for economic stability.

The tourism industry in this study was recognized as comprising of domestic and international tourism. The products of tourism such as sex, wildlife, culture, conferencing, beach tourism, sea tourism, and sports tourism among others were considered as most appropriate for developing domestic tourism and proposed to be

56 marketed in accordance with Elaboration Likelihood Model. People‟s construct and the impacts of strategic planning construed from the constructs were considered to have impacts on economic, political, and policy issues that in themselves also contributed to the nature and extent of tourism products and the tourism industry. In summary, the role played by tourism in order to develop, market, and promote tourism products by use of various channels such as the media were expected to impact on the industry and the society in realms of education, economy, social structure, and even in the international scene.

Conceptual Framework

Independent variables

SOCIO-ECONOMIC FACTORS Dependent variable

Income Gender Marital Status Participation in Domestic Location Education Tourism Occupation Mobility Age

Intervening variables

Legal Framework Policy Framework Marketing Strategies

Source: Author (2014)

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research Design

The overall research designs for this study were the survey and cross sectional-research methods which, according to the Handbook of Survey Research by Marsden and Wright

(2010), employsystematic standardized approaches to collecting information on individuals, households or organizations through questioning systematically identified samples. Further, the broad guideline of research methods as provided by Couperet al

(1999) in the book, “Computer Assisted Survey Information Collection” was considered during data collection and analysis. The broad principles states:

“First, a population…of interest is defined to which the survey results will be

generalized. Second, a sampling frame…of the population members is

constructed. Third, probability methods are used to select a sample from the frame

of the appropriate size to reach reliable conclusions about the population. Forth, a

carefully designed and pretested questionnaire (instrument) is prepared containing

the questions to be asked of the sampled respondents. Fifth, largely successful

efforts are made to complete all relevant items of the questionnaire with each

sampled respondent under relatively comparable conditions of statistical theory to

infer conclusions about the defined population.” Couper et al (1999:6).

This study adopted the descriptive and exploratory research methodology (Hakim, 2000) which included the definition and description of the study population, sampling frame, sampling procedure, questionnaire type and the administration process, and the data

59 collection criteria. The methodology also outlined the process and methods of monitoring data collection, data cleaning process and tools, and the data analysis and the analytical tools(Fink, 2005).

Descriptive and exploratory research methodology was chosen for this study as it has been viewed as useful in unearthing associations (Goodwin and Goodwin, 1996; Monsen and Horn, 2007). Since the variables that were gathered through the instrument of a structured questionnaire were both quantitative and qualitative in nature, Descriptive

Statistics, Pearson's Chi-Square, ANOVA, and Regression Analysis were used as the analytical tools to both reveal underlying associations between variables and also quantify the strengths of those associations.

3.2 Sampling Frame and Size

Places of sampling were purposively identified as Nairobi, Mombasa, and Nakuru, being the towns thathave Kenyans from diverse communities represented, andwere assumed to contribute significantly to domestic tourism activities in the country.The towns were also chosen based on the assumptions that the preferred snow bowl sampling technique would work best in these towns due to proximity of residents within their respective estates.

Collectively and according to government of Kenya (2009) census report, the three towns had a population of approximately 4,420,000 (see the table 3.1). To arrive at the sample size from this populations, guidance was sought from Gill and Johnson (2010) who provided a formula that takes into account sampling error, non-response bias and the

60 extent to which subgroups in the sample are analyzed. The sample size formula suggested by them was the widely used Cochran's (1977) formula that is given as:

푃 100 − 푃 Z2 n = E2 where n is the sample size, P being the proportion of the population with the desired attributes e.g. for the purposes of this study, are potential tourists, Z being the statistical value that corresponds to the confidence level of the research and is normally 1.96 for a

95% confidence level andE is the margin of error or the risk the researcher is willing to take, mostly being 5 percent in social surveys. E is also commonly known as confidence interval.

To further simplify the process of arriving at the sample size, an online sample size calculator located at http://www.surveysystem.com/sscalc.htmthat uses Cochran's (1977) formula was used. The calculator was supplied with the confidence level of 95 percent, sample population of 4,420,000, and a sample interval of 4 percent which was a percentage lower than the recommended 5 percent for social surveys enabling the researcher to minimize on the error margin. From variables, the sample size was calculated as 600.

Table 3.1: Sample Size distribution according to population ratios Town Population* Sample Size Ratio Nairobi 3,200,000 300 0.5 Mombasa 920,000 200 0.33 Nakuru 300,000 100 0.17 Total 4,420,000 600 1 *Population of the towns was estimated from the 2009 Kenya census report.

The sample size of 600 was then divided in three parts where Nairobi was allocated 300 respondents, Mombasa 200 respondents and Nakuru 100 respondents.

3.3 The Target Population

For this research, it was assumed that the majority of would be consumers of tourism products were the emerging middle income earners since they have access to information and the required purchasing power. The target Population size was 4,420,000: from

Nairobi, Mombasa and Nakuru.

3.4 Sampling Design

Research enumerators were required to identify a few individuals from the sampling frame who were knowledgeable about domestic tourism or those who had likely participated in domestic tourism activities. Those who were most likely to participate in the domestic tourism activities were also targeted for interviews. Through snowball sampling technique, the few individuals previously identified were requested to suggest their friends or associates, or other individuals they were aware of who could provide the needed information as regarded the research topic

3.5. Instrumentation

The research study used questionnaires and interview schedules

3.5.1. Validity of Instruments

The instruments accuracy was confirmed by the supervisor, the focus Group and earlier scholars who were experts in the field of study.

3.5.2 Reliability of Instruments

This was pilot-test by a popular of similar characteristics in Eldoret and after a re-test the results revealed high level of consistency. The respondents closely answered the questions in one direction. Hence re-confirming that the instruments were clear, candid and had less ambiguity. The re-test was after a period of one month.

3.6. Data Collection

Primary data was collected by interviewing 600 respondents from the three towns of sample collection. The interviews were conducting by administering a structured questionnaire/unstructured interview schedules aimed at soliciting information on the interviewees' socio-economic, participation , contribution in form of expenditure incurred per visit, and the constraints faced hence affecting full participation in domestic tourism.

The collected data was recorded by enumerators on the questionnaires to minimise errors associated with data recording.

3.7 Data Collection Monitoring and Evaluation

The researcher together with the research assistant travelled across the towns of data collection (Nairobi, Mombasa and Nakuru) to monitor the progress of the data collection, attend to difficulties arising, and also guided the research enumerators accordingly. The research supervisors were also constantly consulted on issues that required their input.

3.8 Data Analysis

The collected data was first and foremost cleaned for analysis. Cleaning involved removing undesired respondents from the list. For instance,those respondents who gave no more than three quarters of the total responsesand those whose responses contained grievous contradictions were not included in the final analysis. However, those whose responses arrived after the deadlinewere carefully evaluated before being included in the analysis.

Analysis involved entering data in Ms Excel 2003 for pre-analysis (that is data exploration and minor descriptions) then transferred to SPSS version 22 for Windows for proper descriptions and comparative analysis. Analysis followed: Pearson‟s Chi-Square thatwas used to detect relationships in trends, Differences in categories wastested by use of ANOVA (F-test), Regression analysis was used to reveal the determinants of domestic tourism, All the analyses were conducted at 95 percent confidence level and the output data from these analyses presented in form of charts, graphs, and tables.

3.9 Study Area

The research was conducted in three of the main towns in Kenya namely Nairobi,,

Mombasa, and Nakuru. As a country, Kenya is a land of many cultures and has one of the fastest growing populations in the world with a growth rate estimated at 2.64percent per annum (World Bank, 2011). According to the report by Kenya National Bureau of

Statistics (2014), in the year 2009 the country‟s population stood at approximately 40 million people3.2 million of which lived in Nairobi while about one million lived in

Mombasa town. Part of the national population, about 300,000 Kenyans, lived in Nakuru town (KNBS, 2014).

The cultural diversity of Kenya has divided the country into distinct administrative lines with major cities in every county some of which include Nairobi the capital city,

Mombasa and Kisumu. Other cosmopolitan towns in Kenya are Kitale, Eldoret, Nakuru,

Kakamega, Kericho, and Thika. Kenya also boasts of a diversity of its natural habitat.

Dubbed Africa in one country(Mwakikagile, 2007), Kenya has a tremendous variety of landscapes, people, and wildlife. From the snow covered peaks of mount Kenya to her tropical beaches, grassland and cities, Kenya has become one of the leading destinations in Africa for holiday makers (Shackley 1996; Kibicho, 2012; Boniface et al, 2012).

3.9.1 Nairobi

According to the history of Nairobi posted in the government's website www.nairobi.go.ke/home/history, the area currently occupied by Nairobi was essentially

65 uninhabited swamp until a supply depot of the Uganda Railway was built by the British in 1899 linking Mombasa to Uganda. The location of the camp was chosen due to its central position between Mombasa and Kampala and also due to a network of rivers that passed through the area. Its elevation above sea level, the cool environment and the fresh water supply made the area a suitable residential place forboth the thousands of Indian labourers who came to Kenya seeking to be employed to work on the railway line, and the British settlers.

Other than Agriculture and Fishing all the other main industries that fuel Kenya's economy are present in Nairobi. The city leads in Infrastructural development including areas of transport network, communication systems, the financial sector, and hosts the country's sole Stock Market, the Nairobi Securities Exchange. The population of Nairobi that according to the 2009 census report stood at3.2 million is largely composed of youthful individuals most of them having migrated from their rural homes to the city either to seek education or to search for jobs (Oucho, 1996;Adazuet al, 2012).

In the tourism front, Nairobi is known for her National Park - being the only city in the world with a National Park within its environs. More tourism attractions include the city itself, the Nairobi Animal Orphanage, Giraffe Centre, and The Snake Park. Other tourism sites that have been marked as a must visit include David Sheldrick Wildlife Trust where rescued elephants can be viewed, Karen Blixen Museum that is a preserved farmhouse of the famous "Out of Africa" author, Nairobi National Museum where visitors can view stuffed birds and mammals, fossils from Lake Turkana, cultural artefacts from various

66 ethnic communities of Kenya and exhibits of local arts, and The Bomas of Kenya that has been dubbed "the living Museum" where visitors learn about the lifestyle, art, music, crafts, and culture of ethnic communities of Kenya.The National Archive, The Railway

Museum, Ngong Hills and Kenyatta International Conference Centre other tourism attractions found in Nairobi (Ondieki, 2005; Fitzpatrick et al, 2009; Harsselet al, 2014).

3.9.2 Mombasa

Mombasa is one of the oldest towns in Kenya with no clear indication on when it was founded, but history school books approximate 900 AD as the founding date of the old town (Mazrui, 1995). The long history of the town has placed the city as one of the

Africa's preferred tourism destinations due to its rich history, cultural heritage, sandy beaches, and the several other tourist attractions scattered around the town and its environs (Ochieng and Maxon, 1992, Zeleka, 1997).

Mombasa has been inhabited by diverse groups of people ranging from the Arabs around

14th century, the Portuguese who took over from the Arabs and during the colonial period the British who gave way to the modern day cosmopolitan nature of residence. By

2009 according the census report of that year, Mombasa was inhabited by about 920,000 people most of whom were aged between 20 and 30 years, and just like Nairobi,

Mombasa has experienced urban city migration mostly for those intending to start new businesses and a few searching for jobs (Oucho, 1996; Adazuet al, 2012).

The city is a major trade centre and hosts the only seaport Kenya has, the Kilindini

Harbour that has enabled the country to remain an economic giant in East Africa.

Mombasa is a centre of coastal tourism though the Island itself is not a main attraction.

Despite that, many people visit the Old Town and Fort Jesus to learn about the ancient history of the town. The main attractions are thetheNyali, Bamburi, and Shanzu beaches located at the north coast and the Shelly, Tiwi, and Diani beaches are located at the south coast.As noted by Bowden (2007), other tourist attractions in the coastal town include

Haller Park renowned for its oldest tortoise, Mombasa Marine National Park, Mombasa tusks that have become the iconic representation of Mombasa city, Mombasa village centre, Bombolulu workshops and culture centre, and Mombasa Go Kart which is the place for kids to enjoy tourism in Mombasa (Westmacott, 2000; Kibicho 2012).

3.9.3 Nakuru

Nakuru was founded five years after Nairobi in 1904 as a railway outpost. The town is located along the east-west transport route that connects Nairobi with the Western towns of Eldoret, Kericho, and Kisumu all the way to Uganda. It is situated at an altitude of

1859 metres above sea level between the Menengai Crater and Lake Nakuru. Nakuru was named after aMaasai word meaning dust as it is located on the floor of Rif Valley where it is susceptible to volcanic soils that engulf the town in dust (McCall, 1967, UN-

HABITAT, 2002)

Nakuru grew to be the fourth largest town in Kenya and before the promulgation of the

2010 Constitution, it was the headquarter of the vast Rift Valley Province but currently

68 the central hub of administrative and commercial functions of Nakuru county (Owuor,

2006). According to the 2009 census report, the estimated population of Nakuru town was 300,000 people with a growth rate of about 7 percent per annum making the current population estimate be around 480,000 people.

Mwangi (2003) informs us that the economy of Nakuru is supported by several industrial investments that include factories that produce cooking oil, batteries, blankets and agricultural implements. Tourism also plays a greater role in the economy of Nakuru as the town hosts Lake Nakuru renowned for its flamingos (Obudho and Kauti, 2000). Lake

Nakuru itself is within the Lake Nakuru National Park that boasts of over 50 different mammal species, over 400 bird species, and over 500 plant species. Kenya has only two premium national parks and Lakuru National Park is one of them. The other one is

Amboseli National Park (Buckely, 2003).

As a county, Nakuru has a wide variety of tourism sites starting from Naivasha town that has the infamous Hells Gate to Njoro that has the Lord Egerton Castle. Other attractions include Mt. Longonot National Park also referred to as Sheer Adventure, Lake Naivasha that is home to over 400 bird species and variety of wildlife, Menengai Crater that is within the borders of Nakuru town, Lake Elementatia that is near Gilgil town, Kariandusi

Site, Hyrx Hill Museum, and Olorgesaillie Prehistoric site (Waugh and Bushel, 2000;

Buckely 2003, NEMA, 2004).

CHAPTER FOUR

DATA ANALYSIS AND PRESENTATION

4.1 Characteristics of the Respondents

The total number of respondents that were sampled and interviewed were 600. Out of that, responses from 34 respondents were rejected based on:Incomplete responses -

Respondents who did not provide more than three thirds of total responses were treated as faulty responses and thus were not considered for analysis, Responses that contained grievous contradictions that indicated either their total lack of misunderstanding of the purpose of the study or their lack of interest to provide truthful information were not used in the final analysis.

The 558 responses whose responses were deemed suitable for analysis were entered in

SPSS version 22 and analyzed. The first analysis was the descriptive analysis that revealed the characteristics of the respondents based on their town of residence, Age,

Education, Gender, and Occupation just to name a few.

4.2. Factors Affecting Participation in Domestic Tourism

This section presents the specific results on the factors affecting participation of Kenyans in domestic tourism. The descriptive results are first presented followed by the results of the F-tests and finally the regression results. To perform a multiple regression analysis

(logistic model) in order to examine the effect of each of the factors on the participation of Kenyans in domestic tourism, all the categorical independent variables were (gender,

70 age, occupation, location, marital status, and education) were recorded into specific dummy variables.

Thus, gender was recorded into two dummy variables of male and female; age was recorded into young and old after computing a new age variable based on these two classifications from the previous categories; occupation was recorded into government employee, private sector employee, own business, student, farming, and other; location was recorded into Nairobi, Mombasa and Nakuru, marital status was recorded into single, in a relationship, married, divorced, widowed, and separated; education was recorded into none, primary, secondary, college, and university. The continuous variable „monthly income‟ was left as it was.

Then, it was important that serial correlations be tested between the computed variables before entering them into a multiple regression equation. One of the methods that have been previously used by a number of researchers to test for the presence of serial correlations is the correlation matrix. Therefore, a correlation analysis was run and the results are shown in the correlation matrix in Table 1. The results show no presence of multi-collinearity between the independent variables as none of the correlations is high enough to be problematic to the regression analysis. None of the correlations exceeded

0.6 from the results and therefore were considered low enough for use in the multiple regression analysis.

Table 1: Correlation Matrix 1 2 3 4 5 6 7 8 1. Participation Pearson Correlation 1 Sig. (2-tailed) N 625 2. Gender Pearson Correlation -.011 1 Sig. (2-tailed) .793 N 625 625 3. Location Pearson Correlation -.202** -.096* 1 Sig. (2-tailed) .000 .016 N 625 625 625 4. Age Pearson Correlation -.125** -.132** .136** 1 Sig. (2-tailed) .002 .001 .001 N 625 625 625 625 5. Marital Status Pearson Correlation -.061 -.014 .048 .084* 1 Sig. (2-tailed) .130 .730 .232 .037 N 617 617 617 617 617 6. Education Pearson Correlation -.166** -.033 .547** -.004 .043 1 Sig. (2-tailed) .000 .414 .000 .925 .289 N 625 625 625 625 617 625 7. Occupation Pearson Correlation -.101* -.114** .171** .299** .058 .101* 1 Sig. (2-tailed) .011 .004 .000 .000 .153 .012 N 625 625 625 625 617 625 625 8. Income Pearson Correlation .128** .021 -.019 -.004 -.006 .050 -.105* 1 Sig. (2-tailed) .002 .623 .662 .927 .893 .240 .013 N 558 558 558 558 550 558 558 558 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

After examining and noting that no serial correlations existed between the independent variables, they were entered into a regression model using “Enter” method in SPSS and analysed. To decide on which variables to enter among the many alternatives from the dummy variables, mean variances were analysed using Anova (F-test) to check which of the options among the variables had to lowest mean (as a mean closer to 1 than to 2 shows that the respondent group participated in domestic tourism more than the rest of the group members. This led to the selection of the following dummy variables to into the model: Mombasa for location, male for gender, young for age, separated for marital status, college for education level, and student for occupation. As had been noted before, income was left unchanged and therefore used as one of the independent variables as a continuous variable in the model. Table 2 shows the summary results of multiple regression analysis.

Table 2: Factors affecting Participation in Domestic Tourism Variables Statistic P 1. Constant 1.198 .000 2. Gender -.013 .658 3. Marital status -.102 .338 4. Location -.090 .019 5. Occupation -.061 .496 6. Education -.073 .037 7. Income 5.816E-7 .010 8. Age -.052 .089 R .243 R square .059 F 4.879 .000

4.2.1. Effect of Location on Domestic Tourism Participation

The study hypothesized that the town of residence of the respondents could effect their participation in domestic tourism. Table 3 shows the distribution of respondents by these locations.

Table 3: Distribution of Respondents by Location Frequency Percent Nairobi 285 50.4 Mombasa 187 30.0 Nakuru 96 19.6 Total 566 100.0

The results show that Nairobi had the highest number of respondents (50.7%) followed by Mombasa (30.8%) and finally Nakuru (18.5%). The results of the study may therefore be biased towards those that at the time of the study stayed in Nairobi as they were half of the respondents. The subsequent results are therefore disaggregated by locations to establish responses from each of these locations. Chart 1 shows a clear picture of these distributions.

Number of response by Town of Residence 350

300

250

200

150

100

Number of Respondents of Number 50

0 Nairobi Mombasa Nakuru Towns of Residence

Number of response by Town of Residence

Chart 1: Number of Respondents by Town of Residence

The study examined whether individuals differed in their participation in domestic tourism by their locations. This was done using an Anovatable and the results are summarized and presented in Table 4.

H0: Town of residence or environment of residence has no effect on domestic tourism.

H1: There is no effect on domestic tourism participation by town of residence.

Table 4: Differences in Domestic Tourism Participation by Location N Mean SD df1 df2 F p Decision Nairobi 304 1.16 .365 2 211 21 .000 Reject Mombasa 185 1.04 .200 Nakuru 111 1.30 .462

The mean analysis showed that Mombasa (M = 1.04, SD = .200) had most of the respondents having participated in domestic tourism followed by Nairobi (M = 1.16, SD=

.365) and finally Nakuru (M = 1.30, SD = .462). These differences were statistically significant, F (2, 597) = 21.211, p< .001. The null hypothesis of no differences in responses by town of residence of the respondents is therefore rejected.

The multiple regression results (Table 2) show that location had a negative and significant effect on participation in domestic tourism, p= .019. The results therefore lead to the rejection of the null hypothesis thus suggesting that the residence of the respondents played a major role in their participation in domestic tourism. Specifically, those in Mombasa were more likely to engage in domestic tourism than those in Nakuru and Nairobi.

This could be attributed to the fact that there are a number of tourist attraction sites in

Mombasa and most of them are available for free or can be accessed at very low costs for most of the residents to access and therefore they were more likely to visit the places.

Such places include the Mombasa Public Beach and Fort Jesus. On the other hand, those in Nairobi would spend more to access sites such as Safari Walk and Nairobi National

Park and most of them would therefore shy away from visiting these places given the costs of entry. For Nakuru, one would need to have a car to access the Nakuru National

Park and given that most of the residents cannot afford such luxuries, most of them would not access such sites. This is consistent with Johnson and Devonish (2008) who found that nationality was a major determinant of tourism.

4.2.2. Gender on Domestic Tourism Participation

Gender was considered as a possible factor that would influence the likelihood of individuals to participate in domestic tourism. Gender of respondents was therefore conceptualized and tested in the present study together with other factors. The gender

76 distribution of respondents was examined through a descriptive analysis and the results are shown in Table 5 by their locations.

Table 5: Distribution of Respondents by Gender and Location Town of Residence Total Nairobi Mombasa Nakuru Male Count 173 81 52 306 % within Location 56.9% 43.8% 46.8% 51.0% Female Count 131 104 62 294 % within Location 43.1% 56.2% 53.2% 49.0% Total Count 304 185 111 600 % within Location 100.0% 100.0% 100.0% 100.0%

The results in Table 5 show that 51.0% of the respondents were male and 49.0% were female. Nairobi was the only place where most of the respondents were male (56.9%) as

Mombasa and Nakuru had most of the respondents as female (56.2% and 53.2% respectively). These results can also be observed from Chart 2.

Number of Respondents by Town of Residence and gender 200 173 180 160 140 131 120 104 100 81 80 62 52 60 40

Number of Respondents of Number 20 0 Nairobi Mombasa Nakuru Towns of Residence

Male Female

Chart 2: Number of Respondents by Town of Residence and Sex

The study examined whether individuals differed in their participation in domestic tourism by their gender. This was done an Anovatableand the results are summarized and presented in Table 6.

H0: There are no differences in domestic tourism participation by gender.

H1: There are differences in domestic tourism participation by gender.

Table 6: Differences in Domestic Tourism Participation by Gender N Mean SD df1 df2 F p Decision Male 306 1.15 .353 1 623 .069 .793 Accept Female 294 1.15 .360

The mean analysis showed that male (M = 1.15, SD = .353) and female (M = 1.15, SD =

.360) had the same level of participation in domestic tourism. The differences were not statistically significant, F (1, 598) = .069, p> .05. The null hypothesis of no differences in participation by gender is therefore accepted.

The regression results in Table 2 confirm the results of t-test as gender had a positive but insignificant effect on participation in domestic tourism, p = .658. This means that gender did not influence participation of individuals in domestic tourism in Kenya and therefore not a significant factor. This is expected as no gender differences had been observed through the t-test results. The results are consistent with Johnson and Devonish (2008) who found that gender did not emerge as an important determinant of tourism.

4.2.3 Age on Domestic Tourism Participation

The study hypothesized that the age of the respondents would influence the participation into domestic tourism. Table 7 presents the descriptive results on the age distribution of respondents by their locations.

Table 7: Distribution of Respondents by Age and Town of Residence Age Distribution Town of Residence Total Nairobi Mombasa Nakuru 18-27 years Count 72 48 27 147 % within Location 23.7% 25.9% 24.3% 24.5% 28-37 years Count 77 74 37 188 % within Location 25.3% 40.0% 33.3% 31.3% 38-47 years Count 64 25 8 97 % within Location 21.1% 13.5% 7.2% 16.2% 48-57 years Count 62 29 19 110 % within Location 20.4% 15.7% 17.1% 18.3% 58-67 years Count 25 7 20 52 % within Location 8.2% 3.8% 18.1% 8.7% Above 67 years Count 4 2 0 6 % within Location 1.3% 1.1% 0.0% 1.0% Total Count 304 185 111 600 % within Location 100.0% 100.0% 100.0% 100.0%

Table 7 shows that most (55.8%) of the respondents were young (37 years of below). The age distribution also shows that Mombasa had the most youthful population as respondents as those aged 18 – 37 years were 65.9%. These results are also shown in

Chart 3.

Number of Respondents by Age and Town of Residence

160 149 18-37 years 140 126 122 38-57 years 120 above 58 years 100

80 64 60 54 40 29 27

Number of Respondents of Number 9 20 3 0 Nairobi Mombasa Nakuru Towns of Residence

Chart 3: Number of Respondents by Age and Town of Residence

The study examined whether individuals differed in their participation in domestic tourism by their age. This was done using an Anova table and the results are summarized and presented in Table 8.

H0: There are no differences in responses in domestic tourism participation by age.

H1: There are differences in responses in domestic tourism participation by age.

Table 8: Differences in Domestic Tourism Participation by Age N Mean SD df1 df2 F p Decision Young 362 1.11 .312 1 623 9.846 .002 Reject Old 238 1.20 .100

The mean analysis showed that the young respondents (M = 1.11, SD = .312) had most participated in domestic tourism than the older respondents (M = 1.20, SD = .100). These differences were statistically significant, F (1, 598) = 9.846, p< .001. The null hypothesis of no differences in responses in domestic tourism participation by age is therefore rejected.

The regression results in Table 2 show that age was marginally significant at 10% and the effect was negative, p = 0.089. There is therefore evidence that age is an important contributor to participation in domestic tourism in Kenya. The results show that as individuals ages progress, their participation in domestic tourism decreases. Younger populations are therefore more favourable for domestic tourism than older populations.

The results are consistent with Johnson and Devonish (2008) who found that age

80 influenced participation in tourism. The age of a person has a very important impact on leisure participation, but its impact may vary depending on the people, and the activity preferred. For example, while young people prefer to attend more energetic leisure activities such as energetic sport activities, older people prefer relatively more secure activities requiring less risk such as walking (Demir and Oral, 2007).

4.2.4 Effect of Marital Status on Domestic Tourism Participation

Studies have shown that marital status of individuals influences leisure participation. This study therefore sought to examine whether marital status of individuals influenced their participation in domestic tourism in Kenya. The distribution of respondents in terms of their marital status and by towns of residence is shown in Table 9.

Table 9: Distribution of Respondents by Marital Status and Location Town of Residence Total Nairobi Mombasa Nakuru Single Count 35 18 6 59 % within Location 11.7% 9.7% 5.4% 9.9% In a relationship Count 55 36 29 120 % within Location 18.3% 19.5% 26.1% 20.1% Married Count 170 92 62 324 % within Location 56.7% 49.8% 55.9% 54.4% Widowed Count 25 22 10 57 % within Location 8.3% 11.9% 9.0% 9.6% Divorced Count 10 12 3 25 % within Location 3.3% 6.5% 2.7% 4.2% Separated Count 5 5 1 11 % within Location 1.7% 2.6% 0.9% 1.8% Total Count 300 185 111 596 % within Location 100.0% 100.0% 100.0% 100.0%

Table 9 reveals that 54.4% of the respondents were married and therefore making up the most of the respondents. Further, those in the relationship were 20.1%. These two groups are important for hotel industry in Kenya for marketing purposes as they tend to go for holidays as pairs and therefore an important group in terms of revenue generation for the

81 hotel industry. These two groups were the largest across all the three towns. These results are also shown in Chart 4.

Number of Respondents by Town of Residence and Marital Status 170 180 Nairobi 160 Mombasa 140 Nakuru 120 100 92 80 62 55 60 35 36 40 29 25 Number of Respondents of Number 18 22 20 10 10 12 6 3 5 5 1 0 Single In a Relationship Married Widowed Divorced Separated Marita Status

Chart 4: Number of Respondents by Town of Residence and Marital Status

The study examined whether individuals differed in their participation in domestic tourism by their marital status. This was done using an Anovatableand the results are summarized and presented in Table 10.

H0: There are no differences in responses by marital status.

H1: There are differences in responses by marital status.

Table 10: Differences in Domestic Tourism Participation by Marital Status N Mean SD df1 df2 F p Decision Singe 50 1.08 .275 5 611 2.109 .063 Accept In a relationship 120 1.14 .345 Married 321 1.15 .360 Widowed 57 1.26 .442 Divorced 20 1.12 .332 Separated 11 1.00 .000

Total 600

The mean analysis showed that those separated (M = 1.00, SD = .000) had most of the respondents having participated in domestic tourism followed by the single (M = 1.08,

SD = .275), the divorced, (M = 1.12, SD = .332), those in a relationship (M = 1.14, SD =

.345), the married (M = 1.15, SD = .360), and finally the widowed (M = 1.26, SD = .442).

These differences were statistically significant at 10% but not at 5% level, F (5, 590) =

2.109, p= .063. The null hypothesis of no differences in responses by marital status of the respondents is therefore accepted.

The multiple regression results in Table 2 show that marital status had a negative but non-significant effect on participation in domestic tourism, p = .338. This can be explained by the fact that most of the respondents in the survey were married and therefore tend to participate less in touring Kenya as they have more responsibilities than the single ones. In general, married people have more responsibilities than single people

(Demir and Oral, 2007) hence the results are consistent with literature.

4.2.5 Occupation on Domestic Tourism Participation

The study also sought to test whether occupation affects participation of individuals in domestic tourism. Table 11 shows the results for the distribution of respondents by location and employment status.

Table 11: Distribution of Respondents by Occupation and Location Employment Status Town of Residence Total Nairobi Mombasa Nakuru Government Count 97 76 15 188 % within Location 31.9% 41.1% 13.5% 31.3% Private Count 101 51 42 194 % within Location 33.2% 27.6% 37.8% 32.3% Own Business Count 74 24 34 132 % within Location 24.3% 13.0% 30.6% 22.0% Farming Count 0 0 10 10 % within Location 0.0% 0.0% 9.1% 1.7% Student Count 23 34 7 64 % within Location 7.6% 18.3% 6.3% 10.7% Other Count 9 0 3 12 % within Location 3.0% 0.0% 2.7% 2.0% Total Count 304 185 111 600 % within Location 100.0% 100.0% 100.0% 100.0%

The results in Table 5 show that most of the respondents were employed (63.6%) either in government (31.3%) or private sector (32.3%). Further, 22.0% of the respondents owned businesses. These results show that a large number of respondents had some form of income and therefore were able to afford the services provided by the tourism industry in Kenya. These results are also presented in Chart 5.

Number of Respondents by Town of Residence and Occupation

120 Nairobi 101 97 100 Mombasa 76 Nakuru 80 74

60 51 42 40 34 34 24 23 15

Number of Respondents of Number 20 10 7 9 0 0 0 3 0 Government Private Own Business Farming Student Other Occupattion

Chart 5:Number of Respondents by Town of Residence and Occupation

The study examined whether individuals differed in their participation in domestic tourism by occupation. This was done using an Anova table and the results are summarized and presented in Table 12.

H0: There are no differences in responses by employment status.

H1: There are differences in responses by employment status.

Table 12: Differences in Domestic Tourism Participation by Occupation N Mean SD df1 df2 F p Decision Employed by Government 188 1.11 .317 5 619 4.367 .001 Reject Employed in Private Sector 194 1.15 .362 Own Business 132 1.20 .405 Farming 10 1.45 .522 Student 64 1.04 .208 Other 12 1.29 .469 The mean analysis showed that students (M = 1.04, SD = .208) were most involved in domestic tourism followed by those employed by the government (M = 1.11, SD = .317) while the least involved were farmers (M = 1.45, SD = .522). These differences were statistically significant, F (5, 594) = 4.367, p= .001. The null hypothesis of no differences in responses by employment status of the respondents is therefore rejected.

The regression analysis results in Table 2 show that occupation had a negative and insignificant effect on domestic tourism participation, p = .496. This is inconsistent with literature but could be due to the fact that most of the respondents were employed or engaged as business men and therefore did not have the time to participate in domestic tourism. Occupation (or employment status) signifies working or non-working. Being employed or unemployed determines availability of discretionary finances for travel and may also determine whether and how much time may be available for leisure travel.

Therefore employment can be directly associated with financial and time constraints

(Khan, 2011).

4.2.6Education on Domestic Tourism Participation

The study further tested whether the level of education was a significant determinant of domestic participation in tourism in Kenya. The distribution of respondents by education and town of residence is analysed and presented in Table 13.

Table 13: Distribution of Respondents by Level of Education and Location Level of Education Town of Residence Total Nairobi Mombasa Nakuru None Count 0 1 3 4 % within Location 0.0% 0.5% 2.7% 0.6% Primary Count 3 2 20 25 % within Location 1.0% 1.1% 18.0% 4.2% Secondary Count 5 16 45 66 % within Location 1.6% 8.6% 40.5% 11.0% Middle College Count 102 161 15 278 % within Location 33.6% 87.1% 13.5% 46.3% University Count 194 5 28 227 % within Location 63.8% 2.7% 24.3% 37.8% Total Count 304 185 111 600 % within Location 100.0% 100.0% 100.0% 100.0%

The results in Table 13 show that most (84.1%) of the respondents had middle college to university education. Nairobi had most of the respondents with university education

(63.8%), Mombasa had the largest group with middle college education (87.1%) and

Nakuru had most of the respondents with secondary education (40.5%). These results are also shown in Chart 6.

Number of Respondents by Location and Level of Education

250 Nairobi 194 Mombasa 200 Nakuru 150 102 100

50 Number of Respondents of Number 0 3 5 0 None Primary Secondary Middle college University Level of Education

Chart 6: Number of Respondents by Location and Level of Education

The study examined whether individuals differed in their participation in domestic tourism by education levels of participants. This was done using tan Anova table and the results are summarized and presented in Table 14.

H0: There are no differences in responses by level of education.

H1: There are differences in responses by level of education.

Table 14: Differences in Domestic Tourism Participation by Education N Mean SD df1 df2 F p Decision None 4 1.25 .500 4 620 4.764 .001 Reject Primary 20 1.28 .458 Secondary 66 1.21 .407 Middle college 273 1.09 .280 University 218 1.19 .397 Total 600

The mean analysis showed that those who mostly participated in domestic tourism were those with college level education (M = 1.09, SD = .280) followed by those with

87 university level of education (M = 1.19, SD = .397) while the ones that least participated were those with primary level education (M = 1.28, SD = .458). These differences were statistically significant, F (4, 595) = 4.764, p= .001. The null hypothesis of no differences in responses by level of education is therefore rejected.

The regression analysis results in Table 2 show that education had a negative and significant effect on participation in domestic tourism, p = .037. This means that individuals with higher levels of education will not participate in domestic tourism as much as those with low levels of education. This is consistent with the findings of

Okello, Kenana and Kieti (2012), who found that the level of education influenced the likelihood of the community to visit the park and appreciate its conservation contribution.

However, the direction of the relationship is inconsistent with the findings of both Okello et al (2012) and Demir and Oral (2007). The later noted that the type and length of education influence the type and frequency of leisure participation. They found that education helps individuals to organize their leisure time efficiently. They noted that educated people are more interested in their physical and mental health than non- or less educated people. It should be however noted that the education that was examined by

Demir and Oral (2007) related to leisure education which is availability of information on tourist attraction arrears and the packages available.

4.2.7.Effect of Income on Domestic Tourism Participation

Income levels of individuals were also tested to find out how they affect the participation of domestic tourism in Kenya. Table 15 shows the results on the distribution of monthly income by locations.

Table 15: Distribution of Respondents by Monthly Income and Location Income Levels Town of Residence Total Nairobi Mombasa Nakuru Up to Sh. 30,000 Count 50 34 33 117 % within Location 17.6% 20.5% 30.6% 21.0% Sh. 30,001 - Sh. 60,000 Count 43 37 30 110 % within Location 15.1% 22.3% 27.8% 19.7% Sh. 60,001 - Sh. 90,000 Count 81 31 15 127 % within Location 28.5% 18.7% 13.9% 22.8% Sh. 90,001 or above Count 110 64 30 204 % within Location 38.7% 38.6% 27.8% 36.6% Total Count 284 166 108 558 % within Location 100.0% 100.0% 100.0% 100.0%

The results show that 36.6% of the respondents earned more than 90,000/=, 22.8% earned between 60,000/= and 90,000/=, 21% earned less than 30,000/= while 19.7% earned between 30,000/= and 60,000/=. Therefore it is clear that most of the respondents who were employed were very big earners. Nairobi had the highest earners followed by

Mombasa and then Nakuru. These results are also shown in Chart 7.

120 110 Nairobi 100 81 Mombasa 80 64 Nakuru 60 50 43 37 40 34 33 30 31 30 15 Number ofRespondents Number 20

0 Up to Sh. 30,000 Sh. 30,000 - Sh. Sh. 60,001 - Sh. Sh. 90,001 or 60,000 90,000 above

Chart 7: Number of Respondents by Town of Residence and Monthly Income

The study examined whether individuals differed in their participation in domestic tourism by the monthly income of individuals. This was done using an Anova table and the results are summarized and presented in Table 16.

H1: There are no differences in responses by income levels

H2: There are differences in responses by income levels

Table 16: Differences in Domestic Tourism Participation by Income Levels N Mean SD df1 df2 F p Decision Yes 475 79,069.26 62,895 556 96 3.316 .002 Reject No 83 103,642.17 91,188

The mean analysis showed that those who participated in domestic tourism (M = 103,642,

SD = 91,188) were less than those who did participate in domestic tourism (M = 79,069,

SD = 62,895). These differences were statistically significant, F (556, 96) = 3.316, p=

.002. The null hypothesis of no differences in responses by income levels is therefore rejected.

The multiple regression results in Table 2 show that income had a positive effect on participation in domestic tourism, p = .010. Thus, higher incomes lead to more participation in domestic tourism in Kenya. Allen and Yap (2009) found that disposable income was a significant factor that influenced Australian domestic tourism demands hence the present results are consistent with prior studies.

4.3.1 Overall Economic contribution by Kenyans participation in Domestic

Tourism

To test the overall participation of the respondents in domestic tourism, the frequency distribution of the number of those who had visited local tourism sites was sought. Chart

8 shows that 15.0% of the respondents had visited a tourism attraction site in Kenya while 85.0% had not.

Further, the number of visits per year by the respondents was sought including the expenditure estimates per visit. The results are shown in Table 17.

Have you Visited any Tourist Attraction in Kenya N=600

YES 15%

NO 85%

Chart 8: Have you visited any tourist attraction in Kenya?

As shown, the results show that on average, the respondents had visited 5.35 sites in a year spending on average Ksh 9,965.41 per visit with some spending up to KSh. 45,000 per visit.

Table 17: Number of Visits in Local Tourism Sites and Expenditure per Visit N Min Max Mean SD Number of visits per year 532 1 33 5.35 4.823 Average expenditure per visit 532 200 45,000 9,965.41 10,108.491

It is important to compare the number of domestic tourism visits to the foreign tourism visits. These results are analysed and presented in Chart 9. As shown, only 14% had participated in foreign tourism while the remaining 86% had not. This shows that not many Kenyans are participating in foreign tourism. In those visits, they spent an average of Ksh. 25,149.43 per visit with some spending up to Ksh. 71,000 per visit.

Have you ever participated in foreign tourism? N=600

Yes; 14%

No; 86%

Chart 9: Have you ever participated in foreign tourism?

4.3.2 Participation and Contribution to Domestic Tourism by Location

The study examined the participation of respondents in domestic tourism by location. The results are summarised and presented in Chart 10. The results show that 50% of those who participated in domestic tourism were from Nairobi, 35% were from Mombasa and

15% were from Nakuru.

Participation in domestic tourism by Location N=600 Nakuru 15%

Nairobi 50% Mombasa 35%

Chart 10: Participation in domestic tourism by Location

The average number of visits by location was also computed in order to show which location had the most frequent participants in domestic tourism. The results in Table 18

92 show that the average number of visits was 5.36 for Nairobi, 7.21 visits for Mombasa and

2.09 visits for Nakuru. Thus, the highest participants in domestic tourism were those in

Mombasa while the lowest were those in Nakuru.

Table 18: Number of Visits to Domestic Tourism Sites by Location Location Mean SD Median Min Max Nairobi 5.36 3.73 5.00 1 33 Mombasa 7.21 6.71 5.00 1 30 Nakuru 3.04 2.09 2.00 1 11

The study also examined the expenditures during domestic visits by location and the results are shown in Table 19. The results show that on average those in Nairobi spent Sh.

11,020, those in Mombasa spent Sh. 11,999 while those in Nakuru spent Sh. 4,296 per visit. This means that those in Mombasa spent the highest while those in Nakuru spent the least on domestic tourism.

Table 19: Expenditures on Domestic Tourism by Location Location Mean SD Median Min Max Nairobi 11,019.58 9,992.63 6,450.00 500 45,000 Mombasa 11,998.72 11,799.56 6,000.00 600 45,000 Nakuru 4,295.89 3,907.88 3,600.00 200 22,000

4.3.3. Participation and Contribution to Domestic Tourism by Gender

The study analysed participation of respondents in domestic tourism by gender. And the results are shown in Chart 11. As shown, 51% of the participants were male and 49% were female. This suggests that both male and female participated in domestic tourism in almost the same way.

Participation in domestic tourism by gender N=600

Female Male 49% 51%

Chart 11: Participation in domestic tourism by gender

The study also examined the number of visits by gender. The results in Table 20 show that the average number of visits was 5.53 for male and 5.67 for female. This suggests that the female made slightly more visits than the male.

Table 20: Number of Visits to Domestic Tourism Sites by Gender Gender Mean SD Median Min Max Male 5.53 5.02 4.00 1 33 Female 5.67 4.90 5.00 1 30

The results in Table 21 show the annual expenditures by gender. As shown, the male spent on average Ksh 9,676 while female spent Ksh 10,986. Thus, female respondents spent more in domestic tourism than the male respondents.

Table 21: Expenditure on Domestic Tourism by Gender Gender Mean SD Median Min Max Male 9,675.61 9,575.93 6,000.00 200 45,000 Female 10,986.10 11,045.36 6,000.00 500 45,000

4.3.4. Participation in Domestic Tourism by Age

The study examined participation in domestic tourism by age of the respondents. The results in Chart 12 show that 26% of the participants were aged 18-27 years, 33% were aged 28-37 years, 16% were aged 38-47 years, 17% were aged 48-57 years, 8% were aged 58-67 years and 1% were aged over 67 years.

Above 67 years 1%

58 - 67 years 8%

48 - 57 years 17%

38 - 47 years 16%

28 - 37 years 33%

18 - 27 years 26%

0% 5% 10% 15% 20% 25% 30% 35%

Chart 12: Participation in domestic tourism by age

Table 22 shows the average number of visits to domestic sites by age. As shown, the age group that had the highest number of visits was 48-57 years with 5.88 average visits followed by those aged 38-47 years with 5.85 visits and those aged 19-27 years with 5.58 visits. The least visits were made by those aged over 67 years with only 3.8 visits.

Table 22: Number of Visits to Domestic Tourism Sites by Age Age Mean SD Median Min Max 19 – 27 years 5.58 5.17 4.00 1 25 28 – 37 years 5.82 4.65 5.00 1 24 38 – 47 years 5.85 5.52 5.00 1 30 48 – 57 years 5.88 5.34 5.00 1 33 58 – 67 years 3.95 3.11 3.00 1 13 Over 67 years 3.80 2.49 2.00 2 7

Table 23 shows the annual expenditures on domestic tourism by age. The study finds that the age group that spent more was the 28 – 37 years with an average expenditure of Sh.

11,263 per visit while the group that spent the least was those aged over 67 years with an average expenditure of Sh 6,400 per visit.

Table 23: Expenditures on Domestic Tourism by Age Age Mean SD Median Min Max 19 – 27 years 10,708.74 10,775.35 6,000.00 500 39,000 28 – 37 years 11,263.82 10,869.42 6,000.00 500 45,000 38 – 47 years 9,262.96 8,896.23 6,000.00 600 43,000 48 – 57 years 10,827.47 10,724.36 6,200.00 500 45,000 58 – 67 years 6,686.49 8,212.33 4,600.00 200 40,000 Over 67 years 6,400.00 5,650.22 5,000.00 1,000 16,000

4.3.5. Participation in Domestic Tourism by Marital Status

The study assessed the level of participation in domestic tourism segregated by marital status. The results shown in Chart 13 reveals that 54% of those who participated in domestic tourism were married, 20% were in a relationship, 11% were single, 8% were widowed, 4% were divorced and 2% were separated. Thus, those who were either married or in relationships most participated in domestic tourism constituting about 74% of those who participated.

Separated 2%

Divorced 4%

Widowed 8%

Single 11%

In a relationship 20%

Married 54%

0% 10% 20% 30% 40% 50% 60%

Chart 13: Participation in Domestic Tourism by Marital Status

Table 24 shows the results of the number of visits segragated by marital status. As shown, it was revealed that the divorced had on average the most visits (7.05 visits) followed by the widowed (6.21 visits). The least visits were by the seperated (4.91 visits).

Table 24: Number of Visits to Domestic Tourism Sites by Marital Status Marital Status Mean SD Median Min Max Single 5.41 3.66 4.00 2 16 In a relationship 5.11 4.84 4.00 1 25 Married 5.66 5.25 4.00 1 33 Widowed 6.21 4.29 5.00 1 17 Divorced 7.05 5.84 5.00 1 24 Separated 4.91 2.34 4.00 2 9

The results in Table 25 show the expenditures segragated by marital status. It was revealed that on average, the biggest spenders were the divorced spending on average Sh

11,948 followed by the widowed (Sh 11,715) and those in relationships (Sh 11,390). The least spenders were those separated with an average expenditure of Sh 7,237 per visit.

Table 25: Expenditures on Domestic Tourism by Marital Status Marital Status Mean SD Median Min Max Single 9,213.51 10,996.67 5,500.00 1,000 45,000 In a relationship 11,389.90 10,679.88 6,000.00 500 39,000 Married 9,825.19 10,069.32 6,000.00 200 45,000 Widowed 11,715.38 10,853.37 8,000.00 1,000 44,000 Divorced 11,947.62 11,164.08 6,000.00 200 38,000 Separated 7,236.36 5,735.20 6,000.00 1,500 20,000

4.3.6. Participation in Domestic Tourism by Level of Education

The study examined participation of the respondents in domestic tourism by the levels of education. As shown in Chart 14, 50% of the participants in domestic tourism had middle college education, 36% had university degree as their highest level of education, 10% had secondary education, 3% had primary level of education and 1% had no education.

None 1%

Primary 3%

Secondary 10%

University 36%

Middle college 50%

0% 10% 20% 30% 40% 50% 60%

Chart 14: Participation in Domestic Tourism by Level of Education

The study also examined the number of cvisits made by the respondents. As shown in table 26, the results show that on average, those with middle college education had the most visits (6.82 visits) followed by those with university education (5.12 visits). The least visits were made by those with primary level of education (2.56 visits).

Table 26: Number of Visits to Domestic Tourism Sites by Education Education Mean SD Median Min Max None 2.67 2.08 2.00 1 5 Primary 2.56 1.15 3.00 1 4 Secondary 2.77 1.96 2.00 1 9 Middle college 6.82 6.16 5.00 1 33 University 5.12 3.09 5.00 1 17

Table 27 shows the results of the expenditure on domestic tourism segragated by education. As shown, those with middle college education spent an average of Sh 12,621 per visit making them the highest spenders. They were followed by those with university education with an average spending of Sh 9,862 per visit. The least spenders were those with primary level of education with an average spending of Sh 2,319 per visit.

Table 27: Expenditure on Domestic Tourism by Education Education Mean SD Median Min Max None 2,566.67 3,429.77 1,000.00 200 6,500 Primary 2,318.75 1,770.58 1,950.00 1,000 6,000 Secondary 3,691.49 2,744.07 3,000.00 200 13,600 Middle college 12,621.21 11,343.38 7,400.00 500 45,000 University 9,862.21 9,431.38 6,000.00 500 45,000

4.3.7. Participation in Domestic Tourism by Occupation

The study examined the level of participation in domestic tourism by occupation of the respondents. The results in Chart 15 shows that 33% of those who participated in domestic tourism were employed by the government, 32% were employed in primary sector, 20% owned businesses, 12% were students while 1% of the participants were farmers.

Farming 1%

Other 2%

Student 12%

Own business 20%

Employed in private sector 32%

Employed by government 33%

0% 5% 10% 15% 20% 25% 30% 35%

Chart 15: Participation in Domestic Tourism by Occupation

Table 28 shows the results on the number of visits to domestic tourist attractions by occupation. The results show that most visits were made by those who owned businesses

(5.91 visits) followed by those employed by the government (5.7 visits) and those employed by private sector (5.64 visits). The least visits were made by farmers (2.6 visits).

Table 28: Number of Visits to Domestic Tourism Sites by Occupation Occupation Mean SD Median Min Max Employed by government 5.70 5.04 4.00 1 30 Employed in private sector 5.64 4.64 5.00 1 25 Own business 5.91 5.78 5.00 1 33 Farming 2.60 1.82 2.00 1 5 Student 4.50 2.45 4.00 2 12 Other 3.89 2.67 4.00 1 9

Table 29 shows the results of the expenditures on domestic tourism by occupation. The results show that those employed in the private sector spent the highest with an average spending of Sh 11,794 per visit while the least spenders were farmers with an average spending of Sh 4,380 per visit.

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Table 29: Expenditures on Domestic Tourism by Occupation Occupation Mean SD Median Min Max Employed by government 9,679.07 9,767.17 6,000.00 500 45,000 Employed in private sector 11,793.94 11,283.52 6,900.00 500 45,000 Own business 10,208.82 10,370.42 6,000.00 200 42,000 Farming 4,380.00 3,473.76 2,200.00 1,800 9,500 Student 5,206.25 3,865.14 4,650.00 500 17,000 Other 8,088.89 6,648.02 6,500.00 1,300 20,000

4.3.8 Participation in Domestic Tourism by Income Level

The study assessed the level of participation of respondents in domestic tourism by income levels. The results shown in Chart 16 reveal that 34% of those who participated in domestic tourism earned more than Sh 90,000, 23% earned between Sh 60,000 and Sh

90,000 while 42% earned less than Sh 60,000.

Above Sh. 90,000 34%

Sh. 60,001 - Sh. 90,000 23%

Sh. 30,001 - Sh. 60,000 21%

Up to Sh. 30,000 21%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Chart 16: Participation in Domestic Tourism by Income Level

Table 30 shows the number of visits to domestic tourism sites by income levels. As shown, it is revealed that the most visits were made by those who earned more than Sh

90,000 (7.79 visits) followed by those who earned between Sh 60,000 and Sh 90,000.

The least visits were made by those who earned below Sh 30,000.

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Table 30: Number of Visits by to Domestic Tourism Sites Income Levels Income (Ksh.) Mean SD Median Min Max Up to 30,000 3.51 3.92 3.00 1 30 30,001 – 60,000 3.74 2.56 3.00 1 15 60,0001 – 90,000 6.23 3.93 6.00 1 22 Above 90,000 7.79 6.28 6.00 1 33

Table 31 shows the results of the expenditures on domestic tourism by income levels. It is revealed that the largest spenders were those who earned above Sh. 90,000 with an average spending of Sh. 19,524 while the least spenders were those who earned up to Sh.

30,000 with an average spending of Sh. 2,804.

Table 31: Expenditures on Domestic Tourism by Income Levels Income (KSh.) Mean SD Median Min Max Up to 30,000 2,803.96 1,734.35 2,000.00 200 6,000 30,001 – 60,000 4,664.36 3,238.60 4,000.00 1,000 17,000 60,0001 – 90,000 8,897.20 6,895.78 6,500.00 600 44,000 Above 90,000 19,523.75 11,397.56 19,750.00 1,700 45,000

4.4.0 Psycho-social FactorstoDomestic Tourism

The study sought to determine the general concepts, attitudes, and preferences of

Kenyans affecting their participation in domestic tourism. This section presents the results on constructs and attitudes of Kenyans toward domestic tourism. The concepts and attitudes are well answered using the constraints to domestic tourism as noted by the respondents. This is presented generally and then disaggregated by a number of respondents‟ characteristics.

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4.5.0 Sustainable performance in domestic tourism in Kenya

4.5.1. Constraint to Domestic Tourism

Chart 17 shows the results of the constraints to domestic tourism. As shown, 48% of the respondents said they cannot afford it, 19% noted that they have family commitments,

15% said they do not have time while 8% did not have enough information on domestic tourism. Another 8% noted that they were committed at work. Thus, most of the respondents cited cost as the biggest impediment to engaging in domestic tourism. This can be illustrated by the respondentsfromNakuru. The Nakuru National Park requires that anyone who wishes to tour the part pay 200/- for entry. Then, no one is allowed to enter without a car. This makes it very costly for common citizens who do not have cars or who cannot afford to hire cars to visit the park.

I can't afford 48%

I have family commitment 19%

I don't have time 15%

I don’t have enough information 8%

I am committed at work 8%

Other 2%

0% 10% 20% 30% 40% 50% 60%

Chart 17: constraints to Domestic Tourism

The results of constraints to domestic tourism were disaggregated by location to test which factors were the main impediments in the three towns where the study was

103 conducted. The results in Chart 18 show that cost was the largest impediment to domestic tourism in Nairobi (62%) and Mombasa (36%). It was also one of the largest impediments for domestic tourism in Nakuru (31%).

70% 62% Nairobi 60% Mombasa 50% Nakuru 38% 40% 31% 31% 30% 25% 20% 20% 13% 13% 12%13% 9% 9% 10% 6% 6% Number ofRespondents Number 10% 4% 0% Other I am I don’t have I don't have I have family I can't afford committed at enough time commitment work information

Chart 18: constraints to Domestic Tourism by Location

The gender analysis in Chart 19 shows that the greatest impediment to domestic tourism by gender was affordability as 50% of men and 47% of women noted that it was the largest constraint. The second most important impediment to women was family commitment (23%) while the second impediment to domestic tourism for men was lack of time (20%). Lack of information also played a key role as an impediment as 9% of women cited it while another 7% of men also cited it.

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0.6 50% 0.5 Male 47% Female 0.4

0.3 23% 20% 0.2 15% 9% 9% 11% 0.1 6% 7%

Number ofRespondents Number 4% 0 Other I am I don’t have I don't have I have family I can't afford committed at enough time commitment work information

Chart 19: constraints to Domestic Tourism by Gender

4.5.2 Number of visits to tourist sites

The study also analysed the number of visits the respondents expected to make if the constraints to domestic tourism were resolved. The results in Table 32 shows that if the constraints to domestic tourism are eliminated, expected number of visits will be 10.54 visits on average per year up from the current 5.35 visits per year. This shows an improvement as the annual visits double if the constraints are addressed adequately. The number of those willing to visit domestic tourism sites also increases from 532 to 611, a

15% rise in numbers.

Table 32: Number of Visits to Domestic Tourism Sites per Year N Min Max Mean SD New number of visits per year 600 1 66 10.54 7.310 Current number of visits per year 532 1 33 5.35 4.823

Table 33 shows an analysis of the differences between the current number of visits and the expected number of visits. The results show that there are statistically significant differences between the mean number of visits currently experienced and the expected number of visits of the constrains are addressed, p < .05. This means that should the current constrains be adequately addressed, the number of visits to domestic tourism sites

105 will significantly improve. To be specific, there are higher chances that the numbers will double.

Table 33: Differences in Number of Visits per Year N Mean SD df p Decision Current 532 5.36 4.82 531 0.00 Reject Expected 532 10.85 7.51

Table 34 shows the number of expected visits to domestic tourism sites by location. The results show that Mombasa would have the highest number of visits of 13.18 visits on average per person per year followed by Nairobi with an average number of 11.03 visits per person per year. This is an improvement from the current numbers.

Table 34: Number of Visits by Location Location Mean SD Median Min Max Nairobi 11.03 6.12 10.00 1 66 Mombasa 13.18 8.85 12.00 2 66 Nakuru 4.49 2.51 4.00 1 17

Table 35 shows the results of the expected number of visits by gender. As shown, male domestic tourists are expected to do on average 10.8 visits per year while the female are expected to do an average of 10.26 visits per year. This is an improvement from the current figures.

Table 35: Number of Visits by Gender Gender Mean SD Median Min Max Male 10.80 7.23 10.00 1 66 Female 10.26 7.40 9.00 1 66

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4.5.3 Expenditures on Domestic Tourism

Table 36 shows the expected number expenditures per visit on domestic tourism sites if the current constrains are addressed. The results show that domestic tourists would spend on average Sh 10,447 per visit slightly up from the current expenditure of Sh 9,965. This shows that the average spending of domestic tourists will improve if the constraints currently facing domestic tourism are addressed. It can also be observed that the number of those who would spend on domestic tourism increase from the current number of 532 individuals to 624 individuals, a 17% improvement in the numbers.

Table 36: Average Expenditures on Domestic Tourism per Visit N Min Max Mean SD New average expense per visit 600 200 49,000 10,447.28 11,249.386 Current expenditure per visit 532 200 45,000 9,965.41 10,108.491

Table 37 presents the results of differences between current and expected expenditures on domestic tourism. The results show a statistically significant difference in the mean expenditures suggesting that indeed the improvements on expenditures per visit will be significant.

Table 37: Differences in Average Expenditures on Domestic Tourism N Mean SD df p Decision Current expenditures 532 9,965.41 10,108.49 531 0.00 Reject Expected expenditures 532 11,879.89 11,589.30

Table 38 presents the results of expected expenditures on domestic tourism by location.

The results show that those in Mombasa would spend the highest on average per visit followed by those in Nairobi. In all the locations, there is a marked improvement in expected average expenditures per visit.

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Table 38: Average Expenditures on Domestic Tourism by Location Location Mean SD Median Min Max Nairobi 11,672.99 11,765.75 7,000.00 500 49,000 Mombasa 12,604.71 11,954.45 8,000.00 1,000 49,000 Nakuru 4,122.02 4,527.17 3,000.00 200 24,000

Table 39 shows the results for the expected expenditures on domestic tourism by gender.

The results show that the male tourists are expected to spend more than the female tourists but the differences are minimal. In both cases, the expected expenditures are an improvement on the current expenditures per visit on local tourism.

Table 39: Average Expenditures on Domestic Tourism by Gender Gender Mean SD Median Min Max Male 10,647.92 11,183.14 6,000.00 200 49,000 Female 10,584.90 11,453.29 5,000.00 500 49,000

4.5.4 Desirability of Current Number of Domestic Visits

The results in chart 20 show the levels of satisfaction with the current number of domestic tours by the respondents. The results show that 70% of the respondents were not satisfied while only 30% were satisfied with the number of visits they had made so far.

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Yes 30%

No 70%

Chart 20: Satisfaction with Current Tours in Kenya

Of those who felt that the number of current visits was not desirable, 51% were male and

49% were female. In terms of their locations, 51% of those who felt that the number of visits was not desirable were in Nairobi, 28% in Mombasa and 21% in Nakuru. These results reveal therefore that most of the respondents were not touring Kenya as they desired and therefore there is need for improvement.

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CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1.0 SUMMARY OF FINDINGS

The study sought to investigate the effect of socio-economic factors on Kenyans participation on domestic tourism activities. This section presents the results of the same in terms of frequency distributions in charts and tables where necessary.

The study found that about 85% of the respondents had participated in domestic tourism as compared to only 14% that had travelled outside the country for tourism purposes.

This clearly shows that most Kenyans are participating in domestic tourism. In terms of locations, half of the participants in domestic tourism were in Nairobi, 35% in Mombasa and 15% in Nakuru. The results also show that 51% of the participants were male and

48% were female. By age, the results show that the majority (56%) of the participants were between 18 – 37 years. Further, the results revealed that just over half of the participants in domestic tourism were married with another 20% being in relationships. In terms of their levels of education, half of the participants in domestic tourism had college level of education and another 36% had university level of education. The study showed that 65% of the participants were employed either in private sector or by the government.

The results also revealed that most of the participants in domestic tourism (54%) earned over Sh. 60,000.

The study also found that generally, the respondents made an average of 5.35 visits per year on domestic tourism sites. These results were segregated by a number of participant characteristics. The results showed that by location, most visits were made by those in

Mombasa who made on average 7.21 visits per year while the least visits were made by those in Nakuru with an average of 3.04 visits. The results also showed that the female

110 made more visits than male averaging 5.67 visits per annum vis-à-vis the 5.53 visits made by male respondents. By age, the results showed that most visits were made by those aged 48 – 57 years with an average of 5.88 visits while the least visits were made by those aged over 67 years who on average made 3.8 visits annually. Further, the results revealed that in terms of marital status the divorcees made the highest number of visits averaging 7.05 visits annually while the least visits were made by those who were separated with an average of 4.91 visits per year. In terms of levels of education, most visits were made by those with middle college education with an average of 6.82 visits annually while the least visits were made by those with primary education who averaged

2.56 visits per year. By occupation, the study found that those who owned businesses had the most number of visits averaging 5.91 visits annually while the least visits were recorded by famers who averaged 2.6 visits per year. In terms of income levels, the study found that those who earned over Sh. 90,000 made the most visits averaging 7.79 visits per year while those who earned up to Ksh. 30,000 made the least visits averaging 3.51 visits annually.

Further, the study examined the contribution of Kenyans to domestic tourism in Kenya.

The results reveal that generally, the respondents spent Ksh 9,965 per visit. In general therefore, each person spent on average Ksh 53,315 annually on domestic tourism. The participants who spent the highest per visit were those in Mombasa with an average expenditure of Ksh. 11,999 while the least expenditures were recorded for those in

Nakuru who‟s spending averaged Ksh. 4,296. The results show that female participants spent more than male participants averaging Ksh. 10,986 as compared to the later who spent on average Ksh. 9,676. This difference was however small and insignificant. The study also found that those aged 28 – 37 years spent the highest on domestic tours with an average of Ksh. 11,264 per visit while the least spenders were aged over 67 years with

111 an average spending of Ksh. 6,400 per visit. The study revealed that the divorcees spent more on domestic tourism with an average expenditure of Ksh. 11,948 while those separated were the least spenders with an average expenditure of Ksh. 7,237 per visit.

The results also revealed that those in middle college education were the most spenders with an average expenditure of Ksh. 12,621 per visit while the least spenders were those with primary education spending on average Ksh. 2,319 per visit. Those employed in the private sector spent the highest per visit with an average expenditure of Ksh. 11,794 while the least spenders were farmers who spent on average Ksh. 4,380 per visit. Finally, those who earned over Ksh. 90,000 spent the highest with an average expenditure of Sh.

19,524 per visit while those who earned up to Ksh. 30,000 spent the least with an average expenditure of Ksh. 2,804 per visit.

This section sought to elaborate the effect of psycho-social factors of Kenyans on domestic tourism. Thus the current attitudes and preferences were examined and reported. The results showed that most of the respondents (48%) could not afford domestic tourism. Thus, Kenyans perceive domestic tourism as expensive. This can be attributed to the entry fees charged by the most of the local national parks as well as other requirements that involve cash outlays that most residents cannot afford. From responses of most of the respondents regarding affordability in chart 17 ,we can deduce that they would be willing to visit some of the places in Kenya but the costs were prohibitive especially those that relate to accommodation as hotels are expensive especially in

Mombasa and Nairobi. The way hotels charge for accommodation also makes it expensive for people to travel as groups as rooms are charged per number of occupants and not per room. While cheaper options might be available, information on the same is lacking and therefore most people do not know where to go to save costs. However, if government intervened through levy reductions, subsidy, contextualised tourism

112 promotions i.e.venacular media and domestic tourism be emphasised through the education curriculum.

The results show that most of the respondents are not satisfied with the number of visits they had made so far hence would be willing to travel more if constraints bedevilling the domestic tourism industry in Kenya can be addressed. As the results showed, the number of visits made per person per year would double from 5.35 to 10.54 visits per person per year if these constraints can be adequately addressed. The number of those willing to travel also increases by 15% of these constraints are addressed.

In terms of expected expenditures, the results show that this will also rise to an average spending of Sh. 10,447 up from Sh. 9,965 and the number of people willing to spend more on domestic tourism is also expected to rise by 17%. From these figures, an individual is expected to spend an average of Sh. 110,428 on domestic tourism annually.

If these targets can be reached, domestic tourism in the country can be boosted and would be beneficial to local tourism sites that have for long depended on foreign tourists to thrive.

The results also showed that most of the respondents (70%) did not consider their current number of visits as desirable. Only 30% agreed that their current number of visits were their desired visits. This was true for male and female as well as cross the three towns surveyed. This leads to the conclusion that the number of visits was not currently the desired target and there is therefore room for more improvement.

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5.2.0 CONCLUSION

The study sought to examine how Kenya‟s domestic tourism can be structured to act as a growth pole industry for the country‟s economy illustrated through the multiplier effect model. The results showed that location was a significant factor affecting participation in domestic tourism. This calls for stakeholders to positively market the domestic tourism destinations in Kenya in order for these places to be known and for more domestic tourists to visit them.

Age was found to have a significant impact on the level of participation in domestic tourism. Specifically, it was found that the younger the individuals, the higher the likelihood of participating in domestic tourism. This calls for practitioners in the tourism industry in Kenya to ensure diversity in domestic tourism so as to appeal to different age groups as not all domestic tourism products are picked up the same way by everyone.

Given the growing number of youth population in the country, it is important that this group be targeted more for domestic tourism.

Education was found to have a positive influence on participation in domestic tourism.

What this means is that those with higher levels of education are more likely to participate in domestic tourism than those that had lower levels of education.

Stakeholders in the industry especially those tasked with marketing the tourism in the country should market tourism products for different people depending on their levels of education. In fact, it would be beneficial for the tourism marketers to target those with college education and below as they are more likely to participate in domestic tourism. stakeholders should also consider having tourism in the education curriculum for students to understand tourism from the formative ages.

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The results also showed that income was positively correlated with participation in domestic tourism. This means that those with higher incomes tend to participate more in domestic tourism than those with less income. This can be linked to the higher costs involved in touring most domestic destinations and therefore those with less income do not have enough disposable income to spend on domestic tour products. Practitioners should therefore design the products that will also appeal to people with lower incomes.

The results showed that the current levels of participation in domestic tourism not only

15% but also that only14% had done foreign trips for tourism. Therefore, majority of the respondents had at least visited some domestic tourism sites in Kenya in their lifetime.

This suggests that more Kenyans could be willing to tour domestic tourism sites and therefore a revenue source for tourism industry in Kenya.

The study found that the biggest impediment to domestic tourism was costs. Most of the respondents were of the opinion that domestic tourism was very expensive. Practitioners therefore need to review their pricing in order to attract more domestic tourists in several local tourism sites as the current rates are considered high and biggest impediment to domestic tourism. The Government should formulate domestic tourism policy which will guide cost structures in the industry.

The second most significant impediment to domestic tourism was family commitment.

This was mostly exhibited by women. The industry therefore needs to understand that the products they currently have do not appeal to those with families. There is therefore need

115 to market products that target families so as to enable those who feel that family commitment impedes them from doing domestic tourism be involved.

The results showed that the number of expected visits as well as the expected expenditures is higher than the current numbers. This is good news for the industry. If the impediments can be adequately addressed through legislative framework especially a government policy that can address these issues, more people will make more visits and spend more than they are currently spending. This will lead to massive growth of the industry as well as ensure sustainability of the industry. With terror strikes making the industry experience low foreign tourists and the fact that foreign tourists are also seasonal, focusing on domestic tourists would provide an alternative and sustainable revenue line for the industry. In fact, most respondents did not think that they had made adequate number of visits suggesting that they are more willing to make more visits if things change. It is therefore in the interest of the industry and the government to address these constraints and help boost the industryperfomance.

5.3.0 RECOMMENDATIONS

5.3.1 Suggestions for Further Research

The study suggests the need to carry out a national survey on the status of domestic tourism in Kenya. This would help inform policy makers at the national level as well as at the county level on where domestic tourism currently stands and how to move the industry forward.

The study also suggests that another study needs to be carried out to examine what factors influence participation in domestic tourism in Kenya with a wider scope and not

116 just limited to the three towns. A national study would better inform policy makers. This way, counties can plan based on their strategic advantages.

Finally, further research can be conducted on strategies and framework for promoting domestic tourism. And research study on legislative and policy measures for sustainable domestic tourismbe developed for the industry.

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