Analysis of Consumer Preference in Product Attributes: A Case of Common Beans in Kiambu County, Kenya

COUNTY, KENYA

HANNAH WAIRIMU GITONGA

A Thesis Submitted

in Partial Fulfillment

of the Requirements

for the

Award of Degree of Master of Science (Agribusiness

Management

and

Trade) in the School of Agriculture

and Enterprise

Development

of

Kenyatta

University

I Hannah Wairimu Gitonga, declare that this thesis is my original work and has not been presented for the award of a degree in any other university or any other award.

Signature: . .

DatJD!rd!.?P.

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Hannah Wairi u Gitonga (AI 03114475/2009) Department of Agribusiness Management and Trade

SUPERVISORS

We confirm that the work reported in this thesis was carried out by the candidate under our supervision and has been submitted with our approval as university supervisors.

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Dr. Eric Bett (PhD),

Department of Agribusiness Management and Trade,

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DEDICATION

ACKNOWLEDGEMENTS

2.7 Methods of measuring willingness to pay 16 2.8 Analytical methods to determine willingness topay 18

2.9 Critical review ofconsumer preference studies 22

CHAPTER

THREE

3.0 MATERIALS AND METHODS 25

3.1 Introduction 25

3.2 Location of study 25

3.3 Sampling technique 26

3.4 Research instrument and data collection 29

3.5 Measurement of variables and data analysis 31

3.5.1 Hedonic model specification 33

CHAPTER FOUR 38

4.0 RESULTS 38

4.1 4.2 4.3 4.3.1 : 4.3.2 4.4 4.4.1 4.4.2 4.5. 4.5.1 4.5.2 4.5.3 4.5.4 4.5.5

Introduction 38

Socioeconomic characteristics, consumption and residence of respondents38 Evaluation of consumer preference for cornmon bean varieties .42 Consumer preference in cornmon bean varieties based on dwelling place ...44

Respondents age and preference in cornmon bean varieties .45 Evaluation of consumer preference in attributes of cornmon beans 45 Consumer preference in bean attributes after pairwise comparison .45

Attribute ranking according to variety 46

Evaluation of consumer willingness to pay for ranked attributes 53

KAT X 56 Gituru 54

KAT B9 56

GLP 2 Rosecoco (Nyayo) 56

GLP 24 Canadian Wonder 56

CHAPTER FIVE 57

5.0 DISCUSSION 57

5.1 Introduction 57

5.2 Respondents' socio economic characteristics 57

5.3 Consumer preference in common bean varieties 59

5.4 Consumer preference in attributes of common bean varieties 62 5.5 Effect ofpreferred attributes on willingness to pay price 69

CHAPTER SIX 76

6.0 CONCLUSION AND RECOMMENDATIONS 76

6.1 Introduction 76

6.2 Conclusion 76

6.3 Recommendations 78

LIST OF TABLES

Table 3.1: Description of study area 26

Table 3.2: Probability proportional to size sampling for bean traders 28 Table 3.3: Description ofvariables that were evaluated 35 Table 4.1: Socioeconomic characteristics of respondents (n=212) 39 Table 4.2: weekly b.ean consumption based on region 42

Table 4.3: Consumer preference scores for common bean varieties (1-7) 43 Table 4.4: Different age groups' Preference in bean varieties 45 Table 4.5: Results of pairwise comparison of common bean attributes 46 Table 4.6: GLP 2 keeping quality ranking by different age groups 48

Table 4.7: Ranking of KAT X 56 Gituru's taste by different age groups 50 Table 4.8: Ranking of GLP 92 Mwitemania color by different age groups 51

LIST OF FIGURES AND PLATE

Fig. 1.1: Decision making process 8

Fig 4.1: Respondents weekly bean consumption and monthly incomes 40

Fig 4.2: Respondents' weekly bean consumption and occupation 41

Fig 4.3: Bean varieties sold in the market 43

Fig 4.4: KAT B9 cooking quality ranking by different age groups 47 Fig 4.5: Market prices and willing to pay price for a kilo of beans 49

APPENDICES

Appendix 1:Map of larger Thika district showing study area 89

Appendix 2: List of released bean varieties since 1980 to 2010 90

Appendix 3: Data collection and analysis plan 92

Appendix 4: Summary of consumer preference literature 93

Appendix 5: Survey questionnaire 98

Appendix 6:Pairwise comparison of bean attributes 106

Appendix 7: Attribute ranking according tovariety 107

Appendix 8: Evaluation of common bean attributes for consumer preference 109

Appendix 9: Consumers source of nutrition information of common beans 109

Appendix 10: Breusche-Pagan heteroscedasticity test results 1JO

Appendix 11: Regression results for seven bean varieties 110

l. Regression results for KAT X 56 Gituru ll0

2. Regression results for KAT B9 Gacuma 111

3. Regression results for GLP 2 Rosecoco .111

4. Regression results for GLP 92 Mwitemania .112

5. Regression results for GLP 24 Canadian Wonder 112

6. Regression results for GLP 585 Wairimu .113

7. Regression results for KAT B 1 Katheka 113

Appendix 12:Variety ranking based on consumers dwelling place 114

Appendix 13:Bean consumption frequency by different age groups 114

Appendix 14: Consumers education level and consumption frequency 114

DEFINATION OF TERMS

Grain color: refers to the color beans impart into the food.

Grain size: refers to the expansion and visibility of the bean upon cooking.

Price: the market price of each variety during the time of study. Bean varieties have different prices no matter the season.

Cooking time: refers to the duration a variety takes to cook. This study evaluated whether the time taken to cook a variety was acceptable to the consumer.

Cooking quality: refers to the structure of the cooked bean, whether it mashes up or remains whole.

Keeping quality: refers to the ability of the variety to stay fresh without spoiling. The benchmark was two days, being the normal time well preserved boiled beans can be stored under natural conditions without spoilage.

Flatulence: refers to the discomfort of excessive gas experienced after consuming beans.

The discomfort experienced is different for each variety.

APA

-CGIAR

CIAT

DAO

FAO

GDP

GLP

GoK

KAT

KNBS

MOA

NARS

WTP

M-asl

ACRONYMS AND ABBREVIATIONS

American Pulse Association

Consultative Group on International Agricultural Research

Centro International de Agricultura Tropical District Agricultural Officer

Food and Agricultural Organization Gross Domestic Product

Grain Legume Programme Government of Kenya Katumani

Kenya National Bureau of Statistics Ministry of Agriculture

National Agricultural Research Systems Willingness To Pay

ABSTRACT

Common bean (Phaseolus vulgaris L.) is an important source of livelihood and food for approximately three million households in Kenya. Consumers appreciate common bean more due to its nutritional value and health benefits. Between 2005 and 2009, a total of 403,604 MT of bean with a value of US$ 199,743,000 was produced in Kenya. The Kenyan bean market has a deficit of 14,256 metric tons and is dominated by old improved bean varieties, an indication of consumer preference for those beans. This is despite new varieties being released into the market following intensive research and breeding work done by research institutions. Consumer preference assessment gives important information on acceptability of a commodity by consumers. The primary objective of this study, therefore, was to analyze consumer preference for common bean varieties by attribute sensory test and willingness to pay for preferred attributes. This study focused on bean consumers and traders in two districts, Thika East and Thika West of Kiambu County. The region was chosen as a test bed for this study due to high utilization of common beans in most of the diets among the residents. Additionally the two districts were selected because of their high population, diverse socioeconomic characteristics of residents, and their rural and urban living setups. Semi structured questionnaires were used to elicit information from 212 consumers and 67 traders who were randomly selected. Bean variety preference was assessed using a preference scale of 1-7 score. A pairwise analysis of eight bean attributes was done to assess preference of bean attributes. This was followed by assessment of attributes in seven bean varieties using likert scale of 1-5 rank. A hedonic price model was used to analyze effect of preferred attributes on price consumers were willing to pay. Data analysis was done using descriptive and inferential statistics in Excel and SPSS software programs. Results showed that beans were an important part of respondents diet with majority of respondents (86%) consuming beans more than once a week. Rural respondents consumed beans more frequently compared to urban respondents; difference in consumption was statistically significant (p-value =0.025). Beans were popular with women (83%) and were consumed by all age groups but there was more consumption in the 31-40 years age group (26.8%). GLP 585 was ranked l ", GLP 2 was ranked 2nd and

KAT X56 was ranked 3rd in preference by 64.7%, 43% 39.8% respondents respectively.

CHAPTER ONE

1.0 INTRODUCTION

1.1 Overview

The chapter gives the basis of the study. It gives the background of the study,

statement of the problem under investigation, objectives, hypotheses, significance, scope

of the study and conceptual framework.

1.2 Background to the problem

Modem food industry faces the challenge of developing food products in

accordance with consumer needs (Bech et al., 1997). This is as a result of global and

regional integration which has exposed consumers to diverse commodities subsequently

changing their preferences. Research in common bean by National Agricultural Research

Systems (NARS) has been oriented towards increasing yield and producing surplus for

sale and improving nutrient content- biofortication, as a strategy for family aut

o-sufficiency, alleviate malnutrition, hunger and poverty (Katungi et al., 2009), occasioned by among others; increasing population growth and increasing cost of agricultural

products especially animal related. Emphasize on improved production technology

research, has left out consumer preference an important component in acceptability and

marketing of products.

The common bean is an important crop for small-scale farmers grown by more

than three million households in Kenya (Katungi, et al., 2010). It has short growth cycle which permits production when rainfall is erratic. It also provides income to the

household and food to the consumer before harvesting of other long season crops such as

October at altitudes between 600-2000 meters above sea level. Bean varieties have

different attributes which determine their attractiveness to consumers. These attributes

are heterogeneous, making each variety distinct. Wortmann et al. (1998) classified

common bean varieties into nine major classes according to color and size as follows:

1. Pure large reds. 2. Medium. 3. Small reds. 4. Red mottled. 5. Purple.

6. Yellow /tans. 7. Cream. 8. Navy/white. 9. Black.

In Kenya, the annual bean production in the period between 2005 and 2009 was

403,604 metric tons worth about US$ 199,743,000 (FAO, 2011) with an annual per

capita consumption of 14 kg to 66 kg (Spilsbury et aI., 2004; Rubyogo et aI., 2007). This

is an indication that bean trading can contribute towards injecting 80-90 billion Kenya

Shillings into the Gross Domestic Product, thus boosting the realization of Vision 2030

(GoK, 2007). However, the amount of bean produced in Kenya is not sufficient to meet

domestic needs. According to Kibiego et al. (2003) and Mauyo et al. (2010), the

unrecorded annual bean imports from Uganda are estimated to be 9,300 MT while

recorded imports are 1,700 MT. Katungi et al. (2009) places imports at 14,256 MT. The

deficit is expected to increase given the increasing population and urbanization (Kibiego

et aI.,2003). The deficit is an indication of local market failure to stimulate production.

Consumer's choice of bean type is influenced by among others, the food dishes to

be made such as; mixture of beans and maize popularly refered to as githeri in Kenya,

Ngata in Malawi, Kande in Tanzania. Bean sauce is another dish which is served with

accompaniments such as rice and chapati, a common pancake in East Africa. Animal

protein is expensive and has been attributed to negative health implications such as

potassium levels in legumes decreases urinary calcium (Massey, 2003). Nutrition content

of beans isabout 60% carbohydrates, two-thirds of which is in the form of starch, 22% to

25% protein and very low fat content. According to Schwartz & Corrales (1989) beans

contribute one-sixth of total per capita protein intake in the East African highlands.

Compared to cereals, they are a valuable source of protein that supplements well, the low

quality proteins in cereal and are highly valued as staple food by consumers (Tapia, 1985;

EI-Tabey, 1992; Ruiz de Londono et al., 2000; Murray, 2010). Nutrition benefits and low

cost make common beans the alternative food choice to animal protein.

Common bean plays an important role in the soil fertility stabilization through

biological nitrogen fixation (Katungi et al., 2009). Rhizobium bacteria in bean nodules

supply the plant with fixed nitrogen, in form of ammonia, and get carbohydrates in

return. This factor is fundamental in mitigation of greenhouse gas emission. Excessive

use of fertilizers results in emission ofclimate change causing gas known as nitrous oxide

(N20) (Smith, 2008). Common bean therefore enhances sustainability of agricultural

production systems.

The important and diverse roles played by common bean, in the farming systems

and in consumer diets, makes it an ideal crop for achieving Millennium Development

Goal (MDG) one, five, six and seven; poverty and hunger eradication, improved maternal

health, low major disease incidences and sustainable environment.

Previous research on beans concentrated on agronomic aspects resulting in high

yielding bean varieties with little attention given to marketing aspects. As a result little is

known about consumer preference for the bean varieties. Market demand, which reflects

Mishili et al. (2009) consumers are the beginning of the value chain from which the flow

of information about food preference moves back to retailers, manufacturers, farmers and

scientific laboratories. Anti-nutritional aspects of beans such as flatulence, long cooking

time may reduce their consumption (AP A, 2010). Information on bean varieties/attribute

preference is therefore fundamental in enhancing utilization, development of bean market

and subsequently in stimulating production. It is also important considering the resources

and efforts which are directed towards development of alterriative varieties and

characteristics of agricultural commodities (Espinosa & Goodwin, 1991).

1.3 Problem statement

There is a wide range of bean varieties with different physical and sensory

properties. As a cheap and beneficial source of protein compared to other animal products

such as meat, there is need to know the effect of these different bean properties on choice

of beans varieties by consumers in order to enhance utilization. There has been extensive

research conducted on dry beans in relation to agronomic aspects which has resulted in

high yielding bean varieties that withstand biotic and abiotic stresses. Despite this, the old

improved low yielding varieties dominate market share and the country is bean deficit.

There is therefore a knowledge gap on what consumers prefer in the old beans that is

probably not in the recently released bean varieties whose agronomic properties have

been improved. Lack of consumer preference analysis, could be a factor that limits

utilization, subsequently low production of newly released varieties. The problem

therefore is insufficient information on the factors that determine choice of beans by

1.4 Overall objective

The overall objective of this study was to evaluate the consumers' preference in

common bean varieties and their willingness to pay for them.

1.4.1 Specific objectives

1. Evaluate the consumer preferred bean varieties in the market for attribute ranking.

2. Evaluate attributes that influence consumer preference for common bean varieties.

3. Evaluate consumer willingness to pay for preferred attributes in common bean

varieties.

1.5 Hypotheses

The study hypothesized that;

• There is no significant difference in consumer preference for different common

bean varieties.

• Consumer preference in attributes of different bean varieties is not significantly

different.

• Consumers' preference in bean attributes does not significantly influence their

willingness to pay for beans.

1.6 Significance of the study

Despite the many bean varieties that have been developed, the old improved bean

varieties continue to be popular with consumers. The study was important in establishing

the sensory attributes that made consumers prefer some bean varieties. Information from

_.

institutions incorporate the recommendations in bean improvement and breeding

programs, it is expected that bean value chain will become more vibrant following supply

of beans with consumer preferred attributes. This in return will increase utilization and

trading of beans, subsequently contributing towards income generation along the bean

value chain, a healthier population through increased bean consumption and contribute to

the country's realization of food self sufficiency through an increase in participation of

bean producers in the bean value chain. Lastly the findings of this study will contribute to

the existing knowledge of consumer behaviour and especially in attribute preference in

relation to bean choice knowledge gap. The findings will also be a base for further

research in the same or other fields.

1

.

7

The study focused on two value chain actors, (trader and consumer) from Thika

East and Thika West districts. The study concentrated on seven bean varieties, namely,

KAT X56 Gituru, KAT B9 (Red Haricot), KAT Bl Kayellow, GLP 2 Rosecoco, GLP 24 Canandian Wonder, GLP 585 Red Haricot and GLP 92 Mwitemania. All the selected

varieties were consumed in the study area as was established during an exploratory study

conducted in the study area in March 2012. It was therefore easy for consumers to

evaluate the beans since they were familiar with them.

1.8 Conceptual framework

Food choice is comprised of decision making process and factors influencing

these decisions. The study was based on consumer theory, where decision process

involves evaluation of alternatives and subsequently making a choice, which is equated to

decision are the consumer needs and resources available. As Figure 1 shows, it was

hypothesized that consumers would evaluate characteristics of different bean varieties

and choose varieties with attributes that provided them with highest utility; the rank they

assigned an attribute depicted the level of utility it provided. Evaluation was based on

previous experience of the bean or information of available alternative varieties.

It was hypothesized that consumers made choices among the many varieties that

were available in the study area. They were therefore expected to assign levels to

varieties they preferred from one (1) in a descending order.

Attributes in the different varieties were expected to contribute to consumer

preference of a particular variety. The consumers were therefore expected to rank the

different attributes in beans. The way to measure consumer preference for attributes,

therefore, was by ranking attributes on a likert scale, 5-1: Exc.ellent to Very bad for each

variety a respondent consumed.

It was expected that consumers would pay a certain price for a variety depending on

the level of satisfaction provided by the attributes in the variety. By stating the amount

they were willing to pay for varieties, it was expected that consumers showed the value

they attached to attributes in those varieties. Hedonic price function was presented as:

Pj

=

Z,

=

Purchase would depend on individual differences, such as available resources and

motivatio~ consumer got after evaluating the attributes. Preferred attributes which was

This will lead to enhanced consumption, whose effect will be more trading in beans and

ultimately production intensification.

Consumer

H

Situation: Need

1

/

recognition attributes (Output) for

Evaluation of Bean technology development.

Attributes;

'

"

,

/

maximize utility

+

Willingness to Pay

for preferred bean

!

attributes

Expected outcomes, Improved

nutrition, Increased incomes

Improved livelihoods

Fig. 1.1: Decision making process

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Introduction

The chapter consists of literature review that is relevant to this study. It covers

bean trading, improvement programs and bean attributes. It reviews methods of

measuring consumer preference and gives critical review of empirical studies on the

same. Overall, literature on bean preference concentrates more on varieties without

evaluating attributes of those varieties.

2.2 Socio economic characteristics

Socio economic characteristics such as age, income and gender playa crucial role

in acceptance of products in the market. They influence the amount of money spent on a

product and consumption patterns. A consumer may use enough amount of product

because he understands its nutritional value based on his level of education. On the other

hand a consumer may use less of a product because it is unaffordable (Mundua, 2010). In

Groote & Kimenju (2008) consumer preference for traits under study were influenced by

consumer socioeconomic and cultural background. Studies on socio economic

characteristics can inform traders and organizers of farmers' markets in coming up with

strategic locations for product outlets (Govindasamy, Italia, & Adelaja, 2002).

2.3 Bean marketing

Farmers grow beans not only for their own domestic consumption, but as a source

of income. Many farmers value beans as a fast-growing crop, which can be converted

easily and regularly to cash, especially during times of need. The availability of market

potential income and food security crop. It is therefore important to understand its

consumers. According to Munene (1993) beans are accepted by different communities in

Kenya going by the beans in markets across all counties in Kenya. This study showed a

composition of 30 bean varieties in 21 markets surveyed with variation in prices.

According to Kimani et al. (2005) and Korir et al. (2005) farmers in Northern Tanzania

and Eastern and Southwestern Uganda produced red-mottled beans for sale in Nairobi

and other urban centers in Kenya. This indicates a ready market for dry beans.

The average annual bean import in Kenya is 14,256 metric tons (Katungi et al.

2009) with 9,300 tons informally imported (Kibiego et al., 2003; Mauyo et al., 2010).

Approximately, 70 % of beans in Thika market are imported (Karanja D. personal

communication March 23 2012). The above studies show existence of preference for

particular bean varieties whose deficit is compensated through importation.

2.4 Bean Improvement Research in Kenya

A Grain Legume Project was established at KARl Thika in early seventies, to

cater for bean research and development. It released six bean varieties in 1980s namely

GLP-2 (Rose coco), GLP-24 (Canadian wonder), GLP-1004 (Mwezi moja), GLP-x.92

(Mwetemania), GLP-x.1127(a) (New Mwezi moja), GLP-585 (Red haricot), (Munene,

1993). Appendix 2 shows the GLPs. KARl Katumani released two varieties in 1987, Kat

Bean 1 and 2 (KEPHIS, 2011). By 2008, twenty one more improved varieties had been

released into the market by Universities and other research institutions. Research has

mainly been geared towards mitigating for biotic and abiotic constraints in order to

increase yield. This is evident in last column of variety release list in appendix two.

their preference regardless of agronomic constraints, (Santalla et aI., 1999; Buergelt et

aI.,2009; GoK a, 2010).

The Katumani (KAT) bean varieties are improved bean varieties developed by

Kenya Agricultural Research Institute (KARl), Katumani. The institute in partnership

with the International Centre for Tropical Agriculture (CIA T) has been promoting the

varieties in different parts of the country under Tropical Legume II project. The project

was initiated to support dissemination and promotion of the improved bean varieties in

Central, Rift Valley and Western Kenya (Karanja, et al.,2012). A similar promotion

program was introduced in Tanzania by CIA T in partnership with East and Central

African Bean Research Network (ECABREN). The program has resulted in an increase

of beans in Tanzania which are exported to Kenya (Katungi et al 2010).

2.5 Consumer preferences

Consumer preference is a tool that is used in marketing research to gauge

consumer satisfaction (utility maximization) and acceptance of a given commodity,

(willingness to pay for a particular commodity). It helps reveal an option that has the

greatest anticipated value among a number of options. Modeling and measuring

consumer preferences is therefore useful in designing of new or upgrading products and

services.

Consumers choose from the market goods that will satisfy their needs given the

amount of money available. According to Economides (2010) to get the best choice,

consumers undertake several steps; they analyze choices available to them given their

limited funds, for example, the different bean varieties that are sold in the market. Next

given to the good whose attribute combination offers maximum utility. When consumer

chooses one level of attribute against a similar one in a different variety in order to

maximize utility, a tradeoff occurs; marginal rate of substitution. An additional unit of

attribute X will increase level of satisfaction of a consumer by the marginal utility of the

attribute X. This study applies Stated Preference Technique by Pearmain et al. (1991) as

explained in Abley (2000), to estimate utility. The technique uses individual respondents'

statement about their preferences in a set of options to estimate utility. The final stage is

to get an optimal choice by combining analysis of the preferences with available choices.

For a long time technology development has been focused on quantity of

commodities, leaving out consumer preferences; an important component in the

marketing chain. Without good acceptability/preference characteristics, a new crop

variety will find no market, and thus be unprofitable to the producer (Luse, 1980). One

way of measuring consumer preferences is by employing Willingness to Pay (WTP)

technique. Willingness-to-Pay is defined as the maximum price that can be charged

without reducing the individual's welfare and utilization of the product. Empirical studies

have documented that some market segments are willing to pay a premium for food

products with differentiated attributes. In a study by Padilla et al., (2007) consumers were

willing to pay 585 pesos more for homemade marmalade with a certified quality label.

Mclennon, (2002) documented that consumers were willing to pay for non-meat biotech

food, compared to biotech meat products.

2.6 Bean attributes in relation to consumption

Consumers start their decision making with attribute comparison and then turn to

existing research literature on customer evaluation of alternatives prior to choice reveals

the crucial role of identifying the attributes affecting the customer's decision in order to

understand customer choice among alternatives. Food intake is determined by among

others, food availability and cost, preparation time, palatability, bulk, anti-nutritional

factors and digestibility (Kaul, 1987). These factors have not always received a due and

balanced consideration in research.

Beans are heterogeneous in varieties and attributes, which appeal to consumers in

different ways (Mbugua

et aI.,

et aI.,

popularity of old bean varieties, evaluation of consumer preferences has become

necessary before development of new varieties, for the farmers to produce marketable

varieties (Munene, 1993; Katungi

et aI.,

et aI.,

evaluation is important to establish positions these characteristics are given by consumers

during the purchase process, for effective bean grain improvement, development and

business establishment.

Beans are found in different sizes namely: small, medium and large sizes. One

way of differentiating beans in the market is by grain size. According to Gichangi

et aI.,

(2011) 69% farmers and 82% traders differentiate bean products by grain size in the

Central Rift Districts. According to Maryange

et al.

there are 900 seeds per kg, medium; 600-899 seeds per kg and large if there are less than

600 seeds per kg. Katungi

et aI.,

than 25g, Medium 25-40g, Large more than 40g per 100 seeds. Individual bean grain

The decision to take the attribute of taste into account when defining acceptance

was based on numerous studies that indicated taste as the single largest determinant of

food choice, directing consumers to eating, regardless of constraints of production

(Spilsbury et ai., 2004). In Deodhar and Intodia (2002) study of traits in clarified butter

that influenced daily price, it was found that consumers were willing to pay a premium

for branded clarified butter over the non-branded. Consumers attached economic

significance to flavor. In a similar study for rice characteristic, done in Ghana by Anang

et al. (2011) aroma had economic significance. Beans have diverse taste ranging from

beany to 'sweet taste (APA, 2010). It is important to establish whether taste influences

consumers preference in common beans.

Beans need to be cooked for long to ensure Lectin; a protein found in lentils is

well cooked. If not well cooked, Lectin can cause allergic reactions in some consumers.

Cooking time has implications for the rural and urban poor, gender equity and

conservation of biodiversity. For decades, 90% of consumers in Brazil have been

consuming a combination of bean and rice meal daily. However consumption of beans

has decreased significantly due to lifestyle changes which leave consumers with little

time to prepare and cook raw beans (Canada, 2009). Fast cooking food commodities save

on time and fuel cost. Different bean varieties have different cooking time (Maryange et

al., 2010) which range from three hours for unsoaked beans in Kenya to 103,96,56 and

105 minutes for CAL 96, MCM 5001, white Haricot and Uganda K2 varieties in Uganda

respectively (Kim ani et ai., 2005). Reduction in cooking time cuts down fuel

consumption, in the process reducing environmental degradation and fuel cost (Diamant

Bean grain color is of great importance to some consumers such that producers

grow beans with particular color that is preferred in a particular area. In Maryange et al.

(2010) bean colors range from white all through the color spectrum to black; either plain

or speckled.

Beans contain oligosaccharide; 3-5 sugars bound together in a way that human

body cannot digest or absorb them. Bacteria in the intestines break and digest

oligosaccharides, producing gas in the process (flatulence). Flatulence can lead to

reduced consumption, a case in point is Brazil where there has been a steady decline in

pulse consumption due to health aspect related to flatulence effect of beans (Canada,

2009). Some beans have low flatulence effect such as the Manteca bean, grown in China

for centuries. This variety produces tannins in the seed coat that bind to calcium in the

intestines in ways that change the pH and chemistry of digested food enough to prevent

gas formation (American Bean Organization, 2008 ). Soya bean (medium sized purple

bean) grown in Northern Tanzania causes low flatulence, (Korir et aI., 2005).

Cooking quality refers to the cooked structure of the bean. The attribute takes into

account cooking time, density, hydration capacity, swelling capacity and whole grain

(Coelho et al., 2009). According to Coelho et al.(2009); Mwangwela in (Maryange etal.,

2010 ) there is notable varition of bean cooking quality in different genotypes, a clear

indication that the attribute is manifested diffently in the various varieties. Splitting and

mushing up of cooked beans is one of the undesirable cooking quality characteristics

2.7 Methods of measuring willingness to pay

To understand consumer behavior and relative importance of various attributes in

food purchase, various techniques have been applied (Kiesel & Villas-Boas, 2007).

Willingness to pay (WTP) techniques are devised to elicit people's monetary valuations

of costs and benefits for goods and services. They can be classified into two, revealed

preferences which can be derived from market data or experiments and stated preferences

derived from direct surveys or indirect surveys. Market data involves collection of

individual purchase data of a customer panel member or sales records from retail outlets.

The advantage of using this method is that real purchases are used instead of stated

purchase intentions. There is limitation in that it is not possible to estimate WTP for new

products or hypothetical products that are not yet in the market. In experiments, purchase

behavior is simulated by giving the subjects an amount of money and asking them to

spend on a specific selection of goods. It is not always possible to obtain the data

required in revealed preferences in order to estimate price-response function. For

example differentiated products have to be manufactured before they can be tested

experimentally. Practically, the expenditure and time needed to carry out experiments

make them less favored in product evaluations (Vo1ckner, 2006 ).

Stated preferences are methods of measuring WTP based on consumer statements.

They can be derived from direct surveys (contingent valuation) and indirect surveys

(choice modeling). Direct surveys can further be classified into - expert judgments and

customer survey. Indirect surveys comprise of conjoint and discrete choice analysis. In

indirect surveys customers are presented with product profiles with systematically varied

or not (Marbeau, 1987). In a study by Mennecke et aI., (2007) respodents were asked to

choose meat they liked from pictures of different beef cuts and combination of meat

origin, animal breeds, nutrition. One of the limitations of using indirect surveys is that the

customer must be willing to purchase the product as presented and at the given price

-status quo product, which is not realistic market behavior (Breidert, Hahsler, &Reutterer,

2006). Using a status quo product may not yield the correct WTP predictions due to

consumer heterogeneity; different participants might consider different products their

best alternatives.

Profiling is complex and difficult to present in some products. A profiling trial

done for this study yielded profiles which left out some of the attributes that had been

selected by consumers. Direct surveys require respondents to state how much they are

willing to pay for a specific product or bundle of attributes. The objective of direct survey

- contingent valuation methods is to provide the researcher with monetary valuations of

the target goods, whereas choice modeling methods target either monetary valuations or

preference order outcomes (Brown, 2003). Open-ended CV is a direct method asking the

respondents to state their maximum willingness to payor minimum willingness to accept

for a change in their utility compared to the status quo situation (Hanley, Maurato, &

Wright, 2001). In dichotomous-choice contingent valuation the respondents are instead

asked to choose whether they would accept or reject a fixed price for a certain product

(Koistinen, 2010). Some of the advantages of using direct surveys are that they are cost

effective and time efficient. They are flexible enough to include product combinations

and allow for individual level estimation. However the estimation might not give real

The approach has been applied in safety and environment related policy

evaluations (Breidert et aI., 2006).

It

commodities such as organic food products (Boccaletti &Nardella, 2000; Gil, Graca, &

Sanchez, 2000).

It

Raffaelli, &Thilmany-Mcfadden, 2011). This study applied Stated Preference Technique

by Pearmain et al. (1991) as explained in Abley (2000) to estimate utility. The technique

uses individual respondents' statement about their preferences in a set of options to

estimate utility. Direct survey with open ended questions was used for data collection, to

elicit more information by giving respondents a chance to make independent choices,

unlike the dichotomous choice questions which limit respondent choice to status quo or

profiled products (Breidert et al., 2006).

2.8 Analytical methods to determine willingness to pay.

Two main approaches to measure consumer preferences are hedonic and discrete

choice models. Hedonic emanated from Lancaster, an American researcher, who came up

with Lancaster preference theory after expounding on the consumer theory of classical

economics on utility maximization (Lancaster 1966). From the theory he argued that

consumer's choice of a good was for satisfaction derived not from the good as a whole

but from the attributes of the good. Within the context of Lancaster preference theory, an

American economist Rosen (1974) introduced the first equilibrium model of market

supply and demand based on product characteristics. The concept underlying hedonic

model is that the price of a heterogeneous good is a function of the attributes of that good

As explained in Picard (2010) discrete models such as logit and probit among

others identify importance of characteristics in commodity purchase decision but do not

explain the commodity price. Multinomial logit model is a discrete model that has been

used in willingness to pay studies. Its estimation procedure is the maximum likelihood

(MLE). It helps identify the important product characteristics in a purchase decision. It

however does not explain product prices but instead examines the variables that

determine whether a consumer makes a purchase or not. Random Utility Model which is

also a discrete model takes the sale prices as representative of market price available to

all consumers and not necessarily representative of characteristics of a product

(Palmquist, 2003). Repeat Sales Price Indexes analyze data of commodities that have

been sold at least twice, they show percentage growth in sale prices over time. They

however do not provide information on value of individual commodity characteristics or

on price levels. The hedonic regression on the other hand reveals the expected value of a

product given the characteristics and the expected contribution of each of the

characteristics to that value. The specification for hedonic model is the linear regression

model and the estimation procedure is the ordinary least squares (OLS). The concept has

been applied in many studies ranging from housing and automobile markets to

agricultural products.

Von Oppen (1978) was the first to define plant breeding goals by applying

hedonic estimation. He developed a preference index to evaluate the acceptance of new

food grains.

In

are related. This means that some cryptic characteristics can be inferred from evident

necessary to evaluate individual characteristic instead of inferring. The notion that red or

yellow apples are sweet while green apples are sour as expressed by (Portugal, 2004),

may not apply in the case of beans.

Abansi et al. (1990) used hedonic pricing model to evaluate consumer preference

for rice quality. The results showed that consumers in Philippines were willing to pay

more for quality characteristics in rice. The study categorized consumers by location;

urban and rural. The findings of the study showed that both groups were price responsive

to changes in quality characteristics. However urban consumers attached higher value to

quality characteristics than rural group. It is therefore important to evaluate preference in

both urban and rural setting to establish whether there is any variation.

In

market, Schamel, Gabbert, & Witzke (1998) introduced a new dimension of a hedonic

analysis; regionl reputation. U.S. consumers preferred Chardonnay (white wine) to

Cabernet Sauvignon (red wine). This adds to the observation made above that region

may influence consumer preference of a product.

Dalton (2003) used hedonic price model to evalute consumption attributes

perceived important by rice consumers in West Africa. The study was to derive economic

value of upland rice and subsequently advice breeders on consumption traits to be

incorporated in the rice seed, which were not considered in the breeding programmes.

Results showed grain elongation and swelling were important in relation to the amount of

rice prepared and the amount that can effectively feed a household. The swelling

characteristic was perceived to increase in volume thus generating more food with less

grain. The value for the characteristic was 4.5 while for tenderness was 4.3 on the Likert

factor for promoting a new variety for official release. However this trait was not

significant in determining willingness to pay. This means production traits do not

necessarily influence consumer choice or preference and thus do not necessarily have to

be included in attribute evaluation for consumer preference. As stated by Dalton (2003)

agricultural agencies should include a broader set of characteristics besides production

during product evaluations, in order to increase producer and consumer surplus in

agrarian economies.

Langyintuo et al. (2004) used hedonic pricing model to evaluate effect of cowpea

characteristics on prices in Cameroon and Ghana. Results showed that seasonality, grain

size, color and insect damage level explained a substantial price variation in both Ghana

and Cameroon.

In

price, relevant characteristics choosen after literature review were moisture content,

Protein, fat, crude fibre, carbohydrates, ash, seed weight, foreign matter, water uptake

capacity, swelling capacity, and color diversity. Mishili et al. (2009) conducted a study in

Tanzania where they applied hedonic price model to analyse consumer preference for

bean grain quality characteristics. The i~vestigated variables included size of bean grains,

grain damage by bruchids, percentage of discolored grain and percentage of mix. Results

showed that consumers placed significant importance on cooking time. All the above

mentioned studies show that hedonic price model is appropriate in evaluating consumer

preference of agricultural products.

In

varieties sold in Nakuru Municipal Market using a hedonic price model. The study

change in market price respectively. Flatulence, color and expansion were significant at

95% level of confidence. The study concentrated on consumers based in an urban set up

who depend on what is offered in the market and not what they were able to produce or

access from local producers as is the case with consumers in rural set up, where

production and consumption go hand in hand. Preference in rural set up may vary given

that products are easily acquired (Edmeades 2005).

2.9 Critical review of consumer preference studies.

Most of the research efforts have focused on demand of common beans in the

market and the results are therefore derived from the traders' perspective and not from

the consumers' point of view. Some of these studies were done by Munene (1993),

Mbugua

et al.

et al.

et al

documented GLPs as the most popular varieties. In Munene (1993) the study was

inclined more to trading than consumers' perspective. Results showed there was price

variation between varieties but the reason for variation was not explained. A consumer

preference study based on evaluation of attributes would probably have given the reason

for price variation. The study by Mbugua

et

grain quality characteristics both for consumption and production were evaluated. The

results were not clear for example GLP-2 variety was among the best ranked varieties

with an average score of 4.7. It was not explained whether the score was due to

production or consumption attributes of the variety. The study by Katungi

et al.

indicated the preferred attributes were short cooking. time, color, seed size. However,

information from this study is derived from producers and traders perspective, leaving

Gichangi et at (2011) in Central Rift Districts of Kenya, it was established that GLP

dominated the market. The results were derived from data collected from wholesale and

retail traders who indicated they preferred a differentiated crop either by color or size.

One of the recommendations was that consumer preferences should be evaluated before

embarking on introduction and promotion of market oriented beans.

In the study by Korir et at. (2005) on bean varietal preference in East African

markets, results showed that Maharage soya was the most preferred variety in Tanzania

while Nyayo was ranked number one in Kenya. This study did not compare attributes for

preference in each variety but gave overall varietal rank in different regions. It would

have been of great value to the breeders if the attributes in the preferred varieties were

known.

In a consumer preference study conducted by Laswai, Shayo, & Kundi (2008) on

sorghum and millet, local tradition varieties were more preferred than improved varieties.

The improved varieties had most of the desired attributes in relation to production such as

high yields. The study showed that there was no advocacy for production and utilization

of local varieties but they were dominating at the time of the study. One of the stated

reasons for their popularity was that they were more palatable than improved varieties.

The study did not elaborate what characterized palatability, information that could have

been important for future grain improvement.

Groote & Kimenju (2008) conducted a consumer preference study for color and

nutritional quality in maize in Nairobi, using dichotomus contingent valuation. Results

showed that there was a strong preference for white maize among urban consumers who

of marketing outlet, income and cultural background played a role in preference of the

two maize types. The study however did not address intrinsic attributes of the two types

of maize that influenced preference which would have greatly contributed in efforts to

CHAPTER THREE

3.0 MATERIALS AND METHODS

3.1 Introduction

The section presents information on the tools used for the study and justification

thereof. It contains a brief of the study area and the technique used to arrive at the

sample. It also gives details on how data was collected and methods used for analysis.

3.2 Location of study

The study was done in Kiambu County. Thika West district was selected because

it is an urban setting while Thika East district and Kakuzi represented rural setting. Two

Thika Districts were selected because of diverse socio-economic orientation. The main

Thika town is an industrial town and population is therefore composed of consumers

from different backgrounds who are expected to have diverse preferences. Majority of

Thika residents purchase beans for consumption making it ideal for a consumer

preference study. It is also centrally located in terms of infrastructure among major bean

growing counties of Meru, Embu, Kirinyaga and the bean deficit areas in the tea zones of

Muranga and Kiambu counties. The main economic activity in the rural area is fanning.

The main market, Jamhuri, is a key outlet for both local and imported beans which means

there are many bean varieties. The main market actors are wholesalers and retailers who

supply beans to the study area and other county markets.

In the year 2010 an average of 8,300 hectares out of the total 44,615 hectares

arable land in Thika was allocated to bean production. This produced, approximately

70,650 bags of 90kg with an estimated value of Kshs. 364.2 million (GoK 201Qa).

Jamhuri market which has 153 bean traders and Madaraka market with 63 bean traders.

Annual per capita consumption was high at 60 kilograms. Table 3.1 presents a

summarized description of the study area.

Table 3.1: Description of study area

Parameter Thika East Thika West Source

Area (krrr') 493 382 (OoK,2010a)

Population 77,073 218,544 (OoK,2009)

Households 18,618 91,000 (OoK,2010a)

Altitude in m.asl Above 1500 1555-2400 (OoK,201Oa)

Flainfall inmm 500-900 500-900 (OoK,2010a)

Temperature range (Oc) 18.7-22.4 18.7-22.4 (OoK,2010a)

3.3 Sampling technique

Since the proportion of bean consumers in the study area was unknown,

estimation was done based on the bean consumption in the country and the per capita

bean consumption in the study area. Estimation was done as follows; Bean consumption

in Kenya in 2009 was 406,970 metric tons. Assuming annual per capita bean

consumption was 60 kg (Broughton et al., 2003; OoK, 2010b) and given that country

population was 40 million, total number of bean consumers was approximately 6,782,833

people. This was 16.95% (0.169) of the whole population.

The required sample was calculated using formula developed by Cochran (1963) and

explained in Israel (1992).

ZZPq

n

=

-z-

e

p

=

q=

1 -

=

Calculation of consumer sample size was therefore:

n

=

=

Consumers were selected from all the six divisions in the two districts where a total of

216 people were interviewed. Research area was grouped into ten areas comprising of

both households and workplaces. Three areas were in the municipality; Makongeni 25,

Majengo 29, Thika district and Municipal offices 15. Two areas in Juja: Muchatha (20)

and Gacororo (20). Two areas in Gatuanyaga: Gatuanyaga and Munyu (22) Ngoliba:

Ngoliba and Mukawa (20). Kakuzi: Ithanga (22), Kakuzi (23) Mitumbiri: Thangira (20).

The study included both urban and rural areas, to eliminate possible bias of results that

could be attributed to easy access of beans in the rural areas where production takes

place. Thika town and surrounding estates such as Makongeni, Majengo, Juja and

government offices within Thika municipality were classified as urban while villages in

Gatuanyaga, Ngoliba, Kakuzi, and Mitumbiri were classified as rural. Four

questionnaires were incomplete and could not be used for analysis. The analyzed data

was therefore from 212 questionnaires.

Few markets were included in the study to gauge the preferred bean varieties and

magnitude of transactions. Identification of the markets with more than 14 bean traders

was done with the help of Divisional Agricultural Extension Officers and municipal

council staff and cereal traders' group officials in the relevant areas. A total of 67 out of

394 bean traders were interviewed for this study. 394 traders were approximately 4.6% of

follows; Jamhuri 25, Madaraka 11, Muchatha 10, Ithanga 7 Ngoliba 7 and Thangira 6.

Selection of traders was proportional to the total number of bean traders in chosen

markets as table 3.2 shows. Traders sample size was calculated using the Chochran

(1963) formula.

Table 3.2: Probability proportional to sizesampling for bean traders

Market No. of cumulative Samples Sample

traders total (sampling interval of 6) size per

(x) market

Ithanga 40 40 6,12,18,24,30,36,42, 7

Jamhuri 153 193 48,54,60,66,72,78,84,90,96, 25

102, 108, 114, 120, 126, 132,

138,144,150,156,162,168,

174, 180, 186, 192,

Madaraka 64 257 198,204,210,216,222,228, 11

234,240,246,252,258,

Muchatha 58 315 264,270,276,282,288,294, 10

300,306,312,318,

Ngoliba 42 357 324,330,336,342,348,354, 7

360,

Thangira 37 394 366,372,378,384,390,396 6

Total (n) 394 302

Sample (s) 67 Sample 394 =6

67 interval

Source: GoK (201 Oa) and author.

As depicted in table 3.2, to get required samples per market, 394 which was the

interval of six. This was successively added and market sample was given when the

cumulative total for each market was reached.

3.4 Research instrument and data collection

An exploratory study was conducted in Thika town where people were asked

which bean varities they liked. A similar study was conducted in Gatuanyaga and

Ngoliba areas where farmer groups were asked which bean varieties they liked. It was

established the popular bean varieties were Kat X 56 Gituru, KAT B9 (Red Haricot),

KAT Bl Kayellow, Gathika GLP 2 Rosecoco, GLP 24-Canandian Wonder, and GLP 585

Red Haricot. GLP 92 Mwitemania was not very popular. It was also established that

consumers considered such attributes as color, grain size, taste, cooking time, cooking

quality, keeping quality, flatulence, and price when purchasing beans. The seven bean

varieties were named with the help of KARl, Thika and Katumani researchers. It was

further established that the varieties fell into two clasifications based on the year of

release into the market. The classifications were Old Improved Bean varieties

(1982-1984) and New Improved Bean varieties (1989 to date). Appendix 2 shows details of

different varieties and the year the varieties were released. The GLPs used in this study

fell in the Old Improved Bean varieties while KA Ts fell in the New Improved Bean

varieties. Each variety was packaged in transparent polythene bags which were presented

to consumers by the enumerators for attribute evaluation in the main study. The seven

No Variety Local Picture Name

Morphological Characteristics

1 GLP2

Calima

Rosecoco,N

yayo

Medium red mottled

2 GLP 585 Wairimu

Red Haricot

Small red

3 GLP92 Mwitemania Medium cream

Pinto Sugar, mottled Pinto

Carioca

4 GLP24 Gituru Slim dark red kidney

Canadian shaped

Wonder

5 KAT Gituru

X56Canadia

n Wonder

Rounded large dark purple kidney shaped

6 KAT B9 Gacuma

Red Haricot

Medium Red

7 KAT Bl Katheka,Ka

yellow

Medium

yellow/green round

shaped

Plate 1: Seven bean varieties used in the study

A semi structured questionnaire was personally administered to consumers in

order to collect primary data. Administration of the questionnaire was done with the help

of research officers from KARl, Thika and Agricultural Extension Officers in the

different divisions of study area. Secondary data was collected from District Agricultural

Offices in Thika East and West Districts, KARl offices and from existing literature.

Information on socio-economic characteristics of respondents, their bean variety and

attribute preferences was collected for analysis. The required data was both descriptive

and diagnostic, in nature, thus fitting a survey design; the study helped establish popular

attributes of beans and frequency with which they were mentioned as preferred attributes

by consumers. It further helped evaluate influence of attributes on consumer preference

for bean varieties. The study further gauged the amount of money respondents were

willing to pay for varieties with preferred attributes.

3.5 Measurement of variables and data analysis

According to Mutai (2000) measurement is a procedure that assigns numerals to

events, characteristics or responses. Measurement of data facilitates it's analysis in order

to obtain statistical results capable of interpretation. Excel and the Statistical Package for

Social Sciences (SPSS) Version 16.0 were used to generate descriptive statistics

(frequencies, means, standard deviations, percentages, t-test values and skewness).

Appendix 3 presents information on source of data, method used for data analysis

and the expected results for this study. Socio-economic factors of the respondents playa

include them in the study to help establish whether there are any differences among the

consumer groups.

Bean consumers were asked to point out the varieties they consumed, they were

then asked to rate those varieties in order of preference, using a 1-7 (qualitative) scale;

one being the most preferred and seven the least preferred variety. This was to ensure the

rates given were as a result of consumer's experience with the beans. A chi square test

was used to test the hypothesis that there was no significant difference in consumer

preference of different common bean varieties. Respondents then evaluated eight

attributes in the seven bean varieties by assigning a rank to each attribute at 1-5 likert

scale; one being "very bad" and five "excellent" (qualitative). Description of variables is

presented in Table 3.3. After evaluating the attributes, consumers were asked open ended

questions on how much it cost them to purchase each of the varieties they consumed

(quantitative). They were further asked how much they were willing to pay per kilo of

each variety based on the evaluation they had done (quantitative). A chi square test was

used to test the hypothesis that consumer preference in attributes of different bean

varieties was not significantly different.

Descriptive statistics were computed on the sample data. The statistics were,

~ ~

sample size (n) and the proportions of participants in each response category (p I, P2...

~

Pk) where k represents the number of response categories. The test statistic for the

~

""

(0 -

E

)2

X=L

E (3)

Where O=observed frequency and E=expected frequency in each of the response

categories. The test compared the observed frequencies in a response category with the

frequencies to be expected if the null hypothesis were true. If the null hypothesis was

true, the observed and expected frequencies would be close in value and the x? statistic

would be close to zero. If the null hypothesis was false, the y}would be large.

Data on willingness to pay and attribute evaluation was used to fit a hedonic price

model. The model was used to estimate the relationship between willingness to pay and

the value consumers assigned to attributes in each variety. The model was tested for

goodness of fit using R-squared, analysis of residuals. Overall statistical significance was

checked with an F-test followed by t-test of individual parameters. The regressionoutputs

are summarized in Appendix 11 (1-7).The decision rule is that, "if the t-value of the regression

coefficient associated with an independentvariable is greater than,the absolute criticalt-value then

the independent variable is significantat the given level of confidence".

3.5.1 Hedonic model specification

A multiple regression was done using a hedonic price model, introduced by Rosen

(1974), to check the significance of bean attributes in predicting the price consumers

were willing to pay for bean varieties. In the model the mean price of an ith variety would

be what consumers were willing to pay. It would be a function of the attributes in the

variety. The general form of hedonic pricing theory as specified by Rosen (1974) is:

Pi

=

+

+

Z, = A vector of bean attributes. Ei= Random error.

The partial derivative of Pi with respect to Zr,

a

a

implicit price. It represents the amount consumers are willing to pay for a change in unit

of attributes.

This is taken as the value consumers place on a particular variety. This value comes about

by weighting the different attributes of the variety in relation to the utility they provide.

In the bean preference analysis, price consumers were willing to pay was regressed on

eight bean attributes namely; Color, Grain size, Price, Taste, Cooking time, Flatulence,

Cooking quality and Keeping quality. Likert scale 1 - 5 was used to rank preference for

attributes where five (excellent) was the highest rank and one (very bad) least rank

allocated to an attribute. Attributes and ranks used were as described in Table 3.3. The

linear model for each variety used in the study would take the form:

Pi

=

Where Pi= price(WTP) for bean variety i

~o = Constant L~i = implicit value of characteristic Z in variety i

Zi = quantity of the characteristic in variety i £i = Stochastic error term

Specification of the model into estimable form for this study was as follows for all

varieties:

Pi =fJo+fJlcoli+fJ2grzi+fJ3prci+fJ4tasti+fJ5ckdn+fJ6ckqlti+fJ7kpqlti+fJ~tui+ei

Where;Pi =Price (Kshs) consumers were willing to pay for a kilo of common bean

variety i.

fJo

Pi

Pi

estimated coefficients of bean attributes. eol, grz, pre, tast, ektm, ekqlt, kpqIt, fltu = bean

attributes as defined in Table 3.3

e

=

dependent variable. Regression analysis calculates coefficients in a way that minimizes

sum of squared errors between actual values and predicted values of beans.

:Ee/=:E

S]= the stated willingness to pay price. S2 = estimated or predicted value of beans.

Coefficients were given by :~2

=

a~j / agrzj

pi

This means the percentage change in Pi brought about by a change in

grz,

other regressors constant. If ~2 =4, then a 0.1 unit increase in grz, leads to a 0.4 percent

increase inPi.

Table 3.3: Description of variables that were evaluated

Variable Variable

Measurement Value Code definition

1 wtp Willingness to Kenya shillings Quantitative pay

2 col Grain color Scale (5,4,3,2,1) Qualitative (Excellent-Very bad)

3 grz Grain Size Scale (5,4,3,2,1) Qualitative Excellent - Very bad

4 prc Price Scale (5,4,3,2,1) Qualitative (Excellent-Very bad)

5 tast Taste Scale (5,4,3,2,1) Qualitative (Excellent-Very bad)

6 cktm Cooking time Scale (5,4,3,2,1) Qualitative (Excellent -Very bad)

7 Ckqlt Cooking Quality Scale (5,4,3,2,1) Qualitative (Excellent-Very bad)

8 kpqlt Keeping quality Scale (5,4,3,2,1) Qualitative (Excellent-Very bad) after cooking

NB: (5) Excellent (4) Good (3) Fair (2) Bad (1) Very bad

This study adopted linear functional form in regression analysis. To ensure

regression assumptions were satisfied, the data was tested for normality,

heteroscedasticity and collinearity.

One of the assumptions of linear regression analysis is that the residuals are

normally distributed. Estimation of coefficients requires that the errors are identically and

independently distributed this ensures the p-values for the t-tests are valid. As a result, all

variables were checked for distribution normality using histograms of the fitted model.

The histograms showed the results were confined within the normal distribution curve

and took the bell shape.

The regression data was tested for multicollinearity between the independent

variables, by running a tolerance and Variance Inflation Factor (VIF) assessment.

Tolerance =

1 -

VIF= tOle!ance (10)

Where:

R2j is the coefficient of determination of a regression of explanatory j on all the other

explanatory variables. In other words tolerance is an indication of the percent of variance

in the predictor that cannot be accounted for by the other predictors, hence very small

values indicate that a predictor is redundant, and values that are less than .10 may merit

further investigation. A tolerance value less than 0.10 and VIF value of 10 and above

indicates a multicollinearity problem. According to Nzau (2003) cited in Kieti (2005), a

correlation coefficient more than or equal to 0.70 is an indication of a strong explanatory