Supervisor: Fred Håkon Johnsen

II

DECLARATION I hereby declare that this thesis is accomplished with my own work and all sources of

literature that I have used are cited. I also assure that this work has not been presented to any

other university.

III

DEDICATION

This work is dedicated to my parents, and my two brothers, Artan and Arian, with much love and thanks.

IV

ACKNOWLEDGEMENT

My special gratitude goes to my supervisor Ass.Prof.Fred Håkon Johnsen for his wise advice

and great support in accomplishment of my master thesis. Great acknowledge goes to

Lånekassen, without its financial support my study program would be onerous manageable. I

wish to acknowledge Prof. Mujë Gjonbalaj for his continues support and encouragement

throughout my study. I also wish to thank my field assistant Rozafa Miftari for her sincerity

and tireless during field work. Great thanks go to those surveyed for their time and patience

during the interviews. My appreciation goes also to Ass.Prof.Mensur Vegara and Ass.Prof.

Hysen Bytyqi, who were the main initiator for my study program at University of Life

Sciences. I also wish to thank my colleagues at University of Prishtina, Prof. Halim Gjergjizi,

Prof. Mustafë Pllana, Prof. Shukri Fetahu, Prof.Skënder Muji, Jehona Shkodra. My special

gratitude go to my dear parents who continuously made sacrifice to keep me in school, thanks

a lot for all their love, support and encouragement that gave it to me. Special thanks go to my

best friend Panadda Larpkern, Besim Gojnovci, Ilbrahim Mehmeti, Arbina Kaja, and Paul

Okullo.

V

Abstract The aim of this study was to analyse consumer buying behaviour, preferences, attitudes,

needs and wants toward dairy products. The study was carried out in five Kosovo regions

(Prishtina, Prizren, Mitrovica, Peja, and Gjilan). The stratified random sampling has been

applied in selecting people who would be included in the sample. The data used in this study

were obtained through direct interviews with Kosovo consumers aged 18 years and older.

Two associated statistical techniques, multiple linear function and binary logistic function

have been used to explain the relationships between the quantity and expenditures on milk

and other dairy products with demographic and socioeconomic household characteristics. The

results show that milk, yoghurt, cream, curd, and cheese were the main dairy products

consumed by majority of Kosovo consumers. Supermarkets and grocery stores were the most

preferred market places by the consumers when buying milk and other dairy products. The

consumer’s preference towards market place was significantly dependent on demographic and

socioeconomic household characteristics Dairy products with shorter shelf life were bought

more frequently by the consumers compared to those with longer shelf life. Apart from

product life, other demographic and socioeconomic factors had significantly impact on the

frequencies of buying milk and other dairy products. The consumers’ demand for milk and

other dairy products was quite stable throughout the year. The consumers’ attitudes toward

product features such as nutritive content, taste, product safety, price, brand, wrapping,

package size, and the origin of product were significantly dependent on demographic and

socioeconomic factors. Kosovo consumers preferred bigger packages for curd and cheese.

Smaller packages were predominantly more preferred for fruit yoghurt and butter. Majority of

the Kosovo consumers had favourable bias towards domestic versus foreign dairy products.

Product attributes such as quality, safety, taste, and price were the main features motivating

Kosovo consumers to purchase domestic dairy products. Television and newspapers were the

media most often used by the consumers to get information about the dairy products. The

preference towards new dairy products and innovation was dependent on respondent’s

characteristics. The Household’s characteristics such as income, size, employment, the

number of children, respondent’s age, and education were significant in explaining variation

in quantity consumed and the expenditures on milk and other dairy products.

VI

TABLE OF CONTENTS

DECLARATION………………………………………………………………...... II

DEDICATION…………………………………………………………………….. III

ACKNOWLEDGEMENT………………………………………………………… IV

ABSTRACT………………………………………………………………………. V

TABLE OF CONTENTS…………………………………………………………. VI

LIST OF TABLES………………………………………………………………... VIII

LIST OF FIGURES……………………………………………………………….. X

LIST OF ACRONYMS…………………………………………………………… XI

CHAPTER I……………………………………………………………………… 1

1. INTRODUCTION……………………………………………………………… 1

1.1 Background……………………………………………………………………. 1

1.2 Overview of the Kosovo Dairy Sector………………………………………... 2

1.3 Agriculture Sector Strategy and Policies……………………………………… 5

1.4 Problem Statement…………………………………………………………….. 8

1.5 Justification……………………………………………………………………. 9

1.6 Objectives and Research questions……………………………………………. 9

CHAPTER II……………………………………… …………………………….. 11

2. THEORETICAL BACKGROUND……………………………………………. 11

2.1 Factor Influences Consumer Behaviour………………………………………. 11

2.2 The Stimulus model of the Consumer Behaviour…………………………….. 12

CHAPTER III……………………………………………………………………. 15

3. METHODOLOGY……………………………………………………………... 15

3.1 The Study Area………………………………………………………………... 15

3.2 Sample Selection……………………………………………………………… 16

3.3 Data Collection………………………………………………………………... 18

3.4 Model Specification…………………………………………………………… 19

3.4.1 Variable Description and Measurement…………………………………….. 21

3.4.2 Estimation procedure………………………………………………………... 23

CHAPTER IV……………………………………………………………………. 25

4. Results and Discussion…………………………………………………………. 25

4.1 Descriptive Statistics on the Household Characteristics……………………… 25

VII

4.2 Descriptive Statistics on Consumer buying Behaviour……………………….. 29

4.3 Model Estimation……………………………………………………………... 40

4.3.1 Binary Logistic linear Estimated Parameters……………………………….. 40

4.3.2 Multiple linear Estimated Parameters……………………………………….. 43

CHAPTER V……………………………………………………………………... 53

5. Conclusion……………………………………………………………………… 53

LIST OF REFERENCES………………………………………………………... 59

APPENDIX……………………………………………………………………….. 62

Appendix A1/Questionnaire………………………………………………………. 62

Appendix A2/Tables………………………………………………………………. 66

VIII

LIST OF TABLES

Table 1. The estimated livestock situation in 2005……………………………... 3

Table 2. Dairy farm structure in Kosovo……………………………………….. 3

Table 3. Evolution of the local processors on daily milk processing…………… 4

Table 4. Dependent variables used in the estimation of the average quantity of

the product i purchased in litre or kilogram in the HHi…………………………

21

Table 5. Dependent variables used in the estimation of the average monthly

expenditures on the product i in euro per month in the HHi…………………….

21

Table 6. Independent variables used in the estimation of the quantity and the

expenditures on milk and six other dairy products……………………………...

22

Table 7. Dependent variables used in the estimation of the fitted model in the

equation 1………………………………………………………………………..

22

Table 8. Dependent variables used in the estimation of the fitted model in the equation 1………………………………………………………………………..

22

Table 9. Independent variables (factors) used in the estimation of the fitted

model in the equation 1………………………………………………………….

23

Table 10. Recoded variables used in the test of independence…………………. 23

Table 11. Summary statistics of the household characteristics…………………. 25

Table 12. Pairwise comparison between females and males…………………… 27

Table 13. Pairwise comparison between rural and urban household…………… 27

Table 14. Pairwise comparison between regions……………………………….. 28

Table 15. Summary statistics of the average quantity of milk and other dairy

products purchased by the HHs………………………………………………….

33

Table 16 The annual average per capita consumption………………………….. 33

Table 17. Summary statistics of the average expenditures on milk and other

dairy products by the HHs………………………………………………………

34

Table 18. Relationship of whether the respondent i buys milk and the

predictors included in the equation 1……………………………………………

40

Table 19. Relationship of whether the respondent i buys yoghurt and the

predictors included in the equation 1……………………………………………

40

Table 20. Relationship of whether the respondent i buys fruit yoghurt and the

predictors included in the equation 1……………………………………………

41

Table 21. Relationship of whether the respondent i buys cream and the 41

IX

predictors included in the equation 1……………………………………………

Table 22. Relationship of whether the respondent i buys curd and the

predictors included in the equation 1……………………………………………

41

Table 23. Relationship of whether the respondent i buys cheese and the

predictors included in the equation 1……………………………………………

41

Table 24. Relationship of whether the respondent i buys butter and the

predictors included in the equation 1……………………………………………

42

Table 25. Relationship of whether the respondent i fulfils the demand for milk

and dairy products and the predictors included in the equation 1………………

42

Table 26. Relationship of whether the respondent i prefers domestic dairy

products and the predictors included in the equation 1………………………….

42

Table 27. Relationship of whether the respondent i prefers foreign dairy

products and the predictors included in the equation 1………………………….

42

Table 28. Relationship of whether respondent i prefers new dairy products and

the predictors included in the equation 1………………………………………..

43

Table 29. Relationship of whether respondent i started buying new dairy

products last year and the predictors included in the equation 1………………

43

Table 30. Test of independence between the market places and demographic

and socioeconomic characteristics………………………………………………

56

Table 31. Test of independence between the frequencies of buying milk, dairy

products and demographic and socioeconomic characteristics………………….

57

Table 32. Test of independence between evaluation of product attributes and

demographic and socioeconomic characteristics………………………………..

58

X

LIST OF FIGURES

Figure 1. Stimulus-response model of the consumer behaviour………………... 13

Figure 2. Stage model of the consumer buying process………………………... 14

Figure 3.Stratification of the sample size……………………………………….. 17

Figure 4. Respondents’ answers in terms of buying milk and six other dairy

products………………………………………………………………………….

31

Figure 5.Market places preferred by consumers when buying milk and other

dairy produces…………………………………………………………………...

32

Figure 6. Frequencies of buying milk and other dairy products………………... 32

Figure 7. Seasonal consumption patterns on milk……………………………… 35

Figure 8. Seasonal consumption patterns on yoghurt…………………………... 35

Figure 9. Seasonal consumption patterns on fruit yoghurt……………………... 36

Figure 10. Seasonal consumption patterns on cream…………………………… 36

Figure 11. Seasonal consumption patterns on curd……………………………... 36 Figure 12. Seasonal consumption patterns on cheese…………………………... 37

Figure 13. Seasonal consumption patterns on butter…………………………… 37

Figure 14. The evaluation of product features in order of importance…………. 38

Figure 15. The level of the mass media used by the respondents………………. 39

XI

LIST OF ACRONYMS

ANOVA Analysis of Variance

ARDP Agricultural Rural Development Plan

CEFTA Central European Free Trade Agreement

EU European Union

GDP Gross Domestic Product

GMP Good Manufacturing Practices

HACCP Hazard Analysis Critical Control Points

HH Household

KARDP Kosovo Agricultural Rural Development Plan

KCBS Kosovo Cluster Business Support

KDPA Kosovo Dairy Processing Association

MAFRD Ministry of Agriculture Forestry and Rural Development

MPS Ministry of Public Services

SOK Statistical Office of Kosovo

SWOT Strengths Weaknesses Opportunities and Threats

UHT Ultra High Temperature

UN United Nations

UNMIK United Nations Mission in Kosovo

VAT Value Added Tax

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CHAPTER I

1. INTRODUCTION

1.1 Background

After the end of the last war in 1999, the United Nations (UN) Security Council

Resolution 1244 (UNSCR1244), placed Kosovo under temporary UN Administration

(UNMIK – United Nations Mission in Kosovo). Building self-governed democratic

institutions, economic re-activation, an effort to build the peace, inter-ethnic reconciliation

and building new bridges of cooperation with the countries in the region were the main

challenges accompanying Kosovo after the last conflict. The decline of the agriculture sector

which started during the 1990s resulted from the emigration of the Kosovo population (18-

20%) to the Western European Countries. While overall stagnation culminated in 1999, most

of the farms stopped production, having consequences to the food processing industry. The

agriculture sector was heavily harmed by the last conflict. The total amount of the destruction

and deprivation in rural areas was estimated to be 737 million dollars (MAFRD, 2003).

Despite the difficult circumstances after the last conflict, Kosovo has gone ahead in the

building of its democratic institutions and macroeconomic stabilization. ‘’It experienced a

strong recovery mostly based on an immense inflow of foreign assistance and external private

inflows estimated to equal up to 50 percent of GDP’’ (Fock, 2007, p.4). The estimated

average real GDP growth between 2002 and 2007 has been slow at less than one and half

percent. ‘’This slow expansion was mainly due to a combination of low investment and the

ongoing withdrawal of the international community in Kosovo’’ (World Bank, 2007, pp.1-2).

The poverty phenomenon has remained persistent and is widespread within the Kosovo

society. According to the World Bank (2007) estimation, about 45% of the Kosovo

population is considered to be poor, while a smaller fraction (18%) was in extreme poverty. In

2005 Kosovo had the highest poverty rate of the countries in the Western Balkans. This large

fraction of the Kosovo population just around the poverty line reflects the socio-economic

phenomenon where sources available to society are used to satisfy the wants of small fraction

while many have not even met their basic needs (Chambers, 1983).

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Agriculture has historically been an important sector in Kosovo’s economy. It is

characterized by small subsistence farms, high input costs, low productivity, poor

infrastructure and poor advisory services (Ministry of Agriculture Forestry and Rural

Development, MAFRD, 2006). The sector’s contribution to GDP during the period 2000-

2005 was 25-27%. The high contribution of the agriculture sector to GDP was not due to high

productivity, but rather to the declining contribution of the other sectors in GDP. Given that

majority (55%) of the Kosovo population is concentrated in rural areas, agriculture has

remained an important sector in mitigating rural poverty. During the period 2000-2005, its

contribution to the employment rate was between 25-30% (informal self-employed agriculture

workers). The proportion of the Kosovo labour force engaged in agriculture sector/farming

was the highest in the Western Balkans countries and it is around five times higher than in

European Union (EU) countries. This high proportion of the labour force involved in

agriculture clearly indicates the low efficiency of this sector. Despite its low efficiency and

loss of traditional export markets, its contribution to the value of total exports in 2005 was

16%. Agriculture remains an important sector and an engine towards economic growth and

EU accession.

1.2 Overview of the Kosovo Dairy Sector

The Kosovo dairy sector is one of the most promising sectors and has consistently

performed well since the end of the last war. As with most other Kosovo sectors the last

conflict in 1999 caused considerable damage to the agricultural sector. Particular damages

affected the dairy sector, where approximately 50% of the livestock was killed and roughly

40% of the livestock infrastructure (stalls) was destroyed (MAFRD, 2003). Due to the

creation of this situation there has been a market shortage in animal and dairy commodities.

Consequently, there has been a sharp increase of the imported animal and dairy products for

market equilibrium to be established as well as to meet the market demand. Moreover, many

donors helped in the restocking of the cattle herd. Since the end of the war ‘’up to the first

quarter of 2003, 10,000 pregnant heifers were imported and distributed to farmers’’ (Rural

Development Plan 2007-2013, 2006, p.19). The livestock census conducted by Statistical

Office of Kosovo (SOK) in November 2003 was used as the determinative base of the animal

numbers in Kosovo. Even though efforts were made to improve the livestock situation

- 3 -

through import and donor support, livestock in Kosovo has still remained at a very low level.

Table represents the livestock census conducted by SOK in 2005.

Table 1. The estimated livestock situation in 2005

Livestock Cattle Milk cows Buffaloes Sheep and goats Number

351,827 186,707 622 151,880

Source: Agriculture Household Survey, 2006, SOK.

It was estimated that 67.7% of rural households have dairy cows, with an average 1.2 dairy

cows per household (HH) (Rural Development Plan 2007-2013, 2006). According to the

MAFRD classification, the most common type of the dairy farms in Kosovo is the

traditional/subsistence farm (94%), followed by the semi-commercial farm (5%) and the

commercial farm (1%).

Table 2. Dairy farm structure in Kosovo

Type of farm No. of farms Percentage No. of cows Traditional/Subsistence 78,124 94% < 5 Semi Commercial 4,378 5% 5 – 9 Commercial 787 1% > 10 Total 83,289 100% Source: ASPAUK Project

Kosovo dairy sector is mainly dominated by milk, yoghurt and white cheese. Milk

production in Kosovo is derived only from the private sector, since the public sector collapsed

during the transition period between 1990 and 1999. According to the official statistical data,

milk production in Kosovo was estimated to be approximately 257,500 tons/year. Local milk

production is mainly intended for the domestic market. Of the total domestic milk produced,

45.6% is consumed on the farm, 41.4% is sold on the local green market1 mostly as raw milk

or white cheese, while only 13% is sold on the processing market. There are 19 dairy

processors where commercial and semi-commercial farms have the possibility to sell the

produced milk (Nushi and Selimi, 2009). The most important elements affecting marketability

of the domestic milk produced to the processing market were milk collection cost, low

technology and low milk quality. As is shown in table 2 most of the Kosovo dairy farms are

small traditional/subsistence farms dispersed across the countryside, with low capacity for 1 Green market - signifies local markets where un-pasteurised milk and cheese is sold directly to consumers by farmers.

- 4 -

milk production and low milk quality2, which makes the milk collection procedure more

expensive.

‘’Considering that milk quality has a big impact on the final product and shelf-life’’, two

technical assistant projects worked in the improvement of the milk quality and ability of dairy

processors to test quality when buying milk (Oldham, Bajraktari and Wittkowsky, 2006).

However, Kosovo milk quality at the farm level as well as dairy products has still remained at

a poorer quality compared to the imported articles. Large market share and successful

penetration in the market of some imported dairy products above Kosovo prices proved that

consumers are willing to pay high prices for better quality. However, there is still not enough

good milk quality coming to the processing plants.

During the period of 2003 through 2007 there was a remarkable investment by local

investors in milk processing capacity. In 2003, the capacity of daily milk intake for processing

was 30,800 litres, which increased to 114,000 litres in 2007. Yearly milk processing capacity

was estimated to be 16,000 tons, but due to the evident impediments highlighted above, only

12,300 tons are processed in the dairy plants (Rural Development Plan 2007-2013, 2006).

Seasonal difference in the quantity of raw milk production is an additional obstacle for the

local dairy processors, which renders them unable to steadily and fully utilize their capacities.

Fresh milk, yoghurt, kos3, and white cheese are the main items produced by the local dairy

processors.

Table 3. Evolution of the local processors on daily milk processing

Year 2003 2004 2005 2006 2007 Milk in litres 30,800 71,000 74,800 92,200 114,000

Source: Kosovo Dairy Processing Association - KDPA

In Kosovo, dairy commodities are dominant in the daily diet and the main source of

protein. A study conducted by Kosovo Cluster Business Support (KCBS, 2008) on dairy

market assessment, proclaimed that among dairy commodities, ultra high temperature

processed (UHT) milk, white cheese, sour cream, fresh milk and yoghurt, were most

frequently dairy commodities purchased by Kosovo consumers. This study revealed that

89.7% of the Kosovo households occasionally buy milk. It was estimated that milk

consumption per capita was approximately 170 litres per year. There are no estimations

available with regard to the quantity consumed of other dairy commodities.

2 Low milk quality in terms of a high bacterial count. 3 Kos - milk drink between yoghurt and sour crème.

- 5 -

Kosovo is considered to be a huge importer of dairy products. Of the total domestic milk

demand, 76% was made up of Kosovo farms supply, while 24% was supplemented by import.

Among the imported dairy commodities, UHT milk accounts for the highest share of the total

value of imports (about 40%) followed by cream and fruit yoghurt. Production of milk and

other dairy products in Kosovo is relatively costly as most of the livestock is reared in a stall-

feed system rather than pasture fed. Moreover, quality at farm and factory level is still

considered to be at a lower level. This is why imported UHT milk and other dairy products

and compete successfully on the local market. Export of Kosovo dairy products is limited to a

few items, mostly UHT milk, cream and cheese. Export increases during the summer season,

while the Albanian market is the main absorber of Kosovo’s dairy commodities.

1.3 Agriculture Sector Strategy and Policies

Administratively, the agriculture sector and policy is set at national level, while the

execution of the policy is accomplished at national and municipality levels. The Ministry of

Agriculture, Forestry and Rural Development (MAFRD) in partnership with other

stakeholders such as the Farmers’ Associations, the Rural Advisory Service, and the

Veterinary and Food Agency play major roles in policy formulation and implementation. In

2006 Kosovo launched its Agriculture and Rural Development Plan 2007-2013 (ARDP)

(MAFRD, 2006) which outlines objectives and key measures for the agriculture sector. The

aim of ARDP was to provide a framework to guide future agriculture sector policies. Its

vision for Kosovo agricultural and rural development during 2007-2013 was to ‘’make a

balanced contribution to the economic, environmental, social and cultural well-being of rural

areas, and Kosovo as a whole, through effective and profitable partnerships between the

private sector, central/local government and local communities within the European context’’.

Based on the Kosovo’s agri-rural situation and after SWOT (strengths, weaknesses,

opportunities, and threats) analysis, general objectives for agricultural rural development in

Kosovo were set as below:

(i) Additional income for farmers and rural population on purpose to improve living

standards and working conditions in rural areas;

(ii) Improved competitiveness and efficiency of primary agricultural production in

order to achieve import substitution and take advantage of export markets;

- 6 -

(iii) Improved processing and marketing of agricultural produce, through increased

efficiency and competitiveness;

(iv) Improved on-farm / in-factory quality and hygiene standards;

(v) Sustainable rural development and improved quality of life (including

infrastructure) through promotion of farming and other environmentally

sustainable economic activities;

(vi) Creation of employment opportunities in rural areas, particularly through

diversification;

(vii) Alignment of Kosovo’s agriculture with that of the EU.

Two key approaches for achieving KARDP 2007-2013 objectives were set as below:

I. Undertake actions that overcome the bottlenecks that are holding back

sustainable rural development in Kosovo, and

II. Start aligning Kosovo’s rural sector with four axes of current EU rural

development strategy.

The achievement of the KARDP 2007-2003 was built on the following four axes with the

eight key measures:

Axis 1: Competitiveness

� Development of vocational training to meet rural needs (Measure 1)

� Restructuring physical potential in the agri-rural sector (Measure 2)

� Managing water resources for agriculture (Measure 3)

� Improving the processing and marketing of agricultural products (Measure 4)

Axis 2: Environment and improved land use

� Improving natural resource management (Measure 5)

Axis 3: Rural diversification and quality of rural life

� Farm diversification and alternative activities in rural areas (Measure 6)

� Improvement of rural infrastructure and maintenance of rural heritage (Measure 7)

Axis 4: Community-based local development strategies

� Support for local community development strategies (Measure 8)

- 7 -

During the period 1999-2004, fiscal rates were was 10% for the standard import duty and

15% Value Added Tax (VAT) for agriculture inputs. Presently, the agriculture sector in

Kosovo does not benefit from direct subsidies by the government. However, indirect subsidies

are given to the sector in terms of zero VAT and zero import duty on agricultural inputs,

reproductive material in animal production, mechanization and on most capital goods used in

agriculture or agro-processing. Kosovo applies 10 percent customs duty on imports, while

there is no encouraging instrument (export subsidy) that induces Kosovo’s export. In 2006,

Kosovo joined the Central European Free Trade Agreement (CEFTA). The market created by

CEFTA comprises nearly 30 million consumers and it was signed by ten countries4. This

trade agreement resulted in market access at zero import duty for all participating countries.

Even though this trade agreement offered Kosovo the opportunity to be part of regional

integration and EU association, there was also an increased pressure in terms of

competitiveness on the local market.

The expectations of ARDP for Kosovo’s dairy sector were to have a modern dairy

industry, by increasing the number of commercial farms with an average of 30 cows, an

achievement of 100% ‘’A’’ grade milk quality, increasing the average of milk productivity up

to 20/litres/cow/day, and increasing milk consumption from 170 to 190 litres/capita/year by

the end of 2013. Due to an increase of milk consumption per capita, demand for milk at the

end of 2013 was estimated at 28% higher than in 2005.

The following key actions were taken by government in order to achieve its objectives

towards dairy sector: ‘’a 10-yearly strategy for livestock development, approval of Law on

Livestock, U/A (Administrative Order) in proceeding, changes in fiscal policy, free trade

agreements, flattening the customs entering price for imported products, improving animal

nutrition, improving the breeding structure, increasing livestock production, lowering imports,

improving quality production and place exemption of customs tariffs and VAT for the

livestock inputs’’(Nushi and Selimi, 2009, p.12). Actually, although several actions were

taken by the government, Kosovo’s dairy sector is still facing significant difficulties with

regard to ‘’land availability, breed quality, high cost of milk production, transport and

distribution, lack of knowledge on new technology and marketing strategies, unfair

competition from import, lack of experience in processing and low marketing capacities due

to small dairies’’ (Nushi and Selimi, 2009, p.11).

4 Member of CEFTA 2006: Kosovo, Albania, Bosnia and Herzegovina, Croatia, Macedonia, Montenegro, Moldova, Serbia. Except, Bulgaria and Romania (from January 1st 2007 are member of EU-27).

- 8 -

Increasing farm size through land consolidation, a developing land market, decreasing

interest rates for agricultural credits, implementing an integrated quality management system

through Good Manufacturing Practices (GMP) and Hazard Analysis Critical Control Points

(HACCP), were seen as key actions to be taken in order to improve the competitiveness of the

Kosovo dairy sector on the local and regional markets.

1.4 Problem Statement

As highlighted in the above section, the dairy sector in Kosovo has performed well and

seems to be one of the most promising agriculture sectors. However, the sector has passed

through a very onerous period during the last decade. Significant economic changes after the

last conflict had dramatically impacted consumers’ buying behaviour, their attitudes, needs

and demand for dairy products. Moreover, there was an enormous increase of foreign

competitors on the domestic dairy market. Therefore, new market approaches such as

improved product quality, wider assortment of the dairy produce, new management on the

sales system, an improved marketing information system, market segmentation, and price

differentiation, were required for the local dairy processors to become a competitive on local

and regional markets.

Previously, the market orientation of Kosovo’s dairy industry was to produce cheap bulk

dairy commodities with a limited assortment. Nowadays marketing has broadened its concept,

contemporary marketers include the study of transfer behaviour as well as transaction of

consumer behaviour (Kotler, 2002). Having information on consumers’ buying behaviour,

their preferences, attitudes, needs and demand for dairy products are considered to be key

determinant factors for the efficiency of dairy. However, the dairy industry in Kosovo lacks

information on consumers’ buying behaviour and demand for dairy products level. Thus,

among other significant problems that the dairy sector is facing, lack of information is an

additional disadvantage upon the sales opportunities and the efficiency of dairies in Kosovo.

- 9 -

1.5 Justification

The consumers represent the last component within the food chain supply but they are set

as a major player on the market, thus they deserve special attention (Kapsdorferová and

Nagyová, 2005). Significant economic changes and continued evolution of consumers’

preferences, needs and wants for the dairy produce, makes the dairy market become more and

more segmented as well as more complex. Therefore, a study of consumers’ buying

behaviour, their preferences, attitudes, needs and wants towards dairy products helps the dairy

industry (local processors) in their plan projections and development of their marketing

strategies. An understanding of the consumers’ buying behaviour and identifying the major

forces influencing structural changes in their consumption patterns, helps businesses of this

industry to improve and get the most rational way to meet the consumers’ needs.

Currently, there is no data available or comprehensive study devoted to the dairy

consumption patterns in Kosovo (Nushi and Selimi, 2009). There is no study on how

consumers’ buying behaviour reacts to demographic and socio-economic factors. Moreover,

no previous studies addressed the estimates of demand and household budget spent on dairy

products with respect to demographic and socio-economic factors. It is thus very important to

have good estimates of how the demand and household budget spent on dairy products reacts

to demographic and socio-economic changes. Furthermore, analyses of changes in

consumption patterns and consumption trends due to the changes of demographic and socio-

economic factors (particularly income changes) are very important and applicable for policy

modelling purposes. Having an accurate analysis and good estimates of demand for dairy

products helps projection of the future development of the dairy sector in Kosovo.

1.6 Objectives and Research questions

The overall objective of this study was to assess the evolution of Kosovo consumers with

regards to their new consumption patterns for dairy products. The study was carried out to

determine factors that influence consumers’ buying behaviour, their preferences, attitudes,

needs and wants towards dairy products. The following research questions were drawn in

pursuit of the outlined objective:

1. ‘’What, Who, Where, How and When” do consumers buy milk and other dairy

products such as yoghurt, fruit yoghurt, cream, curd, cheese and butter?

- 10 -

2. What are the consumers’ preferences for the package size, country of origin, new dairy

products and innovation?

3. What are the consumers’ attitudes toward product attributes such as brand, packing,

nutritive content, economic value, taste, safety, and country of origin?

4. Do the consumers meet their needs for milk and six other dairy products with their

current monthly income?

The aim of this study was also to estimate how the demand and household budget spent on

milk and six other dairy products react to demographic and socio-economic changes. The

following research questions were addressed in pursuit of this objective.

1. What is the average monthly quantity purchased of milk and other dairy products?

2. What is the average monthly expenditure for milk and other dairy products?

- 11 -

CHAPTER II

2. THEORETICAL BACKGROUND

2.1 Factors Influences Consumer Behaviour

Cultural, social and personal factors are always considered to be the major forces

influencing consumers’ buying behaviour (Kotler and Keller, 2006). An understanding of

such factors helps businesses at tailoring products that meet consumers’ needs and wants.

Among important influences on consumer buying behaviour (culture, subculture, and social

class) ‘’culture is the fundamental determinant of a person’s wants and behaviour’’ (Kotler

and Keller, 2006, p.174). Given that ‘’all human societies exhibit social stratification’’,

distinctive consumer buying behaviour and preferences exist as well among social classes

(Kotler and Keller, 2006, p.175). These distinctive buying patterns and preferences among

social classes are significantly determined by occupation, income, wealth, education etc.

Consumers can shift up and down the social strata but the extent of this mobility depends

much on how rigid the social stratification is in a given society (Kotler and Keller, 2006).

An individual’s values, perceptions, preferences and behaviours in a direct or indirect way

are significantly influenced by reference groups (family, friends, neighbours, and co-workers)

to which they belong. Such reference groups expose an individual to new behaviours and

lifestyles, and often have an effect on their attitudes, products or brand choices (Kotler and

Keller, 2006). Family is considered to be one of the most important consumer buying groups

in the society (Tour and Henthorne, 1995). It is thus very important for the marketers to

identify the roles and influence of the family members in the purchase of products. Marketers

may accurately address their marketing messages, only if they fully understand who of the

family members is a leader and has direct influence on the buying decisions. ‘’In countries

where parents live with grown up children, their influence can be substantial’’ (Kotler and

Keller, 2006, p.177). The buyer’s characteristics such as age, stage in the life cycle,

occupation, economic circumstances, personality, self-concept, lifestyle and values have a

significant impact on the consumer behaviours and the buying decisions. The consumption

patterns and taste in food are often shaped by the family life cycle and the number, age, and

gender of people in the household, and occupation (Kotler and Keller, 2006). The consumer’s

decisions in product and brand choices are greatly influenced by the income level, stability,

- 12 -

personality, self-concept, core values and life style. The consumers’ characteristics, buying

behaviour, and preferences vary over the consumers’ lifetime. This is why successful

marketers make an attempt to follow carefully trends and changes of the consumers’

characteristics.

2.2 The Stimulus model of the Consumer Behaviour

Many theorists have addressed the issue of understanding the consumer behaviour in the

decision buying processes and the purchase decisions. The marketing and environmental

stimuli are key enter points of understanding the consumer buying behaviours. The consumer

psychology combined with the consumer characteristics result in the buying decision process

and the purchase decision. The crucial point of consumer behaviour is to understand what

happens in the consumer’s consciousness between the arrival of outside stimuli (marketing

and environmental) and the ultimate purchase decision (Kotler and Keller, 2006). The

consumer responses to the outside stimuli are mainly influenced by the psychological

processes such as motivation, perception, learning, and memory, and the consumer

characteristics such as cultural, social, and personal.

An individual has different needs, biogenic needs that arise from physiological tension and

psychogenic needs that arise from psychological tension. ‘’A need becomes a motive when it

is aroused to a sufficient level of intensity’’ (Kotler and Keller, 2006, p.184).

The best-known theories of human motivation such as Sigmund Freud, Abraham Maslow, and

Frederic Hezberg, are often used by the marketers for consumer analysis, understanding their

behaviours and develop marketing strategies. Freud’s theory supposes that an individual may

not completely understand his/her motivations, as psychological processes shaping his/her

behaviour are mostly unconscious (Kotler and Keller, 2006). Maslow’s theory explains why

people are driven by particular needs at particular times (Maslow, 1954). Based on the

Maslow’s theory a person needs are in order of importance, starting from physiological,

safety, social, esteem, and self actualization needs. This theory was used by the marketers in

understanding of how products fit into the consumer’s plan, goal and life (Kotler and Keller,

2006). Herzberg’s theory distinguishes two-factor theory dissatisfiers and satisfiers factors

(Herzberg, 1966). According to Herzberg’s theory, marketer’s task is first to avoid

dissatisfiers factors and second to identify satisfiers or motivators of purchase in the market

and then supply them (Kotler and Keller, 2006).

- 13 -

Figure 1. Stimulus-response model of the consumer behaviour

Source: Kotler and Keller (2006).

The information about the product is one of the prime determinants of consumer decision

making. Other psychological processes such as motivation, perception, learning, and memory

have a significant impact in the consumer buying decisions. Key consumer behaviour

questions such as “who, what, when, where, how, and why” are used by marketers to

understand reality and every facet of the consumer behaviour (Kotler and Keller, 2006). The

starting point of the buying process is problem recognition. The consumer starts buying, when

he or she recognizes a problem or need (Kotler and Keller, 2006). There are mainly five

stages that the consumer passes through during the buying decision process: problem

recognition, information search, evaluation of alternatives, purchase decision, and post

purchase behaviour. The consumer does not necessarily pass through all stages when he or

she buys the product. He or she may skip some stages if he or she already is familiar with the

product (Kotler and Keller, 2006). Figure 2 represents the five stages of the buying decision

process.

Consumer Psychology Motivation Perception Learning Memory

Buying Decision Process Problem recognition Information search Evaluation of alternatives Purchase decision Post-purchase behaviour

Purchase Decision Product choice Brand choice Dealer choice Purchase amount Purchase timing Payment method

Consumer Characteristics Cultural Social Personal

Marketing Other Stimuli Stimuli Products Economic Services Technological Price Political Distribution Cultural Communications

- 14 -

Figure 2. Stage model of the consumer buying process

Source: Kotler and Keller (2006).

Problem recognition

Information search

Evaluation of alternatives

Purchase decision

Postpurchase behaviour

- 15 -

CHAPTER III

3. METHODOLOGY

3.1 The Study Area

Kosovo is situated in South-East Europe surrounded by Albania, Macedonia, Serbia and

Montenegro, with a total land area of 10.908 km2 and population density 193 people per km2.

Kosovo is divided into 5 regions and 30 municipalities with 1466 settlements (Law on

Territorial Structure, 2004). Taking into account that the system of civil registration was not

up to date and there has not been any census taken since 1981, it is difficult to provide reliable

figures for the population as a whole (Ministry of Public Services-MPS, 2007). Referring to

the latest registration conducted by Statistical Office of Kosovo (SOK) in 1981, the total

number of inhabitants was estimated to be 1,959,714. In terms of population size, Prishtina is

the biggest region with 25.5% of the total population, followed by Peja with 21%, Prizren

with 19.6%, Gjilan with 19.6% and Mitrovica with 14.6%. Traditionally more of the

population of Kosovo lives in the rural areas than in urban areas, with proportion 55:45%.

Kosovo is considered to have the youngest population in Europe, where 33% of the total

population is less than 14 years old, 61% is between 15-64 years old, while only 6% of the

population is over 65 years old. Ministry of Public Services (MPS, 2009a) states that for

every 100 female births there are around 109 male births.

Statistics of Living Standard in 2007 indicated that households living in rural areas as well

those less educated had lower general food consumption than household living in urban areas.

In 2008 food consumption per households living in urban areas was 2.144 euro or 35% of the

total consumption, compared 2384 euro or 44% for those living in rural areas (MPS, 2009b).

Statistical data on the level of education in 2007 has shown that more than 40% of the males

and about 60% of the females had not completed upper secondary education. The

unemployment rate in 2007 for the labour force in the 15-64 age groups was denoted 33.7%

for males and up to 49.2 % for the females.

- 16 -

3.2 Sample Selection

This section emphasizes the sampling principles and methods involved in selecting people

(respondents) who would be asked questions by questionnaire. Before getting to the

fieldwork, matters that concerned this survey were the definition of the population (N),

sample size (n), type of sampling and formulation of the research instrument that suits the

investigation of the topic. The intention of this study was to gain an understanding of

consumers’ behaviours, their attitudes, preferences and demands towards milk and dairy

products. The total number of inhabitants in Kosovo was the targeted population from which

we were interested to draw a sample.

One of the most relevant and basic considerations in conducting a survey research by

interview is the money and time consumed. It was impractical to examine each and every unit

of the population, thus, sampling was needed. Even though a sampling approach was less

costly in terms of money and time, other considerations came to the fore as well. Since the

aim of this survey was to interview a sample drawn from the national population which was

likely to be highly varied, there was a possibility of sampling error and bias. It was important

to ensure that our drawn sample and findings reflected the national population accurately. One

way in which it was possible to deal with the problem of sampling error and to minimize the

bias was to perform stratified random sampling. It is important to emphasize that this does not

mean that this type of sampling can eliminate bias and sampling error completely. According

to Bryman (2004) stratified probability sampling keeps the sampling error in check better than

non-probability sampling.

Another issue that is relevant to this survey relates to the choice of the sample size. A

crucial criterion for the decision about the sample size was the level of standard error that we

were prepared to tolerate in our findings. Considering this criterion we decided our sample

size would be n = 385 (interviewees) out of 1.959.714 which was the total number of

inhabitants in Kosovo. The calculation of the sample size was performed using a sample size

calculator with a 5% margin of error, 95 % level of confidence and 50% response distribution.

The stratified random sampling has been applied as a type of probability sample in

selecting people who would be included in the sample. One of the main reasons that we

performed this type of sampling was that we wanted our sample to exhibit a proportional

representation of the different strata of the population. Bryman (2004) states that by selecting

a stratified sample, the standard error of the mean will be smaller since the variation between

- 17 -

strata is eliminated and the population will be better represented in the sample in terms of the

stratification criteria used. Moreover, this type of sampling permits us to employ a test of

statistical significance and draw inferences about the whole population from which the sample

was drawn.

The stratification of the sample was done according to three criteria: region, age and

gender of the respondents. The sample size of n = 385 respondents was first stratified among

regions according to the number of inhabitants in each region. The allocated number in each

region was then stratified in terms of age and gender. The following figure illustrates the main

stages involved in stratification of the sample units.

Figure 3.Stratification of the sample size

In spite of its advantages, this method of sampling could be feasible and economically

reasonable only when it is relatively easy to identify and allocate units within strata. In our

case stratification of the sample size into the strata entailed a great deal of work since there

was no available list on distribution of the population by age and gender. Due to that, the

actual respondents deviated slighlty in terms of these two strata compared to the one that is

shown in Fig.3 (see Appendix A2 / Table 2).

n = 385

Prishtina

Prizreni

Mitrovica

Peja

Gjilani

Age 18-30

Age 31-40

Age 41-50

Age 51-60

Over 60

Total

Age 18-30

Age 31-40

Age 41-50

Age 51-60

Over 60

Total

Age 18-30

Age 31-40

Age 41-50

Age 51-60

Over 60

Total

Age 18-30

Age 31-40

Age 41-50

Age 51-60

Over 60

Total

Age 18-30

Age 31-40

Age 41-50

Age 51-60

Over 60

Total

Female 16 Male 16

Female 11

Male 11

Female 10

Male 10

Female 8

Male 8

Female 3

Male 4

Female 48

Male 49

Female 13

Male 13

Female 9

Male 9

Female 7

Male 8

Female 6

Male 6

Female 3

Male 3

Female 38

Male 39

Female 9 Male 9

Female 7 Male 7

Female 6 Male 6

Female 4 Male 4

Female 2 Male 2

Female 28 Male 28

Female 13 Male 13

Female 10 Male 10

Female 8 Male 8

Female 6 Male 7

Female 3 Male 3

Female 40 Male 41

Female 12 Male 12

Female 9 Male 9

Female 7 Male 7

Female 6 Male 6

Female 3 Male 3

Female 37 Male 37

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3.3 Data Collection

In order to find out about the consumers’ behaviour, their attitudes, preferences and

demand for milk and dairy products, a structured questionnaire was developed and used for

the collection of data. The main reason for employing this method of interviewing was to

provide exactly the same context of questioning to all interviewees. Given that replies of the

interviewees are in the response to identical interview inducements, the interviewees’

responses can then be aggregated. The advantages of this type of interviewing go further than

this. One advantage that is particularly significant is the reduction of error due to variation in

the questions asked (Bryman, 2004). This method of interviewing also ensures greater

accuracy and an easier way of processing the respondents’ answers.

The data used in this study were obtained through direct interviews (face to face) with

Kosovo consumers aged 18 years and older. In order to avoid overestimation of the market

demand for milk and dairy products, even those households who happen to have their own

cows were included in the interviewing process. The survey was conducted during the period

of January-February in 2009. A pre-test was conducted in order to ensure that survey

questions and instrument as a whole functions well. Interviews with the respondents were

administered by researcher and trained field assistants. Statistical Program for Social Sciences

(SPSS) was employed for processing and analysing of data. The data is on a monthly basis

and provides information on consumers’ buying behaviour, preferences, their attitudes toward

product attributes, the quantity consumed and expenditures for milk and dairy products

(yoghurt, fruit yoghurt, cream, curd, cheese and butter). The survey also provides data on

respondents and households characteristics including sex, age, level of education, profession,

number of family members, number of children aged 14 years and younger, respondent’s

monthly income, HH income and number of employed family members.

Respondents were therefore asked to state whether they fulfil their needs for those

products by monthly income available. The questionnaire also included retrospective

questions on changes that had occurred over the previous year in consumers’ preferences

towards milk and dairy products. These questions give responses about whether the

consumers prefer new dairy products (innovation), what attracted them to start buying new

dairy products and channels they used to become aware of the new dairy products. The main

- 19 -

intention of asking these questions was to find out whether Kosovo consumers belong to

conservative or progressive consumers category with regard to dairy products.

3.4 Model Specification

Two associated statistical techniques, multiple linear function and binary logistic function

have been used to relate the dependent variable(s) to the independent variables. The model of

multiple linear functions was performed for those dependent variables measured on

continuous scales. There were, however, many dependent variables with binary responses that

had to be evaluated for the effects of multiple independent variables on a dichotomous

outcome. For the variables with dichotomous responses the model of binary logit-function

was categorized to take on two values, Y = 1 when the response outcome for each subject is a

“success” and Y = 0 otherwise.

The logit- function with all the main effects is given as:

logit [(Y i = 1)] = α + β1 Age + β2 Education + β3 HH size + β4 Children + β5 Employment + β6 HH income + β7p1

+ β7r1 + β8r2 + β9r3 + β10r4 + β11e1 (1)

Where, Yi indicates the exhibited preference of buying product i by jth respondent {1 =

Yes, 0 = No}; α is intercept; β1……….. β11 are estimators coefficients; {p1} is the indicator

variable for the first (of two) places; {r1, r2, r3, r4} are indicator variables for the first four (of

five) regions; {e1} stands for the first indicator variable (of two) employment R. Consumers’

statements on the question as to whether they buy milk and other dairy products was defined

as dependent variable Y (= 1 if the respondent buys milk and other dairy products and 0 if

not). The statement of the consumers is then estimated as a function of respondent’s age,

respondent’s education, household (HH) size, number of children below 14 years old, number

of employed members within the HH, HH income, place as a factor with two indicator

variables: Rural and urban, region as a factor with five indicator variables: Prishtina, Prizren,

Peja, Gjilan, Mitrovica, respondent’s employment as a factor with two indicator variables:

Yes and no. The model treats place, region, and respondent’s employment as nominal-scale

factors, whereas, respondent’s age, level of education, HH size, children below 14 years old,

employed members within the HH, and HH income are treated as continuous-scale factors.

Similar models have been applied for the statement of the consumers on buying yoghurt, fruit

- 20 -

yoghurt, cream, curd, cheese and butter. A full description of other variables with

dichotomous outcome which are used as dependent variables and estimated with the same

model and the same explanatory variables is presented in Table 8.

In the model of the multiple linear function the quantity of milk and other dairy products

purchased by a household within a month is a function of respondent’s age and education, HH

size, number of children aged 14 and younger, number of employed family members and HH

income. The same model has been used for the total expenditure on milk and other dairy

products.

Linear function equations are specified as follows:

Q i = b0 + b1Ag + b2De + b3Hh + b4Ch + b5Me + b6In + Ui (2)

E i = b0 + b1Ag + b2De + b3Hh + b4Ch + b5Me + b6In + Ui (3)

T = b0 + b1Ag + b2De + b3Hh + b4Ch + b5Me + b6In + Ui (4)

where, Qi is the quantity of product i purchased in litre/month in the HHi ; E i is the

expenditure on product i in euro/month in the HHi; T is total expenditure on milk and six

other dairy products in euro/month in the HHi; (i = 1………..385); b0 indicates the intercept;

Ag denotes age of the respondent i; De indicates education of the respondent i; Hh stands for

the HH size; Ch indicates number of children below 14 years old in the HHi; Me stands for

number of employed family members in the HHi; In indicates average monthly income in the

HHi; b1..........b6 are estimated coefficients of the variable (s); and Ui is a random error term.

Other statistical models were employed in order to analyse the set of data obtained by the

research study. Analysis of variance (ANOVA) has been used to find out whether there were

significant differences between means of the HH size, number of children below 14 years old,

number of employed family members, HH income, respondent’s income and education as a

factor of gender, place and region. Test of independence was performed to study whether

there was dependency between the market places that consumers preferred to buy dairy

products, the frequencies of buying dairy products, and the evaluation of product attributes

with demographic and socioeconomic characteristics.

- 21 -

3.4.1 Variable Description and Measurement

Tables 4, 5, and 6 present a full description of all input variables that were accommodated

into fitted models in the equation 2, 3, and 4, while Tables 7, 8, and 9 describe the variables

used in equation 1.

Table 4. Dependent variables used in the estimation of the average quantity of the product i

purchased in litre or kilogram in the HHi

Dependent Variable Description of Variable

Q Milk (l/month) Quantity of milk purchased in the HHi

Q Yoghurt (l/month) Quantity of yoghurt purchased in the HHi

Q Fruit Yoghurt (kg/month) Quantity of fruit yoghurt purchased in the HHi

Q Cream (kg/month) Quantity of cream purchased in the HHi

Q Curd (kg/month) Quantity of curd purchased in the HHi

Q Cheese (kg/month) Quantity of cheese purchased in the HHi

Q Butter (kg/month) Quantity of butter purchased in the HHi

Note: Q indicates quantity of the product i.

Table 5. Dependent variables used in the estimation of the average monthly expenditures on

the product i in euro per month in the HHi

Dependent Variable Description of variable

E Milk (euro/month) Expenditures on milk in the HHi

E Yoghurt (euro/month) Expenditures on yoghurt in the HHi

E Fruit yoghurt (euro/month) Expenditures on fruit yoghurt in the HHi

E Cream (euro/month) Expenditures on cream in the HHi

E Curd (euro/month) Expenditures on curd in the HHi

E Cheese (euro/month) Expenditures on cheese in the HHi

E Butter (euro/month) Expenditures on butter in the HHi

E Total (euro/month) Expenditures on milk and dairy products in the HHi

Note: E indicates expenditures on the product i.

- 22 -

Table 6. Independent variables used in the estimation of the quantity and the expenditures on

milk and six other dairy products

Independent Variable Description of variable

Age Respondent’s age given in years

Education Respondent’s education given in years

HH size Number of members in the HHi

Children Number of children in the HHi aged 14 and younger

Employment Number of employed members in the HHi

HH income Average monthly income in the HHi

Note: HH monthly income is given in euro

Table 7. Dependent variables used in the estimation of the fitted model in the equation 1

Dependent Variable Description of Variable

Product Exhibited statement on buying product i by jth respondent

Milk Y = 1 if the respondent buys milk and Y = 0 otherwise

Yoghurt Y = 1 if the respondent buys yoghurt and Y = 0 otherwise

Fruit yoghurt Y = 1 if the respondent buys fruit yoghurt and Y = 0 otherwise

Cream Y = 1 if the respondent buys cream and Y = 0 otherwise

Curd Y = 1 if the respondent buys curd and Y = 0 otherwise

Cheese Y = 1 if the respondent buys cheese and Y = 0 otherwise

Butter Y = 1 if the respondent buys butter and Y = 0 otherwise

Table 8. Dependent variables used in the estimation of the fitted model in the equation 1

Dependent Variable Description of Variable

Fulfilment of the

respondent’s needs

Y = 1 if the respondent fulfilled needs for the dairy products

with his/her current monthly income and Y = 0 otherwise

Preferring domestic

dairy products

Y = 1 if the respondent preferred domestic dairy products

and Y = 0 otherwise

Preferring foreign

dairy products

Y = 1 if the respondent preferred foreign dairy products and

Y = 0 otherwise

Preferring new dairy

products

Y = 1 if the respondent preferred new dairy products and

innovation and Y = 0 otherwise

Buying new dairy

products

Y = 1 if the respondent during the last year started buying

new dairy products and Y = 0 otherwise

- 23 -

Table 9. Independent variables (factors) used in the estimation of the fitted model in the

equation 1

Factor Indicator Variable

Place 1 = Rural; 0 = Urban

Region 1 = Prishtina; 2 = Prizren; 3 = Peja; 4 = Gjilan; 5 = Mitrovica

EmploymentR Respondent’s employment 1 = Yes; 0 = No

Table 10. Recoded variables used in the test of independence

Variable Recoded Variable

Age 1= 18-30; 2 = 31-40; 3 = 41-50; 4 = 51-60; 5 = older than 60

Education 1 = Primary school; 2 = Secondary school;3 = University

IncomeR 1= up to 200; 2 = 201-400; 3 = 401-600; 4 = more than 600

HH size 1 = up to 2 members; 2 = 3-4; 3 = 5-6; 4 = more than 6

Children 1 = 1 child in the HH; 2 = 2-3; 3 = 4 and more

Employment 1 = 1 employed family member; 2 = 2-3; 3 = 4 and more

HH income 1 = up to 200; 2 = 201-400; 3 = 401-600; 4 = 601-800; 5 = more than 800

Note: Variable IncomeR indicates respondent’s income; HH income and respondent’s income are given in euro 3.4.2 Estimation Procedure

In view of the fact that this study was designed to answer certain questions about

consumers’ behaviour, preferences and demands for milk and other dairy products, several

predictor variables have been included in the models (equation 1, 2, 3 and 4). Models with

several explanatory variables often suffer from multicollinearity, making it seem that no one

variable is significant when all the others are in the model (Agresti, 2007). The selection

process of the fitted model becomes more complex as the number of explanatory variables

increases. The estimation procedure of the stated preference by consumers for buying milk

and other dairy products began with a preliminary model presented in equation (1). Backward

elimination procedure was used to select an optimal model that tends to have its fitted values

closest to the true expected values. With this algorithm, the fitted model began with all

explanatory variables presented in equation (1), and then the variable that produced the

smallest decrease in residual variance was removed from the model. This method takes out

terms successively, until there are no variables remaining in the equation that could be

removed without significantly increasing the residual variance. Backward elimination uses the

- 24 -

incremental sum of squares as a measure of the increase in the residual sum of squares

associated with removing the variable (Glantz and Slinker, 2001).

The next stage of the model selection checked the model fit. The validity of the fitted

model in the equation 1 was checked by using log-likelihood statistic, while Wald statistic

was used in assessing the contribution of predictors. Multiple linear functions in the equation

2, 3, and 4 were judged by using the goodness of fit (R2) statistical tests of significance (F

statistics for testing whether the fitted model reduced significantly residual variances, t-

statistics for testing individual regression coefficients). 2χ Statistics has been carried out for

testing the independence between variables. The coefficient of Cramer’s V was used for

measuring the strength of association between two categorical variables.

- 25 -

CHAPTER IV

4. Results and Discussion

This chapter presents the results and analyses the set of data obtained from 385 surveyed

respondents. The chapter is structured as follows. Basic statistics description is given to

demonstrate the overall household characteristics. This section provides results of the

pairwise comparisons among levels of gender, place, and region with regard to the household

characteristics. Basic description is presented for some of the key consumer behaviour

questions such as “what, who, where, how and when” they buy milk and six other dairy

produces. It presents the estimated parameters obtained from the two types of functions:

binary logistic and multiple linear functions. To give a clear view and ease interpretation of

the estimated coefficients, estimation models are given successively for each dairy product.

The last section of this chapter presents tests of independence between the market places

preferred by consumers, the frequencies, and the evaluation of product features with

demographic and socioeconomic characteristics.

4.1. Descriptive Statistics on the Household Characteristics

Table 11 presents a summary statistics on the household characteristics according to

demographic and socioeconomic indicators. The average sample scores given below were

summarized for the whole country.

Table 11. Summary statistics of the household characteristics

Household characteristics Minimum Mean Maximum Std. Error Std. Deviation

HH size 1 6.17 27 ± 0.17 3.39

Children 1 2.21 13 ± 0.10 1.53

Employment 1 2.12 8 ± 0.026 1.16

HH income 60 585.79 3500 ± 26.53 52.51

IncomeR 40 281.74 1500 ± 13.29 219.5

Education 3 12.5 20 ± 0.15 3.04

Note: HH income and respondent’s income are given in euro per month

- 26 -

The overall household characteristics were further examined, in order to find out whether

there were significant variations of the average sample scores among the levels of gender,

place and region. The result in table 12 shows that there was significant variation between

females and males in terms of the average monthly income (PANOVA = 0.016). Males had a

tendency to generate higher income compared to females. Publication given by (MPS, 2009a)

states that unemployment rate is much higher for women (55 %) than for men (39 %). The

majority of employed women were engaged in education, health, trade, and the agricultural

sector (MPS, 2009). Disparity on the unemployment rate, as well as engagement of women in

the sectors which yield lower income caused the significant variation on the income levels

between males and females. Highly significant variation was also found in the level of

education (PANOVA = 0.003). This result clearly shows that women in Kosovo were less

educated compared to men. It also corresponds with the statistical data on the level of

education given by MPS (2007), where the proportion of females and males who had not

completed upper secondary education was 40:60 %.

Study results showed that there were discrepancies between rural and urban households, in

terms of the family members. Rural households had significantly more family members than

the urban households (PANOVA = 0.000). Moreover, the average number of children in the

household below 14 years old was distinctly higher in rural households than in the urban

(PANOVA = 0.003). This result was expected, considering that the rural households had

significantly more family members than the urban. It was proved that these two variables, the

household size and the number of children below 14 years old, were highly related with a

Pearson’s correlation coefficient r = 0.750** . However, insignificant variations were found

between rural and urban households, with regard to the number of employed family members

(PANOVA = 0.297). Concerning the average monthly income, rural households had

significantly higher income than urban households (PANOVA = 0.032). But income per capita

was still higher in urban areas (98.41 euro), than in rural areas (88.75). During the period of

1990-99, approximately 18 to 20% of the Kosovo population emigrated at Western European

countries. Majority of them were young people coming from rural areas. Statistics of Living

Standard in 2007 indicated that in Kosovo every tenth household lives by money sent abroad

the country (remittances). Hence, remittances by emigrants can be considered as an influential

factor in the variations of the average household incomes between rural and urban areas. Even

though, there were significant differences in the average household incomes, insignificant

variations were found in terms of the respondent’s average monthly income (PANOVA = 0.088).

Highly significant variations were marked also in the level of respondents education (PANOVA

- 27 -

= 0.005). Respondents coming from rural areas had predispositions to be less educated than

the respondents from urban areas.

The sample data showed highly significant variations on the average household size

between the regions (PANOVA = 0.000). The region of Peja differed significantly from

Prishtina, Prizren and Mitrovica. The households from Mitrovica had significantly more

family members than the households from Prishtina and Gjilan. A significant variation is

found also between Prizren and Gjilan. Significant variations of the household size appeared

to be important in explaining the variations of the number of children below 14 years old

(PANOVA = 0.019). Regions that had significantly more family members were predisposed to

have more children below 14 years. Significant differences were observed between the

Mitrovica and Prishtina, the first one had predominantly more children below 14 years old.

The households from Peja region appeared to have fewer children than any other region.

There were no significant variations between the levels of region, with regard to the

number of employed family members (PANOVA = 0.161). The insignificant variation between

regions was obtained also in terms of the respondent’s monthly income (PANOVA = 0.229) and

the respondent’s education (PANOVA = 0.057). However, highly significant variations were

observed in the level of income (PANOVA = 0.005). The households from Prishtina and Prizren

had significantly higher monthly income than the households from Peja and Gjilan.

Table 12. Pairwise comparison between females and males Gender Female Male Difference

Sample Mean / Std. Deviation X SD X SD (F X - M X )

IncomeR 243.4 199.3 308.4 229.4 -65*

Education 11.69 3.272 12.620 2.709 -0.931**

Note: Significance of variations is denoted as follows: *P < 0.05; ** P < 0.01; *** P < 0.001. Table 13. Pairwise comparison between rural and urban household Place Rural Urban Difference

Sample Mean / Std. Deviation X SD X SD (R X - U X )

HH size 7.593 4.453 5.581 2.630 2.012***

Children 2.639 2.009 2.00 1.178 0.639**

Employment 2.218 1.474 2.080 0.999 0.138

HH income 673.9 649.3 549.2 452.9 124.7*

IncomeR 247.2 188.6 296.6 230.4 -49.4

Education 11.487 3.071 12.430 2.984 -0.943**

28

Table 14. Pairwise comparison between regions

Region r1 r2 r3 r4 r5

Sample mean /

Std. Deviation X

SD X SD X

SD X SD X

SD

Reference r1 r2 r3 r4

Mean difference (r2-r1) (r3-r1) (r4-r1) (r5-r1) (r3-r2) (r4-r2) (r5-r2) (r4-r3) (r5-r3) (r5-r4)

HH size 6.10 3.42 6.96 3.9 5.02 1.9 5.9 3.1 7.25 4.1 0.86 -1.1*** -0.24 1.15 -1.94** -1.097 0.288 0.84 2.23** 1.385

Children 1.94 1.27 2.48 2.0 1.74 0.8 2.4 1.5 2.74 1.7 0.54 -0.20 0.41 0.80 -0.74 -0.13 0.26 0.61 1.00** 0.39

Employment 2.23 1.24 2.31 1.2 1.88 0.8 2.0 1.2 2.11 1.3 0.08 -0.35 -0.20 -0.12 -0.42 -0.28 -0.19 0.15 0.23 0.083

HH income 710 587 673 625 494 427 460 232 548 518 -37.3 -215.5 -250* -162.3 -178.2 -212.8 -125 -34.55 53.16 87.71

IncomeR 315 230 264 176 315 270 263 213 232 183 -51.4 0.27 -51.68 -82.84 51.10 -0.31 -31.47 -51.41 -82.6 -31.16

Education 12.66 3.08 11.5 3.4 12.35 2.67 12 3.12 12.5 2.6 -1.13 -0.31 -0.99 -0.12 0.82 0.14 1.01 -0.68 0.19 0.87

Note: Given characters denote: r-region; r1-Prishtina; r2-Prizren; r3-Peja; r4-Gjilan; r5-Mitrovica; Significance of variations is denoted as follows: *P < 0.05; ** P < 0.01; *** P < 0.001.

29

4.2 Descriptive Statistics on Consumer buying Behaviour Based on the information gathered from interviewed respondents, it was clear that milk

and other dairy produces stand as regular staple food to most of the Kosovo households. Out

of 385 surveyed respondents 87% proclaimed that they buy milk on a regular basis, (see

Figure 4) only 13% of the respondents did not buy milk at all. An attempt was made to come

across the reasons for the proportion of the respondents who did not use to buy milk. Hence,

the remaining percentage 13%, was scrutinized further and it was remarked that 90% of the

respondents were from rural areas, who predominantly own cows and this was the solely

reason given by them for not buying milk. Respondents were questioned whether they buy

other dairy produces such as yoghurt, fruit yoghurt, cream, curd, cheese and butter. With

regard to the buying habits towards yoghurt and fruit yoghurt, about 79 % of the respondents

stated that they regularly buy yoghurt. The proportion of the respondents who were positively

responding to the question on buying fruit yoghurt was 50:50%.

Consumer’s behaviour to product choice is greatly affected by economic circumstances and

lifestyle pattern which is shaped by whether consumers are money-constrained or time-

constrained (Kotler and Keller, 2006 pp182-183). The sample included respondents who

happened to own cows and it was the only reason stated in terms of the response of not buy

milk. The same matter has accompanied other dairy produces, where out of 21%, who did not

use to buy yoghurt 12% stressed the similar reason as those who did not use to buy milk.

Apart from this reason, there were however other factors influencing consumers lifestyle

and their behaviour to dairy products. Income was an additional influential factor affecting

consumers buying behaviour. It was realized that lower income households could not afford

buying yoghurt direct from the market. A cheaper alternative for this consumers group was

buying fluid milk from farmers and process it into value-added products such as yoghurt.

Out of 50% of the respondents who did not use to buy fruit yoghurt, more than 25% do not

prefer and lack the habit of buying it. This attitude was notably noticed among the old age

group. On the other hand, no presence of children in the household was another indirect factor

influencing the product choice, as most of them consider it as a children product. In fact the

responses by this consumers group in terms of not buying fruit yoghurt were in some way

intertwined by lack of preferences, habits and no presence of children in the household. For

the remaining proportion, 23% of the respondents that did not use buying fruit yoghurt,

economic issue stand as the main influential factor on their attitudes towards this product. As

30

a result of budgetary constraints on this consumers group, fruit yoghurt was considered as

luxury product and predominantly unachievable given their current economic circumstances.

Nearly 70% of the respondents claimed that they commonly buy cream, 66% was the

proportion of the respondents who buys curd. The proportion of the respondents who buys

cheese was similar to the one for milk (86%). The proportion of the respondents that use to

buy butter was roughly as one for the fruit yoghurt, which accounted for 52%. As it is

previously cited, an effort was also made to acquire the reasons given by the proportion of the

respondents that did not use to buy cream, curd, cheese and butter. Before getting to the

depiction of the given reasons, it is worth noting that an open ended question was asked and

we ended up with diverse responses as the respondents answered in their own terms.

Consequently, the given responses were interpreted and coded in order to be analyzed

quantitatively.

The study results have shown that the given main reasons for not buying above mentioned

products were related to personal factors, including respondent’s lifestyle, economic

conditions and age. For the respondents who owned cows it still remained the sole reason for

not buying milk, cream, curd, cheese and butter which accounted roughly the same proportion

for all of these produces, 12%. About 11% could not afford buying cream, while 7% was the

proportion of the respondents that did not prefer buying this product. The proportion of the

respondents that did not use to buy curd was 34%. Lack of preference towards this product

was the main reason stated by the respondents, which counted roughly 19%. There were few

respondents that could not afford buying it. The results of this study indicated that 99% of the

respondents were consumers of cheese. The proportion of the respondents that did not

favoured buying butter was almost 32%. There was small a percentage (5%) that could not

afford buying butter. It was particularly marked that buying habits towards butter and cream

differed by personal characteristics as age and geographic niches. Respondents among urban

households and young adults aged 18 to 40 appeared to buy less dairy products with lower

content of fat.

The family is incessantly considered as the most important consumer buying organization

in society, and family members as the most influential primary reference group (Tour and

Henthorne, 1995). Nowadays, most of the Kosovo households consist of husband, wife,

children and often grandparents. To examine the role and influence of family members in the

purchase of milk and other dairy products, key consumer behaviour questions were asked,

like ‘who buys’ and ‘who makes buying decisions’ on the dairy products choice. Involvement

of husband and wife in the purchase of products varies by product category. ‘’Wife has

31

usually acted as the family’s main purchasing agent for food items’’ (Kotler and Keller, 2006,

p.179). However, respondents’ answers showed highest proportion 34%, for joint purchase

(husband and wife) of milk and dairy products. Men were disposed to be more active in the

purchase of milk and dairy products compared to women. The percentage of men accounted

for 30%, which was higher than the proportion of women (21%). The remaining percentage

indicates involvement of children in the purchase of milk and dairy products. Even though

traditional purchasing roles are changing, study result proved that men were buying

substantially more than women. In fact this result was not surprising, given that

unemployment rate was statistically significant higher for the women than for men. Moreover,

income generating ability for the women was considerably less than for men.

Figure 4. Respondents’ answers in terms of buying milk and six other dairy products

0

10

20

30

40

50

60

70

80

90

100

Milk Yoghurt FruitYoghurt

Cream Curd Cheese Butter

Yes No

To understand how the consumers make their buying decisions in connection with milk

and other dairy products, it was particularly important to identify who among the family

members contributes and makes the buying decision. In the purchase decision task, family

members can be initiators, influencers, deciders, buyers, or users (Kotler and Keller, 2006 p

203). Considering different purchasing roles by the family members in the buying decisions,

men had much more buying power and were mainly engaged as buyers of milk and other

dairy products. Women, on the other hand, had a tendency to be much more influencers and

deciders in the purchasing decisions. Nearly 50% of the respondents stated that the wife is the

one of the family members who usually decides what kind of milk and other dairy products

should be purchased. About 20% of the respondents stressed that decisions were usually made

32

jointly by wife and husband. The proportion of the respondents stating that husband makes

buying decisions was smaller, about 15%. Only 8% of the respondents stated that purchasing

decisions towards kind of milk and other dairy products were taken by children.

The identification of the most frequented market place by consumers, when buying milk

and other dairy products was captured by asking ‘where do you usually go when you buy milk

and other dairy products’? As shown in Figure 5, it was revealed that the most frequented

market place by consumers for milk and other dairy products was supermarket.

Figure 5.Market places preferred by consumers when buying milk and other dairy produces

0% 20% 40% 60% 80% 100%

Milk

Yoghurt

Fruit Yoghurt

Cream

Curd

Cheese

Butter

By farmer Street vendor Green market Grocery store Supermarket

Figure 6. Frequencies of buying milk and other dairy products

0% 20% 40% 60% 80% 100%

Milk

Yoghurt

Fruit Yoghurt

Cream

Curd

Cheese

Butter

Every day Twice a week Once a week Twice a month Once a month

33

Figure 6 shows that milk and yoghurt, were the most frequent dairy products bought by the

consumers, followed by fruit yoghurt and cream. The other dairy products such as curd,

cheese, and butter were less frequently bought by the consumers. The obtained results

indicated that the frequency of buying milk and other dairy products was related to freshness

and durability of the products. The above figure illustrates that majority of the Kosovo

consumers prefer more fresh dairy products, particularly milk and yoghurt.

Table 15 provides the averages of monthly quantity of milk and six other dairy products

purchased by the HHs. The research study did not provide directly per capita consumption of

the various dairy products. However, given that the averages of purchased quantity by the

HHs was already known, as well as the HH size, per capita consumption for the various dairy

products could be estimated, and the estimates are presented in Table 16.

Table 15. Summary statistics of the average quantity of milk and other dairy products

purchased by the HHs

Quantity of product i Minimum Mean Maximum Std. Error Std. Deviation

Q Milk (l/month) 3 26.30 90 ± 0.79 14.53

Q Yoghurt (l/month) 2 10.51 40 ± 0.43 7.53

Q Fruit Yoghurt (kg/month) 1 2.51 30 ± 0.22 3.08

Q Cream (kg/month) 1 2.34 35 ± 0.17 2.86

Q Curd (kg/month) 1 3.93 20 ± 0.18 2.88

Q Cheese (kg/month) 1 4.67 20 ± 0.14 2.53

Q Butter (kg/month) 1 1.39 10 ± 0.0887 1.27

Table 16 The annual average per capita consumption

Product Milk Yoghurt Fruit yoghurt Cream Curd Cheese Butter

Quantity l/year l/year Kg/year Kg/year Kg/year Kg/year Kg/year

51.15 20.44 4.88 4.55 7.64 9.08 2.70

The averages of monthly expenditures by the HHs on milk and other dairy products are

given in table 17. The monthly average expenditure was higher for milk, cheese, and yoghurt

than for other products. The total average monthly expenditure by the HHs on milk and other

dairy products was estimated to be 56.17 euro/month (Std. Error ± 1.55; Std. Deviation

29.25). The total average monthly expenditure on milk and other dairy products counted at

34

9.6%, of the total average household monthly income. Regarding the fulfilments of the

consumers’ needs for milk and other dairy products, roughly 57% of the respondents stressed

that they manage to fulfil demand for milk and dairy products with their current monthly

income. About 43% of the interviewed respondents stated that they did not fulfil their needs

with the current monthly income. Majority (72%) of the respondents who did not fulfil their

demand for such products, were from urban areas. This result was not surprising as the

households in the urban areas had significantly lower income (PANOVA = 0.032) compared to

the rural households. Within the proportion of the respondents who did not fulfil the needs

(73%) were households who had lower monthly income (less than 400 euro). It has been

realized that the households who had more than five family members, had less tendency to

fulfil the demand for milk and other dairy products.

Table 17. Summary statistics of the average expenditures on milk and other dairy products by

the HHs

Expenditures on product i Minimum Mean Maximum Std. Error Std. Deviation

E Milk (euro/month) 2 19.37 60 ± 0.59 10.75

E Yoghurt (euro/month) 2 10.02 40 ± 0.40 7.11

E Fruit yoghurt (euro/month) 1 5.85 36 ± 0.35 4.88

E Cream (euro/month) 1 6.11 70 ± 0.39 6.44

E Curd (euro/month) 1 6.69 30 ± 0.31 4.93

E Cheese (euro/month) 2 14.69 50 ± 0.45 8.16

E Butter (euro/month) 1 5.08 40 ± 0.30 4.31

Despite the seasonal variation on milk production, which is characterized by over-

production during the summer season, the demand for milk and other dairy products was quite

stable throughout the year. Figure 7 shows that majority of the Kosovo consumers’ (64 %),

consumed milk on a regular basis throughout the spring, summer, and autumn months. The

consumption of milk appeared to be higher during the winter months. Concerning the

seasonal consumption patterns on yoghurt, Figure 8 shows that the consumers tend to behave

differently compared with the seasonal consumption patterns on milk. The consumption of

yoghurt seemed to be quite stable during the spring, autumn, and winter months, while its

consumption was higher throughout the summer months.

Figure 9, 10, and 13 indicate that Kosovo consumers’ tend to behave in a seasonal manner

with regard to the fruit yoghurt, cream, and butter. The consumption of fruit yoghurt was less

35

throughout the year, with small increases during the spring and the summer months. The

products with the higher content of fat such as cream and butter were prone to be consumed

less throughout the year except of butter, its consumption tends to be higher during the winter

months. Regarding the seasonal consumption patterns on curd and cheese, Figure 11 and 12

show that consumption of these dairy products was quite stable throughout the year.

Figure 7. Seasonal consumption patterns on milk

11.414.8

6.22.9

65.2 64.7 61.6

35.3

22.619.7

31.4

61

0

10

20

30

40

50

60

70

Spring Summer Autumn Winter

MilkLess Constant More

Note: The variable was measured on scale of 1 to 3, where 1 is “when the respondent consume less of the product i for a given season”, 2 is “when the respondent consumes product i on a regular basis”, and 3 is “when the respondent consumes more the product i for given season”.

Figure 8. Seasonal consumption patterns on yoghurt

14.3

3.4

23.4 22.9

60

26.5

62.6 62.9

22.6

67

10.9 11.2

0

10

20

30

40

50

60

70

80

Spring Summer Autumn Winter

Yoghurt

Less Constant More

36

Figure 9. Seasonal consumption patterns on fruit yoghurt

30.6

24.9

35.8 36.4

12.510.4

12.2 11.7

7.8

15.6

2.6 2.6

0

5

10

15

20

25

30

35

40

Spring Summer Autumn Winter

Fruit Yoghurt

Less Constant More

Figure 10. Seasonal consumption patterns on cream

42.6 44.448.6

44.4

23.6 22.124.9 23.4

15.8 15.6

8.614.3

0

10

20

30

40

50

60

Spring Summer Autumn Winter

Cream

Less Constant More

Figure 11. Seasonal consumption patterns on curd

20 2118.7

15.6

38.4 39.7

44.942.9

21.619.2

16.4

21.6

0

5

10

15

20

25

3035

40

45

50

Spring Summer Autumn Winter

Curd

Less Constant More

37

Figure 12. Seasonal consumption patterns on cheese

4.2 5.5 4.4 3.6

65.7 64.4 62.1 59.7

28.8 28.832.3

35.3

0

10

20

30

40

50

60

70

Spring Summer Autumn Winter

Cheese

Less Constant More

Figure 13. Seasonal consumption patterns on butter

56.6 57.451.9

39

5.5 4.9 4.4 4.20.5 0.3

6.2

19.5

0

10

20

30

40

50

60

70

Spring Summer Autumn Winter

ButterLess Constant More

Butter Concerning the consumers’ evaluation on product features, Figure 14 displays that

nutritive content, taste, product safety, price and origin of the product were highly ranked by

the consumers in the order of importance. Package size, wrapping, and brand appeared to be

not very important.

With respect to the consumers’ preference for the package size, the results have shown that

majority (84%) of the respondents preferred a milk package size at 1 litre, a small fraction

(about 8%), preferred a package size at 2 litres. A 1 litre package was the most preferred one

for the yoghurt (60%), followed by 500 ml which counted at 26%, while the responses for the

remaining proportion were quite diverse. The packages from 250, 200, and 180 gram were the

most preferred for the fruit yoghurt. The consumers’ preference towards package size for

cream were 1000, 500, and 200 gram. Bigger packages were preferred for curd and cheese (3,

38

2, and 1 kg), whereas butter packages from 500, 250, and 200 gram were the most preferred

by interviewed respondents. It is important to note that preferences for the package size of

curd, cheese, and butter were positively related with the HH size. This would mean that the

households with more family members, had tendency to prefer bigger packages for curd,

cheese, and butter.

Figure 14. The evaluation of product features in order of importance

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Nutritive Content

Taste

Product safety

Price

Brand

Wrapping

Package size

Product origin

Unimportant Not very important Important Very important

Regarding the preferences of domestic versus foreign dairy products, consumers had a

favourable bias towards domestic dairy products (78%). Quality, safety, taste, and price were

the main product features related to the consumers’ preference for purchase of the domestic

dairy products. Apart from the product features above cited, national patriotism seemed to be

another factor influencing consumers’ preference towards domestic dairy products. Based on

the consumers’ view, the purchase of domestic dairy products helps development of dairy

industry and domestic economy in general. The package, durability, assortment, and visual

aspect, were the main product features motivating consumers to purchase foreign dairy

products. The consumer’s preference towards foreign dairy products was significantly

dependent on the respondent’s location. Majority of the respondents who preferred foreign

dairy products (82%), were from urban areas.

With respect to mass media preferences, Figure 15 shows that television, and newspapers,

were the media most often used by the respondents to get information about the dairy

products. The preference towards media was significantly associated with the consumer’s

education. The preference of the newspaper was positively related with the respondent’s

39

education, which means that newspapers tend to be preferred more by the respondents that

had higher education. The preferences toward television and radio were negatively associated

with the respondent’s education. It means that a media category such as television and radio

was most often used by the respondents who had less education.

Figure 15. The level of the mass media used by the respondents

0

10

20

30

40

50

60

70

Media most rarelyused

Media rarely used Media often used Media most oftenused

New spapers Flyers Television Radio

Note: The variable was measured on scale of 1 to 4, where 1 is “for the media most rarely used”, 2 is “for the media rarely used”, 3 is “for the media often used”, and 4 is “for the media most often used”.

The proportion of the respondents that did not prefer new dairy products and innovation

was 59:41%. It has been realized that the respondents aged 18 to 40 had a tendency to prefer

more new dairy products and innovation. The respondents aged 60 and older were more

conservative, they prefer dairy products that they were used to consume regularly. Majority of

the respondents (85%) did not start buying any new dairy product since the last year. It was a

small fraction (15%) of the respondents who started buying new dairy products. The

consumers’ habit for the new dairy products was significantly dependent on the HH monthly

income. Milk, yoghurt, and fruit yoghurt were the main dairy products that consumers started

buying since the last year. They were strongly motivated by curiosity, quality, taste, and price

of the new dairy products. It is important to note that milk and yoghurt are bought previously

by the consumers. It was mostly a shift from one brand to another. The supermarkets and

television were the main sources of the information for the new dairy products.

40

4.3 Model Estimation

This section provides the parameters estimated by the two statistical techniques, multiple

linear function and binary logistic function. The following subsection describes the

parameters estimated by the logistic function for the outcome variable that is categorical with

the dichotomy response. The next subsection (4.3.2) presents the parameters obtained by the

multiple linear functions.

4.3.1 Binary Logistic linear Estimated Parameters These parameters are estimated by fitting models, based on the predictors in the equation

1. The values of the parameters are estimated using maximum –likelihood estimation “which

selects coefficients that make the observed values most likely to have occurred” (Filed, 2005).

Table 18. Relationship of whether the respondent i buys milk and the predictors included in

the equation 1

Variable B SE Wald Exp (B)

Constant 3.920*** 0.714 30.187 50.425

Place (Rural) -3.731*** 0.765 23.785 0.024

Note: B = Logistic regression coefficient; SE = Standard error; Wald = Wald statistic (which has a special

distribution known as the 2χ distribution); Exp (B) = Indicates the change in odds resulting from a unit change

in the predictor. Significant contribution of each independent variable to the model is denoted as follows: *P < 0.05; ** P < 0.01; *** P < 0.001.

Table 19. Relationship of whether the respondent i buys yoghurt and the predictors included

in the equation 1

Variable B SE Wald Exp (B)

Constant 1.233* 0.582 4.489 3.431

Place (Rural) -1.640*** 0.413 15.811 0.194

Region (Prishtina) 0.438 0.667 0.431 1.550

Region (Prizren) -0.367 0.631 0.338 0.693

Region (Peja) 1.208 0.800 2.278 3.346

Region (Gjilan) -0.747 0.631 1.400 0.474

HH income 0.001 0.000 2.519 1.001

41

Table 20. Relationship of whether the respondent i buys fruit yoghurt and the predictors

included in the equation 1

Variable B SE Wald Exp (B)

Constant 0.408 0.389 1.099 1.504

HH size -0.170** 0.059 8.210 0.844

IncomeR 0.547** 0.191 8.204 1.729

Table 21. Relationship of whether the respondent i buys cream and the predictors included in the equation 1 Variable B SE Wald Exp (B)

Constant -1.024 1.084 0.893 0.359

Place (Rural) -1.700*** 0.445 14.582 0.183

Region (Prishtina) -0.725 0.714 1.032 0.484

Region (Prizren) -1.085 0.703 2.382 0.338

Region (Peja) 0.693 0.829 0.698 1.999

Region (Gjilan) -1.031 0.704 2.148 0.357

Age 0.033 0.018 3.205 1.034

IncomeR 0.003* 0.001 4.857 1.003

HH size -0.134 0.080 2.853 0.874

Employment 0.897** 0.270 11.006 2.453

Table 22. Relationship of whether the respondent i buys curd and the predictors included in the equation 1 Variable B SE Wald Exp (B)

Constant 1.057** 0.324 10.663 2.878

Place (Rural) -2.235*** 0.402 30.994 0.107

HH income 0.001* 0.000 4.099 1.001

Table 23. Relationship of whether the respondent i buys cheese and the predictors included in the equation 1 Variable B SE Wald Exp (B)

Constant 3.506*** 0.586 35.815 33.329

Place (Rural) -3.393*** 0.647 27.477 0.034

42

Table 24. Relationship of whether the respondent i buys butter and the predictors included in the equation 1 Variable B SE Wald Exp (B)

Constant -0.500** 0.172 8.390 0.607

Place (Rural) 1.214*** 0.307 15.599 3.367

Table 25. Relationship of whether the respondent i fulfils the demand for milk and dairy

products and the predictors included in the equation 1

Variable B SE Wald Exp (B)

Constant -0.854 0.589 2.098 0.426

Place (Rural) 0.748 0.436 2.939 2.112

IncomeR 0.006*** 0.002 13.869 1.006

HH size -0.261** 0.075 12.107 0.770

Employment 0.663** 0.230 8.331 1.940

Table 26. Relationship of whether the respondent i prefers domestic dairy products and the

predictors included in the equation 1

Variable B SE Wald Exp (B)

Constant -0.734 0.817 0.808 0.480

Place (Rural) 1.446** 0.544 7.074 4.247

Age 0.055** 0.020 7.758 1.057

HH size -0.123 0.069 3.202 0.884

Employment 0.416 0.245 2.872 1.515

HH income -0.001* 0.000 5.417 0.999

Table 27. Relationship of whether the respondent i prefers foreign dairy products and the

predictors included in the equation 1

Variable B SE Wald Exp (B)

Constant 0.734 0.817 0.808 2.084

Place (Rural) -1.446** 0.544 7.074 0.235

Age -0.055** 0.020 7.758 0.946

HH size 0.123 0.069 3.202 1.131

Employment -0.416 0.245 2.872 0.660

HH income 0.001* 0.000 5.417 1.001

43

Table 28. Relationship of whether respondent i prefers new dairy products and the predictors

included in the equation 1

Variable B SE Wald Exp (B)

Constant -2.329** 0.897 6.732 0.097

Education 0.149* 0.068 4.832 1.161

Table 29. Relationship of whether respondent i started buying new dairy products last year

and the predictors included in the equation 1

Variable B SE Wald Exp (B)

Constant -4.044** 1.340 9.111 0.018

Education 0.261* 0.105 6.133 1.298

IncomeR -0.004* 0.002 4.789 0.996

4.3.2 Multiple linear Estimated Parameters

Fluid milk

The estimated quantity of milk consumed in litre/month in the HHi was:

Q Milk (l/month) = 14.352 + (0.142 Age) + (1.789 Children) + (0.009543 HH income)

3.742*** 1.665 2.535* 4.482***

(R2 = 0.15) (5)

Note: values in italics are t-statistics; significant variable in the model is denoted as follows: *P < 0.05; ** P < 0.01; *** P < 0.001.

The estimated coefficients in the equation (5) indicated that there was positive relationship

between the quantity of milk consumed in the HHi and the three predictors included in the

model. The results suggest that given a year increase in the respondent’s age, fluid milk

consumption increases by 0.142litre/month. This interpretation stands only if the number of

children in the HHi and the average monthly income in the HHi are held constant. One child

more in the HHi is expected to cause an increase in the fluid milk consumption by

1.789litre/month. This interpretation is consistent only if the respondent’s age and the average

monthly income in the HHi are held constant. The fitted model predicts 0.009543litre/month

44

increases in the fluid milk consumption, for a one euro increase in the HH income. This

interpretation is true only if the respondent’s age and the number of children in the HHi are

held constant. The magnitude of the t-statistics showed that the average monthly income in

the HHi had greater contribution to the model, compared to the respondent’s age and the

number of children in the HHi. The R2 is only 15%, though F ratio is 10.587 and P < 0.001,

indicating that the probability of obtaining the value of F by chance was very small.

Yoghurt

The estimated quantity of yoghurt consumed in litre/month in the HHi was:

Q Yoghurt (l/month) = 4.547 + (0.316 HH size) + (0.006767 HH income)

3.767*** 2.154* 5.958***

(R2 = 0.24) (6)

Positive values of the estimated coefficients indicated that there was positive relationship

between the quantity of yoghurt consumed in the HHi and two predictors HH size and HH

income. The fitted model in the equation (6) predicts 0.316litre/month increase in the

consumption of yoghurt, if the HH size increases by one member. This interpretation is true

only if the HH income is held constant. The magnitude of the t-statistics indicated that the HH

income had greater contribution to the model, meaning that explained much more of the

variability in the outcome than the HH size. The consumption of yoghurt increases by

0.006767litre/month, for every additional euro in the HH monthly income. This interpretation

is consistent only if the HH size is held constant. Even though the fitted model produces a

significant reduction in the residual variance, F = 24.844; P < 0.001, the coefficient of

determination (R2) is only 24%.

45

Fruit Yoghurt

The estimated equation for fruit yoghurt was:

Q Fruit yoghurt (kg/month) = - 2.910 + (0.163 Education) + (0.167 HH size) + (0.709 Children)

- 2.113* 1.706 1.834 2.662**

+ (0.001119 HH income)

2.441*

(R2 = 0.30) (7)

The best fitted model in the equation 7 predicted 0.163kg/moth increase in the quantity of

fruit yoghurt consumption, if the respondent’s education increases by one year. This

interpretation is consistent only if other predictors in the equation are held constant. The

increase in the HH size by one member induces fruit yoghurt consumption by 0.167kg/month.

This interpretation is true only if other explanatory variables in the fitted model are held

constant. As it was expected, the number of children below 14 years old in the HHi had much

more impact than the other predictor variables included in the equation (7). One child more in

the HHi increases fruit yoghurt consumption by 0.709kg/month. This interpretation is

consistent only if other predictors in the equation are held constant. The model predicts

0.001119kg/month increases in the fruit yoghurt consumption, for a one euro increase in the

HH monthly income. This interpretation stands only if other predictors in the model are held

invariable. Although, the fitted model did not explained much of the variability in the

outcome by the predictors, R2 = 0.30. There was a significant reduction in the residual

variance, with F = 12.560; P < 0.001.

Cream

The estimated equation for cream was:

Q Cream (kg/month) = - 3.363 + (0.134 Education) + (0.458 HH size) +

- 3.010** 1.722 6.255***

(0.438 Employment)

1.973

(R2 = 0.40) (8)

46

The estimated parameters in the equation 8 showed that there was positive relationship

between the quantity of cream consumed in the HHi and three explanatory variables. The rate

of cream consumption increases by 0.134kg/month, if the respondent’s education increases by

one year. This interpretation stands only if HH size and number of employed members in the

HHi are held constant. One more member in the HHi induces the cream consumption by

0.458kg/month. This estimation is consistent only if two predictors’ education and

employment are held constant. The rate of cream consumption rises by 0.438kg/month, given

a one more member employed in the HHi. This interpretation is true only if other two

predictors are held constant. The quality of the fit is considered as moderate, with R2 = 0.40

and significant reduction in the residual variance F = 31.237, P < 0.001.

Curd

The estimated quantity of curd consumed in kilogram/month in the HHi was:

Q Curd (kg/month) = 0.164 + (0.0466 Age) + (0.169 HH size) + (0.477 Employment)

0.197 2.682** 2.357* 2.191*

(R2 = 0.19) (9)

The estimated parameters in the equation (9) indicated that there was positive relationship

between the quantity of curd consumed in the HHi and three explanatory variables included in

the model. Given a year increase in the respondent’s age, curd consumption increases by

0.0466kg/month. This interpretation is true only if HH size and number of employed

members in the HHi are held constant. The fitted model predicts 0.169kg/month increases in

the curd consumption, if the HH size increases by on member. This estimation is consistent

only if the respondent’s age and number of employed family members are held constant. One

more member employed in the HHi, curd consumption increases by 0.477kg/month. This

interpretation is true only if the respondent’s age and the HH size are held invariable.

Although, the coefficient of determination (R2) was only 19%, the fit of the model produces a

significant reduction in the residual variance, with F = 10.520; P < 0.001.

47

Cheese

The estimated equation for cheese was:

Q Cheese (kg/month) = 1.153 + (0.03274 Age) + (0.221 HH size) + (0.467 Employment)

1.855 2.545* 4.205*** 2.993**

(R2 = 0.26) (10)

Positive values of the estimated coefficients in the equation (10) showed that there was

positive relationship between the predictors and the outcome. Given a year increase in the

respondent’s age, cheese consumption rises by 0.03274 kg/month. This estimation is

consistent only if the other two predictors HH size and employment are held invariable. From

the magnitude of the t-statistics, it was clear that the HH size had greater impact and

explained more the variability on the cheese consumption. One more member in the HHi

induces cheese consumption by 0.221 kg/month. This estimation stands only if the

respondent’s age and number of employed members in the HHi are held constant. The model

predicts an increase in the cheese consumption by 0.467 kg/month, if the number of employed

members in the HHi increases by one. This interpretation is true only if other two predictors in

the equation (10) are held invariable. Although, the fitted model explained only 26% of the

variability in the cheese consumption, there was a significant reduction in the residual

variance, with F = 20.286; P < 0.001.

Butter

The estimated quantity of butter consumed in kilogram/month in the HHi was:

Q Butter (kg/month) = - 0.655 + (0.03069 Age) + (0.432 Employment)

- 1.465 3.084** 4.964***

(R2 = 0.22) (11)

The fitted model in the equation (11) predicts 0.03069 kg/month increase in the butter

consumption, for every year increase in the respondent’s age. This estimation stands only if

the number of employed members in the HHi is held constant. Based on the magnitude of the

t-statistics, the number of employed members in the HHi had greater contribution to the model

48

compared to the respondent’s age. One more member employed in the HHi, increases butter

consumption by 0.432 kg/month. This interpretation is consistent only if the respondent’s age

is held constant. The variability in the butter consumption explained by the predictors was

only 22%. However, the model produces a significant reduction in the residual variance, with

F = 15.911; P < 0.001.

Expenditures on milk

The estimated expenditure on milk in euro/month in the HHi was:

E Milk (euro/month) = 16.513 + (0.007594 HH income)

13.535*** 4.670***

(R2 = 0.11) (12)

Positive value of the estimated coefficient in the equation (12) indicates that there was

positive relationship between the average monthly income in the HHi and monthly

expenditures on milk. The model predicts 0.007594 euro/month increases on the milk

expenditures, if the average monthly income in the HHi rises by one euro. Even though the

quality of the fit is not very good, with R2 = 0.11, there was a significant reduction in the

residual variance, F = 21.810; P < 0.001

Expenditures on yoghurt

The estimated expenditure on yoghurt in euro/month in the HHi was:

E Yoghurt (euro/month) = 1.052 + (0.327 Education) + (0.298 HH size) + (0.005043 HH income)

0.407 1.789 2.108* 4.584***

(R2 = 0.19) (13)

Based on the fitted model in the equation (13) monthly expenditures on yoghurt in the

HHi, increase by 0.327 euro/month given one year increase in the respondent’s education.

This estimation is consistent only if the HH size and average monthly income in the HHi are

held constant. One more member in the HHi increases the expenditures on yoghurt by 0.298

49

euro/month. This interpretation is true only if the respondent’s education and the HH income

are held constant. The magnitude of the t-statistics showed that the average monthly income

in the HHi had greater impact to the model compared to other predictors in the equation (13).

The expenditures on yoghurt increases by 0.005043 euro/month given one additional euro in

the HH income. This estimation is true only if the respondent’s education and the HH size are

held constant. The fitted model produces a significant reduction in the residual variance, with

F = 12.616; P < 0.001.

Expenditures on fruit yoghurt

The estimated expenditure on fruit yoghurt in euro/month in the HHi was:

E Fruit yoghurt (euro/month) = - 3.167 + (0.303 Education) + (1.472 Children)

- 1.563 2.138* 4.996***

+ (0.003348 HH income)

5.066***

(R2 = 0.36) (14)

The fitted model in the equation (14) predicted 0.303 euro/month expenditure increase on

fruit yoghurt in the HHi, given a one year increase in the education level of respondent i,

holding number of children in the HHi and HH income constant. As it was expected the

number of children in the HHi had significant contribution to the fitted model. One child more

in the HHi increases expenditure on fruit yoghurt by 1.472 euro/month, holding respondent’s

education and HH income constant. The magnitude of the t-statistics indicates that the HH

income had the highest contribution to the fitted model. Monthly expenditure in the HHi on

fruit yoghurt is expected to increase by 0.003348, per each euro increase in the HH income,

holding number of children in the HHi and respondent’s education constant. Even though, the

variability on monthly fruit yoghurt expenditures explained by the predictors was 36%.

However, the fitted model produced a significant reduction in the residual variance, with F =

22.740; P < 0.001.

50

Expenditures on cream

The estimated expenditure on cream in euro/month in the HHi was:

E Cream (euro/month) = - 3.287 + (0.950 HH size) + (1.349 Employment)

- 2.957** 5.898*** 2.745**

(R2 = 0.41) (15)

The estimation results indicate that there was a positive relationship of HH monthly

expenditures on cream consumption with the HH size and number of employed family

members. An increase of the HH size by one member increases HH monthly expenditures on

cream consumption by 0.950 euro/month, holding the number of employed family members

constant. One more member employed in the HHi increases monthly expenditures on cream

consumption by 1.349 euro/month. This interpretation is true only if HH size is held constant.

The fitted model produces a significant reduction in the residual variance, with F = 48.979; P

< 0.001.

Expenditures on curd

The estimated expenditure on curd in euro/month in the HHi was:

E Curd (euro/month) = 1.065 + (0.05923 Age) + (0.290 HH size) + (0.797 Employment)

0.696 1.852 2.193* 1.990*

(R2 = 0.15) (16)

The fitted model in the equation (16) predicted 0.05923 euro/month increase on curd

expenditure as the respondent’s age increases by one year. This interpretation stands only if

the HH size and the number of employed family members are held constant. The expenditure

on curd increases by 0.290 euro/month if the HH size increases by one member, holding other

predictors in the equation (16) constant. The model predicts 0.797 euro/month increase on the

curd expenditures if the number of employed family members increases by one. This

interpretation is true only if the respondent’s age and the HH size are held constant. The fitted

model produces a significant reduction in the residual variance, with F =8.077; P < 0.001.

51

Expenditures on cheese

The estimated expenditure on cheese in euro/month in the HHi was:

E Cheese (euro/month) = 7.773 + (0.920 HH size) – (0.941 Children) + (0.005169 HH income)

6.273*** 4.175*** - 1.846 4.685***

(R2 = 0.24) (17)

The fitted model in the equation (17) predicted 0.920 euro/month increase on the cheese

expenditure if the HH size increases by one member, holding number of children in the HHi

and HH income constant. The expenditures on cheese decrease by 0.941 euro/month if the

number of children in the HHi increases by one. This interpretation is true only if the HH size

and the HH income are held constant. The model predicts 0.005169 euro/month increase on

the cheese expenditure if the HH income increases by one euro, holding HH size and the

number of children in the HHi constant. The fitted model produces a significant reduction in

the residual variance, with F = 18.792; P < 0.001.

Expenditures on butter

The estimated expenditure on butter in euro/month in the HHi was:

E Butter (euro/month) = - 2.600 + (0.09451 Age) + (1.973 Employment)

- 1.599 2.611* 6.227***

(R2 = 0.28) (18)

The model in the equation (18) predicts 0.09451euro/month increase on the butter

expenditure if the respondent’s age increases by one year, holding number of employed

family members in the HHi constant. The magnitude of the t-statistics indicates that the

number of employed family members had highly significant contribution to the fitted model.

The expenditure on butter increases by 1.973 euro/month if the number of employed family

members increases by one. This interpretation stands only if respondent’s age is held

constant. The fitted model produces a significant reduction in the residual variance, with F =

21.558; P < 0.001.

52

Total monthly expenditures on milk and other dairy products

The estimated total monthly expenditure on milk and other dairy products in euro/month in

the HHi was:

T = 12.220 + (0.291 Age) + (1.691 HH size) + (6.091 Employment) + (0.02090 HH income)

1.823 2.051* 2.944** 2.883** 4.757***

(R2 = 0.41) (19)

The fitted model in the equation (19) predicted 0.291 euro/month increase in total monthly

expenditures on milk and dairy products if the respondent’s age increases by one year,

holding HH size, number of employed family members and the HH income constant. The

total monthly expenditures on milk and other dairy products increase by 1.691 euro/month if

the HH size increases by one member. This interpretation stands only if other predictors in the

equation (19) are held constant. The increase number of employed family members increases

total monthly expenditures on milk and other dairy products by 6.091 euro/month, holding

other predictors in the equation (19) constant. The magnitude of the t-statistics indicates that

the HH income had the highest contribution to the fitted model. The model predicts 0.02090

euro/month increase in the total monthly expenditures on milk and other dairy products if the

HH income increases by one euro, holding other predictors in the equation (19) constant. The

fitted model produces a significant reduction in the residual variance, with F = 32.787; P <

0.001.

53

CHAPTER V

5. Conclusions

The results of this study indicate that milk and other dairy products stand as regular staple

food to most of the Kosovo households. Milk, yoghurt, cream, curd, and cheese were the main

dairy products consumed by majority of the Kosovo consumers. Men had much more buying

power compared to women. Men’s role in the decisions process for milk and other dairy

products was mainly as buyers. Women’s role in the decisions process was mainly as

influencers and deciders of the product choice. Supermarkets and grocery stores were the

most preferred market places by the consumers when buying milk and other dairy products.

The consumer’s preference towards market place was significantly dependent on

demographic and socioeconomic household characteristics (see Table 30). The frequency of

buying dairy products was associated with the durability of the product. Dairy products with

the shorter shelf life such as milk, yoghurt, and fruit yoghurt were bought more frequently by

the consumers compared to those with longer shelf life. Apart from product life, other

demographic and socioeconomic factors had significantly impact on the frequencies of buying

milk and other dairy products (see Table 31). The consumers’ demand for milk and other

dairy products was quite stable throughout the year. The consumption of milk and butter was

slightly higher during the winter months, while yoghurt and fruit yoghurt were more preferred

during the summer season.

The consumer’s preference towards the package size was positively associated with the

HH size. Kosovo consumers preferred bigger packages for curd and cheese. Smaller packages

were predominantly more preferred for fruit yoghurt and butter. The country of origin

influenced the consumers’ preferences towards locally produced dairy products. Majority of

the Kosovo consumers had favourable bias toward dairy products. Product attributes such as

quality, safety, taste, and price were the main features motivating consumers to purchase

domestic dairy products. According to the consumers’ view, other product features such as

package, durability, assortment, and the visual aspect were seen as disadvantage of the

domestic compared to the foreign dairy products. Respondent’s location had significant

impact on the preferences of domestic versus foreign dairy products. The consumer’s

preference towards new dairy products and innovation was dependent on his/her

54

characteristics such as age, and monthly income. Respondents with higher monthly income

were more willing to try new dairy products that come to the market.

The consumers’ attitudes toward product features such as nutritive content, taste, product

safety, price, brand, wrapping, package size, and the origin of product were significantly

dependent on demographic and socioeconomic factors (see Table 32). Among the product

attributes nutritive content, taste, product safety, price and the origin of dairy products were

most highly ranked by the Kosovo consumers. The study has proved that households with

lower monthly income and more than five family members were less able to meet their needs

for milk and other dairy products.

The location of the respondents (rural or urban) was an important variable of whether the

respondents buy milk and other dairy products. It was proved that rural households buy

significantly less milk, yoghurt, cream, curd, and cheese compared to the urban households.

Income was a significant variable of whether respondents buy fruit yoghurt, cream, and curd.

The odds ratio of buying yoghurt, cream, and curd increases as the respondent’s and

household’s income increase. Household size was a significant variable of whether

respondents buy fruit yoghurt and cream. The probability of buying these dairy products

decreases as the number of family members increases. It was also proved that number of

family members employed had a significant positive effect of whether respondents buy

cream.

Respondent’s monthly income and number of employed family members were significant

variables of whether respondents fulfil their demand for milk and other dairy products. The

odds ratio for fulfilling the demand for milk and other dairy products decreases as the HH size

increases. Respondent’s location and age were important variables of whether respondents

prefer domestic dairy products. Respondents from rural areas significantly preferred more

domestic dairy products. The probability of preferring domestic dairy products increases as

the respondent’s age increases. The odds ratio of preferring domestic dairy products decreases

as the HH size and HH income increases. Education was a positive determinant factor of

whether respondents preferred new dairy products and innovation.

The household income was a highly significant variable in explaining the variation on

milk, yoghurt, and fruit yoghurt consumption, suggesting a trend toward increased

consumption of such products as the household income rises. The household size was an

important variable almost for all dairy products, and quite significant for cream and cheese

consumption. The effect of number of children in the household was highly significant in

explaining the variation in milk and fruit yoghurt consumption. It suggests that an increased

55

number of children in the household will be a determining factor in the increasing demand for

milk and fruit yoghurt. The number of family members employed was a significant variable in

explaining the variation on curd, cheese, and butter consumption. The estimation predicts an

increase on the curd, cheese, and butter consumption as the employment rate increases.

Respondent’s age was significant for curd, cheese, and butter. Changes in the respondent’s

age significantly affect curd, cheese, and butter consumption. Income was a highly significant

variable in determining the household expenditures on milk, yoghurt, fruit yoghurt and

cheese. It suggests that increase of income will significantly increase expenditures on milk,

yoghurt, and fruit yoghurt. An increase in the household size significantly increases

household expenditures on yoghurt, cream, curd, and cheese.

The average quantity of milk consumed by the HHs was estimated to be 26.30 l/month.

The average quantity consumed by the HHs for the other dairy products was estimated as

follows: yoghurt 10.5 l/month, fruit yoghurt 2.51 kg/month, cream 2.34 kg/month, curd 3.93

kg/month, cheese 4.67 kg/month, and the butter 1.39 kg/month. The average of monthly

expenditures by the HHs on milk and other dairy products were estimated as follows:

expenditures on milk 19.37 euro/month, yoghurt 10.02 euro/month, fruit yoghurt 5.85

euro/month, cream 6.11 euro/month, curd 6.69 euro/month, cheese 14.69 euro/month, and the

butter 5.08 euro/month. The total average monthly expenditure by the HHs on milk and other

dairy products was estimated to be 56.17 euro/month or 9.6% of the total average household

monthly income.

56

Table 30. Test of independence between the market places and demographic and socioeconomic characteristics

Product Milk Yoghurt Fruit

Yoghurt

Cream Curd Cheese Butter

Variable 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V

Place 1.423 0.065 0.632 0.046 0.001 0.003 1.795 0.082 8.901 0.188 3.331 0.10 1.156 0.075

Region 17.720 0.115 19.479 0.126 8.082 0.205 20.139 0.159 37.1** 0.191 23.663 0.134 25.49 0.177

Age 10.741 0.090 11.545 0.097 1.861 0.098 3.089 0.062 10.73 0.103 17.341 0.115 13.50 0.129

Education 17.805* 0.163 17.687* 0.17 7.902* 0.202 11.757 0.148 13.76 0.165 19.42* 0.171 15.77* 0.197

EmploymentR 0.806 0.049 1.905 0.079 1.289 0.082 2.445 0.096 5.718 0.150 6.350 0.139 1.081 0.073

IncomeR 12.76 0.112 16.992 0.136 5.389 0.167 20.82* 0.161 14.50 0.138 13.612 0.117 12.55 0.144

HH size 39.7*** 0.199 21.612* 0.154 1.573 0.090 8.307 0.102 19.53 0.160 13.282 0.116 30.3** 0.223

Children 5.981 0.094 19.749* 0.18 0.536 0.053 4.134 0.088 15.7* 0.176 7.747 0.108 5.119 0.112

Employment 24.23** 0.190 21.44** 0.187 8.586* 0.211 14.18* 0.163 24.3** 0.219 28.7*** 0.208 15.62* 0.196

HH income 24.437 0.135 27.041* 0.149 18.42** 0.309 19.002 0.154 33.2** 0.181 41.8*** 0.178 30.30* 0.193

Note: Market place where consumer use to buy milk and other dairy products: by farmer; street vendor; green market; grocery store; supermarket; Cramer’s V measure the

strength of association between two categorical variables; Level of significance is denoted as follows: *P < 0.05; ** P < 0.01; *** P < 0.001.

57

Table 31. Test of independence between the frequencies of buying milk, dairy products and demographic and socioeconomic characteristics

Product Milk Yoghurt Fruit

Yoghurt

Cream Curd Cheese Butter

Variable 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’

s V

2χ Cramer’s

V 2χ Cramer’s

V

Place 6.755 0.142 7.821 0.160 4.015 0.144 10.729* 0.20 8.36* 0.181 4.825 0.121 0.978 0.069

Region 32.4** 0.155 25.529 0.144 29.672* 0.196 23.208 0.147 42*** 0.234 68.3*** 0.227 42*** 0.227

Age 13.506 0.100 8.196 0.082 14.068 0.135 13.593 0.113 15.55 0.143 23.511 0.133 27.7* 0.185

Education 12.163 0.135 14.523 0.154 8.571 0.149 4.422 0.091 11.22 0.148 13.542 0.143 7.020 0.131

EmploymentR 1.917 0.076 17.58** 0.240 3.088 0.126 2.482 0.096 7.268 0.169 6.881 0.144 0.839 0.064

IncomeR 15.067 0.122 21.244* 0.152 11.206 0.139 8.862 0.105 8.872 0.108 8.641 0.093 9.719 0.126

HH size 13.998 0.118 13.029 0.119 9.110 0.125 14.917 0.136 7.810 0.101 13.541 0.117 8.601 0.119

Children 6.189 0.096 6.786 0.105 4.356 0.106 4.026 0.087 5.922 0.108 15.207 0.152 13.60 0.183

Employment 15.030 0.150 3.875 0.080 11.697 0.174 4.691 0.094 6.822 0.116 13.683 0.144 4.785 0.108

HH income 32.8** 0.156 20.678 0.130 22.833 0.172 9.839 0.096 9.643 0.112 23.792 0.134 12.74 0.125

Note: Frequencies were given as follows: every day; twice a week; once a week; twice a month; once a month. Level of significance is denoted as follows: *P < 0.05; ** P <

0.01; *** P < 0.001.

58

Table 32. Test of independence between evaluation of product attributes and demographic and socioeconomic characteristics

Product

attribute

1 2 3 4 5 6 7 8

Variable 2χ Cramer’s

V 2χ Cramer

’s V

2χ Cramer’s

V 2χ Cramer’

s V 2χ Cramer’s

V 2χ Cramer’s

V 2χ Cramer’s V 2χ Cramer’s

V

Place 5.06 0.115 0.048 0.011 0.46 0.035 4.325 0.106 14** 0.188 7.083 0.136 4.451 0.108 6.19 0.127

Region 14.0 0.110 4.355 0.137 7.88 0.101 29.8** 0.161 35*** 0.174 30.7** 0.163 41.5*** 0.190 10.0 0.093

Age 17.9 0.125 12.29 0.127 6.31 0.091 8.382 0.085 17.3 0.123 7.457 0.080 12.309 0.103 16.5 0.12

Education 13.6* 0.133 4.677 0.078 9.23 0.109 13.0* 0.130 12.8* 0.129 10.80 0.118 16.5* 0.146 23** 0.174

EmploymentR 2.26 0.077 7.155* 0.136 3.40 0.094 12.3** 0.179 13** 0.184 4.115 0.103 0.715 0.043 2.83 0.086

IncomeR 5.010 0.066 5.435 0.084 2.12 0.052 61*** 0.231 44*** 0.197 30.8*** 0.163 14.572 0.112 13.4 0.108

HH size 7.684 0.082 3.432 0.067 8.33 0.104 0.240 0.094 8.82 0.087 3.586 0.056 4.448 0.062 16.2 0.12

Children 5.211 0.082 5.829 0.087 5.51 0.085 2.558 0.058 1.22 0.040 7.456 0.098 3.864 0.071 8.92 0.108

Employment 5.198 0.082 6.840 0.094 1.35 0.042 19.1** 0.161 8.14 0.103 11.345 0.121 5.147 0.082 4.91 0.80

HH income 12.74 0.105 7.991 0.102 14.2 0.136 88*** 0.276 36*** 0.177 36.0*** 0.177 13.468 0.108 24.4* 0.145

Note: The variable was measured in order of importance, on scale of 1 to 4, where 1 is “unimportant”, 2 is “not very important”, 3 is “important”, 4 is “ very important”. Numbers stand for 1= Nutritive content; 2 = Taste; 3 = Product safety; 4 = Prices; 5 = Brand; 6 = Wrapping; 7 = Package size; 8 = Product origin. Level of significance is denoted as follows: *P< 0.05; ** P< 0.01; *** P < 0.001.

59

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APPENDIX Appendix A1 / Questionnaire No. of questionnaire ______ Date ______________ Phone no.______________ Place: _______________ Rural ____ Urban ____ Region_________________ Basic information about respondent Gender F_____ M______ Age ______ Formal education (in years) ______ Profession ________________ Employed Yes______ No_____ Average monthly income (per respondent) _________€ Number of family members_______ Number of children aged 14 and younger ________ Employed number of family members_______ Average family income per month ________ € 1. Do you or your family members buy milk or other dairy products? Response/Product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

Yes No If no, due to ________________________________________ If yes, 2. Who usually buys milk and other dairy products in your family? Man_____ Woman_____ Man & Woman_____ Children_____ 3. Who decides what kind of dairy products to buy? Man____ Woman ______ Man & Woman ______ Children _____ 4. Where do you usually buy milk and dairy products given in table below? Location/Product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

By farmer Street vendor Green market Grocery store Supermarket Other

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5. How often do you usually buy the following products? Frequencies/Product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

Every day Twice a week Once a week Twice a month Once a month 6. What is the average of quantity purchased and expenditures within a month for the following products?

Quantity in Kg, l, and expenditures in € / Product

Fluid milk

Yoghurt Fruit yoghurt

Cream Curd Cheese Butter

Monthly quantity purchased per product

Monthly expenditures per product

7. Total monthly expenditure on milk and dairy products (within family) _______ € 8. Are you fulfilling demand for milk and other dairy products with monthly income available? Yes ______ No_______ 9. Do you personally consume the following products? Response/product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

Yes No

10. If no, due to: Motive/product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

Do not like the taste Can not afford Due to allergy Other

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11. Regarding seasonality, when do you consume less/ on a regular basis/ or more the following products? Rank from 1 to 3: by 1 when you consume less, by 2 when you consume on a regular basis and by 3 when you consume more. Season/Product Fluid

milk Yoghurt Fruit

yoghurt Cream Curd Cheese Butter

Spring Summer Autumn Winter 12. How do you appraise attributes given below to milk and other dairy products? Attribute/Appraisal Very important Important Not very important Unimportant Nutritive content Taste Product safety Price Brand Wrapping Package size Product origin Other 13. What is your preference for the package size for the following products? Package size / Product

Fluid milk

Yoghurt Fruit yoghurt

Cream Curd Cheese Butter

Package size in l/gr 14. When you buy milk and other dairy products, how do you give attention on: (rank from 1 to 5, with 1 when you do not give attention to 5 when you give the highest attention)? a) Expired date _____ b) Product content (in package) _____ c) Producer name_____ d) Origin of the product______ e) Other______ 15. As a consumer of the dairy products, do you prefer and buy more? Domestic dairy products_____ Foreign dairy products_____

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16. What are the main reasons preferring/not preferring domestic/imported products? Origin of the product Domestic Imported Preferences Yes / No Yes / No (< Quality) or (> Quality) (<Safety) or (> Safety) (< Price) or (> Price) (< Packing) or (> Packing) (< Taste) or (> Taste) (< Durability) or (> Durability) (< Assortment) or (> Assortment) (<Visual aspect) or ( > Visual aspect) (<Advertisement) or( >Advertisement) Other 17. How do you usually get information about dairy products (rank by 1 to 4, with 1 for source most rarely used, 2 for source rarely used, 3 source often used and with 4 for source most often used? Newspapers____ Flyers_____ TV_______ Radio_____ Other_____ 18. Do you prefer new dairy products and innovation? Yes______ No_____ 19. During the last year, did you start buying any new dairy product which you did not buy it before? Yes_____ No_____ If yes, 20. Which of the dairy products: __________________________________________ 21. What did you find attractive about this product? ____________________________________________________________________ 22. How did you get information about this new product? _____________________________________________________________________

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Appendix A2 / Tables Table 1. The targeted stratification of the sample size Region / Age 18 – 30 31 – 40 41 - 50 51 - 60 Over 60 Total Prishtina 16F, 16M 11F, 11M 10F, 10M 8F, 8M 3F, 4M 48F, 49M Prizreni 13F, 13M 9F, 9M 7F, 8M 6F, 6M 3F, 3M 38F, 39M Mitrovica 9F, 9M 7F, 7M 6F, 6M 4F, 4M 2F, 2M 28F, 28M Peja 13F, 13M 10F, 10M 8F, 8M 6F, 7M 3F, 3M 40F, 41M Gjilani 12F, 12M 9F, 9M 7F, 7M 6F, 6M 3F, 3M 37F, 37M Total 63F, 63M 46F, 46M 38F, 39M 30F,31M 14F,15M n=385 Note: Given characters F and M denote respondent’s gender: F for female and M for male Table 2. The feasibility stratification of the sample size Region / Age 18 – 30 31 – 40 41 - 50 51 - 60 Over 60 Total Prishtina 16F,16M 11F,11M 10F,9M 8F,6M 3F,6M 48F,48M Prizreni 12F,15M 10F,13M 6F,8M 3F,4M 3F,3M 34F,43M Mitrovica 9F, 9M 6F, 7M 8F,6M 4F,3M 3F,2M 30F,27M Peja 14F,13M 11F, 9M 8F,8M 5F,7M 4F,2M 42F,39M Gjilani 14F,11M 8F, 10M 8F,6M 5F,6M 3F,3M 38F,36M Total 65F,64M 46F,50M 40F,37M 25F,26M 16F,16M n=385