Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
Consumer Behaviours for Good Agricultural Products in
Turkey
Selma karabaş* and Osman Karkacıer**
Good Agricultural Applications (GAA) which is a production method that does not
threaten environment and human health has become widespread in the country and in
the world. In this study, GAA is researched as a production management system. GAA
as a production method is set forth to state what extent it is known by consumers
andthe sensitivity of consumers by logit regression analysis. As a conclusion, it is found
that GAA as a controlled and secured production system is to be improved and
generalised for each area and region in an attempt to prevent potential threats in
agricultural sector, this should be carried out by governmental institutions.
Key Words: Good Agricultural Applications, Consumer Analysis Using Logit Regression
Jell Codes: M3
1. Introduction
The sectors in the economy have made different progress in the world. In recent
years, especially together with 2000s, it is seen that the structure of production and
consumption have changed a lot. These changes also lead human behaviours and
consumption patterns into different ways.
In favour of removing factors of late years threatening human health and environment
Good Agricultural Applications (GAA) and Good Agricultural Products (GAP) is a system
needed to be put into effect.
From the point of recent developments in the world, GAA should take its place as
controlled mechanism within production system in agriculture sector at every stage. Yet,
the harm caused by groups lacking education and equipment due to misuse of
technology should no longer be brought under control.
In this study, it is aimed to offer alternatives and provide solutions regarding how to
set up GAAconsidered being obligation as a systemwithin production
management.Firstful, consumer side is discussed and recognition size of GAA and
sensitivity of consumer behaviours are determined. Deductional statistics and logistic
regression analysis intended for consumer behaviours are carried out. The potential
points that may distinguishstrategically in production and marketing side are identified
and searched out for a solution so as to dynamise the system.
It is come to a conclusion that controlled and secured agricultural production
approach, production system contributing to this is needed and management should be
developed and spread to all sectors in which the government should be in charges of all
stages.
_______________________________________________________________
*.Çankırı Karatekin University, Department of Management, Turkey. beyazkar55@gmail.com,
**.Akdeniz University, Department of Economy. Turkey. okarkacier@gmail.com,
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
2. Material and Method
As a research area, Central Black Sea Region is chosen.The aim is to improve
production and marketing strategy for GAA and support model proposals.It is required
data for consumer models to be used in GAA regression analysis. Since these are not
obtained from data networks, original data are collected via questionnaires in these
kinds of specific studies. The questionnaires are conducted via face to face interview
method.
In an attempt to research consumer consciousness in GAA, structural analysis can be
reinforced through logistic regression analysis to develop business strategy. Regression
analysis ensures more reasonable interpretation in examining factors’ impacts of the
phenomenon. Logistic regression model is a non-linear model designed for 2
independent variables and it can be linearized via conversions (Stock and Watson
2007).That Logitcoefficientsare interpreted as Odds ratios, provide advantage for logit
regression method (Gürsakal 2007).
Logistic regression model has the same setting model techniques valid for other
models used in statistics. Namely, it is to set an optimum model in describing the
relation between dependent variable and explanatory variable series. Dependent
variable It ismade use of logistic distribution function in order to state independent
variable Xi and dependent variable Y of binary logistic regression model, that is (Gujarati
2005):
1
Pİ  E Y  1  


X İ  1  e 1   2 X i 

In this function;
Pi is an independent variable and expresses the probability of 1 or 0 occurrence of Y.
In studies, being 0 or 1 varies depends on binary or multinomial status of logitregression
model. In this study, since dummy independent variable is used, binary logit regression
model is preferred.
By applying maximum probability method in logistic regression model, it is possible to
describe and analyse social variables due to the fact that regression estimates obtained
from Least Square method sometimes remains incapable. In that, Least Square method
assumes normal distribution of dependent variable. Since dependent variable has
nominal scale, this assumption is not provided (Kalaycı 2005). Providing the fact that
variables are nominal or categorical scale, logit model is appropriate in these types of
models (FreseeandLong 2006).
In measuring qualitative variables, data are categorised. Ordinal and nominal scaled
data are used. Making use of likert scale, consumer behaviours can be examined.
Definitionalstatistics is the descriptive of important sides of a measurement
set(Bowerman ve ark. 2013).
Logit model is derived from cumulative logistic distribution function. Dependent
variable has two options. As parameter estimates of these models cannot be obtained
from Least Square method, logit transformation is made use of.
Logit transformation is shown below:
1
Pi  E (Y  1 / X i ) 
1  e   xi
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
In this equation, if Z i   xi is (  xi  1   2 X i )
Pi  E (Y  1 / X i ) 
1
is obtained.
1  e Zi
From Z i ,' to  ' a , Pi is between 0 and 1.There is a non-linear relation between Pi
and Z i . Due to that fact that there are grouped or repetitive data, parameter estimates
are derived through Weighted Least Square methods. Hence, respectively;
a. Pi  ni / N i , estimated probability values are calculated.
 

P
b. For each x i , logit is calculated. L= ln  i


1 P
i







For the validity of the model, -2LogL statistics is used (Gujarati 2005). Apart from that,
the intended use of logit regression analysis is to set a model ensuring the description of
the relation between dependent and independent variable. For this reason, the optimum
and reasonable model should be found. In the goodness of fit test of the model “Hosmer
and Lemeshow Test” is made use of(HosmerandLemeshow 2001). In determining
optimum logit regression model, stepwise selection technique is applied. With respect to
independent variable in the model, estimated coefficient (β), related variables’ standard
errors (SE), degrees of freedom, Odds ratios exp (β) and level of significances are taken
into account for interpretation.
3. Findings and Discussion
This section is considered in 2 parts. Firstful, Good Agricultural Applications are
reviewedtheoretically and hypothetically, afterwards findings obtained from consumer
surveys and analysis results are included.Due to the fact that the big retailers
dominating 70-80% of the fresh vegetable and fruit markets in European Union
countries made up EUREPstandards in 1997 are accepted by different countries in the
world, it becomes globally and is named as GLOBALG.A.P.For the agricultural
productsinvolving minimum standards to be followed beginning from field to fork,
GLOBALG.A.P. compromises criteria that are known as good agriculture in Turkey.The
general rules of Good Agricultural Applications takes place on the 5 th clause of “Good
Agricultural Applications Regulations” appeared in the official gazette dated 7th June
2010 with the 27778 number (O.G.- 27778 / 07.12.2010).
There is no transition period in good agricultural applications which is production
model making easy to reach healthy and reliable products for the consumer. Since good
agricultural applications allows for input usage in conventional production, it is a
production model in accord with producers’ habits.
Among good agricultural applications’ actors; there are producers, retailers,
consumers, control and certification institutions and Ministry of Food, Agriculture and
Livestock that constitutes structure of the system.Under“General Directorate of
Vegetables and Animal Production” with the “Organic Agricultural and Good Agricultural
Application Head of Department” coordination, GAA activities are carried out.
Within the scope of GAA activities by the Ministry, along with legislation studies,
projects, demonstration studies and supervision activities are implemented. The Ministry
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
set up a data base for each production system to record all the agricultural activities and
the producers. With regard to vegetable production, it is required to be registered to
ÇKS; animal production activities, it is required to be registered to TÜRKVET and fishery
activities, it is required to be registered to Fishery data base. GAA activities are carried
out in accordance with “Good Agricultural Applications Regulations”. The producers
should comply with the criteria related to production line that they operate. The criteria
and control points to be applied with the conformity degree as vegetable production (52
pages), animal production (75 pages) and water products production (33 pages) have
been declared on the official web site of the Ministry (TKB 2013). Within the context of
GAA, the producers have to apply around 213 criteria. In Turkey, Good Agricultural
Applications were carried out on 5360ha by 651 producers in 2007 (Anonymous2012).
As per 2012, the number of producers has reached to 4500 and the production areas
has reached to 781.174da (Bayulgen 2012). In the world, 112.600 producers
produceunder GLOBALG.A.P. standards in 112 countries on 409 varieties and
certificate these products (GLOBALG.A.P. 2011).
3.1. GAP Consumer Behaviours Model Logistic Regression Analysis Findings
In this section, it is aimed to research the variables that determine consumption of
good agricultural products through logistic regression model. In this study that
researches consumer behaviour, binary logistic regression model is applied. For
dependent variable, consumption of good agricultural product is taken into account to
state the two possible situations. Dummy variables;
1;Consume GAP
0;Do not consume GAP
With the help of proper statistical program, data is interpreted via binary logistic
regression analysis. In an attempt to obtain the initial model functional form of estimated
logistic regression model, step-wise variable addition-elimination transaction has been
applied to make the modelmore harmonised.A great number of variables has been
added and eliminated in the model. For this reason, statistical significance of each
independent variable has been evaluated. However, variable related to social-economic
factors included in the model and the other factors affecting GAP consumption has been
tried, variables having statistical problem has been eliminated from the model. In the
goodness of fit studies, problems have been faced in getting favourable results as of
statistics. The basic reason of this is that GAP is not in the public eyes which cause
difficulty in finding sensitive variables. In the sensitivity analysis,few numbers of
variables sensitive to dependent variable has been defined and most of them are socialeconomic variables. Variables included in the initial model are described below.
Nevertheless, most of them did not take apart in the final model depending on statistical
problem.
In the table 1, estimate results of binary logit regression model related to initial model
used in the optimum model trials through step-wise selection technique is shown. In the
table 1, estimated coefficient (β) for each variable, standard error (SE), degree of
freedom, odds ratios exp (β) and significance levels (Sig) in the model is seen. When it
is referred to t test stating statistical significance of each independent variable in the
model, “Education level of patriarch (EE) and/or matriarch (EK) variables, housewife or
occupation of matriarch(MK) variable, awareness of GAP variable(ID), fruit and
vegetable expenditure level(MSH) and purchase of organic product(OSA)’’ variables
have been found significant as statistical. In the model where variables are found
significance as statistical, coefficients of variables and odds ratios can be interpreted. It
is inconvenient to interpret other variables statistically.
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
The first operation in terms of goodness of fit transactions in logistic regression
analysis model is accordingly. Taking into account of the existence and non-existence of
certain variables, their support in terms of explanatories is determined. The comparison
of observed and estimated values in logistic regression is based on loglikelihood-LL
function. Good model is the model consisting of high probability of observed results
which means small -2LL(AkgünveÇevik2007).
In GAA model including only constantin logistic regression, the value of -2LL is
492,929. In the model involving constant and all independent variables, the goodness of
fit statistics -2LL value is 428,608 and the value including constant is smaller than
492,929.Model “Chi-Square” produces the difference between -2LL including only
constant and -2LL involving all the variables. Model Chi-Square statistics tests null
hypothesis where the coefficients of all independent variables in the model are zero(0)
excluding constant. This is equivalent of F test in regression model(Akgünve Çevik
2007). In the model, Chi-square value is 64,321 which is the difference between two
values.
G= 492,929 -428,908 = 64,321
Since the given test is P=0.000 (at 1% significance level), it shows that at least one of
the coefficient is different from zero. Namely, the estimated model is found significance
in general.
On the basis of significance variables in GAA binary logit regression model, the
coefficients and Oddsratios (Exp(B)) can be interpreted.
Even though it has been required to research the effects of head of family and the
age of partner variables in the model on GAP usage, it has not been interpreted as there
is statistical problem in these variables. Education level is the variable that is found
significance in statistical.
Education level variable is taken as a variable of male and female in the family who
have a voice in consumption decision. EE variable belonging to the head of family or
her partner is found significance at %5 confidence level which is negative. For this
reason, increase in EE variable affect GAP usage negatively. That negative affect of
male decision maker in GAP usage do not conform to theoretic rules spring to mind.
However, it is known that women play a big role in the family with respect to
consumption of foodstuff. It is expected to find significance the education variable of
dominant female on the consumption decision in the model. The EK coefficient of this
variable is 0,304 and positive. As long as the education level of female improves, the
possibility of GAP consumption grows. The Odds ratio for the coefficient of EK is 1,36.
This means that; when EK variable increases one-time, the possibility of GAP
consumption increase by 1,36 times. In other words, when the education level of female
rise to upper level such as from high school graduate to the post graduate, GAP usage
increase by 1,36 times. The coefficient belonging to the occupation of female in the
model that is significance in statistically is 0,174 and positive. When the professional
status improves, possibility of GAP usage rises. The Odds ratio related to the female
occupation is found 1,19. In that, when the professional status of the dominant female
on the consumption decision in the family increase one-time, the possibility of GAP
usage rises by 1,19 times. One of the important variables in GAP consumption
regression model is to be familiarized with GAP that is variable of being knowledgeable
about it. Shortly, the coefficient of this ID variable is 2,598 and positive which show a
positive relation between ID variable and GAP consumption. At 1% confidence level, ID
variable is found significance and its Odd ratio is 13,44. Accordingly, for the ones who
know and who are aware of GAP, the possibility of GAP consumption is 13,44 times
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
more. This is a very important finding with respect to the success of GAA.Hence, the
ones who are knowledgeable on GAA and GAP use GAP at high rate
Table 1.Statistical test results of binary multilogit regression model
B
Age of Male (YE)
S.E.
Wald
Df
Sig.
Exp(B)
-,036
,047
,615
1
,433
,96
,017
,048
,120
1
,729
1,02
-,479
,152
9,923
1
,002**
,62
Female
,304
,170
3,215
1
,073***
1,36
Occupation of Female
0,174
0,70
6,188
1
0,013**
1,19
-,120
,124
,941
1
,332
,89
,131
,129
1,030
1
,310
1,14
2,598
,364
50,824
1
,000*
13,44
,002
,001
3,281
1
,070**
1,02
2,432
,363
44,838
1
,000*
11,38
-1,930
1,205
2,564
1
Age of Female (YK)
Education of Male (EE)
Education
of
(EK)
(MK)
Income of Family (AG)
Number of people in the
family (AKS)
Awareness of GAP (İD)
Fruit-Veg.Exp(MSH)
Purc.organic
product
(OSA)
Constant term
,109
,14
* % 1 statistically significance . **% 5 statistically significance. ***% 10 statistically
significance
The other important variable as statistically is the consumption level of fruit and
vegetable at home. So, as long as the consumption of fruit and vegetable increases, the
usage of GAP rises, the added MSH variable is found significant at the confidence level
of 10%. MSH variable is too closed to zero but it is positive. The Odd ration of this
variable is closed to 1. In case that coefficient is negative, Odd ratio drops to below 1, if
it is zero, coefficient is 1. It can be said that this variable is significant as statistically and
not a determiner for GAPA.
The last significant variable in the model is organic product consumption variable.
The addition of OSA variable to the model is based on the assumption that GAP and
organic products are consumed in a similar way. In other words, the families tend to
consume organic agricultural products also tend to consume GAP. As a result, this
assumption confirms the idea produced in the first instance. That OSA organic product
consumption variable is found significant at 1% level as statistically and its coefficient is
positive can be seen in Table 1. The coefficient is stated as 2,432 and Odds ratio is
11,38. That is to say, for the families that tend to consume organic products, GAP
consumption trend of them increases by 11,38 times. Namely, when organic product
consumption trend increase by 1 unit, GAA consumption trend rises by 11,38 times.
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
This is one of the important findings of this study. In a sense, it can be said easefully
that GAA is an alternative to the organic product consumption of which production is
insufficient.
To summarize the logit regression analysis result, the female in the family has an
effect on GAP consumption, particularly when the education level and professional
status of females improves, the usage of GAP rises. Therefore, organic product
consumers can also be good GAP consumers and the most important thing is that in
case that awareness of GAP grows, the usage of it will increase too fast.
4. Result
The findings based on the analysis of data obtained from the research states the
primary results. Essentially, social-economic characteristics and behavioural factors
effecting consumer behaviours for GAP has been discussed. In the analysis of
consumer behaviour analysis, it is possible to present functional relations with
regression analysis. With this purpose, the variables determining GAP consumption has
been researched via logistic regression model. The data obtained from consumer
questionnaires has been analysed via binary logit regression model. In this study where
consumer behaviour model is assessed, GAP consumption as a dependent variable has
been determined by dummy variables;
a. When it is consumed GAP, it takes 1,
b. When it is not consumed GAP, it takes 0 ( zero)
As per binary logit regression analysis result, the significance variables can be
interpreted accordingly.The female in the family has an effect on GAP consumption,
particularly when the education level and professional status of females improves, the
usage of GAP rises. Therefore, organic product consumers can also be good GAP
consumers and the most important thing is that in case that awareness of GAP grows,
the usage of it will increase too fast. As per more certain findings, when the education
level of female increases by 1 unit, GAP usage will increase by 1,36 times and when
professional status increases by 1 unit, usage of GAP increases by 1,19 times.
According to another logistic finding, for the ones who are knowledgeable on GAP, the
possibility of GAP consumption increases by 13,44 times. This is a very important
finding on the success of GAP. The ones who know GAP and GAA tend to consume
GAP at high rates.
In the findings obtained from logistic regression that make use of odds ratios, organic
product consumers tend to consume GAP. As per results, if there is an 1 unit increase in
organic product consumption trend, GAP consumption trend increases by 11,38 times.
This is one of the most important findings of the study. Namely, it can be said easefully
that GAA is an alternative to the organic product consumption of which production is
insufficient.
In the GAA research, private companies belonging to industry and trade sectors have
to take part in the system at high rates and their marketing strategies are needed to be
defined. The factors to be taken into account in forming customer group subject to GAA
system is according: Demographic factors, Social factors, Economic factors, Geographic
factors, Product users, Political factors, Natural factors, National or international
markets.
On the customer grouping, firstly it should be urged on economic factors and
secondly markets being national or international. In line of GAA and alternative
projections, being national and international market oriented comes to the forefront.
Proceedings of 3rd European Business Research Conference
4 - 5 September 2014, Sheraton Roma, Rome, Italy, ISBN: 978-1-922069-59-7
International markets and its size is so changeable and fragile. With conventional
product techniques and products, international markets have too risky.
In conclusion, the success of the system depends on the application of GAP in whole
agricultural areas and products. Partial applications put the general success of the
system into the shades. It is advised to the public enterprises to be responsible for all
the control and certification transaction, keep private sector away from supervision and
certification transactions. Model is revised in due course and optimal system is
achieved.
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