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BFJ
125,3
Organic food market segmentation
based on the neobehavioristic
theory of consumer behavior
810
Received 2 December 2021
Revised 25 March 2022
Accepted 21 April 2022
Amirhossein Tohidi and Seyedehmona Mousavi
Department of Agricultural Economics, Ferdowsi University of Mashhad,
Mashhad, Islamic Republic of Iran
Arash Dourandish
Faculty of Economics and Agricultural Development, Tehran University,
Tehran, Islamic Republic of Iran, and
Parisa Alizadeh
Department of Agricultural Economics, Ferdowsi University of Mashhad,
Mashhad, Islamic Republic of Iran
Abstract
Purpose – Although Iran is one of the largest producers and exporters of saffron in the world, the organic
saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore,
the study’s primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic
theory and machine learning methods.
Design/methodology/approach – Considering the neobehavioristic theory of consumer behavior, the
organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative
importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity
analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were
collected in Mashhad, Iran in January and February 2020.
Findings – In contrast to the belief that psychological factors are more important in market segmentation than
demographic characteristics, findings showed that the demographic characteristics of consumers, especially
education and income, are the dominant variables in the segmentation of the organic food market. Among the
4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles
to organic saffron market development. Advertising, distribution channel improvement, package downsizing
and online business development are suggested strategies for expanding the organic saffron market in Iran.
Practical implications – The results of the present study will help policymakers and suppliers of organic
saffron to identify their target markets and design short- and long-term marketing strategies to develop the
organic saffron market.
Originality/value – Machine learning methods and the neobehavioristic theory of consumer behavior were
used to segment the organic food market.
Keywords Environmental concern, Health-consciousness, Marketing mix, Artificial intelligence
Paper type Research paper
British Food Journal
Vol. 125 No. 3, 2023
pp. 810-831
© Emerald Publishing Limited
0007-070X
DOI 10.1108/BFJ-12-2021-1269
1. Introduction
The production and consumption of food have a significant influence on the environment,
economics, and the health and well-being of society and its members (Koksal, 2019). Today,
economic and social factors such as increased disposable income, higher education and
greater attention to food quality have changed many consumers’ needs, desires and lifestyles
(Walker and Mullins, 2014). The production, distribution and consumption of agricultural
products have also changed considerably (Koksal, 2019). Rising concerns about the adverse
effects of agricultural intensification and mechanization on the environment, food safety and
health have led to increased attention toward organic food consumption. Due to the greater
consumption of organic food in many countries, the organic market is a globally growing
sector (Pestek et al., 2018; Tleis et al., 2017).
To further expand the organic food market, manufacturers, policymakers and marketers
need to have a clear understanding of consumers’ demographic characteristics, attitudes,
motivations and behaviors (Pestek et al., 2018). However, these attributes are not
homogenous, and consequently, different groups of consumers have different preferences
for buying organic food (e.g. Ankamah-Yeboah et al., 2020; Chryssohoidis and Krystallis,
2005; Drugova et al., 2020; Jaiswal et al., 2020; Janssen et al., 2020; Koksal, 2019; Pestek et al.,
2018; Risius et al., 2019; Sakolwitayanon et al., 2018; Sarabia-Andreu and Sarabia-Sanchez,
2018; Sultan et al., 2018; Verain et al., 2016). It is generally believed that segmentation of the
organic market is necessary for identifying consumers with similar preferences, selecting
target markets and formulating marketing strategies (Sultan et al., 2018). As such, market
segmentation is a central part of marketing management; it helps agribusiness companies
gain more market share and implement a more focused marketing strategy (Walker and
Mullins, 2014). Overall, dividing the market into small, distinct, homogeneous segments
improves marketing and financial performance and increases customer satisfaction
(Schiffman and Wisenblit, 2019).
Due to its varied climate and agricultural biodiversity, Iran has an excellent position in the
international market for agricultural products. Iran is the largest exporter of saffron in the
world, and the Razavi Khorasan province is the largest producer of saffron in Iran. The area
under cultivation for organic agricultural products is more than 2,000 hectares in this
province (Aghasafari et al., 2020). Despite the growth of organic farmland in many countries,
the cultivation of organic saffron in Iran has decreased in recent years (Aghasafari et al.,
2020). The lack of a domestic market is one of the most significant obstacles to the expansion
of organic farming (Soltani et al., 2014), and as a result, consumer education is the most
effective strategy for its development in the Razavi Khorasan province (Aghasafari et al.,
2020). This approach requires identifying the attitudes and preferences of consumers toward
organic agricultural products (Chawla et al., 2020). Market segmentation and accurate
identification of the key factors affecting organic food consumption will pave the way for
improved resource allocation and decision-making, prediction of consumer response to
marketing strategies and identification of potential markets (Ankamah-Yeboah et al., 2020;
Drugova et al., 2020). However, in Iran, very little knowledge is available on this issue so far.
Therefore, the study’s primary purpose is the segmentation of the organic saffron market and
the identification of consumer inclinations to buy this product in the Razavi Khorasan
province of Iran. By identifying the inhibiting and driving factors of organic saffron
consumption in different market segments, the results of this study can be used in designing
marketing strategies for the development of the organic saffron market in Iran.
Machine learning methods are based on the idea that systems can learn from data and
recognize patterns with minimal human intervention. Although machine learning methods
provide accurate results for marketing decisions, the use of these methods in marketing
studies is still at an early stage. Also, the ability of machine learning methods to consider
heterogeneity among consumers has not been proven (Ma and Sun, 2020). In this study,
clustering and artificial neural network (ANN) methods are used to segment the market and
estimate consumer preferences, respectively. Hence, the use of the mentioned methods, which
are based on machine learning techniques, is the novelty of this research. Another novelty of
the research is that it captures the heterogeneity of individual consumers by using the
sensitivity analysis approach on ANN output.
2. Literature review and theoretical background
2.1 Market segmentation
A target market is defined as a set of consumers with similar needs or characteristics to which
a company intends to market their services or products (Kotler and Armstrong, 2018).
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Market segmentation is the process of breaking down total demand into more specific subsets
of consumers with similar needs and wants (Saleem et al., 2018). To select a company’s target
market, it is necessary to know each of the market segments within the whole (Mothersbaugh
et al., 2020), so that a company can offer its products and deliver its marketing strategies to
the customers most likely to have interest in them (Bannor et al., 2020). However, only a few
studies have been done on the segmentation of the organic market in the last thirty years
(Sultan et al., 2018). Most studies on organic food segmentation are conducted in the USA (e.g.
Drugova et al., 2020; Wetherill et al., 2018), Denmark (e.g. Ankamah-Yeboah et al., 2020),
Germany (e.g. Janssen et al., 2020; Risius et al., 2019; Schipmann-Schwarze and Hamm, 2020),
Australia (e.g. Sultan et al., 2018) and Spain (e.g. Sarabia-Andreu and Sarabia-Sanchez, 2018).
For some developing countries, such as Iran, no such studies have yet been conducted.
Therefore, to fill this gap, the main focus of the present study is on the segmentation of the
organic saffron market in Iran.
In terms of the methodology used in the literature, the researchers have so far utilized
a variety of methods to segment the organic food market. Latent class modeling (e.g.
Ankamah-Yeboah et al., 2020; Drugova et al., 2020; Janssen et al., 2020), hierarchical
cluster analysis (e.g. Sakolwitayanon et al., 2018; Sultan et al., 2018) and K-means cluster
analysis (e.g. Fogarassy et al., 2020; Jaiswal et al., 2020; Schipmann-Schwarze and Hamm,
2020) are among the most common methods for organic food segmentation. But in the
real world, a consumer cannot be considered as absolutely belonging to a group or a
segment of the market because of uncertainty and ambiguity. Therefore, in studies
intending to deal with the problems arising from the classical set theory, the fuzzy logic
introduced by Zadeh (1965) is used. According to this concept, each consumer can belong
to multiple groups with different degrees of membership (for more details, see Mota et al.,
2018). Thus, another novel aspect of this study is the application of crisp (i.e. K-means
and K-medoids) and fuzzy (i.e. C-means and Gath-Geva) clustering algorithms and
identification of the most appropriate algorithm for segmentation of the organic saffron
market.
After determining the number of market segments and describing the characteristics
of the consumers in each, consumer preferences for buying organic food are evaluated
for each segment. Most studies use discrete choice random utility (DCRU) models to
identify consumer preferences for organic foods (e.g. Ankamah-Yeboah et al., 2020;
Drugova et al., 2020; Janssen et al., 2020; Risius et al., 2019; Sarabia-Andreu and SarabiaSanchez, 2018; Schipmann-Schwarze and Hamm, 2020). One of the main assumptions of
these parametric models is that each alternative of the choice set is independent and
identically distributed. Upon violation of this assumption in the real world, the estimated
coefficients will be biased, and interpretation of the results will no longer be valid. In
addition, DCRU models cannot consider the high levels of nonlinearity in the choice
process of a consumer (for more details, see Lee et al., 2018). Unlike DCRU models, an
ANN can measure all nonlinear and complex interactions between the system’s
variables. There is no need to specify a mathematical relationship between the input and
output variables. Nevertheless, ANNs are known as black-box models, and lack of
interpretability is their weakness (Lee et al., 2018; Ma and Sun, 2020). To address this
shortcoming, Dimopoulos et al. (1995) proposed a sensitivity analysis method based on
partial derivatives (PaD) to identify the influence of input variables on the output
variables of an ANN and evaluate the input variables’ relative importance (RI). Although
the sensitivity analysis method based on PaD performs better than other similar
methods (Ozonoh et al., 2020), it has not been widely used in marketing studies,
especially in assessing consumer preferences for organic foods. Therefore, the
implementation of ANN and PaD-based sensitivity analysis for evaluating consumer
preferences is another methodological contribution of this research.
2.2 Neobehavioristic theory of consumer behavior
Different theories have been proposed in the literature to explain consumer behavior, which
can be classified into two approaches: behavioristic and neobehavioristic. The behavioristic
approach focuses on the stimulus(S)-response(R) framework, in which marketing stimuli lead
to consumer responses (e.g. purchase activity or intention to buy). According to the S-R
approach, the consumer is considered a black box. Due to the uncertainty of the decisionmaking process within the black box, this process is not addressed in marketing studies
(Schipmann-Schwarze and Hamm, 2020).
In the neobehavioristic view, stimulus(S)-organism(O)-response(R) explains consumer
behavior (Schipmann-Schwarze and Hamm, 2020). This S-O-R approach was first introduced
by Mehrabian and Russell (1974). In it, the black box is substituted with an organism with
which the internal decision-making processes can also be examined (R€odiger and Hamm,
2015). Neobehavioristic theory states that the stimulus (in this case, marketing) affects the
internal organism (the cognitive and emotional processes of consumers) and this process
ultimately leads to response behaviors (Wang et al., 2021). Thus, the organism links
marketing stimuli and consumers’ responses and includes psychological factors such as
attitudes toward organic foods (Wang et al., 2021).
According to the neobehavioristic view, the demographic characteristics of consumers
influence their attitudes and preferences (Schipmann-Schwarze and Hamm, 2020). In this
approach, organisms or psychological factors (such as attitudes) are assumed to be directly
or indirectly measurable (R€odiger and Hamm, 2015). Stimuli include factors beyond the
consumer’s control. When the consumer is exposed to stimuli, these external factors affect
organisms or psychological factors. Eventually, the process by which stimuli affect
organisms leads to either an avoidant response or purchase intention (Rong-Da Liang and
Lim, 2020). The results of previous studies have shown that the S-O-R theory helps to
understand consumer behavior in regards to buying organic food (e.g. Rong-Da Liang and
Lim, 2020; Schipmann-Schwarze and Hamm, 2020). But so far this theory has rarely been
used in the segmentation of the organic food market. To address this research gap, in this
study, the S-O-R or neobehavioristic approach is used to segment the organic saffron
market.
2.3 Marketing mix
According to neobehavioristic theory, it is believed that marketing mix elements (as
external stimuli), in addition to demographic characteristics and consumer attitudes,
influence the purchase intention to buy organic food (Schipmann-Schwarze and Hamm,
2020; Sultan et al., 2018). The marketing mix is one of the most important concepts in
modern marketing. The concept is based on a set of tactical marketing tools known as 4 P’s
(i.e. product, place, price and promotion), used by companies to achieve organizational
goals and meet consumer needs in different market segments (Cha and Park, 2019; Kotler
and Armstrong, 2018). Marketing mix elements are important factors influencing decisions
to buy organic food, and many studies have been done on this issue (for more details, see
Hemmerling et al., 2015). However, the 4 P’s marketing mix is sometimes criticized for
ignoring market demand and only considering the producer. When taking consumers into
account, the 4 P’s marketing mix should be defined as 4 A’s (i.e. acceptability, accessibility,
affordability and awareness). The 4 A’s framework is closely related to the 4 P’s
framework, but each P is evaluated from a customers’ point of view (Kotler and Armstrong,
2018). Therefore, considering neobehavioristic theory, this study considers consumers’
attitudes toward marketing mix elements (4 A’s) as organismic factors. In this way, the
effect of external factors (stimuli or 4 P’s) on consumer behavioral responses can be
investigated.
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2.4 Market segmentation criteria
The complexity of recognizing consumer behavior and isolating its characteristics is one of
the most challenging issues among researchers and policymakers when segmenting the
organic food market (Jaiswal et al., 2020). Marketing literature introduces many criteria for
market segmentation (Grunert, 2019). Demographic characteristics are among the most
important of these and are widely used in studies to segment the organic market (e.g. Chen
et al., 2014; Jaiswal et al., 2020; Nie and Zepeda, 2011; Sultan et al., 2018). Although some
studies emphasize the influence of demographic characteristics on organic food choice (e.g.
Fogarassy et al., 2020; Heyns et al., 2014; Panzone et al., 2016), other studies underline the
psychological factors and consider them more important than socio-demographic variables
(Drugova et al., 2020; Jaiswal et al., 2020; Pestek et al., 2018). However, a positive attitude
toward organic food does not necessarily lead to purchasing such products; other factors,
such as price and availability, also affect consumer behavior (Drugova et al., 2020). Therefore,
the organic market segmenting criteria should reflect the actual behavior of consumers.
According to the marketing literature, purchase intention is one of the main variables for the
accurate prediction of consumer behavior, providing valuable insights into consumer
decisions in both theory and practice (Persaud and Schillo, 2017). Therefore, based on
neobehavioristic theory and research literature, this study uses three groups of variables to
segment the organic saffron market: (1) demographic characteristics of consumers, (2)
organismic factors (consumer attitudes and 4 A’s marketing mix) and (3) intention to buy
organic saffron.
3. Material and methods
3.1 Study area
Mashhad is one of the most important cities in Iran with a history reaching back 1,200 years.
It is the capital of the Razavi Khorasan province, located in Northeastern Iran (see Figure 1),
and with an area of about 290 km2, it is also the largest city in Iran after Tehran. This city has
13 districts and a population of about three million people, making it the second-most
populous city in Iran. About 930,000 families live in the study area. Economic, social and
tourist attractions have caused the population of Mashhad to grow rapidly in recent decades
(Soltanifard et al., 2020). Since the Razavi Khorasan province is an important region of saffron
production in Iran and the world, most Iranian saffron companies operate in Mashhad. In
addition, saffron is one of the most widely used medicinal plants and spices in the food basket
of households living in this city. Therefore, in this study, the segmentation of the organic
saffron market was done in Mashhad.
3.2 Conceptual model
Considering the superior capabilities of the neobehavioristic approach in predicting
consumer behavior, the S-O-R framework is used in this study to segment the organic
saffron market in Mashhad (see Figure 2). According to Figure 2, the steps of this study are as
follows: (1) survey design, data collection and measurement of indicators, (2) market
segmentation and (3) utility function estimation. The next sections discuss each step in detail.
3.3 Survey design, data collection and measurement of indicators
The statistical population of the study includes Mashhadian households that use saffron in
their diet. Since the size of the statistical population is large and unknown, Cochran’s formula
was used to determine the sample size. According to Cochran’s formula for infinite
populations with a 95% confidence level and 0.05 margin of error, the minimum sample size
required was 384, which was rounded to 400 households. Data were collected through
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Figure 1.
Geographical location
of Iran, Razavi
Khorasan province and
Mashhad
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Figure 2.
Conceptual model
structured questionnaires in January and February 2020. In this study, the stratified
sampling method (with proportionate allocation) was used to determine the sample size of
each of the 13 districts of Mashhad (see Table 1).
The questionnaire consists of three parts: (1) demographic characteristics, (2) organismic
factors and (3) consumer response. As described in Table 2, the demographic characteristics
of the consumer include age, gender, income, education and household size. In the second part
of the questionnaire, the consumer’s views about the 4 P marketing mix (i.e. 4 A marketing
mix), environmental concern, health-consciousness and trust are considered the organismic
factors. In the third part of the questionnaire, the consumer intention to buy organic saffron
was measured. Therefore, the designed questionnaire includes 13 variables in total that are
Districts
1
2
3
4
5
6
7
8
9
10
Table 1.
Household populations 11
and sample size of 13 12
Samen
districts of Mashhad
Household populations
Sample size
55,221
157,592
125,659
76,692
48,593
67,036
76,501
29,989
103,093
92,018
60,609
32,517
4,525
24
68
54
33
21
29
32
13
44
40
26
14
2
Variable
Type
Description
Unit of measurement
Intention to buy
Response
The intention to buy
organic saffron
1–10
Acceptability
Organism
1–10
Accessibility
Organism
Affordability
Organism
Awareness
Organism
Environmental
concern
Organism
Healthconsciousness
Organism
Trust
Organism
Age
Demographic
The extent to which
organic saffron exceeds
respondent expectations
The extent to which the
respondent can readily
acquire the organic
saffron
The extent to which the
respondent is willing to
pay the price of organic
saffron
The extent to which the
respondent is informed
about the features of
organic saffron
The extent to which the
respondent is concerned
about environmental
issues
The extent of health
concerns in respondent’s
day to day activities
The extent to which the
respondent has trust in
the saffron being
organic
Age of the respondent
person
Gender
Demographic
Gender of the
respondent person
1 5 Female; 0 5 Male
Household size
Demographic
Household size of the
respondent person
Number
Income
Demographic
Net household head
income per month
104 Iranian Rial
Education
Demographic
Education of the
respondent person
1 5 Diploma or less;
2 5 Associate degree;
3 5 Bachelor;
4 5 Masters;
5 5 Doctorate
Source(s): Literature review, 2020
1–10
Relevant
literature
Persaud and
Schillo (2017),
Jaiswal et al.
(2020)
Kotler and
Armstrong
(2018)
Kotler and
Armstrong
(2018)
1–10
Kotler and
Armstrong
(2018)
1–10
Kotler and
Armstrong
(2018)
1–10
Yin et al. (2010),
Jaiswal et al.
(2020)
1–10
Yin et al. (2010),
Asif et al. (2018)
1–10
Yin et al. (2010),
Dangi et al.
(2020)
Years
Gracia and de
Magistris (2008),
Yin et al. (2010)
Gracia and de
Magistris (2008),
Bannor et al.
(2020)
Gracia and de
Magistris (2008),
Bannor et al.
(2020)
Gracia and de
Magistris (2008),
Yin et al. (2010)
Gracia and de
Magistris (2008),
Yin et al. (2010)
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Table 2.
Measurement
variables in the study
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used for market segmentation (see Table 2). According to the studies by Hempel and Hamm
(2016) and Bernabeu et al. (2018), a rating scale from one to ten is used to calculate the
variables of Table 2 (1 being no extent at all and 10 being high extent).
3.4 Market segmentation
Considering the variables described in Table 2, the K-means, K-medoids, fuzzy C-means and
Gath-Geva fuzzy clustering algorithms are used to segment the organic saffron market in
this paper.
The K-means clustering algorithm is based on the theory of centroids. The centroids are
the mean values of the observations within each cluster. According to this method, the
observations are partitioned into clusters of those most similar to one another but least
similar to the other clusters. In an N 3 n dimensional space, the K-means algorithm assigns
each xk data (xk e Rn, k 5 1, . . ., N) to one of the c clusters so that the sum of the intra-cluster
squares is minimized (Surya and Laurence Aroquiaraj, 2019):
c X
X
xk vi k
(1)
jðX ; V Þ ¼
2
i¼1 k∈Ai
where Ai is the sample set in the ith cluster and vi is the centroid of the ith cluster, which is
calculated as follows:
vi ¼
Ni
1 X
xk ; xk ∈ Ai
Ni k¼1
(2)
where Ni is the number of samples in Ai.
The K-medoids clustering algorithm is similar to the K-means algorithm, but the main
difference between them stems from the calculation of cluster centroids. In the K-medoids
algorithm, the centroid of each cluster is one of the data points, not the average of the data
within the cluster (Surya and Laurence Aroquiaraj, 2019).
The C-means fuzzy algorithm is an important modeling tool for many applications. This
algorithm is based on unsupervised learning and is particularly suitable for problems in
which the decision boundaries cannot be readily defined. The C-means fuzzy algorithm is
based on minimization of the following function (Mota et al., 2018):
jðX ; U ; V Þ ¼
c X
N
X
i¼1
ðμki Þm kxk vi k2;
(3)
k¼1
where X is the data set, xk (xk e Rn, k 5 1, . . ., N) is the kth sample, and vi (vi e Rn, i 5 1, . . ., c) is
the centroid of the ith cluster, which is calculated as follows:
PN
ðμki Þm xk
;
(4)
vi ¼ Pk¼1
N
m
k¼1 ðμki Þ
where n is the number of variables (number of elements of xk), N is the number of samples, c is
the number of clusters, m is the fuzzy constant and mki is the degree of membership of the kth
sample in the ith cluster. Equation (3) determines the total variance between xk and vi, which
should be minimized. The cluster centroids converge when there are no significant changes in
the partition matrix (Mota et al., 2018).
In the Gath-Geva fuzzy clustering algorithm, the maximum likelihood estimation is used
to calculate the distance norm. This algorithm is similar to the C-means fuzzy clustering
method, but it also includes the size, shape and density of the clusters in the analysis. The
Gauss distance function is used in this algorithm (Mota et al., 2018).
This study used MATLAB software to implement the crisp and fuzzy algorithms. The
Alternative Dunn Index (ADI) was used to determine the most appropriate clustering
algorithm and the desired number of clusters. This index is a modified version of the Dunn
index, in which the dissimilarity function between two clusters is replaced by a triangular
inequality, making it easier to calculate. The minimum value of this index indicates the
minimum intra-cluster distance, based on which the best clustering algorithm and the
appropriate number of clusters can be determined (Hossein Morshedy et al., 2019).
3.5 Utility function estimation
As a supervised machine learning method, ANNs are derived from a biological neural
network and can be used to solve problems that the human mind can handle. In fact, the ANN
is inspired by the human brain’s ability to solve problems. Learning, generalizability,
adaptability and identifying complex nonlinear relationships between input and output
variables, and error tolerance are all advantages of ANNs. Due to their ability to model
complex and nonlinear processes, ANNs are known as a popular and acceptable method for
research, and various types have been introduced. The multi-layer perceptron (MLP) is one of
the most common models that can artificially simulate the function of the human brain using
transformation (activation) functions. Typically, an MLP model consists of three layers. In the
first (input) layer, the input variables are identified and sent to the second layer. In the second
or hidden layer, the input signal is received and weighed and then sent to the third (output)
layer. In the third layer, the signal received from the hidden layer is weighted, and the output
or decision variable is estimated. The mathematical form of an ANN model of the MLP type is
as follows (Shabani et al., 2021):
!!
Nh
Ni
X
X
(5)
βj f α0j þ
αij xi
y ¼ g β0 þ
j¼1
i¼1
where y is the output variable, xi is the input variable i, αoj is the bias vector of the hidden layer
neurons, β0 is the bias of the output layer neuron, Nh is the number of hidden layer neurons, Ni
is the number of input variables, αij is the weight of the input i to neuron j, βj is the weight
between the jth hidden-layer neuron and the output-layer neuron, f() is the activation function
of hidden layer neurons and g() is the activation function of the output-layer neuron. The MLP
model with sigmoid activation functions (equation 6) in the hidden layer and a linear
activation function (equation 7) in the output layer can estimate multidimensional and
nonlinear problems arbitrarily well.
1
; 0 < f ðyÞ < 1
(6)
f ðyÞ ¼
1 þ e−y
gðyÞ ¼ y
(7)
In the MLP model, during the training process, the optimal values of weights and biases are
calculated in the hidden and output layers so that the difference between the output of the
MLP and the decision variable is minimized (Shabani et al., 2021). For this purpose, in this
study, the backpropagation algorithm is used to optimize and train the MLP model.
Weight and bias values in ANNs, unlike regression models, are not interpretable and
therefore cannot provide deep insight from the dataset. Several methods have been proposed
to interpret the weights and RI of network input variables to address this shortcoming. When
comparing these methods, previous studies showed that the PaD method has the best
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explanatory power. Given the sigmoid activation function in the hidden layer, Ni inputs, one
single hidden layer and one output, the importance of the input variable xi can be obtained
from the sum of the squares of the partial derivatives (Van Maanen et al., 2010):
SSDi ¼
N
X
dki2
(8)
K¼1
820
where i represents the ith input and k represents the kth observation from N samples. A larger
SSDi value indicates a greater effect of the xi variable on the output of the ANN. Using Nh
neurons in the hidden layer, the kth output neuron derivative relative to the ith input variable
is calculated as follows (Van Maanen et al., 2010):
dki ¼ Sk
Nh
X
βj Ijk ð1 Ijk Þαij
(9)
j¼1
where Sk is the derivative of the output neuron k with respect to input, and Ijk is the response
of the jth hidden neuron to the kth input. According to equations (8) and (9), RI of the input
variable i is calculated by the following equation (Ozonoh et al., 2020):
SSDi
(10)
RIi ¼ N
Pi
SSDi
i¼1
Considering seven neurons in the hidden layer, the structure of the MLP model is shown in
Figure 3, which is estimated using MATLAB software.
4. Results
Considering the variables listed in Table 2, the organic saffron market was first divided into c
(c 5 1, . . .,10) segments using the crisp and fuzzy clustering algorithms. Then, the ADI was
calculated for each clustering algorithm and market segment, as reported in Table 3.
According to Table 3, the minimum value of the ADI is obtained from the Gath-Geva
algorithm with six clusters, which suggests that this algorithm has the best performance
compared to the other methods for identifying the clusters. Hence, the organic saffron market
can be divided into six segments.
The consumers in each market segment are relatively homogeneous in terms of
demographic characteristics, organismic factors and response. Table 4 shows the
demographic characteristics of the consumers, and Figure 4 illustrates the average score
of the organismic factors and responses in each market segment.
According to Table 4, the first segment of the market primarily includes young consumers
with low income and low education, limited to a diploma or less (Young consumers with low
education and income). The consumers of the organic saffron in the second segment of the
market have higher education levels and income than the other market segments (Consumers
with high education and income). In the third segment of the market, most consumers are
females who have high income and education levels (Female consumers with high education
and income). Consumers in the fourth segment of the market are older. The household size is
larger in this segment than the other market segments (Older consumers with high household
size). In the fifth segment of the market, most consumers are women with lower average
education levels (Female consumers with low education). Approximately 30% of consumers
of organic saffron are in the sixth segment of the market; they have less education than a
bachelor’s degree and a low income (Most consumers with low education and income).
Organic food
market
segmentation
821
Figure 3.
MLP model structure
Algorithm name
K-means clustering algorithm
K-medoids clustering algorithm
Fuzzy C-means clustering
algorithm
Gath-Geva clustering algorithm
Source(s): Research findings
Cluster number
5
6
7
2
3
4
0.152
0.120
0.143
0.131
0.079
0.150
0.133
0.105
0.118
0.111
0.092
0.149
0.108
0.143
0.110
0.096
0.075
0.088
0.076
0.040
8
9
10
0.115
0.093
0.096
0.087
0.087
0.092
0.102
0.081
0.102
0.089
0.056
0.092
0.064
0.071
0.047
Table 3.
Values of ADI in terms
0.046 of cluster numbers and
clustering algorithms
According to Figure 4, consumers in the second and third segments of the market have the
highest intention to buy organic saffron, which constitutes 40% of the market share. Also, in
these market segments, the organismic values, consumer education and income levels are
relatively higher than the other segments.
As Figure 4 shows, in the second, third and fourth segments of the market, the lowest
score is related to accessibility. In contrast, the lowest score was given to awareness in other
segments.
Also, in the first, fifth and sixth segments of the market, affordability scores are
relatively lower.
After identifying the segments of the organic saffron market and using the sensitivity
analysis approach on the output of the MLP model, degree of importance and the effect of
BFJ
125,3
822
Demographic
characteristics
Segment size
(n 5 400)
Share of the
total sample
Female
(percent)
Average age
(years)
Segment 1
33
Segment 2
Market segments
Segment 3
Segment 4
Segment 5
87
75
51
8.25
21.75
18.75
12.75
7.25
31.25
54.55
56.32
72
47.06
72.41
50.40
24.39 (3.961)
43.40 (6.389)
32.47 (3.799)
57.24 (4.853)
49.34 (4.138)
37.54 (8.038)
50.98
41.18
79.31
20.69
58.40
40.80
Level of education (percent)
Diploma or less 78.79
20.69
0
Associate
9.09
28.74
5.33
degree
Bachelor
12.12
22.99
62.67
Masters
0
12.64
29.33
Doctorate
0
14.94
2.67
Average
2.52 (0.892)
4.16 (0.981)
2.89 (0.826)
household size
Table 4.
2.64 (0.709)
3.56 (0.786)
3.19 (0.675)
Average
Demographic
income (104
characteristics of
Iranian Rial)
organic saffron
consumers in different Note(s): Values in parentheses are standard deviations
Source(s): Research findings
market segments
29
Segment 6
125
7.84
0
0
4.39 (1.173)
0
0
0
4.24 (0.934)
0.80
0
0
3.35 (1.112)
3.04 (0.766)
2.81 (0.440)
2.70 (0.553)
input variables (demographic characteristics and organismic factors) on the intention to buy
organic saffron was estimated, the results of which are reported in Table 5. Positive values of
PaD in Table 5 show that the respondents with health and environmental concerns are more
likely to buy organic saffron. Also, by increasing trust and improving attitudes toward the
marketing mix elements, consumers’ intention to buy organic saffron increases in all market
segments. Given the positive PaD values for the gender, income and education variables, it
can be stated that female consumers with high income and education levels are more likely to
buy organic saffron, which is consistent with the results of the Gath-Geva fuzzy clustering
algorithm (Table 4 and Figure 4).
The results of Table 5 show that there is a negative relationship between age and intention
to buy organic saffron. In other words, young respondents are more inclined to buy organic
saffron. RI values in all market segments indicate that income and education have the
greatest impact on the intention to buy organic saffron compared to other variables.
5. Discussion
Market segmentation results revealed that the Gath-Geva fuzzy clustering algorithm
performs better than other algorithms. This agrees with the findings of Mota et al. (2018),
which showed that the Gath-Geva algorithm is suitable for learning about knowledge and
decision-making in the field of agricultural problems due to its consideration of the size and
density of clusters and covariance adaptation.
The Get-Goa fuzzy clustering algorithm showed that the organic saffron market could be
interpreted by the income and education variables. Likewise, sensitivity analysis on the
output of the MLP model suggested that education and income play an important role in the
purchase of organic saffron in all market segments. Income and education are positively
correlated with the organismic factors (Chen et al., 2014), and previous studies have also
Organic food
market
segmentation
823
Figure 4.
Average scores for
response and
organismic factors
Table 5.
RI and average PaD of
the MLP model
Market segments
Segment 3
Segment 4
PaD
RI
PaD
RI
Segment 5
PaD
RI
Segment 6
PaD
RI
0.003
0.005 (0.011) 0.003
0.001 (0.004) 0.003
0.001 (0.005) 0.003
0.006 (0.014) 0.004
0.008
0.007 (0.018) 0.008
0.002 (0.006) 0.008
0.002 (0.009) 0.008
0.010 (0.019) 0.007
0.001
0.003 (0.008) 0.001
0.001 (0.002) 0.001
0.001 (0.003) 0.001
0.004 (0.011) 0.002
0.007
0.005 (0.021) 0.010
0.002 (0.006) 0.007
0.002 (0.008) 0.007
0.007 (0.025) 0.010
0.005
0.006 (0.015) 0.005
0.002 (0.005) 0.005
0.002 (0.007) 0.005
0.009 (0.017) 0.006
0.016
0.010 (0.026) 0.016
0.003 (0.009) 0.016
0.003 (0.012) 0.016
0.014 (0.027) 0.015
0.002
0.002 (0.012) 0.003
0.001 (0.003) 0.002
0.001 (0.004) 0.002
0.003 (0.015) 0.004
0.043 0.016 (0.042) 0.042 0.005 (0.014) 0.043 0.005 (0.020) 0.043 0.023 (0.045) 0.040
0.037
0.016 (0.038) 0.037
0.005 (0.013) 0.037
0.004 (0.019) 0.038
0.023 (0.044) 0.038
0.002
0.003 (0.009) 0.002
0.001 (0.003) 0.002
0.001 (0.004) 0.002
0.005 (0.010) 0.002
0.245
0.044 (0.101) 0.254
0.012 (0.034) 0.245
0.012 (0.048) 0.248
0.060 (0.124) 0.297
0.632
0.060 (0.161) 0.620
0.019 (0.055) 0.632
0.017 (0.077) 0.628
0.087 (0.172) 0.576
Segment 2
PaD
RI
824
Acceptability
0.017 (0.016) 0.003
0.011 (0.014)
Accessibility
0.026 (0.024) 0.008
0.019 (0.025)
Affordability
0.011 (0.011) 0.001
0.006 (0.008)
Awareness
0.020 (0.023) 0.006
0.017 (0.023)
Environmental concern
0.023 (0.021) 0.006
0.016 (0.020)
Health-consciousness
0.037 (0.034) 0.015
0.027 (0.035)
Trust
0.008 (0.013) 0.001
0.008 (0.011)
Age
0.061 (0.055) 0.042 0.044 (0.058)
Gender
0.059 (0.054) 0.039
0.041 (0.054)
Household size
0.013 (0.012) 0.002
0.009 (0.012)
Income
0.158 (0.145) 0.283
0.105 (0.137)
Education
0.229 (0.210) 0.593
0.169 (0.220)
Note(s): Values in parentheses are standard deviations
Source(s): Research findings
Variable
Segment 1
PaD
RI
BFJ
125,3
shown that these two variables play a decisive role in the segmentation of the organic food
market (Fogarassy et al., 2020; Pestek et al., 2018; Sultan et al., 2018).
High levels of income and consumer education are common features of the second and third
segments of the market. Previous studies have shown that high- and middle-class consumers
have a higher intention to buy organic and sustainable products than other market segments
(Wang and Somogyi, 2019). Because organic foods are more expensive than non-organic, it is
believed that having a certain level of income is necessary for their consumption; most studies
have confirmed the positive correlation between income and the tendency to consume organic
foods (Kriwy and Mecking, 2012; Wang and Somogyi, 2019). Kim et al. (2018) showed that high
education levels lead to a higher income and more attention to health, which increases the
tendency to consume organic foods, while Kriwy and Mecking (2012) found that environmental
concern and health-consciousness are the most important motivations for buying organic food
among highly educated people. A study by Aertsens et al. (2011) showed that a high education
level increases consumers’ knowledge about organic foods, which positively influences their
perspective on buying them. Previous studies have shown that education and income affect
psychological processes and consumer attitudes at different levels. As a result, these variables
can affect consumer trust in buying organic food. In this regard, the results of a study by Yin
et al. (2016) also showed that education level has a significant favorable influence on consumers’
trust in buying organic food.
The respondents gave the lowest score to accessibility in the second, third and fourth
segments of the market. This finding is consistent with the results of previous studies (e.g.
_
Aertsens et al., 2011; Padel and Foster, 2005; Singh and Verma, 2017; Zakowska-Biemans,
2011). One of the main obstacles to the growth of the organic products market is the lack or
difficulty in access to these markets. Limited access to stores offering organic products has a
negative impact on the intention to buy these products. Due to time constraints, many
consumers are reluctant to search out organic product stores and prefer to buy products that
are readily available (Singh and Verma, 2017). The low availability of organic food in retail
stores thus hinders consistent buying behavior and consumer loyalty (Rana and Paul, 2017).
The lowest score was given to awareness in the first, fifth and sixth segments of the
organic saffron market. This finding is consistent with the view that lack of or inadequate
knowledge about the benefits of organic products is an important obstacle to market
development. The results of previous studies have also shown that awareness is directly
related to the intention to buy organic food (see Al-Swidi et al., 2014; Hsu et al., 2016; Padel and
Foster, 2005; Teng and Wang, 2015). Access to accurate and clear information is essential for
buying organic food. To promote the intention to buy, consumers must be made aware of the
benefits of organic food and the relevant knowledge should be passed on to them (Teng and
Wang, 2015).
Knowledge about organic food plays an important role in consumer behavior: a
phenomenon that is based on cognitive learning. Consumers have different behaviors at
different levels of knowledge (Singh and Verma, 2017). There is evidence that higher organic
knowledge not only increases the intention to buy organic products, but also leads to
increased consumption among existing consumers (Teng and Wang, 2015). However,
consumers in most countries have limited awareness of organic foods. Such low consumer
awareness is one of the factors hindering the development of the organic product market
around the world (Singh and Verma, 2017). Briz and Ward (2009) and Muhammad et al. (2016)
concluded that education has the greatest impact on consumer awareness and organic
knowledge. In another study, Ghorbani et al. (2019) argued that as their education increases,
Iranian consumers’ awareness of organic saffron improves, which has a positive effect on
their intention to buy this product. In the first, fifth and sixth segments of the market, the
respondents’ level of education is lower compared to other segments of the market. Therefore,
low awareness in these market segments may be due to a poor education.
Organic food
market
segmentation
825
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125,3
826
In the first, fifth and sixth segments of the market, in which the income level of the
consumers is relatively low, the affordability marketing mix is lower. This finding is in line
with expectations because affordability depends on consumer income (Briz and Ward, 2009;
_
Dangi et al., 2020; Sultan et al., 2018; Zakowska-Biemans,
2011). By reviewing the literature,
Rana and Paul (2017) argued that high-income consumer groups are the target markets for
organic products because they can pay higher prices for these products. The production and
processing processes of organic foods are more labor-intensive, more expensive and longer.
Hence, the prices of organic foods are higher than non-organic ones, and these products are
perceived as a luxury by low-income consumers (Pawlewicz, 2020). Therefore, the relatively
high price of organic saffron makes low-income consumers more price sensitive.
While some studies have shown that older people prefer to buy organic food due to their
higher purchasing power (Rodrıguez-Berm
udez et al., 2020) and health concerns (Hwang,
2016), the results of the present study suggested that young respondents are more likely to
buy organic saffron. It is believed that young consumers are a promising market for organic
food because they are more eco-friendly. Accordingly, many companies design their
marketing programs and campaigns with a focus on the young population (Ahmed et al.,
2021). In line with the results of the present study, Mehra and Ratna (2014) concluded that
young consumers have a positive attitude toward consuming organic food because they
consider it to be a healthy food option.
6. Conclusions
Lack of deep understanding of consumer behavior is one of the main obstacles to the
development of the organic food market in developing countries, including Iran. Considering
the neobehavioristic theory of consumer behavior, in this study, the organic saffron market in
Mashhad, Iran was segmented and consumer preferences were estimated in each market
segment. The results showed that the organic saffron market can be divided into six segments.
In these market segments, consumers are homogeneous in terms of intention to buy, organismic
factors and demographic characteristics. In contrast to the belief that psychological factors are
more important in market segmentation than demographic characteristics, this study’s
findings show that the demographic characteristics of consumers, especially education and
income, are the dominant variables in the segmentation of the organic saffron market. The
results show that a low level of consumer awareness and accessibility are important barriers to
organic saffron market expansion. According to the research findings, it is concluded that the
demographic characteristics of consumers play an important role in segmenting the organic
saffron market and shaping their behavior.
6.1 Theoretical implications
The results have two key theoretical implications for a better understanding of consumer
behavior toward organic food. First, neobehavioristic theory is one of the important
approaches in studying the behavior of consumers, but this theory has rarely been used in the
segmentation of the organic food market. Considering the external and internal factors
affecting the consumer response, the application of this theory to segmentation of the organic
food market is appropriate and useful.
Second, in studies and marketing practices that use the neobehavioristic approach to
understanding consumer behavior and the S-O-R framework, it is necessary to consider the
demographic characteristics of consumers. Therefore, it is better to add demographic
characteristics to the S-O-R framework (i.e. stimulus-demographic-organism-response) as an
influential factor in describing consumer behavior toward organic food. The demographic
characteristics of consumers should not be ignored when segmenting the organic food
market, especially in developing countries that are in the early stages of organic market
development. In fact, the demographic characteristics of consumers are very closely related to
their behavioral and psychological attributes.
6.2 Practical implications
The findings of this study have several practical implications for policymakers and
companies supplying organic saffron. To develop the organic saffron market in the short run,
companies can focus the design and implementation of their marketing strategies and tactics
on the second and third segments of the market, because the intention to buy organic saffron
in these market segments is relatively higher. Likewise, the second and third segments of the
market have about 40% of the market share and saffron companies can consider them as
target markets. High income and education are the two key characteristics of the second and
third market segments, and therefore, saffron companies can identify them in practice
through these two key variables.
In the long run, development strategies for the organic saffron market should focus on
solving the problems of each segment. In the second, third and fourth segments, low
accessibility is an obstacle to the market’s development. Access to organic saffron in
Mashhad can be increased by expanding distribution channels, online shopping options and
distribution of organic saffron in retail and grocery stores.
In the first, fifth and sixth segments, low affordability and awareness have made
consumers less inclined to buy organic saffron. Informing consumers about the quality and
distinctive characteristics of organic products and their production processes is one effective
strategy to increase consumers’ affordability and persuade them to pay the premium price for
organic saffron. In addition, increasing organic knowledge through social networks, websites
and advertisements may increase the intention to buy organic saffron. The use of labels and
logos to increase consumers’ knowledge of organic saffron is another suggestion of this
study. Because young consumers are more likely to buy organic saffron, companies can
trigger word of mouth campaigns focusing on a younger audience.
6.3 Limitations and further research
Due to time and cost constraints, in the present study, 400 questionnaires were distributed in
Mashhad. For this reason, the size of some segments of the organic saffron market is small. It
is suggested that in future studies, more populations be used to segment the organic saffron
market.
Machine learning methods have a strong ability to model consumer behavior due to their
flexible structures. In future studies, it is suggested that researchers compare the performance
of machine learning methods with econometric and statistical methods. In addition, word of
mouth is one of the factors influencing consumers’ attitudes toward organic food and leading to
market dynamics. Examining the dynamics of different segments of the organic food market as
a result of word of mouth may be another interesting topic in future studies.
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Corresponding author
Amirhossein Tohidi can be contacted at: Amirhossein.tohidi@mail.um.ac.ir
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