Available via license: CC BY 4.0
Content may be subject to copyright.
* Corresponding author. Tel: +989122520885
E-mail addresses: syahkaly@gmail.com (J. Siahkali Moradi)
© 2012 Growing Science Ltd. All rights reserved.
doi: 10.5267/j.msl.2012.03.016
Management Science Letters 2 (2012) 1053–1064
Contents lists available at GrowingScience
Management Science Letters
homepage: www.GrowingScience.com/msl
What is Impulse Buying? An analytical network processing framework for prioritizing factors
affecting impulse buying
Sahel Ehsani Masouleha, Marzieh Pazhanga and Javad Siahkali Moradib*
aM.A. in Marketing Research, Science & Research Branch, Islamic Azad University, Tehran, Iran
bM.A. in Operational Research, Science & Research Branch, Islamic Azad University, Tehran, Iran
A R T I C L E I N F O A B S T R A C T
Article history:
Received October 17, 2011
Accepted 12 March 2012
Available online
16 March 2012
One of the most important issues affecting profitability is to determine the impact of different
factors influencing purchasing activities. In this paper, we perform an extensive literature
survey to detect different purchasing factors influencing customers' behavior. The factors are
categorized in three different groups and they are ranked using analytical network process. The
results of our survey indicate that three factors of personal, product and situational play
important roles in purchasing impulse. The personal item includes different factors where
demographic characteristic factors receive the highest ranking (35%) followed by other factors
are feelings, excitement and fun, self identify, education and novelty. There are also three sub-
factors associated with demographic characteristics including gender, age and race and the
weights are 0.46748, 0.42668 and 0.10584, respectively, which means gender is the most
important factor followed by age and race. Finally, the other factor is associated with situational
factors' group, which includes presence of others, culture, design of store, time available, local
market condition, sales staff and self service with the relative importance of 0.04296, 0.08733,
0.12130, 0.22217, 0.05643, 0.15346 and 0.31635, respectively.
© 2012 Growin
g
Science Ltd. All ri
g
hts reserved.
Keywords:
Impulse buying
Impulse buying effective factors
Analytical network process
Mixed method
1. Introduction
With a glance to different studies, we will easily understand the importance of impulse buying.
According to Liao et al. (2009) impulse buying plays an important role on changing the purchasing
figures in United states. Impulsive customers' buying behavior accounts for up to 80% of all buying
items in certain product categories and it has been recommended that purchases of new products
result more from impulse buying than from prior planning (Abrahams, 1997; Smith, 1996; Sfiligoj,
1996; Liao et al, 2009). There are other studies, which specify that an estimated $4.2 billion annual
store volume was created by impulse sales of items such as candy and magazines (Mogelonsky, 1998;
Liao et al., 2009). Another study indicates that impulse purchases, making operational as unplanned
purchases and they represent between 27 and 62 percent of all department store purchases (Bellenger
1054
et al., 1978). These mentioned studies and others explain the relative importance and the impact of
impulse buying on consumers' behavior. In this research, we decide to provide a comprehensive
definition for impulse buying and determine important factors affecting consumers to change their
planned behavior and show impulsiveness in their purchase behavior.
2. Impulse Buying
Impulse buying is a sudden, compelling, hedonically complex purchase behavior in which the speed
of the impulse purchase decision precludes any thoughtful, deliberate consideration of alternatives or
future implications (Kollat & Willet, 1967; Cobb & Hoyer, 1986; Rook, 1987; Piron, 1991; Beatty &
Ferrel, 1998; Bayley & Nancarrow, 1998; Kacen & Lee, 2002; Vohs & Faber, 2003; Parboteeah,
2005).
Impulse buying is a major research issue among consumer behavior researchers not only because of
its complexities but also its wide-spread prevalence across a broad range of product categories
(Applebaum, 1951; Baumeister, 2002; Beatty and Ferrell, 1998; Clover, 1950; Kacen and Lee, 2002;
Ramanathan and Menon, 2006; Rook, 1987; Vohs and Faber, 2007; West, 1951).
Impulse buying is defined as an unplanned, which is characterized by both relatively rapid decision-
making, and a subjective bias for immediate possession (Rook & Gardner, 1993). Impulse buying is
associated with consumer impulsiveness (CI) trait, positively (Puri, 1996).
According to Ko (1993), impulse buying behavior is a sufficient unplanned attitude when it is
associated with objective evaluation and emotional preferences in shopping. Impulse buying plays
vital role in fulfilling hedonic desires associated with hedonic consumption (Hausman, 2000; Piron,
1991; Rook, 1987). Impulse buying is more emotional than rational, which is why it is normally used
by states of intense feeling. An impulse arises immediately upon confrontation with a certain stimulus
(Wolman, 1973). Consumer impulse buying is an important concept along with product involvement
as they are involved with a specific product (Jones et al., 2003). According to Han et al. (1991),
impulse buying was classified as four types:
(1) Planned impulse buying;
(2) Reminded impulse buying;
(3) Fashion-oriented impulse buying; and
(4) Pure impulse buying.
The purchase is unintended because it is made while shopping, although the individual was not
actively looking for that item, had no pre-shopping plans to buy the item, and was not involved with a
shopping task, such as searching for a gift. Unintended buying arises from an immediate intend to
purchase a particular item while shopping. The decision and interest to buy happens after the person
visits the item (Hoch & Loewenstein, 1991). Unintended and unplanned have long been associated
with impulse buying and is an important item but not sufficient basis for categorizing a purchase as
an impulse purchase (Kollat & Willet, 1967; Rook, 1987; Rook & Fisher, 1995).
Impulse buying is unreflective because the buy is made without engaging in a significant deal of
evaluation. An individual buying on impulse is less likely to consider the consequences or to think
carefully before making the purchase (Rook, 1987).
Kroeber-Riel (1980) explained that impulse buying is a reactive behavior, and often involves an
sudden response to a stimulus (Rook, 1987). Beatty and Ferrell (1998) defined impulse buying as
instantaneous purchase having no previous objective to buy the commodity. Stern (1962) found that
products bought on impulse are usually cheap. According to a number of studies (Rook & Fisher,
1995; Beatty & Ferrell, 1998; Verplanken & Herabadi, 2001; Virvilaite et al., 2009) the main
characteristics of impulsive purchasing behavior are: inclination to impulse buying, spontaneity in
buying, satisfaction felt after unplanned purchase, and lack of shopping list. This refers to the
individual characteristics of the consumer.
S. Ehsani Masouleh et al./ Management Science Letters 2 (2012)
1055
2.1 Impulse buying factors
There are a lot of studies on various factors affecting impulse buying. Stern (1962) characterized nine
factors affecting impulse buying as follows,
1- Low price,
2- Marginal need for item,
3- Mass distribution,
4- Self service,
5- Mass advertising,
6- Prominent store display,
7- Short product life,
8- Small size or light weight, and
9- Ease of storage.
In addition, there are other studies that investigate the role of various factors on impulse buying. For
example the researches of Beatty and Ferrell (1998); Husman (2000); Rook and Gardner (1993);
Youn and Faber (2000) found that emotions and feelings strongly influence buying behaviors, which
result into consumer impulse buying. Babin and Babin (2001) found that in stores consumer’s
purchasing intentions and spending can largely be influenced by emotions. For better focusing on this
area we divide impulse buying factors in three different groups:
2.1.1 Personal factors
In this group, we collect all different factors which are associated with a person who is shopping. For
instance, feelings and education are categorized in this group. Impulse buying behavior is motivated
by a powerful urge (Verplanken & Herabadi, 2001) and feelings of pleasure and excitement
(Hausman, 2000; Rook, 1987; Rook & Fisher, 1995; Ramanathan & Menon, 2002; Peck & Childers,
2006). Some other internal, personal-related factors thought to influence the act of impulse buying
are: educational experience (Wood, 1998) and mood states (Rook & Gardner, 1993).
Stores are the place where buyers buy products whether it’s planned or unplanned purchase. It only
depends on the personal income, which indicates how much and how many times he or she visits
shopping stores to buy products (Tirmizi et al, 2009). Previous researches have shown that different
factors impact impulsive purchasing behavior, including the presence of others (Luo, 2005), the
consumer's mood (e.g., Beatty and Ferrell, 1998; Rook and Gardner, 1993), trait impulsiveness (e.g.,
Jones et al., 2003; Rook and Fisher, 1995; Weun et al., 1998), product category impulsiveness (Jones
et al., 2003), evaluation of the appropriateness of engaging in impulse buying (e.g., Rook and Fisher,
1995), individual and environmental touch (Peck and Childers, 2006), self-identity (e.g., Dittmar et
al., 1995; Lee and Kacen, 1999), cultural orientation (e.g., Kacen and Lee, 2002; Lee and Kacen,
1999), as well as demographic characteristics such as gender (e.g., Dittmar et al., 1995; Rook and
Gardner, 1993) and age (e.g., Helmers et al., 1995; Wood, 1998). An individual’s impulsive behavior
tendencies have also been associated with demographic characteristics such as a consumer’s age.
Based on a national sample of adults in the United States, Wood (1998) found a reverse relationship
between age and impulse buying overall. However, the relationship is non-monotonic — between the
ages of 18 and 39 impulse buying increases slightly and thereafter declines. This is consistent with
Bellenger et al. (1978) who found that shoppers under 35 were more prone to impulse buying
compared to those older than 35 years. Research on trait impulsiveness indicates that younger
individuals score higher on measures of impulsivity compared to older people (Eysenck et al., 1985;
Helmers et al., 1995; Rawlings et al., 1995) and demonstrate less self-control than adults (Logue &
Chavarro, 1992). The theory of individualism and collectivism offers several insights into many of
1056
the variables linked to impulsive buying behavior, including self-identity, normative influences, the
suppression of emotion, and postponement of instant gratification. In the next section, we discuss this
theory and demonstrate that it is well suited to the study of impulse buying.
2.1.2 Product related factors
All the factors, which influence some body's impulse buying and are associated with a product are
categorized in this group. Examples of product related influences are product appearance and design
(Verplanken & Herabadi, 2001). Other external or product-related factors may include actual or
perceived concept from advertisement and spending power, which is associated with price and
discount amount of a product (Beatty & Ferrell, 1998). Product's design and price or its discount can
affect consumers buying. Retailers can increase the number of impulsive purchases through product
displays, store and packaging designs, and contemporary marketing innovations (e.g., 24-hour
convenience stores, television shopping channels, and internet shopping) (Hoyer & Maclnnis, 1997;
Jones et al., 2003). Other additional buying motivators are the price discounts or sales (Parsons, 2003;
Virvilaite et al., 2009).
2.1.3 Situational Factors:
This category is devoted to all factors that can direct impulse buying act out of a person and a
product. We can mention time available and self-service factors in this group. Environmental factors
of the shopping area or the physical surrounding include: (1) general interior design – color, lighting,
aroma, music, equipment, etc.; (2) arrangement of equipment and merchandise within the store; (3)
display of merchandise; (4) point of sale promotional materials (Mihić, 2002).
The more time is available, the higher is the chance for unplanned buying (Iyer, 1989; Iyer et al.,
1989; Herrington and Capella, 1995; Nicholls et al., 1997; Underhill, 1999, Anić & Radas, 2006)
especially when there is no buying task (Beatty & Ferrell, 1998).
Store accessibility and sales staff (Aylott & Mitchell, 1998) as well as the location (Hart & Davies,
1996) will affect the impulse buying act.
We are showing the factors collected from literature review in a model depicted below. As we
mentioned before there are two different groups of factors influencing impulse buying act. As we are
depicting in this model there some relationships between different sub-criteria of each group.
3. Analytical Network Process
Analytic hierarchy process (AHP) is one of the widely used approaches to handle such a multi-criteria
decision making problems. There are several assumptions when AHP is implemented to make
decisions, such as, the independence between higher level elements and lower level elements, the
independence of the elements within a level, and the hierarchy structure of the decision problem
(Saaty 1994, Saaty & Zoffer 2011). However, a significant limitation of AHP is the assumption of
independency among different criteria of decision-making. Analytic network process (ANP), on the
other hand, captures interdependencies among the decision attributes and allows a more systematic
analysis. In addition, the interactions of decision attributes within the same level and the feedbacks
between two different levels are important issues, which should be considered during the decision
making procedure. Therefore, the AHP method does not work accurately when solving such decision
problems (Saaty, 1996). ANP, as an extensive and complementary method of the AHP, was
introduced and further developed by Saaty (1996, 1999, 2001, 2003, 2004, 2005, 2006, and 2008). On
the contrary to AHP, ANP provides a more generalized model in decision-making without making
additional assumptions about the independency of the higher-level elements from lower-level
elements and also of the elements within a level. Despite all these features, the applications of ANP
are not very common in a decision-making problem. However, in recent years, there has been an
increase in the use of ANP in multi-criteria decision-making problems (Jharkhariaa & Shankar,
2007).
S. Ehsani Masouleh et al./ Management Science Letters 2 (2012)
1057
Fig. 1. Different factors affecting impulse buying act
ANP method can be used to make decision problems which cannot be structured hierarchically and
does not have the inner-independent and outer-independent assumptions. Since its introduction, the
ANP method is applied to diverse areas. It also allows inclusion of all the relevant criteria (tangible
or intangible, objective or subjective, etc.) that have some bearing in arriving at the best decision
(Saaty, 2005). The ANP is the most comprehensive framework for the analysis of societal,
governmental and corporate decisions that is available today to the decision makers. ANP models
have two parts: the first is a control hierarchy or network of objectives and criteria that control the
interactions in the system under study; the second are other sub-networks of influences among the
elements and clusters of the problem, one for each control criterion (Saaty, 2008). For devising an
ANP model and solving it, Chung et al provided bellowing steps: (Chung et al, 2005):
Step 1: Model construction and problem structuring
The problem should be stated clearly and decomposed into rational system like network. The
structure can be obtained by the opinion of decision makers through brainstorming or other
appropriate methods.
Personal Product Situational
Impulse Buying
Feelings
Education
Novelty
Discount
Design
Packing
Culture
Presence of Others
Self-Identify
Age Race
Gender
Price
Distribution
Advertisement
Ease of Storage
Local Market
Condition
Design of Store
Time available
Excitement and
Fun
Demographic
Characteristics
Self-Service
Sales Staff
1058
Step 2: Pairwise comparisons matrices and priority vectors
In ANP, like AHP, decision elements at each component are compared pairwise with respect to their
importance towards their control criterion, and the components themselves are also compared
pairwise with respect to their contribution to the goal. Decision makers are requested to respond to a
series of pairewise comparisons where two elements or two components at a time are compared in
terms of how they contribute to their particular upper level criterion (Sarkis, 2003). The relative
values are determined with Saaty's 1-9 scale (Table 1), where a score of 1 represents equal
importance between the two elements and a score 9 indicates the extreme importance of one element
(row component in the matrix) compared to the other one (column component in the matrix) (Sarkis,
2003).
Table 1
Saaty's 1-9 scales for AHP
Definition Equal
importance
Moderate
importance
Strong
importance
Very strong
importance
Absolute
importance
Intermediate
importance
Intensity of
importance
1 3 5 7 9 2,4,6,8
Like AHP, pairewise comparison in ANP is made in the framework of a matrix, and a local priority
vector, which can be derived as an estimate of relative importance associated with the elements (or
components) being compared by solving the following equation:
21 2 2
32
000
0
0
n
ww
WwI
⎡⎤
⎢⎥
⎢⎥
=⎢⎥
⎢⎥
⎣⎦
max
A
ww
λ
×= ×
(1)
where A is the matrix of pairewise comparison, w is the eigenvector, max
λ
is the largest Eigen value of A.
Step 3: Super matrix formation
The super matrix concept is similar to the Markov chain process (Saaty, 2005). To obtain global
priorities in a system with interdependent influences, the local priority vectors are entered in the
appropriate columns of a matrix. As result, a super matrix is actually a partitioned matrix, where each
matrix segment represents a relationship between two nodes (components or clusters) in a system
(Sarkis, 2003).
Let the components of a decision systems be k
C, k=1, 2, n, and each component k has k
melements,
denoted by 1k
e,2k
e,…, kmk
e. The local priority vectors obtained in step 2 are categorized and located
in appropriate positions in a super matrix based on the flow of influence from one component to
another one, or from a component to itself as in the loop. A standard form of a super matrix is as in
below:
Fig. 2. Super matrix
S. Ehsani Masouleh et al./ Management Science Letters 2 (2012)
1059
As an example, the super matrix representation of a hierarchy with three levels as shown in Fig. 2 (a),
is follows (Saaty, 2005),
21
32
000
00
0
h
Ww
wI
⎡⎤
⎢⎥
=⎢⎥
⎢⎥
⎣⎦
,
(2)
where 21
w is a vector that represent the impact of the goal on the criteria, 32
w is a matrix that
represent the impact of criteria on each of the alternatives, I is the identity matrix, and entries of zeros
corresponding to those elements that have no influence.
4. Solving Impulse Buying Model
For solving this model, we reform our model in a simple figure to put in software. Fig. 3 is the
simplified model.
We gathered factors' weights from experts by using special ANP questionnaires. Then we put these
gathered weights in the Supper decision software. Table 2 shows details of the results of our
comparison using ANP implementation in terms of personal group factors,
Education
Feelings
Self-Identify
Novelty
Excitement and Fun
Demographic
Characteristics
Design
Discount
Packing
Price
Distribution
Advertisement
Ease of Storage
Presence of Others
Culture
Designof Store
Time available
Local Market Condition
Self-Service
Sales Staff
Fig. 3. The final model of effective factors and their relationship
1060
Table 2
The results of ANP implementation for ranking personal group factors
Factor Education Feelings Self identify Novelty Excitement and fun Demographic
Relative importance 0.07466 0.27060 0.10504 0.04546 0.15153 0.35272
As illustrated from the results of Table 2, demographic characteristic factors receive the highest
ranking (35%) followed by other factors are Feelings, Excitement and Fun, Self Identify, Education
and Novelty. Table 3 presents details of the weighted matrix in terms of personal figures.
Table 3
Weighted matrix in terms of personal figures
Demographic Education Excitement
and fun
Feelings Novelty Self
identify
Demographic 0.000000 0.370250 0.577265 0.569946 0.560275 0.551301
Education 0.087133 0.000000 0.058647 0.104417 0.047054 0.044230
Excitement and fun 0.191937 0.129051 0.000000 0.210054 0.112570 0.116563
Feelings 0.477833 0.383270 0.237867 0.000000 0.240035 0.252171
Novelty 0.045266 0.048247 0.044826 0.056691 0.000000 0.035735
Self identify 0.197830 0.069181 0.081395 0.058893 0.040066 0.000000
There are three sub-factor associated with demographic characteristics including gender, age and race
and the weights are 0.46748, 0.42668 and 0.10584, respectively. As we can observe, gender is the
most important factor followed by age and race. Table 4 demonstrates details of weighted matrix in
terms of demographic characteristics.
Table 4
Weighted matrix in terms of demographic characteristics
Age Gender Race
Age 0.000000 0.875000 0.166667
Gender 0.888889 0.000000 0.833333
Race 0.111111 0.125000 0.000000
In terms of product, the relative priorities of advertisement, design, discount, distribution, ease of
storage, packing and price are 0.23483, 0.10406, 0.29533, 0.03103, 0.08155, 0.06186 and 0.19133,
respectively. As we can observe advertisement and price are the most important factors in this group.
The weight factors of these items are summarized in Table 5.
Table 5
Weighted matrix in terms of product
Advertisement Design Discount Distribution Ease of storage Packing Price
Advertisement 0.000000 0.244810 0.446136 0.224471 0.220273 0.144890 0.228456
Design 0.128767 0.000000 0.147434 0.060432 0.105816 0.057559 0.084747
Discount 0.356546 0.391287 0.000000 0.359372 0.397042 0.423168 0.528781
Distribution 0.030397 0.032430 0.031456 0.000000 0.034236 0.031213 0.034131
Ease of storage 0.104221 0.082159 0.102732 0.084102 0.000000 0.071168 0.058417
Packing 0.088308 0.058956 0.057116 0.037512 0.054332 0.000000 0.065468
Price 0.291762 0.190359 0.215226 0.234112 0.188310 0.272002 0.000000
The other factor is associated with situational factors' group, which includes presence of others,
culture, design of store, time available, local market condition, sales staff and self service with the
relative importance of 0.04296, 0.08733, 0.12130, 0.22217, 0.05643, 0.15346 and 0.31635,
respectively. The weight factors of these items are summarized in Table 6.
S. Ehsani Masouleh et al./ Management Science Letters 2 (2012)
1061
Table 6
Weighted matrix in terms of situational factors
Presence of
others
Culture Design of
store
Time
available
Local market
condition
Sales staff Self
service
Presence of others 0.000000 0.079506 0.086985 0.119945 0.096241 0.101859 0.092846
Culture 0.070697 0.000000 0.060418 0.103904 0.141247 0.163229 0.152948
Design of store 0.034970 0.049330 0.000000 0.034866 0.073378 0.069528 0.056900
Time available 0.044971 0.034551 0.033251 0.000000 0.047657 0.046817 0.048787
Local market
condition
0.113607 0.121763 0.108395 0.165670 0.000000 0.230578 0.191714
Sales staff 0.513265 0.511892 0.476941 0.432397 0.407001 0.000000 0.456796
Self service 0.222489 0.202957 0.234010 0.144098 0.234405 0.387989 0.000000
Finally, the main three factors of personal, product and situational are ranked using the proposed
model of this paper and the relative importance of these factors are 0.38462, 0.18462 and 0.43077,
respectively. Table 7 shows details of the weighted relative importance.
Table 7
Weighted matrix in terms of the main factors
Personal Product Situational
Personal 0.000000 0.333333 0.750000
Product 0.200000 0.000000 0.250000
Situational 0.800000 0.666667 0.000000
5. Discussion and conclusion
After understanding the importance of impulse buying in forming consumers' behavior, we decided to
extract different factors that are effective in impulse buying. So we categorized these extracted factors
from literature review in three groups and asked experts to weight these factors one by one. We
utilized an ANP model because there are meaningful relationships between sub-factors in each group
and between the groups. Then we put these gathered data in Super decision software and gained
presented results. Based on our experts' opinions, situational factors category is the most important
factor, which must be considered by decision makers. Then personal and product related factors are in
the importance rank. Other ranking results are presented in different tables and decision makers can
use from these ranked factors for directing their customers' behavior on buying impulsively.
For further studies, we suggest other weighting methods like Entropy and utilizing Fuzzy methods for
better conformity.
References
Abrahams, B. (1997). It's all in the mind. Marketing, 1997, 31–33.
Anić, I., & Radas, S. (2006). The impact of situational factors on purchasing outcomes in the
Croatian hypermarket retailer. Ekonomski pregled, 57(11), 730-752.
Applebaum, W. (1951). Studying consumer behavior in retail stores. Journal of Marketing, 16, 32–
40.
Aylott, R., & Mitchell, V. (1998). An exploratory study of grocery shopping stressors. International
Journal of Retail & Distribution Management, 26(9), 362-373.
Babin, B. J., & Babin, L. (2001). Seeking something different? A model of schema typically,
consumer affect. Purchase intentions and perceived shopping value. Journal of Business Research,
54(2), 89-96.
Baumeister Roy F. (2002). Yielding to temptation: self-control failure, impulsive purchasing and
Consumer behavior. Journal of Consumer Resources, 28(4), 670–676.
Bayley, G., & Nancarrow, C. (1998). Impulse purchasing: a qualitative exploration of the
phenomenon. Qualitative Market Research: An International Journal, 1(2), 99-114.
1062
Beatty, S., & Ferrell, E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing.
74(2), 169-191.
Bellenger, D., Robertson, D., & Hirschamn, E. (1978). Impulse buying varies by products. Journal of
Advertising Research, 18, 15 – 18.
Chung, S.H., Lee, A.H.I., & Pearn, W.L. (2005). Analytic network process (ANP) approach for
product mix planning in semiconductor fabricator. International Journal of Production
Economics, 96 (1), 15–36.
Clover Vernon, T. (1950). Relative importance of impulse buying in retail stores. Journal of
Marketing, 25, 66–70.
Cobb, J. C., & Hoyer, W. D. (1986). Planned versus impulse purchase behaviour. Journal of
Retailing, 62, 384 – 409.
Dittmar, H., Beattie, J., & Friese, S. (1996). Gender Identity and Material Symbols: Objects and
Decision considerations in Impulse Purchases. Journal of Economic Psychology, 16, 491-511.
Eysenck, S. B., Pearson, P. R., Easting, G., & Allsopp, J. F. (1985). Age norms for impulsiveness,
venturesomeness and empathy in adults. Personality and Individual differences, 6 (5), 613–619.
Han, Y. K., Morgan, G. A., Kotsiopulos, A., & Kang, P. J. (1991). Impulse buying behavior of
apparel purchases. Clothing and Textile Research Journal, 9(3), 15-21.
Hart, C., & Davies, M. (1996). The location and merchandising of nonfood in supermarkets.
International Journal of Retail & Distribution Management, 24(3), 17-25.
Hausman, A. (2000). A multi-method investigation of consumer motivations in impulse buying
behavior. Journal of Consumer Marketing, 17(15), 403-419.
Helmers, K.F., Young, S.N., & Pihl, R.O. (1995). Assessment of measures of impulsivity in healthy
male volunteers. Pers Individ Differ, 19(6), 927–935.
Herrington, J. D., & Capella, L. M. (1995). Shoppers reactions to perceived time pressure.
International Journal of Retail & Distribution Management, 23(12), 13-20.
Hoch, S. J., & Loewenstein, G.F. (1991). Time-inconsistent preferences and consumer self-control.
Journal of Consumer Resources, 17, 492– 507.
Hoyer, W. D., & Maclnnis, D. J. (1997). Consumer Behavior. New York; Houghton Mifflin.
Iyer, E. S. (1989). Unplanned purchasing: Knowledge of shopping environment and time pressure.
Journal of Retailing, 65(1), 40-57.
Iyer, E. S., Park, C. W., & Smith, D. C. (1989). The effects of situational factors on in-store grocery
shop. Journal of Consumer Research, 15(4), 422-433.
Jharkharia, S., & Shankar, R. (2007). Selection of logistics service provider: an analytic network
process (ANP) approach. Omega, 35, 274–289.
Jones, M. A., Reynolds, K. E., Weun, S., & Beatty, S. E. (2003). The product specific nature of
impulse buying tendency. Journal of Business Research, 56(7), 505-511.
Kacen, J., & Lee, J. A. (2002). The influence of culture on consumer impulsive buying behavior.
Journal of Consumer Psychology, 12(2), 163 – 176.
Ko, S. (1993). The study of impulse buying of clothing products. unpublished Master’s thesis, Seoul
National University, Seoul.
Kollat, D. T, & Willett, R. P. (1967). Customer impulse purchasing behavior. Journal of Marketing
Research, 4 , 21-31.
Kroeber-Riel, W. (1980). Marketing in nächsten Jahrzehnt, teil 1. Marketing ZFP, 4(1), 5-11.
Liao, S.L., Shen, Y.C., & Chu, C.H. (2009). The effects of sales promotion strategy, product appeal
and consumer traits on reminder impulse buying behaviour, International Journal of Consumer
Studies ISSN 1470-6423.
Lee, J.A., Kacen, J.J. (1999). The relationship between independent and interdependent self-concepts
and reasons for purchase. Journal of Euro-Market, 8 (1/2), 83–99.
Logue, A. W. & Chavarro, A. (1992). Self-control and impulsiveness in preschool children. The
Psychological Record, 42, 189–204.
Luo, X. (2005). How does shopping with others influence impulsive purchasing. Journal of
Consumer Psychology, 15 (4), 288 – 294.
S. Ehsani Masouleh et al./ Management Science Letters 2 (2012)
1063
Mihić, M. (2002). Utjecaj izgleda prodavaonice i prodajnog osoblja na ponašanje u kupnji, PhD
dissertation, Faculty of Economics Split, Split
Mogelonsky, M. (1998). Keep candy in the aisles. Am Demogr, 20:32.
Nicholls, J. A .F., Roslow, S., & Dublish, S. (1997). Time and companionship: key factors in
Hispanic shopping behavior. Journal of Consumer Marketing, 14(2/3), 194 -205.
Parboteeah, V. (2005). A model of online impulse buying: An empirical study. Doktorska disertacija,
Washington State University
Parsons, A. G. (2003). Assessing the effectiveness of shopping mall promotions: customer analysis.
International Journal of Retail & Distribution Management, 31(2), 74-79.
Peck, J., & Childers, T. L. (2006). If I touch it I have to have it: Individual and environmental
influences on impulse purchasing. Journal of Business Research, 59, 765-769.
Piron, F. (1991). Defining impulse purchasing. Advances in Consumer Research, 18, 509-513.
Puri, R. (1996). Measuring and modifying consumer impulsiveness: A cost benefit accessibility
framework. Journal of Consumer Psychology, 5(2), 87-113.
Ramanathan, S., & Menon G. (2006). Time-varying effects of chronic hedonic goals on impulsive
behavior. Journal of Marketing Resources, 43(4), 628–641.
Ramanathan, S., & Menon, G. (2002). Don't know why, but I had this craving: Goal-dependent
automaticity in impulsive decisions. Working paper. New York University.
Rawlings, D., Boldero, J., & Wiseman, F. (1995). The interaction of age with impulsiveness and
venturesomeness in the prediction of adolescent sexual behavior. Personality and Individual
differences, 19, 117–120.
Rook, D. W. (1987). The buying impulse. Journal of Consumer Research, 14, 188-199.
Rook, D. W, & Fisher, R. J. (1995). Normative influences on impulsive buying behavior. Journal of
Consumer Resources, 22, 305–313.
Rook, W. D., & Gardner, M. P. (1993): In the Mood: Impulse Buyings' Affective Antecedents, in
Janeen Arnold-Costa and Russell W. Belk (Eds.). Research in Consumer Behavior, 6, Greenwich,
CT: JAI Press, 1-28.
Saaty, T.L. (1994). Fundamentals of the Analytic Hierarchy Process. RWS Publications, 4922
Ellsworth Ave., Pittsburgh, Pennsylvania.
Saaty, T.L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process.
Pittsburgh, Pennsylvania: RWS Publications. ISBN 0-9620317- 9-8.
Saaty, T.L. (1999). Fundamentals of the analytic network process. ISAHP 1999, Kobe, Japan, August
12-14.
Saaty, T.L. (2001). Decision Making with the Analytic Network Process (ANP) and Its "Super
Decisions" Software: The National Missile Defense (NMD) Example. ISAHP 2001 Proceedings,
Bern, Switzerland, August, 2-4.
Saaty, T.L. (2003). Theory of the Analytic Hierarchy and Analytic network process - Examples Part
2.2. The International Journal of Systems Research and Information Technologies, 2.
Saaty, T.L. (2004).Fundamentals of the Analytic Network Process: Dependence and Feedback in
Decision-Making with a Single Network. Journal of Systems Science and Systems Engineering,
published at Tsinghua University, Beijing, 13(2), 129-157.
Saaty, T.L. (2005). Theory and Applications of the Analytic Network Process: Decision Making with
Benefits, Opportunities, Costs and Risks. Pittsburgh, Pennsylvania: RWS Publications. ISBN 1-
888603-06-2.
Saaty, T.L. (2006). Rank from comparisons and from ratings in the analytic hierarchy/ network
processes. European Journal of Operational Research, 168, 557-570.
Saaty, T.L. (2008).The Analytic Network Process. Iranian Journal of Operations Research, 1(1), 1-
27.
Saaty, T. L., Zoffer, H.J., (2011) Negotiating the Israeli-Palestinian Controversy from A New
Perspective, DOI: 10.1142/S021962201100421X, International Journal of Information Technology
& Decision Making, 10(1), Jan 2011.
1064
Sarkis, J. (2003). Quantitative models for performance measurement systems alternate considerations.
International Journal of Production Economics. 86 (1), 81–90.
Sfiligoj, E. (1996). Helping the little guy to merchandise. Periscope, 20.
Smith, D. (1996). The joy of candy. National Petroleum News Supplement, S2.
Stern, H. (1962). The Significance of Impulse Buying Today. Journal of Marketing, 26, 59-62.
Tirmizi, M. A, Kashif, U., & Iqbal, S. M. (2009). An Empirical Study of Consumer Impulse Buying
Behavior in Local Markets. European Journal of Scientific Research, 28 (4), 522-532.
Underhill, P. (1999). Zašto kupujemo: Znanost kupnje. Zagreb: Olympic International d.o.o.
Virvilaite, R., Saladiene, V., & Bagdonaite, R. (2009). Peculiarities of impulsive purchasing in the
market of consumer goods. Inzinerine Ekonomika-Engineering Economics, Commerce of
Engineering Decisions, 2, 101 – 108.
Vohs, K., & Faber, R. (2003). Spent Resources: Self-Regulation and Impulse Buying.
http://www.sauder.ubc.ca/faculty/research/docs/vohs/ vohs-impulse-buying.pdf
Vohs Kathleen, D., & Faber R. J. (2007). Spent resources: self-regulatory resource availability affects
impulse buying. Journal of Consumer Resources, 33(4), 537–47.
Verplanken. B. & Herabadi. A. (2001). Individual différences in impulse buying tendency: Feeling
and no thinking. European Journal of Personality, 15, 71-83.
West, J. C. Results of two years of study into impulsive buying. Journal of Marketing, 15, 362–363.
Weun S, Jones M.A, & Beatty S.E. (1998). The development and validation of the impulse buying
tendency scale. Psychol Rep, 82, 1123–33.
Wolman, B. (1973). Dictionary of Behavioral Science. New York: Van Nostrand Reinhold.
Wood, M. (1998) Socio-economic status, delay of gratification, and impulse buying. Journal of
Economic Psychology, 19, 295–320.
Youn, S. & Faber, R. J. (2000). Impulse buying: its relation to personality traits and cues. Advances
in Consumer Research, 27, 179-185.
