Graduate School of Management
Master in Management Program
MASTER THESIS
PRODUCT RETURNS RELATED TO IMPULSIVE
BUYING IN E-COMMERCE
Master’s Thesis by the 2 nd year student
CEMS MIM
Valeriia Fedorova
Research advisor:
Doctor of Economics, Professor Vitally I. Cherenkov
St. Petersburg
2017
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Федорова Валерия Анатольевна, студентка второго курса магистратуры направления
«Менеджмент», заявляю, что в моей магистерской диссертации на тему «Возврат товара,
связанный с импульсивным покупательским поведением в электронной коммерции»,
представленной в службу обеспечения программ магистратуры для последующей передачи
в государственную аттестационную комиссию для публичной защиты, не содержится
элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из защищенных
ранее выпускных квалификационных работ, кандидатских и докторских диссертаций
имеют соответствующие ссылки.
Мне известно содержание п. 9.7.1 Правил обучения по основным образовательным
программам высшего и среднего профессионального образования в СПбГУ о том, что «ВКР
выполняется индивидуально каждым студентом под руководством назначенного ему
научного руководителя», и п. 51 Устава федерального государственного бюджетного
образовательного
учреждения
высшего
профессионального
образования
«Санкт-
Петербургский государственный университет» о том, что «студент подлежит отчислению
из Санкт-Петербургского университета за представление курсовой или выпускной̆
квалификационной работы, выполненной другим лицом (лицами)».
_______________________________________________ (Подпись студента)
________________________________________________ (Дата)
2
STATEMENT ABOUT THE INDEPENDENT CHARACTER OF THE MASTER THESIS
I, Valeriia Fedorova, second year master student, program «Management», state that my master
thesis on the topic “Product returns related to impulsive buying in e-commerce”, which is
presented to the Master Office to be submitted to the Official Defense Committee for the public
defense, does not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses, PhD and
doctorate theses which were defended earlier, have appropriate references.
I am aware that according to paragraph 9.7.1. of Guidelines for instruction in major curriculum
programs of higher and secondary professional education at St. Petersburg University «A master
thesis must be completed by each of the degree candidates individually under the supervision of
his or her advisor», and according to paragraph 51 of Charter of the Federal State Institution of
Higher Professional Education Saint-Petersburg State University «a student can be expelled from
St. Petersburg University for submitting of the course or graduation qualification work developed
by other person (persons)».
________________________________________________ (Student’s signature)
________________________________________________ (Date)
3
АННОТАЦИЯ
Автор:
Название магистерской
диссертации:
Факультет:
Направление
подготовки:
Год:
Федорова Валерия Анатольевна
«Возврат товара, связанный с импульсивным покупательским
поведением в электронной коммерции»
Высшая
Школа
Менеджмента
Государственный Университет)
(Санкт-Петербургский
38.04.02 «Менеджмент» (профиль: CEMS MIM)
2017
Научный руководитель: доктор экономических наук, профессор Черенков В. И.
Описание цели, задач и
основных результатов:
Ключевые слова:
Цель исследования - изучить потребительское поведение
возврата товара, связанное с импульсивными покупками в
электронной коммерции. Для достижения этой цели были
сформулированы несколько гипотез на основе анализа
существующей литературы. Тестирование гипотез было
проведено на основе многофакторной регрессионной модели.
Эмпирическое исследование было основано на выборке из 153
потребителей поколения Y. Результаты исследований
свидетельствуют о том, что использование кредитных карт и
либеральная политика возврата положительно связаны с
импульсивной покупательской тенденцией, которая, в свою
очередь, может привести к негативной эмоциональной
реакции после покупки. Отрицательные эмоции после покупки
могут привести к возврату товара. Кроме того, было
установлено, что причинно-следственная связь между
импульсивной покупательской тенденцией и отрицательными
эмоциями после покупки может быть смягчена подарками.
Исследование показало, что послепокупочная коммуникация с
клиентами интернет-магазинов отрицательно влияет на
негативную эмоциональную реакцию после покупки.
Импульсивное покупательское поведение, импульсивные
покупки, возврат товара, электронная коммерция, кредитные
карты, политика возврата
4
ABSTRACT
Author:
Master Thesis Title:
Faculty:
Major subject:
Year:
Academic advisor’s
name:
Description of the goal,
tasks, and main results:
Keywords:
Valeriia Fedorova
“Product returns related to impulsive buying in e-commerce ”
Graduate School of Management (St.-P. State University)
38.04.02 “Management” (specialization: CEMS MIM)
2017
Doctor of Economics, Professor V. I. Cherenkov
The purpose of the research was to investigate consumer product
return behavior related to impulsive buying in the online retailing
environment. To address this goal, several hypotheses were
formulated based on the analysis of the extant literature. To verify
the hypotheses data collected from a sample of 153 Russian
Generation Y consumers was statistically analyzed. Research
findings state that credit card use and perceived return policy
leniency are positively related to impulsive buying tendency. There
is evidence that impulsive buying tendency in its turn may result in
post-purchase negative emotional response. Post-purchase negative
emotions may lead to online product return behavior. Additionally,
the causal relationship between impulse buying tendency and postpurchase negative emotions was found to be moderated by gifts.
While there was no significant interactive effect found between
impulsive buying and post-purchase communication with online
stores’ customers, the study revealed that post-purchase
communication negatively influences post-purchase negative
emotional response.
Online impulsive buying, impulsive buying tendency, product
return, return policy, credit card use, post-purchase negative
evaluation, e-commerce
5
TABLE OF CONTENTS
INTRODUCTION .........................................................................................................................7
CHAPTER I. LITERATURE REVIEW ...................................................................................11
Impulsive buying definition.......................................................................................................11
Impulsive buying tendency ........................................................................................................14
Impulsive buying in the online environment .............................................................................15
Credit card use as an antecedent of impulse buying ..................................................................16
Buying decision process and post-purchase behavior ...............................................................17
Return policy and perceived risk ...............................................................................................18
Return policy and online product return behavior .....................................................................19
Post-purchase negative emotions...............................................................................................21
Product return behavior as a response to negative emotions .....................................................22
Research gap ..............................................................................................................................23
Hypotheses development ...........................................................................................................24
Conceptual model ......................................................................................................................28
CHAPTER II. RESEARCH METHODOLOGY .....................................................................29
Research strategy .......................................................................................................................30
Data collection ...........................................................................................................................30
Sampling procedure ...................................................................................................................31
Questionnaire structure ..............................................................................................................32
Measures ....................................................................................................................................32
Mediation testing method ..........................................................................................................35
CHAPTER III. DATA ANALYSIS ...........................................................................................36
Characteristics of the sample .....................................................................................................36
Preliminary analyses ..................................................................................................................39
Hypotheses testing .....................................................................................................................41
Theoretical and practical implications .......................................................................................50
Limitations and future research directions ................................................................................53
CONCLUSION ............................................................................................................................55
REFERENCES ............................................................................................................................57
APPENDIX...................................................................................................................................61
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INTRODUCTION
Impulsive buying recently defined as “individual’s desire for abrupt ownership of the
product” (Bagdaiyan and Verma, 2014), is a pervasive shopping tendency inherent to consumerist
culture and lifestyle. American shoppers alone have generated around $4 billion worth of impulse
purchases (Kacen and Lee, 2002). Research findings have indicated that impulse purchases may
amount up to 60% of total purchases (Mattila and Wirtz, 2008). On the other hand, compulsive
buying which involves impulse and excessive buying in a severe out of control form is considered
a psychiatric disorder that affects only 1.1% of consumers (Lejoyeux et al., 1996).
Due to technological advances and massive e-commerce growth, online impulsive
purchasing has become a widely spread phenomenon. According to the estimations, online
impulsive buying accounts for 40% of all online consumer expenditure (Liu et al., 2013). The
online shopping boom, that has taken over consumerist societies of the United States and Europe,
has gradually come to the developing world. Online retailers have emerged as a new shopping
destination for millions of consumers in Russia, who enjoy the benefits of convenient product
delivery, accelerated purchase process and access to the endless choice of products. Shopping
experience offered by online stores has lifted some of the limitations attributed to offline retailers
(e.g. social pressure from sales assistants or other shoppers, limited opening hours, inconvenient
store locations, the need to carry the products). Today e-commerce websites are argued to have
created a more favorable environment for impulsive purchasing as opposed to brick-and-mortar
stores (Eroglu et al., 2001). The importance of online impulse buying and its ability to generate
sales was acknowledged by marketers. They attempt to tap into impulsive shopping tendency by
employing limited promotions and offers, developing vivid and appealing website design, offering
the next day delivery etc. Credit card payment and lenient return policy adopted by e-commerce
retailers may also stimulate consumers to buy impulsively when shopping online.
Owing to online retailers’ efforts to ensure the security and safety of bank card payment
transactions in order to reduce perceived risk associated with revealing personal information,
credit cards have become one of the most widely used methods of payment for e-commerce
transactions. In the US and Europe, credit/debit card is a payment method of choice. In Russia
despite the long-standing consumer preference to make cash payments on delivery, with the
growth of cross-border orders, bank card has become the most popular payment mode. Credit cards
offer a convenient means of payment that instantly increases consumer money availability pushing
cardholders to overspend. Individuals who frequently use credit cards are less conscious about the
price and tend to purchase higher priced products. Credit card use has been identified as one of the
antecedents of impulsive buying. Since the order payment is often made by a credit card,
consumers may experience the urge to buy on impulse at e-retailers.
7
Facing peer pressure, e-commerce retailers often adopt lenient return policies despite
tremendous costs associated with product returns. The return policy is considered an important
tool for attracting customers and generating sales. When shopping online consumers have to deal
with a higher risk compared to brick-and-mortar stores, as they are not able to see or touch a
product before placing an order. Flexible product return conditions serve as a risk reliever that
allows consumers to cancel their purchase decisions upon having received and inspected a product
in real life. Lenient return policy compensates for consumers’ inability to physically evaluate a
product before making a purchase. Online shoppers place great importance on return policy and
tend to review product return conditions prior to the purchase. Additionally, consumers may make
judgments about the trustworthiness and quality of an online store based on its return policy. Thus,
return policy is crucial in driving consumer purchase decisions. Consumers are likely to buy more
when they perceive return policy as lenient. If a shopper is certain that he can painlessly return
products and get a refund, he may experience the urge to buy impulsively while browsing the
online store.
However, impulsive buying is often followed by powerful feelings of guilt and regret.
When making an impulse purchase an individual is so consumed by positive emotions and desire
for immediate gratification, that he does not reflect on such aspects as the utilitarian value of the
product, budget constraints and the necessity of the purchase. Consumer research studies have
demonstrated that impulse purchasing frequently results in a state of psychological pain and
anxiety, particularly when consumers overspend when buying on impulse. When consumers come
to the realization that their purchase decision was wrong because they actually did not need the
product or its benefits did not meet their expectations or they cannot afford it, they experience
negative emotions. The post-purchase negative emotional response is associated with low
customer satisfaction which is argued to have a negative impact on brand loyalty, repurchase
intention and word of mouth about the brand. Post-purchase negative reaction results in product
return behavior. Thus, online impulsive buying may negatively influence e-commerce retailers’
bottom line, especially considering the fact that most of them have a very lenient return policy.
Although return policy is a strategic tool for online retailers to increase sales, customer
loyalty and repurchase intention, it may lead to product return behavior. Product return rate is
estimated from 25 to 40% across different product categories, which is much higher than in brickand-mortar stores (Dennis, 2017). E-commerce trend of fully refunded returns with free shipping,
initiated by the industry’s main players such as Amazon, has become a great issue for online
retailers’ profitability. Considering that shipping, return and exchange costs are handled by
retailers and returned merchandise is often sold at markdown due to its defective condition, ecommerce margins are squeezed. Indeed, the majority of e-retailers have lower operating profit
8
with product return as a massive cost driver as opposed to their brick-and-mortar counterparts.
Today, the major challenge of e-commerce business is to find an equilibrium between ensuring
higher margins by cutting down product return costs without alienating consumers and curbing
impulse buying as a result of adopting rigid return policies.
Despite managerial relevancy of the issue, extant research has paid limited attention to
product return behavior in the e-commerce environment, considering that it is a relatively new
research field and there is not much knowledge about it. The majority of the studies on this topic
focused on operational and supply chain aspects of product return, analyzed how product return
policy impacts e-retailers’ profitability and how return policies can be optimized to deliver costefficient and timely returns. Online impulsive buying has been a topic of interest for marketing
scholars, however, prior research has primarily investigated external and internal motivators of
impulse buying. There is far less knowledge about the post-purchase phase of impulsive
purchasing, which is critical as it is distinct from regular consumer behavior and is often
accompanied by post-purchase regret, which can have negative consequences both for consumers
and marketers. In this context, in order to tackle consumers’ abusive practice of product returns it
is crucial to understand online product return related to impulse buying from consumers’
perspective.
Making a contribution towards the understanding of negative consumer behaviors in the ecommerce environment could be of value both from managerial and theoretical perspective.
Therefore, the purpose of this research is to investigate consumer product return behavior related
to impulsive buying in the online retailing environment. The research question of the current
study is formulated as follows:
RQ: How product returns related to impulsive buying can be reduced in the e-commerce
environment?
To address this question, the following objectives of the study were identified:
•
To explore product return behavior in online environment and identify the factors that
contribute to it;
•
To analyze extant research and to identify a research gap;
•
To develop a methodological approach and outline the scope of current study;
•
To gather primary data from a sample of Russian consumers;
•
To conduct statistical data analysis and verify formulated hypotheses;
•
To retrieve the results of data analysis;
9
•
To develop coping strategies for managing excessive product returns for e-commerce
marketers.
This thesis consists of the introduction, three chapters, conclusion, reference list, and
appendix. The first chapter lays a theoretical foundation and formulates the hypotheses for the
current research. The second chapter is dedicated to discussing methodological approach
employed in the study, more specifically it presents research strategy and design, data collection
method and questionnaire structure. Finally, the third chapter focuses on data analysis and
discussion of the results of the study. It is comprised of five major sections respondents’
characteristics, descriptive statistics, model fit analysis, hypotheses testing, discussion and
managerial implications.
10
CHAPTER I. LITERATURE REVIEW
Impulsive buying definition
The phenomenon of impulsive buying started to attract the attention of scholars in the field
of consumer and marketing research over 60 years ago. This attention resulted in a considerable
academic effort to develop a definition of impulsive buying: almost every researcher made an
attempt to provide his own definition that perfectly captured the complex nature of the concept.
As a consequence, over the course of the XX century, impulsive buying definition has undergone
significant transformation.
In the early 50s, when the importance of impulsive purchasing was first brought to light in
marketing literature, academics considered impulsive buying largely synonymous with unplanned
purchasing, i.e. any purchase a consumer makes without advance planning (Clover, 1950). The
next research phase is characterized by describing impulsive purchasing with a simplified formula:
“Impulsive purchasing = unplanned purchasing + exposure to a stimulus” (Piron, 1991).
Applebaum (1951) was the first to suggest that consumer’s exposure to external stimulus may lead
to impulsive buying and developed the following definition: “buying that presumably was not
planned by the customer before entering a store, but which resulted from a stimulus created by a
sales promotional device in the store”.
A significant contribution to the extant research was made by Hawkins Stern, who
developed a classification of impulsive buying, that is still one of the most cited papers in the area
of impulsive buying research today. Stern (1962) identified four categories of impulsive buying:
•
Pure impulse buying: a purchase that has not been planned in advance which goes beyond
normal buying pattern.
•
Reminder impulse buying: the central element of this type of impulsive purchasing is the
previous experience a consumer has with a product or product knowledge that is recalled
in store when seeing an item. It is described as a purchase that occurs when consumer upon
seeing a product, remembers that the stock of this particular product at home is low and
has to be refilled, or a shopper is reminded of an advertisement or some information about
the product.
•
Suggestion impulse buying: it takes place when a shopper coming across a product for the
first time identifies a need for it without having prior knowledge about it.
•
Planned impulse buying: it occurs when a consumer has planned part of the purchases
before his visit to the store, while the purchase decisions about the other part of the
products are made on the spot based on sales promotions offered by the store.
Stern’s framework is built around the notion that impulsive purchasing is an escape or
novelty buy made without advance planning that is triggered in the store environment going
11
beyond consumer’s shopping habits. Impulse purchases stem from exposure to an external
stimulus such as coming across a product, discounts, special offers and other promotion activities
at the store level.
The stance that was taken by scholars in early studies on impulsive buying was limited and
subjective since they made an attempt to understand the phenomenon primarily from the retailer’s
perspective. They put the emphasis strictly on product attributes and did not take into account
consumer traits. Initial definitions of the construct were rather simplistic: impulsive purchase is
equal to unplanned purchase motivated by external stimuli that are controlled by marketers within
the confines of the store.
The first study on impulsive buying to shift the focus from product cues to consumer’s
personal characteristics was conducted by Rook and Hoch (1985). They believed that it is
consumers and not products who experience the need to buy impulsively, thus, to fully understand
this particular type of buying behavior it is crucial to examine consumer’s cognitive and emotional
reaction. From this psychological perspective, impulsive buying cannot be accurately explained
as just an unplanned purchase. Rook indicated that due to the fact that store layout helps consumers
to recognize the need for a product, not all unplanned purchases can be considered impulsive.
Today most researchers agree that all purchases made on impulse can be considered unplanned,
while not all unplanned purchases can be labeled as impulsive (cited in Amos et al., 2014). A
purchase is truly impulsive when a consumer being exposed to a product experiences a complex
reaction which may come as far as an emotional conflict (Rook, 1987). Rook offered one of the
most widely accepted definitions of impulsive buying that has been used in numerous studies:
“Impulsive buying occurs when a consumer experiences a sudden, often powerful and
persistent urge to buy something immediately. The impulse to buy is hedonically complex and
may stimulate emotional conflict. Also, impulse buying is prone to occur with diminished regard
for its consequences” (Rook, 1987).
This opened a research stream that concentrates on the behavioral dimension of impulsive
buying that explores internal motivators of impulsive behavior and the interaction of internal and
external stimuli. A considerable number of consumer research scholars have reached a consensus
about the complex hedonic nature of the construct. Beatty and Farrell (1998) stated that impulsive
buying refers to unplanned spontaneous purchase that is strongly associated with feelings of
excitement and pleasure along with a powerful urge to buy. Previous research indicated that this
urge is so powerful that individuals have difficulty to control it (Hoch and Loewenstein, 1991;
Rook and Fisher, 1995). Consumers describing their impulsive purchase episodes self-report that
when seeing a product it becomes so desired that it is impossible to resist the temptation to buy it
(Roberts and Manolis, 2012). Impulse buying temptations originate from consumer’s craving for
12
instant gratification through consumption (Vohs and Faber, 2007). As the ultimate goal of a
consumer in the act of impulse buying is immediate gratification and satisfaction, the concern for
consequences is very low or nonexistent (Taute and McQuitty, 2004; Punj, 2011). Baumeister
(2002) elaborated on this notion stating that when an individual engages in impulse buying, there
is no careful evaluation of the options and consideration of long-term goals, values, decisions, and
plans.
Sharma e al. (2010) also pointed to irrationality of impulse decision process due to its very
short span, proposing one of the most precise definitions of the phenomenon to date: “a sudden,
hedonically complex purchase behavior in which the rapidity of the impulse purchase precludes
any thoughtful, deliberate consideration of alternative or future implications”.
Taking into account the multitude of studies and definitions of impulsive buying, it is very
important to distinguish the term of impulsive buying from related concepts described in marketing
literature. First of all, the urge to buy impulsively does not equal to impulsive buying. The urge to
buy impulsively is tightly connected to impulsive behavior. However, when an individual faces
the urge to buy impulsively, it does not necessarily mean that he is going to respond to it. A
consumer may experience impulsive urges frequently, successfully resisting some of them, while
yielding to others. In other words, the urge may or may not lead to the actual purchase. Secondly,
impulsive buying and compulsive buying are two separate concepts. Compulsive buying is
considered to be abnormal consumer behavior. The voluminous body of psychiatric research
studying compulsive buying defines it as an uncontrolled, excessive buying behavior that can lead
to psychological distress and adverse consequences in individuals’ lives and financial debt
(Dittmar, 2005). The central element of compulsive buying disorder is its destructive influence
which stems from an individual’s inability to control buying impulses.
Building on the extensive stream of previous research, we have identified the following
distinctive characteristics of impulse buying:
•
Spontaneity and immediacy. The decision time span, i.e. the time spent on making the
decision to purchase a product after visual stimulation is very short. When seeing a
product, a consumer experiences a sudden urge for immediate ownership of a product.
During the episode of impulsive buying, a consumer reacts hastily to the impulse and
spontaneously decides to buy a product.
•
Hedonic dimension. Impulsive buying evokes intense feelings and emotions in consumers.
It may be associated with a state of psychological disequilibrium, when an individual goes
from feeling happy and excited yielding to the temptation of purchasing a product to
eventually feeling guilty about it. The act of impulse buying is primarily driven by a
13
powerful desire for instant gratification via consumption as opposed to satisfying a specific
need.
•
Low cognition. Making an impulse purchase, a consumer tends to disregard future
implications and costs incurred. The decision to purchase is made without reflection due
to arousal and hedonic temptation. While planned rational purchase decision may be
associated with a strong emotional reaction as well, there is a cognitive process behind it.
•
Exposure to a stimulus. External stimuli have a direct influence on the occurrence of the
impulse purchase. External stimuli may refer to the product per se, sensory stimuli, retail
environment (store atmospherics, store layout) and marketer-controlled cues.
Impulsive buying tendency
Impulsivity or impulsiveness refers to a spontaneous action made without reflection. The
concept of impulsiveness has been studied in various disciplines of social science. Self-control
failure stemming from an inability to resist powerful urges leads to impulsiveness. In general,
impulsiveness is associated with the lack of behavioral control and immediate desire to yield to
temptation. Extant research findings demonstrated that impulsive buying behavior is tightly
connected with impulsiveness (Hoch and Loewenstein, 1991; Sharma et al., 2010). Consumer
behavior literature actually provided evidence that individuals’ impulsive buying proneness stems
from their personal impulsiveness tendency, that is also found to be mutually related to other traits
such as variety seeking (e.g. Olsen et al., 2016) and materialism (e.g. Podoshen and Andrzejewski,
2012). Therefore, consumer’s impulsive buying trait or tendency is conventionally treated as a
subtrait of general impulsiveness.
Early studies that explored purchase behavior from personal impulsiveness tendency
perspective, developed lack of control scale that measured the inability to resist the impulse for
instant gratification (Amos et al., 2013). Consumers who exhibit high lack of control scores are
reckless and tend to make spontaneous decisions on impulse rather than sticking with a plan. Rook
and Fisher developed the first measure of impulsive buying tendency. Some individuals are
predisposed to buy on impulse since they have a higher impulsiveness tendency than other
individuals. This group of consumers tends to be more spontaneous in making their purchase
decisions and breaking normal shopping pattern. Besides, these individuals have low cognitive
control when it comes to purchasing, there is not much cognitive process behind their decision to
buy the product. Highly impulsive consumers also immediately respond to the urge to buy.
Additionally, they experience powerful urges more frequently as opposed to consumers with lower
impulsive buying tendency. Rook and Fisher’s scale is aimed to assess impulsiveness tendency in
the context of purchasing behavior (Rook and Fisher, 1995). The initial impulsive buying tendency
14
scale introduced by Rook and Fisher is still the most widely used measure of buying impulsiveness
adopted in the majority of studies of this phenomenon. Later on consumer behavior scholars have
introduced other buying impulsiveness scales (e.g. Beatty and Ferrell, 1998). They are most
commonly referred to as impulsive buying tendency (IBT) in marketing literature. These scales
basically measure to what extent an individual is predisposed to experiencing sudden buying urges
and making spontaneous purchase decisions in response to these urges. These measures were
empirically tested by a considerable number of researchers and indicated that people do vary in
their level of impulsiveness. It is very important to note that while IBT assesses personal trait, it
was also adopted to measure consumer’s decision to act on impulse when shopping (e.g. Park et
al.).
Impulsive buying in the online environment
With technological advances and massive e-commerce growth, today online impulsive
behavior has become a pervasive phenomenon. Online shopping has lifted the constraints of
conventional shopping such as social pressure form sales assistants, inconvenient locations and
limited opening hours. E-commerce websites are open 24/7, offer a wide variety of products and
accelerated buying process, allowing consumers to spend less time on contemplating their choice.
Thus, online retailers have created favorable conditions that encourage consumers to buy on
impulse (Eroglu et al., 2001).
Madhavaram and Laverie’s study that investigated impulsive buying in the online
environment, indicated that 22% of participants who completed the questionnaire have bought on
impulse when shopping online. The majority of this group of respondents have also made an
impulsive purchase in the retail setting. The results of this study suggest that similar to impulsive
buying in brick-and-mortar stores, online impulse purchase is predicted by exposure to stimuli that
go beyond the product per se. Online store browsing, positive emotions, and mood are also found
to have an impact on online impulsive buying behavior. Hence, online impulsive buying is very
similar to regular impulsive purchasing (Madhavaram and Laverie, 2004). Nevertheless, we have
to take into account that there are major differences between e-commerce and traditional retailers.
Online shopping is accompanied by a higher level of risk, as consumers cannot physically inspect
products before making a purchase. In contrast, in brick-and-mortar stores, shoppers can conduct
a visual and sensory product evaluation. Online retailing is associated with higher uncertainty and
perceived risk compared to conventional retailing. Consumers also tend to be reluctant to shop
online due to bank card payment security and shipping and return concerns.
Over the last decade, online impulsive buying has started to attract the attention of scholars.
One of the most widely accepted definitions of online impulsive buying is formulated as follows:
15
online impulsive buying is “a result of a purchaser’s immediate reaction to external stimuli that is
often hedonically charged. An impulse buying episode signifies a change in purchaser’s intention
to purchase that particular product before and after exposure to stimuli. The stimuli are not limited
to just the product, and change in purchaser’s intention does not include a reminder item that is
simply out of stock at home” (Madhavaram and Laverie, 2004).
The literature on impulsive buying in the online environment can be divided into two
principal research directions. The first research direction focused on investigating how
antecedents, that were found to predict impulsive purchasing in the conventional retailing
environment, affect impulse buying behavior in the online setting. An extensive body of literature
has studied the influence of marketer controlled stimuli such as price discounts, bonus packs and
promotions on online impulsive buying behavior (Dawson and Kim, 2010; Xu and Huang, 2014).
Kim and Eastin (2011) have conducted a study investigating hedonic consumption tendency and
its influence on online impulsive buying. The second research stream examined the impact of
website attributes on consumers’ impulsive buying tendency. Various studies investigated the
relationship between e-commerce website quality on impulsive purchase intention (e.g. Shen and
Khalifa, 2012).
Credit card use as an antecedent of impulse buying
Previous research indicated that payment method affects the so-called pain of payment.
According to Prelec and Loewenstein’s mental accounting model, cash payments are perceived
differently by consumers compared to other payment methods such as bank cards, i.e. when
consumers pay with cash, they experience greater pain of payment, even though the amount of
money to be paid is equivalent (Prelec and Loewenstein, 1998). Extant research findings suggest
that credit cards being a less vivid mode of payment feel different from cash payments. When
consumers would like to purchase something and face limited availability of money they typically
have a choice: to save money and postpone the purchase or to resort to credit and immediately buy
the product. Credit cards, that can be easily obtained by most individuals, instantly increase
consumers’ purchasing power and drive them to overspend and consequently encourage impulsive
buying.
Individuals who are impulsive in their purchase decisions, tend to pay by credit cards when
their emotional state is very unstable, i.e. they may be very excited or depressed. Credit cards
become an instant solution for responding to impulsive buying urges and push consumers to
disregard the consequences of purchase decisions. Highly impulsive consumers are likely to use
credit cards since they allow them to experience immediate gratification through consumption. In
contrast, consumers with high self-control level tend to carefully plan their purchases and respect
16
their budget constraints. The study by Roberts and Jones (2011) demonstrated that attitudes
towards money, anxiety, and power are tightly connected with compulsive buying behavior and
credit card use among American college students who are found to overspend for social status and
peer pressure reasons.
Buying decision process and post-purchase behavior
In 1968 Engel, Kollat and Blackwell introduced a model of consumer decision-making
process, which is still relevant for consumer behavior research today and it has been widely
adopted in a lengthy stream of literature (cited in Darley et al., 2010). The original Engel-KollatBlackwell model or EKB model has been widely discussed by the academic community and
modified throughout the years, however, its essence remained unchanged. It consists of five stages,
which are problem/need recognition, information search, evaluation of alternatives, purchase
decision and post-purchase evaluation (Figure 1).
Figure 1. Five stages of buying process (adopted from Darley et al., 2010).
Problem or
Need
Recognition
Information
Seeking
Evaluation
of
Alternatives
Purchase
Decision
PostPurchase
Evaluation
The first four stages of the model refer to consumer decision-making process, while the
final stage is the outcome of the preceding stages. According to Kotler, when a consumer has a
need, problem or recognition occurs. This need may be provoked by internal stimuli such as hunger
or thirst, or external stimuli such as marketer controlled price discounts and sales promotions.
Then during the stage of information search, a consumer may develop an interest in a product or
service, or he may search for information regarding this product or service. The evaluation of
alternatives stage implies that a consumer will contemplate his choice by comparing various
alternative options in an attempt to grasp which of the product meets his needs best. The purchase
decision is the next stage in the decision-making process when a consumer makes a mindful
decision about purchasing a product. It is important to note that a consumer may reverse his
decision due to opinion of other people (e.g. a relative or a friend that does not agree with
consumer’s positive product evaluation) or unforeseen events such as salary reduction.
Post-purchase evaluation is the closing stage of the decision process model. There are two
scenarios on this stage: either a consumer is satisfied with his purchase or he is unsatisfied with
the product. Customer satisfaction arises when product performance either corresponds to
consumer expectations or exceeds them. In contrast, a consumer is dissatisfied with the purchase
when the product falls short of his expectations. Consumer behavior in the post-purchase stage
17
typically is driven by the level of satisfaction. High customer satisfaction results in repurchase
intention, while low customer satisfaction leads to product returns or negative product reviews in
social media or e-commerce websites. Kotler also indicated that product use and disposal in the
post-purchase phase has to be monitored. For instance, consumers may negatively evaluate the
product but never return it to the store, they would rather keep it but never actually use it.
Building on this theoretical model and taking into account the nature of impulsive buying,
we assume that impulsive buyers would skip the first three stages and go straight to the purchase
decision stage. This pattern may lead to consumers experiencing negative emotions. Dealing with
their feelings, consumers try to justify their impulsive behavior. If the product falls short of their
expectations, consumers are also likely to regret their decision.
Return policy and perceived risk
Today e-commerce is going through a phase of major no-hassle product return trend.
Return policy has become an integral part of numerous online retailers’ value offering. It also has
a signaling effect on consumers who tend to make judgments about an online store’s reputation
and product quality based on return conditions. When deciding whether or not to purchase from
an e-commerce website, consumers consider not only its product range and price points but
product return procedure as well. As a consequence, leading e-commerce companies have adopted
very lenient return conditions with full refunds and free return shipping. This trend has transformed
consumer behavior in online retailing. Online shopping bears higher risk and uncertainty
considering that consumers cannot physically inspect products before making an order. Liberal
return policy compensates for this risk and acts as a purchase decision driver. Although consumergenerated content, specifically product reviews plays an important role in relieving the risk, it still
remains higher compared to brick-and-mortar stores. Lenient product return conditions are an
effective tool for tackling the issue of uncertainty related to online shopping and it may be
considered as a risk reliever that has a potential to stimulate sales (Janakiraman et al., 2016).
No-hassle product returns are introduced by online retailers to allow shoppers to reverse
purchase decisions they are not happy about without having to cover any additional fees. Basically,
if online store customers are dissatisfied with product performance or they simply do not need it
anymore, they are free to return their orders getting a full refund with no questions asked. On the
contrary, online shoppers do not seem to be very enthusiastic about stricter return policies, which
are perceived as a drawback. They are likely to avoid online stores with complicated return
procedures which imply that consumers have to pay return shipping fees, extra fee for restocking,
be compensated with store credit instead of a full refund and respect strict deadlines. Lenient return
18
policies have a positive effect on consumers and persuade them to buy products. It can also lead
to increased trust, brand commitment and loyalty (Bower and Maxham, 2012).
The no-hassle return policy is crucial in purchase decision-making process. There is a wide
range of online stores to choose from and consumers are likely to order from a store with liberal
product returns, as it does not bear additional financial risks and helps with relieving perceived
risk. When no additional fees are charged and barriers for returns are low, online shoppers tend to
buy several sizes or colors of the same clothing piece, for instance, when they are not sure which
one would suit them best. Thus, liberal return policy may trigger unnecessary ordering, which
occurs as consumers realize that they can easily reverse their purchase decisions and buy more
items than they have planned (Reinartz and Kumar, 2002).
Return policy has been prioritized by online retailers in an attempt to improve their
customer service since online shoppers typically consider lenient return conditions as a
prerequisite of store’s reputation, customer service quality and perceived value (Parasuraman et
al., 2005). Today e-commerce players rely on no-hassle return policy to successfully compete with
their rivals. Due to ever intensifying peer pressure in the sector, e-retailers are forced to offer easy
product returns. Regulatory legislation and fierce competition push online retailers to adopt lenient
return conditions despite the fact that they impose high costs and squeeze profit margins (Lantz
and Hjort, 2013). In this context, e-commerce players place great importance on how they
communicate product return conditions as they are believed to have a signaling effect on
consumers who evaluate intrinsic product attributes and service quality, and therefore, have a
potential to promote sales (Wood, 2001). This is the reason behind e-retailers’ large investments
in marketing campaigns that amplify the message about easy product returns among existing and
future customers to inform them and to boost their interest in ordering from the store (Petersen
and Kumar, 2009). There is empirical evidence that more than 70% of online shoppers consider
an online store’s return policy prior to making a purchase (Su, 2008). High awareness of return
policy, specifically refund procedure and return shipping, may clearly result in increased demand
and sales. Bower and Maxham’s research findings demonstrate that shoppers are likely to purchase
more products in case free product returns are offered by an online store than when additional fees
are charged (Bower and Maxham, 2012).
Return policy and online product return behavior
Even though return policy plays an important role in decreasing the risk associated with
online shopping and has a potential to enhance sales, it can also lead to excessive buying and
multiply product returns and consequently the impose higher costs on online retailers (Li et al.,
2013). Previous research has made an attempt to explore how return policy effects retailers’ bottom
19
line. For instance, different return policy factors were studied and research findings suggest that
lenient return policy has a positive impact on retailers’ profits when certain conditions are in place
(Batarfi, 2017). Nevertheless, only limited attention has been paid to product returns from
consumer behavior perspective.
Research papers dedicated to examining product return behavior in offline retailing
environment indicated that consumers have various reasons for product returns. In the study
exploring product returns among mail order buyers, Foscht et al. (2013) introduced a classification
of consumers based on their frequency of product returns. This classification has four groups of
product returners: heavy returners, medium returners, light returners and occasional returners.
Product returner groups differ not only in how frequently they engage in returns but in initial
motives behind their purchases and their spending habits.
Wachter et al. (2012) developed another classification regarding consumers who exhibit
product return behavior. It also distinguishes four groups of returners: the planned or unethical
returner (customers who intentionally plan unethical returns), the eager returner (customers who
consider product returns as a right decision and experience positive feeling when returning
products) and the reluctant or educated returner (customers who perceive product return
embarrassing and/or tend to experience guilt when returning products). Extant research
demonstrated that demographic characteristics such as age, gender, and income level may partially
explain product return behavior (Harris, 2010).
In this light, some consumers may have solid justification for returning products, which
they bought or ordered online, while other consumers may be simply abusing lenient return policy.
This phenomenon is called “fraudulent returns”, which is defined as “the returning of a product
broken by the customer after purchase or the returning of a non-faulty product after it had been
used” (Harris, 2010). Lantz and Hjort (2013) have examined this type of product return behavior
and found that apparel online stores also face the problem of fraudulent behavior, more specifically
retail borrowing when consumers exploit lenient return policy and return products that they have
used. Their research findings also demonstrated that liberal return policies reinforce retail
borrowing.
Overall, return policies have become a strategic point for online retailers which are striving
to strengthen their position in the market and ensure growth. Consumers tend to take advantage of
no-hassle return conditions and may not always have a legitimate reason for returning their
purchase. Various types of return behavior can be identified. On the one hand, product returns can
be unintended, when a product is negatively evaluated by a consumer, i.e. he is just not happy with
it. On the other hand, some customers can engage in product returns on purpose having this goal
in mind before even making the purchase, which definitely has an unethical component to it. Thus,
20
the timing of product return decision is key to retailers, as they can control product return process
in a timely manner. Online retailers can actually counteract consumer abusive return practices by
efficiently managing returns through timely provision of information.
Moreover, product return procedure may be initiated by consumers or by retailers. The
aspect of responsibility for initiating product return procedure in e-commerce can extend our
understanding of this ever-transforming phenomenon. Who is primarily responsible for triggering
online product returns? According to previous research implications, it is beneficial for e- retailers
to develop strategies aimed at effective management of product return behavior (Powers and Jack,
2013). While e-commerce has zero power to exterminate opportunistic behavior at its core, but
what online retailers can do is to identify customer groups who frequently resort to fraudulent
returns and profile them based on their purchase history and demographic characteristics.
Customer segmentation by product return behavior may be an effective tool in curbing excessive
returns for e-commerce sector, which requires insights on what motivations stand behind product
returns (Hjort and Lantz, 2012). Online stores can employ this information and design
differentiated return service, which can actually become a competitive advantage. E-retailers can
provide a better experience for existing customers and at the same time attract new ones by offering
return policy that accommodates purchase and product return patterns of different customer
segments (Powers and Jack, 2013).
Post-purchase negative emotions
Despite the fact that post-purchase evaluation is an integral part of commonly accepted
buyer decision process model, discussed earlier in this chapter, research has primarily focused on
purchase decision stage rather than on post-purchase consumer behavior (Kang and Johnson,
2009). In the post-purchase evaluation stage, consumers realize if the product matches their
expectations or not. If a product either meets or exceeds customer expectations they had prior to
purchase, positive post-purchase evaluation arises. In contrast, if a product falls short of customer
expectations he had before making a purchase decision, consumers are likely to have negative
post-purchase evaluations (Lee and Cotte, 2009). The post-purchase evaluation may stem from
product performance, but it is not the only factor that contributes to post-purchase evaluations.
Post-purchase evaluation cannot be considered a purely rational process when a product is assessed
based on its properties. Feelings, which do not have anything to do with product performance, play
an important role in forming post-purchase evaluations, especially when it comes to impulsive
buying (Kang and Johnson, 2009).
Although impulsive buying is frequently accompanied by strong positive emotions such as
happiness or excitement, impulsive buyers often experience negative emotions such as guilt and
21
regret in the aftermath of an impulse purchase (Rook, 1987). Individuals who buy on impulse are
prone to regret their purchase decisions since there was not much cognitive activity involved prior
to making that decision (Kang and Johnson, 2009). During an episode of impulsive purchase
positive feelings are so strong that they are typically not sustained in the post-purchase phase. As
a result, impulsive buyers are having a hard time to feel satisfied with their purchase that does not
match their high expectations. Consequently, impulsive buying behavior is associated with
negative product evaluations (Gardner and Rook, 1988). After an impulse purchase episode,
consumers are likely to experience negative emotions.
Post-purchase negative evaluations bear several implications regarding consumer behavior
(Bui et al., 2011). When negative evaluation is associated not only with the product but with a
brand, consumers may opt for other brands. Another scenario is keeping the product but never
actually using it in attempt to leave behind the unpleasant purchase experience. When dealing with
negative evaluations, consumers can complain about products not meeting their expectations to
their friends and relatives or sales assistants. In an e-commerce environment where online retailers
offer generous return policy, consumers enjoy hassle-free product returns or exchange products
without providing a feasible reason. Online shoppers are able to easily return their purchase even
though the product is in perfect condition, only because they experience guilt and regret in the
aftermath of an impulse purchase. In this light, easy product returns adopted by online retailers
provide an effective solution for consumers in case of post-purchase negative evaluation.
Product return behavior as a response to negative emotions
Online retailers’ product return rate is estimated to be between 25 and 40% across different
product categories. The majority of the products are returned not because of the defects but because
of negative product evaluations. Nevertheless, motivations of product return behavior in online
retailing have not been studied extensively in previous research. Consumers typically assess their
purchase based on product characteristics and performance, personal traits and store attributes
(Kang and Johnson, 2009). The influence of product characteristics on post-purchase evaluations
has been widely examined by the academic community. At the same time, personal consumer traits
such as impulsive buying tendency, along with store attributes e.g. return policy leniency which
seem to affect product return behavior in online setting, are limitedly explored in extant literature.
Individuals with high impulsive buying trait are typically less concerned about the
consequences of their purchase decisions and are not involved in a great deal of cognitive process
to evaluate product attributes (Rook, 1987). Furthermore, when impulsive shoppers are offered a
lenient return policy, they are likely to engage in the act of impulsive buying. Credit card payment
may spur consumer spending and push shoppers to buy on impulse since it instantly extends money
22
availability. These circumstances encourage impulse buying, knowing that they can easily return
products and afford to spend more due to credit money, online shoppers may not use rational
thinking to reflect on such issues as budget constraints. When online impulse buyers receive their
online orders, they may come to realization that they do not have the funds to support their
purchases or their expectations are unmet. Future financial realities may lead to rational
reassessment of the purchase and product return behavior. Negative post-purchase emotions such
as guilt or regret may encourage consumers with high impulsive buying trait to return e-commerce
merchandise.
Research gap
With tremendous e-commerce growth, online retailers have been booming over the past
decade. In extant management, literature e-retailers have been studied extensively regarding their
business model and practices. However, limited attention has been paid to online retailing from
consumer behavior perspective. From what is observed online stores create a very appealing
environment for impulse purchasing. Lenient return policies that have been adopted by the most
reputable online retailers and have become an integral part of their value offering, on the one hand,
has a potential to drive sales. On the other hand, it may fuel unnecessary ordering and increase
product returns. Today product returns are a huge cost driver for online retailers, which erodes
their margins. Despite the managerial relevance of the topic, product returns have been primarily
investigated from operational and supply chain management perspectives. Several studies have
examined the impact of return policy on profitability and proposed ways to optimize product return
and logistics to cut costs associated with it. While researchers have examined the antecedents and
effects of return policy, there is not enough knowledge about it from consumer’s perspective: how
it influences buying behavior, what are the reasons behind product return and how to mitigate it.
We hope to shed light on product returns associated with online impulsive buying. In order to
manage the problem of excessive product returns, it is crucial to know the characteristics of
consumers who are prone to returns. Based on that information, online retailers can develop
optimal return policies to curb excessive returns.
In addition, impulsive buying research has mainly focused on studying antecedents of
impulsive buying (external, demographic, personal), attempting to understand what triggers
impulse buying behavior in different settings. The post-purchase phase of impulsive buying has
been limitedly studied. Most importantly, since impulse buying is known to frequently result in
the negative emotional response, research should be conducted on extending the understanding of
the post-purchase phase and providing insight on how to reduce negative response related to
impulse buying.
23
Hypotheses development
This section is dedicated to presenting the theoretical background of research hypotheses
and the basis for the proposed conceptual model regarding product return behavior related to
impulsive buying in the e-commerce environment.
Lenient return policies are regarded as a strategic tool in improving customer service in the
online retailing environment. If consumers are not satisfied with their purchase, i.e. their
expectations were not met, they may be willing to return the merchandise to the store. Return
policy acts as a risk reliever considering that consumers are unable to physically inspect the
product prior to making a purchase. Consumers tend to be reluctant to buy from an online retailer
that does not have a liberal return policy in place. Hence, return policy is more important for online
retailing as opposed to conventional stores (Yalabik et al., 2005). Research findings suggest that
around 70% of online shoppers consider an online store’s product return procedure prior to placing
an order (Su, 2008). The return policy is crucial in consumer decision-making process as it
stimulates purchase decision. When online shoppers are sure that they can effortlessly cancel their
purchase decision in case the product would not live up to their expectations, they are likely to buy
more. Customer awareness about product return conditions, more specifically refund policy and
return shipping fee, may increase demand and drive sales. There is empirical evidence that
consumers tend to buy more products in case free product returns are offered by an online store
than when additional fees are charged (Bower and Maxham, 2012). In line with this argument, we
assume that lenient return policy can stimulate consumers to buy on impulse. The first hypothesis
is derived as follows:
H1: Perceived return policy leniency is positively related to online impulsive buying behavior.
Previous studies have made an attempt to understand how credit card use affects consumer
expenditure, compulsive buying tendency, and price perceptions. Research findings indicate that
credit card use leads to increased consumer expenditure (Feinberg, 1986). Individuals who
frequently pay for their purchases by credit cards, tend to spend more compared to individuals
who use other payment methods. Moreover, credit card holders are likely to go over their available
credit amount (Pirog and Roberts, 2007). Besides, credit cards allow consumers to experience a
lifestyle they otherwise could not afford (Cohen, 2007). Young consumers who tend to buy on
impulse are prone to accumulate debt due to heavy credit card use when shopping for goods (Wang
and Xiao, 2009). Additionally, credit card holders have a tendency to be less price conscious and
as a consequence to buy products with higher price points (Roberts and Jones, 2001).
24
Extant research findings indicated that credit card use is positively related to compulsive
buying behavior (Roberts and Jones, 2001), which is an intense form of impulsive buying. Credit
card users are found to have a high level of compulsive buying tendency (Park and Burns, 2005).
Credit cards instantly extend consumers’ financial resources availability and increase the
likelihood of impulse buying behavior. In the online retailing environment, the most popular
payment method is by debit or credit card, thus the likelihood that consumers pay for online
purchases is rather high. Considering established positive association between credit card use, we
hypothesize that:
H2: Credit card use has a positive effect on online impulsive buying behavior.
Impulsive purchasing is characterized by diminished concern for the consequences. This
careless approach may frequently result in overspending and negative emotional response in the
post-purchase phase when consumers actually receive their orders (Kang and Johnson, 2009). The
considerable body of research has tried to define an impulsive purchase and identify its
characteristics. Solomon has distinguished three types of impulsive purchases: unplanned purchase
that arises in unfamiliar store environment, or under time constraints, or when consumers are
reminded about the need to buy some item; impulsive purchase when consumers cannot resist the
temptation of instant gratification through consumption; compulsive buying that results from
consumers’ emotional distress, boredom or anxiety. The core difference between impulsive and
compulsive buying is that impulsive purchase is about specific product and moment, while
compulsive buying is about the continuous purchasing process (Solomon, 2008). Compulsive
buying is a chronic form of impulsive buying that arises as a coping mechanism in the situation of
negative feelings. The online retail setting is associated with higher level risk due to customer’s
inability to physically inspect the product prior to purchase and when they actually receive their
order they may be disappointed with it (Lim et al., 2016). Building on these research findings, we
assume that impulsive buying is positively related to post-purchase negative emotional response.
Thus, the third hypothesis is derived as follows:
H3: Online impulsive buying behavior is positively related to post-purchase negative emotional
response.
Product return becomes a great option for consumers when they are not happy with their
purchase. If consumer expectations have not been met by the product, they are likely to return it
to the retailer. Impulsive buyers have a tendency to be disappointed in the post-purchase phase
25
even if there is nothing wrong with the product and it is in perfect condition, as they are prone to
experiencing negative feelings of guilt and regret after committing an impulse purchase (Rook,
1987; Bayley and Nancarrow, 1998; Beatty and Ferrell, 1998). Online shoppers with high
impulsive buying tendency may be dissatisfied with their purchase decision due to feeling guilty
about their impulsive behavior. One way of dealing with negative emotions in the post-purchase
stage is to engage in product return. Impulsive buyers may try to forget their negative experience
by returning undesired e-commerce products. According to this logic, the fourth hypothesis is
derived as follows:
H4: Post-purchase negative emotional response promotes product return behavior.
Extant research findings demonstrate that relationship marketing activities have a positive
impact on customer satisfaction and loyalty. Customer relationship marketing is aimed at building
“long-term mutually satisfying relations with consumers as to earn and retain their long-term
preferences” (Sharifi and Esfidani, 2014). From this definition, it is known that these relations
begin when the purchase occurs. Once an order is placed, online retailers can initiate the
relationship with a client. Communication as one of the tools in relationship marketing arsenal has
a potential to minimize post-purchase negative evaluations. Customers tend to be happy with the
purchase experience, owing to high level of personal contact and customer engagement, which
results in customer satisfaction (Ndubisi, 2007). Furthermore, post-purchase communication can
actually decrease post-purchase regret (Chen, 2011). Taking into account that consumers
frequently experience the feelings of guilt and regret after an episode of an impulse purchase,
reinforcing their choice might be an effective way to make them feel better. Previous research
findings have shown that emails reinforcing consumer decision to reassure customers have a
potential to positively influence post-purchase product evaluations and make them better
(Nadeem, 2007).
Thus, we assume that post-purchase communication email campaigns an effective way to
engage with impulse buyers and reduce the intensity of negative response by increasing trust and
commitment. Besides, free gifts with a purchase are argued to increase customer satisfaction in
the online retailing environment. Gift giving in e-commerce can be considered an effective tool to
improving customer experience. Online shoppers are unable to see and touch the products before
placing an order online. At the moment when products are delivered, a complimentary gift form
an online store actually boost positive emotions and creates an overall pleasant online shopping
experience, driving customer satisfaction in case customer expectations are met. When an online
store customer expectations are not met, a gift that comes with his order smoothens out negative
26
emotions (Zhu et al., 2015). Building on previous research findings, we assume that post-purchase
communication and gift giving mitigates negative response related to online impulse buying. The
fifth hypothesis was formulated as follows:
H5: Post-purchase communication and gift giving moderates the relationship between impulse
buying and post-purchase negative emotional response.
Liberal return policies have been widely adopted by e-commerce players. Most of the times
online shoppers benefit from easy product return procedure enjoying full refund with no questions
asked. As a result, online stores’ customers are not very thorough in picking the right sizes/colors
and product configurations in general. On the one hand, lenient return policy is an effective means
of driving consumer purchase decision in online environment and consequently boosting sales
volume. On the other hand, consumers are often taking advantage of liberal product return
conditions, which spurs excessive ordering and leads to higher product returns and inflated costs
associated with it (Li et al., 2013). A field experiment conducted in Sweden that observed
consumer response to free shipping and returns in fashion e-commerce, demonstrated that lenient
product return conditions increase sales and product returns simultaneously (Lantz and Hjort,
2013). In addition, research findings indicate that return policy awareness leads to product return
behavior (Powers and Jack, 2013).
In this light, some consumers may abuse lenient return conditions and buy merchandise
with no intention to keep it. Fraud related to product returns clearly has become a major issue for
online retailers (Hjort and Lantz, 2012). It is argued that lenient return policy is likely to have an
impact of product return frequency in e-commerce. Previous studies demonstrate that
consideration for return policy is positively correlated with product return behavior in the context
of fashion merchandise (Kang and Johnson, 2009). No-hassle product return conditions induce
shoppers to be more reckless with their online orders. Perceived return policy leniency leads to
increased sales volume but at the same time inflates product return rates. In contrast, if stricter
return rules are in place, consumers are likely to be very careful and to put much thought into
making the right choice to avoid having to return back undesired products. Based on this rationale,
we hypothesize that:
H6: Perceived return policy leniency spurs product return behavior.
27
Conceptual model
The conceptual model of the current study is presented in Figure 2. The model aims to
explore the relationship between perceived return policy leniency, credit card use, impulsive
buying, post-purchase negative emotions and product return behavior in online retailing. Credit
card use and return policy leniency were expected to act as stimuli to impulse buying behavior.
The association between impulse buying behavior and negative emotions is portrayed. Negative
post-purchase emotions following impulse purchases were anticipated to lead to return behavior.
Figure 2. The conceptual model of the study.
H6
Return policy
Impulse
buying
Credit card
H3
Negative
emotions
H5
Product return
H4
Gifts
Post-purchase communication
28
CHAPTER II. RESEARCH METHODOLOGY
This chapter presents the methodology of the current research. It starts with the overview
of the methodological approach, research philosophy, research approach, research strategy and
design, data collection method and questionnaire structure. The methodological approach of the
current study is summarized in Figure 3.
Figure 3. The methodological approach of the study.
This paper employs positivist research philosophy, that implies objective observation and
description of reality. Current study aims to observe social reality, collect primary data through a
survey, conduct statistical data analysis and provide findings that can be generalized. Positivist
paradigm contends that only knowledge acquired by observing the reality is valid. Positivist
doctrine adheres to the view that the truth is determined by objective reality observation that is
when the role of the researcher is to gather and analyze data not interfering with constructs under
29
study. Hypotheses formulated in this thesis were based on theories described in the extant
literature. Hypotheses testing was executed through statistical analysis of collected data. Positivists
believe that researchers are independent when observing the social world and human interests are
irrelevant for the study. According to positivist doctrine, science and common sense should be
distinguished and studies should not be biased by common sense (Easterby-Smith et al., 2015).
Current study adopts a deductive approach as it is considered the most suitable for positivist
studies. We develop the hypotheses building on existing theories explaining impulsive buying and
product return behavior described in marketing and consumer behavior literature. Data collection
method was also selected in accordance with positivist paradigm. Hypotheses are supported or
rejected by statistically analyzing the data. As the current study goal is to confirm of reject
formulated hypotheses built upon existing theoretical foundation, and examine hypothetical causal
relationships between constructs, the research design nature is conclusive. In order to explore these
causal relationships, data was collected from a sample of Russian consumers using a selfadministered questionnaire. The data drawn from the sample was statically analyzed. To test the
reliability of scales and collected data and to examine relationships between variables the
Statistical Program for Social Scientists (IBM SPSS) was used.
Research strategy
Quantitative research strategy is deemed to be suitable for the purpose of the current study,
which is to verify the hypotheses. Quantitative research strategy as the most appropriate for
hypotheses testing through exploring casual relationships between constructs. Variables are
quantifiable and therefore, can be measured and analyzed statistically. As it was stated earlier in
this chapter, deductive approach and generalization are associated with positivist research. It is
important to take into account that a researcher has to tackle bias and ensure the independence of
observation. It seems that quantitative strategy addresses these issues in an effective manner and
accommodates current study objectives. Primary data was collected using a survey method. The
reason behind that is that surveys are widely employed in business research, since they allow to
answer on “who, what, where, how much and how many” questions (Saunders et al., 2003). In
addition, survey method is effective in collecting large volume of data from large population
portions. Survey method was employed in a form of self-administered questionnaire.
Data collection
Once the research problem is identified a researcher has to initiate data collection process.
The choice of data collection method to be applied in the study is determined by the type of data.
Academics distinguish two types of data: primary and secondary. The primary data refers to data
30
that has been collected for the first time and is tailored to research questions raised in a particular
study, i.e. the character of primary data is original. On the contrary, secondary data has been
already gathered and processed using statistical tools. It implies that the data and the results of
data analysis can be relatively easily accessed by a researcher, since they are presented in extant
literature. Obtaining secondary data does not bear the difficulties inherent to primary data
collection. However, secondary data has to be used with caution, since the suitability and reliability
of this type of data might be questionable when taken out of the context; the question of
inconsistency with current research objectives and the problem under scrutiny may arise. In this
light we deem primary data collection suitable for the current research.
Sampling procedure
Non probability sampling technique was employed in the current study. The convenience
sampling method refers to data, which is collected in an effective manner, taking into account
different factors such as access, time and cost.
The data were collected from a convenience sample of 157 individuals aged between 18 to
35 years via a self-administered questionnaire that was published online. A mixed sample of
millennials was considered appropriate in the context of this research for various reasons. First,
young people aged 18-35 are the most active customers of online retailers, they are exposed to
online shopping and have considerable experience with online retailing. Millennials are likely to
have knowledge about several online stores and their return procedure. Compared to generation X
consumers who are still reluctant to embrace e-commerce due to perceived risks associated with
online payment process and inability to physically evaluate products prior to purchase, millennials
place great importance on convenience and speed of the shopping process, as well as wider range
of products and access to information and insights such as product ratings and reviews from other
consumers.
Besides, being a truly digital generation, millennials are very online savvy and online
shopping is an integral part of their lives. Secondly, Generation Y consumers today are young
adults in their 20s and 30s representing a considerable proportion of the population who are getting
their degrees and building their successful careers. Millennials’ economic impact is already strong.
Their purchasing power is growing very fast and they are projected to be the highest spending
consumer group in the near future. Therefore, we concentrated on consumers younger than 35
years old and excluded other individuals from the survey compilation, since the probability that
consumers older than 35 have had solid experience with online retailers and have returned products
is rather low.
31
Questionnaire structure
The questionnaire was composed mostly using measurement items that were developed
and empirically validated in extant marketing literature (see Appendix 1). The items of the
questionnaire evaluated the following variables of the study: credit card use, perceived leniency
of return policy, impulsive buying, post-purchase emotional reaction and product return behavior.
Additionally, the survey collected demographic characteristics of participants and their online
shopping patterns.
In the preliminary part of the questionnaire participants were asked if they bought anything
online over the previous six months. In case a participant did not made an online purchase in the
last six must he was instructed to submit the form. Participants who had an experience of buying
products online, continued through the preliminary section by indicating which websites they had
ordered products from, how often they usually buy products via e commerce websites, which
product categories they usually purchase online and what is their preferred method of payment for
online purchases. Respondents were also asked to indicate how many credit cards (if any) they
owned. Credit card holders were directed to the next section of the survey dedicated to credit card
use. The rest of the respondents skipped this section and proceeded with the questions about online
stores’ return policy.
The purpose of the main part of the questionnaire (section two to six) was to assess the
constructs under study. Each section was aimed to measure credit card use, perceived return policy
leniency, impulsive buying, post purchase emotional reaction and product return behavior. The
seventh section of the questionnaire collected the information on post-purchase communication
and incentives that online shoppers typically receive when buying from online stores. The final
section of the survey included questions on respondents’ demographic characteristics. They were
asked to indicate their gender, age, education level and monthly income.
Measures
This study primarily relied on the multi-item scales that were verified and empirically
tested by researchers in extant marketing literature, apart from the perceived return policy leniency
scale that was developed specifically for this research. A 5-point Likert scale with a range from 1
= strongly disagree to 5 = strongly agree is applied to assess each item in the subsections two to
six. The scales adopted in this study are summarized in the table below:
Table 1. Multi-item scales.
Variable
Credit card use
Items
Source
7 items
Roberts and Jones (2001)
32
Impulsive buying
6 items
Rook and Fisher (1995)
Negative emotions
11 items
Gardner and Rook (1995)
Return behavior
3 items
Chatvijit Cook and Yurchisin (2017)
Credit card use
Respondents’ credit card use was measured adopting the scale that was developed by
Roberts and Jones (Roberts and Jones, 2011). They studied the impact of credit card use on
compulsive buying tendency among college students in the United States. To construct the scale
for credit card use measurement, they conducted several focus groups with students who
elaborated on how they manage their financial affairs, focusing on credit card use. The scale was
comprised of twelve items and tested on a sample of 122 students has shown a high level of
reliability of 0.81. A string of later studies in marketing literature has adopted this scale on different
samples not only in the US and it proved to be reliable. For instance, Park has used the scale to
evaluate credit card use of Korean fashion-oriented consumers (Park and Burns, 2005). The
original credit card use scale was adapted to the current research context: repetitive items and the
items that did not seem to be relevant for the study were eliminated. There is evidence that reduced
scale of credit card use comprising seven items out of twelve still delivers reliable results (Saleh,
2012). The resulting credit card use scale included items such as “I am more impulsive when I
shop with credit cards”, “I spend more money when I use a credit card” and “I am less concerned
with the price of a product when I use a credit card”.
Leniency of online retailers’ return policy
Perceived leniency of return policy scale was developed to fit the context of the current
study. Essentially, the leniency of online retailers has three aspects: fully refunded product return,
the shipping cost for returning products are handled by an online retailer and extensive time frame
for product return. The resulting scale contained three items: “I would not incur any costs If I had
to return a product to an online retailer”, “I would easily get my money back if I had to return a
product to an online retailer” and “I have plenty of time to decide if I want to keep the products
once I receive them”.
Online impulsive buying
The questionnaire included items to assess both impulsive buying behavior and impulsive
buying tendency. To measure online impulse buying behavior a one-item scale was adopted, as
suggested by Kacen and Lee (Kacen and Lee, 2002), since it is very understandable for
respondents and does not bulk up the questionnaire. Impulsive buying behavior is a simple
construct that can be effectively evaluated with one question: “How often do you typically buy
33
things online on impulse?”. The answer is measured on the Likert scale from 1-almost never to 5always. To assess impulsive buying tendency in the online retailing environment the study relied
on the scale proposed by Rook and Fisher, the most reliable and widely used impulsive buying
tendency scale in consumer behavior literature (Rook and Fisher, 1995). The scale was modified
to correspond with the context of the current study. The original scale comprised nine items and
reported a good level of reliability (0.88). However, to better fit the model and to avoid a very
lengthy questionnaire, the scale was reduced to 6 items. Six-item impulsive buying tendency scale
has shown sufficient reliability coefficients in several studies (Nor et al., 2014). The examples of
items are “I often buy things online spontaneously”, “I carefully plan most of my online purchases”
(reverse coded) or “Sometimes I am a bit reckless about what I buy online”.
Post-purchase emotional response
An eleven-item scale constructed by Gardner and Rook (Gardner and Rook, 1988) was
adapted to measure post-purchase emotional reaction. Although the original paper did not include
reliability level of this scale, similar scales adopted in papers on post-purchase emotional response
and satisfaction have demonstrated a high level of reliability. For instance, a similar scale was
used in the context of fast fashion retailers and its reported reliability level was satisfactory (0.83)
(Chatvijit Cook and Yurchisin, 2017). The scale comprised both positive such as excitement and
pleasure, and negative emotions such as guilt and regret. The survey included statements on how
consumers may feel after an online impulse purchase, for example, “After I buy something on
impulse online, I feel guilty”. The respondents were asked to assign the value from 1-strongly
disagree to 5-strongly agree to each of the statements. Positive post-purchase emotions were
reverse coded so that lower value corresponded with a negative experience.
Online product return behavior
To evaluate consumers’ product return behavior in the online retailing environment the
scale developed by Chatvijit Cook and Yurchisin (Chatvijit Cook and Yurchisin, 2017) was
adopted. The scale included three items that were slightly modified considering current research
context. Respondents were instructed to assign the value from 1-strongly disagree to 5-strongly
agree to the statements regarding their product return patterns in the online retailing environment.
The examples of items are “I frequently return products that I purchase online”, “I usually do not
return products that I purchase from online stores”.
34
Mediation testing method
Since the conceptual model of the current study contains mediation, we decided to adopt
methodological approach developed by Baron and Kenny (1986). This is a several step approach
that implies testing for mediation with several regression analyses examining the significance of
relationships between variables at each step. During the first step, the hypothetical causal
relationship is tested between variable X and Y. The second step is to run a single regression with
variable X predicting mediator variable. The third step is to conduct a single regression analysis
with a mediator predicting variable Y. If the above relationships are found to be significant, we
assume that there is some form of mediation and proceed with the final step. To test for mediation
multiple regression analyses is run with variable X and mediating variable predicting variable Y.
If mediating variable is found to be statistically significant along with the predictor variable, we
assume that there is partial mediation. If variable X is no longer significant in multiple regression,
while mediator is, there is full mediation. This approach has been widely used in extant research
and has proved to deliver valid results.
35
CHAPTER III. DATA ANALYSIS
This chapter is dedicated to data analysis and discussion of the results of the study. It is
comprised of five major sections respondents’ characteristics, descriptive statistics, model fit
analysis, hypotheses testing, discussion and managerial implications. The first section starts with
the overview of the sample characteristics and participants’ online shopping patterns. The next
section presents the descriptive statistics of variables under study: perceived return policy
leniency, credit card use, impulsive buying behavior, negative post-purchase emotions and product
return behavior. Then we continue with the model fit, hypotheses testing and reporting of the
results. Finally, the chapter is concluded with the discussion of the findings and practical
implications for marketers.
Characteristics of the sample
A total of 167 online questionnaire forms were submitted. 14 questionnaires were excluded
from the analyses since they were completed by individuals who are over 35 years old (the focus
of the study were generation Y consumers). The final sample comprised 153 valid questionnaires.
The data was collected by sending out the online survey to the followers of popular online stores’
official pages in Russian social network. The electronic link to the survey was sent out to 1000
users of the social network. The response rate amounts to 15.3%.
Demographic characteristics of the sample are presented in Table 2. Descriptive analysis
of collected data revealed that women represent the overwhelming majority of the sample: 74.5%
of valid questionnaires were filled out by female online shoppers. The remaining part (25.5%) of
the sample are male online shoppers. This skewness may be attributable to the population of the
social network; its female user base is actually larger than its male user base. Besides, considering
the topic of the questionnaire which is dedicated to online shopping behavior, no wonder that the
sample is skewed towards women. Female consumers are known to be more passionate about
shopping and they are, in general, more prone to impulsive buying behavior compared to men.
The largest age group is 22-25 years old with 34% of the total number of respondents followed by
the group of 26-30 years old with 28.8%. The youngest respondents aged between 18 and 21 years
and senior age group between 31 and 25 years represent 17% and 20.3% of the sample
respectively. The majority of respondents’ monthly income ranges from 20000 to 40000 RUB
(29.4%) followed by over 80000 RUB (20.9%). The monthly income of 20.3% of participants is
40000-60000 RUB, 15% of the sample has a monthly income of 60000-80000 RUB. Finally, the
lowest income group is presented by 14.4% of respondents. The vast majority of participants have
a university degree (79.1%) and the remaining part of the sample is represented by students.
36
Table 2. Demographics of the sample.
Variable
Frequency
Percentage
Male
39
25.5%
Female
114
74.5%
18-21
26
17%
22-25
52
34%
26-30
44
28.8%
31-35
31
20.3%
Less than 20000 RUB
22
14.4%
20000-40000 RUB
45
29.4%
40000-60000 RUB
31
20.3%
60000-80000 RUB
23
15%
Over 80000 RUB
32
20.9%
University
121
79.1%
Student
32
20.9%
Gender
Age
Income (monthly)
Education level
Total
153
Additionally, information regarding respondents’ online shopping habits was collected. It
is reported in table 3. Respondents indicated that they mostly purchased products from the USbased online retailer Amazon (49%), British online fashion and beauty store Asos (39.2%),
Russian online fashion retailer Lamoda (32%) and Russian e-retailer Ozon (26.1%) followed by
American shopping website eBay (19.6%), official brand websites e.g. Inditex group brands
(17.6%), Chinese online retailing platform AliExpress (14.4%), Russian-based online fashion
retailer Wildeberries and Italian online fashion outlet Yoox (13.7%), Russian e-retailer Ulmart
(13%) and British online beauty and personal care store Feelunique (10.4%).
As for product categories purchased online, 67.3% respondents indicated that they
typically buy apparel and accessories making it the most popular product category in e-commerce.
The second most bought online product category is books/music/video with 58.8% followed by
consumer electronics and beauty and health with 41.8% and 41.2% respectively. One fifth of
participants (20.9%) stated that they typically purchase sports and recreation products form online
37
stores, home and garden product category is usually ordered online by 17.6% of the sample.
Jewelry and watches product category was indicated by 15.7% of respondents and furniture,
appliances and equipment products are typically purchased from online retailers by 12.4% of the
sample. The least popular product categories for online shopping are office supplies and groceries
with 8.5% and 7.8% respectively. When it comes to online shopping frequency, high number of
respondents place online orders at least once a month (38.6%), followed by two or three times a
month (27.5%). One-fifth of the sample indicated that they usually purchase products form ecommerce websites two or three times a year. Some participants engage in online shopping once
a week (9.8%). Finally, only 3.9% of the sample makes an online purchase once a year. The
payment method of choice is debit card for the vast majority of respondents accounting for 59.5%
followed by credit card with 32.7% of the sample. The least used payment methods are cash on
delivery and PayPal with 6.5% and 1.3% respectively. The overwhelming majority of respondents
do not own credit cards (56.2%) while 26.1% of the sample holds one credit card and rather high
number (17.6%) of respondents hold two or three credit cards. Considering that credit card holders
are primarily represented by the older group of millennials aged between 31 and 35 years, we
assume that younger respondents avoid credit use due to the lack of stable revenue and negative
perceptions about consumer credit in general. From this analysis we can see that the respondents
have been exposed to online shopping and have extensive experience with online stores to
adequately fill out the questionnaire. We can also conclude that generation Y consumers are
comfortable with online bank card payments despite Russian consumers’ reluctance to reveal their
bank card credentials due to security reasons and general trend for cash on delivery on the Russian
e-commerce market.
Table 3. Online shopping behavior of the sample.
Variable
Frequency
Percentage*
Amazon
75
49%
Asos
60
39.2%
Lamoda
49
32%
Ozon
40
26.1%
eBay
30
19.6%
Official brand online store
27
17.6%
AliExpress
22
14.4%
Yandex.Market
22
14.4%
Online stores patronized
38
Wilberries
21
13.7%
Yoox
21
13.7%
Ulmart
20
13%
Feelunique
16
10.4%
Apparel & accessories
103
67.3%
Books/music/video
90
58.8%
Consumer electronics
64
41.8%
Health & beauty
63
41.2%
Sports & recreation
32
20.9%
Home & garden
27
17.6%
Jewelry & watches
24
15.7%
Furniture, appliances & equipment
19
12.4%
Office supplies
13
8.5%
Groceries
12
7.8%
Once a month
59
38.6%
2 or 3 times a month
42
27.5%
2 or 3 times a year
31
20.3%
Once a week
15
9.8%
Once a year
6
3.9%
Debit card
91
59.5%
Credit card
50
32.7%
Cash on delivery
10
6.5%
PayPal
2
1.3%
0
86
56.2%
1
40
26.1%
2 or 3
27
17.6%
Products typically purchased online
Online shopping frequency
Preferred payment method
Credit cards owned
*If the percentage exceeds 100%, several response options could have been chosen.
Preliminary analyses
Although in order to measure variables the study primarily relies on multi-item scales
developed and empirically validated in previous research, the reliability of scales had to be
39
verified. Considering that these scales were built by Western scholars and they were, for the most
part, tested in developed countries, we had to make sure that these scales are applicable to Russian
consumers as well. Additionally, the perceived leniency of return policy has been developed
specifically for the purpose of the current study and has never been empirically verified. To test
scale reliability, Cronbach’s alpha was calculated. Cronbach’s alpha is a widely adopted
coefficient to measure the reliability of psychometrically developed scales (Aaker, 2007).
Cronbach’s alpha was also used to test the internal consistency of scales. Cronbach’s alpha value
can range from 0 to 1, where 0 refers to completely unreliable scale and 1 indicates a completely
reliable scale. The majority of studies in A-list marketing journals suggest that the minimum
coefficient value is 0.70 for latent variable scales.
Preliminary reliability analysis showed that the majority of scales have a good level of
reliability well above the threshold of 0.70. However, credit card use scale initially indicated a
somewhat questionable Cronbach’s alpha coefficient (0.612). In order to tackle this issue, it was
decided to carry out confirmatory factor analysis, which was also adopted for the goodness of fit
estimation of the model proposed in the current study.
Since we collected the measures of latent variables through a questionnaire, we used
confirmatory factor analysis to test for construct distinctiveness. Amos software was used to build
the model and to examine loading coefficients of each item of the scales. This procedure revealed
poor factor loadings in the credit card use scale, the one that exhibited low reliability level in the
initial reliability testing. Post-purchase negative emotional response scale has also indicated low
factor loadings of some items. These items with low loading coefficients were excluded from both
scales and from further statistical analysis. Chi-square difference tests indicated that a five-factor
model (perceived leniency of return policy, credit card use, impulse buying, post-purchase
negative emotional response and product return behavior) demonstrated good fit to the data (chisquare/df 2.329; CFI 0.895; IFI 0.897; LISREL GFI 0.801).
After CFA another reliability test was conducted to verify credit card use and post-purchase
negative emotional response (where the items were eliminated). Both scales have demonstrated a
high level of reliability. Table 4 presents the reliability coefficients of all the scales adopted in the
current study (for SPSS output see Appendix 2).
Table 4. Reliability of scales.
Variable
Credit card use
# of items
Reliability
3*
0.884
40
Return policy leniency
3
0.926
Impulsive buying
6
0.936
Post-purchase negative
emotional response
7*
0.877
Product return behavior
3
0.848
*The number of items after some items were eliminated from the scale.
Prior to performing multiple regression analyses, Pearson’s product-moment correlation
for the main study constructs was run to make sure that there is a considerable correlation between
the variables of the model and it makes sense to execute regression analyses. The correlation
matrix was also used to screen the data for multicollinearity. Correlation coefficients for study
variables are reported in table 5 (for SPSS output see Appendix 3). Pearson’s correlation revealed
high correlation coefficients among variables under study, justifying regression analyses for
verifying the direction of dependence among variables. According to proposed conceptual model,
there are two independent variables that are not supposed to be correlated with each other,
otherwise, multicollinearity issue would arise and regression analysis would not be accurate. There
was a small positive correlation detected between credit card use and return policy leniency r =
0.277, p < 0.01. These dynamics are in line with the conceptual model and formulated hypotheses.
Table 5. Pearson’s correlations for main study variables.
Return policy
leniency
Post-purchase
Credit card use
Impulsive buying
emotions
Credit card use
.277*
Impulsive buying
.679*
.469*
.642*
.520*
.610*
.626*
.525*
.591*
Post-purchase negative
emotions
Product return
negative
.648*
*p < 0.0005
Hypotheses testing
In order to test all the hypotheses of the current study, a series of single and multiple
regressions were conducted. The data was first tested for assumptions. The assumption of linearity
41
was met as assessed by partial regression plots and a plot of studentized residuals against the
predicted values. The data met the assumption of independent residuals (Durbin-Watson statistic
close to the value of 2). The scatterplot of standardized predicted values indicated that the
assumption of homoscedasticity was not violated. The histogram of standardized residuals
demonstrated that the distribution of errors was close to normal. The P-P plot of standardized
residuals contained points that were distributed very close to the line. The data also satisfied the
assumption of collinearity as indicated by tolerance values greater than 0.1.
The first dependent variable analyzed in the current study was impulsive buying tendency.
A multiple regression was run to test the effects of credit card use and perceived return policy
leniency on impulsive buying tendency (Hypotheses 1 and 2).
The model itself proved to significantly predict impulsive buying tendency, F (3, 149) =
52.974, p < 0005, adj. R2 = 0.506. The model accounted for approximately 51% of the variance in
the dependent variable. Regression coefficients and standard errors are summarized in table 6 (for
SPSS output see Appendix 4). The first hypothesis stated that credit card use has a positive effect
on impulsive buying tendency. Linear regression revealed that credit card use is positively related
to impulsive buying tendency (β = 0.190, p = 0.004). Hence, the first hypothesis developed in the
current study is supported. The second hypothesis claimed that impulsive buying tendency is
positively influenced by perceived return policy leniency. Multiple regression indicated that there
is a statistically significant relationship between perceived return policy leniency and impulsive
buying tendency (β = 0.574, p < 0.0005). Thus, the second hypothesis is supported as well.
Additionally, gender was identified as a significant predictor of impulsive buying (β = 0.166, p =
0.004).
Table 6. Multiple regression analyses predicting impulsive buying.
B
SE B
(Constant)
Credit card use
.335
.309
.366
.106
.190**
Return policy leniency
Gender
.531
.447
.060
.164
.574***
.166**
Variable
***p < 0.0005
**p < 0.01
The second dependent variable of the current study is the post-purchase negative emotional
response. To test for mediation, we adopted the approach developed by Baron and Kenny (1985),
which implies performing several regression analyses. According to our conceptual model, credit
card use and perceived return policy leniency act as predictors of post-purchase negative emotions,
42
while impulsive buying acts as a mediator. We first run the regression to test the effect of credit
card use and perceived return policy leniency on the post-purchase negative emotional response.
Multiple regression model established the statistically significant association of credit card use and
perceived return policy leniency with the post-purchase negative response, F (2,150) = 67.009, p
< 0.0005, adj. R2 = 0.465. The model explained roughly 47% of the variance in the dependent
variable. The coefficients and standard errors are presented in table 7 (for SPSS output see
Appendix 5). All variables added significantly to the prediction: both credit card use (β = 0.277, p
< 0.0005) and return policy leniency (β = 0.510, p < 0.0005) are identified as predictors of negative
post-purchase emotions.
Table 7. Multiple regression predicting post-purchase negative emotions (Model 1).
Variable
(Constant)
Credit card use
Return policy leniency
B
SE B
.618
.406
.423
.293
.099
.056
.277***
.510***
***p < 0.0005
The next stage in mediation analysis was to test the effect of impulsive buying tendency
on the post-purchase negative response. According to the third hypothesis, impulsive buying
tendency may be positively related to post-purchase negative emotional response. The multiple
regression was performed to predict the post-purchase negative emotional response from
impulsive buying tendency and demographic variables such as gender and income. The linear
regression model demonstrated that post-purchase negative emotional response is statistically
significantly predicted by impulsive buying tendency F (4,148) = 28.929, p < 0005, adj. R2 = 0.424.
The model accounted for roughly 42% of the variance in post-purchase negative emotional
response. Regression coefficients and standard errors are reported in table 8 (for SPSS output see
Appendix 6). Multiple regression established the relationship between impulsive buying and postpurchase negative response, (β = 0.601, p < 0.0005. Thus, there is evidence that allows us to reject
the null hypothesis and conclude that the third hypothesis is supported.
Table 8. Multiple regression analyses predicting post-purchase negative emotions (Model 2).
Variable
(Constant)
Impulsive buying
Gender
Age
B
SE B
.773
.539
-.337
.066
.453
.057
.165
.019
.601***
-.131*
.265**
43
Income
***p < 0.0005
**p < 0.01
*p < 0.05
-.197
.063
-.241**
The findings of the regression analyses confirm that the relationship among credit card use,
perceived return policy leniency (independent variable) impulsive buying tendency (mediator) and
post-purchase negative response (dependent variable) do exist. Since there were these statistically
significant relationships, we assume that some form of mediation takes place. To verify the
mediation effect of impulsive buying tendency on the post-purchase negative emotional response,
a multiple regression was performed. The multiple regression model statistically significantly
predicted post-purchase negative emotions, F (3, 149) = 51.015, p < .0005, adj. R2 = .497. It
explained approximately 50% of the variance in post-purchase negative emotions. The results of
multiple regression analyses are summarized in table 9 (for SPSS output see Appendix 7). Both
the predictors (credit card use and perceived return policy leniency) and the mediator (impulsive
buying) were proven to influence post-purchase negative response. These results suggest that there
is partial mediation.
Table 9. Multiple regression analyses predicting post-purchase negative emotions (Model 3).
Variable
(Constant)
Impulsive buying
Credit card use
Return policy leniency
B
SE B
.529
.234
.334
.296
.286
.072
.098
.067
.261*
.228*
.356*
*p < 0.0005
Moderation analyses
Finally, to investigate whether established in the study relationship between impulsive
buying tendency and the post-purchase negative emotional response is moderated by gift giving
and post-purchase communication (Hypothesis 4). In order to do that hierarchical regression was
performed. We first ran the hierarchical regression with gifts as a moderator variable. The results
are summarized in table 10 (for SPSS output see Appendix 8). The model proved to statistically
significantly predict post-purchase emotional response, F (6, 146) = 52.121, p < 0.0005, adj. R2 =
0.669. It explained roughly 67% of the variance in the dependent variable, which is the negative
post-purchase emotional reaction. Hierarchical regression indicated that the relationship between
impulsive buying tendency and post-purchase negative emotions is moderated by gifts from online
retailers, i.e. the interactive effect proved to be significant, β = -0.603, p < 0.0005. From the
regression analyses, we can see that impulsive buying tendency is a predictor of post-purchase
44
negative emotions and that the strength of the relationship between impulsive buying tendency
and the negative emotional response is moderated by free gifts. The moderating effect of gifts is
evident since R square increased when the moderator was entered into the regression. There is
evidence that gifts from online retailers weaken the link between impulsive buying tendency and
post-purchase negative emotions.
Table 10. Multiple regression analysis (Moderator gifts).
Variable
B
SE B
(Constant)
1.611
.402
Impulsive buying
.499
.071
.556***
Moderator 1
-.480
.105
-.603***
Gifts
.285
.355
.126
Gender
-.325
.125
-.126*
Age
.050
.014
.201**
Income
-.170
.049
-.207**
***p < 0.0005
**p < 0.01
*p < 0.05
We then proceeded with the second hierarchical regression with post-purchase
communication as a moderating variable. The results of multiple regression analysis are reported
in table 11 (for SPSS output see Appendix 9). The model itself was found to be significant, F (4,
148) = 37.007, p < 0.0005, adj. R2 = 0.487, accounting for around 49% of variance in the dependent
variable. While the direct effect of post-purchase communication on post-purchase negative
emotions was significant, β = -0.292, p < 0.0005, the interactive effect of impulsive buying
tendency and post-purchase communication with online stores’ customers was not significant.
Therefore, there is no evidence of moderation effect of post-purchase communication. The fourth
hypothesis formulated in the current study is partially supported.
Table 11. Multiple regression analysis (Moderator post-purchase communication).
Variable
(Constant)
Impulse buying
Moderator 2
PPC
Age
B
SE B
1.467
.499
-.048
-.658
.030
.449
.068
.051
.177
.014
.557***
-.077
-.292***
.120*
45
*p < 0.05
***p < 0.0005
Since the direct effect of post-purchase communication on post-purchase negative
emotions was found to be statistically significant, another hierarchical regression was executed to
assess combined impact of post-purchase communication and interactive effect of impulse buying
and gifts. The regression coefficients and standard errors are presented in table 12 (for SPSS output
see Appendix 10). The model statistically significantly predicted post-purchase negative emotions,
F (4, 148) = 68.947, p < 0.0005, adj. R2 = 0.641, explaining approximately 64% of the variance in
the dependent variable. The interactive effect between impulsive buying tendency and gifts was
still significant, β = -0.485, p < 0.0005, while post-purchase communication effect was not
significant.
Table 12. Multiple regression analysis (predictors: moderators: gifts and post-purchase
communication).
Variable
B
SE B
(Constant)
2.586
.195
Impulse buying
.465
.046
.518***
Moderator 1
-.386
.047
-.485***
PPC
-.160
.137
-.071
-.276
.128
-.107*
Gender
***p < 0.0005
*p < 0.05
The third dependent variable of the current study is product return behavior. To test for
mediation, we first had to make sure that the relationships between independent variables,
mediator variables, and dependent variable were statistically significant. According to the
conceptual model of the current study, credit card use and perceived return policy leniency act as
predictors of product return behavior, while impulsive buying tendency and post-purchase
negative emotional response mediate this relationship.
The first multiple regression was run to assess the effects of credit card use and perceived
return policy leniency on product return behavior. The model was proven to significantly predict
product return behavior, F (2, 150), p < 0.0005, adj. R2 = 0.417. Approximately 42% of the variance
in product return behavior was explained by the model. Multiple regression coefficients are
reported in table 13 (for SPSS output see Appendix 11). Both credit card use, β = 0.227, p = 0.001
and perceived return policy leniency, β = 0.519, p < 0.0005 were significant predictors of product
return behavior.
46
Table 13. Multiple regression analysis predicting product return (Model 1).
Variable
B
SE B
(Constant)
-.208
.322
Credit card use
.359
.109
.227**
Return policy leniency
.451
.060
.519***
***p < 0.0005
**p < 0.01
The next step was to examine the hypothetical association of post-purchase negative
emotional response with product return behavior by performing a single linear regression
(Hypothesis 5). The linear regression model statistically significantly predicted product return
behavior F (1, 151) = 109.072, p < .0005, adj. R2 = .416. Regression coefficients and standard
errors can be found in table 14 (for SPSS output see Appendix 12). The findings suggest that postpurchase negative response is positively related to product return behavior, β = 0.648, p < 0.0005.
Thus, the fifth hypothesis of the study is supported.
Table 14. Regression analysis predicting product return (Model 2).
Variable
B
SE B
(Constant)
.335
.248
Post-purchase negative emotions
.722
.069
.648***
***p < 0.0005
Based on the above single and multiple regression results, we conclude that there were
statistically significant associations among predictors, mediators, and dependent variable. Under
these conditions, we can assume that there may be some form of mediation. Hence, the final phase
of mediation testing was initiated. In order to find out whether the relationship between product
return behavior (dependent variable) and perceived return policy leniency and credit card use
(independent variables) is mediated by impulsive buying tendency and post-purchase negative
emotional response, multiple regression was run. The multiple regression model proved to be
statistically significant in predicting product return behavior, F (4, 148) = 42.371, p < .0005,
adj. R2 = 0.521. Regression coefficients and standard errors can be found in table 15 (for SPSS
output see Appendix 13). Both the predictors and mediators added statistically significantly to the
prediction, p < .05. Therefore, there is empirical evidence of partial mediation.
Table 15. Multiple regression analysis predicting product return (Model 3).
Variable
(Constant)
B
SE B
-.597
.314
47
Post-purchase negative emotions
.341
.089
.306**
Impulsive buying
.161
.081
.161*
Credit card use
.292
.111
.179**
Return policy leniency
.218
.078
.235**
**p < 0.01
*p < 0.05
To test the hypothetical relationship between perceived return policy leniency and product
return behavior a single regression was executed (Hypothesis 6). The model was significant and
indicated that perceived return policy leniency has a positive impact on online product return
behavior, F (1, 151) = 93.523, p < 0.0005, adj. R2 = 0.378. The model accounted for around 38%
of the variance in product return behavior. Regression coefficients and standard errors can be
found in table 16 (for SPSS output see Appendix 14). The regression analysis results showed that
online product return behavior is positively influenced by perceived return policy leniency, β =
0.618, p < 0.0005. Hence, there is empirical evidence of the positive association between return
policy leniency and online product returns. The sixth hypothesis is supported.
Table 16. Regression analysis predicting product return (Model 4).
Variable
B
SE B
(Constant)
.623
.207
Return policy leniency
.537
.056
.618***
***p < 0.0005
Post-hoc analysis
Since gender was found to have a statistically significant effect on impulsive buying
tendency during hypotheses testing, we decided to conduct a post-hoc analysis. Descriptive
statistics for impulsive buying tendency are reported in table 17 (for SPSS output see Appendix
15). On average, female respondents scored considerably higher in impulsive buying tendency
compared to male respondents. In other words, women tend to be more impulsive with their
purchases than men, which seemed very feasible.
Table 17. Descriptive statistics for impulsive buying by gender.
Variable
Mean
Std. Deviation
Impulsive buying
Males
Females
2.795
3.442
1.250
1.219
48
To test whether the difference in impulsive buying tendency scores between female and
male consumers was statistically significant, ANOVA was performed. The data met the
assumption of homogeneity of variances with Levene’s statistic of 0.547. Impulsive buying
tendency score was statistically different between males and females, F (1, 151) = 8.076, p = 0.005.
Thus, there is empirical evidence that female consumers are more impulsive in their purchase
decisions than male consumers.
The results of hypotheses testing are summarized in the table below:
Table 18. The results of hypotheses testing.
H1
Hypothesis
Results
Perceived return policy leniency is positively related to online impulsive
Supported
buying behavior.
H2
Credit card use has a positive effect on online impulsive buying behavior.
Supported
H3
Online impulsive buying behavior is positively related to post-purchase
Supported
negative emotional response.
H4
H5
Post-purchase communication and gift giving moderates the relationship
Partially
between impulse buying and post-purchase negative emotional response.
supported
Post-purchase negative emotional response promotes product return
Supported
behavior.
H6
Perceived return policy leniency spurs product return behavior.
Supported
49
Figure 4. The results of hypotheses testing on the conceptual model.
β =.618***
Return policy
= .161*
Negative
emotions
Impulse buying
Credit card
= .306**
= -.603***
gifts
Product return
= -.292***
PPC
Theoretical and practical implications
The current study provides insight for both academicians and practitioners. Theoretically,
the results of the current study contribute to a greater understanding of consumer behavior in ecommerce environment in general. More specifically, the current study extends the knowledge
base pertaining to the impulsive buying behavior in online retailing. Furthermore, this research
contributes to the field by providing a complete picture of the post-purchase phase of impulsive
buying purchase behavior in the online setting. In addition, the findings suggest that emotional
response in the post-purchase stage of consumer behavior lead to disposition decisions regarding
e-commerce merchandise. Current paper made an attempt to extend the understanding of online
purchase behavior from acquisition to disposal. The author also has successfully attempted to
examine product returns in online retailing from a different angle, that is from consumer’s
perspective and enriched consumer behavior literature on this issue. Finally, our research has shed
light on the behavior of Russian consumers and has empirically tested marketing scales that were
developed in previous literature and mostly tested in developed countries. The scales adopted in
the current study have proven to be applicable not only to Western countries but to a developing
country as well. This paper has extended the knowledge about Russian consumers’ behavior in the
online retailing environment and proposed a conceptual model specifically developed for ecommerce impulse buying and product return behavior.
As for managerial implications, our findings may be used by online retailers. The results
of the current study clearly demonstrate that credit card use and perceived return policy leniency
are predictors of impulsive buying tendency. Online retailers have created an environment that
50
drives impulsive buying and impulsive buying is frequently followed by the negative emotional
response, which in its turn triggers product return behavior. Considering that product returns are a
major cost driver that erodes e-retailers’ profit margins, it is necessary to develop strategies for
tackling excessive product returns associated with impulsive buying.
The results of the study suggest that online shoppers who perceive product return
conditions as lenient are likely to buy on impulse. E-commerce sector can benefit from these
findings and make informed decisions when developing their marketing strategies. On the one
hand, liberal return policies are crucial for driving purchase decisions, since they compensate for
the higher perceived risk of online shopping related to customer’s inability to physically inspect
products prior to purchase. Additionally, return policy has a signaling effect on consumers, who
judge about stores’ reputation and product quality based on return policy conditions. Therefore,
the trend of no-hassle return policies cannot be reversed and e-commerce business cannot simply
adopt stricter product return policies without jeopardizing their sales, as today consumers are
accustomed to an easy return procedure.
On the other hand, online retailers have control over how they choose to communicate their
product return conditions. For instance, if no-hassle return policy becomes an element of online
retailers’ marketing strategy, i.e. the message about easy product returns is very evidently
conveyed, return policy is an integral part of value offering and it is often one of the first things
online shoppers see on the webpage (see Appendix 16). Following the rationale of the current
study, this approach may stimulate impulsive buyers to make purchase decisions that they are very
likely to regret later and as a consequence, they may engage in product return. A better approach
regarding return policy communication is a subtle message placed on the store’s front page that
informs online shoppers about favorable return conditions but does not stress it excessively (see
Appendix 17). Another option is not placing any information about return conditions on the front
page, which implies that customers have to search for product return conditions on purpose in case
they are particularly interested in this information. Not integrating a no-hassle return policy in the
value offering and making it an element of marketing strategy may help online retailers to avoid
unnecessary ordering as well as product returns stemming from impulsive buying.
Another direction that online retailers can follow using our research findings to curb
product return behavior is to identify customers who are likely to frequently engage in product
returns in the aftermath of impulsive purchases. Online stores’ customers who tend to pay for their
online orders by credit cards are likely to act on impulse regarding their purchase decisions.
Besides, women are found to be more impulsive when shopping online compared to men. It does
not cost e-retailers anything to profile these clients based on the history of their purchases by
selecting female shoppers who frequently pay by credit cards. They can develop a customized
51
approach for these customers in order to prevent them from buying on impulse and return ecommerce merchandise. For instance, online stores’ can adopt an email marketing strategy that
implies sending out fewer email letters with promotions and offers.
Impulse buying behavior can result in post-purchase negative evaluation in the online
retailing environment, leading to customer dissatisfaction. Participants of the study returned
products to online stores after they experienced negative post-purchase emotions. Online retailers
have to understand the reasons behind online product returns related to impulsive buying to prevent
customer dissatisfaction and to tackle the issue of excessive product returns. Consumers’ negative
feelings from previous-purchase disappointment may lead to reluctance to repurchase products
from e-retailers. E-commerce marketers have to be aware of this issue and find marketing
strategies to increase customers’ satisfaction after impulsive buying episodes. The findings of the
current study suggest one method that may be effective in reducing post-purchase negative
reaction and consequently preventing excessive product returns. Gift giving proved to moderate
the association between impulsive buying and post-purchase negative emotional response. In other
words, impulse buying does not always result in post-purchase negative evaluations and gift giving
helps to weaken the strength of this relationship. The respondents of the study who received gifts
with their orders from online retailers were less likely to experience feelings of guilt and regret in
the aftermath of an impulsive purchase.
E-commerce marketers can use this information not only to increase customer satisfaction
but to minimize product returns related to impulsive buying. Based on the customer database and
purchase history marketers can identify clients that are likely to be impulsive and experience
negative emotions and employ gift-giving strategy to create a positive purchase experience and
increase customer satisfaction. If the customer is happy after his impulse purchase or at least not
as unhappy if he could have been without a gift from an online store, he may be less likely to
reverse his purchase decision by returning products bought on impulse. Additionally, the current
study revealed that post-purchase communication with online stores’ customers reduces postpurchase negative emotions and since negative emotions may lead to product returns in the online
environment, these findings cannot be underestimated by marketers. Online retailers can develop
marketing programs for post-purchase email communications to build long-term relationships with
customers, creating positive purchase experience and increasing customer satisfaction. The main
purpose of such email letters, especially in case of impulsive purchases, is emphasizing the
excitement after the purchase and reinforcing customers that they have made the right choice.
Reassuring customers about their purchase decisions is very effective in preventing impulsive
buyers from experiencing guilt or regret after the purchase. In order to successfully convey this
message, it is crucial to avoid impersonal mailouts (see Appendix 18) and to try to connect with
52
the customer on a personal level (see Appendix 19). It is also important to channel the brand voice
and identity of an online store, making the post-purchase phase very positive and upbeat. The
layout of the letter should be visually stimulating (Appendix). This can obviously go beyond
simple thanking customers for their purchase email letters. For example, e-commerce marketers
can launch campaigns on social media and ask customers to share their purchase experience (see
Appendix 20). Furthermore, marketers can also engage with customers by asking them to leave a
product review providing a monetary incentive such as a discount voucher for the next purchase.
These measures can help online retailers to drive positive post-purchase emotions and increase
customer satisfaction, which can result in lower product returns associated with online impulsive
buying.
Limitations and future research directions
This study as any research paper had a number of limitations that can be covered in the
future research. The sample of the current study was considerably skewed towards female
consumers. It will be beneficial to draw and examine a more balanced sample since e-commerce
websites sell goods both to men and women. Future research could conduct an in-depth
comparison of female and male online purchase behavior and to explore the differences in
impulsive buying and product return behavior associated with demographic characteristics, the
income level for instance. Besides, the current thesis sample was rather small and research can
benefit from investigating the relationships among credit card use, perceived return policy
leniency, impulsive buying, post-purchase negative emotional response and product return
behavior using a larger sample. Additionally, the proposed model was only empirically tested on
Russian consumers and future studies may apply the developed model to other countries and see
whether it is applicable to a wider range of countries and nationalities and whether the results of
the current research can be generalized beyond the Russian market.
The data for this thesis was drawn from a convenience sample. Although the respondents
have clearly had an extensive experience with online retailing to adequately complete
questionnaires, the responses of individuals from a non-convenient sample would offer important
insight for e-commerce sector. Moreover, respondents were asked about their online shopping
patterns and tendencies towards impulsive buying, post-purchase negative emotions, and product
returns. Scholars can examine the actual emotional reactions and return behavior in a field study
with an experimental design to extend the understanding of post-purchase consumer behavior in
the online retailing setting. Besides, future research can focus on conducting a follow-up study
with individuals who ordered products form e-retailers to investigate their post-purchase emotional
responses and actual product return behavior.
53
Another research avenue can investigate how product return behavior related to impulsive
buying differs in the online retailing environment and in brick-and-mortar stores. While current
study focused on e-commerce setting, product return behavior related to impulsive buying can
differ in across retail channels. Future researchers could examine the differences between prepurchase and post-purchase both in online and offline environments to develop managerial
recommendations for curbing product returns and reducing costs associated with them.
In addition, the effect of culture on the post-purchase emotions stemming from impulsive
buying can be studied. There is empirical evidence that collectivist and individualist societies
differ in impulsive buying behavior. Since collectivist cultures put great value on self-control,
consumers from these countries may be less likely to engage in impulsive buying and therefore
may be more prone to feeling guilty afterward. Meanwhile, individualist countries cultivate
hedonic consumption and consider shopping to be a leisure activity. Consumers from such
countries may buy impulsively more frequently and experience strong positive emotions in the
aftermath of self-indulgence. Exploring cultural aspect of impulsive buying and its post-purchase
phase may extend the understanding of consumers across the globe and allow retailers to develop
strategies tailored to different markets.
54
CONCLUSION
With proliferation of Internet and tremendous growth of e-commerce, online impulsive
buying has become e pervasive phenomenon. Lifting some of the conventional shopping restraints
such as social pressure from sales assistants, limited opening hours and inconvenient locations,
online stores have created an environment that fuels impulsive purchasing. Meanwhile, the trend
of liberal return policies in e-commerce can lead to excessive products returns, which have become
a major cost driver for online retailers that erodes their profitability.
The purpose of this research was to investigate consumer product return behavior related
to impulsive buying in the online retailing environment. We investigated product returns from
consumer’s perspective and made an attempt to better understand the phenomenon of online
product returns from the consumer’s perspective through the prism of impulsive buying.
The research question of this thesis: How product returns related to impulsive buying can
be reduced in the e-commerce environment?
To answer this question, the factors that may influence online product returns have been
identified and the conceptual model was developed. Hypothetical relationships among credit card
use, the perceived leniency of return policy, impulsive buying, post-purchase negative emotional
response and product return behavior were investigated.
Current study adopts a quantitative strategy. Questionnaire method of data collection was
employed. Primary data was collected from a convenience sample of 153 Russian consumers. The
series of single and multiple regressions were performed to test the hypotheses developed in the
study.
Research findings clearly state that credit card use and perceived return policy leniency are
positively related to impulsive buying tendency. There is evidence that impulsive buying tendency
in its turn may result in the post-purchase negative emotional response. The hypothesis that
predicted that post-purchase negative emotions may lead to product return behavior in the online
retailing environment was supported as well in the current study. Additionally, the interaction
effect of impulsive buying tendency and gifts from online retailers was found to be significant, in
other words, there is empirical evidence that the causal relationship between impulse buying
tendency and post-purchase negative emotions is moderated by gifts. While there was no
significant interactive effect found between impulsive buying and post-purchase communication
with online stores’ customers, it was found that post-purchase communication negatively
influences post-purchase negative emotional response.
Regarding theoretical contribution of the current paper, it has extended the knowledge
about consumer behavior in e-commerce and more specifically increased the understanding of
online product return behavior from consumer’s point of view. This study also has enriched
55
marketing literature on impulsive buying and its post-purchase phase which has been limitedly
studied in previous research.
The results of the current research can be also used by online retailers to tackle the issue of
excessive product returns associated with impulsive buying. Online retailers should carefully
communicate their return policies and develop strategies to reduce product returns that are not
related to product defects. They can do so by identifying online shoppers that may engage in
product return due to negative post-purchase evaluations based on their purchase history. To
minimize negative emotional reaction in the aftermath of an impulsive purchase e-commerce
marketers can send gifts to target customers and adopt post-purchase email communication
programs, which have a potential to create an overall positive shopping experience and increase
customer satisfaction. As a result of curbing negative emotions, they may prevent product returns
related to impulsive buying.
56
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60
APPENDIX
Appendix 1. Questionnaire
61
Appendix 2. Reliability analyses SPSS outputs
1) Credit card use scale
Reliability statistics
Cronbach's
alpha
N of items
,884
3
2) Perceived return policy leniency
Reliability statistics
Cronbach's
alpha
N of items
,926
3
70
3) Impulsive buying tendency
Reliability statistics
Cronbach's
alpha
N of items
,936
6
4) Post-purchase negative emotional response
Reliability statistics
Cronbach's
alpha
N of items
,877
7
5) Product return behavior
Reliability statistics
Cronbach's
alpha
N of items
,848
3
Appendix 3. Pearson’s correlation matrix SPSS output
Correlations
CREDIT_S
UM
CREDIT_S
Pearson correlation
POLICY_SU IMPULSE_SU
M
1
M
NEGAT_SU RETURN_SU
M
M
,277**
,469**
,520**
,525**
,000
,000
,000
,000
153
153
153
153
1
**
**
,626**
,000
,000
,000
UM
Sig. (2-tailed)
N
POLICY_S
Pearson correlation
153
**
,277
,679
,642
UM
IMPULSE_
Sig. (2-tailed)
,000
N
153
153
153
153
153
,469**
,679**
1
,610**
,591**
Sig. (2-tailed)
,000
,000
,000
,000
N
153
153
153
153
153
**
**
**
1
,648**
Pearson correlation
SUM
NEGAT_SU Pearson correlation
,520
,642
,610
M
Sig. (2-tailed)
,000
,000
,000
N
153
153
153
,000
153
153
71
,525**
,626**
,591**
,648**
Sig. (2-tailed)
,000
,000
,000
,000
N
153
153
153
153
RETURN_S Pearson correlation
1
UM
153
**. Correlation is significant at the 0.01 level (2-tailed).
Appendix 4. Multiple regression results predicting impulsive buying tendency
Model Summaryb
Std. Error of
R
Adjusted R
the
Model
R
Square
Square
Estimate
1
,718a
,516
,506
,88164
A. Predictors: (Constant), POLICY_SUM, GENDER,
CREDIT_SUM
B. Dependent Variable: IMPULSE_SUM
ANOVAa
Model
1
Regressio
n
Residual
Total
Sum of
Squares
Mean
Square
Df
123,527
3
41,176
115,816
239,342
149
152
,777
F
Sig.
52,974
,000b
A. Dependent Variable: IMPULSE_SUM
B. Predictors: (Constant), POLICY_SUM, GENDER, CREDIT_SUM
Coefficientsa
Model
1
(Constant)
GENDER
Unstandardized
Coefficients
B
Std. Error
,067
,332
,477
,164
CREDIT_S
,309
,106
UM
POLICY_S
,531
,060
UM
a. Dependent Variable: IMPULSE_SUM
Standardize
d
Coefficients
Beta
,166
T
,201
2,904
Sig.
,841
,004
,190
2,925
,004
,574
8,816
,000
72
Appendix 5. Multiple regression results predicting post-purchase negative emotions
(Model 1)
Model Summaryb
Std. Error of
R
Adjusted R
the
Model
R
Square
Square
Estimate
1
,687a
,472
,465
,82310
A. Predictors: (Constant), POLICY_SUM,
CREDIT_SUM
B. Dependent Variable: NEGAT_SUM
ANOVAa
Sum of
Squares
Model
1
Regressio
n
Residual
Total
Mean
Square
Df
90,797
2
45,399
101,624
192,421
150
152
,677
F
Sig.
67,009
,000b
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), POLICY_SUM, CREDIT_SUM
Coefficientsa
Model
1
(Constant)
CREDIT_S
UM
Unstandardized
Coefficients
B
Std. Error
,618
,293
,406
POLICY_S
,423
UM
A. Dependent Variable: NEGAT_SUM
Standardize
d
Coefficients
Beta
T
2,107
Sig.
,037
,099
,277
4,108
,000
,056
,510
7,555
,000
Appendix 6. Multiple regression results predicting post-purchase negative emotions
(Model 2)
Model Summaryb
Model
1
R
,662a
Std. Error of
R
Adjusted R
the
Square
Square
Estimate
,439
,424
,85420
A. Predictors: (Constant), INCOME, IMPULSE_SUM,
GENDER, AGE
B. Dependent Variable: NEGAT_SUM
ANOVAa
Model
1
Regressio
n
Sum of
Squares
Mean
Square
Df
84,433
4
F
21,108
Sig.
,000b
28,929
Residual
107,988
148
,730
Total
192,421
152
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), INCOME, IMPULSE_SUM, GENDER, AGE
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
Model
1
(Constant)
IMPULSE_
SUM
GENDER
AGE
,773
,453
,539
,057
-,337
,066
INCOME
-,197
A. Dependent Variable: NEGAT_SUM
Standardize
d
Coefficients
Beta
T
Sig.
1,708
,090
,601
9,414
,000
,165
,019
-,131
,265
-2,048
3,522
,042
,001
,063
-,241
-3,154
,002
Appendix 7. Multiple regression results predicting post-purchase negative emotions
(Model 3)
Model Summaryb
Model
R
R
Square
Adjusted R
Square
1
,712a
,507
,497
A. Predictors: (Constant), POLICY_SUM,
CREDIT_SUM, IMPULSE_SUM
B. Dependent Variable: NEGAT_SUM
Std. Error of
the
Estimate
,79816
ANOVAa
Model
Sum of
Squares
Df
Mean
Square
F
Sig.
74
1
Regressio
n
Residual
Total
97,499
3
32,500
94,922
192,421
149
152
,637
51,015
,000b
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), POLICY_SUM, CREDIT_SUM, IMPULSE_SUM
Coefficientsa
Model
1
(Constant)
Unstandardized
Coefficients
B
Std. Error
,529
,286
IMPULSE_
,234
SUM
CREDIT_S
,334
UM
POLICY_S
,296
UM
A. Dependent Variable: NEGAT_SUM
Standardize
d
Coefficients
Beta
T
1,850
Sig.
,066
,072
,261
3,244
,001
,098
,228
3,400
,001
,067
,356
4,402
,000
Appendix 8. Multiple regression results testing for moderation effect of gifts
Model Summaryb
Model
1
R
,826a
Std. Error of
R
Adjusted R
the
Square
Square
Estimate
,682
,669
,64767
A. Predictors: (Constant), INCOME, MOD1, GENDER,
IMPULSE_SUM, AGE, GIFTS
B. Dependent Variable: NEGAT_SUM
ANOVAa
Model
1
Regressio
n
Residual
Total
Sum of
Squares
Mean
Square
Df
131,178
6
21,863
61,243
146
,419
192,421
152
F
52,121
Sig.
,000b
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), INCOME, MOD1, GENDER, IMPULSE_SUM,
AGE, GIFTS
75
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
1,611
,402
Model
1
(Constant)
IMPULSE_SU
M
MOD1
GIFTS
GENDER
AGE
INCOME
Standardize
d
Coefficients
Beta
T
4,004
Sig.
,000
,499
,071
,556
7,030
,000
-,480
,285
-,325
,050
-,170
,105
,355
,125
,014
,049
-,603
,126
-,126
,201
-,207
-4,577
,803
-2,593
3,501
-3,470
,000
,424
,010
,001
,001
A. Dependent Variable: NEGAT_SUM
Appendix 9. Multiple regression results testing for moderation effect of post-purchase
communication
Model Summaryb
Model
R
R
Square
Std. Error of
the
Estimate
Adjusted R
Square
1
,707a
,500
,487
A. Predictors: (Constant), AGE, MOD2,
IMPULSE_SUM, PPC
B. Dependent Variable: NEGAT_SUM
,80623
ANOVAa
Model
1
Regressio
n
Sum of
Squares
96,220
Mean
Square
Df
4
24,055
F
37,007
Sig.
,000b
Residual
96,201
148
,650
Total
192,421
152
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), AGE, MOD2, IMPULSE_SUM, PPC
Coefficientsa
Model
Unstandardized
Coefficients
Standardize
d
Coefficients
T
Sig.
76
1
(Constant)
IMPULSE_SU
M
B
1,467
Std. Error
,449
,499
,068
,051
,177
,014
MOD2
-,048
PPC
-,658
AGE
,030
A. Dependent Variable: NEGAT_SUM
Beta
3,266
,001
,557
7,334
,000
-,077
-,292
,120
-,940
-3,711
2,057
,349
,000
,041
Appendix 10. Multiple regression results testing for moderation effect of gifts and direct
effect of post-purchase communication
Model Summaryb
Model
1
R
,807a
Std. Error of
R
Adjusted R
the
Square
Square
Estimate
,651
,641
,67383
A. Predictors: (Constant), GENDER, MOD1,
IMPULSE_SUM, PPC
B. Dependent Variable: NEGAT_SUM
ANOVAa
Sum of
Mean
Model
Squares
Df
Square
F
Sig.
1
Regression
125,222
4
31,305 68,947
,000b
Residual
67,200
148
,454
Total
192,421
152
A. Dependent Variable: NEGAT_SUM
B. Predictors: (Constant), GENDER, MOD1, IMPULSE_SUM, PPC
Coefficientsa
Model
1
(Constant)
IMPULSE_SU
M
Unstandardized
Coefficients
B
Std. Error
2,586
,195
Standardize
d
Coefficients
Beta
T
13,295
Sig.
,000
,465
,046
,518
10,005
,000
MOD1
-,386
,047
-,485
-8,131
,000
PPC
GENDER
-,160
-,276
,137
,128
-,071
-,107
-1,173
-2,154
,243
,033
77
A. Dependent Variable: NEGAT_SUM
Appendix 11. Multiple regression results predicting product return behavior (Model 1)
Model
1
Model Summaryb
R
Adjusted R Std. Error of
Square
Square
the Estimate
,424
,417
,89349
R
,651a
ANOVAa
Model
1
Regression
Residual
Sum of
Squares
88,265
119,748
Df
2
150
Mean
Square
44,133
,798
F
55,282
Sig.
,000b
Total
208,013
152
A. Dependent Variable: RETURN_SUM
B. Predictors: (Constant), CREDIT_SUM, POLICY_SUM
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
-,208
,322
Model
1
(Constant)
POLICY_SU
M
CREDIT_SU
M
Standardize
d
Coefficients
Beta
T
-,646
Sig.
,519
,451
,060
,519
7,536
,000
,359
,109
,227
3,302
,001
Appendix 12. Multiple regression results predicting product return behavior (Model 2)
Model Summaryb
Std. Error of
R
Adjusted R
the
Model
R
Square
Square
Estimate
1
,648a
,419
,416
,95896
A. Predictors: (Constant), NEGAT_SUM
B. Dependent Variable: RETURN_SUM
78
Model
1
Regression
ANOVAa
Df
Mean Square
Sum of
Squares
100,303
Residual
138,860
Total
239,163
A. Dependent Variable: RETURN_SUM
B. Predictors: (Constant), NEGAT_SUM
1
100,303
151
152
,920
Coefficientsa
Unstandardized
Standardize
Coefficients
d
Coefficients
B
Std. Error
Beta
,335
,248
,722
,069
,648
Model
1
(Constant)
NEGAT_S
UM
A. Dependent Variable: RETURN_SUM
F
Sig.
,000b
109,072
T
Sig.
1,349
10,444
,179
,000
Appendix 13. Multiple regression results predicting product return behavior (Model 3)
Model
R
Model Summaryb
Adjusted R
R Square
Square
Std. Error of the
Estimate
1
,731a
,534
,521
,86793
A. Predictors: (Constant), POLICY_SUM, CREDIT_SUM, NEGAT_SUM,
IMPULSE_SUM
B. Dependent Variable: RETURN_SUM
ANOVAa
Model
1
Regressio
n
Residual
Sum of
Squares
Mean
Square
Df
100,303
1
138,860
151
F
100,303 109,072
Sig.
,000b
,920
Total
239,163
152
A. Dependent Variable: RETURN_SUM
B. Predictors: (Constant), NEGAT_SUM
79
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
Model
1
Standardize
d
Coefficients
Beta
(Constant)
NEGAT_S
UM
IMPULSE_
SUM
-,597
,314
,341
,089
,161
,081
CREDIT_S
,292
,111
UM
POLICY_S
,218
,078
UM
A. Dependent Variable: RETURN_SUM
T
Sig.
-1,899
,060
,306
3,823
,000
,161
1,977
,045
,179
2,628
,009
,235
2,806
,006
Appendix 14. Single regression results predicting post-purchase negative emotions
Model Summaryb
Model
1
R
Adjusted R
Square
R Square
,618a
,382
Std. Error of the
Estimate
,378
,92233
ANOVAa
Model
1
Regressio
n
Residual
Sum of
Squares
Mean
Square
Df
79,559
1
79,559
128,454
151
,851
F
Sig.
93,523
,000b
Total
208,013
152
A. Dependent Variable: RETURN_SUM
B. Predictors: (Constant), POLICY_SUM
Coefficientsa
Model
1
(Constant)
Unstandardized
Coefficients
B
Std. Error
,623
,207
POLICY_SU
,537
,056
M
A. Dependent Variable: RETURN_SUM
Standardize
d
Coefficients
Beta
,618
T
3,009
Sig.
,003
9,671
,000
80
Appendix 15. ANOVA results for differences in impulsive tendency scores between
GENDERs
Descriptives
IMPULSE_SUM
N
Mean
Std.
Std. Error
95% Confidence Interval
Deviation
Minimum
for Mean
Maximu
m
Lower
Upper
Bound
Bound
,0
39
2,7949
1,25042
,20023
2,3895
3,2002
1,00
4,50
1,0
114
3,4415
1,21850
,11412
3,2154
3,6676
1,00
4,83
Total
153
3,2767
1,25484
,10145
3,0763
3,4771
1,00
4,83
Test of Homogeneity of Variances
IMPULSE_SUM
Levene
Statistic
Df1
Df2
Sig.
,364
1
151
,547
ANOVA
IMPULSE_SUM
Sum of
Squares
Between
Groups
Within Groups
Total
Mean
Square
Df
12,151
1
12,151
227,191
239,342
151
152
1,505
F
8,076
Sig.
,005
81
Appendix 16. Return policy as an integral part of e-commerce marketing strategy example
Appendix 17. Return policy as a subtle signal example
Appendix 18. Impersonal post-purchase email example.
83
Appendix 19. Personal post-purchase e-commerce email examples
84
Appendix 20. Customer engagement example
85
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