Empirical Investigation of Consumers’ Impulse Purchases from
Television Home Shopping Channels:
A Case of Order Cancellation Behavior
Sang Hee Bae
New York University
Stern School of Business
Sungjoon Nam
Rutgers Business School
Sang-Hoon Kim
Seoul National University
This version: Feb 2011
VERY PRELIMINARY
ABSTRACT
Due to retail channel proliferation and mega size retail stores, marketers are putting more efforts in instore marketing tactics to induce more impulse purchases while customers are at the stores. In particular,
customers are more likely to purchase on impulse when they discover a product by chance with a
price promotion or a bundling/multi-unit packaging offer. Although impulse buying is a widespread
phenomenon, previous literature heavily relies on interrupt survey data or lab experiments on unplanned/
impulse purchases. Also, the empirical data have been limited to a small number of product categories
such as fashion and grocery.
This paper empirically investigates the consumers’ retrospective canceling behavior on previous
impulsive purchases for pricing, product bundling, and packaging offer. We use order canceling data from
a television home shopping channel to proxy impulse purchase behavior. Order cancellation occurs when
a customer places a product order, and subsequently cancels the order even before the product is shipped.
The unique aspect of our data can distinguish between impulse purchases and unplanned purchases. We
find that a small price discount significantly lowers consumer order cancellation by 30%. Also impulse
buying is more likely to occur when products are sold with bundling offers for hedonic goods, but not
for utilitarian goods. Furthermore, order cancellation varies by multi-unit packaging of products. These
findings give managerial implications for in-store price promotion and bundling practices.
Keywords: impulse buying, order cancellation behavior, television home shopping
1.
Introduction
New media proliferation and emergence of mega size retail stores make it hard to induce consumers
to recognize and to buy new products with conventional advertising driven marketing activities. As
traditional marketing efforts becomes less effective, marketers are trying to drive sales relying on more
on in-store marketing tactics such as in-store coupon, various pricing display, improved shelf displays,
active bundling and multi-unit packaging. To attract shoppers at the moment of purchase, retailers and
manufacturers devise innovative packaging or display technologies. Live product demonstrations, large
flat TV display with video clips about new products are commonly seen in retail stores. These efforts
are to inform shoppers in stores and to boost more sales through impulse purchases at the moment of
purchase. The Point-of-Purchase Advertising Institute (POPAI) found that two-thirds of supermarket
purchases are the result of an in-store decision (Agnew 1987). “Marketing Actions That Influence
Shopper Behavior” is a focus of the Marketing Science Institute’s 2010 “Shopper Marketing” research
initiative. Consumers no longer go to a retailer to buy the product they desired. They buy products when
they they are in shopping mode in stores.
Impulse purchasing becomes a prevalent phenomenon. More than $10 billion generated annually in
impulse purchases worldwide, and between 27% and 62% of all department store purchases are identified
as ‘unplanned’ purchases. Also during the past 15 years, an average of 38% of adults responded positive
to the prompt: “I am an impulsive buyer.” Wellas (1986) suggested that nine out of ten shoppers
occasionally buy on impulse.
However, previous marketing literature has limitations to predict impulse purchase behaviors given
various in-store marketing tactics lacking appropriate accurate measures on impulse purchases. Unlike
the previous research which relies on surveys, we obtain objective input and output measures on
impulse purchases using a new data set, TV home shopping data. The key difference is that unlike
the “unplanned” shopping list in survey data, we use the order canceling data which come from
consumers’ retrospective regretting decisions.
The goal of this paper is to investigate the consumers’ impulse purchase behaviors using a new
objectively measured product canceling behavioral data in respect to price discount, various product
bundling, packing options and product life cycles. It is obtained from a major television home shopping
retailer (such as QVC and HSN in the United States) in Korea, a world top five retailer with 17 million
customers. It carries a large range of products (Figure 1) including well-known national brands and
local new products exclusive to this home shopping channel. These unique characteristic to help us to
investigate the impulse purchases accurately and to make our results generalizable to much broader
product categories. We find significant effects of pricing, bundling, and multi-unit packing effects on
order canceling behaviors.
Impulse buying behavior was first examined by the Du Pont Company in 1945 where it was defined only
as unplanned purchasing. Stern (1962) further classified impulse buying into several distinct categories
of pure, reminder, suggestion, and planned impulse buying1. More recent research shows the elements
of impulsive purchases (Wood, 1998; Rook 1987; Hausman 2000); (a) little or no planning, (b) sudden
and spontaneous desire or urge to buy something immediately or “on the spot”, (c) the presence of a
heightened emotional state, and (d) a reduction in cognitive evaluation and reduced consideration for
the consequences of impulse purchasing. Previous literature approaches the impulse purchase behaviors
using purchase survey data and lab experiments in limited product categories. A series of lab experiments
were conducted by researchers in consumer behavior literature examining the impulsive spending for
utilitarian/hedonic product categories (e.g., Narasimhan et al., 1996), for physical proximity (Faber
1
Pure impulse buying is characterized by a total lack of preplanning, while reminder impulse buying refers to
buying when a shopper sees an item and remembers that the stock at home is exhausted. Suggestion impulse buying
refers to buying when a shopper sees a product for the first time and visualizes a need for it though there is no
previous knowledge of it. Lastly, planned impulse buying refers to buying when a shopper enters a store with a
specific purchase in mind but has intentions to make other purchases dependent on price, coupon specials, and the
like. To better capture and understand consumer impulse purchasing behavior, we would exclude reminder impulse
buying as the items are bought based on true needs.
and Vohs 2004), for self-control (Vohs and Faber 2007), for consumer demographics (e.g. Puri 1996;
Vohs and Faber, 2007; Ramanathan and Williams 2007), for power distance belief as a cultural construct
(Zhang et al. 2010), and even for nutrition composition (Mishra and Mishra 2010). However the problem
in this research steam is that the explanatory variables are not what marketers can control so that it is
hard to draw managerial implications in practice. Also it measures its intentions in lab environment with
limited representative respondents (e.g., undergraduate students at universities), it has limitations to
predict what would happen in real purchase situations.
To obtain data in more realistic data, surveys are often collected at retail channels like grocery stores or
shopping malls. Especially, unplanned purchases are often used to proxy the impulse purchase behavior
(XXX). The most common form of the survey is conducted by asking consumers in grocery stores.
Personal interviews have been conducted in the grocery retailers prior to entering and after exiting a
store. These “exit interviews” are conducted immediately after consumers complete their shopping trips,
minimizing forgetting and improving data accuracy. Based on “enter” and “exit” interviews, researchers
identify the unplanned items which are used for proxy of impulse purchases.
The problems with this type of method are formulation of a list before entry and lack of marketing
variables in stores. Shopping lists are basically self-reported data. It is possible that asking people about
their purchase planning could influence how they shop after the survey (Podsakoff, et al 2003). Also
unplanned purchases could be driven by aggressive in-store marketing tactics like buy one get one free,
attractive bundled products, or live demonstration.
To overcome the problems of self-reported data, researchers attempt to use objective and behavioral
data in purchase occasions in stores. Huang et al. (2010) tracks shopper’s in-store shopping behavior
relying on several measures to answer what causes unplanned purchases in a grocery setting. Several
measurement devices can be employed such as RFID (Radio Frequency Identification device) cart tracker
(“where does the consumer go”—path data), eye cam (“where does the consumer look”), and scanner
data (“what did the consumer buy”). Their work should be credited to use several measures including eye
tracking data to capture unplanned purchases. Still, the core of impulse purchase identification is captured
by the entrance survey data (consumer shopping list, demographic data) relating to the unplanned
purchases.
The caveat is that unplanned purchases are not necessarily related to impulse purchases (Rook 1987).
It is vary hard to verify the stimulus as an input for impulse purchasing. We cannot know whether a
certain purchase output is actually driven by the eye tracking input. It is hard to rule out other alternative
explanations such that it is a reminder purchase based on true needs. Furthermore, in the supermarket
context, consumers usually have prior brand information during their shopping trips. For this reason, prior
exposure to a brand can influence their purchasing behavior regardless of impulsive behavior. Moreover,
the exposure to the product, the input of impulse purchase, could be self-selected. Consumers who
are interested in paper products might be more likely to visit paper towel section, and might pay more
attention to promotional activities. Hui et. al. (2009) shows that shoppers are more purposeful in their
shopping trip spending less time on exploration and more likely to “back-track” once a shopper enters an
aisle. Even if we objectively observe the input (i.e. eye tracking data) and output (i.e. product purchasing
behavior) at the moment of purchase, it is hard to know whether the purchase event is driven by impulsive
desires.
Due to these problems associated with unplanned purchases, it is critical to show that how impulse
purchasing behavior is defined apart from unplanned purchases which also involve little planning,
emotional desire, and short deliberation. Wood (1998) distinguishes the unplanned purchases and
impulse purchase such that impulse purchases involves buyer dissatisfaction and regret with retrospective
judgment about the previous purchase occasion. Therefore it is desirable to have retrospective judgment
about the previous purchases distinct from the “unplanned” purchases.
When consumers are not satisfied with their previous purchases, they can cancel the order, return the
product, or take no action for their dissatisfaction. The no action case is not objectively measurable.
Ridgway et al. (2008) documented that returning an item is a significant consequences of impulse buying.
The returning behaviors could be recorded, but may be compounded with other alternative explanations.
A government sponsored survey in Korea2 reports that wrong color and wrong size fit, the difference
between the advertisement and the actual, and product defections account for 36.6%, 32%, and 13.4%
respectively in consumers’ reported reasons for return behaviors. Therefore the remaining candidate for
impulse purchase behavior metric is canceling behavior. It is objectively measured, and could rule out
other alternative motivations like wrong size, color, defection because the product was not even delivered
yet. If the consumer do not see the product, the canceling behavior would reflect the regret from the
previous impulsive product purchases.
As described earlier, the data comes from a TV home shopping retailer in Korea. This retailer airs its live
show 24-hours a day, 365 days a year. One Korea major television home shopping company reported that
48% of their purchases were impulse types. Also, the customers reported that among those products they
returned, 13.3% say impulse purchase (e.g. regret, unnecessary item purchased) was a main reason for
their action of return. The advantages using the new TV home shopping canceling data are described in
details below.
First, consumers are exposed to the live show by chance so that we can rule out the self-selected,
purposeful exposure to input measure. Since the major TV home shopping channels are located between
major national TV channels, TV viewers occasionally have to see the home shopping channels. In a TV
home shopping and Internet shopping usage survey conducted in Korea in 20043, 85.4% of respondents
do not check the home shopping program channel schedule, and 56.8% reports that they habitually watch
these shopping program when they turn on TV. According to survey data conducted in Korea (Lee and
2Korea
Consumer Agency, Consumer Education Department, Consumption Culture Team, 2004. 04. Kim
Heajin
3Korea Consumer Agency, Consumer Education Department, Consumption Culture Team, 2004. 04. Kim
Heajin
Jong, 2003), customers reported that 58% did not have any previous information about the product they
watched. Only 37.6% said they do price search before they place an order. Most purchases are driven by
randomly aired advertisement.
Second, unlike shopping at retail stores where consumers spent less than a second on display for one
of thousands of products, consumers watch the TV home shopping program at least 10 minutes when
they buy it. There is no other products aired that attracts the viewer’s attention. The input measure is
very straight forward ruling out lack of attention to the details of in-store promotional activities. The
average aired time (exposure time of a product) in TV home shopping is 45 minutes. It is very unlikely to
remember the product and order it hours or days later. In TV home shopping industry, survey respondents
in Korea report4 that 17.2% places orders right at the moment they see the product, overall 91% ordered
during the show program on air. We also 10 minutes level purchase data given a TV program. 70% of
purchases occur in 30 minutes once a TV program is aired (Figure 4). In retail store setting, consumers
could buy a chocolate bar without paying attention to the message, price level, or other promotional
activities. Such weak links between marketing inputs to the output measure make it hard to identify the
effect of these in-store marketing activities.
Third, we use objectively measured canceling behaviors as an output measure to proxy the impulse
purchase behaviors. In television home shopping context, consumers watch the television program and
place orders by phone. Consumers have an option to cancel the order before it’s shipped without penalty.
Consequently, canceling decisions occur by consumers’ retrospective judgment especially regretting their
previous orders. Since we can associate each real-time television programs to the ordering and delivering
behaviors at a specific time, we can test the impulse purchase behaviors across hundreds of products with
various promotional activities.
Fourth, the data includes the full sample of products including almost every product categories that
4Korea
Consumer Agency, Consumer Education Department, Consumption Culture Team,
2004. 04. Kim Heajin
could be sold in online, offline, and TV platform. It is not the kind of surveys or experiments on a few
products. We test impulse purchasing behavior by various product categories including electronics, home
appliances, fashion, and food, and by mixed bundling and packaging settings. These marketing variables,
which marketers could control and practice, could give us managerial implications in practice. These
characteristics distinguish our paper from the previous research based on “unplanned” purchase survey
data.
Fifth, along with numerous national brand products, we observe local new product launches exclusive
to the retailer, so that we can rule out the previous brand recognition, awareness effects. These features
identify impulse purchases with potential sequential product exposure, which helps us to show the effect
of impulse purchases throughout the product life cycle.
As far as we know, this is the first paper that investigate the impulse purchase behaviors given product
bundling and packaging and product life cycle distinguishing the unplanned purchase and impulse
purchases with objectively measured order canceling data. It can be generalizable to almost every product
categories. This paper could shed some on the opportunities to increase both the completion rate of
planned purchases and the percentage of unplanned purchases through improved shopper engagement and
conversion.
The rest of the paper is organized as follows: In the next section, we review the previous literature. In
section 3 we provide the background regarding the television home shopping industry and describe the
data. Our model and results are outlined and presented in section 4. Section 5 provides a discussion of the
results and section 6 concludes.
2. Literature Review
Our research is closely related to the literature on consumer impulse/unplanned purchasing behavior, and
consumer product cancel and return behaviors.
2.1 Literature on Impulse Purchases as a general in-store purchasing behavior
A considerable body of literature in psychology and consumer behavior has examined consumer impulse/
unplanned purchase behavior. A planned purchase in marketing theory is characterized by deliberate,
thoughtful search and evaluation that usually results in rational, accurate and better decisions (Halpern,
1989). Meanwhile, impulse purchasing is a spontaneous and immediate purchase (Rook and Fisher, 1995)
which occurs when the consumer is not actively looking for a product and has no prior plans to purchase
(Beatty and Ferrel, 1998). We define impulse purchases in the television home shopping environment
as those that occur when a consumer makes an unintended, unreflective, and immediate purchase (Rook
1987; Rook and Fisher, 1995). [Note that researchers frequently use another term “unplanned” purchases,
instead of impulse purchases. In some studies, the two terms are exchangeable and unplanned buying
has long been associated with impulse buying. However, many researchers have made a conceptual
distinction between unplanned and impulse purchases. As Arnould, Price, and Zinkhan (2002, p.349)
note, mostly all impulse purchases are unplanned, but not all unplanned purchases are impulse buys.
Therefore, we differentiate two terms as unplanned purchase is considered to be a necessary but not
sufficient basis for categorizing a purchase as an impulse purchases (Kollai and Willet, 1967; Rook, 1987;
Rook and Fisher, 1995). Unplanned purchases include “forgotten needs” while impulse purchases do
not. In our context of the television home shopping industry, it is unlikely shoppers will cancel an order,
however unplanned, if that purchase represents a forgotten need.]
Previous research on planned and impulse buying has described impulse buying as a psychological
trait (Prus, 1991; Rook and Fisher, 1995), where consumers vary in terms of their impulse buying
tendency. Also, there is some evidence that consumer demographics are able to explain whether a
purchase is planned or impulsive (e.g. Ramanathan and Williams 2007). There exists literature on the
mental processes underlying impulsive behavior. The cognitive views suggest that impulsive behavior
arises from a tendency to overvalue benefits and undervalue long-term consequences (e.g., Ainslie and
Haslam 1992). In their view, people try to maximize the immediate utility of consumption, even as
they come into conflict with the goal of maximizing a higher-order, long-term utility. Moreover, it was
argued that impulsive individuals are more likely to make impulse purchases (Rook and Fisher, 1995;
Vohs and Faber, 2007). Researchers who are interested in studying individual differences in behavior
have examined impulsivity as a personality variable (e.g., Puri 1996). Stilley et al. (2010) and found
a significant relationship between individual impulsiveness and aisles shopped in grocery trips. Also,
Hausman (2000) asserted that if the hedonic components are considered, impulse buying is a valuable
pastime which is more than just a means of acquiring goods. Urbany et al. (1996) referred to them
as “psychosocial returns”. Naramsimhan et al. (1996) examined whether sales promotion and “dealproneness” of the consumer are associated with consumers’ impulse spending but found no statistically
significant relationships.
Turning to the interaction of individual traits and external factors, Bucklin and Lattin (1991) use
supermarket scanner data to show that shoppers who have planned their purchasing do not respond to
in-store stimuli and show no response to point-of-purchase promotions, while consumers who have not
planned their purchasing in the category may respond to in-store stimuli and are strongly influenced
by promotions. Inman et al. (2010) examined the product category and customer characteristics that
affect consumers’ likelihood of becoming involved in unplanned purchases using large scale field
study. The information whether the purchases were planned or unplanned was included in the data.
They found that category characteristics such as purchase frequency and displays as well as customer
characteristics affect in-store decision making. However, the authors mainly considered in-store stimuli
as triggers to unrecognized or forgotten needs, leading to unplanned purchases, so that shoppers may
find their purchases necessary at least. Most recently, Bell et al. (2010) examined off-store factors
such as overall trip goals and store shopping objectives as important drivers for unplanned category
purchases by employing Poisson model and concluded that “out-of-store” marketing has no direct effect
on unplanned buying. Rowley (1999) found that the ratio of leisure shopping to buying is as high as 95
percent in apparel products and one advertiser estimated that women’s and children’s wear generate more
unplanned, impulse buying than any other category. Based on a mall survey of 100 consumers in Boston
area, Narasimhan et al. (1996) examined 108 product categories using a two-item impulse buying scale
and found that high impulse-buying categories include pastry/doughnuts, bath products, candy, and chips
and snacks while low impulse buying categories are diet pills, cleaners, lard, milk and cough syrup.
To summarize, the general view emerging from this research is that consumer impulse purchase behavior
is characterized by individual traits (e.g. impulsive personality traits, lifestyle and demographics such
as income, age, sex, and household size), product characteristics such as category type, and marketing
activities (e.g. price, promotion, coupons). However, results are conflicting at times. In this research
project we try to obtain a more integrated and complete picture of those antecedents of consumer
impulse purchase behavior. Furthermore, most of the studies concerned frequently-purchased, low-priced
consumer goods. Therefore, there is a need to perform consumer impulse purchase research on a broader
range of products.
This paper would contribute to direct marketing, shopper marketing, and impulsive buying literature, and
create meaningful managerial implications for resource allocation strategy for retailers and manufacturers.
For example, an important potential application can be found at the traditional retail store level for
allocating in-store promotions so that greater emphasis can be placed on those products with a high
percentage of impulse purchases. Also, retailers may be able to improve their product range and improve
the shopping environment to trigger impulse purchases.
2.2 Literature on Consumer Product Cancel and Return Behavior
Consumers often return a product to a retailer because they learn after purchase that the product does
not match as well with preferences as had been expected. In the Direct Marketing industry, the average
return rate is about 20% (Rogers and Tibben-Lembke 2001; Hess and Mayhew 1997). This is a costly
issue for retailers and manufacturers. In fact, it is estimated that the U.S. electronics industry alone spent
$13.8 billion dollars in 2007 to restock returned products (Lawton 2008). Of those returned products,
95% were non-defective items that were not what the consumer was expecting. Also, Anderson et al.
(2008) documented that item return rates varied from 0% to 100% in the apparel mail order catalogue
industry. Managerial theory holds that if a product is easy to return, it is more likely to be returned (Davis
et al. 1998). Bonifield et al. (2010) argue that the advent of online channels has led to an increase in the
number and complexity of product returns. Still, empirical evidence of this is relatively sparse. Hess and
Mayhew (1997) is one of the few empirical studies that measures individual customer return behavior in
the apparel catalog order industry; however, they do not consider customer purchase decisions and instead
focus on predicting whether and when a return occurs using actual and simulated data. Also, Wood’s
(2001) paper is one of the few exceptions to examine that consumer product return policy in remote
purchase environments as a two-stage decision process. Their results from laboratory experiments suggest
that return policy leniency benefits the retailer. Anderson et al. (2009) document evidence that product
returns provide customers with an option value that can be captured with individual level data and show,
using field experiments, how varying product return policies can affect firm profits. Another paper by
Anderson et al. (2008) using aggregate level data in the apparel catalog industry showed that lower prices
lead to additional consumer surplus, which decreases the likelihood that a customer will return an item.
Peterson and Kumar (2009) empirically demonstrate the role of product returns and their consequences
on future customer and firm behavior. They argue that product returns are inevitable but by no means
detrimental in the catalogue mail order industry. Che (1996) explores the consumer learning implications
of return policies by developing a theoretical model where customers realize idiosyncratic valuations of
the good after their purchase. However, this paper focuses on consumer learning and delayed purchase
with analysis on the trade-off for a monopoly seller. One explanation for impulse buying would be an
overestimation of net benefits characterized by projection bias (Loewenstein et al. 2003). A recent work
by Colin et al.(2007) documents and models this projection bias in the field using individual level catalog
sales and returns data. However, their investigation is limited to projection bias in terms of current and
future weather temperature in apparel industry.
It is apparent that these models are lacking in richness of data information and availability about
consumer impulse purchases. Our paper is distinct from extant consumer product return research as it has
largely been limited to capturing consumer impulse purchases. This is because product return behavior
entails reasons such as product default, products’ poor performance or wrong size. By using television
home shopping data, we will directly explore the issue of customer impulse purchases. Our preliminary
analysis shows that there is a significant level of product order cancellation. We will thus contribute
to the literature by demonstrating the evidence of consumer impulse purchases and subsequent order
cancellation behavior, thereby supplementing the existing stream of research on consumer product return
behavior.
2.3. Bundling and multi-unit packages
Bundling. Bundling is a pervasive and strategically important marketing strategy in today’s markets. By
definition, bundling is the sale of two or more separate products in one package (Stramersch and Tellis
2002). Guiltinan (1987) defines bundling as "the practice of marketing two or more products or services
in a single package for a special price." One interesting phenomenon these days on bundling practices
is that the retailer may offer the bundles of both cross-category unrelated products and complementary
and related products. While prior research has mainly focused on bundling of related products, one
recent paper by Khan and Dhar (2010) examined the practice of such cross-category bundling. In this
paper, we use the terms heterogeneous bundles and traditional homogeneous bundles and those terms are
exchangeable with bundling of unrelated and related products.
3. Data
3.1 Korea television home shopping industry
Since mid-90s, the Korea television home shopping market has grown rapidly. The size of the industry
in 2008 has reached $4.4 billion on a revenue basis. The television home shopping refers to the home
shopping channels industry, which includes television-based companies such as HSN, and QVC in
America and they have become one of the most important sectors in the retail outlets5. The television
home shopping allows consumers to shop for goods from the privacy of their own home, as opposed to
traditional shopping, which requires one to visit physical stores. In particular, the purchase decision can
be framed as a three stage process (see Figure 1). Specifically, customers may place orders by phone
when they view the products on television, and the products are then delivered. 95% of payments are
done by credit cards.
Direct response from messages of urgency and ease of ordering, key characteristics of this industry,
can encourage impulse purchases. All the marketing activities during the television home shopping
channels can be considered as those at the last stage of the choices process (i.e. the point of purchase)
in the standard setting. Surprisingly, among women customers, 28.3% of survey participants reported
that they watch television-shopping channels every day. Moreover, shopping hosts induce consumers
to make impulse purchases at the time of airing products (e.g., expressions such as “the cheapest price
among all the retailers”, “the last time offer”, “buy one now and get one free”). One survey report6 in
Korea revealed that more than half of the respondents identified themselves as having at least one of
the following: impulsive buying tendency, compulsive buying, excessive spending, and buying to get
discounts or free gifts. To exclude the reminder purchasing as impulse purchasing behavior, we can
select product categories which are new and exclusively sold for the home shopping channels, therefore
eliminating the possibility that consumers are recalling needs or have previous brand information on the
items.
5The
two most successful shopping channels—HSN and QVC—in the United States, both major players
in the retail industry, generate a combined total of over 10 billion dollars in sales and a combined total
yearly net revenues of 11 billion dollars.
6Korea Consumer Agency, Consumer Education Department, Consumption Culture Team, 2004. 04. Kim
Heajin
3.2. Korean television home shopping data
As described above, much literature on consumer impulse purchases have been based on laboratory and
field experiments with limited product categories. It has largely used supermarket data and intercept
survey data (e.g., “exit” interviews). In contrast, unique features of the new data from a major television
home shopping retailer will allow us to measure magnitude of impulse buying objectively. Our data is an
aggregate level data with the complete product ordering and canceling information of all the television
programs over one year period spanning Sep 2004 - Aug 2005. Each item is identified by an unique
item number, allowing for easy identification when ordering. The retailer offers a 30-day money-back
guarantee on all of its items and lenient cancellation policy without penalty. The overall average cancel
rate (weighted by the number of orders) is 26.7%. However, the cancel rate across all product categories
is about 37% because the higher the number of orders placed per program, the lower the cancel rate is
(Figure 2). Table 1 shows the summary statistics.
Our data include each program’s product’s number of orders and number of orders delivered. We subtract
the number of orders delivered from the number of orders to obtain the number of canceled orders. Each
program in our data has a unique product ID number (a total of 3000 different IDs). The data comprise
product category, brand name, marginal profit, price, price promotion and program audience information.
The overall product order cancellation rate is about 37% across all the product categories. Details are
reported in Table 2 and Figure 3 (See summary statistics in Table 1). The order cancellation rate varies
from 0% to 100%.
One cautionary aspect of using the TV home shopping data is whether we can rule out consumers’ prior
knowledge about the product or brand aired on TV program.
The packaged mackerel is one of the interesting product categories we observe. We consider the
packaged mackerel hedonic good. In Korea, we take the mackerel xxxxxxx and the program itself is very
appetizing. The product has launched in Oct 2004, the starting point of our data period, and is a great
success. Also, the program airing time, day, and date are all consistent across programs.
Television home shopping retailers recognize the issue of high order cancellation and one
representative says that they try to ship the ordered items as soon as possible (i.e. products are often
shipped on the day of order) in order to reduce the possibility of order cancellation. The publicly available
return rate is reported as 12.5%, which is much lower than the order cancellation rate because of hassle
and cost (e.g., return postage, re-packaging items).
To obtain bundling and packing information, we use character matching function to identify
whether the character or the words among product names contain “+” which indicate that the product is
offered as bundling of items or “package” (in Korean) as packaged goods with multiple identical products
(e.g., 6-pack, 8-pack, or 12-pack of one brand of beer). We find that 12.4% of all the programs were
featured as bundled products, and 1.6% was sold as multiunit packages.
Moreover, we classify the product categories into utilitarian and hedonic goods. Product
categories such as fashion, jewelry, grocery, culture are classified to hedonic goods, and those like
electronics, health related products, home appliances do to utilitarian. Table 2 shows the detailed
categories by utilitarian and hedonic criterion. Overall 50.7% of goods is hedonic and 49.3% is utilitarian.
The estimates of day, month, time, television viewership, and product category dummy variables are
relative to Monday, January, 2-5AM, for female less than 20 years old, and health related products,
respectively, as baselines.. The exposure time reports the net exposure time dedicated to the product in the
television home shopping program. On average each product is exposed to the television viewers for 45
minutes.
We also have access to another dataset containing television viewership information from TNS
Korea. This is an aggregate-level audience measurement for each television home shopping program.
Along with the number of audiences for each program, we have gender and age range information. The
viewership data are aggregated from 10 minutes time slot to 3 hours time slot so that we can merge with
the purchase data. The viewership data contain all the combinations of age and gender. Women of ages
between 35 and 39 years old have the highest television viewership rate followed by women of ages
between 30-34 years old.
4. Model and Results
We now turn our attention to the model set-up and present the results from the data analysis.
We investigate the impulse purchase behavior based on order canceling behavioral data using logistic
regression. The data includes all the products that were aired for 12 months period (Sep 2004 - Aug
2005). The order cancellation rate is obtained for each television home shopping program (the number of
cancelled orders divided by all the orders placed for 18,000 different programs). We use cancellation rate
as our dependent variable for further analysis. Notice that the “cancellation rate” refers to the proportion
of products that a customer orders and then subsequently cancels. We distinguish this proportion from
the “number of cancellation”, which represents a raw count of how many products are cancelled after
being placed an order.
We use the logistic regression model to analyze the canceling behaviors by various independent
variables. Although, the bounded dependent variables can be analyzed with conventional regression or
linear probability model, the logistic regression model has some advantages. The predictive variable is
bounded from 0 to 1 and the marginal effect differs by the level of mean canceling rate. One of drawbacks
is interpretation of the parameters. In conventional regression, the parameter of a variable X shows the
marginal effect of X on y. It is straight and simple. However, the marginal effect of logistic regression
depends on the average dependent variable so that the interpretation of the estimation parameters is
different from those in conventional regression. The marginal effect is a non linear function of the mean
level of dependent variable.
The logisitic regression could be converted to conventional regression by converting the
dependent variable into odd ratio. We adopt this linearization to the cancel rate, our dependent variable.
The linearization enables us to estimate several hundreds of products, various marketing variables of
television home shopping data. Also the canceling rates could be varied by product categories so that
we include category specific fixed effects and we run the regression separately for two broad category
groups. One of the usual problem of converting dependent variable to odd ratio is the limitation of the
dependent variable. The extremes 0 and 1 are not convertible. In canceling perspective, it is very unlikely
to have no or all cancellation (Figure 2).
Previous researchers have measured impulse purchase across different types of products (e.g.,
Kollat and Willett 1967). Also on a given occasion, a consumer may have planned purchases in some
categories, but not in others. This is consistent with the empirical findings of Kollat and Willet (1967),
and Park, Iyer, and Smith (1989). Specifically, Bellenger et al. (1978) found from personal interviews that
product categories such as meals and snacks, women’s clothing and footwear, and costume jewelry are
more impulsively purchased than those like men’s clothing, toiletries, books and stationary. However, the
findings are not always consistent across studies. Inman, Winer, and Ferraro (2009) considered product
hedonicity, inter-purchase cycle, coupon and display as product category characteristics. In this paper,
we find that all product categories have significant effects. Overall hedonic product categories such as
jewelry, fashion, fashion accessory, home decor have similar canceling rates than utilitarian product
categories including electronics and home appliances. The industry specific fixed effects are shown in
column [6] in Table 2.
We begin by evaluating how on-screen stimuli including product characteristics and marketing
activities such as price, promotion and product exposure time affect the order cancellation behavior
across product category j in time t. ProductCat is a categorical variable to identify each product category
(6 product category considered as Figure 2b), reflecting category fixed effect. This product “fixed
effects” allow the intercept to vary across each product and control for all product effects such as
product attributes that are constant across seven time zones. Alternatively, as a starting point, we may
substitute product fixed effect for the dummy variable which represents product type: hedonic versus
utilitarian. (For deciding which products belong to hedonic or utilitarian category, we follow Crowley
et al.’s findings (1992).) StartTime (= 1, …, 7) is a categorical variable which refers to a time zone when
the television home shopping program starts. (1 = 11pm-2am, 2 = 2-5am, 3=5-8am, 4 = 8-11am, 5 =
11am-2pm, 6 = 2-5pm, 7 = 5pm-8pm, 8=8pm-11pm). Weekday is a dummy variable (1 = weekday, 0 =
weekend). CustomerChar represent the aggregated audience information data such as gender and age.
CancelRatejt=j=1nαj+ProductCatj+β1Pricejt+β2Promotionjt+β3ExposureTimejt+β4StartTimejt+β5Week
dayjt+β6(Price*StartTime)jt+CustomerCharjt+εjt
By including only Price variable together with separate models in which we add the other
independent variables both separately and jointly, we can see from the R-squared results that which
independent variable are important factors when deciding the number of order cancellation (See Table 5
in Appendix for model comparison).
In other words, the independent variables in the regression include price($), price discount
depth (%), bundling and packing dummy variables, product exposure time (minutes), aired time slot
dummies, calendar month dummies, and product category dummies. The results are shown in Table 2.
For marketing activities, we use price discount promotion and product exposure time as our on-screen
marketing activities. Time slot dummies, product category controls for the program specific effects on
that program, television program viewer characteristics, and category specific fixed effects.
Table 2 shows the estimation results across various model specifications. Each column shows
a model specific parameter estimates. The first column shows the names of variables, and the second to
ninth column represents model [1] to model [8]. The first model [1] has only time related variables which
are monthly dummies, days of the week dummies, and time slot dummies. These variables explain about
11% of all the variation in canceling rate. The second model [2] adds television viewership data and the
length of exposure of the product in the television program. The viewership shows the effect of age and
gender specific effects, but the explanatory power barely improves.
The third model [3] includes price and price discount depth information. The R square doubles to
0.23. The higher the price is, the more likely the cancel rate is. This is consistent with previous research
that more expensive items are more likely to be returned (Hess and Mayhew, 1999; Anderson et al. 2008)
except the difference between returning and canceling behaviors.
The fourth model expands the third model by discrediting the price levels so that non-linear
pricing effects could be captured. The results indicates that the price discount has non-monotonic effects
on canceling behaviors. When there is a price discount greater than 3%, the canceling rate drops by
around 30% regardless the magnitudes of the discounts (Figure 4). In other words, a consumer’ impulse
buying behavior could be significantly reduced by the fact that she got price mere discounts at the
moment of purchase.
The fifth [5] and sixth [6] model test whether product bundling and multi-unit packaging affect
the impulse purchasing behaviors. The model [6] controls for the heterogeneous canceling rates using
product category specific fixed effects. Compared to model [5], the explanatory power doubles to 0.49
with these product category fixed effects. The bundling effect is significant and positive. When a product
is sold with other products bundled, the overall canceling rate is increased by 6%. The overall multi-unit
packaging effect is significant and negative. The canceling rate is decreased by 17% when products are
sold in multi-unit packages. The details on product category specific bundling and multi-unit packaging
effects are discusses below the paragraph.
Finally we divide the data into two groups of product categories; utilitarian and hedonic goods.
We define that hedonic products are desired for pleasure and fun, whereas utilitarian goods tend to satisfy
basic needs (Babin, Darden, and Griffin, 1994). We classify the product categories into these two based
on the gross product categories (Table 3). The model [7] and [8] shows these category specific regression
results. We find that the effects of price level, price promotion, bundling and multi-unit packaging are
quite different for utilitarian and hedonic goods.
The bundling effects are significant in both utilitarian and hedonic goods. The estimates show
that the canceling rates are increased by 8.5% when products are sold with bundling offer for utilitarian
products, and are decreased by 8.9% for hedonic goods. The magnitudes are comparable, but signs are
opposite. For utilitarian goods such as air-conditioners, consumers tend to regret their purchases more
likely when the items are sold with unrelated bundling offer such as with digital cameras. Consumers
may realize that the bundled products are not needed after the television program. In contrast, for hedonic
goods such as ear rings, consumers are more likely to be satisfied with their purchases when they are
offered with a necklace (e.g., bundling offer with related products). These findings are the first attempt to
investigate the impulse purchase behaviors in respect to bundling promotion activities.
The multi-unit packaging effect is significant and negative for utilitarian goods, but insignificant
for hedonic goods. When products are sold with multiple item packages, the canceling rate is decreased
by 23% for utilitarian goods. Prior research has shown that hedonic purchases are more guilt inducing
than utilitarian purchases (Khan and Dhar 2006). Consumers who bought multiple utilitarian items,
compared to hedonic items, might have higher mental cost to cancel the order (XXX needs more
literature).
5. Discussions
We first showed the relative effects of product category fixed effects, consumer demographics and
marketing activities on consumer impulse purchasing behavior from television home shopping channels.
Our results reveal the importance of product categories. We have found preliminary evidence of impulse
purchases inferred by high order cancellation rate across all product categories. We wish to find empirical
support for behavioral theories relating impulse purchases and other factors to shopping behavior. Our
results will provide managerial insights for retailers and manufacturers in the presence of consumer
impulse buying. As our first objective is to find the relative importance of situational and individual
factors, our research will help firms decision regarding their resource allocation. For example, if the
product characteristics are important factors for consumer purchase and order cancellation decision,
the retailer can put its effort on program scheduling and planning (e.g. product type such as hedonic
versus utilitarian goods, national brand versus exclusive home shopping channel brand, or durable versus
perishable). If customer characteristics play a significant role in this context, the retailer can target their
customer segments by scheduling programs for specific time period (e.g., selling electronics for late night
and women apparel in weekday morning). Ultimately, we would like to show that making inferences from
observed choice under the traditional rational expectation hypothesis can lead to biases in the estimation
of consumer preferences.
Managerial Implications
Moreover, In retailers’ perspectives, the demand at the moment of customers’ placing orders can be
considered as a “fake demand” since not all demand are actually realized and generate profits. Since there
exist costs associated with the event of cancelled orders (e.g., handling inventory, increasing customer
service, etc.), it would be profitable for retailers to minimize the cancellation events.
Limitations and Further Research
We use retrospective product order canceling as a proxy measure for impulse purchase behavior given
the fact that sales is occurred in prior to canceling. The impacts of pricing, bundling and packaging on
the initial sales are not investigated. It is possible that there is a trade-off between boosting sales and
managing order cancellation. It would be desirable to investigate the initial ordering and canceling jointly
and draw the optimal marketing tactics to maximize the profits. Those are out of the scope of this paper’s
goal. Also, no action and returning product after impulse purchases are not included in the analysis.
Future research could delve into collecting more detailed information about post purchase behaviors and
investigate these behaviors.
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Table 1: Summary Statistics of the TV Home Shopping Data
Variable
Obs
Mean
Std. Dev.
Min
Max
Year
17848
2004.64
0.48
2004
2005
Week
17848
26.90
15.48
1
53
Program
17848
119.91
71.47
40
370
17848
45.20
15.81
0
129
v21
17847
0.43
0.43
0
3.65
#orders
17848
437.69
596.46
1
9058
price ($)
17848
295.14
426.18
0.001
13063
pricedisc(%)
17848
1.50
2.38
0
26.63
cancel
17848
0.38
0.20
0
1.00
female20~24
17925
0.02
0.04
0
0.45
Length(min)
Exposure
time (min)
female25~29
17925
0.05
0.07
0
1.00
female30~34
17925
0.10
0.10
0
0.95
female35~39
17925
0.15
0.12
0
1.00
female40~44
17925
0.07
0.06
0
0.64
female45~49
17925
0.07
0.08
0
1.00
female50~54
17925
0.06
0.06
0
0.69
female55~59
17925
0.04
0.06
0
0.92
female60olde
17925
0.06
0.07
0
0.71
male 20less
17925
0.04
0.05
0
0.40
male20~24
17925
0.01
0.03
0
0.55
male25~29
17925
0.01
0.04
0
0.53
male30~34
17925
0.03
0.05
0
0.76
male35~39
17925
0.04
0.06
0
0.84
r
male40~44
17925
0.03
0.07
0
1.00
male45~49
17925
0.03
0.04
0
0.61
male50~54
17925
0.07
0.14
0
0.98
male55~59
17925
0.02
0.03
0
0.56
male60older
17925
0.05
0.08
0
1.00
Table 2. Summary Statistics by Product Categories
Table 3. Utilitarian vs. Hedonic Product Categorization
Utilitarian
Hedonic
Home Appl.
Fashion
Health
Fashion Acc.
Kids
Food
Home Décor
Culture
Elect
Cosmetics
Computer/Edu
Sports/Outdoor
Jewelry
Service
Underwear
Table 4 Estimation Results
Variables
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]Hedonic
Utilitarian
Tuesday
0.0800***
0.0822***
0.0562**
0.0637**
0.0458*
0.0119
0.0107
0.00183
Wednesda
0.126***
0.113***
0.103***
0.111***
0.0939***
0.0454**
-0.00719
0.0738***
Thursday
0.137***
0.132***
0.100***
0.103***
0.0903***
0.0388*
0.0533*
0.0166
Friday
0.223***
0.218***
0.192***
0.191***
0.170***
0.0816***
0.0456
0.0929***
Saturday
0.232***
0.238***
0.218***
0.210***
0.202***
0.0607***
0.0281
0.0707**
Sunday
0.154***
0.203***
0.147***
0.137***
0.123***
-0.00577
-0.0245
-0.00428
month_2
-0.0239
-0.0149
-0.00264
0.00477
0.00838
-0.00496
0.0014
-0.0123
month_3
-0.0185
-0.032
-0.0262
-0.0213
-0.0252
-0.0526*
-0.0163
-0.0803**
month_4
-0.00443
-0.0295
0.0165
0.0262
0.0252
-0.0455*
-0.0703*
-0.0121
month_5
-0.0494
-0.0619*
-0.0222
-0.0107
-0.0127
-0.0875***
-0.108***
-0.0416
month_6
-0.0744*
-0.0917**
-0.0315
-0.0264
-0.04
-0.0976***
-0.124***
-0.0549
month_7
-0.164***
-0.186***
-0.127***
-0.118***
-0.114***
-0.144***
-0.164***
-0.123***
y
month_8
-0.255***
-0.269***
-0.188***
-0.168***
-0.176***
-0.181***
-0.176***
-0.171***
month_9
0.0355
0.0275
0.022
-0.00384
0.00144
-0.000193
-0.0301
0.0355
month_10
0.0689*
0.0462
0.0621*
0.0487
0.0619*
0.00583
0.0223
0.00892
month_11
0.136***
0.109***
0.0814**
0.0745**
0.0809**
0.0206
0.0589
-0.0109
month_12
0.126***
0.108***
0.0939***
0.0956***
0.104***
0.0486*
0.0853**
0.00446
5-8AM
-0.0660*
-0.0756**
-0.156***
-0.183***
-0.173***
-0.00686
-0.0211
-0.0416
8-11AM
0.232***
0.244***
0.0747**
0.0428
0.0499
0.0768***
0.0124
0.0774**
11AM-2PM
0.346***
0.322***
0.177***
0.158***
0.154***
0.0434*
-0.0564
0.0663*
2PM-5PM
-0.135***
-0.116***
-0.218***
-0.235***
-0.215***
-0.0559**
-0.134***
-0.0144
5PM-8PM
0.202***
0.302***
0.0469
-0.0000568
0.00353
0.0258
-0.0298
-0.0346
8PM-11PM
0.534***
0.599***
0.317***
0.289***
0.285***
0.155***
0.0256
0.221***
11PM-2AM
0.456***
0.535***
0.423***
0.386***
0.385***
0.358***
0.262***
0.418***
0.137
0.107
0.0735
0.0475
0.00623
0.109
-0.0387
-0.0672
-0.0875
-0.039
0.00759
-0.0709
-0.0738
0.0369
-0.0684
-0.117
-0.0829
-0.0506
-0.127
-0.133
-0.0154
female20~
24
female25~
29
female30~
34
female35~
0.114
0.0659
0.0882
0.0935
-0.109
-0.173
0.0515
0.649***
0.496**
0.484**
0.468**
-0.077
0.0433
-0.0845
0.25
0.162
0.172
0.154
-0.0614
-0.0267
0.00155
0.0681
-0.0613
0.0229
0.0301
-0.147
-0.109
-0.0111
-0.00332
-0.0598
-0.024
0.0254
-0.00232
0.0739
0.0433
-0.135
-0.226
-0.239
-0.233
-0.0939
-0.0907
-0.0312
-0.23
-0.361
-0.332
-0.246
-0.142
-0.0737
-0.092
male20~24
0.257
0.102
0.175
0.135
0.0304
0.166
-0.0198
male25~29
-0.138
-0.198
-0.159
-0.139
-0.0639
0.0608
0.0948
male30~34
-0.0611
-0.091
-0.133
-0.145
-0.324*
-0.233
-0.373*
male35~39
-0.199
-0.243
-0.195
-0.175
-0.248*
-0.221
-0.158
male40~44
-0.1
-0.266
-0.243
-0.241
-0.233*
-0.171
-0.185
39
female40~
44
female45~
49
female50~
54
female55~
59
female60ol
er
male
20less
male45~49
-0.304
-0.322
-0.319
-0.326
-0.127
-0.114
0.026
male50~54
0.0341
-0.00973
0.00896
0.0349
-0.142
-0.162
-0.0222
male55~59
-0.093
-0.331
-0.287
-0.367
-0.109
-0.0972
-0.026
male60olde
-0.265
-0.322*
-0.303*
-0.298*
-0.316**
-0.301
-0.203
-0.00289***
-0.0105***
-0.0103***
-0.00972***
0.00296***
0.00660***
-0.000431
0.000765**
0.000817**
0.000780**
0.000559**
0.000435**
0.00102***
*
*
*
*
*
0-3%
0.127***
0.0881***
-0.0593***
-0.0615**
-0.0408*
3-5%
-0.244***
-0.285***
-0.189***
-0.201***
-0.135***
5-7%
-0.294***
-0.333***
-0.148***
-0.171***
-0.0855**
7-10%
-0.403***
-0.438***
-0.283***
-0.425***
-0.104**
10%~
-0.190***
-0.221***
-0.148***
-0.268***
-0.0308
r
Exposure
time
Price level
Price
-0.0465***
Promotion
contain"+"
0.0973***
0.0596***
0.113***
-0.0429*
contack"pa
-0.573***
-0.271***
-0.364***
-0.115
Fashion
0.822***
0
0.266***
Underware
0.149***
0
-0.338***
Fashion
0.513***
0
-0.0606
0.212***
0.348***
0
Electronics
-0.194***
-0.062
0
Computers/
-0.252***
-0.102**
0
-0.0834*
0
-0.575***
Culture
-0.650***
0
-1.135***
Food
-0.835***
0
-1.332***
Home Appl
-0.414***
-0.276***
0
ck"
Acc
Home
Décor
Edu
Sports/
Outdoor
Cosmetics
-0.258***
0
-0.725***
Kids
-0.589***
-0.440***
0
Jewelry
0.790***
0
0.217**
Others
0
0
0
Const.
-1.027***
-0.942***
-0.503***
-0.624***
-0.621***
-0.827***
-1.043***
-0.364**
N
14858
14851
14851
14851
14851
14796
7229
7567
R-sq
0.105
0.114
0.232
0.248
0.258
0.556
0.393
0.675
adj. R-sq
0.104
0.111
0.229
0.246
0.255
0.554
0.389
0.673
Figure 1
Figure 2
Figure 3
Figure 4. Histogram of Cancellation Rate across all
items
Figure 5. Sales by minutes after start of a program
Figure 6.Cancel rate and the number of
orders
Figure 7 Cancel rate by price discount
rate
Figure 8 Salted
Mackerel