shipping costs and online shopping behavior
TRANSCRIPT
Shipping costs and online shopping behavior
The effect of shipping costs on basket size and shopping
frequency
Amsterdam, June 27, 2015
Bachelor thesis business administration
Supervisor: dr. E. Korkmaz
Academic year: 2014/2015
Ryan de Regt, 10342109
1
Contents 1. Introduction ................................................................................................................................. 4
2. Literature review ......................................................................................................................... 6
2.1 Non-linear pricing ................................................................................................................. 6
2.3 Effects of shipping fee on online shopping behavior ............................................................ 8
2.3.1 Effect of shipping fee on basket size ............................................................................. 8
2.3.2. Effect of shipping fee on purchase frequency ............................................................. 14
3. Conceptual framework .............................................................................................................. 14
3.1 Shipping fee ........................................................................................................................ 14
3.2 Basket size in euro .............................................................................................................. 15
3.3 Purchase frequency ............................................................................................................. 15
3.4 Control variables ................................................................................................................. 16
3.4.1 Day of the week and month ......................................................................................... 16
3.4.2 Number of products per purchase ................................................................................ 16
4. Methodology ............................................................................................................................. 17
4.1 Research design .................................................................................................................. 17
4.2 Data collection .................................................................................................................... 17
4.3 Measures ............................................................................................................................. 18
4.3.1 Independent variable shipping fee ............................................................................... 18
4.3.2 Dependent variables: basket size and shopping frequency .......................................... 19
4.3.3 Control variables .......................................................................................................... 19
4.4 Data analysis ....................................................................................................................... 20
5. Results ....................................................................................................................................... 20
5.1 Descriptive statistics ........................................................................................................... 20
5.1.1 Sample characteristics and descriptives of dependent and independent variable ........ 21
5.1.2 Descriptives of control variables ................................................................................. 21
5.1.3 Number of different time slots per customer ............................................................... 24
5.2 Intercorrelations .................................................................................................................. 25
5.3 Regression analyses .............................................................................................................. 0
5.3.1 Relationship between shipping fee (IV) and basket size (DV) ...................................... 0
2
5.3.2 Relationship between average shipping fee per customer (IV) and purchase frequency
(DV) ........................................................................................................................................ 1
6. Discussion ................................................................................................................................... 1
6.1 The effects of shipping costs on online shopping behavior .................................................. 2
6.1.1 Effect of shipping costs on basket size .......................................................................... 2
6.1.2 Effect of shipping costs on purchase frequency ............................................................ 4
6.2 Theoretical- and managerial implications ............................................................................. 4
6.3 Limitations and suggestions for further research .................................................................. 5
7. Conclusion .................................................................................................................................. 6
8. Bibliography ............................................................................................................................... 7
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Foreword I, Ryan de Regt, wrote this thesis for my Bachelor degree in Business Administration at the
University of Amsterdam. While I wrote this thesis, my supervisor dr. E. Korkmaz supported me
exceptionally well by discussing possibilities and providing excellent feedback during our very
efficient meetings. Therefore, I would like to thank dr. E. Korkmaz for her exceptional guidance
and the suggestions she gave me.
I hope you will enjoy reading my thesis.
Statement of originality This document is written by Student Ryan de Regt who declares to take full responsibility for the
contents of this document. I declare that the text and the work presented in this document is
original and that no sources other than those mentioned in the text and its references have been
used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of
completion of the work, not for the contents.
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Abstract According to the Center for Retail Research1, online retail will grow with 18.4 percent next year.
Online retailers cope with a cost that offline retailers do not have: shipping costs. There are several
different ways online retailers charge shipping fee to their customers. However, there is not much
literature available about the effects of shipping fee on online shopping behavior. Therefore, this
research focuses on providing additional evidence for the effects of shipping fee on online
shopping behavior. Based on non-linear pricing- and economic order quantity theories, we propose
that shipping fee would affect basket size and purchase frequency. More specifically, we
hypothesize that shipping fee is positively related to basket size, and negatively related to purchase
frequency. These hypotheses are tested by performing regression analyses on a dataset consisting
of 25050 transactions and 849 customers of a Dutch online retailer. Based on this data set, we did
not find support for both hypotheses. Instead, we found a positive relationship between shipping
fee and purchase frequency, and a slightly negative relationship between shipping fee and basket
size. We argue that such unexpected results might stem from the highly heterogeneous customer
base, as we are aware that this online retailer serves both businesses and households.
1. Introduction
As one of the components of the marketing mix, pricing is a well-researched topic in the academic
field. According to Hossain and Morgan (2006, p.1), dividing the price in two pieces is a common
marketing practice. Amstrong and Vickers (2009, p. 30) call this phenomenon non-linear pricing:
the price of a product will consist of a fixed and a variable piece. Morwitz et al. (1998, p. 453) call
those pieces the base price and the surcharge. Let’s clear things up with some examples. In a
restaurant in the US, the menu lists a price of $29 for a tournedos. However, you are expected to
add a tip for the service. Consumers will experience non-linear pricing in more industries. Another
great example of charging a base price and surcharge is in the travel industry. Consider two
possible hotels to stay next weekend: Double Tree by Hilton hotel, 5 stars with excellent service
which costs $100 per night, or Ibis hotel, 3 stars with moderate service for $75 per night. At the
first sight, paying the $25 premium for staying in a 5 star Hilton hotel with excellent service sound
1 Center for Retail Research, http://www.retailresearch.org/onlineretailing.php
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reasonable. However, most consumers do not take into account that the surcharges at Hilton, for
example $40 for parking, $80 for dinner and $25 for breakfast are probably higher than the
surcharges at Ibis hotel. This example shows how offline retailers can take their advantage by
practicing non-linear pricing: at first sight, there does not seem to be a lot difference between both.
However, by taking all surcharges into account, there will be a considerable difference.
There is considerable literature about non-linear pricing, bundling, surcharges and two-part
tariff (Adams and Yellen, 1976; Amstrong, 1996; Adams and Yellen, 1976; Amstrong and
Vickers, 2009; Ahn and Lee, 1999; Park et al. 1983). However, most research within this field of
research has been on offline retailing. Analysts of the Center for Retail Research2 expect online
sales in Germany, France, Sweden, The UK, The Netherlands, Italy, Poland and Spain to grow
from 156,28 billion euro in 2014, to 185,39 billion euro in 2015. This results in a growth of 18,4
percent in one year. Non-linear pricing is also a common practice in online retailing: the consumer
pays the base price for the product(s) itself, and an surcharge for shipping and handling (Hossain
& Morgan, 2006, p. 1). From the customer point of view, the marginal cost of the second article is
therefore lower than the costs of the first article. Therefore, it is likely that the customer will
increase the average products in this basket (Hossain & Morgan, 2006, p. 1; Lewis et al., 2006, p.
52).
As mentioned before, most research about non-linear pricing has been in an offline rather
than an online setting. However, there is some research about non-linear pricing in online retailing.
Hossain and Morgan (2006) for example, investigated the effect of shipping fee on the willingness
to pay for products on E-bay. They found that consumers are willing to pay more for an item when
shipping and handling costs are mentioned separately, rather than when shipping fee is included
in the price. A possible reason for this could be that consumer tend to overlook shipping costs due
to the framing effect (Kahneman & Tversky, 1984, p.343; Hossain & Morgan, 2006, p, 21). Lewis
(2006) and Lewis, Singh and Fay (2006) already investigated the effect of shipping fee on basket
size and purchase frequency. However, they used the same dataset from the same online grocery
e-tailer. In order to provide additional evidence for the relationships between shipping fee and
consumer purchase quantity and purchase frequency, this study focusses on providing further
2 Center for Retail Research, http://www.retailresearch.org/onlineretailing.php
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evidence for those relationships. Therefore, the research question is: “What is the effect of shipping
fee on consumer purchase quantity and shopping frequency?”
In order to answer the research question, secondary data of an online retailer consisting of
25000 transactions from around 800 consumers will be used. In advance to analysing the the data,
literature will be reviewed. The goal of this literature review is to further strengthen our
propositions by examining possible explanations.
As mentioned before, this paper will follow up with a literature review. The literature
review will treat different aspects and characteristics of nonlinear pricing online. On top of that, it
serves to link several theories and to underpin why the research question mentioned above has
been chosen. After reviewing the literature, the conceptual framework, research design and
methodology will be presented. Methodology will be followed by the results of the analyses. The
results of the analyses, will be discussed thereafter. This section includes possible explanations for
our findings, discusses the limitations of this study, and provides suggestions for further research.
This thesis will end with a conclusion.
2. Literature review
In this section, existing literature will be examined to emphasize the relevance of our research
question. First, literature about non-linear pricing will be discussed. This includes definitions and
applications of nonlinear pricing. Next, incentives to perform non-linear pricing will be discussed.
After discussing non-linear pricing in general, the applicability and opportunities for online
retailers will be highlighted by discussing current literature about the topic. Several theories,
including the economic order quantity and framing effect theory, will be addressed.
2.1 Non-linear pricing
Most of the firms and markets are operating based on a linear pricing strategy, which means that
the basket price is the sum of the prices of its components (Amstrong and Vickers (2009, p. 30).
An example of a linear pricing strategy can be found in grocery. When heading to the grocery store
for a bottle of Coke and some bananas, the grocery store will probably charge the consumer the
sum of both prices. However, according to Amstrong and Vickers (2009, p. 30) an increasing
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number of firms and markets base their prices on non-linear pricing. In practice, this means that
the marginal price of the second, third or even twentieth product will relatively be lower than the
marginal price of the first product.
An example can be found in the industry of carton boxes for packaging. The average price
for hundred carton boxes is $0,40 per box, resulting in a total price of forty dollar. Due to non-
linear pricing, the average price for two hundred boxes could for example be $0,30 per box,
resulting in a total of sixty dollar. This indicates that the marginal price per box for the extra
hundred boxes is just $0,20 dollar per box. By performing non-linear pricing, customers will
receive a discount when purchasing larger quantities from the same retailer (Amstrong & Vickers,
2009, p. 30). Since the marginal costs for the retailer decrease if customers purchase more items
at the same time, retailers have the opportunity to practice this kind of pricing (Pindyck and
Rubinfeld, 2013, p. 87).
Another type of nonlinear pricing is called the two-part tariff. By using a two-part tariff,
firms divide their prices in a fixed charge (annual, monthly, or per transaction) for the access to
purchase an item and a variable part for actually using the service. The variable part is the marginal
price per secondary or complementary item (Feldstein, 1972, p. 175). This type of pricing can be
found in various organisations. In the following part, this will we illustrated with some examples.
The study association SEFA practice this way of pricing during the first day of college of
many Economics and Business students of the University of Amsterdam. In order to purchase
study books at their store, students have to pay a fixed fee of 10 euros per year for becoming
member of SEFA. This is a typical example of paying an access fee. Amusement parks also used
to practice this way of pricing: consumers had to pay a fee in order to enter the park. In addition
the access fee, they had to pay-per-ride for the attractions. Another industry which used to have
the two-part tariff is the telecom sector (Park, Wetzel and Mitchell, 1983, p. 1700). Theory of
telephone demand makes the distinction between the demand for access and the demand for usage:
consumers had to pay a given price per call, and a variable price per minute. If the price per call
decreases, consumers will make more calls (Park, Wetzel and Mitchell (1983, pp. 1700-1701).
Online retailers, including myself, also practice non-linear pricing by charging a shipping fee.
Once consumers are willing to pay a shipping fee, they can add as much items to their basket as
they want for the same, fixed price per item. In the next section, this phenomenon will be discussed
further.
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2.1.1 Nonlinear pricing and shipping fee
As mentioned in the previous section, online retail is expected to grow by 18.4 percent to 185,39
billion euro in 2015 (Center for Retail Research). This provides the opportunity for e-tailers to
practice another form of non-linear pricing: shipping costs. From the retailers point of view, the
marginal cost of the second, third or even tenth product to the same customer could be lower than
the marginal cost of the first product. This is likely because the retailer has to pay shipping costs
only once for multiple products instead of paying shipping costs for every product separately. This
offers the opportunity for e-tailers to practice non-linear pricing by charging the customer a fixed
shipping fee, irrespectively the amount of items a customer will add to his basket.
From the customer's point of view, the marginal price of the second product is lower than
the marginal price of the first product as the shippings costs only have to be paid once. The effect
of shipping fee on shopping behavior will be discussed in the next section.
2.2 Effects of shipping fee on online shopping behavior
According to the work of Lewis (2006), the level and structure of shipping and handling fee will
have effects on several aspects of consumers shopping behavior. Lewis (2006) has found evidence
that the level of shipping fee is correlated with customer acquisition, customer retention and basket
size. Since this research is focussed on basket size and customer retention, the following sections
will provide literature overview about those relationships.
2.2.1 Effect of shipping fee on basket size
In this section, current literature about the effect of shipping fee on basket size will be examined.
As mentioned before, there are currently two papers who did an empirical research about the effect
of shipping fee on basket size and bids on Ebay. Therefore, these paper will be discussed first.
After discussing those papers, this section contains possible theories which can explain the insights
Hossain & Morgan (2006) and Lewis (2006) generated. In the last part of this section, we will
discuss some theories which come up with counter evidence.
Research from Hossain and Morgan (2006) shows that two-part pricing of a product does
have an influence on the total price a consumer is willing to pay for an item on Ebay. They found
that when they charge consumers high shipping costs and a low opening bid, the number of bidders
and total revenue will increase. This is consistent with findings from Morwitz et al. (1998, p. 460).
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Their results also suggest that two-part pricing tend to increase the consumers’ product demand
compared combined prices. If those findings can be generalized to online retailers, charging higher
shipping costs combined with a lower item price will increase the willingness to pay for an item
and could therefore increase basket size and profits.
Lewis (2006) is one of the first scholars who investigates the relationship between shipping
fee and basket size. In his research, he empirically investigates the effect of shipping costs on
customer retention, customer acquisition and average basket size. To answer his research question,
Lewis (2006, p. 16) used data from an online retailer specialized in non-perishable grocery and
drugstore items. The dataset consists of information about the first 502 days of operation and
contains information of around 30000 customers. In the dataset, the online grocer experimented
with different shipping schedules: increasing shipping fees for higher order sizes, free shipping at
a certain order size and free shipping at every order size. Lewis (2006) found that shipping fees do
influence order frequency, which means that if consumers have to pay a higher shipping fee, they
will order less frequent. He also found that shipping fee significantly influence average basket size.
As possible reason for this phenomenon, Lewis (2006) mentions the fact that shipping
prices are an element of a non-linear pricing strategy. By providing relative discounts for higher
order sizes, shipping fees provide an economic incentive for consumers to enlarge their purchases
quantities.
In addition to the paper of Lewis (2006), results from Ma et al. (2011) also provide evidence
for the relationship between shipping fee and average basket size. Ma et al. (2011) examine the
effect of gas prices on shopping behavior of consumers in almost three hundred product categories
across different retail formats. This research can be considered as reliable, additional evidence
since gas prices are, just as shipping fee, part of shopping costs in terms of getting access to
shopping and products (Ma et al., 2011). In practice, this means that if consumers are shopping at
multiple offline retail stores, the total gas costs will increase, just as shopping at different online
retailers will result in paying higher total shipping costs. Another way to reduce the total level of
gas is to make less shopping trips to the retailer. If total purchase volume will remain the same,
the consumer will have to make larger purchase quantities at the time. Therefore, the effect of
higher gas prices should result in a reduction of the number of shopping trips (Ma et al., 2011, p.
21).
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It is likely that the effect described above will not only occur at offline retail store. Due to
shipping fee, shopping at multiple online retailers and shopping more frequent at the same online
retailer will result in a higher level of total shopping costs. A possible theory to explain this effect
could be the “Optimal Order Quantity” theory.
Consumers can use the theory of optimal order quantities just as professional organizations
do. According to Slack et al. (2013, p. 376), managing the inventory of a firm requires operation
managers to make three major decisions: how much to order, when to order and how to control the
inventory system. Since the latter two are not relevant for this paper, we will focus on explaining
the former one: how much to order. To make this decision, operation managers or consumers have
to make this decision by balancing two sets of costs: the costs of holding stock, and the cost
associated with purchasing the items (Slack et al., 2013, p. 376). Slack et al.(2013, p. 376) describe
the following costs which should be considered when purchasing stock: cost of placing the order,
which can include shipping fee, personnel time, comparing pricing among different suppliers or
internal administration. Second, Slack et al. (2013, p. 377) mentions the cost of price discounts.
When purchasing large quantities, suppliers often provide discounts. Other costs may include
stock-out costs, working capital costs, storage costs, obsolescence costs and operating efficiency
costs.
Cost of placing the order, price discounts and stock-out costs tend to decrease if the order
size increases, whereas working capital costs, storage costs, obsolescence costs and operating
inefficiency costs generally increase if the order size increases (Slack et al. (2013, pp. 376-377).
To determine the economic order quantity, consumers should determine holding costs,
which could be calculated as holding cost per unit*average inventory. This results in the following
equation*:
𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 = ℎ𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 𝑝𝑒𝑟 𝑢𝑛𝑖𝑡 ∗ 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑖𝑛𝑣𝑒𝑡𝑜𝑟𝑦
𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 =𝐶ℎ ∗ 𝑄
2
* In which Ch= cost of holding inventory per unit, Q=order quantity
The second part that should be determined is the cost of ordering. These costs can include shipping
fee and the costs of comparing prices among different suppliers/retailers. Ordering costs are
determined by the following equation**:
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𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 = 𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 ∗ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑟𝑑𝑒𝑟𝑠 𝑝𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑
𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝑐𝑜𝑠𝑡𝑠 =𝐶𝑜 ∗ 𝐷
𝑄
** In which Co=Cost of ordering per order, D=total demand per period (often used per year),
Q=order quantity
The total costs associated with managing the inventory could be calculated with the following
equation:
𝑇𝑜𝑡𝑎𝑙 𝑐𝑜𝑠𝑡𝑠, 𝐶𝑡 =𝐶ℎ ∗ 𝑄
2+
𝐶𝑜 ∗ 𝐷
𝑄
The rate of change of total cost is given by the first differential of the total cost equation with
respect to Q.
𝑑𝑦
𝑑𝑥=
𝐶ℎ
2−
𝐶𝑜 ∗ 𝐷
𝑄𝑜2
Consequently, lowest total cost will be lowest if 𝑑𝑦
𝑑𝑥= 0 that is:
0 =𝐶ℎ
2−
𝐶𝑜 ∗ 𝐷
𝑄𝑜2
Rearranging this expression to Qo equals the economic order quantity gives:
𝑄𝑜 = 𝐸𝑂𝑄 = √2 ∗ 𝐶𝑜 ∗ 𝐷
𝐶ℎ
Consistent with the theory of economic order quantity, Park et al. (1983, p. 1700) proposes
that before people choose a phone subscription, they will determine how many phone calls it will
make before subscribing. This could also be applied with other products, lets illustrate this with a
example of an household purchasing ice-cream which is exclusively sold at a specific store.
First of all, the total demand per year should be examined. Let’s suppose this household
consumes 100 boxes of ice-cream per year. After determining demand, the costs of holding ice-
cream and the costs of ordering should be determined.
Assume that energy costs due to cooling the ice is 150 dollar per year. This would result in
1,50 dollar holding costs per unit per year. Other holding costs to consider could be the
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obsolescence risk costs and working capital costs. To determine the cost of ordering, the consumer
has to consider the time it takes to get to the store each trip and the costs of driving to the store by
car. Let's assume the total cost of ordering are 15 dollar. The economic order quantity for ice-
cream for this household would be:
𝐸𝑂𝑄 = √2∗15∗100
1,50
𝐸𝑂𝑄 = 54.7
The most economical order quantity in this case would thus be 54.7 boxes of ice-cream.
The paper of Hossain and Morgan (2006, p. 4) suggests that bidders bid more on items of
Ebay when if the total price a bidder has to pay is 115 percent of their bid. They suggest that this
is due to the fact that bidders can not easily calculate the total cost of their bid and thus are likely
to bid more. Other research by Gaibax and Laibson (2005, p. 2) support this idea that consumer
do not think about add-ons when the buy goods. The theory of economic order quantity is
applicable in online retailing. However, due to cognitive limitations it could be difficult for most
of the consumers to determine the optimal order quantity they have to purchase because they tend
to overlook add-ons (Gaibax & Laibson, 2005). Therefore, it is possible that average basket size
would not be affected by the level of shipping cost. Research from Hossain and Morgan (2006)
show that in an auction, bidders effectively bid more when the winner has to pay 115 percent of
his or her bid, compared to paying a fixed auction fee. This could indicate that if the total price
could not easily be calculated, average bids will be higher. If it would be the case that shipping fee
is not positively correlated with average basket size, this could be a possible explanation.
Other research from Kahneman and Tversky (1984) also proposes several theories that
contradict the literature mentioned above. First of all, shipping fee and the base price of a product
could be displayed in a way that the focus is off the shipping fee. This is what Kahneman and
Tversky (1984, p. 343) call the framing effect. Possible outcomes can be framed in different ways.
Kahneman and Tversky (1984) come up with the following example of framing possibilities: the
U.S. is preparing for an outbreak of an unusual Asian disease, which is expected to kill around six
hundred people. There are two programs to combat the disease. If program A is adopted, 200
people will be saved. If program B is adopted, there is a one-third probability that 600 people will
be saved, and a two-third chance that nobody will be saved. If human are fully rational, both
choices are equal. However, surprisingly 72 percent of the respondents chose for program A. This
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framing effect could also exist for shipping fee. Imagine you are a consumer buying a new
Macbook Pro at www.apple.com for 1400 dollar and shipping costs are displayed in different
ways. Option A is the base price, 1400 euro + 14 dollar shipping fee. Option B is the base price,
1400 dollar and just 1 percent shipping fee. Option C could be the base price, 1400 dollar and just 1
percent shipping fee. This example is of course not empirically researched. However, according to the
framing effect of Kahneman and Tversky (1984), one could expect that the latter two options are
more favorable for consumers than the first option.
Research from Morwitz et al. (1998) also suggests that consumers often tend to overlook
shipping fee. Morwitz et al. (1998) provide two reasons for this statement. First of all, bias will
take place when consumers will first read the base price of an item instead of the shipping fee,
which is likely in online retailing. If people will anchor the first price they see, they will perceive
the first price, the base price, as the most important. Therefore they pay less attention to the
additional shipping fee. Another possible scenario for online retailers is that consumers ignore the
shipping fee completely, by not noticing it at all. Given the fact that shipping fee and base price
are often displayed distant from each other, this could be a reason why shipping fee and average
basket size are not correlated with each other.
By summarising the literature, it is clear that there are several theories that are not
congruent with each other. First of all, Hossain and Morgan (2006) show that having a two-part
tariff increases the willingness to pay for an item. On top of that, Lewis (2006) found statistical
evidence for the positive correlation between shipping fee and average basket size. Ma et al. (2011)
found evidence that consumers tend to increase their basket size if gas prices are high. Gas prices
and shipping fee can be seen both as an access fee to shop. There could be a theory that explains
this relationship, namely the economic order quantity. This theory/equation states that if the costs
for ordering rise, the optimal order quantity rises too.
However, it seems likely that consumers are not likely to make such complex calculations
due to cognitive limitations. On top of that, research from Gaibax and Laibson (2005) suggest that
consumers do not think about add-ons when they purchase goods. Meaning that shipping costs are
often overlooked by consumers. This idea is also proposed by Hossain and Morgan (2006) and
Morwitz et al. (1998). Last but not least, Kahneman and Tversky (1984) propose the framing effect
which contradicts the theories above.
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2.2.2. Effect of shipping fee on purchase frequency
As mentioned in the section above, Lewis (2006) suggest that shipping fee is positively correlated
with basket size. This means that if online retailers will charge a higher shipping fee, consumers
basket size will be higher and vice versa. If online retailers charge a high shipping fee, a logical
consequence is that consumers will purchase larger quantities at once and thus will purchase less
frequent. Results from Lewis et al. (2006, p. 52) provide evidence for this consequence. They
examined whether shipping fee and order-incidence rates are related to each other and found that
free-shipping promotions do increase purchase frequency, but lead to smaller order amounts.
Since shipping fees can be seen as a access fee for shipping, literature provide additional
evidence for a possible relationship between shipping fee and purchase frequency. Ma et al. (2011)
investigated the impact of gasoline prices on grocery shopping behavior and found a negative
effect of gasoline prices on shopping frequency. This means that if gasoline prices increase and
thereby increase the access fee for shopping, people will make shop less frequent.
According to the economic order quantity theory as explained in section 2.3.1., a reaction
to higher shippings costs, and thus higher costs of ordering, will lead to consumers purchasing
larger quantities at once and purchase less frequent.
In sum, results Lewis (2006) and Ma et al (2011) both provide evidence for the relationship
between shipping fee or access fee and purchase frequency. This relationship can possible be
explained by the economic order quantity theory.
3. Conceptual framework
After discussing relevant literature in respect to the research question, the variables which will be
used for the analyses will be operationalized in the section. In addition to that, propositions will
be made in this section.
3.1 Shipping fee
How and how much to charge for the delivery of their products is one of the key marketing
decisions for online retailers (Lewis et al., 2006, p. 51). Research from Ernst and Young (1999,
In: Lewis et al.,2006, p.51) points out that over 50 percent of consumers who purchase online lists
15
shipping fee as one of their main complaints about online shopping. As pointed out in the previous
section, shipping costs can be seen as a two-part tariff. Two-part tariffs can be misleading when
they are in some way hidden or not directly visible for the customer (McDowell 1996, In: Morwitz
et al., 1998, p. 454).
Research from Hossain and Morgan (2006) highlights the importance of shipping costs by
providing evidence the relationship between shipping fee and the number of bids on Ebay. Their
results suggest that increasing shipping fee on Ebay and simultaneously lowering the opening bid
will lead to higher profits. In addition to that, Lewis (2006) empirically proves that shipping costs
have an effect on customer acquisition, customer retention and average basket size. In this
research, shipping fee is measured in euros for every transaction and is the independent variable
which influences basket size in euros and purchase frequency. Those dependent variables are
discussed in more detail in the following sections.
3.2 Basket size in euro
In this research, average basket size per customer in euro (ABS) is the dependent variable. Based
on previous literature, we expect that ABS will be positively influenced by the level of the shipping
fee. Therefore, the following hypothesis is proposed:
H1: Shipping fee is positively correlated with average basket size of individuals in euro.
3.3 Purchase frequency
Purchase frequency is the second dependent variable in this research. Based on literature, we
expect that the level of shipping fee will negatively influence the purchase frequency of online
shoppers. Therefore the following hypothesis is proposed:
H2: Shipping fee is negatively correlated with purchase frequency.
In order to test the hypotheses, a dataset consisting of 25000 transactions from 848
customers will be analyzed. Therefore, every customer did on average around 29.48 transactions
over a period of two years. To investigate the relationship between shipping fee and purchase
frequency, the average shipping fee and total number of transactions by that specific customer will
be used. It is possible to average the shipping fee since the level of shipping fee is determined by
a specific time slot. It is likely that a customer will order mostly in the same time slot because of
the characteristics of that customer. For example, the shipping fee is higher between Monday
16
between 09.00am and 11.00am because businesses are willing to pay more for their order at the
beginning of the week, rather than the end. To test whether customers do indeed order in the same
period, statistics about the amount of time slots per customer will be provided in section 5.1.3.
3.4 Control variables
After discussing the independent and dependent variables in the previous section, the control
variables that will be used in the analysis will be discussed in this paragraph. In this research,
various control variables are incorporated in the regression analysis to provide a clear results of
the effect of shipping costs on basket size. By controlling for day of the week, month and the
number of products, we account for the effects of these variables. While testing the hypothesis
stated earlier, the following control variables will be added during the analysis: day of the week,
month and the number of products.
3.4.1 Day of the week and month
Day of the week, month and time slot will be used as a control variable in this research as they
could possibly explain some of the variance in the dependent variables. For example, it could be
that businesses order larger quantities on some days. Because of the weekend, monday could for
example be more popular for larger order quantities than other days. Month is another important
control variable as customers could possibly order higher quantities or are willing to pay more for
shipping due to differences in demand during high- and off-season. Let’s illustrate this with a clear
example. McConnel (1977, p. 189) mention that there people are willing to pay more for access to
a recreation area during summer and when temperatures are high.
3.4.2 Number of products per purchase
Except for day of the week and month, the regression will also be controlled for the number of
products per purchase. The number of products are important control variables as they can explain
why basket size is higher. If consumers shift to buying different kind of products which are
possibly more expensive due to higher shipping cost, this could result in unreliable measures.
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4. Methodology
In the sections before, relevant literature and the conceptual framework related to the research
question are discussed. In this section, the research design and method will be discussed further.
4.1 Research design
In order to answer the research question, a quantitative research approach will be adopted. A
deductive, quantitative research approach is considered best way to answer the research question
as it is possible to collect large amounts of data and hopefully provide statistical evidence for the
propositions. During our regression analyses, we will control for several other variables, including
number of products, day of the week, month, and time slot. After developing hypotheses and
propositions based on academic literature, collecting quantitative data to test these propositions is
possible for this research question. Since there is literature available about this topic, a deductive
research approach could be adopted to verify current literature (Saunders et al., 2012, p. 144). On
top of that, previous studies from Lewis et al. (2006), Lewis (2006) and Hossain and Morgan
(2006) also adopted a quantitative, deductive research approach.
4.2 Data collection
In order to provide statistical evidence to answer the research question, a large amount of data and
access to collect such data is necessary. I, as a researcher, do not have a large online retail business
myself to collect such amounts of data. Therefore, secondary data from a large online retailer will
be used to answer the research question. Saunders et al. (2012, p. 304) describe secondary data as
data that has already been collected for other purposes. Secondary data can be analyzed further
and used can be used for other research to provide different or additional knowledge or
interpretations. Saunders et al. (2012, p. 304) mention that secondary data is often used if students
or scholars do not have the time, money and access to resources necessary to obtain or collect
large, detailed datasets.
The dataset which will be used to answer the research question is obtained from a large
online retailer from the Netherlands. The dataset consists of 25000 transactions from 848 different
customers of that online retailer. Data is collected over a period of one year, in which shipping
fees differ along the year. Shipping fees vary between 3.96 and 7.16. At the same time, the number
18
of items and the average basket size are measured. Those measures will be relevant to answer the
research question. Besides the shipping fee, number of products and average basket size, there is
also data available about the day of the week, and the time-slot in which the transactions took
place.
4.3 Measures
As mentioned before, data from 25000 transactions is obtained in a two-year period. During this
one-year period, the independent variable shipping fee has varied over time to measure the effects
of it on basket size and shopping frequency. The following sections describe how these variables
are measured. First, the measurement of shipping fee will be discussed, followed by basket size
and purchase frequency. Last, the measurement of control variables such as day of the week,
month, time slot, type of products and the total number of products will be discussed.
4.3.1 Independent variable shipping fee
The independent variable shipping fee is measured in euros. Lewis (2006) investigated the effect
of shipping fee on purchase frequency and basket size by varying the shipping fee schedule over
time. Lewis’ (2006, p. 16) dataset consists of data from 502 days in which he varied the shipping
fee. For example, during the first forty days customers had to pay 3.99 dollar shipping fee for
orders under fifty dollar. The next period, the 41th till the 137th day of data collection, customers
had to pay 4.99 dollar for orders under 50 dollar and 6.97 dollar for orders between 50-75 dollar.
In total, Lewis (2006) used five shipping schedules during the 502 days of data collection.
This could result in biased measures since it is possible that the same customer have to pay
different shipping fees over time. The effect on basket size can be measured that way, however the
effect of shipping fee on purchase frequency could be biased. Therefore, the data that is collected
for this research is collected differently.
The variance in the level of shipping fee is realised by charging different shipping fees in
different time slots and days. The company charges different shippings fees in order to balance
demand across different time slots, as well as days. This results in different shipping fees for every
day-timeslot combination. When customers order during the seventh time slot on Mondays which
means ordering between 17.00pm and 19.00pm on Monday, the firm will charge them 5.56 euro
19
for shipping. In contrast, when customers order at Tuesday from 11.00pm and 13.00pm the
company charges them 4.76 euro for shipping.
4.3.2 Dependent variables: basket size and shopping frequency
In this section, the dependent variables basket size and shopping frequency will be discussed. The
first dependent variable, basket size, is measured for every transaction in euros. As mentioned
before, the dataset consist of 25000 transactions from 848 customers. This means that on average
we have data from 29.48 transactions per customer over a two-year period. In order to measure the
effect of shipping fee on shopping frequency, these transactions are combined on a customer level.
By combining these transactions, average shipping fee and the number of transactions per
customer have been calculated. By doing so, we are able to perform a linear regression analysis to
check whether there is a relationship between shipping fee and purchase frequency.
By using average shipping fee, the results of our analysis could get biased as there could
possibly be a lot of variance in the shipping fee paid by the customer. However, the shipping fee
schedule is bounded by time and day of the week. Since most the customers place an order in the
same time slot, there could be less variance in shipping fee on a customer level. We will show this
later in Table 5.
4.3.3 Control variables
After discussing the independent variable shipping fee and both dependent variables basket size
and purchase frequency, the measurement the control variables such as time slot, day of the week,
month of the year, number of items and type of items will be discussed in this section.
As mentioned earlier, shipping fee is based on eight time slots and six working days:
monday till Saturday. The days and times slots are both nominal variables. The first time slot
represents the time period 8.00-10.00 am, the second represents 9.00-11.00am, the third 10.00-
12.00 am, the fourth 11.00 am-13.00 pm, the fifth 12.00-14.00 pm, the sixth 16.00-18.00 pm, the
seventh 17.00-19.00 pm, and the eighth 18.00-20.00 pm.
The last control variable related to the moment of ordering is month of the year. As
mentioned in the conceptual framework, some products are bounded to a specific season. If this is
the case, some months the basket size or purchase frequency can be higher due to seasonal effects
rather than the effect of shipping fee. During data collection, the date is measured as the day of the
20
year. For example, the 5th of February is measured as 36. To use month of the year as a control
variable, these numbers have to be converted into nominal variables. By converting the variables,
12 values are left in which 1 represents the first till the 31th day of the year, 2 represents the 32th
till the 50th day of the year, et cetera.
While performing a regression analysis, we will also control for the number of products a
the customer purchases at the time. This control variables are relevant because it is possible that
there is no increase in basket size because the customer shifted from purchasing larger quantities
of different products for a lower price. The number of products are measured as a scale variable.
4.4 Data analysis
To answer the research question, a linear regression analysis will be executed. While doing so, we
will control for day of the week, time slot and month. Day of the week and time of the day could
have an influence on shopping behavior as employed consumers could for example have more
time during the weekend or in the evening to spend their time shopping online. Month could also
have an influence on average basket size. Christmas holidays in the December could for example
influence shopping behavior in December, and also in January because people have spend a lot of
money in December.
5. Results
In the previous section, the research design, measurements and our way of analysing data have
been discussed. The results from the analysis of these measures will be provided in this section.
The first part of this section will provide descriptives of our data, followed by the results from
the regression analyses.
5.1 Descriptive statistics
In this section, general descriptives of the sample will be discussed to get to know the measures.
First, the characteristics of the sample and descriptives including the average, highest- and
lowest measure, and the standard deviation of the independent and dependent variables will be
presented. Second, descriptives of the control variables will be discussed. These include the
21
number of transactions, average basket value, average number of items, average shipping fee and
the spread between product categories per month, per day of the week and per time slot.
5.1.1 Sample characteristics and descriptives of dependent and independent variable
Data of 25050 transactions from 849 customers are randomly selected from the transactional data
of the company. The following data is collected to answer the research question: date, day of the
week, time slot, customer ID, basket size, the number of items, and the shipping fee.
During data collection, customers of the company have paid a shipping fee per order
varying between 3.96 and 9.56. On average they paid 5.74 (SD=1.12, N=25050). Those
customers ordered between 1 and 72 times in the 365 days period. The average number of orders
per customer is 29.50 (SD=10.62, N=849). The basket size in euros varied between 39.99 and
889.71. The average basket size of our dataset is 97.56 (SD=52.19, N=25050). An overview of
these descriptives are shown below in Table 1.
Table 1. Descriptives of independent and dependent variables
Average Standard Deviation Minimum Maximum
Shipping fee (in €) (N=25050) 5.74 1.12 3.96 9.56
Basket size (in €) (N=25050) 97.56 52.19 39.99 889.71
Purchase frequency (N=849) 29.50 10.62 1 72
5.1.2 Descriptives of control variables
After discussing the independent and dependent variables, this part of the descriptives will focus
on the control variables including day of the week, month of the year, time slot, number of
products and product categories,
First of all, basket size and shipping fee per month will be discussed. The average basket
size in our dataset is 97.56. The lowest average basket size per month was in September, 94.76.
The highest average basket size per month was in December, 106.53. The standard deviation
varies between 49.95 and 54.94. More information average basket sizes per month are shown in
Table 2. The average shipping fee per month varies between 5.64 (SD=1.07, N=2536) in March
and 5.95 (SD=1.39, N=1654) in December.
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Table 2. Average basket size per month
Average basket size
(in €)
Standard deviation Percentage of
total orders
January (N=2365) 97.19 51.56 9.4%
February (N=2166) 95.60 50.43 8.6%
March (N=2536) 97.03 51.99 10.1%
April (N=2050) 98.48 54.69 8.2%
May (N=2009) 98.70 54.69 8.0%
June (N=2332) 97.28 50.43 9.3%
July (N=1654) 96.90 54.41 6.6%
August (N=1962) 97.07 52.04 7.8%
September (N=2123) 94.76 48.67 8.5%
Oktober (N=2015) 95.66 50.20 8.0%
November (N=2184) 97.64 49.95 8.7%
December (N=1654) 106.53 57.96 6.6%
After discussing differences in basket size and shipping fee per month, differences per
day of the week will be discussed. The average basket size per day of the week varies between
94.22 (SD=50.16, N=5084) on Tuesday, and 105.84 (SD=55.78, N=3211) on Friday. Shipping
fee per day varies between 4.79 (SD=0.71, N=4676) on Wednesday, and 6.70 (SD=0.64,
N=2165) on Saturday. More information about the average basket size and shipping fee per day
of the week can be found in Table 3.
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Table 3. Average basket size and average shipping fee per day of the week
Average
basket size
Standard
deviation
Average shipping
fee (in €)
Standard
deviation
Percentage of
total orders
Monday (N=6946) 95.55 51.45 6.21 1.17 27.7%
Tuesday (N=5084) 94.22 50.16 5.08 0.77 20.3%
Wednesday (N=4676) 95.01 52.16 4.79 0.71 18.7%
Thursday (N=2968) 102.83 57.36 5.57 0.80 11.8%
Friday (N=3211) 105.84 55.78 6.67 0.52 12.8%
Saturday (N=2165) 97.95 43.95 6.70 0.64 8.6%
After discussing the descriptives of the control variables month of the year and day of the
week, the control variable time slot will be discussed now. The average basket size is highest
(108.30, SD=62.87, N=2443) in the first time slot from 08.00 till 10.00, and is lowest (90.53
,SD=38.50, N=4223) in the last time slot from 18.00 till 20.00. Average shipping fee varies per
time slot from 6.99 (SD=0.76, N=2372) in the second time slot from 09.00 till 11.00, till 4.71
(SD=0.86, N=4049) in the sixth time slot from 16.00 till 18.00. More information about the
average basket size and average shipping fee per time slot can be found in Table 4.
Table 4. Average basket size and average shipping fee per time slot
Average
basket size (in
€)
Standard
deviation
Average shipping
fee (in €)
Standard
deviation
Percentage of
total orders
08.00-10.00
(N=2443)
108.30 62.87 6.45 0.77 9.8%
09.00-11.00
(N=2372)
101.80 55.90 6.99 0.76 9.5%
10.00-12.00
(N=2629)
95.30 56.32 6.05 1.42 10.5%
11.00-13.00
(N=2409)
96.43 59.03 5.74 1.13 9.6%
24
12.00-14.00
(N=2317)
97.34 56.72 5.10 1.05 9.2%
16.00-18.00
(N=4049)
97.50 51.37 4.71 0.86 16.2%
17.00-19.00
(N=4608)
98.22 45.42 5.74 0.69 18.4%
18.00-20.00
(N=4223)
90.53 38.50 5.78 0.71 16.9%
5.1.3 Number of different time slots per customer
As mentioned in section 3.3, this section provides statistics about the number of different time
slots a customer uses when ordering. By counting the number of different time slots per
customer, we figured out that on average, customers order in 4.32 (SD=1.586) different time
slots out of eight. The minimum number of time slots is one, and the maximum number of time
slots per customer is eight. On the one hand, this means that there are customers that have
ordered in every timeslot available. The percentage of such flexible customers is very low,
namely only two percent. On the other hand, there are customers that order just in one time slot.
Statistics show that 29 of the customers have ordered in just one time slot, 97 customers
have ordered in two times lots, 123 customers have ordered in three time slots, 201 customers in
four time slots, 207 customers in five time slots, 121 customers in six time slots, 54 customers in
seven different time slots, and 17 customers in eight time slots. An overview of how many
customers order in which number of different time slots is provided in table 5.
Table 5. Number of different time slots per customer
Number of time slots Number of customers Percentage of customers
1 29 3.41%
2 97 11.43%
3 123 14.49%
4 201 23.67%
5 207 24.38%
25
6 121 14.25%
7 54 6.36%
8 17 2.00%
Total 849 100%
5.2 Intercorrelations
In this section, the intercorrelations between the dependent, independent, and control variables
will be discussed. An overview of the intercorrelations is shown in table 6.
As shown in table 6, basket size is significantly correlated with shipping fee (0.053,
p<0.01), the number of items (0.759, p<0.01), day of the week (0.054, p<0.01) and time slot (-
0.067, p<0.01). Shipping fee is significantly correlated with basket size (0.053, p<0.01), number
of items (0.090, p<0.01), day of the week (0.186, p<0.01), time slot (-0.325, p<0.01), and month
of the year (-0.031, p<0.01). Shopping frequency is not significantly correlated with any of the
other variables. The number of items is correlated with basket size (0.759, p<0.01), shipping fee
(-0.090, p<0.01), day of the week (0.020, p<0.01), and time slot (-0.112, p<0.01). The control
variable Day of the week is significantly correlated with basket size (0.054, p<0.01), shipping fee
(0.186, p<0.01), the number of items (0.020, p<0.01), time slot (-0.238, p<0.01), and month of
the year (-0.24, p<0.01). The control variable Time slot is significantly correlated with basket
size (-0.067, p<0.01), shipping fee (-0.325, p<0.01), number of items (-0.112, p<0.01) and day of
the week (-0.238, p<0.001). The control variable Month of the year is significantly correlated
with shipping fee (-0.031, p<0.01) and day of the week (-0.24, p<0.01).
As these correlations are on a very high level, we continue with providing the results
from the regression analyses.
Table 6. Intercorrelations
1 2 3 4 5 6 7
1. Basket size (-) 0,053** (-0,028) 0,759** 0,054** (-0,067)** 0,01
2. Shipping fee 0,053** (-) 0,044 0,090** 0,186** (-0,325**) (-0,031**)
3. Shopping frequency (-0,028) 0,044 (-) (-0,009) 0,003 (-0,017) 0,017
4. Number of items 0,759** 0,090** (-0,009) (-) 0,020** (-0,112**) (-0,007)
5. Day of the week 0,054** 0,186** 0,003 0,020** (-) (-0,238**) (-0,24**)
6. Time slot (-0,067)** (-0,325**) (-0,017) (-0,112**) (-0,238**) (-) 0,006
7. Month of the year 0,01 (-0,031**) 0,017 (-0,007) (-0,24**) 0,006 (-)
Note: **= Correlation is significant at the 0.01 level
5.3 Regression analyses
To answer the research question, two regression analyses are performed. First, we discuss results
of the regression analysis that explains the relationship between the independent variable
shipping fee and the dependent variable basket size. These results are followed by the results of
the regression analysis of the relationship between the independent variable shipping fee and the
dependent variable purchase frequency.
5.3.1 Relationship between shipping fee (IV) and basket size (DV)
A linear regression analysis has been performed to investigate the relationship between the
independent variable shipping fee and the dependent variable basket size. The results of that
analysis will be discussed now.
During the regression analysis, two models are tested. First, a linear regression has been
performed to investigate whether there is a relationship between shipping fee and basket size.
This model gives a significant (t=8.429; p<0.001) standardized beta coefficient of 0.053. In
practice, this means that if shipping increases with one percent, basket size will increase on
average by 0.05 percent.
To get a more robust result of the regression analysis, a second model has been tested in
which the following control variables are added: day of the week, time slot, month of the year
and the number of items. After including these control variables to the model, a significant (t=-
3.883, p<0.001), negative standardized beta coefficient of -0.017 has been found. This indicates
that after controlling for the effects of day of the week; time slot; month of the year; and the
number of items, there is a negative relationship between shipping costs and basket size. In
practice this means that if shipping costs increase by one percent, basket size will on average
decrease by 0.017 percent.
Since our results indicate that there is a significant, negative relationship between
shipping fee and basket size, the analysis of this data set does not provide evidence for the first
hypothesis: Shipping fee is positively correlated with average basket size of individuals in euro.
In the next section, the results of the regression analysis of the dependent variable shipping fee
1
5.3.2 Relationship between average shipping fee per customer (IV) and purchase frequency
(DV)
To investigate the relationship between the independent variable average shipping fee per
customer, and dependent variable shopping frequency, a linear regression analysis has been
performed. As during the analysis the the relationship between shipping fee and basket size, two
models are tested. The first model investigates the relationship between the independent variable
average shipping fee and the dependent variable purchase frequency. After that, the second
model including the control variables has been tested.
The first model gives a significant (t=4.227; p<0.001) standardized beta of 0.144. This
result indicates that there is a positive relationship between the independent variable shipping fee
and the dependent variable purchase frequency. In practice this result means that if the average
shipping fee increase by one percent, the number of transaction per customer will on average
increase by 0.144 percent.
In the second model, the control variables average number of items, average basket size
and the average number of transactions have been included. This model also gives a significant
(t=4.313; p<0.001) standardized beta coefficient of 0.147. This results provide evidence for the
relationship between average shipping fee per customer and the number of transactions per
customer. Since we expected that the average shipping fee per customer should be negatively
correlated with purchase frequency, the analysis of this data set does not provide evidence for the
second hypothesis: Shipping fee is negatively correlated with purchase frequency.
In the next section, the results of the analysis will be discussed. The discussion of the
results will be followed by the theoretical, as well as managerial implications of our research.
6. Discussion
In this section, the findings of this research will be discussed. First, the findings from this
research will be compared with previous studies. The discussion of those findings are followed
by discussing the theoretical- and managerial implications of this study. In the last part of this
section, the limitations of this study will be discussed and suggestions for further research will be
proposed.
2
6.1 The effects of shipping costs on online shopping behavior
This research tried to empirically investigate the effects of shipping fee on online shopping
behavior. More specific, the effect of shipping fee on basket size and the effect of shipping costs
on purchase frequency are investigated. Surprisingly, the regression analyses do not support both
our hypotheses. However, the analyses did result in some interesting findings. Those findings
and possible explanations for them will be discussed in the next sections.
6.1.1 Effect of shipping costs on basket size
One of the goals of this thesis was to find additional evidence for the relationship between
shipping costs and basket size. At first, a slightly positive beta coefficient of 0.053 between
shipping costs and basket size has been found. However, when controlling the effects of day of
the week; month of the year; number of items; and time slot, this slightly positive beta
coefficient turns into a slightly negative one of -0.017. This finding was unexpected as Lewis
(2006) found statistical evidence for this relationship. The slightly negative relationship we
found in this paper is not in line with the economic order quantity (Slack et al., 2013, p. 376).
However, it is in line with the theories of Kahneman & Tversky (1984) and Morwitz et al.
(1998). Morwitz et al. (1998) suggest that customers usually pay less attention to additional
prices such as shipping costs. Gaibax and Laibson (2005) also suggest that customers do not
think about add-ons when they purchase goods. Therefore, the level of shipping fee would not
affect basket size.
There are several theories that support our findings. However, there is also evidence
against it. There are some explanations that could possibly explain why the results of this
research do not support our hypotheses. Those will be discussed in more detail below.
First, variance in shipping fee is determined by time slot-day combinations. This results
in the fact that one single customer could have paid many different amounts of shipping fee. As
mentioned in Table 5, only 3.41 percent of the customers have ordered in a single time slot only.
If customers do not exactly know the amount of shipping fee they have to pay in a certain time
slot and determine what they want to order up front, the level of shipping fee won’t influence
basket size for that customer. Contrary to Lewis (2006), shipping fees in this data set have
fluctuated continuously during the whole period of data collection. Lewis (2006) used a different
method to get variance in the independent variable shipping fee by charging customers different
3
levels of shipping fee during a certain period of time. Since his customers can not choose the
level of shipping fee they want to pay by substituting to another time slot, that method could be
more effective.
However, bringing variance to the independent variable the way we did in this paper
enables us to control for other factors such as day of the week, month of the year and time of the
day. By using these control variables, a clearer view of the relationship between shipping fee and
basket size could have been given than Lewis (2006) did.
After discussing the differences in methods between this paper and that of Lewis (2006),
some other factors that could disturb our results will be discussed now. Those include
heterogeneity in the sample, previous pricing action from the retailer and the multicollinearity
problem.
A possible reason for our unexpected results could be that there is a lot of heterogeneity
in the sample. This means that there could be multiple customer groups with different buying
habits. If this was the case, the different buying characteristics of the different customers can
outweigh each other. For example, non-business customers could be willing to pay more for
shipping costs related to their basket size than business customers. A possible reason for this
could be that they do not need large quantities of items because they won’t use it on a large scale.
Having business customers in the sample will thus weaken the relationship between shipping fee
and basket size, if there would have been a positive relationship without the non-business
customers. Unfortunately, the data about customer type is not available in the dataset. Therefore,
we could not take customer heterogeneity into event.
Another factor that could have disturbed the relationship between shipping fee and basket
size is the way a company has developed its shipping fee pricing schedule. If there is a group of
customers that would be willing to pay more for shipping fee in a certain time period, the
company could have anticipated on that by charging a higher shipping fee in a time slot
corresponding to that period. Since those customers are willing to pay more for shipping fee and
are less affected by it, the difference in shipping fee would not cause those customers to enlarge
their basket size when shipping fee is higher in that period.
As shown in Table 6, most of the variables included in the model are correlated with each
other. If those intercorrelations are high, which some of them are, the multicollinearity problem
will occur. According to Saunders et al. (2012, p. 524), high correlation among two or more
4
independent variables will make it difficult to determine the effects of the individual variables
separately.
6.1.2 Effect of shipping costs on purchase frequency
According to the economic order quantity, purchase frequency should lower if shipping fee
increases. However, by performing a regression analysis between the independent variable
average shipping fee and the dependent variable purchase frequency, a significant beta
coefficient of 0.147 is found. This means that if shipping fee increase, customers will purchase
more frequent. Just as described in the previous section, this could be a result of the
multicollinearity problem and high heterogeneity, such as less price sensitive and frequent
business buyers in the data. However, could also some other explanations for this result.
First, our results suggest that basket size decreases slightly if shipping costs increase. A
logical consequence of this could be that because customer order smaller quantities, they will
purchase more frequent. This will only hold if we assume that demand will remain the same.
Second, by using the average shipping fee per customer the results could be biased. Since
every customer orders in 4.32 times lots on average, heterogeneity in the level of shipping fee is
not taken into account in our regression analysis. To get a reliable result, shopping frequency
should be measured over a longer period of time in which shipping fee is constant.
6.2 Theoretical- and managerial implications
In this section, the theoretical as well as the managerial implications of this study will be
discussed. This study tried to come up with additional evidence for the relationship between
shipping fee and basket size, as well as for the relationship between shipping fee and purchase
frequency. Contrary to our hypothesis and previous research, we found a significant negative
relationship between shipping fee and basket size. This finding is an important theoretical
contribution since there is only one paper that supports the opposite (Lewis, 2006). This implies
that more research is needed to figure out which other factors influence the relationship between
shipping fee and basket size.
Our finding that shipping fee and basket size are just slightly negatively correlated
confirms what many scholars are already proposing. Gaibax and Laibson (2005), Hossain and
Morgan (2006), and Morwitz et al. (1998) have all proposed the idea that the relationship
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between shipping fee and basket size is moderated by the fact that add-ons such as shipping costs
tend to be overlooked by customers.
Another implication of this paper is that the economic order quantity is not applied by
every customer. If it would be the case, the results from the regression analyses should support
our hypotheses. A possible explanation for the finding that the economic order quantity theory
could not be applied in this case would be that most of the customers are cognitively limited and
thus are not able to make such complex calculations. The economic order quantity theory is hard
to apply in every situation as it can only be applied easily in highly standardized, stable purchase
processes.
The results of this research is different from findings of previous research. Therefore, it is
necessary for managers to investigate the relationship between shipping costs and basket size in
their specific situation. Because customers can possess different buying habits, generalization is
not possible across different industries, companies and target groups. On top of that, our research
highlights the importance of decisions related to the pricing strategy of shipping fee. It is
important for managers to take into account other factors that could be influenced by shipping
fee as well. For example, Lewis (2006) found that customer acquisition and customer retention
are also partly explained by the level of shipping fee.
6.3 Limitations and suggestions for further research
The way variance is added in the level of shipping fee and the limited information about the
characteristics of the customers are limitations to this study. Because variance in shipping fee is
determined by time slot-day combinations, which are determined by the level of demand during
the week, the measures of this dataset could be biased. These measures could be biased since
customers who are willing to pay a higher price for shipping do not care about the level of
shipping fee relative to the basket size.
The limited information about the characteristics of the customers is another limitation to
this study. Due to the limited information, it is not possible to make homogeneous groups of
customers and analyse differences between those groups.
For further research, I would recommend making sure there is more additional
information about the customers available to make more homogeneous groups of customers.
Further research could for example differentiate between business and non-business customers.
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Gender, living environment (urban vs. sub-urban) and age are examples of characteristics that
can be used to group customers and analyse whether there are differences or similarities among
those groups.
Another suggestion for further research would be investigating the effect of shipping fee
on purchase frequency of customers over a longer period of time. By having a more constant
level of shipping fee over a longer period, it is possible to get a clear view on the relationship
between shipping fee and purchase frequency.
7. Conclusion
This research intended to provide additional evidence for the effect of shipping fee on online
shopping behavior. More specific, we intended to provide more insights in the relationship
between shipping fee and basket size, and the relationship between shipping fee and purchase
frequency. In this quantitative study, we used secondary data from a Dutch, online retailer to
answer the research question. The data used for this study includes 25050 transactions from 849
customers.
Although the results of this study do not support both hypotheses, both regression models
are significant. For the relationship between shipping fee and basket size, a slightly negative,
significant standardized beta coefficient of 0.017 (t=-3.883, p<0.001) has been found. This
implies that for this dataset, basket size tend to decrease by 0.017 percent if shipping fee
increases by one percent. For the relationship between shipping fee and purchase frequency, a
positive, significant standardized beta coefficient of 0.147 (t=4.313; p<0.001) has been found.
This implies that if shipping costs increase by one percent, purchase frequency will increase by
0.147 percent.
This research made some important contributions to the literature about the effects of
shipping fee on online shopping behavior. Since the results are not in line with other research
available about this topic, it highlight the importance of a contingency approach to shipping fee.
A contingency approach should be adopted because different groups of customers can have
different purchase habits and characteristics. Differences between groups of customers could be
determined in further research.
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