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Free Shipping 3.0: Leveraging Scarcity and Popularity Information
– A Randomized Field Experiment
ABSTRACT
We conducted a randomized field experiment to investigate the impact of free shipping policy
on an online retailer’s conversion rates and to examine the extent to which the use of persuasion
principals such as scarcity information and popularity information affects consumers’ purchase
likelihood. We found the introduction of unconditional free shipping leads to 2.5% higher
conversion rates compared to threshold-based free shipping. Notably, contrary to the
conventional belief, the new free shipping promotion leads to not only more orders, but most
importantly higher average order and revenue. We also found that visitors from rural areas react
more strongly to the free shipping promotion compared to the ones from urban areas. In
addition, the presence of scarcity information and popularity information increases consumers’
purchase likelihood (57% and 32% respectively). However, surprisingly, when both information
cues are displayed, it decreases sales compared to when only one of the two information cues is
displayed. This study is the first to empirically examine the combined effects of scarcity and
popularity information. The findings suggest that online retailers should pay attention to the
differential effects of urban and rural visitors when introducing free shipping policies. Adapting
unique shipping promotions that incorporate the high (urban) or low (rural) concentrated areas
may give economic advantages to retailers.
Keywords: delivery, free shipping, popularity information, scarcity information, randomized
field experiment.
1. INTRODUCTION
According to eMarketer (2014), e-commerce sales are expected to grow by 17.4% on
average in 2012-2017. Despite such a growth, the global conversion rates across industries are
on average less than 4% and especially for online retailers drop below 2% 1. Shopping cart
abandonment is considered as one of the major reasons for such low conversion rates (Hoffman
2005). The shopping cart abandonment rate has been steadily over 60%, indicating majority of
online shoppers abandon their shopping carts (eMarketer 2013; Listrak 2015). Although
shopping carts may be used by consumers as wish lists or “save for later” tools (Kukar-Kinney
and Close 2010), shipping cost is considered as one of the major causes for shopping cart
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index.fireclick.com; http://www.wolfgangdigital.com/blog/digital-marketing/wolfgang-e-commerce-kpi-study-2014/
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abandonment (eMarketer 2013). From an online retailer perspective, such figures underline the
importance of adjusting shipping policies to reduce shopping cart abandonment rates and to
improve online retailers’ performance. Prior research shows that shopping cart abandonment
rates drop to 25% when policies such as free shipping are implemented (Mulpuru et al. 2010).
Product shipment is an essential component of online retailing. Whereas in offline retailing,
the buyers often absorb the transportation costs, the spatial remoteness (between firms and
customers) of online retailing necessitates the firms taking over the costs of order delivery
(Koukova et al. 2012; Rosen and Howard 2000). The essential role of shipping costs in online
retailing further highlights them as an important component of competitive advantage among
retailers, especially given that consumers are sensitive to both the product price and the shipping
cost (Dinleroz and Li 2006; Smith and Brynjolfsson 2001). Therefore, decisions related to
shipping charges are important for online retailers.
Shipping fee decisions revolve around the level of fees and their link to the order size.
Companies increasingly experiment with threshold-based free shipping and unconditional free
shipping. Threshold-based free shipping refers to free shipping when orders above a specific
amount (yet charge otherwise); and (unconditional) free shipping refers to free shipping
irrespective of the order value (Dinleroz and Li 2006; Koukova et al. 2012; Lewis 2006). These
free shipping policies are important for attracting online conversions, yet evidence is mixed
regarding their performance (Leng and Becerril-Arreola 2010; Lewis 2006; Yang et al. 2005).
In this paper, using a randomized field experiment, we investigate the benefits of switching
from threshold-based free shipping to unconditional free shipping and examine how online
retailers can use scarcity information and popularity information – two major persuasion cues –
to improve the performance of their free shipping promotion. We found that unconditional free
shipping increases not only conversion rates and the number of orders; contrary to the
conventional wisdom (Lewis 2006), it also increases average order and revenue. In addition, we
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found that the effectiveness of free shipping is larger for customers from rural areas compared to
urban areas. In terms of information cues, we found that showing scarcity information or
popularity information regarding a free shipping policy improves the performance of free
shipping (compared to a free shipping policy without any additional information) in terms of
purchase probability (increases by 57% and 32% respectively). However, when both scarcity
and popularity information are shown, it negatively affects consumers’ likelihood to purchase
compared to when only one of the two information cues is displayed. In particular, the closer the
free shipping promotion expiry date approaches, the larger the effect of displaying scarcity
information becomes. The findings imply that there is room for customization in free shipping
promotion, using persuasion principals and consumers’ geographic information, to achieve the
next level of free shipping (i.e., Free Shipping 3.0).
2. THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT
2.1. Shipping Fee Policies
The growth of e-commerce has emphasized the importance of shipping fees for business
models. Since physical goods are spatially separated from consumers at the time of purchase,
the shipment of these products is an essential component of online businesses (Koukova et al.
2012; Lewis 2006; Rosen and Howard 2000). Firms typically recover such shipment costs by
relegating them to consumers in the form of shipping fees. However, designing a shipping
policy is a critical decision for online firms, as they need to trade-off between increasing the fees
to recover their costs and decreasing shipping fees to attract consumers (Koukova et al. 2012).
From this perspective, shipping fee policies have become an essential tool against competition,
Consumers equally respond to shipping fees as with product prices. Consumers may also
become skeptical towards shipping fess as they may consider them a source of additional profit
for retailers (Schindler et al. 2005).
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A distinctive characteristic of firms’ shipping policies compared to retailer attributes such as
reputation and brand name is that they are often explicitly separated (as a shipping fee) rather
than being implicit in the total price (as a premium due to increased brand equity). Such a
partitioned pricing increases the visibility and the importance of shipping fee in consumers’
decision-making (Chakravarti et al. 2002; Xia and Monroe 2004). Although partitioned pricing
of shipping fees may lead to consumers’ underestimation of total costs (Morwitz et al. 1998), it
increases consumers’ weight attribution to one additional cost-related dimension; hence they
become very elastic to changes in shipping fees (Hamilton and Srivastava 2008; Smith and
Brynjolfsson 2001). Therefore, firms’ decisions related to shipping policies are fundamental
both from a marketing and economic perspective.
There are four shipping policies that are widely used in the current e-commerce practices.
Flat rate shipping fees are constant regardless of the order value. Progressive shipping fees
increase with the order size (monetarily or the number of items). Threshold-based free shipping
offers free shipping after a predetermined order amount has been reached. Unconditional free
shipping means retailers absorb all shipping fees regardless of the order value (Dinleroz and Li
2006; Koukova et al. 2012; Leng and Becerril-Arreola 2010; Lewis 2006).
2.2 Free Shipping Policy
Free shipping, either threshold-based or unconditional free shipping, is the most frequently
used shipping policy. Customers are more easily persuaded to make a purchase when a free
shipping component is communicated to them. The use of free shipping attracts new customers
and impacts order incidence and order size, yet evidence is mixed regarding its overall
performance (Leng and Becerril-Arreola 2010; Lewis 2006; Yang et al. 2005). Threshold-based
free shipping policy is currently one of the most commonly used shipping policies. Such a
policy encourages customers to increase the total order amount to surpass the imposed threshold
(Becerril-Arreola et al. 2013). However prior research shows that imposing a threshold-based
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free shipping is only beneficial for consumers when the order value meets the threshold
(Schindler et al. 2005). When the threshold is not met, the lost opportunity of free shipping is
used as a negative reference against any shipping fee imposed (Koukova et al. 2012). Despite
the popularity, the performance between different free shipping policies remains unknown.
Zero pricing distinguishes from any other price in a non-linear form. The perceived value
difference between a free product and a product with the price of one (in any given currency) is
higher than the difference between the price of one and the price of two (Shampanier et al.
2007). A well-known example is related to the introduction of Amazon’s free shipping policy in
Europe. While free shipping was introduced to most countries, France reduced the shipping
costs to one French franc (instead of zero). As a result, the sales increase in France was
substantially lower than that of the other countries used the unconditional free shipping.
The benefits of different free shipping policies have been documented in previous studies. A
threshold-based free shipping policy may induce customers to shift to larger order sizes in order
to reach that threshold. However, in contexts where customers are shopping for a specific
product (goal-directed purchase behavior) they may defer from increasing their order value just
to meet the threshold, since they might perceive such shipping policy as unfair (Koukova et al.
2012). Unconditional free shipping, compared with threshold-based free shipping, results in
more orders even though the effect on order size per transaction is not necessarily comparable
(Khan et al. 2009; Lewis 2006). Shifting from threshold-based free shipping to unconditional
free shipping can increase consumers’ propensity to make a purchase, especially for repeated
customers, because they may perceive the psychological value of such a shift to a price of zero
more greatly than the actual price shift, and thus would be more likely to buy. We expect:
•
Hypothesis 1: Unconditional free shipping will lead to greater conversion rates
compared to threshold-based free shipping.
There is evident heterogeneity across consumers in the way they respond to free shipping
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promotions (Lewis 2006). When unconditional free shipping is introduced in place of thresholdbased free shipping, we expect that visitors in the rural areas (compared to the urban areas) to
respond more strongly. This is because individuals living in urban areas are less willing to pay
shipping costs as compared to people living in less urban areas, because people in urban areas
have easier access to physical stores. A main source of convenience of online retailers is the lack
of physical travel distance needed between their offline retail competitors and the customers
(Choi et al. 2012). Forman et al. (2009) showed that a reduced travel distance to offline
alternatives makes online retailers less attractive to customers. Similarly, Sinai and Waldfogel
(2004) found that the farther people live from the nearest store, the more they are inclined to buy
products via internet or catalogues. Consumers living in rural areas are on average farther away
from offline sellers and therefore are likely to benefit more from unconditional free shipping
compared to urban customers.
•
Hypothesis 2: The positive effect of unconditional free shipping on conversion rates will
be larger for visitors from rural areas compared to visitors from urban areas.
2.3. The Role of Persuasion Principles
We consider two commonly used persuasion principles (scarcity information and popularity
information) that influence individual behavior when free shipping policy is introduced.
Scarcity Information. Scarcity is defined as the presence of limitations on the supply of a
resource (Lynn 1989). The principle of scarcity reflects individuals’ stronger desire to obtain a
resource that is scarce to them and serves as a fundamental persuasion driver (Cialdini 2001).
Scarce resources make individuals to think in loss terms, as they contemplate what they may
lose if they miss the opportunity to acquire a scarce resource, instead of what they may gain if
they choose that resource. Prospect theory suggests that the psychological value of a loss is
higher than a respective gain (Kahneman and Tversky 1979). Individuals inflate the value of a
limited resource based on the belief that a scarce resource has higher objective value (Cialdini
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1993; Inman et al. 1997; Lynn 1991); therefore the presence of a scarce resource increases
consumers’ choice propensity. A limited unconditional free shipping policy triggers a feeling of
urgency in consumers and accelerates their decision-making, since they want to avoid the
potential regret of losing the gains from a scarce promotion. Such anticipated regret from losing
on a scarce resource is a persuasive driver of choice occurrence (Eppen et al. 1981). An
alternative explanation for the effect of scarcity information is related to the attention effect,
based on which, individuals more thoroughly evaluate information about scarce offers (Inman et
al. 1997). Therefore an unconditional free shipping promotion would be more likely taken into
consideration and become a choice attribute for customers. A deadline effect suggests that the
scarcity effect is highlighted when the expiration time approaches to an end and is often
observed in negotiations (Roth et al. 1988) and auctions (Ariely and Simonson 2003). Limitedtime coupon promotions increase sales and accelerate purchases especially towards the end of
the promotion period (Krishna & Zhang, 1999). Respectively, the less time is left for an
unconditional free shipping policy, the more likely customers become persuaded as the time
scarcity makes the use of that policy more immediate. We expect:
•
Hypothesis 3a: Displaying scarcity information (i.e., the countdown number of days left)
regarding an unconditional free shipping policy increases consumers’ likelihood to
purchase.
•
Hypothesis 3b: When scarcity information is displayed, displaying a smaller countdown
number positively affects consumers’ likelihood to purchase.
Popularity Information. Popularity information is an information cue on the frequency
with which a product or service is purchased by other individuals (Tucker & Zhang 2011).
When a product is purchased by a large number of individuals, consumers are likely to perceive
a higher product quality and can justify their purchase decision (Cai et al. 2009). According to
the theory of observational learning, individuals derive information from observing the behavior
of other peers (Chen et al. 2011) and are influenced by their peers (Muchnik et al. 2013). Prior
studies have found a positive impact of popularity information on sales in various contexts,
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including auctions (bidders participate more in auctions with more bids) (Simonsohn and Ariely
2008), digital downloads (users download music based on popularity even with contradictory
quality signals) (Salganik et al. 2006), peer lending (lenders infer the creditworthiness of
borrowers through lending decisions of others (Zhang & Liu 2012), online book purchases
(Chen et al. 2011), and software downloads (Duan et al. 2009). Such information has evidently
suggested as a fundamental principle of persuasion (Cialdini 2001). Individuals that observe
information about the popularity of an unconditional free shipping policy among other
customers, are more likely to be convinced and desire to use that policy themselves. Especially
when popularity information signals a large number of other individuals being persuaded into
using an unconditional free shipping, customers can more easily and effortlessly justify their
decision to use such a popular resource as well. We expect that:
•
Hypothesis 4a: Displaying popularity information (i.e., the number of customers having
used free shipping) regarding an unconditional free shipping policy increases
consumers’ likelihood to purchase.
•
Hypothesis 4b: When popularity information is displayed, displaying a higher number
of popularity information positively affects consumers’ likelihood to purchase..
However, when both information cues are displayed, the overall expected persuasiveness is
expected to lower than the sum of the independent effects. According to prospect theory, gains
have diminishing marginal returns in individuals’ utility function (Kahneman and Tversky
1979). Although gains have high psychological value in the beginning, the marginal
psychological value of a second gain related to the information cue would be smaller, since a
baseline persuasion has been achieved with the first persuasion cue. In other words, the marginal
value of adding a persuasion cue (e.g. popularity information) about an unconditional free
shipping policy is decreased when another persuasion cue (e.g. scarcity) is already displayed
(compared to when there is no other persuasion cue displayed). Therefore:
•
Hypothesis 5: When both scarcity and popularity information regarding an
unconditional free shipping policy are displayed, it negatively affects consumers’
likelihood to purchase compared to when one of the two information cues is displayed.
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3. METHODOLOGY
We conducted a randomized field experiment in collaboration with an online retailer in The
Netherlands. We chose this online retailor because it was interested in finding a better shipping
policy as it suffers from a severe shopping cart abandonment problem. Before our study, the eretailer used a threshold-based free shipping policy and would like to introduce an unconditional
free shipping policy. During the experiment, we introduced the unconditional shipping policy
for 30 days and used a 2×2 between-subject experimental design and manipulated scarcity
information (yes/no) and popularity information (yes/no). After the experiment, the e-retailer
went back to the threshold-based free shipping. We obtained data from Google Analytics and
the company’s sales database.
To increase the chances that visitors notice the changes in shipping policy, we displayed a
picture on the right side of the website with a person holding a white sign with different
experimental conditions: (1) Free shipping; (2) Free shipping + (#) days left; (3) Free shipping +
Recently, (#) people made use of free shipping; (4) Free shipping + Recently, (#) people made
use of free shipping + (#) days left. The number of days left displays a countdown number until
the free shipping promotion is over and is updated every day. The popularity number is based on
the actual number of sales since the beginning of the experiment. We made sure each unique
visitor was randomly assigned to an experiment condition. We also made sure the website works
with even the lowest screen format so that all visitors would be able to see the banner clearly on
the right place of the website without any graphical distortions.
4. MODEL AND RESULTS
In this section, we first discuss the results related to the introduction of the unconditional
free shipping policy and test hypotheses H1 and H2. Second, we discuss the effects of scarcity
and popularity information and provide hypothesis testing for H3 to H5, and present the analysis
of the economic impacts. We also conduct some additional analyses to check the robustness of
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the results. The summary statistics of the variables are shown in Table 1.
4.1. The Effect of Unconditional Free Shipping
Model Specification. To understand the effect of introducing unconditional free shipping
policy, we first used a dataset that consists of the daily-city-level conversion data for 30 days prior
and 30 days during the experimental period. We model the effect of introducing the policy using
Equation (1), where µ, γ, and ρ are parameters to be estimated.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑡𝑡 = 𝜇𝜇 + 𝛾𝛾1 × 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡 + 𝛾𝛾2 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡 + 𝛾𝛾3 × 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑡𝑡 +
𝜌𝜌 × 𝑍𝑍𝑡𝑡
(1)
Conversiont is the conversion rate at each day t. FreeShippingt and Ruralt are two dummy
variables. FreeShippingt is equal to 1 when the unconditional free shipping policy is introduced.
Ruralt is equal to 1 when a city is in a rural area; when a city is in an urban area, it is equal to 0.
To define if a city belongs to a rural or an urban area, we gathered information from the Central
Bureau of Statistics (www.cbs.nl). Zt is the visitor related controls (i.e., the number of visitors)
and ρ is the associated coefficient.
Results. We first examine the effect of introducing unconditional free shipping on
conversion rates and summarize the results in Table 2. Column (1) includes only the direct
effect of FreeShipping. The parameter estimate is positive and significant compared with before
introducing the unconditional free shipping policy (β = 0.025, p < 0.01). This suggests that the
introduction of unconditional free shipping increases conversion rates by 2.5% compared with
threshold-based free shipping, supporting H1. Column (2) enters the direct effect of Rural. The
direct effect of FreeShipping remains positive and significant; and the estimate of Rural is
negative however not significant. Column (3) includes both the direct effects and the interaction
term between FreeShipping and Rural. FreeShipping remains positive and significant; Rural is
negative and significant (β = -0.008, p < 0.01), suggesting customers in the rural areas have
0.8% lower conversions compared to customers from urban areas. The estimate for the
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interaction effect FreeShipping × Rural is positive and significant (β = 0.015, p < 0.01). This
suggests that customers in the rural areas reacted more strongly to the unconditional free
shipping policy, supporting H2.
In Column (4), (5) and (6), we used alternative dependent variables and examined the effect
of unconditional free shipping on number of orders, average order (number of orders per
visitor), and revenue. The direct effect of FreeShipping remains positive and significant,
suggesting the unconditional free shipping increases not only conversion rates, but also number
of orders, average order, and revenue. The direct effect of Rural on number of orders is positive
and significant (β = 0.023, p < 0.01), suggesting customers in the rural areas place 2.3% more
orders compared to customers from urban areas.
Robustness Checks. We performed a difference-in-difference analysis based on the dailylevel conversion data from one month before and one month during the experiment and also for
the same period from the previous year. This method allows us to control for unobserved factors
driving conversions over time by comparing daily conversions against the same period of the
previous year; and to eliminate the website inherent factors by comparing daily conversions
during the experiment with the ones before the experiment. As such, we estimated the effect of
how conversions change after introducing unconditional free shipping compared to the changes
in conversions in the control group over the same period in the previous year. We found that the
direct effects of treatment and time are positive and significant. In addition, the interaction is
significantly positive, indicating the conversions increase after the introduction of the
unconditional free shipping policy.
4.2. The Effect of Scarcity Information and Popularity Information
Model Specification. To examine the extent to which displaying scarcity and popularity
information affects consumers’ likelihood to purchase, we used a dataset that consists of the
visitor-level observations during the experimental period. Our model estimates the probability of
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𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
purchase for each visitor, which we denote as 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖
. We model the latent
probability of purchasing as a logit function of scarcity information and popularity information.
Equation (2) summarizes the specification:
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
𝑈𝑈𝑖𝑖
𝑃𝑃𝑢𝑢𝑢𝑢𝑢𝑢ℎ𝑎𝑎𝑎𝑎𝑎𝑎
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖
=
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
exp(𝑈𝑈𝑖𝑖
)
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼
exp�𝑈𝑈𝑖𝑖 � +
1
= 𝛼𝛼𝑖𝑖 + 𝛽𝛽1 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖 + 𝛽𝛽2 × 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 + 𝛽𝛽3 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖 × 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 + 𝛽𝛽4 ×
(#𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 | 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖 ) + 𝛽𝛽5 × (#𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 | 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 ) + 𝛽𝛽6 × (#𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 | 𝐵𝐵𝐵𝐵𝐵𝐵ℎ𝑖𝑖 ) +
𝛽𝛽7 × (#𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 | 𝐵𝐵𝐵𝐵𝐵𝐵ℎ𝑖𝑖 ) + 𝜃𝜃 × 𝑋𝑋𝑖𝑖 + 𝛿𝛿𝑖𝑖 + 𝜀𝜀𝑖𝑖
𝐼𝐼𝐼𝐼𝐼𝐼𝑜𝑜
In the above specification, 𝑈𝑈𝑖𝑖
(2)
denotes the latent utility of a purchase. αi is the constant that
captures individual’s inclination to purchase. We included two dummy variables that measure the
level effect of the treatment condition for scarcity information and popularity information.
Scarcityi is equal to 1 when only the number of days is displayed to visitor i, Popularityi is equal
to 1 when only the number of sales is displayed to visitor i. The interaction term
Scarcity&Popularityi captures the effect of the scarcity information and popularity information are
shown together to a visitor. The conditional terms capture the effect of the number of days #Daysi
and the number of sales #Salesi when displayed, either in isolation or in combination.
Xi is the consumer controls (i.e., first time visitor) that account for the unobserved fixed
effects in consumers’ visiting behaviors. The vector θ is the associated coefficients. To control
for visitors’ different arrival time at the website, we included a vector of time effects, δi, which
consists of fixed effects for the day of the week and the week. εi consists of the idiosyncratic
error terms. We assume an independent and identically distributed extreme value distribution of
the error term in the logit model.
In the traditional treatment-control sense, randomized field experiments can avoid
endogeneity and causality biases. That is, the experiment randomization controls for
consumers’ unobservable heterogeneity that might confound our results. Differences in
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consumer purchase likelihoods are then attributed to the treatment effects of scarcity and
popularity information relative to the control condition of no information.
The Effect of Scarcity and Popularity Information. The key empirical results of our
model are summarized in Table 3. The dependent variable is an individual customer’s decision
to purchase. Column (1) includes only the control variables as the baseline predictions, Column
(2) and (3) enter the scarcity information and popularity information variables. As shown in
Column (3), the parameter estimate for the effect of scarcity information is positive and
significant compared with not showing any information (β = 0.351, p < 0.01), and the estimate
for popularity information is also positive and significant compared with not showing any
information (β = 0.155, p < 0.05). The results show that displaying scarcity information and
popularity information increases purchase probabilities compared with not displaying any
information. This supports H3a and H4a.
Column (4) includes the interaction terms of displaying both scarcity and popularity
information in combination. The parameter estimate of the interaction is negative and
significant (β = -0.213, p < 0.10), supporting H5. Column (5) includes the number effects and
presents the full model as in Equation (2). The parameter estimate for the interaction is positive
and significant (β = 2.256, p < 0.01). These results provide evidence for the significant effects
of combinations of scarcity and popularity information. The coefficient for # days | both (β = 0.738, p < 0.01) was negative and significant, supporting H3b. This suggests that, when both
scarcity information and popularity information are shown in combination, the lower the
number of days (i.e., closer to the deadline) significantly increases consumers’ likelihood to
purchase. The coefficient for # sales | both was not significant. This does not support H4b.
Economic Importance of Combining Scarcity and Popularity Information. We describe
the economic impact of combining scarcity and popularity information strategies using the odds
ratios. Based on the result in Column (4), displaying scarcity information compared with not
14
displaying any information produces an increase in the odds of purchasing by 57% (1.57 =
exp(0.450)), holding other variables constant. Displaying popularity information compared with
not displaying any information produces an increase in the odds of purchasing by 32% (1.32 =
exp(0.279)), holding other variables constant. However, displaying both scarcity and popularity
information together, compared with displaying only one of the two, yields an decrease in the
odds of purchasing by 24% (1.24 = exp(2.266)), holding other variables constant. When
looking at the number effect in Column (5), the result suggests that showing one day closer to
the free shipping deadline increases the odds of purchasing by 1.1 times (2.09 = exp(0.738)).
Robustness Checks. We took several additional steps to check the robustness of our results
and summarized the results in Table 4 for both the main effect model as well as the model with
the number effect. First, we checked for different model specifications. Column (1) and (2)
report the estimates for a probit model and the results are qualitatively the same. Second, we
made sure the observed effects of scarcity and popularity information cannot be explained by
alternative explanations resulting from different online behaviors. In Column (3) and (4), we
controlled for the effect of consumer onsite behavior in terms of time-on-site. The estimate of
the interaction effect Scarcity × Popularity in the main effect model is negative but not
significant. In the number effect model, the estimate for # sales | both is, surprisingly, negative
and significant (β = -0.208, p < 0.05). Third, we checked for visitors that only briefly visited the
website and removed bounces (i.e., single page sessions). Column (5) and (6) exclude visitors
that had only one page view. The results are similar to our main results. Fourth, we checked the
subsamples of our data based on the population of the areas. Column (7) and (8) estimate the
model for the areas that have more than 200,000 people, whereas Column (9) and (10) estimate
the model for the areas that have less than 200,000 people. The results are robust to this
subsample analysis. In fact, it shows that the negative interaction effect of Scarcity ×
Popularity is much larger for areas with less population, suggesting combining both
15
information has a stronger negative impacts on consumers in rural areas (vs. urban areas).
5. CONCLUSION
In this paper, using a randomized field experiment in collaboration with an online retailer,
we first investigated the benefits of switching from threshold-based free shipping to
unconditional free shipping. We found that unconditional free shipping increases conversion
rates, the number of orders, average order, and revenue. The effectiveness of free shipping is
larger for customers from rural areas. Second, we examined the extent to which online retailers
can leverage the persuasive role of scarcity and popularity information when introducing
unconditional free shipping policy. The findings suggest that showing both information cues in
separation improves consumers’ purchase probability. However, when both information are
shown in combination, it negatively affects consumers’ likelihood to purchase compared to
displaying only one of the two information cues.
Showing how retailers can use persuasion cues to further improve the performance of their
free shipping policy, as well as understanding the potential of different identifiable segments can
further improve its performance are very crucial from a theoretical as well as a practical
perspective. The findings imply that there is room for customization in free shipping policy
implementation. Using geographic information, retailers can classify their customers into
segments and adjust the shipping fees accordingly.
Further, retailers can leverage the persuasive power of scarcity and popularity information to
attract more customers or accelerate their order decisions. Showing scarcity information about a
free shipping policy increases consumers’ likelihood to purchase; and such an effect becomes
stronger the less number of days left for such a promotion. Showing popularity information
increases consumers’ purchase probability, however, the popularity number does not seem to
influence their purchase probability. Popularity information in the context of a shipping policy
serves mostly as a cue that induces consumers’ trust about such a promotion. As long as an
16
adequate number of other consumers have trusted this promotion, a larger number does not
further increase the persuasive power of that information cue.
Our study has some limitations. First, the retailer provided no information about shipping
costs per order, a factor that might influence the profitability of a free shipping promotion.
However, shipping costs in the Netherlands are uniform across regions and relatively small
compared to the average order value in the experiment period. Another aspect of shipping that
we did not consider is the timing of delivery. Whereas customers might be willing to pay more
for fast delivery, retailers can also reduce their costs if they can afford a less fast delivery of
their orders. Future studies could look into these elements of shipping policies. Finally, we will
explore alternative definitions for urban/rural areas to test the robustness of our findings.
REFERENCES
1. Ariely, D., & Simonson, I. (2003). Buying, bidding, playing, or competing? Value assessment
and decision dynamics in online auctions. Journal of Consumer Psychology, 13(1), 113-123.
2. Becerril-Arreola, R., Leng, M., & Parlar, M. (2013). Online retailers’ promotional pricing,
free-shipping threshold, and inventory decisions: A simulation-based analysis. European
Journal of Operational Research, 230(2), 272-283.
3. Cai, H., Chen, Y., and Fang, H. (2009). Observational learning: Evidence from a randomized
natural field experiment. American Economic Review, 99(3), 864-82.
4. Chakravarti, D., Krish, R., Paul, P., & Srivastava, J. (2002). Partitioned presentation of
multicomponent bundle prices: evaluation, choice, and underlying processing effects. Journal
of Consumer Psychology, 12(3), 215–29.
5. Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: A natural experiment on
word of mouth versus observational learning. Journal of Marketing Research, 48(2), 238-254.
6. Choi, J., Bell, D. R., & Lodish, L. M. (2012). Traditional and IS-enabled customer acquisition
on the Internet. Management Science, 58(4), 754-769.
7. Cialdini, R. (2011). Harnessing the Science of Persuasion. Harvard Business Review, 72-79.
8. Cialdini, R. (1993). Influence: The Psychology of Persuasion, New York: Harper Collins.
9. Duan, W., Gu, B., & Whinston, A.B. (2009). Informational cascades and software adoption on
the Internet: An empirical investigation, MIS Quarterly 33(1), 23-48.
10. eMarketer (2014). Global B2C Ecommerce Sales to Hit $1.5 Trillion This Year Driven by
Growth in Emerging Markets. Available from: www.emarketer.com/Article/Global-B2CEcommerce-Sales-Hit-15-Trillion-This-Year-Driven-by-Growth-Emerging-Markets/1010575,
accessed on June 19, 2015.
11. eMarketer 2013 http://www.emarketer.com/Article/Retailers-Rethink-Shopping-CartAbandonment/1009645, accessed on June 19, 2015
12. Eppen, G. D., & Lieberman, J. (1981). A theoretical and empirical evaluation of price deals for
17
consumer nondurables. Journal of Marketing, 45(1), 116-129.
13. Foreman, Chris, Anindya Ghose, & Avi Goldfarb (2009), Competition between local and
electronic markets: How the benefit of buying online depends on where you live. Management
Science, 55 (1), 47-57.
14. Hamilton, R. W., & Srivastava, J. (2008). When 2 + 2 is not the same as 1 + 3: Variations in
price sensitivity across components of partitioned prices. Journal of Marketing Research,
45(4), 450 - 461.
15. Hoffman, D. L. (2005). Beyond the basics: Research-based rules for internet retailing
advantage. Sloan Center for Internet Retailing.
16. Inman, J. J., & McAlister, L. (1994). Do coupon expiration dates affect consumer behavior?
Journal of Marketing Research, 31(3), 423.
17. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk.
Econometrica, 47(March), 163–91.
18. Khan, R., Lewis, M., & Singh, V. (2009). Dynamic customer management and the value of
one-to-one marketing. Marketing Science, 28(6), 1063-1079.
19. Koukova, N. T., Srivastava, J., & Steul-Fischer, M. (2012). The effect of shipping fee structure
on consumers’ online evaluations and choice. Journal of the Academy of Marketing Science,
40(6), 759-770.
20. Krishna, A., & Zhang, Z. J. (1999). Short-or long-duration coupons: The effect of the
expiration date on the profitability of coupon promotions. Management Science, 45(8), 10411056.
21. Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers’ online shopping
cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240-250.
22. Leng, M., & Becerril‐Arreola, R. (2010). Joint pricing and contingent free‐shipping
decisions in B2C transactions. Production and Operations Management, 19(4), 390-405.
23. Lewis, M., Singh, V., & Fay, S. (2006). An empirical study of the impact of nonlinear
shipping and handling fees on purchase incidence and expenditure decisions. Marketing
Science, 25(1), 51-64.
24. Lewis, M. (2006). The effect of shipping fees on customer acquisition, customer retention, and
purchase quantities, Journal of Retailing, 82(1), 13-23.
25. Listrak Shopping Cart Abandonment Index. Available from: http://www.listrak.com/digitalmarketing-solutions/email/shopping-cart-abandonment-index.aspx, accessed on June 19, 2015.
26. Lynn, M. (1991). Scarcity effects on desirability: A quantitative review of the commodity
theory literature. Psychology & Marketing, 8, 43–57.
27. Morwitz, V., Greenleaf, E., & Johnson, E. J. (1998). Divide and prosper: Consumers' reaction
to partitioned prices. Journal of Marketing Research, 35, 453-463.
28. Mulpuru, S., Hult, P., Evans, P.F., McGowan, B., (2010). Understanding shopping cart
abandonment: Customers are often unprepared to buy and stunned by shipping costs.
Forrester.
29. Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment.
Science, 341(6146), 647-651.
30. Rosen, K. T., & Howard, A. L. (2000). E-retail: Gold rush or fool’s gold. California
Management Review, 42(3), 72–100.
31. Roth, A. E., Murnighan, J. K., & Schoumaker, F. (1988). The deadline effect in bargaining:
Some experimental evidence. The American Economic Review, 78(4), 806-823.
32. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and
unpredictability in an artificial cultural market. Science, 311(5762), 854-856.
33. Schindler, R. M., Morrin, M., & Bechwati, N. N. (2005). Shipping charges and shippingcharge skepticism: Implications for direct marketers’ pricing formats. Journal of Interactive
18
Marketing, 19(1), 41-53.
34. Shampanier, K., Mazar, N., & Ariely, D. (2007). Zero as a special price: The true value of free
products. Marketing Science, 26(6), 742‐757.
35. Simonsohn, U., & Ariely, D. (2008). When rational sellers face nonrational buyers: Evidence
from herding on eBay. Management science, 54(9), 1624-1637.
36. Sinai, T., & Waldfogel, J. (2004). Geography and the Internet: Is the Internet a substitute or a
complement for cities? Journal of Urban Economics, 56(1), 1‐24.
37. Smith, M. D., & Brynjolfsson, E. (2001). Consumer decision making at an internet shopbot:
brand still matters. Journal of Industrial Economics, 49(4), 541–58.
38. Tucker, C., & Zhang, J. (2011). How does popularity information affect choices? A field
experiment. Management Science, 57(5), 828-842.
39. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.
Science, 185(4157), 1124-1131.
40. Xia, L., & Monroe, K. B. (2004). Price partitioning on the internet. Journal of Interactive
Marketing, 18(4), 63-73.
41. Yang, Y., Essegaier, S., & Bell, D. R. (2005). Free shipping and repeat buying on the internet:
theory and evidence. Manuscript, Wharton School, University of Pennsylvania.
42. Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science,
58(5), 892-912.
19
Table 1. Summary Statistics
Variable
Mean
Std. Dev.
Min
Panel A. Free Shipping
0.045
0.159
0.000
0.501
1.403
0.000
0.143
0.451
0.000
0.437
0.496
0.000
0.545
0.498
0.000
1.147
0.540
0.693
Panel B. Scarcity and Popularity Information
Mean
Std. Dev.
Min
0.06
0.23
0.00
0.51
0.50
0.00
0.43
0.49
0.00
0.54
0.50
0.00
.71
1.18
0.00
1.39
2.73
0.00
.53
1.08
0.00
1.34
2.65
0.00
Conversion Rate
Average Order
Sales
Experiment
Rural
Number of Visitors
Variable
Purchase
Scarcity
Popularity
First Visit
# days | scarcity only
# sales | popularity only
# days | both
# sales | both
Max
1.000
7.849
10.000
1.000
1.000
4.454
Max
1.00
1.00
1.00
1.00
3.40
7.52
3.40
7.52
Note. In Panel A, the variables number of visitors and average order are log transformed. In Panel B, the
variables related to # days and # sales are log transformed.
Table 2. Effect of Free Shipping
(1)
(4)
Number of
Conversion Conversion Conversion
Orders
Free Shipping
0.025***
(0.003)
(2)
0.025***
(0.003)
-0.002
(0.003)
(3)
(5)
Average
Order
(6)
Revenue
0.017***
0.043***
0.196***
0.167***
(0.004)
(0.012)
(0.036)
(0.036)
Rural
-0.008*** 0.023***
0.025
0.016
(0.003)
(0.009)
(0.027)
(0.027)
Free Shipping × Rural
0.015***
0.000
0.008
0.033
(0.005)
(0.014)
(0.044)
(0.045)
Number of Visitors
0.007***
0.006***
0.006***
0.391***
0.999***
1.039***
(0.002)
(0.002)
(0.002)
(0.017)
(0.028)
(0.030)
Constant
0.029***
0.030***
0.034***
-0.329*** -0.708*** -0.733***
(0.004)
(0.005)
(0.005)
(0.023)
(0.048)
(0.049)
Week Fixed Effect
Yes
Yes
Yes
Yes
Yes
Yes
Observations
15,303
15,303
15,303
15,303
15,303
15,303
Adjusted R2
0.007
0.007
0.007
0.216
0.154
0.159
Note. We estimated the models with robust standard errors. Standard errors in parentheses. Significance
levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
20
Table 3. Effect of Scarcity and Popularity Information
VARIABLES
Scarcity
(1)
Purchase
(2)
Purchase
(3)
Purchase
(4)
Purchase
0.348***
(0.062)
0.351***
(0.062)
0.155**
(0.064)
0.450***
(0.086)
0.279***
(0.098)
-0.213*
(0.128)
(5)
Purchase
0.859***
(0.294)
Popularity
-0.212
(0.510)
Scarcity × Popularity
2.266**
(0.982)
# days | scarcity only
-0.145
(0.108)
# sales | popularity only
0.091
(0.080)
# days | both
-0.738***
(0.129)
# sales | both
-0.089
(0.101)
First Visit
0.274***
0.251***
0.231***
0.230***
0.219***
(0.064)
(0.063)
(0.064)
(0.064)
(0.064)
Constant
-3.080***
-3.255***
-3.328***
-3.376***
-3.209***
(0.111)
(0.116)
(0.120)
(0.122)
(0.132)
Time Fixed Effect
Yes
Yes
Yes
Yes
Yes
Observations
21,144
21,144
21,144
21,144
21,144
Chi2
33.52
63.02
70.61
71.40
128.5
Pseudo R2
0.00409
0.00770
0.00836
0.00868
0.0141
Log likelihood
-4584
-4567
-4564
-4563
-4538
Note. Dependent variable is a binary indicator for whether a customer purchased on the website. Time
Fixed Effect includes: day-of-week and week fixed effects. We estimated the models with robust
standard errors. Standard errors in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
21
Table 4. Robustness Checks
Probit
(2)
VARIABLES
(1)
Scarcity
0.209***
(0.040)
0.129***
(0.044)
-0.099*
(0.059)
0.387***
(0.133)
-0.082
(0.230)
1.149**
(0.468)
-0.063
(0.050)
0.039
(0.036)
-0.361***
(0.064)
-0.050
(0.048)
(3)
Time on Site
(4)
0.448***
(0.090)
0.224**
(0.104)
-0.151
(0.135)
(5)
Bounce
(6)
Population > 200,000
(7)
(8)
Population ≤ 200,000
(9)
(10)
0.860*** 0.435*** 0.755*** 0.404**
1.132*** 0.464*** -0.627
(0.302)
(0.085)
(0.291)
(0.161)
(0.395)
(0.102)
(0.926)
Popularity
0.127
0.265*** 0.156
0.126
-0.698
0.394*** -0.224
(0.512)
(0.098)
(0.513)
(0.172)
(2.574)
(0.121)
(0.612)
Scarcity × Popularity
3.053*** -0.218*
2.823*** 0.284
1.534
-0.569*** 7.361***
(1.007)
(0.128)
(1.008)
(0.219)
(5.116)
(0.157)
(1.982)
# days | scarcity only
-0.149
-0.114
-0.399*
0.385
(0.111)
(0.108)
(0.213)
(0.317)
# sales | popularity only
0.030
0.030
0.137
0.106
(0.081)
(0.080)
(0.355)
(0.102)
# days | both
-0.884***
-0.814***
-0.997***
-1.612***
(0.135)
(0.132)
(0.211)
(0.447)
# sales | both
-0.208**
-0.194*
0.068
-0.239*
(0.106)
(0.105)
(0.595)
(0.125)
Time on Site
0.674*** 0.679***
(0.020)
(0.020)
First Visit
0.107*** 0.102*** 0.379*** 0.371*** 0.088
0.079
0.118
0.103
0.297*** 0.292***
(0.030)
(0.030)
(0.068)
(0.068)
(0.065)
(0.065)
(0.102)
(0.102)
(0.083)
(0.083)
Constant
-1.842*** -1.767*** -7.353*** -7.266*** -2.920*** -2.805*** -3.336*** -3.413*** -3.326*** -3.274***
(0.056)
(0.061)
(0.188)
(0.197)
(0.123)
(0.135)
(0.185)
(0.190)
(0.140)
(0.151)
Time Fixed Effect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
21,144
21,144
21,144
21,144
16,860
16,860
7,907
7,907
13,237
13,237
Chi2
71.94
123.5
1269
1298
52.01
112.8
41.80
82.80
52.91
79.61
Pseudo R2
0.00859
0.0140
0.134
0.140
0.00680
0.0129
0.0138
0.0216
0.0102
0.0134
Log likelihood
-4563
-4538
-3986
-3957
-4293
-4266
-1700
-1687
-2849
-2840
Note. Time Fixed Effect includes: day-of-week and week fixed effects. We estimated the models with robust standard errors. Standard errors in parentheses.
Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.