Spending real money: Purchasing patterns of virtual goods in an online social game

Abstract

Researchers have found that "social" factors contribute to purchasing intentions of virtual goods in an online social game, but little is known about actual purchasing behavior. Study 1 examined the relationship between social factors and virtual goods purchasing patterns using large scale data obtained by server logs of an online social game. Exchange of virtual goods and number of friends increased the likelihood of spending real money compared to no spending. Among those who did spend real money, giving virtual goods to others was the strongest factor associated with the amount of spending. Study 2 examined purchasing patterns of players who spent real money: High real-money spenders were buying items for visual customization while low spenders were buying consumable items necessary to sustain playing the game.

Full text

Spending Real Money: Purchasing Patterns of Virtual
Goods in an Online Social Game
Donghee Yvette Wohn
Northwestern University
2240 Campus Drive, #2115
Evanston, IL 60208
yvettewohn@gmail.com
ABSTRACT
Researchers have found that “social” factors contribute to
purchasing intentions of virtual goods in an online social
game, but little is known about actual purchasing behavior.
Study 1 examined the relationship between social factors
and virtual goods purchasing patterns using large scale data
obtained by server logs of an online social game. Exchange
of virtual goods and number of friends increased the
likelihood of spending real money compared to no spending.
Among those who did spend real money, giving virtual
goods to others was the strongest factor associated with the
amount of spending. Study 2 examined purchasing patterns
of players who spent real money: high real-money spenders
were buying items for visual customization while low
spenders were buying consumable items necessary to
sustain playing the game.
Author Keywords
social game; virtual goods; big data; consumer behavior;
social exchange; customization; e-commerce
ACM Classification Keywords
K.4.4. Electronic Commerce
General Terms
Human Factors; Design; Measurement.
INTRODUCTION
Purchasing virtual goods is increasingly becoming a
common feature of virtual worlds and online games
[3,9,16,24]. The global virtual goods market has been
rapidly growing: one market report estimated that the global
market value of virtual goods was $14.8 billion in 2012 and
would increase at an annual growth rate of 12.5% to 2016
[6]. Moreover, sales of virtual goods—once seen only in
massively multiplayer online games (MMOs) and social
network sites based in Eastern Asia—are now becoming a
common feature of social network sites in the United States
and Europe, especially in social network game (SNG)
applications [9,24].
Despite the prevalence of virtual good purchasing behavior
in online games, there is little academic research that has
examined actual virtual good purchasing behavior. Part of
this is due to the fact that game companies do not share
such information. Thus much of the research on virtual
goods has been conducted from the player’s (consumer’s)
perspective, and examining intention to purchase rather
than actual behavior. Study 1 examines the relationship
between behaviors captured by log data and players’ actual
spending behavior. This data-driven approach takes
observable variables that are informed, but not exact
proxies of psychological variables studied in prior research,
providing a model that can be easily replicated in other
game contexts. Study 2 examines spending patterns based
on different types of virtual goods. By differentiating the
spending behaviors of low and high real-money spenders,
the results inform targeted design of virtual goods.
Understanding the factors that are associated with
purchasing patterns of virtual goods is important to the HCI
community because game companies are increasingly
relying on sales of these goods as their main source of
revenue. Traditionally, online games were subscription-
based, requiring the player to pay a fixed amount every
month or year to play the game. Now, newer games employ
a free-to-play model, in which the game is free, but players
can purchase virtual goods with real money to enhance their
playing experience, whether that is to make their virtual
character or space more visually pleasing, or to accelerate
their progress.
Since most people play without purchasing anything, game
designers must appeal to the pocket of the paying player
without turning away the non-paying player in order to
retain a large number of players. This poses a design
challenge because the system must attract and retain two
different types of players. This distinction is important
because, as mentioned above, traditional online games with
a single business model (subscription-based) were designed
under the assumption of a uniform user.
Virtual Goods in Online Games
Most early online games—especially those popular in the
Western hemisphere—did not require players to use real
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http://dx.doi.org/10.1145/2556288.2557074

money to buy virtual goods. For example, World of
Warcraft had an internal economic system that required
players to buy virtual goods with currency that could only
be earned within the game. However, more game
companies are now changing their revenue models to
incorporate sales of virtual goods. In the context of online
multiplayer games, which include MMOs and SNGs, one
popular genre of virtual goods is that related to visual
customization. These goods include clothing, hairstyles,
and accessories for virtual characters (avatars), as well as
furniture and home décor for virtual space. Another genre
of virtual goods is related to game mechanisms, such as
experience points. In many cases, these two genres are not
mutually exclusive. For example, a piece of clothing may
be aesthetically pleasing and be related to a game
mechanism, such as enhancing certain skills.
Most often these virtual goods can be purchased from an in-
game “store” that is run by the game company. Maple Story
is a game that is a successful example of using virtual
goods sales as a source of revenue: players can buy clothing
for their avatars, digital pets, and items that are required to
take care of the pet.
Not all virtual goods are purely eye candy. Farmville, for
example, a SNG that lets players build a virtual farm,
enables players to purchase virtual goods that will expedite
their progress in the game. Kart Rider, an online social
racing game, allows players to purchase virtual goods that
will help themselves (such as a speed booster) or hinder
others (such as a bomb).
Study Context: Puppy Red
This paper presents two studies that examine virtual goods
purchasing behavior in the context of Puppy Red, a 3D
social game service based in South Korea. Puppy Red is
similar to social media services such as Webkins or Club
Penguin and targets female players. Each player is provided
with an empty island, which they can decorate with virtual
goods.
Launched in 2003, the game has 5 million registered
players and provides more than 15,000 different virtual
items that range from avatar clothing and home decorations
to pet accessories. Players create and dress up their own
avatar and decorate their house. They can visit other
players’ houses, congregate in a public space, and engage in
mini games or tasks within the game—such as picking
apples—which will give them virtual currency in the form
of beans.
Players can then use these beans to purchase clothes and
animations for their avatar, furniture and decorations for
their house, and food and accessories for their pets among
others. The game operates on a free-to-play business model.
There is no membership fee, but players have the option to
purchase coins with real money. Coins are not necessary to
play the game, but can be used to purchase special items.
Some items can only be purchased with coins, while others
can be purchased with both beans and coins.
ECONOMIC BEHAVIOR IN ONLINE GAMES
Much of the research on economic behavior in online
games has been conducted in the context of MMOs such as
World of Warcraft and Everquest. Players of MMOs can
visually represent themselves through a virtual character, or
avatar, and traverse in an immersive environment that
resembles a physical space. The economy within the game
emerges out of the activities of the aggregate behavior of
individuals through complex mechanisms of individual and
interactive behavior. Scholars found that activities that the
behaviors players engage in within the game draw parallels
to “real world” activities that economists call production,
trade, consumption, and labor [17].
Although there have been studies on macro-economic
trends in online games [3,4], there has been relatively little
literature on micro-economic behavior, such as consumer
behavior, that incorporates behavioral data. Studies on
micro-economic behavior—in particular, purchasing
behavior of players—have been limited by the difficulty in
acquiring large enough samples. Only a small proportion of
players actually spend real money to buy virtual goods in
online games, which makes it extremely difficult for
academics to identify the players who spend real money,
even if there are tens of thousands of people who fall into
this category. Industry statistics have shown that about 34
percent of people who played MMOs bought virtual goods
and 23 percent bought goods in a social network game [23].
Even for Nexon, a company that is considered a successful
case in terms of profiting from virtual goods sales—it
operates at a 35 percent margin—only 90 percent of the
people who play its games actually pay actual money [2].
The literature on virtual goods purchases mainly comes
from two perspectives. The first is a player perspective,
examining psychological motivations and decision
processes that are involved in the purchase of virtual goods.
In the context of MMOs, Nojima [20] looked at the
relationship between players’ motivations and revenue
models, finding that certain motivations—such as
immersion—are correlated with higher levels of spending.
Lehdonvirta [17] identified several different motivations
unique for explaining virtual goods purchases: advancement
in status, competitive advantage, keeping up with other
players, experiencing new content, customization, and self-
expression. He found that players’ attitudes towards virtual
good purchases are correlated with their motivations.
Researchers have also tested the technology acceptance
model of how perceived usefulness and attitude leads to
purchase intention of virtual goods through player surveys
[5,18].
The second perspective taken by scholars studying virtual
good purchases is a pragmatic one that focuses more on
technical affordances or design factors that affect
purchasing. For example, Oh and Ryu [21] looked at how

game mechanics can be used to create and sustain demand
for virtual goods. Hamari and Lehdonvirta [12] identified
several mechanics that drive the desirability of virtual
goods and then looked at how different types of game
mechanics based on segmentation of players can generate
repeated purchases or create settings for additional virtual
goods.
These two perspectives are useful in understanding general
purchasing behavior in an online environment, but this
study will focus on the player perspective. In an online
game that is inherently social in nature, interpersonal and
group dynamics could strongly influence people’s
behaviors. The following section thus examines the social
factors involved in purchasing behavior.
STUDY 1: SOCIAL FACTORS ASSOCIATED WITH
SPENDING
Literature points to three social factors that have been
empirically examined in the context of virtual goods
purchases: social motivations, social presence, and social
influence,
Social motivations have been found to be associated with
spending. In the context of SNGs, researchers [24] found
asked adult Facebook game players about whether or not
they spent real money to buy virtual cash in the games.
They found a significant positive correlation between social
motivations (e.g., I play games on Facebook to… “find
people like me,” “to feel like I belong to a group”) and
spending. Motivation, however, is something that is
difficult to examine with behavioral data, so we were
unable to derive any testable hypotheses.
Social presence in a virtual environment has also been
found to be positively correlated with purchasing intention
[14]. Social presence is the feeling that an individual has
that they are with other people in the virtual world [13], or
the degree of salience of other people in an interaction [22].
In an experimental study, social presence and social
interaction in Second Life were found to be positively
related to purchase intention [14]. Social presence was also
found to be positively correlated with purchasing intention
of virtual goods in studies of Second Life [1] and Habbo
Hotel [19].
The relationship between social influence and spending is
mixed. Kim et al. [15] surveyed players of Cyworld and
Habbo Hotel and, and found that players who had higher
desire for online self-presentation were more likely to
motivate their purchasing behavior. They explained that this
self-presentation desire was driven by social norms [15].
However, although social influence has been hypothesized
to predict spending in virtual worlds [10], empirical
evidence has not been able to find a direct correlation
between social influence and spending behavior in the
context of Second life [11].
The above literature suggests that various social factors are
correlated with spending. These constructs, however, are
primarily psychological, requiring a self-report from the
player. What has not been explored, however, is how social
behaviors—such as exchange of virtual goods—and
network factors—such as number of in-game friends—are
associated with real-money spending. These factors do not
require administration of a survey, and can be easily
examined through log data by the designer. While not exact
representations of the psychological constructs discussed
above, these elements were selected with theories of social
behavior in mind:
RQ1: Are social factors (number of friends, giving and
receiving virtual goods) associated with the likelihood of
spending money in the game?
The above research question examines how much social
factors contribute to a player’s likelihood to spend money in
a game. However, do they explain the degree of spending?
To our knowledge, consumer behavior literature has not
examined this question, as studies have mainly looked at
purchasing behavior as a dichotomous variable. If one were
to look at spending as a continuous variable (i.e., how much
money a player spends), would social factors still account
for who spends more? We thus have an open research
question that examines the relationship between social
factors and the amount of real money spent by the player.
RQ2: Are social factors (number of friends, giving and
receiving virtual goods) associated with how much money a
player spends in the game?
One demographic factor to consider, especially in the
context of this particular game, is age. Because the game
caters to both under-age players and adults—and adults
have more money to spend—these demographic groups
should be examined separately.
RQ3: Are there different spending patterns between
adults and minors?
Study 1 Methods
Tri-D provided access to three months of log data from
Puppy Red. The company had been keeping track of all
players’ actions within the game for the entire seven years it
had been in service, but did not provide all the data due to
proprietary reasons. The log data comprised of
demographic, behavioral and network variables that were
recorded by the game server. Demographic data was based
on the player’s national ID information in their game
account, which indicates birth date and gender. Behavioral
data was captured when the individual performed a visible
activity (i.e., clicking) that required a response from the
main computer. This included the aggregate number of
virtual items that were sent to other people by the player,
aggregate number of virtual items received by the player,
aggregate number of beans (virtual currency) collected by
the player, and amount of real money (coins) spent in the

game. The server also recorded when the player logged in
and out, thus enabling us to calculate how frequently a
player visits and how much time they spend on the site. The
data also showed how many in-game connections, or
“friends” the player has. Much like the concept of Facebook
Friends, this was not an indicator of true friendship, but a
signal that two players mutually agreed to officially connect
with each other within the game.
Puppy Red players were mostly female (76.4%), aged nine
to 85 (M=20.91, SD=14.927). However, age information
was based on the account information, which may not
reflect their actual age. The game required players to enter
their government-issued ID number, which confirms their
date of birth—this is a common form of authorization used
in South Korea. Players who are 18 years old or younger
must receive authorization from their parents through a
mobile age-authentication system, a system commonly used
in South Korea. Underage players who went through this
authentication process would have their actual age reflected
in their game profile. However, it is very probable that
parents create the account and have their children use it.
Due to the nature of how age data was collected, it could be
that some children used their parent’s or grandparent’s ID,
but it would be unlikely for an adult to use a child’s ID.
Thus any effects of age seen in adult data should be
interpreted with caution, but age in the data for children’s
behavior may be a more valid measure.
Membership length ranged from less than one year up to
seven years. The lower quartile of players had been on the
site for less than a year while the upper quartile consisted of
players who had been on the site for four or more years.
Analysis was confined to players who accessed the site at
least once during the past three months, as there were many
who had been inactive during that timeframe. We defined
these players as “active players” (N=264,934). Among this
population, 69,269 players (26.1% of total active players)
had spent real money at least once during the three-month
period. We defined this sub-sample (N=69,269) of active
players who had spent real money as “active spenders.”
Active spenders were mostly female (79.7%), aged nine to
53 (M=22.75, SD=14.77).
Study 1 Results
Modeling likelihood of spending real money
To identify the factors that contribute to spending, a
Binominal Logistic Regression was conducted on the data
of all active players. The dependent variable measured
spending of real money, with “not spending any money”
coded as 0 and “spending money” coded as 1. Independent
variables included the two social interaction factors (giving
and receiving virtual goods), and number of friends. Game-
related variables included membership length, frequency of
visits, time spent on the site, and number of harvested beans,
as someone who is more invested in the game may be more
inclined to spend more real money. Lastly, individual
factors such as gender and age were included into the model.
Two models were created to see the added effect of social
factors. The first model contained demographic and game-
play variables (time spent, number of visits), while the
second model added social variables (number of friends,
virtual goods given and received). Both models were
significant with a Hosmer & Lemeshow Goodness-of-Fit
Test of the models’ chi-square statistic (p<.001). Model 1
explained 78.5% of spending correctly (Nagelkerke R2=.24)
while Model 2 explained 83.7% of spending correctly
(Nagelkerke R2=.41). Adding the social variables made a
significant change (p<.001) in the R2. Variance Inflation
factors (< 2.8) indicated that colinearity was not an issue.
As can be seen in Table 1, all coefficients are statistically
significant, which is expected with such a large sample size,
Model 1
Model 2
Variables
Beta
Wald
Exp
Beta
Wald
Exp
Constant
-1.45**
16929.37
.23
-1.94**
16736.40
.14
Age
-.01**
2196.56
.99
-.01**
1660.26
1.10
Gender (Male)
-.04**
13.36
.96
-.09**
43.95
.55
No. of visits
.03**
2630.40
1.03
.02**
1066.02
1.02
Time spent in game
.00**
854.55
1.00
.00**
266.11
1.00
No. of beans
.00**
3004.64
1.00
.00**
655.93
1.00
Virtual goods given
.62**
7507.63
1.86
Virtual goods received
.04**
320.36
1.04
No. of friends
.02**
14137.55
1.02
Model Chi-square [df]
45,402.98 [5] (p<.001)
87,931.68[8] (p<.001)
Block Chi-square [df]
40,869.38[3] (p<.001)
% Correct Predictions
78.5
83.7
Nagelkerke R square
.24
.41
Dependent variable: spending/not spending, **p<.001
Table 1: Modeling likelihood of spending money among active players (N=264,934)

thus interpretation of the coefficients should be focused
more on the effect size, which can be seen through the odds
ratio. Giving virtual goods (=.62, odds ratio=1.86) to other
players and receiving virtual goods (=.04, odds ratio=1.04)
from other players were positively related with likelihood to
spend real money. Each additional friend increased the
likelihood of spending real money by 1.02 (=.02, odds
ratio=1.02). Comparing spenders with non-spenders, a post-
hoc T-test comparison of means indicated that spenders
(M=46.09, SD=55.71) had more friends than non-spenders
(M=7.34, SD=22.76), t(264,932)=253.656, p<.001.
In terms of demographic variables, younger players were
more likely to spend real money and females were more
likely to spend real money than males. However, variables
related to game play had a minimum impact on the
likelihood to spend. In particular, time spent playing the
game and virtual currency earned within the game almost
had no effect at all on the likelihood to spend.
As it could be that adults simply have more money to spend
that children and teens, the same analysis was conducted
only looking at the subset of the sample that was under the
age of 19, which is the legal age in South Korea. The results
in regards to the effect of social interaction variables were
very similar (Table 2), showing a very strong positive
relationship between giving virtual goods to others and the
likelihood of spending real money. Age, however, had the
opposite effect; among players who were under 19, older
players were more likely to spend real money.
Explaining amount of money spent
As noted above, only 26% of the population had spent real
money in the game. The range of money spent during the
three-month period was 455 won (about $.40) to 662,500
won ($587.15) with the mean at 3,160 won ($2.80). To
identify the factors that contribute to how much more
money is spent among those who spend money, a negative
binomial regression was executed on just the population of
active spenders, looking at the amount of real money spent
as a dependent variable. Somewhat similar to an OLS
regression, a negative binomial allows a continuous count
variable to be the dependent variable, but is used when the
dependent variable is over-dispersed (standard deviation is
higher than the mean) and the distribution is skewed. In the
case of this data, the skewness was 11; skewness under 2 is
considered to be a normal distribution.
Independent variables were the same as the ones used for
the first three hypotheses; these included social factors such
as number of friends, giving and receiving virtual goods; as
well as time spent on the site, number of visits, number of
harvested beans, gender, and age.
The first model was for active spenders who were 19 years
old and older (Table 3). The mean age for players in this
group was 51 years. The regression model was significant
(Likelihood ratio Chi-square= 2,620.77, df= 8, p<.001).
Again, due to the large sample, results should be interpreted
by looking at the effect size in addition to statistical
significance. Social interaction factors were a positive
indicator of spending, although they were extremely weak.
Giving virtual goods to other players was positively
associated with the amount of real money spent. Receiving
virtual goods from other players and the number of friends
had a very small effect close to zero.
Game variables had a weaker effect in explaining how
much real money a player spends in the game. Spending
more time in the game or playing more frequently had next
to no relationship to amount spent. Also, having a large
amount of virtual currency was not statistically related to
Model 1
Model 2
Variables
Beta
Wald
Exp
Beta
Wald
Exp
Constant
-1.45**
16929.37
.23
-2.56**
3480.44
.08
Age
.05**
287.37
1.01
.05**
272.81
1.05
Gender (Male)
-.08**
16.68
.92
.04*
4.35
1.02
No. of visits
.03**
1100.15
1.03
.02**
503.88
1.02
Time spent in game
.00**
537.62
1.00
.00**
186.53
1.00
No. of beans
.00**
1846.84
1.00
.00**
427.03
1.00
Virtual goods given
.57**
3493.38
1.76
Virtual goods received
.03**
102.12
1.03
No. of friends
.02**
8164.58
1.02
Model Chi-square [df]
22541.29 [5] (p<.001)
43501.05[8] (p<.001)
Block Chi-square [df]
20959.75[3] (p<.001)
% Correct Predictions
75.6
80.1
Nagelkerke R square
.22
.39
Dependent variable: spending/not spending, *p<.05, **p<.001
Table 2: Modeling likelihood of spending money among active players under age 19 (N=138,166)

spending more real money. Gender was insignificant and
there was almost no effect of age.
Beta
SE
Sig.
(Intercept)
9.38
.02
.000
Age
-.004
.00
.000
Gender (male)
.008
.00
.580
Time spent in game
1.99E-9
2.35E-10
.000
No. of visits
-.001
.00
.020
No. of beans
-1.77E-8
2.99E-8
.554
No. of Friends
.001
.00
.000
Virtual goods given
.056
.00
.000
Virtual goods received
.003
.00
.000
Table 3: Factors explaining amount of spending among active
spenders 19 and older (N= 28,608)
The second model (Table 4) examined active spenders
under 19 years of age, Likelihood ratio Chi-square=
2,015.45, df= 8, p<.001. Older players and females were
more likely to spend more real money. Similar to the adult
data, the amount of time spent in the game was weakly
linked with using real money to buy virtual goods. Different
from the adult data was a significant effect of gender: males
were more likely to spend real money. Giving virtual goods
was positively associated with amount of spending;
receiving virtual goods was very weakly associated with
amount of spending.
Beta
SE
Sig.
(Intercept)
8.72
.03
.000
Age
.025
.00
.000
Gender (male)
.131
.02
.000
Time spent in game
1.18E-9
2.05E-10
.000
No. of visits
.001
.00
.036
No. of beans
9.60E-8
2.99E-8
.001
No. of Friends
.001
.00
.000
Virtual goods given
.053
.00
.000
Virtual goods received
.002
.00
.000
Table 4: Factors explaining amount of spending among active
spenders under 19 (N= 40,661)
STUDY 2: PURCHASING PATTERNS: HIGH SPENDERS
VS LOW SPENDERS
Study 1 looked at factors that contribute to likelihood of
spending real money and factors that contribute to more
spending of real money. However, the data did not provide
any insights into what types of virtual goods were being
purchased. The purpose of Study 2 was therefore to
examine what kind of items players are purchasing and
what currencies (real or virtual?) they are using to purchase
those items because game had dual currencies: one that can
be earned through play (beans), and another that can be
purchased with real money (coins).
In the context of Puppy Red, it was difficult to compare the
value of these two currencies because virtual currency
requires time and effort inside of the game, while real
money requires time and effort outside of the game. For
certain currencies—such as gold in World of Warcraft or
Linden dollars in Second Life—there are exchange systems
where people can exchange real money for virtual currency.
The famous “gold-farms” of China are a wonderful
example of how people who want to save time purchase
virtual currency from others [7]. However, not all items in
the game can be bought with real money; in World of
Warcraft, certain items can only be obtained by fulfilling a
quest, and these items are not transferrable to other players.
Similarly, in simulation games such as Farmville, there are
items that can only be bought with real money, regardless
of how much time one spends on the game. This raised
additional questions about the relationship between players’
behaviors in regards to the two different types of currency
in an environment where the two currencies are not
exchangeable:
RQ4: What is the relationship between currency purchased
with real money and currency earned through play?
RQ5: What are sales patterns of items by type of virtual
good?
RQ6: What are the purchase patterns of high spenders vs.
low spenders?
Study 2 Method
Two separate data sets were obtained from Puppy Red: an
item sales log and transactional data of players. The item
sales log was an inventory list of all items and their sales
during a one-month period, which included how many
items were sold, what currency was used to purchase the
item, and the price of the item. It did not contain any
information on who the buyers were. The player transaction
data was based on a random sample of 1,295 players who
spent real money during the past three months as of the
time of data collection. The game operator provided a log
showing what items they bought and what type of currency
(real or virtual) they used to purchase items.
First, categories of items were identified taking into
consideration the existing taxonomy of items in the game’s
virtual shops and Lehdonvirta’s [17] categorization of
virtual goods. There were three main categories: avatar-
oriented items, space-oriented items, and play-oriented
items. Avatar-oriented items served the purpose of
decorating or customizing the look of the avatar. An avatar
is a virtual character that often represents the player and is
created according to the player’s preferences [8]. Items in
this category included clothing items, beauty items—which
can give variations on the avatar’s hair and facial features—
and accessories such as bags, jewelry, and gloves.
Space-oriented items were those that could be used for
household decoration or landscaping. In the game, a humble
house with bare furnishings and an empty island are
provided to each player by default. Players can expand or
renovate their space by purchasing items. Like avatar-

oriented items, space-oriented items were all about visual
customization. They include home décor such as wallpaper
and furniture, and landscaping items such as ponds and
bridges. Although some expensive items served utilitarian
functions—for example, a ferris wheel could provide
avatars a ride—but most items were merely decorative.
Play-oriented items were perishable items that would assist
raising pets or caring for crops. The consumable aspect
made play-oriented items different from avatar or space-
oriented items, because they diminish with use.
Study 2 Results
RQ4 inquired into the relationship between spending and
the two types of virtual currencies. Results indicated that
increased membership length generally increased the
average amount of virtual currency that was being spent,
suggesting that players become more efficacious in how to
earn virtual currency. For real money, spending increased
for players up to four years, then declined. This may be
reflecting an exit pattern, or lack of new items that can be
purchased with real money. However, given that the system
generates new items every week, it is more likely to be the
former explanation than the latter.
Figure 1: Relationship between amount of virtual currency
accumulated (X-axis) and real currency purchased (Y-axis)
based on membership duration
To see the relationship between the two different types of
virtual currency, players were divided into seven groups in
one-year intervals depending on how long they had been a
member. As indicated in Figure 1, “0 year” indicates the
player group whose membership period is less than 1 year
and “6 years” are players who were on the site for more
than six years but under seven years .The bubble size
represents the number of players belonging to each group
and the values of X- and Y-axis respectively indicate the
groups’ average amount of collected beans and coins
bought with real money per month.
The “0 year” group has the largest player pool, but has the
least amount of coins, indicating that newest players spend
the least real money. The number of coins increases and
peaks for players who were on the site for three or more
years but less than four years, but subsequently declines.
To explore RQ4, item sales were aggregated within each
category and the frequencies were converted to percentages
(see Table 5). A price index for each category was
calculated by dividing the average price of items affiliated
to each category by the average price of total items. The
price index for avatar items was 1.20, indicating that the
price of avatar items are 1.20 times the average price of all
items.
As mentioned above, Puppy Red uses two kinds of currency
in parallel: coins (real money) and beans (virtual currency).
There were noticeable differences regarding how people
spent these different currencies. In terms of the quantity of
items bought with real money, players spent most real
money on avatar-oriented items (60.6%), followed by
space-oriented items (26.8%) and play-oriented items
(12.6%). However, the order was reverse with items bought
using virtual currency (beans): players spent most beans on
play-oriented (45.8%) items, followed by space-oriented
(41.7%) and avatar-oriented (12.5%) items. This suggested
that players are spending real money on items that enable
visual customization, while items used for game mechanics
were purchased with beans, which can be obtained through
play. Play-oriented items required continuous
replenishment, which may be why players were more
inclined to purchase those items with beans.
Item
category
Sales (Units)
Sales (Price)
Price
index
Beans
Coins
Beans
Coins
Avatar
12.5
60.6
36.5
65.1
1.20
Space
41.7
26.8
41.3
28.0
0.78
Play
45.8
12.6
22.1
6.8
1.08
Total
100.0
100.0
100.0
100.0
Table 5: Percentage of virtual goods spending patterns by
category
This item-buying pattern is similar to that of the real world
in the sense that people first satisfy basic needs to sustain
living with hard-earned money and then allocate their extra
resource into luxuries later. Players spent their beans to buy
the play-oriented items in terms of necessities for sustaining
services (such as feeding their pet), and then used real
money to pursue the higher hierarchy of needs such as
cosmetic products or cars.
Once virtual items are designed and released, the marginal
cost for reproducing these items is close to zero for the
characteristics of distribution of digital contents, therefore
selling expensive items means higher profit to the service
operator. The price index is a relative measure of how
expensive the item is in comparison to other items. The
price index of each item category in Table 5 shows that the

price of play-oriented items (consumable items used to take
care of pets or plants) is cheaper than avatar-related items.
This indicated that despite the fact that “rearing” items were
relatively less expensive to buy with coins compared to
other items, players still chose to buy those items with
beans.
To examine RQ5, players were divided into three groups
based on their level of spending real money. These three
groups were labeled “high spenders,” “low spenders,” and
“non-spenders.” Players who never spent real money were
defined as non-spenders. Non-spenders did not spend real
money to buy virtual items; they only used beans.
Spenders were players who had logged into the game at
least once in the past three months and had spent real
money at least once during that time. In this population,
players had spent real money on virtual items ranging from
$.05 to $210, with the mean amount at $9. High and low
spenders in this player sample were defined by sorting the
players by the amount they spent during the sampled period
and then drawing a boundary that equates the aggregate
sum of money spent between the high and low spenders; in
other words, 89.7% (low spenders) cumulatively spent as
much as the other 10.3% (high spenders).
Item-buying patterns between these two groups were
examined by calculating the percentage of real money spent
on each category of items then testing the significance of
the difference between the two groups through means
comparison T-tests. Low spenders spent 48.6% of their real
money on play-oriented items mainly for taking care of
their pets, followed by avatar (28.6%) and space (22.8%)
items. In contrast, high spenders preferred to spend their
money on items to customize their avatar (48.4%) and
space (44.8%) rather than consumable play-oriented items
(6.8%). The differences between these two groups were
significant at the p<.001 level for all three item categories.
DESIGN IMPLICATIONS
In Study 1, log data of a 3D social game was used to
examine factors that contribute to spending of real money.
Using two regression models, results indicated that
engaging in more social interaction, such as giving virtual
goods, and having more in-game friends increased the
likelihood of spending. However, social factors played a
weaker role in terms of how much money players were
spending.
Study 2 investigated what types of virtual items are sold
and how the item-purchase patterns differ according to the
extent of spending. High spenders mostly buy decorative
avatar-oriented and space-oriented items using real money,
while low spenders mostly buy consumable play-oriented
items with hard-earned virtual currency.
The Effect of Social Factors
Time spent on site and earning virtual currency on the site
had no effect on the likelihood to spend real money. This
suggests that merely playing a lot on these sites doesn’t
encourage spending, but social playing does. In particular,
giving virtual goods to other players was the strongest
factor of all independent variables that contributed to
whether or not the player spent real money. This suggests
that game operators, when marketing their virtual goods to
players, should focus on creating advertisements,
promotional campaigns, or in-game quests that appeal to
“gifting” behavior.
For example, Puppy Red sends out a weekly newsletter via
email to its players informing them of the new items added
to the virtual shop. Instead of presenting a static list of
items, adding a persuasive message, such as “your friend
may appreciate this new item” or framing certain items as a
“perfect housewarming gift” may be a way to encourage
players to purchase the item to give to their friend. Since
giving virtual goods to others increases the likelihood that
someone will spend real money, game designers may also
want to provide reminders to players about their friends or
even suggest reciprocity when the player receives a virtual
item from another player.
Having more friends in the game also increased the
likelihood of spending real money; this implies that game
designers can devise in-game quests that encourage
connecting with other people, or build more communication
channels within the game to facilitate social interaction.
Social factors were strongly associated with whether or not
an individual spent real money. However, they were not
very strong in explaining how much money people spent.
This suggests that different design strategies are required
when trying to get people to spend more. Even with the
weak associative value, social interaction factors can still
contribute to significant increase in revenue when looking
at large-scale populations. Of note, game-related factors,
such as time spent and frequency of play, were not salient at
all in explaining the extent of spending real money.
For both adults and minors, giving virtual goods to others in
the game was the strongest positive factor associated with
how much the player spends in the game. Time was a very
weak factor. Time, which is often seen as a proxy of
engagement, is strongly correlated to habit strength [25], so
for game designers, efforts that are aimed at keeping
players engaged in the game may not directly relate to their
revenue. This may be particularly important for free-to-play
games, because this indicates that players do not have to
spend more time in the game to be spending money. Thus
keeping the players engaged in the site in a short timeframe
becomes just as important, if not more, than attracting
players into the game. Constantly introducing new virtual
goods that are related to aesthetics may be a way to retain
high spenders.
People who had more friends were more likely to spend
money than people who had less friends, but the added
number of friends did not explain increased spending

among spenders. These results suggest that the quality, not
quantity, of social relationships within the site could affect
why players spend more. Creating more features in the
game that allow players to strengthen those strong ties
could lead to more spending, while features in the game
that allow players to build weak ties could encourage
players who do not spend anything to spend at least
something.
Age Differences in Spending
Among younger players, more virtual currency earned in
the game was weakly but positively associated with real
money spent in the game. This may seem counter intuitive,
because if you had a lot of virtual currency, why would you
want to use real money? However, the dual currency system
of Puppy Red may work in their favor—as mentioned
earlier, some virtual goods in Puppy Red can only be
bought with real money. Thus one currency does not
necessarily decimate the value of the other, and people who
are more likely to purchase virtual items show the same
pattern using both virtual and real currency. This suggests
that having a dual currency system where the two
currencies are not interchangeable can be advantageous to
the game designer because it encourages the player to spend
both time in the game and real money. However, this
pattern was not present among older players. For players
who were 19 years old or older, the amount of virtual
currency they had in the game did not have any relation
with the amount of real money they spent. This may be
because the value of the time required to acquire virtual
currency is greater for adults. Further exploration is needed
to see why there are differences between adults and
children, and if there are differences between adults who
are playing for their own pleasure and adults who are
playing because of a child.
High spenders vs. Low Spenders
The results of study 2 showed that players who spend a lot
of real money purchase items that have decorative but no
functional value, while players who spend a little money
purchase items that are more consumable. This mirrors
purchasing behavior of luxury items in the physical world
and supports research on MMOs [3] that have found
parallels between in-game economy and real-world
economies.
From a marketing perspective, these results provide insights
about how different marketing strategies should be used for
these different sub groups. Currently, most gaming
companies send out bulk newsletters, or have the same
introductory screen when players log into the game. If the
companies are able to identify what type of spender the
player is, they will be able to deliver a more effective
marketing strategy.
From a design perspective, it may be advantageous to keep
the price range for consumable items narrow, since low
spenders are the ones who are purchasing those items, and
increase the price range for avatar and space-related items.
Creating visually unique items, or limited-edition items
may increase the appeal for high spenders.
Limitations
This study is based on log data of a social game service,
which makes behavioral data more accurate than self-
reported behaviors via surveys. However, this methodology
has limitations in that it does not explain why the players
are engaging in such behavior. Future studies should try to
merge both player perception through surveys and pair it
with behavioral data, which would provide more insight
into why players engage in certain spending patterns or
what elements of social interaction influence different types
of spending.
The game service is based in South Korea where broadband
penetration rate is high, youth have high Internet usage, and
micro-payment services are widespread [21]. This may
make our results difficult to generalize to all countries or
cultures, especially as some studies have found that cultural
differences in game play are associated with virtual goods
spending [16]. However, these purchasing patterns at the
very least may generalize to other collectivist cultures, and
could be valuable to countries that are beginning to see
rapid developments in micro-transaction revenue models
involving virtual goods. These results may not generalize to
all games, especially those games that are action-oriented or
have more linear narrative-driven game play. However,
findings may apply to the understanding behavior of players
in games that have strong mechanisms for visual
customization (e.g., avatar decoration, virtual space
decoration) and caretaking (e.g., raising a pet, growing
crops).
CONCLUSION
As selling virtual items become a major revenue source for
social network and social game service operators, this study
takes a big data analysis approach to factors that are
associated with virtual goods purchasing patterns. This
study tested variables that were informed by theory-based
empirical studies, and while prior studies mainly examined
behavioral intention, this study was able to examine actual
behavior.
By examining actual behavior, this study found that social
factors play different roles in terms of explaining an
individual’s likelihood of spending and the extent of their
spending. Most consumer theories of purchasing do not
make the distinction between these two concepts. This
behavioral data suggests that the socio-psychological
mechanisms involved in purchasing intention are different
from those that are involved in how much money an
individual spends, and informs development of future
theories.
The results of this study also inform game design by
identifying how different types of players based on their

spending patterns. Study 1 suggests that game elements that
enhance social presence, such as virtual goods exchange
and having more in-game friends, are important in
distinguishing real money spenders vs. non-spenders. When
it comes to looking within spenders, however, Study 2
shows that there are major differences in purchasing
patterns between high spenders and low spenders.
Understanding player types from the perspective of how
much they spend will allow for different strategies,
especially in the design of different types of virtual goods
and different types of marketing strategies of those goods.
ACKNOWLEDGMENTS
Thanks to staff at TriD and EK Na for providing access to
log data.
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