=Paper=
{{Paper
|id=Vol-2186/paper11
|storemode=property
|title=Economic decision-making in free-to-play games: A laboratory experiment to study the effects of currency conversion
|pdfUrl=https://ceur-ws.org/Vol-2186/paper11.pdf
|volume=Vol-2186
|authors=Mikko Salminen,Simo Järvelä,Niklas Ravaja
|dblpUrl=https://dblp.org/rec/conf/gamifin/SalminenJR18
}}
==Economic decision-making in free-to-play games: A laboratory experiment to study the effects of currency conversion==
https://ceur-ws.org/Vol-2186/paper11.pdf
Economic decision-making in free-to-play games: A laboratory
experiment to study the effects of currency conversion
Mikko Salminen
Department of Information and Service Management, Aalto University School of Business, Finland.
Gamification Group, Laboratory of Pervasive Computing, Faculty of Computing and Electrical
Engineering, Tampere University of Technology, Finland.
mikko.salminen@aalto.fi
Simo Järvelä
Department of Information and Service Management, Aalto University School of Business, Finland.
simo.jarvela2@aalto.fi
Niklas Ravaja
Faculty of Medicine, University of Helsinki, Finland.
Department of Information and Service Management, Aalto University School of Business, Finland.
niklas.ravaja@helsinki.fi
Abstract: We present initial results from a controlled laboratory experiment where the economic decision-
making typical in free-to-play games was studied. The participants were presented with a series of scenarios,
where they rated how much they were willing to pay (in euros, in hard currency, or in soft currency) for
common in-app virtual goods (booster, unlock, timer). The goal of the study was to examine how the
multiple currency conversions and the amount of resources affect the perceived value of the virtual goods
and the willingness to pay for them. The results don’t support the notion that the currency conversions would
lead to increased spending. When comparing the willingness to pay in different currencies by first
converting them to a unitary currency, the participants were willing to use highest amount of resources when
considering purchases in euros and least when considering purchases in soft currency. However, when
considering purchases with euros, the participants were willing to pay most when they had moderate amount
of virtual currency. But with gold the willingness to pay was highest when the amount of resources was the
highest. This finding highlights the differences in how the players may process real money and in-game
currencies. In addition, the results imply that regardless of the currency type, the participants were willing
to pay most for unlocking of new game content. It is suggested that the economic decision making in free-
to-play games could be studied also with abstract and simplified laboratory experiments.
1. Introduction
In online game market there are two dominant business models: subscription-based model where
the player must pay a monthly fee to access games, and free-to-play model (Park & Lee, 2011).
Free-to-play games can be obtained free of charge, however, the games are designed such that to
proceed in the game one must buy different types of virtual goods, such as boosters that enhance
the performance, or openings for time locks that hinder the progress in the game (e.g., Hamari,
Hanner, & Koivisto, 2017). Thus, the core service or product is free, and the actual revenue is
collected by in-game purchases and premium services (Hamari, Hanner, & Koivisto, 2017; Kumar,
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2014; Liu, Au, & Choi, 2014). The free-to-play revenue model has been used in games on various
platforms: computers, gaming consoles, and especially in mobile games.
Altit (2013) divides the free-to-play games into two categories based on their monetization
mechanisms. First category is the “Free to download but not necessarily free to play” games, which,
at least in principle, can be played for free, but proceeding in the game without making any
purchases is made difficult; for example, Pokémon GO and Trials Frontier utilize this mechanism.
The other category is “Pay or wait” games, where the player has an option to unlock time locks to
speed up gameplay by making in-game purchases, although, technically it is possible to play the
game without investing any real money, by waiting the time lock to unlock. This mechanism is
utilized in some of the highly popular free-to-play games, such as Candy Crush Saga and Clash of
Clans. It must be noted, however, that a single game may utilize both these mechanisms, and thus,
a strict categorization by a one monetization mechanism may not be informative. There have also
been attempts to taxonomize the in-game purchasable virtual goods. Luton (2013), for example,
has divided the possible virtual items that could be purchased in games to four different classes: 1)
Downloadable content, like new levels and characters; 2) Convenience, something that would have
been laborious to achieve by playing; 3) Competitive advantage, typically against the game or other
players; and 4) Customization, for example personalizing items for the character.
Usually number of currency conversions are needed before the virtual goods can be obtained in a
free-to-play game (e.g. Alha et al., 2014). A widely used mechanism is to have the player first
purchase hard currency (e.g. diamonds in Clash of Clans game) with the real money (e.g., euros).
The soft currency (e.g. coins in Clash of Clans) can be purchased with the hard currency; and
finally, the virtual item in the game can be purchased with the soft currency. In some games the
soft currency can be obtained also by playing the game, but the hard currency can only be obtained
with the real money, and there may be exclusive virtual goods that can be obtained only with the
hard currency. On the other hand, in some games in addition to purchasing with real money, also
the hard currency can be obtained by playing, but only to certain amount. Typically, the hard
currency is sold in bundles, for example ten diamonds could be $1.99, and one hundred diamonds
could be $18.99 (Hanner & Zarnekow, 2015).
The “double currency model” used in free-to-play games effectively obfuscates the amount of real
money being spent when buying the virtual goods. This could lead to spending more money in the
game than the player originally intended. Another reason for utilizing these conversions is to keep
the gameplay smooth and not interfere it with real world elements, such as real money. This
suggestion was supported in a study by Lin and Sun (2011), who reported that players viewed
buying items with virtual money as gaming behaviour and buying items with real money as
shopping behavior (which could interfere with the enjoyment of the game).
Previous studies have shown that people resort to various strategies, some of them which are not
optimal, when mentally converting between different real currencies (Lemaire & Lecacheur, 2001;
Lemaire, 2007). The use of non-optimal strategies in currency conversion may be due to innate
tendency to save cognitive resources and avert actual calculations between the currencies. As
suggested by the theory of cognitive myopia, one may focus during decision making only on
information that is immediately related to the decision, for example to the shown price of a virtual
good in an in-game currency without conducting the cognitively laborious conversion to real
money (Huang & Lin, 2017; Hsee et al., 2003). When the exchange rate between the real and the
virtual currency is not shown to the player, they may perceive the price of a virtual item higher,
since the players resort to the most salient price information and the virtual item’s price in the
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virtual currency is higher in numerosity (Huang & Lin, 2017; Bagchi & Davis, 2016). This effect
of considering a product to be more expensive if the price is in a currency of higher numerosity is
also referred as face value effect (Huang & Lin, 2017; Wertenbroch, Soman, & Chattopadhyay,
2007). In the context of free-to-play games the real currency would be of lowest numerosity, hard
currency second highest, and soft currency of highest numerosity. On the other hand, the high
numerosity of the virtual currency may lead the player to distance it from the rea currency and to
consider it as “play money”, and thus spend it more carelessly.
2. Current study
Economical decision making has been studied extensively, also in the context of purchasing virtual
goods (e.g. Guo & Barnes, 2012). However, free-to-play games have certain defining
characteristics that may affect the decisions to buy the in-game content. Often the free-to-play
games can be played without making any purchases. However, to proceed in the game multiple
purchases (each of them often of small value) are needed. Thus, the motivational factors affecting
each of these small purchases are different from, for example, purchasing more traditionally a
whole game at once, before even playing it. Specifically, the double currency model of the free-to-
play games (see, Chapter 1) is suggested to affect the perception of the value of the virtual currency.
This may affect the processes of economic decision making. Existing studies on in-game purchase
of virtual goods have focused mainly on the player’s motivations for obtaining the goods, or to
related social factors (e.g., Huang & Lin, 2017; Wohn, 2014). The issues related to buying
decisions in free-to-play games should be studied more thoroughly. After all, as high as 98% of
players of a casual game have been reported to never spend their money on it (Campbell, 2014). In
addition, monetary sacrifices have been reported to be the strongest factor in predicting the players
to abandon a game (Wei et al., 2015).
In this study we focus to the effects of the in-game currency conversions to the willingness to pay
for virtual in-game products. To the best of our knowledge, there is a lack of previous studies on
this topic conducted with the current methodology.
As an exploratory research question we study how the currency conversion affects willingness to
pay when considering to purchase virtual goods. Previous studies have shown that resource scarcity
may lead to decreased spending, e.g. Carrol, Hall, & Zeldes, 1992). Thus, we also explore the effect
of the amount of current resources in this decision-making process. Finally, since the in-game
currencies may be bought with real money or they may be obtained by playing (see, Chapter 1),
we also study exploratively the effects that how the resources have been obtained may have on the
willingness to pay.
3. Methods
Given that economic decision-making in free-to-play games is a complex phenomenon to study, it
is suggested that diverse methodology should be employed. We present as one possible approach
to use controlled laboratory experiments where the participant is presented with simplified and
abstract scenarios. Due to the laborious data collection, within-subject design is the most
commonly used design type in psychophysiological decision-making experiments. Thus, we chose
to use within-subjects design.
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Altogether 21 participants completed the experiment. The participants had experience in playing
free-to-play games. The participants responded in each of the independent 27 scenarios how much
they would be willing to pay for the offered in-game purchase. The offered goods were a timer
which would open a time-lock, a booster which would help in completing a level, and an unlock
that would open new levels to play. A 3 x 3 x 3 setting was employed, consisting of the following
manipulations: 1) the amount of resources the player has: scarce, moderate, or plenty; 2) the virtual
good that is offered: timer, unlock, or booster; and 3) how the currency has been obtained (virtual
currency earned by playing, virtual currency earned by buying with real currency, real money
used).
At the beginning of the experiment the participants were described how they would have played
their favorite free-to-play game and during the playing the game would present an offer to purchase
an item. The participants were informed also about the relations between the different currencies.
The euro (€) to diamonds (D) rates were: 2€ = 25D, 5€ = 55D, 12€ = 135D. The diamonds to gold
(G) rates were set as: 4D = 65G, 15D = 250G, 50D = 830G. Thus, the rates represented linear
relations between the currencies, but this was made difficult to notice for the participants by
presenting amounts of currencies that could not be easily divided mentally. Two professional game
developers were consulted to set the limits for different resource levels; in the conditions of scarce
resources the participants were informed that they had 10/200D or 50/1000G, in the condition of
moderate resources the amounts were 40/200D and 200/1000G, and in the conditions of plenty
resources the amounts were 100/200D or 500/1000G.
The data were analyzed with SPSS by the linear mixed-models (LMM) procedure with restricted
maximum likelihood estimation and an AR1 covariance structure for the residuals. Willingness to
pay (in euro, diamonds, gold) was set as the dependent variable, and the Amount of resources, How
the currency was obtained, and Product, were set as independent variables. Presentation order of
the stimulus was set as the repeated variable and a model was specified where there were fixed
effects for Amount of resources, How the currency was obtained, and Product.
4. Results
When examining the participant’s overall willingness to pay over all products and resource
conditions, it was observed that, not surprisingly, the willingness was highest for the most
numerous currency, the Gold (Fig 1., left panel), then for the Diamonds (p < .001, for pairwise
comparison). However, when converting the willingness to pay in Euro and the willingness to pay
in Gold to Diamonds, it was observed that, after all, the participants were willing to use largest
amount of resources when considering purchases in Euros, then Diamonds and least with Gold (p
< .001, for pairwise comparison between Diamonds and Gold; Fig 1., right panel).
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Figure 1. Left panel: willingness to pay in different currencies. Right panel: willingness to
pay in different currencies with the Euro and Gold currencies converted to Diamonds. The
error bars represent 95% confidence interval.
The participants were willing to pay most for the Unlock product when considering purchases in
each of the currencies, in euros (F(2, 352.59) = 13.46; p < .001), in diamonds (F(2, 714) = 6.49; p
= .002), and in gold (F(2, 714) = 43.518; p < .001; Fig. 2).
Figure 2. Willingness to pay in euro (left), diamonds (center), and gold (right) for different
products. The error bars represent 95% confidence interval.
The participants were willing to pay most euros when the amount of resources (in virtual
currencies) was moderate, compared to when there were scarce or plenty of resources (F(2, 354.32)
= 3.706; p = .026; Fig. 3); although, the difference between Moderate and Plenty amount of
Resources failed narrowly to reach statistical significance (p = .074), and needs to be verified when
data from more participants is collected. On the other hand, the participants were willing to pay
highest amount of gold when they had plenty of resources (F(2, 714) = 57.32; p < .001; Fig. 2).
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Figure 3. Willingness to pay in euros (left), in diamonds (center), and gold (right) for
different amounts of resources. The error bars represent 95% confidence interval.
5. Discussion
The results of the current study suggest that the currency conversions didn’t obfuscate the value of
the currency being used when considering purchases. When comparing the willingness to pay in
different currencies by first converting them to a unitary currency, the participants were willing to
use highest amount of resources when considering purchases in Euros, then in Diamonds, and least
in Gold. It is suggested, that the (multiple) currency conversions led the participants to act
cautiously and thus to prefer purchases made in a real currency (Euros) where no mental
conversions had to be made. In addition, the use of the in-game currencies (Diamonds, Gold) would
entail the conducting of mentally taxing currency conversions, (e.g., Huang & Lin, 2017; Hsee et
al., 2003) which possibly affected to the observed results.
It must be noted, that the lack of any game-like features possibly led the participants to process the
buying decisions differently from actual free-to-play games. In real games the players, for example,
have an actual motivation to proceed in the game, the purchases may have long-lasting effects in
the game, there may be various social aspects related to the purchases (e.g., Hamari et al., 2017).
When these aspects are lacking, the participants possibly considered the purchase decisions more
rationally than they would have in actual games. This finding challenges the using of these kind of
laboratory experiments in the study of free-to-play games, and the validity of this approach should
be examined critically in future studies.
Interestingly, the amount of resources had an effect to the participant’s willingness to pay for the
virtual goods; for euros the participants were willing to pay most when the amount of resources
(presented in virtual currency) was moderate, but for gold the willingness to pay was highest when
the amount of resources (in virtual currency) was also highest. This difference may be caused by
the two conversions that had to be made between the euros and gold (euros → diamonds → gold),
thus, the relation from gold to euros may have been ambiguous to the participants at the moment
of decision. That is, the presented information about the abundance of the virtual currencies
(diamonds and gold) had a differential effect when considering the purchases in virtual money
(gold) when compared to considering the purchases in euros. When considering the purchase in
gold, it is possible, that the participants felt “wealthy” only when the amount of the resources was
highest; when the numerosity of the currency was highest also the mental currency transforming
would probably be most demanding cognitively. On the other hand, with real euros, in the scenarios
where there was the highest amount of resources in virtual currencies (diamonds and gold), the
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participants possibly considered using real euros as peculiar and would have opted to use the
abundant virtual currencies.
Regardless of the used currency, the players were willing to pay most for the Unlock product,
which unlocks new game content. This is reasonable, after all, the Unlock opens actual new playing
content and thus its value was probably considered as the highest. This finding is also in line with
the previous findings of Hamari and colleagues (2017), who identified “Unlocking content” as one
of the six motivations that explain in-game content purchases.
One of the limitations of the current study is the rather small sample size, which inhibits the
generalizability of the findings. Thus, we plan to collect more data to reach 40 total participants.
After the additional data has been collected we intend to analyze also the collected EEG
(electroencephalography) data to study the neural mechanisms related to the economic decision-
making processes. In addition, we will explore the possibility to convert this setting to a web
experiment, which would enable the collection of a significantly larger sample. In future studies
we intend to also gradually add and manipulate for example the visual-aesthetic features of the
“game” and to study how these would affect the decision making.
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