The changing face of desktop video game monetisation: An exploration of exposure to loot boxes, pay to win, and cosmetic microtransactions in the most-played Steam games of 2010-2019

RESEARCH ARTICLE

The changing face of desktop video game

monetisation: An exploration of exposure to

loot boxes, pay to win, and cosmetic

microtransactions in the most-played Steam

games of 2010-2019

David Zendle

*, Rachel Meyer

, Nick Ballou

1 University of York, York, United Kingdom, 2 Queen Mary University of London, London, United Kingdom

* [email protected]

Abstract

It is now common practice for video game companies to not just sell copies of games them-

selves, but to also sell in-game bonuses or items for a small real-world fee. These purchases

may be purely aesthetic (cosmetic microtransactions) or confer in-game advantages (pay to

win microtransactions), and may also contain these items as randomised contents of uncer-

tain value (loot boxes). The growth of microtransactions has attracted substantial interest

from both gamers, academics, and policymakers. However, it is not clear either how fre-

quently exposed players are to these features in desktop games, or when any growth in

exposure occurred. In order to address this, we analysed the play history of the 463 most-

played Steam desktop games from 2010 to 2019. Results of exploratory joinpoint analyses

suggested that cosmetic microtransactions and loot boxes experienced rapid growth during

2012–2014, leading to high levels of exposure by April 2019: 71.2% of the sample played

games with loot boxes at this point, and 85.89% played games with cosmetic microtransac-

tions. By contrast, pay to win microtransactions did not appear to experience similar growth

in desktop games during the period, rising gradually to an exposure rate of 17.3% by

November 2015, at which point growth decelerated significantly (p<0.001) to the point

where it was not significantly different from zero (p = 0.32).

Introduction

The way that the video game industry makes money has undergone important changes in

recent decades. In the 1990s and early 2000s, industry profits were largely based around the

sale of copies of games [1]. These copies might take the form of cartridges, discs, or even digital

downloads. Under this model individuals were handing over money in return for the either

the ownership of a complete product, or the license to play that product for a potentially

unlimited period of time [2]. Similarly, ownership of a product might occur via a subscription-

PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 1 / 13

a1111111111

OPEN ACCESS

Citation: Zendle D, Meyer R, Ballou N (2020) The

changing face of desktop video game

monetisation: An exploration of exposure to loot

boxes, pay to win, and cosmetic microtransactions

in the most-played Steam games of 2010-2019.

PLoS ONE 15(5): e0232780. https://doi.org/

10.1371/journal.pone.0232780

Editor: Jose

C. Perales, Universidad de Granada,

SPAIN

Received: November 16, 2019

Accepted: April 21, 2020

Published: May 7, 2020

Peer Review History: PLOS recognizes the

benefits of transparency in the peer review

process; therefore, we enable the publication of

all of the content of peer review and author

responses alongside final, published articles. The

editorial history of this article is available here:

https://doi.org/10.1371/journal.pone.0232780

access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All relevant data are

held openly on the OSF repository located at

https://osf.io/wpqx7/.

based model: It has been common for decades for players of online role-playing games to pay a

flat monthly charge for access to a game.

However, at some point in the early 2000s, monetisation in video games underwent a signif-

icant shift. As well as selling games as complete products, publishers also began offering gam-

ers the ability to purchase additional items, bonuses or services within the game itself for a

real-money fee, known as a ‘microtransaction’ [3].

Cosmetic microtransactions

As noted in [4], many microtransactions allow players to purchase decorations and alternative

costumes that “offer no in-game advantage and are purely aesthetic“. In the context of this

paper, we refer to any situation in which spending additional money leads to an aesthetic

change within a game but no in-game advantage as a ‘cosmetic microtransactions’.

The cosmetic microtransactions that may be made in video games are varied. For example,

in the multiplayer battle royale game Fortnite, players can spend real-world money to buy in-

game ‘emotes’ that allow them to express ideas and feelings via the movements of their in-

game avatar. In the vehicular soccer game Rocket League, players can pay to purchase new

‘goal explosions’ that allow them to celebrate in-game victory with unique visual effects. And

in the third-person shooting game Anthem, players can buy new armour pieces for their in-

game mechanical suits of armour. These pieces do not confer any in-game boosts or advan-

tages in terms of fighting: They simply look different.

Pay to win microtransactions

However, as noted in [5], not all microtransactions in games are purely cosmetic in nature.

Players of many modern video games are also given the option to purchase virtual items and

bonuses that increase their chances of in-game success. In this paper, we define any situation

in which players are able to exchange real-world money for something that increases their

chance of in-game success as a ‘pay to win’ microtransaction.

Some ‘pay to win’ microtransactions do not have any effect on the aesthetic of a game. For

example, players of the multiplayer mode in The Last of Us can pay real-world money for

advantages such as the ability to sneak up on other players silently via an ‘Agility perk’. This

in-game advantage does not change how the game itself looks: It merely alters how the game is

played.

However, other “pay to win” microtransactions also change how a game looks. For exam-

ple, players of the game Awesomenauts can spend real-world money to purchase additional in-

game characters. These new characters can convey an in-game advantage. However, they also

have unique and special looks, and therefore have cosmetic value as well. Within the context of

this paper, if a microtransaction changes both how a game looks and confers an in-game

advantage, we would categorise that microtransaction as ‘pay to win’.

It is import to note that some games separately offer both cosmetic microtransactions and

pay to win microtransactions. An example of this is Assassin’s Creed: Odyssey. In this game,

players may pay real-world money to purchase a boost that enables them to level up more

quickly, but does not change how the game itself looks. This is a pay to win microtransaction.

Conversely, they may spend real-world money to purchase a ‘skin’ for their in-game mount

that changes how it looks, but does not affect gameplay–this is an entirely cosmetic

microtransaction.

Pay to win microtransactions are thought to have originated with online multiplayer games

such as MapleStory in the early 2000s [6], and have garnered controversy amongst both gamers

and academics alike. Criticisms of pay to win microtransactions are wide-ranging. Some

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 2 / 13

Funding: Nick Ballou is a PhD student whose

doctoral training is funded by the EPSRC Centre for

Doctoral Training in Intelligent Games & Game

Intelligence (IGGI).

Competing interests: The authors have declared

that no competing interests exist.

academics provide ethical critiques of how they may change “the game from a competition

where the best player wins to . . . who wants to and can pay the most” [7]; others posit a belief

that this model makes games unfair for less affluent players [8]. In [9], researchers suggest that

they may encourage the entrapment of players. They posit that games such as Candy Crush

may set up situations in which in-game goals are almost attained (‘near misses’) in order to

encourage pay to win purchasing; and that this strategy may lead to continued play and spend-

ing “to the similar extent of wins as demonstrated in the gambling literature”.

Controversies over pay to win have led some game developers to explicitly reject these

microtransactions as an element of their design philosophies [10]. Furthermore, despite the

popularity of games with pay to win elements, many individuals have publicly voiced their dis-

pleasure with their incorporation in the games that they play [11].

Loot boxes

As described above, microtransactions can lead to both aesthetic differences, and gameplay

advantages. However, when making a purchase, players are not always aware what advantage

or difference they are buying due to a monetisation strategy known as loot boxes. A definition

of loot boxes is given in [12] as follows:

1. Loot boxes are items in video games that may be bought for real-world money, but which

provide players with a randomized reward of uncertain value

This may be considered a restrictive definition of loot boxes: For example, a sceptic might

suggest that in-game items rewarded purely through gameplay be considered loot boxes. We

would defend the above definition on the basis that it explicitly invokes the potential for mone-

tisation, which sits at the heart of many issues regarding loot boxes. Furthermore, it was used

in oral testimony to a recent UK Parliamentary Select Committee, and was subsequently used

by this legislative subcommittee in their official report regarding the potential for harm present

in loot boxes [13, 14]. It therefore provides a widely-used and useful definition of loot boxes.

This definition is used throughout this paper.

Loot boxes take diverse forms. Some may be considered pay to win: For example, players of

the fighting game Marvel: Contest of Champions may pay real-world money to open sealed in-

game crystals that contain characters from Marvel franchises. Owning powerful and rare char-

acters can help the player win in-game fights. However, when a player hands over their money

to open a crystal, they have no way of knowing whether the character that crystal contains is a

rare and powerful one, or a weak and common one.

Others loot boxes may be considered purely cosmetic microtransactions. For example,

players of Counter-Strike: Global Offensive may spend real world money to unlock sealed

‘weapon cases’. Each case contains a novel aesthetic for an in-game gun or knife. However,

when paying to open a weapon case, players do not know which cosmetic upgrade they are

paying for.

Loot boxes are thought to be extraordinarily lucrative for the video games industry, with

one source estimating that they may have generated as much as $30 billion in revenue in 2018

alone [15]. However, there are distinct concerns about this monetisation strategy. As noted in

[16], loot boxes share distinct similarities with gambling. This has led to concerns that engag-

ing with loot boxes may lead to increases in gambling amongst gamers [17]. Evidence for this

causal mechanism is unclear. Spending on loot boxes has been repeatedly linked to problem

gambling. However, it is uncertain whether this is because loot boxes cause problem gambling,

or whether it is because individuals with pre-existing gambling problems spend more money

on loot boxes [18–20].

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 3 / 13

The present research

It is widely acknowledged that both pay to win microtransactions, cosmetic microtransactions,

and loot boxes have become more common in recent years. This has been accompanied by

substantial interest.

However, how these features are changing over time is unclear. For example, some news

reports have recently suggested that loot boxes are currently becoming more widespread [21],

whilst others report that loot boxes are currently in decline [22]. Still more imply that the prev-

alence of specific in-game features may render them relatively unimportant: A recent state-

ment from one industry representative characterises loot boxes as “a particular form of

randomised in-game purchase which feature[s] in a minority of games” [23]. However, to the

best of our knowledge no piece of academic research has investigated changes in exposure to

either loot boxes, pay to win microtransactions, or cosmetic microtransactions.

This piece of research therefore sets out to explore the changing rate of exposure to loot

boxes, pay to win microtransactions, and cosmetic microtransactions by analysing historical

data on how many individuals play games with these features each day.

The Steam platform is often considered to be the dominant way for desktop video games to

be both sold and delivered [24, 25]. In this piece of research, we create a dataset of the number

of players of each of the most-played Steam games. This dataset records the peak number of

simultaneous players for each game on each day from the 22

March 2010 to the 22

April

2019. We then code each of these games for the presence of loot boxes, pay to win microtran-

sactions, and pay to win microtransactions. We then explore how these features change within

the sample over time via a joinpoint analysis.

Method

Ethics

This research consisted of an analysis of SteamDB data. SteamDB is a publicly available

database that lists the daily number of players of a variety of games. For example, the daily

number of players of Counter-Strike can be viewed by browsing to https://steamdb.info/app/

730/.

This study solely makes use of SteamDB data. Due to the publicly available and naturally

anonymous nature of the aggregate data used in this study, ethical approval was not applied

for when conducting this study. Upon submission of this manuscript, a formal waiver from

the lead author’s host institution was requested by journal staff. Said waiver was applied for

and granted by the ethics officer for the lead author’s department on the basis that this project

uses a publicly available database of information that is not personally identifiable.

SteamDB is a browsable database of aggregate play data from a variety of desktop games.

The design and conduct of this research did not violate the terms and conditions of SteamDB.

Design

A list was made of the all-time most-played desktop games on the Steam platform. This was

operationalised as any game that had achieved over 10,000 simultaneous players. This led to

the creation of a list of 474 games that fit this criterion on the 22

April 2019 via reference to

the SteamDB website [26], which keeps a record of the peak number of simultaneous players

for each game on the Steam platform.

The complete play history of each of these games was then extracted in turn from SteamDB.

Inspection of these records revealed that a daily log of peak simultaneous players was kept for

each game by the Steam platform from 22

March 2010.

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 4 / 13

This process was achieved by first navigating a browser to the SteamDB website, where a list of

games ordered by all-time peak simultaneous players is displayed. All entries with 10,000 or more

simultaneous players were noted down by a researcher. The researcher then used a browser to navi-

gate to the SteamDB page for each of the 474 games. A link on each of these pages allowed the

direct download of a.csv file containing the complete history of the number of players of that game.

Measures

The following three variables were then measured for each of these games:

1. The presence of loot boxes,

2. The presence of pay to win microtransactions

3. The presence of cosmetic-only microtransactions.

The presence of loot boxes. Using the definition of loot boxes given earlier, coders were

instructed to record that a game tested positive for the presence of loot boxes if it contained in-

game items that could be bought for real-world money but which contained randomized

rewards. An example of a game that would test positive for loot boxes is NBA2K18, a basketball

game in which gamers can pay real-world money to purchase ‘player packs’ that contain a ran-

domised assortment of new basketballers for their team. An example of a game that would test

negative for loot boxes is The Elder Scrolls: Oblivion. Players of this game could pay real-world

money to purchase new in-game content (for example, armour for their horses). However,

when handing over their money they always knew what they would get in return.

The presence of pay to win microtransactions. Using the definition of pay to win micro-

transactions given earlier, coders were instructed to record that a game tested positive for the

presence of pay to win microtransactions if players could pay real-world money to in any way

increase their chances of in-game success. An example of a game that would test positive for

pay to win microtransactions is Grand Theft Auto V, in which players may pay real-world

money for in-game currency, that can be used to purchase powerful new weapons. A game

that would test negative for pay to win microtransactions is the team-based strategy game

DOTA 2. In this game, players can pay real-world money to unlock new aesthetics for their in-

game characters: However, spending money can never confer an in-game advantage.

The presence of cosmetic microtransactions. Using the definition of cosmetic micro-

transactions 2given earlier, coders were instructed to record that a game tested positive for the

presence of cosmetic microtransactions if players could pay real-world money for things that

offered no in-game advantage and purely led to an aesthetic change. An example of a game

that would screen positive for cosmetic microtransactions is Rocket League, in which a variety

of decals, goal explosions, and other aesthetic effects may be bought for real-world money.

However, none of these purchases are able to change how the game is played. A game that

would test negative for cosmetic microtransactions is the digital collectible card game Artifact,

in which players can pay real-world money to purchase new cards, all of which have some the-

oretical gameplay value.

It should be noted that some games could be coded as containing both cosmetic and pay to

win microtransactions: A good example of this is Assassin’s Creed: Odyssey, as detailed in the

literature review. Similarly, should a game contain loot boxes, it would also be coded as con-

taining ‘pay to win’ or ‘cosmetic’ microtransactions on the basis of whether those loot boxes

contained cosmetic rewards or pay to win rewards. NBA2K18, for example, whose randomised

‘player packs’ contain new basketballers that give gamers an in-game advantage, would be

coded as containing both pay to win and loot boxes.

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 5 / 13

A final note should be made regarding the time at which the presence or absence of these

features was recorded. The presence or absence of all of the above features was coded on the

basis of those features currently appearing in-game at the time of analysis. It was deemed infea-

sible to consistently determine whether games had added or removed these features at any

point during the period under study (2012–2019). As covered in our discussion, some games

may have inserted or removed loot boxes, pay to win microtransactions, or cosmetic micro-

transactions during the period. This is considered in our discussion.

The presence of each of the features outlined above were measured by having two research-

ers separately code each game for their presence or absence. A single illustrative example of

Counter-Strike was provided as an exemplar at the beginning of the coding process. Following

this, coders explored the presence or absence of relevant in-game features primarily through a

combination of reading the game developer’s documentation and descriptions, and searching

for other information regarding the games online such as in forum posts. If this was insuffi-

cient, researchers engaged in watching videos of others playing the games and, as a last resort,

playing the games in question themselves.

An initial round of coding resulted in near-perfect agreement between coders when it came

to the presence of loot boxes (97%, Cohen’s Kappa = 0.90). However, there was only substan-

tial agreement when it came to the presence of pay to win (85.5%, Cohen’s Kappa = 0.66) and

cosmetic microtransactions (84.6%, Cohen’s Kappa = 0.68).

Disagreement between human coders is an extremely common feature of any reliability

analysis. Indeed, as noted in a standard textbook on the topic, for many researchers, a raw

agreement rate of 80–90% is taken as sufficient in a variety of contexts [27].However, given the

importance of a reliable coding scheme to our analyses, we resolved to be more stringent.

Cohen’s Kappa measures the degree of agreement between coders when chance is taken into

account, and is the most commonly used way to measure inter-coder reliability. A Kappa sta-

tistic of greater than or equal to 0.81 is categorised according to multiple common benchmark-

ing schemes as representing ‘near perfect agreement’ [28]. As in previous work on similar

topics (e.g. [12]), it was determined that after achieving a minimum acceptable Kappa level,

any remaining disagreements between coders would then be resolved through dialogical inter-

subjectivity to yield a dataset whose accuracy we were confident in.

Disagreements in coding were first discussed before re-coding the data. From these discus-

sions, it emerged that disagreements in coding may have been due to a lack of clarity about

whether downloadable content (DLC) such as expansion packs should be classified as either

pay to win or cosmetic microtransactions. This is a subtle point. The simulation game Farming

Simulator 15, for example, has 4 DLC releases that contain new branded machinery that may

minorly improve a player’s farming capabilities. Should this be classed as a game with pay to

win microtransactions?

In order to resolve this, it was agreed that cosmetic and pay to win microtransactions would

be classified as in-game items and rewards that are purchasable with real-world money but do

not add substantial additional game content. This was undertaken in order to distinguish as

best as possible between the addition of small amounts of additional content via microtransac-

tion, and the offer to purchase substantial video game expansion packs such as in Skyrim. For

example, the Echoes of Auriga Pack in Endless Legends may give the player in-game skins such

as the Drum of Gios. However, it also comes with a substantial additional content in the form

of a new soundtrack, and thus was not coded as a cosmetic microtransaction.

Every game in the dataset was then recoded separately by both coders using this new defini-

tion. This round of coding led to near-perfect agreement for both pay to win (96.5%, Cohen’s

Kappa = 0.91) and cosmetic microtransactions (96.3%, Cohen’s Kappa = 0.92). Eleven games

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 6 / 13

remained uncoded at this point. These were either demos, test servers, or other non-game

products (e.g. an SDK).

Both coders then met and discussed the remaining games on which their codes conflicted.

The resolution of these cases via dialogic intersubjectivity led to perfect agreement, and a final

dataset of games annotated with the presence of both loot boxes, pay to win features, and cos-

metic microtransactions.

Overall, 463 games were included in the final dataset after removing the eleven that could

not be categorised. There were 75 games with loot boxes, 388 games without them, and 11 games

that could not be categorised. There were 135 games with pay to win microtransactions, 328

games without them, and 11 games that could not be categorised. There were 203 games with cos-

metic microtransactions, 260 games without them, and 11 games that could not be categorised.

The presence of both multiplayer and co-operative features were additionally measured for

each of these games. Analysis of these features is not presented here.

Changes in exposure to loot boxes, pay to win, and cosmetic microtransactions was mea-

sured by first recording the number of players of each game under test for each of the 3,319

measured days from 22

March 2010 to 22

April 2019. This was accomplished by extracting

the complete history for each game from the SteamDB website. Any missing days were filled in

via linear interpolation. The total number of players was summed for each day. The number of

players of games with each specific feature on each of these days was then calculated. This fig-

ure was then divided by the total number of players overall for that day, and multiplied by 100

to yield a percentage measure of exposure.

Statistical analysis

Changing trends in video game features were explored using joinpoint regression. Joinpoint

regression is a technique for procedurally fitting a segmented regression model to trend data in

order to identify points in a dataset at which a trend changes [29]. It begins by fitting a linear

model to the dataset under test, and then iteratively tests whether the segmentation of this model

via one or several ‘joinpoints’ leads to an improvement in overall fit. Joinpoint regression is suitable

for the analysis of time series data, and commonly used to analyse change in trends over time. It is

most commonly used in the analysis of changes in cancer rates over time. However, it has been

used for analysing changes in trends as diverse as sales of pipe tobacco [30]; suicide rates [31]; fatal

car crashes [32]; workforce growth [33]; and the prevalence of coronary heart disease [34].

Joinpoint regressions can be computationally expensive, and data were therefore trans-

formed into weekly means in order to make analysis tractable. The National Cancer Institute’s

Joinpoint Regression Program Version 4.7.0.0 was used for these analyses. Due to the serial

nature of the data, adjustments for autocorrelation were made according to [29]. Model selec-

tion was conducted by measuring the fit of each model via the calculation of BIC3, a variant of

the Bayesian Information Criterion [35, p. 3]. In order to prevent the development of an over-

fitted model, we elected for a maximum of three joinpoints to be fit to the data, and for a mini-

mum of 8 weeks to occur between joinpoints.

Results

Changes in exposure to loot boxes

Exploratory joinpoint regression was first carried out on the relationship between time and the

percentage of individuals in the sample who played games which featured loot boxes. Exposure

to loot boxes was initially estimated at 4.2% of the sample in 22

-26

March 2010, rising to

71.2% of the sample by 16th-22nd April 2019. Results indicated that the best-fitting model

(BIC3 = 2.63) contained two joinpoints: 1

-8

January 2012, and 12

-19

March 2014.

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 7 / 13

Exposure first increased at an average annual rate of 5.3%, from 4.2% at the beginning of

observation to 14.0% in the period of 1

-8

January 2012 (β = 0.10, t = 2.82, p = 0.004). At this

point, the trend increased significantly in steepness (change in β = 0.28, t = 6.50, p<0.001) to

an average annual increase of 20.3% (β = 0.39, t = 15.54, p<0.001). Finally, at the second inflec-

tion point during 12

-19

March 2014, exposure was estimated at 59.4%. At this point, the

trend in the data became significantly more shallow (change in β = -0.34, t = -12.96, p<0.001).

This led to a more gradual rise in exposure to 71.2% by 16th-22nd April 2019 at an average

annual increase of 2.0% (β = 0.04, t = 4.78, p<0.001).

Changes in exposure to pay to win microtransactions

Joinpoint regression was then carried out on the relationship between time and the percentage

of individuals in the sample who played games with pay to win features. Exposure to pay to

win features was initially estimated at 5.0% of the sample, rising to 15.9% of the sample by

16th-22nd April 2019. Results indicated that the best-fitting model (BIC3 = 1.51) contained a

single joinpoint during 12

-19

November 2015.

Exposure first increased at an average annual rate of 2.1%, from 5.0% during 22

-26

March 2010 to 17.3% during 12

-19

November 2015 (β = 0.04, t = 10.12, p<0.001). At the

inflection point of 12

-19

November 2015, this trend decreased significantly in steepness

(change in β = -0.04, t = -5.40, p<0.001) to an average annual rate that was not significantly

different from zero (β = -0.008, t = -0.99, p = 0.32).

Changes in exposure to cosmetic microtransactions

Joinpoint regression was finally carried out on the relationship between time and the percent-

age of individuals in the sample who played games which featured cosmetic microtransactions.

Exposure to cosmetic microtransactions was initially estimated at 8.3% of the sample during

-26

March 2010, rising to 85.8% of the sample by 16th-22nd April 2019. Results indi-

cated that the best-fitting model (BIC3 = 2.34) contained two joinpoints: 12

-19

February

2012, and 20

-27

August 2013.

Exposure first increased at an average annual rate of 7.7%, from 8.3% at 22

-26

March

2010 to 23.4% at 12

-19

February 2012 (β = 0.149, t = 5.13, p<0.001). At the first inflection

point during 12

-19

February 2012, this trend increased significantly in steepness (change in

β = 0.40, t = 9.02, p<0.001) to an average annual increase of 28.9% (β = 0.555, t = 16.18,

p<0.001), leading to an estimated exposure of 67.8% during 20

-27

August 2013. At this

point, the trend in the data became significantly more shallow (change in β = -0.49, t = -14.15,

p<0.001). This led to a more gradual rise in exposure to 85.8% at 16

–22

April 2019 at an

average annual increase of 3.1% (β = 0.06, t = 8.84, p<0.001).

The resulting models from all joinpoint regression analyses are shown below as Fig 1.

Discussion

These results corroborate reports of an overall growth in loot boxes and cosmetic microtran-

sactions in the period 2010–2019. At the beginning of the period, only a small minority of

gamers were exposed to these features: 5.3% and 8.3% of the sample respectively. However, by

the end of the studied period, the majority of gamers were playing games that featured both

loot boxes (71.2%) and cosmetic microtransactions (85.8%). This does not contradict state-

ments by games industry representatives that loot boxes only appear in a minority of games:

After all, a mere 75 of the 463 games analysed during this study contained loot boxes. How-

ever, they do suggest that the games which do contain loot boxes such as DOTA 2 and Player

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 8 / 13

Unknown’s Battlegrounds may be so popular that, whilst the minority of games may have loot

boxes, the majority of gamers are exposed to this feature.

It is important to note that the data under test also provides no evidence of a diminishment

in the exposure to either loot boxes or cosmetic microtransactions: None of the regression anal-

yses within any joinpoint model contained a negative coefficient. However, they do suggest that

the growth of both cosmetic microtransactions and loot boxes have decelerated in recent years.

The majority of growth in exposure to loot boxes was modelled as taking place between Jan-

uary 2012 and March 2014. During this period exposure to loot boxes increased at a rate of

20.38% per year to a point where more than half of the sample played games with loot boxes.

Similarly, between February 2012 and August 2013, exposure to cosmetic microtransactions

increased at a rate of 28.9% per year to the point where more than two-thirds of the sample

played games with cosmetic microtransactions. However, immediately after these rapid peri-

ods of growth, the increase in exposure to both these features dropped significantly to rela-

tively low rates: 2.0% and 3.1% per year respectively. These low rates remained in place for the

subsequent 5–6 years.

Some might be surprised by the growth in loot box exposure occurring as early as 2012–

2014. They may assume that increases in loot box exposure occurred much later in time. For

example, in [16], researchers state that “there were as many games released with loot boxes in

2016–2017 as there were before this time”, suggesting that loot box exposure may similarly

experience rapid increases after 2016. The novelty of the result observed here may be due to

two factors. The first is that we are measuring exposure rather than prevalence: It may well be

the case that changes in the number of games that contain loot boxes occur differently to

changes in the number of players who are exposed to loot boxes.

Fig 1. Time series graph showing the percent of the sample playing games with each relevant feature during the period under test. Models produced by three

separate joinpoint regression analyses are superimposed on the graph as lines on top of each relevant time series.

https://doi.org/10.1371/journal.pone.0232780.g001

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 9 / 13

However, another explanation for this difference in analysis is possible: data quality. The

statement made above was based on scrutinising a list of games containing loot boxes that was

collated and sourced from the video game journalism site Giant Bomb. Said data is subject to

various forms of bias and inaccuracy. Furthermore, the underlying processes that are used to

generate said data are not available for public scrutiny, and may not conform to scientific ide-

als. The data presented here is superior in this regard. Further work to determine the changing

prevalence rates (as well as exposure rates) of loot boxes over time are necessary.

Exposure to pay to win microtransactions appeared to change in a somewhat different

manner to the features outlined above. Whilst loot boxes and cosmetic microtransaction

growth was characterised by a sharp increase leading to a slow period of gradual growth, pay

to win microtransactions did not experience a similar temporary acceleration. Instead, expo-

sure was modelled as rising at a gradual rate of only 2.1% per year from the beginning of obser-

vation until 12

-19

November 2015, at which point this rate declined (p<0.001) to an

increase that was not significantly different from zero (p = 0.32). Consequently, by the end of

the sampled period, only 15.9% of the sample were playing games that featured pay to win

microtransactions.

Limitations

The analyses presented here are limited in several ways. The dataset used captures the data of a

large number of individuals: Indeed, an average of over 4 million players were recorded each

day within our dataset by the conclusion of the studied time period. However, it is important

to note that this data represents the players of only the 463 most popular games on Steam. The

data of all less-popular games are is therefore not included in this dataset, and it is likely that

these games may have a different distribution of features to the most popular games on the

market.

Additionally, each game was coded as containing a specific feature if it contained that fea-

ture at the time of coding. Theoretically, a game may have only introduced a feature such as

cosmetic microtransactions in 2017 or 2018. Yet, when coding took place, all datapoints for

that game would be coded as coming from a game which contained such a feature. If this is the

case, the models produced below could underestimate the size of increases in exposure within

the sample. Furthermore, it is also possible that games in the sample had previously contained

loot boxes, and then subsequently removed them. These games would be coded as not contain-

ing loot boxes, and their presence in the sample might lead to the overestimation of increases

in exposure to loot boxes.

Finally, and most importantly, this dataset consists only of information about desktop

games available via the Steam marketplace. It is therefore unable to provide information about

the exposure to cosmetic microtransactions, loot boxes, and pay to win features on other plat-

forms such as mobile devices.

One must also note that this data cannot make any claims about the number of players who

actually purchased microtransactions of any kind; rather, it speaks to the frequency with

which these features appeared in popular games, and the proportion of gamers who are

exposed to these features in the games they play.

A final limitation of this study concerns the joinpoint analysis that was used. Joinpoint anal-

yses are able to establish both when changes in the slope of a regression line occurred, and the

steepness of a slope after each change-point. This makes them appropriate to the aims of this

project: They allow researchers to understand how exposure to various in-game features has

changed in specific ways over the past decade. Here, for example, they are used to estimate a

rate of increase in exposure between specific dates. However, their utility in understanding

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 10 / 13

other features of exposure is limited: They are not commonly used as predictive models to

understand future levels of exposure; and their ability to estimate the shape of trends is limited:

They cannot be used to understand if, for example, increases in exposure fit an exponential

curve. These are interesting and important analyses for future researchers to conduct. All data

associated with our analyses are publicly available, and it is our hope that future work will

address these questions.

Similarly, the process of joinpoint regression necessitates researchers defining specifc

apriori constraints on their statistical models. For example, the regression undertaken here

was constrained to contain a maximum of three joinpoints. It should be noted that these con-

straints were informed by standard practice in the literature: For example, it is a common

strategy to allow a maximum of three joinpoints to occur within a model, presumably in order

to resolve tensions between computational efficiency, overfitting, and model accuracy [36–38].

A sceptic may note that this analytic strategy may allow for situations of analytic flexibility:

One might receive different results by specifying four, or five, or two joinpoints. Further work

may focus on determining the impact of model constraint decisions on analytic outcomes:

Our data is open and available with no reservations should other parties wish to reanalyse it

for this purpose, via, for example, a form of multiverse analysis.

Conclusions

The exploratory analysis presented above suggests that pay to win microtransactions continue

to be an uncommon feature of desktop video games. Increases in exposure to this feature

appeared to only gradually rise from 2010 onwards, and to plateau in 2015, leading to relatively

low levels of exposure in 2019.

By contrast, cosmetic microtransactions and loot boxes appear to be present in games played

by the majority of desktop gamers within the sample. Over 70% of gamers played a game with

loot boxes in by the end of the studied period; over 80% played a game with cosmetic micro-

transactions. This increase in exposure does not appear recent: Indeed, the data suggests that

these features may have risen to a dominant position in desktop games as early as 2014.

Academics and policymakers have expressed interest and concern in the potential conse-

quences of the incorporation of the features outlined above in modern video games. Recent

reports have suggested that loot boxes may recently have experienced either a decline in popu-

larity, or a rise in popularity. This study instead suggests that, at least on desktop platforms,

gamers experienced a sudden increase in exposure to both loot boxes and cosmetic microtran-

sactions during approximately 2012–2014, followed by a period of steady and gradual growth.

Author Contributions

Conceptualization: David Zendle.

Data curation: David Zendle, Rachel Meyer, Nick Ballou.

Formal analysis: David Zendle, Rachel Meyer, Nick Ballou.

Methodology: David Zendle, Rachel Meyer, Nick Ballou.

Writing – original draft: David Zendle.

Writing – review & editing: Rachel Meyer, Nick Ballou.

References

1. Lizardi R., ‘DLC: Perpetual commodification of the video game’, Democr. Commun., vol. 25, no. 1,

2012.

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 11 / 13

2. Consalvo M. and Paul C., ‘Paying to play: The evolving structure of game pricing and industry legiti-

macy’, AoIR Sel. Pap. Internet Res., vol. 5, 2015.

3. Tomić N. Z., ‘economic Model of Microtransactions in video Games’, J. Econ. Sci. Res. Vol., vol. 1, no.

01, 2018.

4. Marder B., Gattig D., Collins E., Pitt L., Kietzmann J., and Erz A., ‘The Avatar’s new clothes: Under-

standing why players purchase non-functional items in free-to-play games’, Comput. Hum. Behav., vol.

91, pp. 72–83, 2019.

5. Reza A., Chu S., Khan Z., Nedd A., Castillo A., and Gardner D., ‘Skins for Sale: Linking Player Identity,

Representation, and Purchasing Practices’, in Information in Contemporary Society, Cham, 2019, pp.

124–131, https://doi.org/10.1007/978-3-030-15742-5_11

6. Davidovici-Nora M., ‘Innovation in business models in the video game industry: Free-To-Play or the

gaming experience as a service’, Comput. Games J., vol. 2, no. 3, pp. 22–51, 2013.

7. Heimo O. I., Harviainen J. T., Kimppa K. K., and Ma¨kila¨ T., ‘Virtual to Virtuous Money: A Virtue Ethics

Perspective on Video Game Business Logic’, J. Bus. Ethics, vol. 153, no. 1, pp. 95–103, Nov. 2018,

https://doi.org/10.1007/s10551-016-3408-z

8. Alha K., Koskinen E., Paavilainen J., Hamari J., and Kinnunen J., ‘Free-to-play games: Professionals’

perspectives’, Proc. Nord. DiGRA, vol. 2014, 2014.

9. Larche C. J., Musielak N., and Dixon M. J., ‘The Candy Crush Sweet Tooth: How “Near-misses” in

Candy Crush Increase Frustration, and the Urge to Continue Gameplay’, J. Gambl. Stud., vol. 33, no.

2, pp. 599–615, Jun. 2017, https://doi.org/10.1007/s10899-016-9633-7 PMID: 27435416

10. ‘Yves Guillemot: “We have no pay-to-win elements in our games”‘, GamesIndustry.biz. [Online]. Avail-

able: https://www.gamesindustry.biz/articles/2019-10-24-yves-guillemot-we-have-no-pay-to-win-

elements-in-our-games. [Accessed: 27-Oct-2019].

11. ‘A Guide To The Endless, Confusing Star Wars Battlefront II Controversy’, Kotaku. [Online]. Available:

https://kotaku.com/a-guide-to-the-endless-confusing-star-wars-battlefront-1820623069. [Accessed:

01-Nov-2019].

12. Zendle D., Meyer R., Waters S., and Cairns P., ‘The prevalence of loot boxes in mobile and desktop

games’, Addiction, Jan. 2020.

13. ‘Immersive and addictive technologies report published—News from Parliament’, UK Parliament.

[Online]. Available: https://www.parliament.uk/business/committees/committees-a-z/commons-select/

digital-culture-media-and-sport-committee/news/immersive-technology-report-17-19/. [Accessed: 26-

Feb-2020].

14. ‘Oral evidence—Immersive and addictive technologies—22 Jul 2019’. [Online]. Available: http://data.

parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/digital-culture-media-and-

sport-committee/immersive-and-addictive-technologies/oral/103954.html. [Accessed: 11-Sep-2019].

15. Juniper Research, ‘In-Game Gambling ~ The Next Cash Cow for Publishers’, Apr-2018. [Online]. Avail-

able: https://www.juniperresearch.com/document-library/white-papers/in-game-gambling-~-the-next-

cash-cow. [Accessed: 15-Jul-2018].

16. Drummond A. and Sauer J. D., ‘Video game loot boxes are psychologically akin to gambling’, Nat. Hum.

Behav., pp. 530–532, Jun. 2018, https://doi.org/10.1038/s41562-018-0360-1 PMID: 31209310

17. Zendle D. and Bowden-Jones H., ‘Loot boxes and the convergence of video games and gambling’, Lan-

cet Psychiatry, vol. 6, no. 9, pp. 724–725, 2019.

18. Brooks G. A. and Clark L., ‘Associations between loot box use, problematic gaming and gambling, and

gambling-related cognitions’, Addict. Behav., 2019.

19. Zendle D., Meyer R., and Over H., ‘Adolescents and loot boxes: links with problem gambling and moti-

vations for purchase’, R. Soc. Open Sci., vol. 6, no. 6, p. 190049, Jun. 2019, https://doi.org/10.1098/

rsos.190049 PMID: 31312481

20. Zendle D. and Cairns P., ‘Video game loot boxes are linked to problem gambling: Results of a large-

scale survey’, PLoS One, vol. 13, no. 11, p. e0206767, 2018. https://doi.org/10.1371/journal.pone.

0206767 PMID: 30462669

21. Wright M., ‘Video gamers will be spending $50 billion on “gambling-like” loot box features by 2022,

according to analysts’, The Telegraph, 17-Apr-2018.

22. Davenport J., ‘Battle passes are replacing loot boxes, but they’re not necessarily a better deal’, PC

Gamer, 05-Jul-2018. [Online]. Available: https://www.pcgamer.com/uk/battle-passes-are-replacing-

loot-boxes-but-theyre-not-necessarily-a-better-deal/. [Accessed: 27-Oct-2019].

23. Boland H., ‘British children spending £270m a year on mobile game “loot boxes” and in-app purchases”‘,

The Telegraph, 28-Aug-2019.

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 12 / 13

24. Brunt C. S., King A. S., and King J. T., ‘The influence of user-generated content on video game

demand’, J. Cult. Econ., pp. 1–22, 2019.

25. Lin D., Bezemer C.-P., and Hassan A. E., ‘An empirical study of early access games on the Steam plat-

form’, Empir. Softw. Eng., vol. 23, no. 2, pp. 771–799, 2018.

26. ‘Home � SteamDB’, Steam Database. [Online]. Available: https://steamdb.info/. [Accessed: 02-Nov-

2019].

27. Saldana J., The Coding Manual for Qualitative Researchers. SAGE, 2015.

28. Gwet K. L., Handbook of Inter-Rater Reliability, 4th Edition: The Definitive Guide to Measuring The

Extent of Agreement Among Raters. Advanced Analytics, LLC, 2014.

29. Kim H.-J., Fay M. P., Feuer E. J., and Midthune D. N., ‘Permutation tests for joinpoint regression with

applications to cancer rates’, Stat. Med., vol. 19, no. 3, pp. 335–351, 2000. https://doi.org/10.1002/

(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z PMID: 10649300

30. Morris D. S. and Tynan M. A., ‘Fiscal and Policy Implications of Selling Pipe Tobacco for Roll-Your-Own

Cigarettes in the United States’, PLOS ONE, vol. 7, no. 5, p. e36487, May 2012, https://doi.org/10.

1371/journal.pone.0036487 PMID: 22567159

31. Lee S.-U., Park J.-I., Lee S., Oh I.-H., Choi J.-M., and Oh C.-M., ‘Changing trends in suicide rates in

South Korea from 1993 to 2016: a descriptive study’, BMJ Open, vol. 8, no. 9, p. e023144, Sep. 2018,

https://doi.org/10.1136/bmjopen-2018-023144 PMID: 30269071

32. Nakahara S., Katanoda K., and Ichikawa M., ‘Onset of a declining trend in fatal motor vehicle crashes

involving drunk-driving in Japan’, J. Epidemiol., p. JE20120134, 2013.

33. Gruca T. S., Nelson G. C., Thiesen L., Asprey D. P., and Young S. G., ‘The workforce trends of physi-

cian assistants in Iowa (1995–2015)’, PLOS ONE, vol. 13, no. 10, p. e0204813, Oct. 2018, https://doi.

org/10.1371/journal.pone.0204813 PMID: 30296294

34. O’Flaherty M., Allender S., Taylor R., Stevenson C., Peeters A., and Capewell S., ‘The decline in coro-

nary heart disease mortality is slowing in young adults (Australia 1976–2006): a time trend analysis’, Int.

J. Cardiol., vol. 158, no. 2, pp. 193–198, 2012. https://doi.org/10.1016/j.ijcard.2011.01.016 PMID:

21288580

35. ‘BIC3’, Joinpoint Help System 4.7.0.0. [Online]. Available: https://surveillance.cancer.gov/help/

joinpoint/setting-parameters/method-and-parameters-tab/model-selection-method/bic-3. [Accessed:

24-Oct-2019].

36. Vujcic I. S., Sipetic S. B., Dubljanin E. S., and Vlajinac H. D., ‘Trends in mortality rates from coronary

heart disease in Belgrade (Serbia) during the period 1990–2010: a joinpoint regression analysis’, BMC

Cardiovasc. Disord., vol. 13, no. 1, p. 112, 2013.

37. Thomas K., Chang S.-S., and Gunnell D., ‘Suicide epidemics: the impact of newly emerging methods

on overall suicide rates-a time trends study’, BMC Public Health, vol. 11, no. 1, p. 314, 2011.

38. Nistal-Nuño B., ‘Joinpoint regression analysis to evaluate traffic public health policies by national tempo-

ral trends from 2000 to 2015’, Int. J. Inj. Contr. Saf. Promot., vol. 25, no. 2, pp. 128–133, 2018. https://

doi.org/10.1080/17457300.2017.1341937 PMID: 28675063

PLOS ONE

The changing face of desktop video game monetisation

PLOS ONE | https://doi.org/10.1371/journal.pone.0232780 May 7, 2020 13 / 13