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JURNAL

SOSAINS

JURNAL SOSIAL DAN SAINS

VOLUME 3 NOMOR 1 2022

P-ISSN 2774-7018, E-ISSN 2774-700X

THE IMPACT OF PERCEIVED VALUES AND

PEER INFLUENCES TOWARD IN-APP PURCHASE

INTENTION IN MOBILE GAME

Aditya Eka Pranandiansyah

University Of The Budio Luhur

Email : eka.aditya.p@gmail.com

Kata kunci: Game

Seluler, Pengaruh

Teman, Nilai yang

Diyakini

Keywords: Mobile

Games, Friends

Influence, Believed

Values

ABSTRAK

Latar Belakang : Dalam beberapa dekade terakhir, Internet dan smartphone sering

dianggap sebagai kebutuhan dasar bagi kehidupan manusia. Menurut survei yang

dilakukan oleh Datareportal.com, pengguna internet meningkat sebesar 3,5 persen

pada tahun ini hingga Oktober 2022, mencapai 5,07 miliar saat kita memasuki kuartal

terakhir tahun ini. 171 juta pengguna baru selama 12 bulan terakhir telah

meningkatkan penetrasi internet global menjadi 63,5%.

Tujuan : Bertujuan untuk menyatakan kembali proposisi bahwa game seluler,

terutama game sosial, mengharuskan pemain untuk berkomunikasi dengan pemain lain

dan berdiskusi tentang game itu sendiri, dan diskusi sering kali condong ke arah cara

membelanjakan uang mereka secara efisien untuk pembelian dalam aplikasi.

Metode : Dalam penelitian ini akan digunakan metode "Normal P-P Plot" untuk

menentukan apakah data tersebut didistribusikan secara normal atau tidak.

Hasil : Hasil ini menunjukkan bahwa pengguna game seluler lebih cenderung membeli

Pembelian Dalam Aplikasi ketika mereka melihat sesuatu yang mereka butuhkan atau

inginkan dalam game, baik untuk tujuan estetika atau praktis. Kemungkinan untuk

membeli juga akan ditentukan oleh genre atau jenis permainan.

Kesimpulan: Dapat diartikan bahwa tidak ada pengaruh yang signifikan antara Nilai

Hedonik terhadap Niat Pembelian Dalam Aplikasi. Ini menunjukkan bahwa meskipun

motivasi hedonik memainkan peran besar dalam hal keterlibatan dalam game itu

sendiri, itu tidak berarti itu akan memiliki dampak yang cukup besar dalam hal niat

Pembelian Dalam Aplikasi.

ABSTRACT

Background: In the past few decades, the Internet and smartphones are often

considered as basic necessities for human life. According to a survey conducted by

Datareportal.com, Internet users increased by 3.5 percent in the year to October 2022,

reaching 5.07 billion as we entered the final quarter of the year. 171 million new users

over the past 12 months have taken global internet penetration to 63.5%.

Purpose: Aim to restate the proposition that mobile games, especially social games,

require players to communicate with other players and have discussions about the

game itself, and discussions often lean towards how to efficiently spend their money on

in-app purchases.

Method: In this study, the "Normal P-P Plot" method will be used to determine

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whether the data is normally distributed or not.

Results: These results suggest that mobile game users are more likely to buy In-App

Purchases when they see something they need or want in a game, whether for aesthetic

or practical purposes. Likelihood to buy will also be determined by genre or game

type.

Conclusion: It can be interpreted that there is no significant effect between Hedonic

Value on In-App Purchase Intention. This suggests that even if hedonic motivation

plays a large role when it comes to engagement in the game itself, that doesn't mean it

will have a appreciable impact in terms of In-App Purchase intent.

INTRODUCTION

In the past few decades, Internet and smartphone is often considered as basic

necessities for human lives. According to a survey conducted by Datareportal.com,

Internet users increased by 3.5 percent in the year to October 2022, reaching 5.07 billion

as we enter the final quarter of the year. 171 million new users over the past 12 months

have taken global internet penetration to 63.5%. Global mobile users have reached 5.48

billion, with smartphones accounting for almost 4 in 5 of the mobile handsets in use

today. The world’s mobile user base has grown by 170 million since this time last year,

with 68.6 percent of all the people on Earth now using some form of mobile phone

(KEMP, 2022). As Internet providers and mobile technologies becoming more affordable,

there will be more people who are connected to the Internet. It is predicted that more than

two-thirds of the world’s population will be connected to the internet by the end of 2023.

Hence there would be growing demand for mobile applications to support our daily needs

for various purposes, including entertainment purposes. And one of the very promising

innovations, that is currently growing rapidly in mobile entertainment industry, is mobile

gaming.

Mobile games are defined as a form of game that are played on mobile devices

(Terlutter & Capella, 2013). Mobile games have some advantages compared to the other

game segment (such as console and PC games), which is easy accessibility and

convenience to play whenever and wherever the user wish to play. This makes mobile

games are able to cater to larger share of the market due to their flexibility, thus making it

quickly become popular (Titov, 2022). As a highly popular form of entertainment, mobile

game has a very high potential to be profitable. In 2021, a survey from Statista reports

that there are 2.7 billion unique user who played mobile games worldwide, and it boasts a

massive $103.5 billion revenue during the same year (Lynkova, 2022). In a recent survey

from Statista.com, it has been mentioned that the average annual revenue per user

(ARPU) projection in mobile games segment will reach $87.32 in 2022 (“Mobile Games

- Worldwide,” 2022). Mobile games segment currently are the most thriving segment of

game market, with year-on-year revenue growth reaching 5.1%.

Fig. 1 Statistics of global games market growth rates from Newzoo

Indonesia holds the 16th place in the world and is the largest gaming market in

Southeast Asia. The market size of mobile games market in Indonesia is valued at $853

million in the year and is expected to reach $2,188 billion by the end of the year 2025

(Reogma, 2022). In terms of revenue, Indonesia’s mobile game segment are projected to

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Aditya Eka Pranandiansyah 170

reach $630 million in 2022, with an annual growth rate of 7.41%, resulting in a projected

revenue of $900 million by 2027 (Statista.com, 2022). Mobile games segment are

developing well in Indonesia, and several Asian mobile game giants (Tencent, Moonton,

Hoyoverse, and other companies) are looking to get into Indonesia’s market soon.

Fig. 2 Projected revenue in Indonesia’s mobile games market from

Statista.com

What makes mobile game segment interesting is, there are approximately 37.5% of

the mobile game player known as “mobile game payer” – A group of players that spends

real money on mobile gaming, and this number are expected to grow in the following

years. This shows that mobile game industry is a very interesting segment to look into.

Even though most of the mobile game currently available in the app store are free-to-play

game, these mobile game payer does pay to have additional features for them that might

not be available for the majority of mobile game player that are otherwise not spending

money. This kind of business model are also known as “Freemium” business model.

Freemium is a business model in which a product is provided for free with its basic

functionality, with additional benefits, features, or services which is charged to user who

wish to expand beyond the scope of the given basic functionality (Reime, 2011). In the

case of mobile games, the game which implemented this kind of freemium business

model is also known as free-to-play, or F2P game to be short. These kinds of mobile

games are free to be downloaded and played, however several virtual items that would

help the players’ gameplay and progressions are locked behind a paywall, unless the

players are willing to pay for those additional features via microtransaction – a business

model in which the users can pay with real money for virtual items via in-app purchase

(Rahiem & Fitrananda, 2021). By combining freemium and microtransaction business

model, mobile game publishers can make virtual goods, in a virtual economic

environment, using virtual currency in place of actual money, which also adheres to real

life currency exchange rate.

Nowadays, many mobile games utilize the model of in-app purchases where

players use real money for virtual goods. This form of “freemium” business model

requires the game design to tempt players to do in-game purchases. The in-app purchases

itself can be classified into two broad groupings: functional and aesthetic (Hellsten,

2019). Functional in-game purchases like these add something to the gameplay and game

enjoyment e.g., more powerful tools or increases flow of the game. On the contrary,

games also utilize in-game purchases that are purely for aesthetic purposes e.g., character

customization. Aesthetic virtual goods do not add anything to the gameplay itself but are

still proven to be very efficient as a monetization tool, that is utilized by many game

developers. Both types of virtual goods are popular ways to monetize the game and

usually determine the way the game is designed.

Some virtual items are designed to be exclusive and one-of-a-kind, and that

makes the price of those virtual items absurdly high. For example, in January 4

, 2021, a

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South East Asian mobile game called Ragnarok Online Mobile : Eternal Love, managed

to shock the internet with its virtual auction value. The virtual auction sold Key of

Heaven, a one-of-a-kind virtual mount and the only one in the server at that time, which

was sold with the price of 360.000 BCC (Rp 900 million). Since then, there are at least

several virtual goods that had been sold in the auction with bid value reaching almost Rp

1 billion. But the same game managed to once again amazed the mass in the same year by

releasing EVA Unit-01, a giant robot character costume, which was then sold for 490.000

BCC (Rp 1.2 billion). The interesting thing is, two of those virtual items are in the

possession of Indonesian player with the nickname of MambaaElChap.

Fig. 3 Two of the most expensive virtual items in Ragnarok Online Mobile :

Eternal Love

Besides the freemium and microtransaction business models, there is one more

layer of business model that is widely implemented in most of mobile games currently

available nowadays, which is loot box mechanism, or in Japanese term are referred to as

“Gacha”. The term Gacha itself is derived from Japanese word “Gashapon”, a type of

capsule toys vending machine which is very popular in Japan. This mechanism is based

on random chance, creating a “box” which is a form of a refined lottery system where

users choose to pay for a chance to enter a real-time “lucky draw” to acquire these virtual

items, contrary to normal transaction in which one player purchases certain virtual goods

with a fixed price point (Koeder, Tanaka, & Hitoshi, 2018). The adaptation of gacha

mechanism in mobile games creates another complex layer of abstraction, as it allows the

game company to use the gacha mechanism as both “reward system” and “honey trap”.

Related to the freemium pricing strategy, gacha mechanism can be seen as an extension

of the free features given by the F2P game, in a form of reward by giving the players

several free chance to try the lucky draw, also in tandem they provide some premium in-

game currency that can be bought with real money to further encourage the player to buy

additional chance to draw the lucky lots. The gacha behavioral impact of this of this

Gacha business model are so significant that there are some controversies about Gacha

between mobile game developers, players, and regulators in Japan because its perceived

relationship to over-spending behavior (Shibuya, Teramoto, Shoun, & Akiyama, 2019).

There are a lot of different mobile games with gacha systems can be found in the

app store. But in the history of mobile games, perhaps the one with most significance in

building gacha games history is a Japanese gacha game named Granblue Fantasy, which

developed by Cygames, Inc., one of the biggest game company in Japan (AlDakhil, Al

Taleb, Al Ghamlas, & Al-Megren, 2019). Granblue Fantasy is initially designed as a 2D

game with turn-based RPG gameplay, and also contains summons and a class system that

alters the main character's move-set and growth. Characters in Granblue Fantasy gain

levels and abilities by accruing experience; by collecting certain materials, some

character may earn an extra star (which is called full limit break or "FLB"). Summons

and weapons equipped also confer characters with bonuses on attack power and HP. The

characters themselves are gained either via quests (the main story quests or special event

quests) or by using in-game currency to receive random crystal fragments, which may

contain special weapons that add specific characters to the party. Characters, summons,

and weapons are ranked (from best to worst) as SSR, SR, R, or N. Since March 2016,

Granblue Fantasy offers English translation as part of their marketing strategies to

expand playerbase outside of Japan.

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Aditya Eka Pranandiansyah 172

Fig. 4 Real footage of Granblue Fantasy game, developed and published by

Cygames, Inc.

For some small, active part of the playerbase, the goal of Granblue Fantasy is to

collect and upgrade as much weapon and characters as possible to be able to contend in a

guild-vs-guild battle event called “Unite and Fight”, as well as clearing the end game

raids available in game. But the majority of the playerbase choose to be more laid back

and casual, only collecting characters that roused their interests. It is because one of the

main appeal of Granblue Fantasy, is that character designs are very intricate and detailed,

also they are voiced by various popular voice actors in Japan. But to be able to obtain

these characters, players have to do some gacha pulls and pray that they are lucky enough

to draw their desired characters. In Granblue Fantasy, the normal chance of getting an

SSR is 3%, and is doubled on special occasions such as Premium Gala, which usually

occurs at the end of the month; and Flash Gala, which usually occurs on the middle of the

month. Those occasion usually offers limited characters, so as to build pressure on the

player to spend money to get them before the time runs out.

Fig. 5 Examples of Premium Gala and Flash Gala Banner on Granblue Fantasy

Back in the early days of Granblue Fantasy, this limited occasion often leads to

overspending incident, moreover so when there are no upper limit on how one can spend

before finally getting the characters they want. One of the extreme case of the incident in

Granblue Fantasy is in March 2016, where the game introduced a character named

Anchira, a character based on a Chinese Zodiac. Since 2016 is the year of monkey,

Anchira is designed to be a limited “Divine General” series. Being the first character of

the limited Divine General series, the players are rushing to get her. The chance of getting

her are supposed to be 6% since it is on the rate up occasion. However, one of the player

named Taste did a stream to pull Anchira, and spend about $6.065 (around Rp 90 million)

before finally getting her. He did 2.276 pulls, before getting something that was supposed

to be have a 6% rate up (Nakamura, 2022). This, of course, resulting in one of the biggest

backlash in the history of gacha games, and Cygames as the publisher publicly

apologizing by giving a massive refund in the form of their virtual currency for all players

who pull for Anchira. This incident, also known as ”monkeygate”, remains as one of the

most historical moment in the history of gacha games (EXCEL COANANDA, 2021).

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Fig. 6 Anchira, The Divine General, a character that starts the “Monkeygate”

incident

After the “monkeygate” incident, JOGA (Japanese Online Games Association)

investigates this matter. After their investigation, it is revealed that the problem lies in the

chances of gaining a rare item were not always truly presented to the player (Koeder et

al., 2018). This lack of transparency led to a law announced by the Computer

Entertainment Supplier’s Association in 2016 in Japan (George-Gabriel & Anastasia,

2022). As part of the law, companies must disclose item probabilities in paid gachas so

consumers can understand their chances of winning (CESA, 2016). According to CESA

(CESA, 2016), instead of displaying the gacha item offer ratios, game publishers can

select and focus on any of the following restrictions :

1. The upper limit of the estimated amount of money to acquire any of the gacha rare

items is within 100 times of the charge amount per paid gacha. If the upper limit is

exceeded, the estimated price or multiplying factor must be displayed on the gacha

page.

2. The price limit to obtain rare gacha items should be within 50,000 yen. If the limit

exceeds, the estimated price must be displayed on the gacha page.

3. Display the upper and lower distribution rates of rare gacha items.

4. The offer ratio is displayed for each type of gacha item.

These laws were passed on April 27

, 2016, in Japan. Then soon after, other

countries like China and South Korea begun to develop their own regulation regarding

gacha games. Looking at the implementations of gacha business model above in mobile

game industry, we can see issues arise regarding the mobile game player, be it for the free

user or the paying one. In the case of typical freemium model implementation, it is

expected that a number of players might consider purchasing the additional premium

features. But when we include the lucky draw gacha mechanism, there will be a situation

where players are more likely failing to get the desired virtual items with resources that

are accessible to free players. This will raise a questionable condition in which the paying

players still choose to buy additional gacha chance, despite of its low chance of getting

higher rarity items.

Before starting this research, a simple survey has been conducted in Nov 14

, 2022

until Nov 17

, 2022 on a sample of 100 people who plays various mobile games in the

past year. This survey consists several simple questions, such as what kind of gacha

games they are currently playing, whether they have ever been do in-app purchase on the

before, how much money are they putting in their mobile games for these past 12 months,

and the factors that leads them to do in-app purchases. In this survey, 93% of the

respondent said that they have been doing in-app purchases, while 7% have not. And the

statistics for the spending levels are as follows:

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Aditya Eka Pranandiansyah 174

5. 30% belongs to F2P/Low spender group, with spendings between Rp 0 – Rp

500.000 a year

6. 24% belongs to Casual spender group, with spendings between Rp 500.001 – Rp

1.000.000 a year

7. 23% belongs to Regular spender group, with spendings between Rp 1.000.001 –

Rp 5.000.000 a year

8. 11% belongs to Competitive spender group, with spendings between Rp 5.000.001

– Rp 10.000.000 a year

9. 10% belongs to Hardcore spender group, with spendings between Rp 10.000.001 –

Rp 50.000.000 a year

10. 2% belongs to Whale group, with spendings above Rp 50.000.000 a year.

Fig. 7 Mobage spending level on the past 1 year, data taken from 100 respondent

As for the factors influencing the in-app purchase of mobile gacha games, there are

several common factors that often mentioned by the respondents of previous survey. The

first and the most common reason is that they feel that the in-app purchase is worth the

money spent for them, which is mentioned by 69% of the respondent. Second reason is

they are attracted by the characters that appears in the draw pool, mentioned by 67% of

the respondent. Next reason is that they feel the in-app purchase will help their progress

in the game, mentioned by 44% of the respondent. Then, 43% of the respondent said that

they want to support the game. 26% of the respondent said that they want to be stronger

in terms of competitiveness. Lastly, 13% of the respondent said that they are influenced

by their play mates or friends. Beside those common reason, there are several interesting

reasons that is mentioned by the respondents. Some respondents said that they bought in-

app purchase since they are just missing a bit before getting the guaranteed unit with free

obtainable gems. Some other respondents do in-app purchase to further enhance the story

experience in the game they played. And lastly, there are respondents that do in-app

purchase on impulse since they have the money to spare.

Based on the general definition for the theory of consumption value, it is

mentioned that perceived values consisted of several components such as : functional

value, social value, emotional value, epistemic value, and conditional value (Boksberger

& Melsen, 2011). In the context of mobile games, multiple driving factors influencing

players intention to make an in-app purchase, such as loyalty and good value of money.

are shown in recent studies (Hsiao & Chen, 2016). Another studies also shows that

emotional factors are also driving in-app purchases in mobile games, such as addiction

and brand loyalty, which influences each other in the process of influencing players

intention to make in-app purchase (Balakrishnan & Griffiths, 2018). The forementioned

studies have shown that emotional values, social values, and good value of money are the

factors influencing in-app purchase intention (Tirtasamita, 2020), which aligns with the

theory of consumption value mentioned before. Mobile game players also might be

influenced by their peers such that they found higher satisfaction or intention in

purchasing digital items so as to escalate their level of performance (Chow, 2021). In the

case that mobile game players have some disposable income, they might do some planned

impulse purchase (Shapiro, 2014)

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Although the theoretical angles from Behavioral Economics on lottery analysis in

mobile game environment has been briefly explained in (Koeder et al., 2018), the lack of

comprehensive and empirical explanation on this phenomenon creates a gap of

understanding that needs to be addressed. Moreover, the motivation and driving factors

behind the mobile game free players and paying players are still unclear and worth

discovering. In regards to the previous research, the objective of this research is to

address the main factors driving the in-app purchase intention of mobile game players.

RESEARCH METHODS

This research aims to determine the relationship between the independent variable

to dependent variable by using quantitative approach. This research uses numerical data

to determine the relationship. This research has no specific location as search location, as

all of the data used will be gathered via online questionnaire. However, the respondent

location will be compiled as well to improve this research integrity. Population is a

generalization of area that includes some sort of object or subject that have certain quality

or characteristics that can be used to conclude a particular hypothesis (Creswell &

Creswell, 2009). The population of this research is all player who plays mobile game that

implements gacha mechanism in their game.

Sample is a form of representation of a population that is able to explain the

population as a whole (Creswell & Creswell, 2009). This research uses random sampling

method due to the sheer size of the population. A population size of mobile game player

requires at least 300 sample size for 5% margin of error. As stated in the research

background, a simple survey has been conducted to identify the key factor influencing in-

app purchase in mobile game with gacha mechanism. There are 100 respondents of the

survey, all of them are active players of various mobile games for the past year period.

Statistical analysis of their responses reveal that there are four major determinants of in-

app purchase intention, which is: the joy in having things they want, the needs to become

stronger in-game, worth of the value gained compared to money spent, the need to show

off or bragging rights, and attitude from friends who played the same game (Chuang,

2020; Hamari, 2015; Hsiao & Chen, 2016; Tirtasamita, 2020). Based on the result, this

research will categorize these determinants into three dimensions of perceived value

(hedonic value, utilitarian value, economic value), and one dimension of peer influence

(Hsu & Lin, 2016; Sweeney & Soutar, 2001).

RESULTS AND DISCUSSION

A. Result

1. Classical Assumption Testing

a. Normality test

According to normality test is used to test whether the data in a variable

is normally distributed or not. If data in a variabkle is not normally distributed,

then the result of statistical analysis will not be accurate. In this research, we

will use “Normal P-P Plot” method to determine whether the data is normally

distributed or not. The result of the normality tests is as follows:

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Aditya Eka Pranandiansyah 176

Fig. 8 Normality test results

The above graph explains that the probability plot has normal distribution

value since the data spread follows average residual plotline. In the histogram

we can also see that the normality curve is right in the middle, so we can

conclude that the data is normally distributed.

b. Multicolinearity test

Table 1 Multicolinearity test

Coefficients

Model

Collinearity

Statistics

Tolerance

VIF

(Constant)

HEDONIC

VALUE(X1)

.448

2.231

Utilitarian Value

(X2)

.911

1.098

Economic Value

(X3)

.280

3.574

Peer Influence

(X4)

.196

5.111

a. Dependent Variable: IAP intention (Y)

Since there are no tolerance value below 0.1 and VIF value above 10,

then we can conclude that there are no correlation between the independent

variables.

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Fig. 9 Heteroscedasticity

As we can see, there is no pattern nor equalized data scatter in the

residual, also the data spreads over and below the 0 value on Y axis. So, by this

result, we can say that the heteroscedasticity assumption is fulfilled.

2. Hypotheses Testing

a. Correlation Test

Correlation test explains the amount of relation from the independent

variable (X) to dependent variable (Y). In this correlation test, we will use r-

table value = 0.159. The results of correlation test for Hedonic Value (X1),

Utilitarian Value (X2), Economic Value (X3), and Peer Influence (X4) toward

In-App Purchase Intention (Y) can be found in the table below : Based on

significance value shown above, we can conclude that:

1) Significance value between Hedonic Value (X1) and IAP intention (Y) is

0.000, which is less than 0.05, which means that the correlation between

Hedonic Value (X1) and IAP intention (Y) is significant

2) Significance value between Utilitarian Value (X2) and IAP intention (Y) is

0.000, which is less than 0.05, which means that the correlation between

Utilitarian Value (X2) and IAP intention (Y) is significant

3) Significance value between Economic Value (X3) and IAP intention (Y) is

0.000, which is less than 0.05, which means that the correlation between

Economic Value (X3) and IAP intention (Y) is significant

4) Significance value between Peer Influence (X4) and IAP intention (Y) is

0.000, which is less than 0.05, which means that the correlation between

Peer Influence (X4) and IAP intention (Y) is significant

Then, we will compare r

count

value with r

table

value. Since our N = 157, df

= N-2 = 157-2 = 155, and our significance level is 0.05, then our r

table

for

(0.05;155) = 0.159. This means that every item that have r

count

above 0.159 will

be count as valid. Hence, we can conclude that:

1) The r

count

value for Hedonic Value (X1) is 0.613. greater than our r

table

value

(0.159), so we can conclude that there are correlations between Hedonic

Value (X1) with In-App Purchase Intention (Y)

2) The r

count

value for Utilitarian Value (X2) is 0.319. greater than our r

table

value (0.159), so we can conclude that there are correlations between

Utilitarian Value (X2) with In-App Purchase Intention (Y)

3) The r

count

value for Economic Value (X3) is 0.815. greater than our r

table

value (0.159), so we can conclude that there are correlations between

Economic Value (X3) with In-App Purchase Intention (Y)

4) The r

count

value for Peer Influence (X4) is 0.791. greater than our r

table

value

(0.159), so we can conclude that there are correlations between Peer

Influence (X4) with In-App Purchase Intention (Y)

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Aditya Eka Pranandiansyah 178

b. Multiple Linear Regression Analysis

The result of multiple linear regression analysis can be seen in the table below :

Table 2 Multiple Linear Regression Analysis

Coefficients

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std.

Error

Beta

(Constant)

1.320

1.889

.699

.486

HEDONIC

VALUE(X1)

.103

.064

.105

1.616

.108

Utilitarian

Value (X2)

.101

.044

.106

2.323

.022

Economic

Value (X3)

.499

.080

.513

6.251

.000

Peer Influence

(X4)

.242

.095

.250

2.547

.012

Based on the above model we can interpret it further as follows:

1) The constant is 1.320, means that if every X variable is 0 then Y will have a

positive value of 1.320

2) The regression coefficient of Hedonic Value (𝑋

) is 0.103; it means that for

every unit (𝑋

), it will impact the In-App Purchase Intention (𝑌) by 0.103.

The positive value coefficient means that there is a positive relationship

between Hedonic Value (𝑋

) and In-App Purchase Intention (𝑌)

3) The regression coefficient of Utilitarian Value (𝑋

) is 0.101; it means that

for every unit (𝑋

), it will impact the In-App Purchase Intention (𝑌) by

0.101. The positive value coefficient means that there is a positive

relationship between Utilitarian Value (𝑋

) and In-App Purchase Intention

(𝑌)

4) The regression coefficient of Economic Value (𝑋

) is 0.499; it means that for

every unit (𝑋

), it will impact the In-App Purchase Intention (𝑌) by 0.499.

The positive value coefficient means that there is a positive relationship

between Economic Value (𝑋

) and In-App Purchase Intention (𝑌)

5) The regression coefficient of Peer Influence (𝑋

) is 0.103; it means that for

every unit (𝑋

), it will impact the In-App Purchase Intention (𝑌) by 0.103.

The positive value coefficient means that there is a positive relationship

between Peer Influence (𝑋

) and In-App Purchase Intention (𝑌)

a. T-test

Table 3 T-test

Model

Unstandardized

Coefficients

Standardized

Coefficients

Sig.

Std.

Error

Beta

(Constant)

1.320

1.889

.699

.486

HEDONIC

VALUE(X1)

.103

.064

.105

1.616

.108

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Utilitarian Value

(X2)

.101

.044

.106

2.323

.022

Economic Value

(X3)

.499

.080

.513

6.251

.000

Peer Influence

(X4)

.242

.095

.250

2.547

.012

Based on the t value and significance level above, we can interpret it as follows:

1) The t-count value of Hedonic Value (X1) is 1.616. Since the t-count value is

lower than t-table (1.976), then it means the null hypothesis is accepted, or the

alternative hypotheses is rejected. Which means, Hedonic Value (X1) is not

statistically significant to In-App Purchase Intention (Y)

2) The t-count value of Utilitarian Value (X2) is 2.323. Since the t-count value is

lower than t-table (2.323), then it means the then it means the null hypothesis

(H0) is rejected, or the alternative hypotheses (Ha) is accepted. Which means,

Utilitarian Value (X1) is statistically significant to In-App Purchase Intention

(Y)

3) The t-count value of Economic Value (X3) is 6.251. Since the t-count value is

lower than t-table (6.251), then it means the null hypothesis (H0) is rejected, or

the alternative hypotheses (Ha) is accepted. Which means, Economic Value

(X3) is statistically significant to In-App Purchase Intention (Y)

4) The t-count value of Peer Influence (X4) is 2.547. Since the t-count value is

lower than t-table (2.547), then it means the null hypothesis (H0) is rejected, or

the alternative hypotheses (Ha) is accepted. Which means, Hedonic Value (X1)

is statistically significant to In-App Purchase Intention (Y)

b. F-test

Table 4 F-test

ANOVA

Model

Sum of

Squares

Mean

Square

Sig.

Regression

5208.927

1302.232

94.736

.000

Residual

2089.366

152

13.746

Total

7298.293

156

a. Dependent Variable: IAP intention (Y)

b. Predictors: (Constant), Peer Influence (X4), Utilitarian Value (X2), HEDONIC

VALUE(X1), Economic Value (X3)

According to the ANOVA table above, we have F-count value = 94.736,

which is higher than our F-table value (2.431). As such, we can conclude that there

exists a significant influence on independent variables simultaneously towards the

dependent variable

c. R-square test

Table 5 R-square test

Model Summary

Model

Square

Adjusted

Square

Std.

Error of

the

Estimate

Change Statistics

Durbin

Watso

Square

Change

df1

df2

Sig. F

Change

.845

.714

.706

3.708

.714

94.736

152

.000

2.168

a. Predictors: (Constant), Peer Influence (X4), Utilitarian Value (X2), HEDONIC VALUE(X1),

Economic Value (X3)

Dependent Variable: IAP intention (Y)

The Impact Of Perceived Values and Peer Influences

Toward In-App Purchase Intention In Mobile Game

2023

Aditya Eka Pranandiansyah 180

Based on the above table we can see that the R value is 0.845, it means

that all the independent variable is strongly correlated to Y. Also, we can see that

the determinant coefficient R square is 0.714, which means that In-App Purchase

Intention (Y) is influenced by the independent variable by 71.4%, and the

remainder (28.6%) influenced by factors outside the regression model or other

variables outside of this research.

B. Discussion

1. Impact of Hedonic Value (X1) towards In-App Purchase Intention (Y)

Based on the statistic results, from the correlation test we found that Pearson

Correlation value for Hedonic Value is 0.613, with sig. value of 0.000. Since the Pearson

correlation value is higher than R table value of 0.159, and the sig. value is lower than

0.05, it means that there is a positive linear correlation between Hedonic Value (X1) and

In-App Purchase Intention (Y).

Even so, high correlation coefficient does not imply causality between the

independent variable and the dependent variable. We can see the results of T-test of

Hedonic Value, whereas the t-count value of Hedonic Value (X1) is 1.616. Since the t-

count value (1.616) is lower than t-table (1.976), and sig. value then it means the null

hypothesis (H

) is accepted, or the alternative hypotheses (Ha) is rejected. Which means,

in terms of relationship, Hedonic Value (X1) is not statistically significant to In-App

Purchase Intention (Y). In other words, every increase in Hedonic Value does not

necessarily have positive increase in In-App Purchase Intention.

Contrary to the findings in previous researches such as Tirtasasmita (Tirtasamita,

2020), which states that there are a positive—albeit not significant—influence from

emotional value towards purchase intention, and similarly, research from Tjhin &

Hendratno (2021), states that fun, fantasy, and challenge—which is part of hedonic

value—has a positive but not significant influence towards purchase intentions, this

research does not show any relationship between Hedonic Value (X1) towards In-app

Purchase Intention. This research findings is more similar to the results, which states that

there are no significant influence from emotional value towards in-app purchase

intentions.

2. Impact of Utilitarian Value (X2) towards In-App Purchase Intention (Y)

Based on the statistic results, from the correlation test we found that Pearson

Correlation value for Utilitarian Value is 0.319, with sig. value of 0.000. Since the

Pearson correlation value is higher than R table value of 0.159, and the sig. value is lower

than 0.05, it means that there is a positive linear correlation between Utilitarian Value

(X2) and In-App Purchase Intention (Y).

Even so, high correlation coefficient does not imply causality between the

independent variable and the dependent variable. We can see the results of T-test of

Utilitarian Value, whereas the t-count value of Utilitarian Value (X2) is 2.323. Since the

t-count value (2.323) is lower than t-table (1.976), and sig. value then it means the null

hypothesis is rejected, or the alternative hypotheses is accepted. Which means, in terms of

causality, Utilitarian Value (X2) is statistically significant to In-App Purchase Intention

(Y). In other words, every increase in Utilitarian Value will positively impact In-App

Purchase Intention. This research finding is similar to Chuang (Chuang, 2020), which

states that utilitarian value have positive impact on purchase intention via satisfaction and

loyalty.

3. Impact of Economic Value (X3) towards In-App Purchase Intention (Y)

Based on the statistic results, from the correlation test we found that Pearson

Correlation value for Economic Value is 0.815, with sig. value of 0.000. Since the

Pearson correlation value is higher than R table value of 0.159, and the sig. value is lower

than 0.05, it means that there is a positive linear correlation between Economic Value

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181 http://sosains.greenvest.co.id

(X3) and In-App Purchase Intention (Y). Even so, high correlation coefficient does not

imply causality between the independent variable and the dependent variable.

We can see the results of T-test of Economic Value, whereas the t-count value of

Economic Value (X3) is 6.251. Since the t-count value (6.251) is lower than t-table

(1.976), and sig. value then it means the null hypothesis is rejected, or the alternative

hypotheses is accepted. Which means, in terms of causality, Economic Value (X3) is

statistically significant to In-App Purchase Intention (Y). In other words, every increase

in Economic Value will positively impact In-App Purchase Intention. This research

finding is similar to Tirtasamita (Tirtasamita, 2020), Chuang (Chuang, 2020), and Hsiao

& Chen (Hsiao & Chen, 2016), which states that economic value or good value of money

will positively impact purchase intentions.

4. Impact of Peer Influence (X4) towards In-App Purchase Intention (Y)

Based on the statistic results, from the correlation test we found that Pearson

Correlation value for Peer Influence is 0.791, with sig. value of 0.000. Since the Pearson

correlation value is higher than R table value of 0.159, and the sig. value is lower than

0.05, it means that there is a positive linear correlation Peer Influence (X4) and In-App

Purchase Intention (Y).

Even so, high correlation coefficient does not imply causality between the

independent variable and the dependent variable. We can see the results of T-test of Peer

Influence, whereas the t-count value of Peer Influence (X4) is 2.547. Since the t-count

value (2.547) is lower than t-table (1.976), and sig. value then it means the null

hypothesis is rejected, or the alternative hypotheses is accepted. Which means, in terms of

causality, Peer Influence (X4) is statistically significant to In-App Purchase Intention (Y).

In other words, every increase in Peer Influence does not necessarily impact In-App

Purchase Intention.

This research finding is similar to Hsieh & Tseng (2018), which states that

influence of online and/or offline groups, as well as sense of online and/or offline

community have positive impact on purchase intention.

CONCLUSION

Based on the research findings mentioned in previous chapter, several conclusions

can be drawn. It can be concluded that there is no significant influence between Hedonic

Value over In-App Purchase Intention. This shows that even though hedonic motivations

play a big part in terms of the game engagement itself, it does not mean that it would have

enough impact in terms of In-App Purchase intention. This research has a different

conclusions compared to the previous research findings from Tirtasasmita (Tirtasamita,

2020), which states that there are positive relationship between emotional value or

hedonic value towards purchase intention, albeit not a significant one.

The reason being even though people would be more willing to spend money if

they are happy playing the game, but it does not necessarily imply that people would buy

In-App Purchase straight away. There are more to consider when it comes to do In-App

Purchase, such as economic situation, personal welfare, and maybe other factors that

encourage the In-App Purchase. Based on the findings of this research, where the

respondent profile is leaning towards low-to-middle income players, the hedonic factor

might not be as prominent as the other factor when it comes to driving up in-app

purchase. However, these results might be requiring further study, as the relationship

observed in this research can only be attributed to low to middle income players based on

this research results. High income players might behave differently and might place

hedonic value way higher compared to other player as those kinds of gamers are.

There is a positive and significant influence between Utilitarian Value over In-App

Purchase Intention. This result shows that mobile game user would be more inclining to

buy In-App Purchase when they see something they need or want in the game, be it for

The Impact Of Perceived Values and Peer Influences

Toward In-App Purchase Intention In Mobile Game

2023

Aditya Eka Pranandiansyah 182

aesthetical or for practical purposes. The inclination to buy would also be determined by

the game genre and/or type. for example, grind-heavy games are more likely to generate

transactions by increasing the amount of limited product that focuses on making

progression easier. There is a positive and significant influence between Economic Value

over In-App Purchase Intention. As mentioned before in the previous part, economic

situations and personal welfare are some of few factors determining In-App Purchase

intentions. Mobile game users might be more inclined to buy In-App Purchase when they

feel what they would get is worth the money they spend. Although, the amount of money

they are willing to spend will be widely varied from one mobile game player to another.

There is a positive and significant influence between Peer Influence over In-App

Purchase Intention. The existence of game communities and social groups also influence

in-app purchase behavior through enjoyable experience of “playing with peers” which

enables the members to provide suggestions relating to In-App Purchase. This research

reaffirms such proposition that mobile games, especially social games, require players to

communicate with other players and discuss about the game itself, and the discussion

often leans on how to efficiently spend their money on in-app purchases.

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