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Perceived Aggressive Monetization: Why Some Mobile Gamers Will Not Spend Money on In-app Purchases

Imam Salehudin

PhD in Marketing (University of Queensland)

Supervisor: Frank Alpert & Chris Hodkinson

​

BI Institute Brown Bag Presentation

2 March 2022

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First paper:

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Second paper:

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First News media publication:

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Global Mobile Game App Revenue

5

Source: Dogtiev (2017), adapted

CRICOS Provider Number 00025B

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Imbalanced Source of Revenue

Source: Sterling (2016) & Shaul (2016)

5%

Revenue

Revenue

CRICOS Provider Number 00025B

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Examples of In-app Purchase: Currencies

CRICOS Provider Number 00025B

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Examples of In-app Purchase: Loot Boxes

CRICOS Provider Number 00025B

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Examples of In-app Purchase: Special Offer

CRICOS Provider Number 00025B

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Paper 3:

  • Perceived Aggressive Monetization: Why Some Mobile Gamers Will Not Spend Money on In-app Purchases
    • Quantitative oriented
    • Developed and validated the measures
      • Pretest (5 marketing experts and 5 mobile gamers),
      • Pilot Test (103 Australian mobile gamers)
    • Online Survey (527 US and 526 Australian mobile gamers)
    • Scenario-based Experiment (264 Australian mobile gamers)
  • Currently: Under review, after a major revision, at Electronic Commerce Research

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Main Research Question

  • Why do mobile gamers spend money on in-app purchases (IAP)?
    1. Which key factors explain a user’s decision to spend?

​

    • Which factors explain how much a user spend?

​

    • How can the existing IAP business model be improved to increase conversion and/or reduce churn?

​

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Main Research Methods: Participants

  • Purposive sample
  • 1,053 mobile gamers
  • Qualtrics data panel
  • 18+ years old
  • Active user of free-to-play mobile games (plays at least 30 minutes per day in the last seven days).
  • Roughly half of the respondents are US sample, and the other half are Australian sample.

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Main Research Methods: Analysis

  • Main hypotheses tested using Cragg’s hurdle regression (Cragg, 1971)
  • Ordinary linear regression models use the assumption of multivariate normality and require the dependent variable to be perfectly continuous.
  • The actual spending data collected in this study exhibit semi-continuous property, with excess zeros and a truncated distribution.
  • Hurdle regression method has been used extensively to model spending behaviour for addictive consumptions (Geisner et al., 2017; Trivedi and Teichert, 2017; Oncini and Guetto, 2018)

​

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Respondent Demographics

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Respondent Backgrounds

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Descriptive Statistics: Actual Spending

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Descriptive Statistics: Willing to Spend

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Descriptive Statistics: Time Spent Playing

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Descriptive Statistics

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Variables

Mean

Median

Std. Dev.

Perceived Manipulativeness

4.66

4.68

1.39

Perceived Addictiveness

4.69

4.71

1.36

Perceived Riskiness

3.93

4.00

1.57

Perceived Intrusiveness

4.19

4.28

1.42

Perceived Overpricing

4.97

5.05

1.40

Perceived Threat to Freedom

4.42

4.49

1.27

Perceived Threat to Fairness

4.62

4.68

1.27

Perceived Aggressive Monetisation

4.52

4.60

1.22

Perceived Fairness

3.92

4.00

1.25

Self-Control

5.33

5.66

1.34

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Measurement Validation

Dimensions

1st Order

PAM

2nd Order PAM

Coefficient

Alpha

Perceived

Threat

to Freedom

Perceived

Threat

to Fairness

Perceived

Manipulativeness

0.92

0.78

0.49

0.82

Perceived

Addictiveness

0.83

0.85

0.22

0.75

Perceived

Riskiness

0.88

0.46

0.75

0.75

Perceived

Intrusiveness

0.86

0.83

0.31

0.84

Perceived

Overpricing

0.79

0.32

0.94

0.79

Main Study (527 US and 526 Australian mobile gamers)

Perceived Aggressive Monetisation

Perceived Manipulativeness

Perceived Addictiveness

Perceived Intrusiveness

Perceived Riskiness

Perceived Overpricing

Perceived Threat to Freedom

Perceived Threat to Fairness

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Result of Hypotheses Testing

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Perceived Fairness

Perceived Aggressive Monetisation

Self-Control

Time Spent Playing

H2

+

-

H3

H4A -

H4B

-

+

H5A

+

H5B

​

Actual

IAP Spending

​

​

​

​

​

​

User

Conversion

Size of Spending

Willingness to Spend on IAP

1.02*

+0.11*

  • H1A: Willingness to Spend on IAP increases the likelihood of User Conversion: Supported

​

  • H1B: Willingness to Spend on IAP increases the Size of Spending, for those who convert (spend money on IAP): Supported

​

*) p-value < 0.05

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Result of Hypotheses Testing

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+

H5A

+

H5B

Time Spent Playing

Self-Control

H4A -

H4B

-

H1A

+

H1B

+

Willingness to Spend on IAP

Perceived Fairness

Perceived Aggressive Monetisation

1.13*

​

​

0.87*

​

Actual

IAP Spending

​

​

​

​

​

​

User

Conversion

Size of Spending

  • H2: Perceived Fairness of IAP increases the likelihood of User Conversion: Supported

​

​

  • H3: Perceived Aggressive Monetisation of IAP reduces the likelihood of User Conversion: Supported

​

*) p-value < 0.05

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Result of Hypotheses Testing

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+

H5A

+

H5B

H1B

+

H1A

+

H2

+

Perceived Aggressive Monetisation

-

H3

Time Spent Playing

Perceived Fairness

Self-Control

0.91*

-0.05

*

Willingness to Spend on IAP

​

Actual

IAP Spending

​

​

​

​

​

​

User

Conversion

Size of Spending

  • H4A: Self-Control reduces the likelihood of User Conversion: Supported

​

  • H4B: Self-Control reduces the positive effect of Willingness to Spend toward the Size of Spending, for those who convert: Supported

​

*) p-value < 0.05

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Result of Hypotheses Testing

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H2

+

H1B

+

H1A

+

H4A -

H4B

-

Perceived Aggressive Monetisation

-

H3

Perceived Fairness

Self-Control

Willingness to Spend on IAP

Time Spent Playing

1.03*

+0.20*

​

Actual

IAP Spending

​

​

​

​

​

​

User

Conversion

Size of Spending

  • H5A: Time Spent Playing increases the likelihood of User Conversion: Supported

​

  • H5B: Time Spent Playing increases the Size of Spending, for those who convert: Supported

​

*) p-value < 0.05

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Replication of Main Hypotheses

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Scenario-based Experiment Methods: Participants

  • Purposive sample
  • 264 mobile gamers
  • Qualtrics data panel
  • 18+ years old
  • Active user of free-to-play mobile games (plays at least 30 minutes per day in the last seven days).
  • Randomly assigned three between-subject treatments (2 x 2 x 2 experimental groups)
  • One within-subject treatment (baseline currency, loot box IAP, special offer)

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Experimental Design

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Treatment Group

n

%

1a

125

47.3%

1b

139

52.7%

Total

264

 

Group

n

%

2a

32

12.1%

2b

32

12.1%

2c

31

11.7%

2d

30

11.3%

2e

35

13.3%

2f

35

13.3%

2g

35

13.3%

2h

34

12.9%

Total

264

​

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Result of Hypotheses Replication

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H5A

H5B

H9

H6

Time Spent Playing

H8

H7

Stated Probability

2X Value

Special Offer

Size of IAP Transaction

Mode of Currency

User

Conversion

Price Premium

  • H2: Perceived Fairness of IAP increases the likelihood of User Conversion (RWTS=1): Reproduced
  • H3: Perceived Aggressive Monetisation of IAP reduces the likelihood of User Conversion: Reproduced
  • H4A: Self-Control reduces the likelihood of User Conversion: Reproduced

​

*) p-value < 0.05

Perceived Fairness

Self Control

Perceived Aggressive Monetisation

0.83*

0.77*

1.57*

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

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Result of Hypotheses Replication

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Perceived Fairness

Self Control

Perceived Aggressive Monetisation

H4A

H3

H2

H9

H6

H8

H7

Stated Probability

2X Value

Special Offer

Size of IAP Transaction

Mode of Currency

1.04*

0.08*

Time Spent Playing

User

Conversion

Price Premium

  • H5A: Time Spent Playing increases the likelihood of User Conversion: Reproduced

​

  • H5B: Time Spent Playing increases the Price Premium, for those who convert: Reproduced

​

*) p-value < 0.05

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

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Result of Additional Hypotheses

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H7

Mode of Currency

H9

Stated Probability

H8

2X Value

Special Offer

H5A

H5B

H4A

H3

H2

Time Spent Playing

Self Control

Perceived Aggressive Monetisation

Perceived Fairness

-0.96*

Size of IAP Transaction

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

User

Conversion

Price Premium

  • H6: Offering a larger size of IAP transactions reduces user willingness to spend relative to the stated price (Relative Willingness to Spend): Supported

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Result of Additional Hypotheses

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H6

Size of IAP Transaction

H9

Stated Probability

H8

2X Value

Special Offer

H5A

H5B

H4A

H3

H2

Time Spent Playing

Self Control

Perceived Aggressive Monetisation

Perceived Fairness

-0.46*

Mode of Currency

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

User

Conversion

Price Premium

  • H7: Stating the price of an IAP offer in real money reduces user Relative Willingness to Spend, compared to the price stated in in-game currency (Gems): Supported

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Result of Additional Hypotheses

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Size of IAP Transaction

Mode of Currency

2X Value

Special Offer

H6

H7

H9

H5A

H5B

H4A

H3

H2

Time Spent Playing

Self Control

Perceived Aggressive Monetisation

Perceived Fairness

Stated Probability

0.26

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

User

Conversion

Price Premium

  • H8: Stating the probability of a Loot Box IAP offer increases user Relative Willingness to Spend, compared to not stating the probability: Rejected

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Result of Additional Hypotheses

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H6

H7

Size of IAP Transaction

Mode of Currency

Stated Probability

H8

H5A

H5B

H4A

H3

H2

Time Spent Playing

Self Control

Perceived Aggressive Monetisation

Perceived Fairness

Relative

Willingness to Spend

​

​

​

​

​

​

​

​

​

​

User

Conversion

Price Premium

  • H9: Offering a 2x Value Special Offer increases user Relative Willingness to Spend, compared a regular IAP offer: Rejected

2X Value

Special Offer

0.17

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Post-hoc Analyses: Five User Archetypes

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​

Whales

Dolphins

Minnows

Remoras

Barnacles

Paying users, estimated to be 30% of total users

Free users, estimated to be 70% of total users

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Whales

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Big spenders (>$100 per week)

Dataset: 0.8% of total respondents

Money spent on IAP

Per week: $159.50

Willingness to Spend

Per week: $120.12

>

Excess Spending

Per week: +$39.38

Overall Average

Money spent on IAP Willingness to Spend

Per week: $15.98 Per week: $15.65

Excess Spending:

$ 0.34

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Dolphins

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Medium spenders ($30.01-$100 per week)

Dataset: 2.8% of total respondents

Money spent on IAP

Per week: $44.78

Willingness to Spend

Per week: $33.86

>

Excess Spending

Per week: +$10.92

Overall Average

Money spent on IAP Willingness to Spend

Per week: $15.98 Per week: $15.65

Excess Spending:

$ 0.34

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Minnows

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Small spenders ($0.01-$30 per week)

Dataset: 26.4% of total respondents

Money spent on IAP

Per week: $8.85

Willingness to Spend

Per week: $16.47

<

Potential Spending

Per week: +$7.62

Overall Average

Money spent on IAP Willingness to Spend

Per week: $15.98 Per week: $15.65

Excess Spending:

$ 0.34

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Remoras

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Potential spenders ($0 per week, >$0 Willingness to Spend)

Dataset: 32.3% of total respondents

Money spent on IAP

Per week: $0

Willingness to Spend

Per week: $10.93

<

Potential Spending

Per week: +$10.93

Overall Average

Money spent on IAP Willingness to Spend

Per week: $15.98 Per week: $15.65

Excess Spending:

$ 0.34

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Barnacles

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Non-spenders ($0 per week, $0 Willingness to Spend)

Dataset: 37.8% of total respondents

Money spent on IAP

Per week: $0

Willingness to Spend

Per week: $0

=

Potential Spending

Per week: +$0

Overall Average

Money spent on IAP Willingness to Spend

Per week: $15.98 Per week: $15.65

Excess Spending:

$ 0.34

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Discussion (1)

  • PAM is a formative construct that consists of five dimensions: Manipulativeness, Overpricing, Riskiness, Intusiveness, and Addictiveness
  • There is a two part decision mechanism that explain conversion (to spend money or not) and size of spending (how much money is actually spent).
  • The first part (conversion) is rational/intentional, but the second part (size of spending) is mostly impulsive
  • Perceived Aggressive Monetisation (PAM) explained conversion, but not size of spending

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Discussion (2)

  • Size of money spent is more determined by time spent playing and willingness to spend money, with a negative interaction with Self-Control: Higher SC reduces the effect of willingness to spend to the actual amount of money spent.
  • Mobile game publishers who aim to increase the conversion of free users to paying users will want to minimise the users’ perceived aggressive monetisation by using low-key monetisation strategy to ease the barrier. Sometimes “Less is More”.
  • Arguably, mobile games who want to get maximum profit from the big spenders could probably get away with aggressive monetisation, as long as they can keep the users playing. However, it will be at the cost of less small to medium spenders and free users.

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S

P

A

M

trongly

erceived

ggressive

onetisation

MORE

​

​

LESS

Barnacles

​

​

Paying Users

&

LEADS TO

LESS

Whales

BUT

NOT

Take Away Points

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Thank you

THANK YOU!

CRICOS code 00025B