Investigating the influence of mobile game addiction on in-app purchase intention in PUBG mobile: the mediating roles of loyalty, negative e-WOM and perceived risk

Figures & data

Table 1. Demographic characteristics of the sample.

Profile	Category	n	%
Gender	Male	185	53.3
	Female	162	46.7
Age (years old)	16–20	125	36.0
	21–25	196	56.5
	26–30	11	3.2
	31 or above	15	4.3
Education	Junior high school	3	0.9
	Senior high school	131	37.8
	Bachelor’ degree	201	57.9
	Master’ degree	12	3.5
Monthly income (IDR)	Below 1 million	153	44.1
	1−3 million	125	36.0
	3–6 million	50	14.4
	Above 6 million	19	5.5
Time spent playing mobile games (minutes a day)	29 or below	111	32.0
	30–60	63	18.2
	60–90	35	10.1
	90–120	48	13.8
	120 or above	90	25.9
Number of in-app purchases of mobile games (in a month)	Never	153	44.1
	1–5	102	29.4
	5–10	42	12.1
	11 or above	50	14.4
Total		347	100

Note: Percentage (%), frequency (n), (IDR) Indonesian Rupiah.

Table 2. Convergent validity test.

Variable	Dimension	Indicator	Loadings	AVE dimension	AVE variable
Mobile game addiction	Salience	SLC01	0.883	0.818	0.541
		SLC02	0.939
		SLC03	0.890
	Tolerance	TLR01	0.895	0.806
		TLR02	0.931
		TLR03	0.867
	Mood modification	MM01	0.770	0.724
		MM02	0.880
		MM03	0.897
	Relapse	RLS01	0.876	0.745
		RLS02	0.839
		RLS03	0.873
	Withdrawal	WDR01	0.895	0.846
		WDR02	0.923
		WDR03	0.941
	Conflict	CFL01	0.891	0.806
		CFL02	0.913
		CFL03	0.888
	Problems	PRL01	0.858	0.659
		PRL02	0.835
		PRL03	0.738
Mobile game loyalty		LYT01	0.864	–	0.805
		LYT02	0.903
		LYT03	0.909
		LYT04	0.914
Perceived risk	Online payment risk	OPR01	0.941	0.904	0.708
		OPR02	0.962
		OPR03	0.949
	Financial risk	FR01	0.932	0.843
		FR02	0.928
		FR03	0.895
	Performance risk	PFR01	0.926	0.853
		PFR02	0.924
		PFR03	0.920
Negative E-WOM		NEW01	0.819	–	0.699
		NEW02	0.885
		NEW03	0.846
		NEW04	0.762
		NEW05	0.864
In-app purchase intentions in mobile game		NBA01	0.876	–	0.751
		NBA02	0.856
		NBA03	0.815
		NBA04	0.879
		NBA05	0.905

Note: SLC: salience; TLR: tolerance; MM: mood modification; RLS: relapse; WDR: withdrawal; CFL: conflict; PRL: problems; LYT: loyalty; OPR: online payment risk; FR: financial risk; PFR: performance risk; NEW: negative e-WOM; IPI: in-app purchase intentions; AVE: average variance extracted.

Table 3. Reliability test.

Variable	Cronbach’s alpha	Composite reliability
Game mobile addiction	0.957	0.961
Game mobile loyalty	0.919	0.943
Perceived risk	0.948	0.956
Negative E-WOM	0.892	0.921
In-app purchase intentions game mobile	0.917	0.938

Note: The independent variable is addiction game mobile, the dependent variable is in-app purchase intentions game online and the mediating variables are loyalty, perceived risk and negative e-WOM.

Table 4. Discriminant validity.

	1	2	3	4	5
Mobile game addiction	0.736
In-app purchase intention	0.649	0.867
Mobile game loyalty	0.659	0.716	0.897
Negative e-WOM	0.584	0.459	0.349	0.836
Perceived risk	0.448	0.382	0.288	0.478	0.841

Note: The italics number denote the correlation between constructs and its indicators.

Figure 1. Conceptual framework.

Figure 2. Structural equation model results.

Table 5. Hypothesis testing.

Hypothesis	Original sample	Sample mean	Standard deviation	T statistics	p Values
AGM → LYT	0.659	0.657	0.033	20.076	.000**
LYT → IPI	0.518	0.517	0.057	9.052	.000**
AGM → NEW	0.584	0.585	0.043	13.635	.000**
NEW → IPI	0.121	0.120	0.054	2.232	.026**
AGM → PR	0.448	0.447	0.061	7.347	.000**
PR → IPI	0.087	0.088	0.042	2.060	.040**
AGM → IPI	0.649	0.648	0.037	17.371	.000**

Note: AGM: addiction game mobile; IPI: in-app purchase intention; LYT: loyalty game mobile (LYT); NEW: negative e-WOM; PR: perceived risk.

*Represents test statistical significance at 10% level.

**Represents test statistical significance at 5% level.

***Represents test statistical significance at 1% level.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Rakotoarisoa Maminiaina Heritiana Sedera, upon reasonable request.