Testing a Model to Predict Problem Gambling in Speculative Game Users

Hyangjin Park; Suk-Sun Kim

doi:10.4040/jkan.2018.48.2.195

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Park and Kim: Testing a Model to Predict Problem Gambling in Speculative Game Users

Original Article

J Korean Acad Nurs 2018;48(2):195-207.

Published online: 15 January 2018

DOI: https://doi.org/10.4040/jkan.2018.48.2.195

Testing a Model to Predict Problem Gambling in Speculative Game Users

Hyangjin Park^1,², Suk-Sun Kim^2,

¹Department of Nursing, Chodang University, Muan

²College of Nursing, Ewha Womans University, Seoul, Korea

Address reprint requests to : Kim, Suk-Sun College of Nursing, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea Tel: +82-2-3277-2885 Fax: +82-2-3277-2850 E-mail: suksunkim@ewha.ac.kr

Received 31 August 2017 Revised 3 April 2018 Accepted 4 April 2018

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

Abstract

Purpose

The purpose of the study was to develop and test a model for predicting problem gambling in speculative game users based on Blaszczynski and Nower's pathways model of problem and pathological gambling.

Methods

The participants were 262 speculative game users recruited from seven speculative gambling places located in Seoul, Gangwon, and Gyeonggi, Korea. They completed a structured self-report questionnaire comprising measures of problem gambling, negative emotions, attentional impulsivity, motor impulsivity, non-planning impulsivity, gambler's fallacy, and gambling self-efficacy. Structural Equation Modeling was used to test the hypothesized model and to examine the direct and indirect effects on problem gambling in speculative game users using SPSS 22.0 and AMOS 20.0 programs.

Results

The hypothetical research model provided a reasonable fit to the data. Negative emotions, motor impulsivity, gambler's fallacy, and gambling self-efficacy had direct effects on problem gambling in speculative game users, while indirect effects were reported for negative emotions, motor impulsivity, and gambler's fallacy. These predictors explained 75.2% problem gambling in speculative game users.

Conclusion

The findings suggest that developing intervention programs to reduce negative emotions, motor impulsivity, and gambler's fallacy, and to increase gambling self-efficacy in speculative game users are needed to prevent their problem gambling.

Keywords: Pathological gambling, Self-efficacy, Gambling, Structural models

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Figure 1.

Conceptual framework.

Figure 2.

Path diagram for the final model.

Table 1.

General Characteristics of Participants and Descriptive Statistics of Observed Variables (N=262)

Variables	Categories	n (%) or M±SD	Skewness	Kurtosis
Gender	Male	222 (84.7)
	Female	40 (15.3)
Age (yr)		49.41±10.27
	20~29	4 (1.6)
	30~39	36 (13.7)
	40~49	102 (38.9)
	50~59	80 (30.5)
	≥60	40 (15.3)
Marital status	Never married	6 (2.3)
	Married	93 (35.5)
	Living with partner	26 (9.9)
	Separated	19 (7.3)
	Divorced	101 (38.5)
	Widowed	17 (6.5)
Education	≤Middle School	95 (36.3)
	High school	112 (42.7)
	≥College	55 (21.0)
Family history of gambling disorder	Yes	149 (56.9)
	No	113 (43.1)
Age of beginning problem gambling (yr)	10~19	41 (15.6)
	20~29	94 (35.9)
	30~39	76 (29.0)
	40~49	34 (13.0)
	≥50	17 (6.5)
Frequency of gambling per month	1~2	50 (19.0)
	3~4	110 (42.0)
	5~6	83 (31.7)
	≥7	19 (7.3)
Time spent gambling per episode (hours)	≤2	41 (15.6)
	3~4	49 (18.7)
	5~6	70 (26.7)
	7~9	74 (28.2)
	≥11	28 (10.8)
Problem gambling		7.88±5.80	−0.09	−1.24
	No risk gambler (0 scores)	63 (24.0)
	Low risk gambler (1~2 scores)	12 (4.6)
	Moderate risk gambler (3~7 scores)	39 (14.9)
	Problem gambler (≥8 scores)	148 (56.5)
Negative emotions		38.56±14.47	−0.82	−.46
Attentional impulsivity		21.69±3.92	0.15	−.57
Motor impulsivity		28.64±5.26	0.33	−.52
Non-planning impulsivity		19.33±3.16	−0.24	−.35
Gambler's fallacy		103.44±17.25	−0.19	−.24
Gambling self-efficacy		28.47±5.23	0.26	−1.03

Table 2.

Descriptive Values and Correlations among Variables (N=262)

Variables	Categories	FL	C.R	CCR	AVE	Cronbach α	r (p)
1. Negative emotions^†	Depression	.95	12.06	.88	.71	.95 1	1
	Anxiety	.96					(<.001)
	Stress	.91	13.04
2. Attentional						.84	.42	1
impulsivity							(<.001)
3. Motor impulsivity						.84	.53	.23	1
							(<.001)	(.005)
4. Non-planning						.77	.22	.57	.12	1
impulsivity							(<.001)	(<.001)	(.099)
5. Gambler's fallacy^†	Gambling-related	.91		.90	.65	.94	.68	.38	.64	.20	1
	expectancies						(<.001)	(<.001)	(<.001)	(.005)
	Perceived inability to	.91	8.06
	stop gambling
	Interpretative control/	/ .89	8.36
	Bias
	Illusion of control	.86	8.84
	Predictive control	.86	8.54
6. Gambling						−	−.74	−.37	−.58	−.18	−.75	1
self-efficacy							(<.001)	(<.001)	(<.001)	(.002)	(<.001)
7. Problem gambling						.96	.77	.37	.66	.20	.79	−.79	1
							(<.001)	(<.001)	(<.001)	(.004)	(<.001)	(<.001)

AVE=Average variance extracted; CCR=Composite construct reliability; C.R=Critical ratio; FL=factor loading.

^†Latent variables.

Table 3.

Effects of Predictor Variables in the Hypothetical Model (N=262)

Endogenous variables	Exogenous variables	β	C.R.	p	SMC	Direct	Indirect	Total
						β (p)	β (p)	β (p)
Problem gambling	Negative emotions	.30	5.60	.004	.782	.31(.004)	.30(.002)	.59(.005)
	Attentional impulsivity	−.02	−.48	.628		−.02(.628)	.03(.235)	.01(.732)
	Motor impulsivity	.15	3.68	.003		.15(.003)	.09(.003)	.34(.005)
	Non-planning impulsivity	.03	.91	.361		.03(.361)	−.01(.885)	.03(.485)
	Gambler's fallacy	.31	5.03	.006		.31(.006)	.10(.002)	.41(.008)
	Gambling self-efficacy	−.25	−4.66	.002		−.25(.002)	−	−.25(.002)
Gambling self-efficacy	Negative emotions	−.40	−6.70	.003	.634	−.40(.003)	−.19(.002)	−.60(.003)
	Attentional impulsivity	−.05	−.99	.321		−.05(.321)	−.02(.369)	−.06(.229)
	Motor impulsivity	−.09	−1.79	.073		−.09(.073)	−.16(.002)	−.26(.006)
	Non-planning impulsivity	.03	0.63	.526		.03(.526)	−.01(.899)	.02(.580)
	Gambler's fallacy	−.41	−6.01	.004		−.41(.004)	−	−.41(.004)
Gambler's fallacy	Negative emotions	.48	8.34	.006	.679	.48(.006)	−	.48(.006)
	Attentional impulsivity	.04	.84	.402		.04(.402)	−	.04(.402)
	Motor impulsivity	.41	8.05	.002		.41(.002)	−	.41(.002)
	Non-planning impulsivity	.01	.17	.891		.01(.891)	−	.01(.891)

C.R.=Construct Reliability; SMC=Squared Multiple Correlation. β=Estimate of standardized regression weight.

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