Structural equation modeling was used to examine our online game playfulness model.
Latent variables were in-game motivation (Motive), in-game playfulness (Playful), and
subjective vitality (Vitality). Observed variables were in-game competence (Compe),
in-game autonomy (Auto), in-game relatedness (Relate), concentration (Conce), enjoyment
(Enjoy), curiosity (Couri), vitality1 (Vit1), vitality2 (Vit2), self-esteem1 (Est1), and
self-esteem2 (Est2). We also examined estimated coefficients for causal relationships
between constructs that validated the hypothesized effects. A covariance matrix of the
variables is presented in Table 8.
--Insert Table 8 about here--
LISREL software was used to estimate model parameters, standard errors, and overall fit
indices33. Estimated coefficients and their significance in the structural model are shown in
Figure 2. The chi-square statistic for the overall fit model was 19.34 (df=31, p=.000,
RMSEA=0.068 < 0.08), and values for the other fit indices were within acceptable rangers
(CFI=.97, NFI=.95, NNFI=.96). Standardized estimates of path coefficients for all three
measurement variables (representing causal effect magnitude) ranged from 0.26 to 0.88. Total
magnitudes of causal relationships take into account the direct and indirect (mediated) effects
of latent variables on one another. Interpretations of absolute values are: < 0.1, small effects;
≈ 0.30, medium effects; > 0.5, large effects35.
The data indicate that in-game motivation had significant direct effects on both in-game
playfulness (thereby supporting H1) and self-esteem (thereby supporting H2); the
magnitudes of each impact were 0.88 and 0.69. In-game motivation had a significant effect
on subjective vitality as mediated by in-game playfulness (thus supporting H3); impact
magnitude was 0.55. In-Game Motivation was not found to have a positive effect self-esteem
on mediated by In-Game Playfulness and Subjective Vitality (Hypothesis 4). No positive
effect was found for in-game motivation on self-esteem as mediated by in-game playfulness
and subjective vitality, therefore H4 is rejected. However, a positive effect was found for
in-game playfulness on subjective vitality, meaning H5 is supported (magnitude = 0.62). No
positive effect was found for in-game playfulness on self-esteem as mediated by subjective
vitality, meaning H6 is rejected.
The measure’s composite reliability being 0.6 or higher, calculated as
Composite variables were identified as motivation (0.62), playfulness (=0.68), subjective
vitality (=0.82), and self-esteem (= 0.69)—all above the 0.6 minimum.
--Insert Figure 2 about here--
5. Discussion and Conclusion
According to the study 1 results, three factors account for online gamer motivation:
competence, autonomy, and relatedness. This finding agrees with the conclusion reported by
Ryan et al.1. The microcomputer playfulness7 was related to the in-game playful
questionnaire and illustrates the criterion related validity. The relation index from our
application of a “time spent playing Kart Rider per week” factor to examine criterion-related
validity with intrinsic motivation was found to be statistically significant (r=.204, p<.01). Our
results also indicate a statistically significant relationship between online game playfulness
(trait) and in-game playfulness (state) (r=.255, p<.01), but no significant relationship between
in-game playfulness (trait) and number of hours spent playing per week. Furthermore,
statistical significance was noted between motivation and playfulness state (r=.607, p<.001)
as well as between motivation and playfulness trait (r=.409, p<.001), suggesting that
playfulness state exerted a stronger influence on online game playfulness.
According to the results of our regression analyses, in-game motivation was a valid
predictor of in-game playfulness (r2=.362, p<.001). In addition, of the three motivation
factors that were tested, both relatedness and autonomy could be used to predict playfulness
(r2=.388, p<.001). This supports Jansz and Tanis’s10 finding that social interaction motivation
is the strongest predictor of time spent gaming. Our data also indicated that in-game
combination of playfulness and competence motivation could be used to predict self-esteem
(explaining 12.5% of total variance; F=8.406, p<.001) (Table 7). These results support our
contention that self-determination theory can be viewed as accounting for a large part of
online players’ motivation, playfulness, vitality, and self-esteem—that is, they support the
findings of Ryan et al.1.
As part of our second study, we utilized a structural equation model to examine
correlations among motivation, playfulness, and vitality. Similar to Study 1 results, in-game
motivation had significant direct effects on in-game playfulness and self-esteem. Motivation
has been defined as the inner drive of an individual and a force that compels people to act.
Among adolescents, intrinsic motivation to play online games is strongly connected to
perceived needs for autonomy, to display competence, and to feel connected to others. Their
motivation would have positive effects on adolescence players’ in-game playfulness and
self-esteem. In contrast, the sense if in-game playfulness (consisting of concentration, enjoy,
and curiosity) among adolescent players was not found to have any effect on self-esteem in
Study 2. One possible explanation is that adolescent players’ in-game playfulness is possibly
hard to enhance adolescence players’ personal worth and self-esteem.
Among the adolescent players in the second study, in-game playfulness had a
significant effect on subjective vitality (0.62 magnitude) and in-game motivation had a
significant effect on subjective vitality as mediated by in-game playfulness. The results
suggest that when adolescent players have a strong sense of in-game playfulness, they either
simply lose track of their fatigue or feel energized. Ryan et al.1 concluded that prolonged
exposure to online games may determine player vitality. Our data suggests that in-game
playfulness may help reduce fatigue and enhances player perceptions of subjective vitality. In
terms of spent playing per week, our 150 participants (41.3%) reported 7 hours or less, 89
(24.5%) between 8 and 16 hours 150 (41.3%) reported 7 hours or less, 89 (24.5%) between 8
and 16 hours, 56 (15.4%) between 16 and 24 hours, and 58 (15.9%) 25 hours or more. Most
of them were not prolonged exposure to online games, and they simply lose track of their
fatigue.
Previous researchers have gathered evidence showing that online games exert negative
effects on individual players’ self-esteem 28,29 and vitality1. In contrast, our data indicate that
the in-game intrinsic motivation of adolescent gamers exert positive effects on in-game
playfulness, subjective vitality, and self-esteem. A possible explanation it tied to Ryan et
al.’s1 suggestion that the motivation component of self-determination theory accounts a great
deal for online players’ motivation. Our two findings suggest self-determination theory (SDT)
could be applied to investigate the positive influence on adolescence players’.
Regarding the use of SDT to investigate online game players’ motivation, playfulness,
vitality, and self-esteem, Bartle22 and Yee2 postulated that players can be categorized into
four motivation types: killers, achievers, socializers, and explorers. Ryan et al1 argue that a
true motivation theory should not focus on behavioral classifications constrained by the
structures of specific games, but instead focus on (a) factors associated with enjoyment and
persistence across players and genres, and (b) how games that differ in controllability,
structure, and content appeal to human motivation tendencies and psychological needs. Our
findings support the idea that SDT can account for a significant amount of player motivation,
and that player motivation has a direct effect on playfulness and self-esteem.
Similarities exist between playfulness and Csikszentmihalyi’s15 flow theory, which has
been used to explain intrinsic motivation and sense of involvement in many activities. A
number of researchers are using flow theory to examine recreation and game playing. For
example, Hwang13 and Wan and Chiou14 applied flow state to investigate the psychological
motives of online games, and found the contradistinction between flow and addiction. We
also expect to apply flow theory to investigate online game playfulness and motivation in
future studies. Other potential topics for further research include a meta-analysis of evidence
on how publication bias in terms of online game violence effect the literature36. Accordingly,
there needs to be a stronger research focus on the positive effects of online gaming. Finally,
more effort is needed to conduct longitudinal studies to provide insights into gamers’
developmental stages and how adolescent game choices and play patterns evolve as they age.
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Table 1. Zero order correlations among play hours, motivation, play trait, and playful state.
Number of Play Hours/Week
Motivation Play State Play Trait
Number of Play Hours/Week
-
Motivation .204* -
Play State .269** .607** - -
Play Trait .109 .409** .255** .255**
*p <.05 ; **p <.01
Table 2. Regression for in-game playfulness.
Factor R △R2 F β t
In-Game Intrinsic Motivation .67 .362 60.03* .607 7.748*
*p <.001
Table 3. Regression for in-game playfulness.
Factor R △R2 F β t
Step 1: Relatedness .607 .363 60.182*** .445 4.262***
Step 2: Autonomy .632 .388 33.997*** .240 2.302*
*p<.05; *** p<.001.
Table 4. Regression for subjective vitality.
Factor R △R2 F β t
Step 1: Motivation .621 .379 64.585* .445 4.770*
Step 2: Playfulness .662 .427 39.781* .289 3.097**
*, p<.001; **, p<.05
Table 5. Regression for self-esteem.
*p <.01; ** p<.001.
Factor R △R2 F β t
Step 1: Playfulness .329 .100 12.509* .329 3.537**
Table 6. Regression for the self-esteem.
*p <.001; **p <.01; ***p<.05.
Factor R △R2 F β t
Step 1: Playfulness .329 .100 12.509** .329 3.537**
Step 2: Competence .376 .125 8.406* -.183 -1.987***
Table 7 Score statistics for Study 2 scales.
Scales M SD Cronbach’s Alpha
In-Game Intrinsic Motivation1 (Ryan et al., 2006) In-Game Playfulness (Ahn et al.30)
Concentration Player Vitality25 (Ryan & Frederick, 1997)
Vitality 1
Table 8. Covariance matrix for the study variables.
Variable 1 2 3 4 5 6 7 8 9 10
Playfulness
1. concentration (0.81)
2. Enjoyment 0.25 (0.65)
3. Curiosity 0.30 0.33 (0.72) 4. Vitality 1
0.26
0.29 0.26 (0.90) 5. Vitality 2 0.21 0.33 0.34 0.70 (1.13) 6. Self-esteem1
-0.13 0.18 0.04 0.08 0.11 (0.82)
7. Self-esteem 2 0.17 0.25 0.21 0.21 0.28 0.15 (0.05) Motivation
8. Competence 0.26 0.19 0.24 0.22 0.26 0.07 0.26 (0.48)
9. Autonomy 0.13 0.19 0.23 0.17 0.18 0.09 0.13 0.14 (0.51)
10. Relatedness 0.23 0.28 0.31 0.17 0.20 0.08 0.23 0.20 0.21 (0.66)