| Literature DB >> 33256148 |
Yuqi Liu1, Yao Song2, Ryoichi Tamura1.
Abstract
As an important branch of video games and the integration of emerging motion-sensing technology, home motion-sensing games cannot only bring hedonic entertainment but also promote utilitarian benefits including exercise and social interaction for people to improve their physical and psychological health. As one of the most populous countries in the world, China has the largest number of households in the world but quite a low home game penetration rate due to the 13 year game industry winter for international enterprises. Whether Chinese customers have the intention of using motion-sensing games to improve their health status in the home environment will directly determine the commercial potential of the relevant industry in the Chinese market. In order to understand the motives of users and explore the market possibility and prospects of the game industry, this study adopts empirical research and structural equation modeling to construct a motivation model of Chinese consumers toward motion-sensing gameplay behavior in the household environment. We distributed 515 questionnaires to conduct a survey; 427 valid responses have been received, and 203 data, which meet the inclusion criteria of the required game experience, have been analyzed by SPSS25.0 and AMOS25.0. A structural equation model for the gameplay motivation has been constructed. The result shows that the three functional motivators, exercise (Path efficient = 0.40, p < 0.01), entertainment (Path efficient = 0.27, p < 0.01), and social interaction (Path efficient = 0.36, p < 0.01) of home motion-sensing games have a significantly positive impact on the user's intention to play. Furthermore, the diversity and the time-and-place flexibility variables exert an important positive influence on the users' gameplay behavior through their effects on the three main functional motive variables. To sum up, (1) exercise, (2) entertainment, and (3) social interaction are the main functional motivations of the Chinese consumers' gameplay behaviors; (4) diversity and (5) time-and-place flexibility are the two main attribute motivators. The acceptance of Chinese users for home motion-sensing games remains positive and high. The motion-sensing game industry has broad market prospects in China through its potential in promoting consumer's wellness and health in the home environment.Entities:
Keywords: Chinese market; empirical study; motion-sensing game; motivation; wellness and health
Year: 2020 PMID: 33256148 PMCID: PMC7730092 DOI: 10.3390/ijerph17238794
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1The conceptual framework of hypotheses.
Constructs and measurement items.
| Construct | Measure Item | Reference |
|---|---|---|
| Diversity (DIV) | DIV1: Motion-sensing games have rich game types and play modes. | [ |
| DIV2: Motion-sensing games have rich game contents. | ||
| DIV3: Motion-sensing games can bring me rich gaming experiences. | ||
| Time-and-place flexibility (TPF) | TPF1: I can play home motion-sensing games without the time limit. | [ |
| TPF2: I can play home motion-sensing games without the place limit. | ||
| TPF3: I can begin and stop playing motion-sensing games at home anytime. | ||
| Entertainment (ENT) | ENT1: I play motion-sensing games because it’s funny. | [ |
| ENT2: I play motion-sensing games because it’s cool. | ||
| ENT3: I play motion-sensing games because it’s exciting. | ||
| Exercise (EXE) | EXE1: I play a motion-sensing game because it can help me to lose weight and sculpt my figure. | [ |
| EXE2: I play a motion-sensing game because it can help me to improve my physical health. | ||
| EXE3: I play a motion-sensing game because it can exercise different parts of my body via different control methods. | ||
| Social interaction (SI) | SI1: When playing motion-sensing games with family members, it can help to promote the communication and enhance the emotional bonds. | [ |
| SI2: When playing motion-sensing games with friends, it can help to strengthen our relationship. | ||
| SI3: When playing motion-sensing games, I can know new friends. | ||
| Intention to play (IPL) | IPL1: I am willing to play motion-sensing games. | [ |
| IPL2: I will try to play motion-sensing games. | ||
| IPL3: I will play motion-sensing games. |
Demographic information and attitude for home motion-sensing game.
| Attributes | Value | Frequency | Attributes | Value | Frequency |
|---|---|---|---|---|---|
| Gender | Male | 85 | Acceptance in Home Motion-Sensing Game | Low | 6 |
| Female | 118 | Relatively low | 14 | ||
| Age | 15–20 | 16 | Medium | 60 | |
| 21–30 | 122 | Relatively high | 99 | ||
| 31–40 | 47 | High | 24 | ||
| 41– | 18 | Future for Home Motion-Sensing Game | Pessimistic | 4 | |
| Education | Some colleges | 71 | Relatively pessimistic | 5 | |
| Undergraduate | 113 | Neutral | 19 | ||
| Postgraduate | 19 | Relatively optimistic | 104 | ||
| Optimistic | 71 |
Reliability and unidimensionality.
| Construct | Cronbach’s Alpha | Variable | Mean | Standard | Standardized | C.R. | SMC | AVE | Composite |
|---|---|---|---|---|---|---|---|---|---|
| Diversity (DIV) | 0.824 | DIV1 | 4.01 | 0.783 | 0.763 | - | 0.583 | 0.617 | 0.829 |
| Time-and-place flexibility (TPF) | 0.843 | TPF1 | 4.18 | 0.638 | 0.784 | - | 0.614 | 0.655 | 0.850 |
| Entertainment (ENT) | 0.868 | ENT1 | 4.07 | 0.805 | 0.774 | - | 0.598 | 0.701 | 0.875 |
| Exercise (EXE) | 0.823 | EXE1 | 3.94 | 0.839 | 0.750 | - | 0.543 | 0.612 | 0.825 |
| Social interaction (SI) | 0.905 | SI1 | 3.88 | 0.781 | 0.858 | - | 0.736 | 0.770 | 0.909 |
| Intention to play (IPL) | 0.848 | IPL1 | 4.09 | 0.863 | 0.793 | - | 0.628 | 0.655 | 0.850 |
Note: C.R. (t-value) = composite reliability; SMC = square multiple correlations; AVE = averaged variances expected.
Correlation matrix of the constructs.
| CR | AVE | MSV | ASV | IPL | DIV | TPF | SI | EXE | ENT | |
|---|---|---|---|---|---|---|---|---|---|---|
| IPL | 0.850 | 0.655 | 0.581 | 0.510 | 0.809 | |||||
| DIV | 0.829 | 0.617 | 0.610 | 0.513 | 0.762 *** | 0.786 | ||||
| TPF | 0.850 | 0.655 | 0.610 | 0.488 | 0.749 *** | 0.781 *** | 0.809 | |||
| SI | 0.909 | 0.770 | 0.472 | 0.374 | 0.687 *** | 0.667 *** | 0.647 *** | 0.878 | ||
| EXE | 0.825 | 0.612 | 0.507 | 0.410 | 0.712 *** | 0.700 *** | 0.646 *** | 0.534 *** | 0.782 | |
| ENT | 0.875 | 0.701 | 0.440 | 0.380 | 0.655 *** | 0.663 *** | 0.658 *** | 0.497 *** | 0.591 *** | 0.837 |
Note: CR = composite reliability; AVE = averaged variances expected; MSV = maximum shared variance; ASV = average shared variance; IPL = intention to play; DIV = diversity; TPF = time-and-space flexibility; SI = social interaction; EXE = exercise; ENT = entertainment; *** p < 0.01.
Goodness-of-fit test.
| Category | Measure | Acceptable Values | Value |
|---|---|---|---|
| Absolute fit indices | Chi-square | 195.561 | |
| d.f. | 125 | ||
| Chi-square/d.f. | 1–5 | 1.564 | |
| GFI | 0.90 or above | 0.906 | |
| SRMR | 0.08 or below | 0.027 | |
| RMSEA | 0.05–0.08 | 0.053 | |
| Incremental fit indices | NFI | 0.90 or above | 0.922 |
| IFI | 0.90 or above | 0.970 | |
| TLI | 0.90 or above | 0.963 | |
| CFI | 0.90 or above | 0.970 |
Note: GFI = goodness-of-fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of approximation; NFI = normed fit index; IFI = incremental fit index; TLI = Tucker–Lewis index; CFI = comparative fit index.
Figure 2Path coefficients resulting from structural equation modeling (SEM). Note: ** p < 0.05; *** p < 0.01.
Hypothesis testing.
| Path Direction | Standardized Coefficient | Standard Error | C.R. (t-Value) | Result | |
|---|---|---|---|---|---|
| H1 | EXE → IPL | 0.393 *** | 0.089 | 4.819 | Accepted |
| H2 | ENT → IPL | 0.267 *** | 0.078 | 3.708 | Accepted |
| H3 | SI → IPL | 0.355 *** | 0.072 | 4.974 | Accepted |
| H4 | TPF → EXE | 0.265 ** | 0.147 | 2.040 | Accepted |
| H5 | TPF → ENT | 0.352 *** | 0.141 | 2.822 | Accepted |
| H6 | TPF → SI | 0.326 *** | 0.148 | 2.672 | Accepted |
| H7 | DIV → EXE | 0.519 *** | 0.137 | 3.805 | Accepted |
| H8 | DIV → ENT | 0.403 *** | 0.127 | 3.183 | Accepted |
| H9 | DIV → SI | 0.419 *** | 0.133 | 3.371 | Accepted |
Note: TPF = time-and-space flexibility; DIV = diversity; EXE = exercise; ENT = entertainment; SI = social interaction; IPL = intention to play; ** p < 0.05; *** p < 0.01.