The games industry has been growing prosperously with the development of information technology. Recently, with further advances in social networks and mobile services, playing mobile social gaming has gradually changed our daily life in terms of social connection and leisure time spending. What are the determinant factors which affect users intention to play such games? Therefore in this research we present an empirical study on WeChat, China’s most popular mobile social network, and apply a technology acceptance model (TAM) to study the reasons beneath the popularity of games in mobile social networks. Furthermore, factors from social and mobile perspective are incorporated into the conventional TAM and their influence and relationships are studied. Experimental study on accumulated online survey data reveals several interesting findings and it is believed that this research offers the researchers in the community further insight in analysing the current popularity and future potential of mobile social games.
With the development of information technology, video games have become one of the most important applications and are wildly popular with all kinds of people of all ages. Playing video games has gradually changed people’s life style, particularly in terms of how leisure time is spent [
The smartphones provide a new platform for both social networking and video games. The mobile platform for social networks allows users to influence their friends [
Due to the fact that playing games and using social networks are two of the most popular applications used daily on smartphones [
The WeChat App was first released in January 2011 as a mobile social network application which provides text, image, video, and voice messaging communication service. On 5th of August in 2013, Tencent released WeChat 5.0 which included a gaming centre. Several WeChat games were released with incredible numbers of games being downloaded. For example, a game called “Craz3 Match” was ranked 1st in App store just five hours after it was first released with more than 20 million downloads over the following three days. Subsequently many more games have been released which were also top ranked after their initial release.
Given the popularity of WeChat based games, we study in depth the reasons behind their broad acceptance. Many techniques in the literature can be used to analyse such behaviour patterns and technology acceptance model (TAM) [
Besides these fundamental factors, there are other variables which also contribute to the popularity of WeChat games. A lot of previous researches on user’s intention of using social networks and/or playing mobile games have been conducted and can provide inspiration in social network based game analysis. For example, an extended TAM model was proposed by D.-H. Shin and Y.-J. Shin to investigate the factors affecting user’s acceptance of social network games [
In this research, we try to explain why people continue to play mobile social games and investigate the main determinants and their relationships. Specifically, this work proposes an extended TAM model and adds several additional variables, such as social interaction, enjoyment, and altruism to enhance the understanding of user’s intention to play such games. The evaluation and validation of the proposed model are conducted by analysing questionnaires accumulated online and several interesting findings are revealed.
The remainder of this paper is organised as follows. In Section
In the area of information systems, there is a need for researchers to understand the reasons behind the users’ actual usage of IT systems. To solve this problem, many technologies have been proposed, for example, Theory of Reasoned Action (TRA) [
In the earliest TAM model, it is argued that the actual system use is predicable by user motivation, which is also directly influenced by external variables, that is, system features, capabilities, and so on [
Currently the video game has become one of the most important usages of advanced information technology. It has greatly transformed all people’s behaviour pattern in spending their spare time [
With the development of mobile devices, particularly the smartphone, playing online games in a mobile environment has become more and more popular as it extends the variance of place and time for users to play online games. According to the studies of Liu and Li, the effect of use context on the formation of users perceptions of mobile services is powerful [
Social networks, such as Facebook, Twitter, and WeChat, have greatly changed our daily life [
From the discussion above, it is clear that playing online games and surfing social network services are the two major mobile applications. It is found that social network games have been widely implemented further into mobile devise as applications [
Mobile social gaming is a new platform for people to play games with other friends. In this research we will use WeChat games as a case study to understand such attraction. To understand the popularity of a game platform, a large number of factors can be attached for importance; for example, people may concern about privacy in social game as users could use personal information to buy equipment. Such information stored in the app’s cloud environment is sensitive [
Proposed model.
The proposed research model is an extension of the conventional TAM model. Therefore, the hypotheses of belief-attitude-intention-behaviour causal chain [ Perceived ease of use (PEU) positively influences user’s perceived usefulness (PU) of playing mobile social games. Perceived ease of use (PEU) positively influences user’s attitude (ATT) on social based mobile games. Perceived usefulness (PU) positively influences user’s attitude (ATT) on mobile social gaming. Perceived usefulness (PU) positively influences user’s intention (BI) to play mobile social games. Attitude (ATT) positively influences user’s intention (BI) to play mobile social games.
Perceived enjoyment (PE) is the extent to which an activity is perceived to be enjoyable without considering any performance consequences [ Perceived enjoyment (PE) positively influences attitude (ATT) on mobile social gaming. Perceived enjoyment (PE) positively influences intention (FL) to play mobile social games. Perceived enjoyment (PE) positively perceived usefulness (PU) of playing mobile social gaming.
Use context (UC) refers to the environment where the technology will be used [
As many contextual factors may have great effect on user adoption, our study focuses on two factors which are highly related to mobile games, that is, the place where the people are and how the people feel at that time. For example, when people are in crowded public transportation and they feel bored, using a laptop is not possible but there is space to use a mobile phone. People can play mobile games to pass the time and enjoy themselves. Hence, two hypotheses are posited as follows: Use context (UC) positively influences perceived enjoyment (PE) of mobile social gaming. Use context (UC) positively influences perceived ease of use (PEU) to play mobile social games.
Flow (FL) was first put forward by M. Csikszentmihalyi and I. Csikszentmihalyi and defined as the holistic experience when involved in the action [ Flow (FL) positively influences intention (BI) to play mobile social gaming.
Interaction is a kind of behaviour between two or more objects. In prior studies interaction is usually classified into two types. The first is the interaction between the user and the system, and the second is user-to-user interaction [ Social interaction (SI) positively influences perceived enjoyment (PE) of mobile social gaming. Social interaction (SI) positively influences use context (UC) of mobile social gaming.
Altruism (AL) can be classified into kin altruism and reciprocal altruism [ Altruism (ALT) positively influences social interaction (SI) of mobile social gaming.
In this research we published questionnaires on an online survey agency to collect the experimental data. The original questionnaire consists of two parts. The first part has 8 questions to collect the basic information of the informants, such as sex, age, and use experience with WeChat and/or games. The second part is the main component of the questionnaire and consists of 32 questions to investigate the 9 factors introduced in previous section. Each question is measured on a 7-point Likert scale with the end points of “strongly agree (7)” and “strongly disagree (1)”.
The data collection process uses a two-step approach. Firstly we conducted a pilot test to verify the questionnaire’s accuracy, which results in the removal of 4 questions from the original questionnaire. As a result the final questionnaire consists of 28 questions, among which two questions are designed as reverse questions to help judge insincere responses. Table
Design of the questionnaire.
Factor | Abbreviation | Question number | Verification questions (Y/N) |
---|---|---|---|
Social interaction | SI | 3 | N |
Altruism | ALT | 3 | N |
Perceived enjoyment | PE | 4 | Y |
Perceived usefulness | PU | 3 | N |
Perceived ease of use | PEU | 3 | Y |
Flow | FL | 3 | N |
Attitude | ATT | 3 | N |
Use context | UC | 3 | N |
Behaviour intention | BI | 3 | N |
Questionnaire.
Factor | Item | Measure |
---|---|---|
Social interaction (SI) | SI1 | I like to play the game which my friends play. |
SI2 | WeChat games provide a platform for me to play games with my friends. | |
SI3 | I like to play games with friends. | |
|
||
Altruism (ALT) | ALT1 | I will give my friends gifts or other in-game help. |
ALT2 | I often help my friends when they need help in WeChat games. | |
ALT3 | My friends often give me feedback when I offer help they need in WeChat games. | |
|
||
Perceived enjoyment (PE) | PE1 | It is interesting to play WeChat games. |
PE2 | Playing WeChat games brings enjoyment to my daily life. | |
PE3 | I always feel happy when I am playing WeChat games. | |
|
||
Perceived usefulness (PU) | PU1 | Playing WeChat games makes my life different. |
PU2 | Playing WeChat games makes my life better. | |
PU3 | Playing WeChat games is useful for me. | |
|
||
Perceived ease of use (PEU) | PEU1 | It is easy for me to play WeChat games. |
PEU2 | It is easy for me to master the rules of the games. | |
|
||
Flow (FL) | FL1 | I will not be tired of WeChat games in a short time. |
FL2 | I will not lose interest in WeChat games in a short time. | |
FL3 | It happened often for me to ignore the time past when I play WeChat games. | |
|
||
Attitude (ATT) | ATT1 | It is a good idea for me to play WeChat games during my free time. |
ATT2 | I feel good towards WeChat games. | |
ATT3 | I like playing WeChat games. | |
|
||
Use context (UC) | UC1 | Playing WeChat games is a way to spend free time for me. |
UC2 | I will consider to play WeChat games when I am bored. | |
UC3 | I will consider to play WeChat games when I have free time. | |
|
||
Behaviour intention (BI) | BI1 | I want to play more kinds of WeChat games later. |
BI2 | I will keep playing WeChat games. | |
BI3 | I will play WeChat games with my friends together. |
A total of 491 responses were collected from the online survey. In order to improve the quality of the data we filter out responses which fit the following criteria: (1) Eliminate the responses of respondents who have never played a WeChat game. (2) Eliminate the insincere responses through data filtering on the two verification questions. (3) Eliminate the insincere responses which look like “Straight-Line” or “Wave” [
Data filtering result.
Item | Number |
---|---|
Total responses | 491 |
Not played WeChat games | 122 |
Insincere response | 61 |
Effective responses | 308 |
In order to analyse the effectiveness of the original data, the first step of the experiment is to conduct data standardisation. In this step we calculate the average and standard deviation of each question result and also the average for each category. The results are shown in Table
Question standardisation and reliability analysis.
Factor | Question | AVG | SD | AVG |
---|---|---|---|---|
SI | SI1 | 5.98 | 0.943 | 5.88 |
SI2 | 5.86 | 1.040 | ||
SI3 | 5.81 | 1.085 | ||
|
||||
ALT | ALT1 | 5.94 | 1.003 | 5.92 |
ALT2 | 5.97 | 0.920 | ||
ALT3 | 5.87 | 0.947 | ||
|
||||
PE | PE1 | 5.87 | 0.989 | 5.84 |
PE2 | 5.86 | 0.909 | ||
PE3 | 5.80 | 0.958 | ||
|
||||
PU | PU1 | 5.36 | 1.220 | 5.37 |
PU2 | 5.41 | 1.153 | ||
PU3 | 5.36 | 1.305 | ||
|
||||
PEU | PEU1 | 5.93 | 0.773 | 5.98 |
PEU2 | 6.03 | 0.786 | ||
|
||||
FL | FL1 | 5.32 | 1.265 | 5.44 |
FL2 | 5.50 | 1.035 | ||
FL3 | 5.51 | 1.163 | ||
|
||||
ATT | ATT1 | 5.87 | 0.946 | 5.84 |
ATT2 | 5.85 | 0.939 | ||
ATT3 | 5.79 | 0.929 | ||
|
||||
UC | UC1 | 6.04 | 0.855 | 5.99 |
UC2 | 6.01 | 0.941 | ||
UC3 | 5.92 | 0.963 | ||
|
||||
BI | BI1 | 5.98 | 0.950 | 5.94 |
BI2 | 6.05 | 0.910 | ||
BI3 | 5.80 | 1.080 |
Afterwards we further employ Cronbach’s alpha coefficient to show the convergent validity and internal reliability of the factors, which are listed in Table
Cronbach’s alpha coefficient of each factor.
Factor | Cronbach’s alpha coefficient |
---|---|
SI | 0.789 |
ALT | 0.776 |
PE | 0.799 |
PU | 0.872 |
PEU | 0.718 |
FL | 0.749 |
ATT | 0.786 |
UC | 0.727 |
BI | 0.735 |
Total (26 questions) | 0.947 |
Meanwhile, discriminant validity is verified as to ensure that variables relate more strongly to their own factor than to other factors. As shown in Table
Intercorrelations between factors.
SI | ALT | PE | PU | PEU | FL | ATT | UC | BI | |
---|---|---|---|---|---|---|---|---|---|
SI | 1.000 | ||||||||
ALT | 0.612 | 1.000 | |||||||
PE | 0.637 | 0.492 | 1.000 | ||||||
PU | 0.527 | 0.470 | 0.417 | 1.000 | |||||
PEU | 0.530 | 0.479 | 0.562 | 0.292 | 1.000 | ||||
FL | 0.651 | 0.509 | 0.631 | 0.621 | 0.494 | 1.000 | |||
ATT | 0.563 | 0.452 | 0.475 | 0.602 | 0.379 | 0.598 | 1.000 | ||
UC | 0.657 | 0.508 | 0.618 | 0.568 | 0.518 | 0.687 | 0.638 | 1.000 | |
BI | 0.695 | 0.556 | 0.674 | 0.603 | 0.541 | 0.673 | 0.600 | 0.699 | 1.000 |
The next step of data analysis is to conduct principal component analysis (PCA). Before that, it is necessary to test the adequacy of data. In this research, KMO Testing and Bartlett Testing are employed to validate whether the data are suitable for PCA process [
KMO and Bartlett Testing.
Kaiser-Meyer-Olkin | .942 |
---|---|
Bartlett Testing | |
|
4518.333 |
df | 325 |
Sig. | .000 |
Rotation matrix.
Component | |||||||||
---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
SI1 | 0.106 | 0.339 |
|
0.246 | 0.129 | 0.322 | 0.086 | 0.037 | 0.149 |
SI2 | 0.227 | 0.151 |
|
0.212 | 0.196 | 0.040 | 0.195 | 0.189 | 0.074 |
SI3 | 0.190 | 0.167 |
|
0.129 | 0.199 | 0.152 | 0.080 | 0.083 | 0.070 |
ALT1 | 0.227 | 0.104 | 0.189 |
|
|
0.125 | 0.213 | 0.340 | 0.114 |
ALT2 | 0.043 | 0.165 | 0.114 |
|
0.198 | 0.310 | 0.071 |
|
0.023 |
ALT3 | 0.211 | 0.106 | 0.171 |
|
0.180 |
|
|
0.105 | 0.183 |
PE1 | 0.279 | 0.322 | 0.320 | 0.096 |
|
0.108 | 0.207 | 0.400 |
|
PE2 | 0.255 | 0.188 | 0.237 | 0.145 |
|
0.200 | 0.191 | 0.023 | 0.120 |
PE3 | 0.232 | 0.175 | 0.219 | 0.230 |
|
0.092 | 0.086 | 0.174 | 0.266 |
PU1 |
|
0.183 | 0.222 | 0.103 | 0.096 |
|
0.243 | 0.302 |
|
PU2 |
|
0.226 | 0.118 | 0.115 | 0.309 | 0.134 | 0.130 |
|
|
PU3 |
|
0.118 | 0.168 | 0.211 | 0.136 | 0.128 | 0.102 | 0.031 | 0.146 |
PEU1 | 0.108 | 0.037 | 0.079 | 0.090 | 0.222 | 0.201 | 0.128 | 0.048 |
|
PEU2 |
|
0.401 | 0.160 | 0.296 | 0.050 | 0.157 |
|
0.225 |
|
FL1 | 0.176 | 0.117 | 0.135 | 0.072 | 0.127 | 0.019 |
|
0.260 | 0.021 |
FL2 | 0.253 | 0.281 | 0.133 | 0.089 | 0.196 | 0.221 |
|
|
0.163 |
FL3 | 0.383 | 0.262 | 0.192 | 0.307 |
|
0.132 |
|
0.038 | 0.116 |
ATT1 | 0.130 |
|
0.121 | 0.163 | 0.398 | 0.163 | 0.177 | 0.485 | 0.125 |
ATT2 | 0.209 |
|
0.216 | 0.024 | 0.288 | 0.016 | 0.284 | 0.067 | 0.075 |
ATT3 | 0.199 |
|
0.195 | 0.239 | 0.315 | 0.209 | 0.004 | 0.156 | 0.134 |
UC1 | 0.080 | 0.362 | 0.176 | 0.133 | 0.454 |
|
0.122 | 0.021 | 0.228 |
UC2 | 0.074 | 0.117 | 0.013 | 0.206 | 0.133 |
|
0.151 | 0.233 | 0.151 |
UC3 | 0.148 | 0.116 | 0.387 | 0.053 | 0.152 |
|
|
0.101 | 0.140 |
BI1 | 0.235 | 0.366 | 0.238 | 0.136 | 0.308 | 0.312 | 0.220 |
|
0.112 |
BI2 | 0.107 | 0.064 | 0.175 | 0.181 | 0.065 | 0.457 | 0.046 |
|
0.195 |
BI3 | 0.349 | 0.061 | 0.343 | 0.118 | 0.312 | 0.243 | 0.140 |
|
0.093 |
To evaluate the proposed model and validate the proposed hypotheses, eight fit indices are employed in this research, that is,
Fit indices for the measurement.
Results | Recommended criteria | |
---|---|---|
|
1.844 |
|
GFI | 0.886 |
|
AGFI | 0.854 |
|
RMSEA | 0.052 |
|
RMR | 0.043 |
|
CFI | 0.947 |
|
NFI | 0.892 |
|
IFI | 0.947 |
|
The aim of path analysis is to evaluate the veracity and reliability of the hypothetical model and measure the strength of the causal relationship between variables. We examined the structural equation model by testing the hypothesised relationships between various factors, as shown in Figure
Analysis of significance of path coefficient.
Hypothesis | Estimate | Supported? |
---|---|---|
(H1) PEU |
|
N |
(H2) PEU |
|
Y |
(H3) PU |
|
N |
(H4) PU |
|
Y |
(H5) ATT |
|
Y |
(H6) PE |
|
Y |
(H7) PE |
|
Y |
(H8) PE |
|
Y |
(H9) UC |
|
Y |
(H10) UC |
|
Y |
(H11) FL |
|
Y |
(H12) SI |
|
Y |
(H13) SI |
|
Y |
(H14) ALT |
|
Y |
Path verification.
This study developed a theoretical framework and discussed the structural equation modelling analysis of the proposed theoretical framework for mobile social game adoption. Consistent with previous studies focusing on online games and mobile social network services [
From this study it is found that perceived enjoyment and perceived ease of use are the chief determinants of user attitudes to play mobile social games. This may suggest that (1) players regard the level of enjoyment from playing mobile social games as the most significant factor and (2) players prefer to play some easier to get started mobile social games which would not cost them much effort. Of these two factors, perceived enjoyment shows a much stronger effect than perceived ease of use, which implies that entertainment oriented technologies will be paid much attention by the markets. Furthermore, this model shows insignificant role of perceived usefulness, which sharply contrasts perceived enjoyment and perceived ease of use, in affecting user attitude to play mobile social games. From this research it is concluded that perceived usefulness also does not have very strong effect on the actual behaviour intention, which corroborates previous studies [
Considering the importance and the significance of perceived enjoyment, it is deserved to conduct further investigation to study the relationship between it with other factors. From this research, it is reasonable to argue that enjoyment can enhance perception of flow. In fact, the popularity of some WeChat games is partly because of its mechanism of making fun from keeping playing to beat friends. However, due to the fact that normally users play WeChat games to kill the boring time, for example, when using public transportation, it is not surprising to see that flow does not exert significant effect on the intention.
Our findings also shows that social interaction does have strong influence on perceived enjoyment while it also has significant influence on use context. Mobile social games provide a new platform for users to communicate with each other and then close the relationship among them. For example, in WeChat games, users can compete against, offer help to, and/or interact promptly with their friends, thereby making the gaming more interesting. In this research, it is also found that social interaction in WeChat games is also supported by altruism. Offering help in the games does bring a lot of fun and social reputation among friends. As such it suggests that social interaction plays a key role in increasing the enjoyment, thereby increasing the user attitude to play WeChat games.
Meanwhile, since users can use WeChat to communicate with each other when they have spare time, it is easier for a user to realise other friends’ activity in WeChat games with portable smartphones. The use context for easily accessing and playing mobile social games by social connection does provide more chance for users to get involved into WeChat games, which is also the major cause of WeChat games spreading. This result supports previous research on use context [
This proposed extended TAM model has several practical and theoretical implications for researchers and engineers to develop popular mobile social games. This study provided some in-depth analysis of popularity of WeChat games in China and then can be applied into development of games industry. It is argued that successful mobile social games should exert significant efforts to deliver enjoyable games in an easily accessible way as well as to provide excellent social interaction experience to encourage users to share their fun.
Nowadays along with the development of social network service and mobile devices, social network based mobile gaming has become wildly popular. In this research we provide a use case analysis of the factors affecting acceptance of mobile social games on WeChat. To this end, we employ a technology acceptance model and integrate some amending predictors from social and mobile perspective. Our analysis of over 300 valid questionnaire respondents provides revealing findings on the influence of 9 factors on the acceptance of mobile social games. We believe that this research provides invaluable insight for mobile social game service providers, enabling better understanding of adoption behaviour and thus further improving their services.
Similar to other researches, there are several limitations in this study which deserve future effort to address. The major issue is related to the users of WeChat. The questionnaire in this research is in Chinese and all responses are from Mainland China. Furthermore, WeChat is not the only service for the social network though it is the most popular one in China indeed. Using WeChat as case study in this paper does provide some interesting findings; however, the results may be not easy to generalise. It would be interesting to extend this work into an international context and perhaps consider other social networks.
In contrast with other studies, there may be some important factors which may significantly contribute to the integrated model and deserve to be further investigated. For example, considering the possibility for WeChat games to involve payment and advertisement in terms of virtual gift, it can be forecast that user’s comprehensive sense of security would have significant influence on user attitudes towards to mobile social games, thereby making perceived security an essential factor for further study. Furthermore, continuous usage of mobile games is also important as attracting users to use a game is a challenge but keeping the users to play with games is another even more challenging task. Therefore, analysis of factors for mobile game’s continuous usage deserves to be studied further in the future research.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the State Key Laboratory of Software Development Environment of China (no. SKLSDE-2015ZX-23), the National Natural Science Foundation of China (no. 61472021), and the Fundamental Research Funds for the Central Universities.