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The impact of WeChat use intensity and addiction on academic performance.

WeChat has been the most popular form of social media in China in recent years (Gan, 2017). Since its launch in China by Tencent in 2011, researchers have examined the social, physical, and mental effects WeChat has on its users. For example, Wen, Geng, and Ye (2016) found that WeChat users' intrinsic use motivation significantly predicted their subjective well-being. Zhang, Li, Wu, and Li, (2017) found that WeChat users' continuance intention was influenced by such factors as network externalities, social interaction ties, and perceived value. However, little research has been done regarding WeChat use intensity and addiction or its impact on student users' academic performance.

Given China's rapid development in recent decades, increasing numbers of international students are choosing to pursue their postsecondary education in China (Lu & Zhao, 2017). After coming to China, most of these international students would use WeChat. This led us to ask the following questions: What are these international students' WeChat use behaviors? Do they have a problem with WeChat addiction? And will their WeChat use behavior actively or passively impact their academic performance? To explore possible answers to these questions, we selected Yemeni international students in China as participants and examined their WeChat use behaviors, as well as the effect of WeChat use intensity and addiction on their academic performance.

Literature Review

Yemeni International Students in China

The first group of Yemeni students came to China in the 1960s. According to data provided by the Chinese Ministry of Education to the Embassy of Yemen in Beijing in 2016, the number of Yemeni scholarship holders who had registered at Chinese universities in 2016 had reached 800. In addition, there are increasing numbers of self-financing Yemeni students in China. The counselor estimated that the approximate number of Yemeni students in China was between 2,000 and 2,500 at the end of 2016 (A. Alnoah, personal communication, February 18, 2017).

WeChat Use Intensity

According to the 2017 Tencent WeChat User Data Report, the number of WeChat users has increased rapidly, from 1.58 million at the end of 2012 to 902 million at the end of the third quarter of 2017 (Tencent, 2017). Among WeChat's functions, the three used most frequently are Moments, Send and Receive Messages, and Public Account.

Previous researchers have found mixed results regarding the effect of using WeChat and other online activity on users' lives. For example, Xu et al. (2016, p. 9) found that WeChat users had better sleep quality than did non-WeChat users. One of their explanations was that "WeChat could be used to relieve stressful situations and feelings of depression or anxiety." In contrast, Demirci, Akgonul, and Akpinar (2015) found that greater smartphone use was associated with depression and anxiety, which, in turn, resulted in sleep problems. There are several possible reasons for individuals continuing to use WeChat. For example, Lien, Cao, and Zhou (2017) found that WeChat's environment and outcome service quality is positively related to users' use intention, with stickiness (users' willingness to stick to WeChat) playing a mediating effect in this relationship.

WeChat Addiction

Scholars from multiple countries have found that the occurrence of mobile phone addiction in adolescents is above 30% (Liu et al., 2017). Importantly, it has been found that mobile phone addiction negatively impacts both the physical and mental health of users and can result in other problems such as academic failing, interpersonal problems, depression, anxiety, and even suicidal ideation (Liu et al., 2017). As for WeChat addiction specifically, Zhu (2015) found that the phenomenon does exist among Chinese undergraduate users.

Vujic (2017) found that improper use of social networking sites negatively affected users' academic performance and was one of the major sources of distraction among students. Indeed, smartphone overuse is negatively related to concentration and rational thinking (Vujic, 2017). Other researchers have also found that college students' academic self-efficacy was affected significantly by Internet addiction. Specifically, Odaci (2011) found that academic self-efficacy was a significant predictor of problematic Internet use among Turkish college students. Similarly, Chiu (2014) found that smartphone users' learning efficacy mediated the relationship between life stress and smartphone addiction.

Based on this literature review, we formed the following research objectives for this study:

Objective 1: To determine Yemeni international students' WeChat use intensity, WeChat addiction status, and academic performance level, and the correlations between these three variables.

Objective 2: To examine the impact of demographic characteristics on Yemeni international students' WeChat use intensity and WeChat addiction.

Objective 3: To examine the effect of WeChat use intensity on participants' academic performance and the mediating effect of WeChat addiction in this relationship.

Method

Participants

Participants were 427 randomly selected Yemeni international students currently studying in China. This group of students was selected because one of the study researchers is a Yemeni international student who is pursuing a doctorate at Zhejiang University. We had observed that most Yemeni international students in China are connected to each other by WeChat, so the participants were recruited by WeChat connection.

Among the 427 participants, 371 were men and 56 were women. Participants were language learners, undergraduates, master's students, and doctoral students. Their majors included medicine (94), engineering (89), social sciences (76), humanities (56), information (56), agriculture, life sciences, and environmental sciences (10), technology (11), and other (35). Of the participants, 74 started using WeChat in 2011, 70 in 2012, 83 in 2013, 82 in 2014, 70 in 2015, 47 in 2016, and one in 2017.

Measures

We developed a four-part survey to measure international students' WeChat use intensity, addiction, and impact on their academic performance. The first part was used to collect the students' demographic information, including gender, academic major, educational level, and length of time using WeChat.

The second part of the survey involved a four-item WeChat use intensity scale, a sample item of which is "Daily time on WeChat." Participants rated each item on a 5-point scale, with responses ranging from 1 (Level I) to 5 (Level V). The confirmatory factor analyses (CFA) showed that this scale had good structural validity: [chi square]/df = 2.50, goodness-of-fit index (GFI) = .997, normed fit index (NFI) = .99, incremental fit index (IFI) = .996, and root mean square error of approximation (RMSEA) = .06. The path estimates were all meaningful in size, ranging from 0.26 to 0.56, indicating that all items significantly contributed to use intensity. Cronbach's alpha was .72.

The third part of the survey comprised an eight-item WeChat addiction scale. We adopted and revised Koc and Gulyagci's (2013) Facebook Addiction Scale. A sample item is "I lose sleep over spending too much time on WeChat." A 7-point Likert-type scale was used to measure students' attitudes (1 = totally disagree, 7 = totally agree). CFA showed that this scale had good structural validity: [chi square]/df = 4.13, GFI = .96, NFI = .93, IFI = .95, and RMSEA = .08. The path estimates were all meaningful in size, ranging from 0.52 to 0.75. Cronbach's alpha was .84.

The fourth part of the survey comprised one item to gather data related to academic performance. Self-reported grade point average (GPA) was used to provide a comprehensive representation of students' academic performance. Participants were asked to rate their GPA from among five choices: 1 = very high (4.5-5.0), 2 = high (4.0-4.5), 3 = average (3.5-4.0), 4 = low (3.0-3.5), and 5 = very low (below 3.0).

Data Collection

The initial survey was in Chinese, which was translated into Arabic by one researcher then uploaded to a survey website (SoJump). The link to the survey was then sent to Yemeni international students through WeChat groups between January 10 and February 1, 2017. Participation was anonymous. Reliability and validity of the survey were examined prior to data analysis. SPSS 21.0 and AMOS 20.0 were used for data analysis.

Results

Descriptive and Correlation Results

As per Objective 1, Table 1 shows the extent of Yemeni international students' WeChat use intensity.

Table 2 shows the descriptive and correlational results of each variable. Correlational analyses found that the three variables significantly correlated with one another.

Demographic Difference Results

As per Objective 2, t tests and F tests were used to investigate the impact of gender and educational level on participants' WeChat use intensity and WeChat addiction. Results show that male students scored significantly higher than did females on the WeChat use intensity scale, t(425) = 5.58, p < .01, and WeChat addiction scale, t(425) = 3.90, p < .01. Results also indicate that education level had a significant impact on participants' WeChat use intensity, F(3, 423) = 14.25, p < .01, and WeChat addiction, F(3, 423) = 5.91, p < .01. Specifically, results of least-significant difference indicate that undergraduates scored significantly higher than did language learners and doctoral students in WeChat use intensity and WeChat addiction.

Mediating Analysis Results

As per Objective 3, using regression analysis, we tested the single effect of WeChat use intensity on WeChat addiction ([beta] = .43, p < .001) and academic performance ([beta] = .43, p < .001), as well as the effect of WeChat addiction on academic performance ([beta] = .14, p < .001). Regression results indicate that the predictive results met the requirements for mediation, and we thus used hierarchical regression to test the mediating effect of usage addiction.

First, we added the control variables (gender and education level, dummy variables) into the model, then added the independent variable (WeChat use intensity), and finally added the mediating variable (WeChat addiction). As shown in Table 3, after controlling for gender and education level and including the mediating variable in the model, WeChat use intensity no longer significantly predicted students' academic performance ([beta] = .08; p < .133). However, the effect of WeChat usage intensity on WeChat addiction ([beta] = .43, p < .001) and the effect of WeChat addiction on academic performance remained significant ([beta] = 0.09, p < .05). Therefore, WeChat addiction fully mediated the relationship between WeChat use intensity

Discussion

In this study, we found several noteworthy and meaningful characteristics of WeChat usage among Yemeni international students in China. First, over 60% of Yemeni international student users had more than 200 friends on WeChat, whereas approximately 40% had more than 400 friends on WeChat. The majority of users spent more than 1 hour on WeChat every day, and about 40% spent more than 4 hours on WeChat every day. Compared with data from the 2017 WeChat User Data Report (Tencent, 2017), the percentage of Yemeni student users who had more than 200 friends and who spent more than 1 hour on WeChat was much higher than that of Chinese users.

Second, we found that Yemeni international students' WeChat use intensity and WeChat addiction was at a low-medium level. Gender and educational level significantly impacted students' WeChat use intensity and their WeChat addiction level. Specifically, male students' WeChat use intensity and addiction levels were significantly higher than that of their female counterparts. Undergraduates' WeChat use intensity and addiction levels were significantly higher than that of language learners and doctoral students. These findings expand on those of previous researchers regarding the existence of WeChat addiction among college students (Wen et al., 2016; Zhu, 2015) by adding new evidence that international students also exhibit a certain degree of WeChat use intensity and addiction, with significant differences as a function of gender and educational level.

Finally, we found that WeChat use intensity, WeChat addiction, and academic performance significantly positively correlated with one another. Mediation analyses further demonstrated that WeChat addiction fully mediated the relationship between WeChat use intensity and academic performance. Our findings confirm Vujic's (2017) conclusion that improper use of social media can passively impact users' academic performance.

A limitation of the present study is the simplification of the WeChat Use intensity measurement. Future researchers should investigate a more effective measurements of WeChat Use intensity.

As a powerful and influential social media tool in China, WeChat has brought about many benefits for its users. However, our findings provide evidence that overuse of WeChat may cause addiction and, in turn, negatively influence international student users' academic performance. Therefore, faculty members in Chinese universities should pay special attention to international students' WeChat usage and develop strategies to help reduce the risk of their addiction.

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central University of Zhejiang University (Project No. DCL001, Instructional Design and Classroom Learning) and Specialized Funding of Teaching and Scientific Research for Faculty Members of Liberal Arts at Zhejiang University.

References

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Demirci, K., Akgonul, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4, 85-92. https://doi.org/cmvj

Gan, C. (2017). Understanding WeChat users' liking behavior: An empirical study in China. Computers in Human Behavior, 68, 30-39. https://doi.org/f9nt5x

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish college students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16, 279-284. https://doi.org/f4stt4

Lien, C.-H., Cao, Y., & Zhou, X. (2017). Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Computers in Human Behavior, 68, 403-410. https://doi.org/f9nvwr

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Odaci, H. (2011). Academic self-efficacy and academic procrastination as predictors of problematic internet use in university students. Computers & Education, 57, 1109-1113. https://doi.org/cpvjqv

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Xu, X. L., Lin, Q. Y., Zhang, Y., Zhu, R. Z., Sharma, M., & Zhao, Y. (2016). Influence of WeChat on sleep quality among undergraduates in Chongqing, China: A cross sectional study. Springer Plus, 5, 1-12. https://doi.org/cnkn

Zhang, C.-B., Li, Y.-N., Wu, B., & Li, D.-J. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284-293. https://doi.org/f9t42p

Zhu, Y. X. (2015). Media social addiction of college students from the dependency theory [In Chinese]. Today's Media, 2, 44-44.

Yan Li (1), Marwan H. Sallam (1), Yinghua Ye (1)

(1) Department of Curriculum and Learning Science, Zhejiang University

How to cite: Li, Y., Sallam, M. H., & Ye, Y. (2019). The impact of WeChat use intensity and addiction on academic performance. Social Behavior and Personality: An international journal, 47(1), e7331

CORRESPONDENCE Yinghua Ye, Institute of E-learning, Department of Curriculum and Learning Science, College of Education, Zhejiang University, Tian Mu Shan Rd. #148, 310028, Hangzhou, Zhejiang, People's Republic of China. Email: yeyin2hua79@126.com

https://doi.org/10.2224/sbp.7331
Table 1. Participants' WeChat Use Intensity

Number of friends            Number/%  Daily time on
on WeChat                              WeChat

Level I (0-100)               67/15.7  Level I (0-1hour)
Level II (101-200)            84/19.7  Level II (1-2 hours)
Level III (201-300)           57/13.3  Level III (2-3 hours)
Level IV (301-400)            43/10.1  Level IV (3-4 hours)
Level V                      176/41.2  Level V
([greater than or equal to]             ([greater than or equal to]
400)                                     4 hours)

Number of friends            Number/%  Number of
on WeChat                              WeChat
                                       groups

Level I (0-100)                32/7.5  Level I (0-10)
Level II (101-200)            82/19.2  Level II (11-20)
Level III (201-300)           69/16.2  Level III (21-30)
Level IV (301-400)            76/17.8  Level IV (31-40)
Level V                      168/39.3  Level V
([greater than or equal to]             ([greater than or equal to]
400)                                     40)

Number of friends            Number/%  Number of WeChat
on WeChat                              public accounts

Level I (0-100)              189/44.3  Level I (0-10)
Level II (101-200)           145/34.0  Level II (11-20)
Level III (201-300)           43/10.1  Level III (21-30)
Level IV (301-400)            14/3.3   Level IV (31-40)
Level V                       36/8.4   Level V
([greater than or equal to]            ([greater than or equal to]
400)                                       40)

Number of friends            Number/%
on WeChat

Level I (0-100)              220/51.5
Level II (101-200)           116/27.2
Level III (201-300)            38/8.9
Level IV (301-400)             24/5.6
Level V                        29/6.8
([greater than or equal to]
400)

Note. N = 427.

Table 2. Descriptive Statistics and Correlations Among the Three
Variables

                      M     SD    WeChat use  WeChat     Academic
                                  intensity   addiction  performance

WeChat use intensity  2.73  0.98  --
WeChat addiction      3.98  1.27  0.33 (**)   --
Academic performance  2.25  0.96  0.17 (**)   0.18 (**)  -

Note. N = 427.
(**) p < .01.

Table 3. Results of Hierarchical Regression Analysis

Dependent variable: Academic performance     Model 1      Model 2

First step   Control      Gender             -0.180       -0.100
             variables
                          Education Level 1  -0.160       -0.200
                          Education Level 2  -0.450 (**)   0.370 (**)
Second step  Independent  WeChat use                       0.110 (*)
             variable     intensity
Third step   Mediating    WeChat addiction
             variable
F                                            13.480 (***)   11.480 (***)
[[DELTA]R.sup.2]                              0.087 (***)    0.011 (*)

Dependent variable: Academic performance     Model 3

First step   Control      Gender             -0.070
             variables
                          Education Level 1  -0.190
                          Education Level 2   0.360 (***)
Second step  Independent  WeChat use          0.080
             variable     intensity
Third step   Mediating    WeChat addiction    0.090 (*)
             variable
F                                            10.480 (***)
[[DELTA]R.sup.2]                              0.012 (*)

Note. Education Level 1 = language learners and undergraduates.
Education Level 2 = master's and doctoral students.
(*) p < .05, (**) p < .01, (***) p < .001.
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Author:Li, Yan; Sallam, Marwan H.; Ye, Yinghua
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9CHIN
Date:Jan 1, 2019
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