







































 Humanities and Social Science Research; Vol. 8, No. 3; 2025 

ISSN 2576-3024   E-ISSN 2576-3032 

https://doi.org/10.30560/hssr.v8n3p108 

 108 Published by IDEAS SPREAD 

 

Research on Influencing Factors of College Students' Willingness to 

Use Mobile Learning 

Qun Wang1 & Yong Yu1 

1 Hubei University of Medicine, China 

Correspondence: Yong Yu, Hubei University of Medicine, Shiyan, Hubei, 442000, China. 

 

Received: May 28, 2025; Accepted: June 9, 2025; Published: June 12, 2025 

 

Abstract 

As a new way of learning, mobile learning is no longer limited by time and space. College students, the main 

audience group of mobile learning, study the factors that affect college students' willingness to use mobile learning 

is helpful to improve their motivation for mobile learning. Therefore, based on the theory of technology acceptance 

model, information literacy theory, self-management theory, and social influence theory, this study designed a 

questionnaire on factors influencing college students' willingness to use mobile learning by taking variables such 

as information literacy, perceived usefulness, perceived ease of use, self-learning management, social influence, 

and flow experience as dimensions. In this paper, 535 students at Hubei University of Medicine were investigated, 

and structural equation model analysis was used to verify the theoretical model and discuss the effect relationship 

among the variables. The results proved that information literacy has a positive impact on perceived ease of use 

and perceived usefulness; perceived ease of use has a positive effect on perceived usefulness; perceived usefulness, 

self-learning management, and social influence had positive effects on immersion experience. 

Keywords: mobile learning, usage willingness, technology acceptance model, influencing factors 

1. Introduction 

The development of mobile communication, the continuous upgrade of mobile devices and the gradual 

improvement of mobile software makes it more and more common for learners to use mobile learning, which 

breaks the traditional way of in person learning and greatly satisfies people's need for continuous learning [1]. 

When conducting mobile learning, people can use mobile devices to obtain many learning resources in a good 

network communication environment, and make reasonable use of fragmented time to learn, so as to improve their 

comprehensive quality [2]. Professor Huang Ronghuai of Beijing Normal University put forward that "mobile 

learning is the learning that learners take place in non-fixed and non-preset positions, or the learning that takes 

place by effective use of mobile technology" [3]. At present, the definition of mobile learning refers to a kind of 

learning that can take place at any time and place with the help of mobile devices. 

Mobile computing devices used in mobile learning must be able to effectively present learning content and provide 

two-way communication between teachers and learners. Mobile learning has changed the time, place, method, and 

content of learning dramatically. With the popularity of mobile devices, convenient mobile learning is more and 

more popular among college students [4]. The sudden outbreak of COVID-19 affected offline classroom teaching, 

and mobile learning gradually became the mainstream learning method during COVID-19 [5], which helped 

teachers and students realize the goal of "stopping classes without stopping learning". Although mobile learning 

has many advantages, students’ willingness to use it will be affected by some factors. Foreign scholars Ahmed 

Alsswey and others found that cultural and social factors will have an impact on the acceptance and adoption of 

mobile learning in the Arab Gulf countries [6]; Chinese scholar He Huimin's research on influencing college 

students' willingness to use mobile devices to learn English by using Unified Theory of Acceptance and Use of 

Technology (UTAUT) has found [7] that, among the design variables, perceived self-efficacy, content and 

resources of educational resources, and performance expectation have great influence on behavioral intention. In 

this paper, the influencing factors of college students' willingness to use mobile learning were explored by taking 

college students as the research object. Based on the technology acceptance model (TAM), the influencing factors 

of college students' willingness to use mobile learning were discussed. 

 

 



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2. Theoretical Basis and Research Hypothesis 

2.1 Theoretical Basis 

2.1.1 TAM 

TAM was proposed by American scholar Fred D. Davis in 1989 and developed based on the Theory of Reasoned 

Action (TRA) in the field of information system/computer technology. It is mainly used to explain and predict 

people's acceptance degree of the emerging information technologies [8]. TAM takes external variables, perceived 

usefulness, perceived ease of use and usage willingness as important structures. As the two key variables of the 

model, perceived usefulness and perceived ease of use will affect the user's attitude to use, and then the user's 

specific willingness to use, and finally the user's specific use behavior. Perceived usefulness refers to the extent to 

which a person believes that using a specific system can help his or her work. Perceived ease of use refers to the 

extent to which a person accepts the difficulty of using a system [9]. 

2.1.2 Information Literacy Theory 

The concept of information literacy was first put forward by P. G. Zurkowski in 1974, which refers to people's 

comprehensive ability to identify, acquire and apply information [10]. With the continuous development of 

information technology, information literacy has been given new connotation, and the elements of information 

literacy have also been improved and developed. The four elements of information literacy include information 

awareness, information knowledge, information ability and information morality. Information awareness refers to 

people's keen perception, judgment, and insight of information; information knowledge refers to theories, 

knowledge and methods related to information; information capability refers to the ability to understand and 

acquire information and the ability to use information and information technology; information morality is the 

ideology and behavior standard used to regulate the relationship between people. In this study, information literacy 

refers to learners' attitudes, methods, and abilities towards information in the process of mobile learning. 

2.1.3 Self-Management Theory 

Drucker, a contemporary management scientist, put forward the theory of "self-management" in 1954. Self-

management refers to the self-management of an individual to himself and his goals, thoughts, behaviors, and 

psychology to motivate, restrain and manage himself. The main contents of self-management include their own 

management of their own time, health, learning, emotional intelligence, and other content. The main contents of 

self-management include the management of your own time, health, learning, emotional intelligence, etc. People 

constantly meet their own needs, enrich the original knowledge structure, obtain valuable information, and 

ultimately achieve success without independent learning, and independent learning depends on self-management 

of learning. In this study, self-learning management refers to learners' arrangements and plans for their mobile 

learning. 

2.1.4 Social Influence Theory 

Kelman, the proponent of the social influence theory, hold the view that social influence refers to the fact that 

individuals' thoughts, attitudes, and behaviors will be more or less affected under the influence of others or the 

external environment. The social influence will affect the subjective norms of individuals through compliance, 

identification, and internalization [11]. Some scholars have revealed that social influence will have an indirect 

impact on college students' willingness to use mobile learning [12]. In this study, social influence refers to the 

extent to which learners' own willingness to use is influenced by others important people, such as teachers, friends, 

parents, etc. 

2.2 Research Hypothesis 

According to the technology acceptance model and relevant theoretical basis, this study takes information literacy, 

self-learning management and social influence as external variables, retains the two factors of perceived usefulness 

and perceived ease of use in the model, and adds the intermediate variable of flow experience combined with 

relevant literature, which has strong subjective feelings. Since only the influencing factors of college students' 

willingness to use mobile learning are discussed, the result variable is only the usage willingness. The research 

model established in this study is shown in Figure 1. 

Information literacy and perceived usefulness, perceived ease of use: The information literacy of college students 

is that they can judge when they need information and know how to obtain information and how to evaluate and 

effectively use the information they need according to their own ability. When college students have good 

information literacy, they can use their information ability to better choose, understand and use information. 

Therefore, information literacy has a positive impact on perceived usefulness and perceived ease of use. Kuang 



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Yun et al. believed that information literacy has a significant positive impact on perceived usefulness and perceived 

ease of use in the study of the factors affecting the acceptance of flipped classroom of students in a vocational 

college [13]. The following hypothesis was proposed. 

H1: Information literacy positively affects perceived usefulness. 

H2: Information literacy positively affects perceived ease of use. 

Perceived ease of use and perceived usefulness: In the technology acceptance model, perceived usefulness is 

affected by perceived ease of use. In this study, the more convenient mobile learning is, the more useful it is for 

college students. Through an empirical study, Xu Shun discovered that in the study on influencing factors of 

learners' willingness to use mobile learning platforms, perceived ease of use variable would have a significant 

impact on perceived usefulness [14]. The following hypothesis was proposed. 

H3: Perceived ease of use positively affects perceived usefulness. 

Perceived usefulness, perceived ease of use, self-learning management, social influence, and flow experience: 

Research shows that the higher the sense of usefulness is, the stronger the sense of flow experience is [15]. Hsu C 

L and Lu H P used the technology acceptance model to predict users’ acceptance of online games and believed 

that perceived ease of use would have a positive impact on the flow experience [16]. People with strong self-

learning management ability have strong self-discipline and strong anti-interference ability, and thus have stronger 

flow experience in mobile learning. Lulu Chen found that social influence would have a positive impact on mobile 

live broadcast users' flow experience in her research on factors influencing their willingness to pay [17]. The 

following hypothesis was proposed. 

H4: Perceived usefulness positively affects flow experience. 

H5: Perceived ease of use positively affects flow experience. 

H6: Self-learning management positively affects flow experience. 

H7: Social influences positively affects flow experiences. 

Flow experience and usage willingness: The impact of flow experience on usage willingness is positive in many 

research fields. When college students are immersed in mobile learning, it means that they have a good experience 

of using mobile learning, and the pleasure brought by mobile learning will increase their intention to use mobile 

learning. 

H8: Flow experiences positively affects usage willingness. 

 
Figure 1. Model assumption 

 

 

 



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3. Research Method 

3.1 Sample 

According to relevant theories, a questionnaire was prepared and an online survey was conducted by using the 

questionnaire star. The respondents were undergraduates of Hubei University of Medicine. A total of 842 

questionnaires were collected, and 535 valid questionnaires were obtained after screening, which means that all 

the respondents have experience in using mobile learning. Among college students with mobile learning 

experience, male students account for 42.8%, female students account for 57.2%. In terms of the duration of using 

mobile devices for mobile learning, 204 students use mobile devices for 2-4 hours a day, accounting for 38.1% of 

the total. Secondly, 136 people use mobile devices to study for 4-6 hours every day, accounting for 25.4%. 

Meanwhile, 134 students, accounting for 25% of the total, study on mobile devices for 0-2 hours a day. The number 

of people who use mobile devices for 6-8 hours is 42, accounting for 7.9%. At least 19 people (3.6%) use mobile 

devices to study for more than 8 hours. The overall conclusion is that college students use mobile devices to study 

for more than 2 hours a day. 

3.2 Reliability and Validity Analysis 

3.2.1 Validity Analysis 

Validity test is to verify the validity of the questionnaire, that is, whether the questions in the questionnaire can 

truly and effectively reflect the purpose of the research. In this paper, principal component analysis and variance 

maximization orthogonal rotation method in exploratory factor analysis were used to extract common factors, and 

then confirmatory factor analysis was used to verify the degree of fitting between the generated model and the 

sample data. Exploratory factor analysis and confirmatory factor analysis were analyzed with SPSS23.0 and 

AMOS24.0 software, respectively. 

3.2.1.1 Exploratory Factor Analysis 

Before the factor analysis, it is necessary to conduct KMO test and Bartlett sphericity test to judge whether the 

sample data is suitable for the factor analysis. The results illustrated that KMO value is 0.907 and Bartlett sphericity 

test is less than 0.010, which indicated that the data is suitable for the factor analysis. Exploratory factor analysis 

was carried out on all items, and the factor load of the item "If the conditions of equipment and resources are met, 

I am willing to conduct mobile learning" was less than 0.4, and the item "I am willing to recommend people around 

to use mobile learning" did not support the dimension of perceived ease of use in theory, and was also deleted. 

The KMO value of the second factor analysis was 0.903, the Bartlett sphericity test was less than 0.010, and the 

remaining 28 items were restricted to extract 7 common factors. The variance of the total variance interpretation 

obtained by the principal component analysis method was 62.707%. The component matrix after rotation obtained 

by the maximum variance method is shown in Table 1, including the factor load values of 28 items in 7 dimensions. 

 

Table 1. Results of exploratory factor analysis 

Items 
Component 

1 2 3 4 5 6 7 

Mobile learning can improve my academic 

performance. 
 0.778      

Mobile learning can improve my learning efficiency.  0.763      

Mobile learning allows me to study in my spare time.  0.749      

Mobile learning is helpful to my life.  0.751      

I can skillfully use mobile devices for learning.    0.746    

Mobile learning interface interaction is simple.    0.795    

It doesn't take much effort to adapt to mobile 

learning. 
   0.673    

Mobile learning always makes me forget about the 

world around me. 
    0.784   

When it comes to mobile learning, time always 

seems to fly by. 
    0.739   

When I use mobile learning, my attention will not be 

distracted elsewhere. 
    0.705   

When mobile learning encounters difficulties, I will 

use network information as an important reference to 
  0.709     



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find answers. 

When conducting mobile learning, I can quickly 

identify which learning resources are really useful. 
  0.678     

In mobile learning, I can summarize and integrate the 

information obtained. 
  0.573     

In mobile learning, I will consciously resist bad 

information, not to create and spread junk 

information. 

  0.467     

I can use mobile learning to solve the problems I 

encounter. 
  0.476     

When I encounter difficulties in learning, I believe I 

can solve them independently. 
  0.468     

I can reasonably arrange my time for mobile learning 

according to my learning goals. 
0.605       

After starting mobile learning, I can complete the 

learning task without interference. 
0.644       

I am a self-disciplined person in learning and 

research. 
0.806       

In life, I can easily deal with the relationship between 

entertainment and learning. 
0.763       

I can effectively manage my study time and complete 

my homework easily. 
0.744       

I can independently make learning plans and 

complete learning tasks. 
0.718       

My friend's recommendation will affect my use of 

mobile learning. 
     0.758  

My teacher's recommendation will influence my use 

of mobile learning. 
     0.823  

My family's recommendation will influence my use 

of m-learning. 
     0.693  

I enjoy interacting with teachers and students when 

I'm in mobile learning. 
      0.656 

I like others to praise and reply to my learning 

experience. 
      0.748 

I like to share interesting things about my study on 

social media. 
      0.809 

Extraction method: Principal component analysis.        

Rotation method: Kaiser's method of maximum 

variance normalization. 
       

a. The rotation has converged after 7 iterations.        

 

3.2.1.2 Confirmatory Factor Analysis 

Structural validity: In the first confirmatory factor analysis, items with factor load lower than 0.5 in the dimension 

were deleted. Among them, the items "when mobile learning encounters difficulties, I will take network 

information as an important reference for seeking answers" and "when mobile learning, I will consciously resist 

bad information and not create and spread junk information" had factor loads less than 0.5, so they were deleted. 

The fitting index was obtained after the second confirmatory factor analysis of the remaining 26 items, as shown 

in Table 2. All indicators meet the fitness criteria, which indicates that the model fits well and the structure validity 

of the questionnaire is satisfactory. 

 

Table 2. Fitting index 

 χ2/df GFI AGFI CFI IFI TLI SRMR RMSEA 

Fitting index 2.418 0.908 0.884 0.927 0.927 0.914 0.045 0.052 
 



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Convergent validity: Convergent validity emphasizes those measures that should belong to the same factor 

(indicator) and do fall under the same factor when measured. Average variance extraction (AVE) and combined 

reliability (CR) are often used to evaluate the convergent validity under the same dimension. Studies have 

demonstrated that AVE values >0.4 and CR values >0.6 indicate that the convergent validity is within the 

acceptable range [18]. In this study, the AVE value of each dimension is 0.413-0.571, and the CR value is 0.686-

0.877, which indicates that the combination reliability and convergence validity of each dimension pass the test. 

Discriminant validity: To verify the differentiation validity of each factor, this study conducted a confirmatory 

factor test with Amos 24.0 software to compare the fit of seven factor, six factor, five factor, four factor, three 

factor, two factor and single factor models. It can be seen from Table 3 that the seven-factor model has the best 

fitting degree, so the seven-factor model has good discriminative validity. 

 

Table 3. Discriminative Validity Analysis 

Model Factors χ2 df χ2/df RMESA GFI CFI 

Model 1(Seven-factor 

model) 
PU,PEU,FE,IL,SLM,SI,UW 672.264 278 2.418 0.052 0.908 0.927 

Model 2 (Six-factor model) PU+PEU,FE,IL,SLM,SI,UW 930.601 284 3.277 0.065 0.87 0.88 

Model 3 (Six-factor model) PU,PEU+FE,IL,SLM,SI,UW 1050.409 284 3.699 0.071 0.842 0.857 

Model 4 (Six-factor model) PU,PEU,FE+IL,SLM,SI,UW 918.978 284 3.236 0.065 0.869 0.882 

Model 5 (Five-factor model) PU+PEU+FE,IL,SLM,SI,UW 1242.94 289 4.301 0.079 0.826 0.823 

Model 6 (Five-factor model) PU,PEU+FE+IL,SLM,SI,UW 1096.605 289 3.794 0.072 0.842 0.85 

Model 7(Five-factor model) PU,PEU,FE+IL+SLM,SI,UW 1112.427 289 3.849 0.073 0.841 0.847 

Model 8(Four-factor model) PU+PEU+FE+IL,SLM,SI,UW 1407.892 293 4.805 0.084 0.803 0.793 

Model 9 (Four-factor model) PU,PEU+FE+IL+SLM,SI,UW 1378.405 293 4.704 0.083 0.794 0.798 

Mode 10(Three-factor 

model) 
PU+PEU+FE+IL+SLM,SI,UW 1859.81 296 6.283 0.099 0.732 0.709 

Model 11(Three-factor 

model) 
PU,PEU+FE+IL+SLM+SI,UW 1647.412 296 5.566 0.092 0.769 0.749 

Model 12(Two-factor model) PU+PEU+FE+IL+SLM+SI,UW 2131.158 298 7.152 0.107 0.71 0.659 

Model 13(Single factor 

model) 
PU+PEU+FE+IL+SLM+SI+UW 2296.493 299 7.681 0.112 0.694 0.628 

PU: Perceived usefulness; PEU: Perceived ease of use; FE: Flow experience; 

IL: Information literacy; SLM: Self-learning management; SI: Social influence; 

UW: Usage willingness. 

 

3.2.2 Reliability Analysis 

Reliability test is the reliability test of the questionnaire, which is to test the internal consistency of each dimension 

measurement items. This paper used Cronbach's Alpha coefficient to test the reliability of the data. The larger 

Cronbach's Alpha coefficient is, the higher the reliability of each variable is. In general, Cronbach's Alpha 

coefficient is above 0.6, which indicates that the questionnaire data had good reliability [19]. SPSS 23.0 was used 

for the reliability analysis of the questionnaire data. Cronbach's Alpha coefficient of each latent variable in the first 

reliability test is 0.641-0.876. After the validity test, the Cronbach's Alpha coefficient results of each latent variable 

in the second reliability test are shown in Table 4. Cronbach's Alpha coefficients of all potential variables are 

greater than 0.6, which indicates that the measurement reliability of potential variables in the questionnaire 

measured by this model is relatively good. 

 

Table 4. Reliability Analysis 

Variable Cronbach’s α coefficient Quantity of measurement items 

Perceived usefulness 0.838 4 

Perceived ease of use 0.718 3 

Flow experience 0.750  3 

Information literacy 0.733 4 

Self-learning management 0.876 6 

Social influence 0.742 3 

Usage willingness 0.685 3 



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4. Model Analysis 

Structural equation model (SEM) was used to test the relationship between constructs. After examining multiple 

indicators suitable for the models according to Bollen's (1989) suggestion, the goodness of fit of each model was 

determined using several statistical functions. The parameters were Chi-square/degree of freedom (χ2/df), adjusted 

goodness of fit index (AGFI), comparative fit index (CFI) and root mean square error approximation (RMSEA) 

[20]. AMOS24.0 software was used for data analysis. 

5. Research Results 

Through structural equation model analysis, the fit of the whole model was tested, and the results showed that the 

fit of the model is good. χ2/df=2.665, which was less than 3 suggested by Kline [21]. Among the similarity 

indicators, the goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), 

incremental fit index (IFI) and Tucker-Lewis index (TLI) are all close to 0.9, which indicates that the model 

similarity indicators fit well. In the anisotropy indexes, root mean residual (RMR)<0.05 and root-mean-square 

error of approximation (RMSEA)< 0.08, which suggests that the model anisotropy indexes are well fitted (See 

Table 5 for data results). Information literacy and perceived usefulness (β=0.471, P<0.001), information literacy 

and perceived ease of use (β= 0.663, P<0.001), perceived ease of use and perceived usefulness (β= 0.224, P<0.01), 

perceived usefulness and immersion experience (β=0.274, P<0.001), self-learning management and flow 

experience (β= 0.454, P<0.001), social impact and immersion experience (β= 0.180, P<0.01) and immersion 

experience and willingness to use (β= 0.493, P<0.001) indicate that the hypothesis H1, H2, H3, H5, H6, H7, and 

H8 is supported. According to the results of the test model, no evidence was found to support hypothesis H4. The 

results of path analysis to verify the hypothesis model are shown in Figure 2 and Table 6. 

 

Table 5. Model fitting index 

 χ2/df GFI AGFI CFI IFI TLI RMR RMSEA 

Fitting index 2.665 0.896 0.873 0.911 0.911 0.899 0.041 0.056 

 

 

Figure 2. Final model 

 

Table 6. Path analysis for hypothesis testing 

Hypothetical path Standardization coefficient β C.R. P Pass the test 

PEU <--- IL 0.663 8.522 *** Yes 

PU <--- IL 0.471 6.193 *** Yes 

PU <--- PEU 0.224 3.04 0.002 Yes 

FE <--- PU 0.274 4.282 *** Yes 



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FE <--- SI 0.18 2.988 0.003 Yes 

FE <--- SLM 0.454 6.772 *** Yes 

FE <--- PEU -0.096 -1.475 0.14 No 

UW <--- FE 0.493 6.866 *** Yes 

PEU: Perceived ease of use; IL: Information literacy; PU: Perceived usefulness; FE: Flow experience; 

SI: Social influence; SLM: Self-learning management; UW: Usage willingness 

*P<0.05**P<0.01***P<0.001 

 

6. Discussion 

Based on TAM, this study explored the effects of information literacy, perceived usefulness, perceived ease of use, 

self-learning management, social influence, and flow experience on college students' willingness to use mobile 

learning. Information literacy, self-learning management and social influence are the external variables, and flow 

experience is the intermediate variable. Our research results proved that the factor that directly affects college 

students' willingness to use mobile learning is immersion experience. When college students are completely 

immersed in mobile learning, they will have great pleasure in mobile learning and will not be bored for a long 

time, and then will have a stronger willingness to use. The variables that directly affect flow experience are 

perceived usefulness, self-learning management, and social influence. The influence intensity of the three factors 

was self-learning management (0.45) > perceived usefulness (0.27) > social influence (0.18). College students 

with stronger self-learning management have stronger self-discipline and are more likely to be immersed in the 

state of mobile learning. However, studies have shown that whether college students can enter the state of 

immersion learning has nothing to do with self-learning management [22]. More mobile learning users think that 

the knowledge of mobile learning is useful. When they devote themselves to mobile learning, they will have better 

immersion experience. Flow experience is often affected by the external environment, and the positive influence 

of the external environment will significantly improve the level of flow experience. The positive influence of 

perceived usefulness on information literacy and perceived ease of use was 0.47 and 0.22, respectively, and the 

positive influence of perceived ease of use on information literacy was 0.66. College students with higher 

information literacy have stronger information awareness, attitude, and evaluation ability, which has a positive 

impact on the perceived usefulness and ease of use of mobile learning. Similarly, perceived ease of use has a 

positive impact on perceived usefulness in line with the assumptions in the technology acceptance model. 

The intensity of the variables that have a positive impact on college students' willingness to use mobile learning is 

as follows: immersion experience (0.49)>self-learning management (0.22)>perceived usefulness (0.14)>social 

impact (0.09)>information literacy (0.08)>perceived ease of use (0.03). According to our research, immersion 

experience will have a positive impact on college students' intention to use mobile learning, which is consistent 

with the research results that immersion experience will have an impact on the intention to use/participate 

in/purchase something [23-24]. Once immersed in mobile learning, mobile learners will have a pleasant experience 

of mobile learning. The stronger the sense of immersion is, the stronger the willingness to use mobile learning will 

be. This requires mobile learning developers to focus on the specific needs of college students' mobile use and 

introduce gamification elements to make mobile learning more attractive to users. College students with strong 

self-learning management ability have the strong willingness to use, and self-learning management will have a 

positive effect on the willingness to use mobile learning [25]. The COVID-19 has made online mobile learning 

the main way of learning. Although online learning abandons the disadvantages of traditional learning, it increases 

the factors that interfere with and tempt learning [26]. Therefore, only by actively improving self-learning 

management ability can college students consciously resist the temptation and enhance their willingness to use 

mobile learning. In the technology acceptance model, perceived usefulness and usage attitude jointly influence 

usage willingness, while usage attitude is jointly influenced by perceived usefulness and perceived ease of use. 

Therefore, perceived usefulness and ease of use will directly or indirectly have a positive impact on usage 

willingness. The results of this study are consistent with the theory of TAM. When college students feel that mobile 

learning is simple, convenient, and fruitful, they will have a higher willingness to use mobile learning [27]. The 

willingness to use mobile learning is often influenced by mobile learning users such as classmates, friends or 

teachers, and individual willingness is largely influenced by society. Therefore, the acceptance and support of 

mobile learning in the external environment will promote college students to use mobile learning more actively. 

Finally, the results of this study revealed that college students with high information literacy will have a strong 

intention to use mobile learning. Therefore, improving college students' information literacy will help improve 

their intention to use mobile learning. 

 



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Acknowledge 

This work was supported by the 2018 Hubei Provincial Education Department Humanities and Social Sciences 

Research Project (Grant No. 18D072) 

Author Contributions 

Qun Wang (1981-), female, Luzhou, Sichuan province, professor: Conceptualization, Writing original draft, Data 

curation, Formal analysis, Funding acquisition.  

Yong Yu (1982-) (corresponding author), male, Shiyan, Hubei Province, professor: Methodology, Software, 

Supervision, Review & editing.  

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