English learning motivation with TAM: Undergraduates’ behavioral intention to use Chinese indigenous social media platforms for English learning

Abstract

As the largest proportion of Chinese netizens, undergraduates’ use of social media platforms to learn English has become a focus of Chinese higher education. Based on technology acceptance model (TAM) and theory of language learning motivation, through a structural equation modeling approach, this paper explored factors that influenced the behavioral intention of Chinese undergraduates to use two typical Chinese indigenous social medial platforms (i.e. WeChat and Bilibili) to learn English. By analyzing data from 834 Chinese undergraduates who answered an online questionnaire, we found that perceived ease of use, perceived usefulness, and English learning motivation had positive effects on intention to use both platforms to learn English. Perceived enjoyment had a positive influence on the intentional use of WeChat, but not Bilibili. Additionally, perceived usefulness acted as a mediator between perceived ease of use and intentional use, as well as between perceived enjoyment and intentional use; however, it did not mediate the relationship between English learning motivation and intentional use for both platforms. This study is beneficial to English teachers, developers of social media platforms, and other related stakeholders, as it provides insight into Chinese undergraduates’ use of indigenous platforms to learn English, while incorporating motivation theory with technology acceptance model.

Keywords:

• Chinese indigenous social media platforms
• English learning motivation
• technology acceptance
• structural equation modeling

1. Introduction

Social media has become a pervasive part of modern life, particularly among young people. It is a form of virtual communication that enables users to interact with each other through networks (Kandpal et al., 2023). In recent years, the use of social media has been found to have a positive impact on learning and academic performance (Al-Jarrah et al., 2019). It provides learners with the ability to generate, comprehend, and interact ideas with a wide range of people (Lin et al., 2013), and facilitates active learning, engagement, and improved learning outcomes (Alshuaibi et al., 2018). Research has also shown that those who view social media platforms as a useful tool are more likely to be engaged in their education than those who do not (Morton et al., 2019).

It has been suggested that social media platforms can be a beneficial tool in L2 education, as it has a positive impact on learners’ behavioral, social, cognitive and emotional engagement, as well as L2 learning outcomes (Yu et al., 2020). Instant messaging and multi-modal communication, which are available on social media platforms, offer an environment for learners to collaborate (Akkara et al., 2020) and are beneficial for non-English speakers to communicate with English speakers (Lin et al., 2013). Studies have shown that the majority of L2 learners agree that social media use is beneficial in improving their language proficiency (Desta et al., 2022).

Despite the fact that social media platforms can be effective for L2 learning, certain learners find it challenging to get the most out of them and some are even reticent to use them (Lim & Newby, 2020). To take full advantage of technologies in teaching and learning, it is essential to understand the factors that influence learners’ behavioral intention to use social media platforms for L2 learning. Without acceptance and intention from learners, the potential of such platforms cannot be realized.

Up to December 2022, China has become home to 1.67 billion netizens, with a large percentage of them being university students who are particularly drawn to social media for interactive capabilities. English-medium platforms such as YouTube, Twitter, Instagram are not accessible in mainland China (Mei et al., 2017). Consequently, Chinese indigenous social media platforms, such as WeChat and Bilibili, have become increasingly popular. WeChat is an app that facilitates users to send text or voice messages, host video conferences, play video games, and share photos, videos, and locations, thus serving as an instant messaging and social media platform. Bilibili is a video streaming and sharing platform, similar to YouTube, but with its own distinct community culture and other distinguishing features. The majority of prior studies have investigated the utilization of English-medium social media for L2 education. Consequently, further research is necessary to explore the acceptance and utilization of Chinese indigenous social media platforms (CISMPs) by learners in China for English learning, as the results may not be applicable to the Chinese educational context.

2. Literature review and hypotheses development

2.1. Technology acceptance model

The technology acceptance model (TAM) was devised by Davis (1989) to explain and predict users’ adoption and utilization of new technologies. The model explains the adoption process of a new technology based on four main constructs: perceived ease of use (PEOU), perceived usefulness (PU), attitude towards use (AU), and behavioral intention (BI) to use. PEOU measures the effort required to incorporate the technology into the user’s working practice. PU assesses the user’s perception of the usefulness of the technology. AU evaluates the user’s (un)favorable attitude towards the technology. BI measures the user’s intention to use the technology (Figure 1) (Davis, 1989).

Figure 1. Technology acceptance model (Davis, 1989).

Note: PEOU = perceived ease of use; PU = perceived usefulness; AU = attitude towards use; BI = behavioral intention.

Numerous studies have confirmed that TAM is a reliable predictive model when it comes to the acceptance of technology, with evidence found in multiple contexts, including mobile banking (Zhou et al., 2010), e-learning (El-Masri & Tarhini, 2017), e-training (Bello & Bhatti, 2017), and virtual reality (Huang, 2023).

In educational settings, TAM has also been found to be a useful tool for understanding technology adoption in different contexts among different populations. Intentional utilization of computer-assisted language learning 2.0 by pre-service EFL teachers (Mei et al., 2017), intention to use web 2.0 technologies by university teachers (Faizi, 2018), acceptance of digital media by students (Pumptow & Brahm, 2020), L2 learners’ adoption of virtual reality (Klimova, 2021), K-12 students’ use of online learning (Zou et al., 2021), university students’ acceptance of mobile learning (Al-Rhami et al., 2022), and learners’ technology adoption for STEM in higher education (Oladele et al., 2023) have all been observed.

However, the original TAM has been criticized for its oversimplification and lack of consideration for other variables that may affect the acceptance process (Huang & Teo, 2021). This can lead to issues in the variance explained when TAM is used in exploratory studies (Legris et al., 2003). Additionally, it has been argued that TAM does not provide enough practical advice on how to improve PEOU and PU (Luo et al. 2021). Therefore, this study proposes a modified TAM that is applicable to the context and objectives of this study by adding two constructs such as perceived enjoyment (PE) and English learning motivation (ELM). The modified model retains the constructs of PEOU, PU, and BI from the original TAM (Davis, 1989) but removes AU. AU, which was included in the first version of TAM, was removed in later studies such as Venkatesh and Davis (2000) and Venkatesh and Bala (2008) as it only partially moderates the effects of PEOU and PU on BI. This removal allows for a better understanding of the effects of the constructs on BI (Venkatesh, 2000). In the educational field, AU is also often removed to create models that are more suitable for the educational context (Tan et al., 2014). Therefore, the following hypotheses for the constructs of PEOU, PU, PE, ELM, and BI were proposed.

2.1.1. Perceived ease of use and behavioral intention to use

Perceived ease of use (PEOU) is the degree to which a person believes that utilizing a technology is effortless. Studies have indicated that when a technology is deemed simple to use, it is more likely to be accepted. Conversely, if users find it difficult to become familiar with a new technology, their intention to use it decreases (Teo et al., 2008). Khairi and Baridwan (2015) found that PEOU of an accounting information system in Sharia banking had an impact on usage. Balouchi and Samad (2021) determined that PEOU had a positive correlation with behavioral intention to accept online technology for informal English learning. Humida et al. (2022) showed that PEOU had a positive impact on behavioral intention to utilize e-learning. Accordingly, this study hypothesizes that PEOU has a positive effect on the behavioral intention of Chinese undergraduates to utilize CISMPs for their English learning:

H1:

PEOU of CISMPs positively predicts behavioral intention to use these platforms for English learning.

2.1.2. Perceived usefulness and behavioral intention to use

An individual’s perception of how a technology can enhance their job performance is referred to as perceived usefulness (PU). PU has been demonstrated to be a reliable and valid construct across cultures, influencing a person’s behavioral intention to use the technology (Balouchi & Samad, 2021). Studies have highlighted the positive effect PU has on the behavioral intention to use English mobile learning systems (Chang et al., 2013), teaching blogs (Chen et al., 2015), cloud services (Huang, 2016), and e-learning (Humida et al., 2022). This study seeks to assess PU in terms of how an undergraduate perceives CISMPs to be useful for English learning. It is suggested that those who view CISMPs as useful for English learning are likely to use them. It is therefore suggested that:

H2:

PU of CISMPs positively predicts behavioral intention to use these platforms for English learning.

2.1.3. Perceived enjoyment and behavioral intention to use

Perceived enjoyment (PE) is a concept that measures how enjoyable and pleasurable using a technology is (Venkatesh & Davis, 2000). It was incorporated into the original TAM together with PEOU and PU and has been demonstrated to be a factor in motivating people to accept new technology (Huang & Ren, 2020). It is necessary to consider PE, an intrinsic source of motivation, to understand users’ acceptance of new technology, in contrast to PEOU and PU, which are extrinsic sources of motivation (Davis et al., 1992).

Research has examined the impact of PE on users’ acceptance of technological innovations in various contexts, such as cloud computing (Shiau & Chau, 2016), online English learning (Balouchi & Samad, 2021), online payment systems (Rouibah et al., 2016), and virtual reality (Tao et al., 2019). Bagdi and Bulsara (2023) found that PE had a fully significant relationship with the behavioral intention to use online learning. These findings suggest that PE is a significant factor in predicting users’ acceptance of a technology. Although Venkatesh (2000) asserted that PE was a weaker predictor of users’ behavioral intention to use a new technology than PEOU and PU, recent studies have demonstrated that PE is positively associated with users’ behavioral intention to adopt a technology (Ramírez-Correa et al., 2019). As learning English through CISMPs is an activity of personal interest and is voluntary in nature, the pleasure derived from using the technology should be taken into account. Therefore, this study hypothesizes that:

H3:

PE of CISMPs positively predicts behavioral intention to use these platforms for English learning.

2.1.4. Perceived ease of use and perceived usefulness

Studies have demonstrated that PEOU of a technology has a direct influence on its PU (Winarno & Putra, 2020; Li, Chau and Lou 2021). Feng et al. (2021) conducted two model-based meta-analytic reviews and found that PEOU had a medium positive effect on PU. Cao et al. (2021) found that PEOU had a direct effect on students’ perception of the usefulness of intelligent tutoring systems. Huang et al. (2021) found that PEOU positively impacts higher education teachers’ perception of the usefulness of information and communications technology. Sun and Gao (2020) showed that those who find smartphones and related applications easier to use tend to view them as more beneficial for English learning activities. Al-Adwan et al. (2023) discovered that PEOU significantly and positively affects PU of university students’ acceptance of Metaverse-based learning platforms. Bailey et al. (2022) also proved that PEOU positively affects PU with video conference tools among university students. In light of these studies, we propose that PEOU of CISMPs positively affects Chinese university students’ PU of these platforms to learn English. This idea has led to the formulation of the following hypothesis:

H4:

PEOU of CISMPs positively predicts PU of these platforms for English learning.

2.1.5. Perceived enjoyment and perceived usefulness

Research has demonstrated that PE has a positive effect on PU when using various technologies, including augmented reality teaching platforms (Balog & Pribeau, 2010), instant messaging (Li et al., 2005), search engines (Liaw & Huang, 2003), and systems such as multimedia e-learning systems, web-based training systems (Al-Gahtani, 2016; Hanif et al., 2018). Li et al. (2021) discovered that PE had a positive effect on PU of e-learning for students studying English in Chinese universities. Pratama (2021) demonstrated that PE of mobile learning among secondary school students in Indonesia impacted their PU. Furthermore, Linardatos and Apostolou (2023) showed that PE of high school students was the strongest factor in influencing their perception of usefulness of digital comics creation in classroom learning. The findings suggest that if a technology is perceived as enjoyable, it is likely to be perceived as more useful (Sun & Zhang, 2008). Therefore, the use of CISMPs to learn English can be motivated by PE, which is an intrinsic motivation and is expected to lead to increased extrinsic motivation, such as PU. Consequently, it is hypothesized that:

H5:

PE of CISMPs positively predicted PU of these platforms for English learning.

2.1.6. The mediating effect of perceived usefulness

It has been found that PU can mediate the relationship between a predicting variable and a predicted variable. For example, Santhanamery and Ramayah (2018) proposed that PU mediated the relationship between trust in an e-filing system and continuance usage intention of that system in Malaysia. Kim et al. (2021) found that PU of AI fosters more positive attitudes and stronger perceived realism of AI. Humida et al. (2022) found that PU mediated the relationship between experience and behavioral intention to use an e-learning system, as well as between PEOU and PE and behavioral intention to use in Bangladesh. Kim and Song (2022) confirmed the mediating role of PU on the relationships between task-technology fit, teaching presence, and continuance intention to use MOOCs among Korean learners. This study proposes that PU mediates the relationship between undergraduates’ PEOU and PE of CISMPs for English learning, and their intentional adoption of these platforms for English learning purposes. As a result, this study suggestes the following hypotheses:

H6:

PU of CISMPs mediates the relationship between PEOU and behavioral intention to use these platforms for English learning.

H7:

PU of CISMPs mediates the relationship between PE and behavioral intention to use these platforms for English learning.

2.2. English learning motivation

According to Dörnyei (2019), language learning is greatly impacted by one’s level of motivation, surpassing other individual variables. Motivation can be classified into two distinct types: intrinsic and extrinsic. Intrinsic motivation is sourced internally and associated with one’s identity, whereas extrinsic motivation is external and relies on rewards from the external environment. Motivation, particularly of the intrinsic kind, can be a great aid in the process of L2 learning, as it can help to sustain learners’ enthusiasm. This study seeks to examine the behavioral intention of Chinese undergraduates to make use of CISMPs for English learning in a voluntary and self-motivated learning environment. Consequently, only intrinsic motivation for English learning has been taken into consideration as a possible factor that can affect undergraduates’ intention to adopt such platforms. Ryan and Deci (2000) proposed that intrinsic motivation of English learners was measured through their free choice, perceived interests and enjoyment. This study aimed to measure the intrinsic motivation of Chinese undergraduates in English learning by assessing their interest/enjoyment and self-reported efforts.

Learner motivation is considered to be a crucial factor in determining the success of language learning (Iwaniec, 2014; Xu & Gao, 2014). Despite the numerous studies that have been conducted on this topic, there is a lack of investigation into how individual language learning motivation affects the acceptance of technology (Lee et al., 2015). This is especially true when considering how English learning motivation affects the intention of Chinese undergraduates to use CISMPs for English learning.

Previous research on technology acceptance has mainly focused on users’ adoption of various technologies, yet their study-related motivation remains largely unexplored. To bridge this gap, this study focused on not only motivation for technology adoption but also motivation for language learning, particularly in the context of social media platforms for English learning. Therefore, English learning motivation was considered a possible factor that may influence learners’ intentional adoption of CISMPs for English learning. By including it as a parallel factor to PEOU, PU, and PE, the following hypotheses are suggested:

H8:

ELM positively predicts behavioral intention to use CISMPs for English learning.

H9:

ELM positively predicts PU of CISMPs for English learning.

H10:

PU of CISMPs mediates the relationship between ELM and behavioral intention to use these platforms for English learning.

In accordance with the hypotheses, a conceptual model was proposed and illustrated in Figure 2.

Figure 2. The conceptual model.

3. Method

3.1. Participants

A sample of 900 first-year and second-year undergraduates from a university in eastern China was randomly selected from a population of 4474 first-year and 4190 second-year students. Of these, 834 participants completed an online questionnaire. The gender distribution of the participants was 220 (26.40%) male and 614 (73.60%) female. The average age of the participants was 19.37 (SD = 1.001), with 453 (54.30%) first-year undergraduates and 381 (45.70%) second-year undergraduates. In terms of the duration of English learning, 226 (26.62%) had been studying for more than twelve years, 98 (11.54%) for twelve years, 180 (21.20%) for eleven years, 160 (18.85%) for ten years, 95 (11.19%) for nine years, and 90 (10.60%) for less than nine years.

3.2. Survey instrument

This study employed an online questionnaire to measure participants’ PEOU, PU, PE, ELM, and behavioral intention (BI) to use two CISMPs (WeChat, Bilibili) for English learning. The two chosen CISMPs share some similarities but have their unique features: WeChat is mainly used for communication and interaction, Bilibili is focused on sharing and enjoying videos. Each platform will be examined separately. A brief introduction was given prior to the survey to explain the definition and types of CISMPs, and demographic information, familiarity with CISMPs, and the frequency of using WeChat and Bilibili in daily life and for English learning were collected.

3.2.1. Perceived ease of use

Assessing the participants’ PEOU of CISMPs (WeChat, Bilibili) for learning English, a 4-item measure was employed, based on Tarhini et al. (2016). The items asked participants to rate their agreement with statements such as “The Chinese indigenous social media platforms (WeChat, Bilibili) are easy to use when learning English” on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree).

3.2.2. Perceived usefulness

A 6-item measure, adapted from Balouchi and Samad (2021), was utilized to measure participants’ PU of CISMPs (WeChat, Bilibili) for learning English. Participants were asked to rate statements such as “The Chinese indigenous social media platforms (WeChat, Bilibili) enhance my English learning” on a 5-point Likert-type scale.

3.2.3. Perceived enjoyment

To evaluate the participants’ PE of CISMPs for English learning, 4 items were adapted from Chang et al. (2013). They were asked to rate their level of agreement with statements such as “I find using the Chinese indigenous social media platforms (WeChat, Bilibili) to be enjoyable for fulfilling my needs to learn English” on a 5-point Likert scale.

3.2.4. English learning motivation

A 5-item, 5-point Likert scale, derived from Ryan and Deci (2000), was employed to assess ELM, with statements such as “I enjoy learning English very much”.

3.2.5. Behavioral intention to use

Participants’ BI to use CISMPs (WeChat, Bilibili) for English learning was evaluated by four items with a 5-point scale, which was adapted from Balouchi and Samad (2021). Participants were asked to answer questions such as “I will recommend the Chinese indigenous social media platforms (WeChat, Bilibili) for English learning to others.” A list of the measurement items for these constructs can be found in the Appendix.

3.3. Data collection

Ethical approval was obtained prior to data collection. The researcher provided the students with the objectives of the study and ensured them of the anonymity and voluntary nature of the participation. A sample of 900 undergraduates from a university in eastern China was randomly selected from a population of 4474 first-year and 4190 second-year students, as English is a compulsory subject in the first two years of university in China. An online anonymous survey was used to collect data, with a link hosted on https://www.wjx.cn sent to participants via QQ, WeChat, or email and open for three weeks. After removing incomplete and unqualified responses, 834 valid responses were obtained for analysis.

3.4. Data analysis

Descriptive statistics were calculated using SPSS 26.0 and structural equation modeling was performed using Mplus 8.3. To evaluate the reliability and validity of the measurement model, internal consistency reliability, convergent validity, and discriminant validity were employed. To assess the fitness of the model and data, indices such as the Comparative Fitting Index (CFI), Tuck-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standard Root Mean Square Residual (SRMR) were utilized. To detect the presence of mediating effects, Hayes’ (2009) bootstrap method was employed, with 2000 iterations used to generate the confidence intervals.

4. Results

4.1. Descriptive statistics

The survey results indicated that participants were familiar with CISMPs (M = 3.050, SD = 1.234). Frequency of use was determined for WeChat (M = 4.18, SD = 1.130) and Bilibili (M = 3.20, SD = 1.285), with both platforms being used frequently. Despite their familiarity and frequent use of CISMPs, participants reported a lower frequency of use for English learning: WeChat (M = 2.76, SD = 1.222), Bilibili (M = 2.84, SD = 1.228).

After assessing the correlation between demographic variables and the outcome variable BI, we found no significant relation between grade and BI for both WeChat (r = −0.042, p = 0.225) and Bilibili (r = −0.030, p = 0.380). Likewise, major was not significantly linked to BI for both WeChat (r = −0.014, p = 0.683) and Bilibili (r = 0.006, p = 0.866). Gender, however, showed a weak correlation with BI for WeChat (r = 0.182, p < 0.001) and Bilibili (r = 0.172, p < 0.001). Therefore, grade and major were not taken into consideration in the following analysis, while gender was controlled for.

4.2. The measurement model

Before examining the proposed hypotheses, the reliability and validity of the measurement items (indicators) and scales (constructs) should be tested (Hair et al., 2019). The evaluation of the measurement model necessitates an assessment of internal consistency reliability, convergent validity, and discriminant validity. To evaluate internal consistency reliability, Cronbach’s alpha (α) and composite reliability (CR) were utilized. According to Hair et al. (2009), an α score of 0.9 or higher is considered excellent, while scores between 0.7 to 0.9 and 0.6 to 0.7 are considered good and acceptable, respectively. Moreover, CR scores between 0.7 to 0.9 and 0.6 to 0.7 are deemed satisfactory and acceptable, respectively (Hair et al., 2014). The results of α and CR for PEOU, PU, PE, BI, and ELM for WeChat and Bilibili indicated that the measurement model had satisfactory internal consistency reliability (Table 1).

Table 1. Reliability and validity of the measurement model

Items	Factor loading		AVE		CR		Cronbach’s alpha
	WeChat	Bilibili	WeChat	Bilibili	WeChat	Bilibili	WeChat	Bilibili
PEOU			0.710	0.681	0.906	0.894	0.855	0.837
PEOU1	0.900	0.895
PEOU2	0.886	0.873
PEOU3	0.881	0.825
PEOU4	0.684	0.694
PU			0.832	0.816	0.967	0.964	0.960	0.955
PU1	0.925	0.909
PU2	0.920	0.907
PU3	0.915	0.908
PU4	0.912	0.900
PU5	0.903	0.909
PU6	0.898	0.888
PE			0.623	0.599	0.866	0.853	0.788	0.761
PE1	0.868	0.874
PE2	0.859	0.861
PE3	0.798	0.778
PE4	0.603	0.535
BI			0.778	0.848	0.946	0.957	0.938	0.940
BI1	0.943	0.946
BI2	0.921	0.922
BI3	0.913	0.909
BI4	0.897	0.906
ELM			0.762		0.941		0.920
ELM1	0.932
ELM2	0.931
ELM3	0.918
ELM4	0.848
ELM5	0.717

PEOU = perceived ease of use; PU = perceived usefulness; PE = perceived enjoyment; BI = behavioral intention to use; ELM = English learning motivation; CR = composite reliability; AVE = average variance extracted.

The assessment of the convergent validity was conducted using average variance extracted (AVE) and factor loading. According to Fornell and Larcker (1981), AVE is a reliable measure for determining convergent validity, with a minimum threshold of 0.5. The AVE scores in this study surpassed the stipulated threshold, with PEOU, PU, PE, BI, and ELM having scores of 0.710, 0.832, 0.632, 0.778, and 0.762 respectively. Furthermore, the factor loading of each item on its respective construct ranged from 0.535 to 0.946, which was near to or higher than the suggested value of 0.60. The results of both factor loading and AVE indicated that the measurement model displayed convergent validity (Table 1).

To measure the degree to which a particular construct in a survey instrument is distinct from the other constructs, discriminant validity is utilized (Hair et al., 2009). By comparing the square root of AVE for each construct with the correlations between the constructs (Fornell & Larcker, 1981), it was evident that the diagonal elements were higher than the off-diagonal elements, thereby demonstrating that discriminant validity was established for both platforms (Table 2).

Table 2. Discriminant validity of the measurement model

WeChat		PEOU	PU	PE	ELM	BI
	PEOU	.843
	PU	.506	.912
	PE	.407	.750	.789
	ELM	.286	.487	.429	0.873
	BI	.449	.742	.671	0.562	0.882
Bilibili		PEOU	PU	PE	ELM	BI
	PEOU	.825
	PU	.615	.903
	PE	.480	.740	.774
	ELM	.288	.446	.427	0.873
	BI	.543	.768	.623	0.540	0.921

PEOU = perceived ease of use; PU = perceived usefulness; PE = perceived enjoyment; BI = behavioral intention to use; ELM = English learning motivation; The bold diagonal elements are the square roots of AVE of each construct; off-diagonal elements are correlations of constructs.

The proposed model was evaluated for its congruence with the empirical data using indices such as CFI, TLI, SRMR, and RMSEA. To obtain a good fit, the values of CFI and TLI should exceed 0.90, while those of RMSEA and SRMR should be below 0.08. An excellent fit is indicated by values higher than 0.95 for CFI and TLI, and lower than 0.06 for both RMSEA and SRMR (Hu & Bentler, 1999). The results of this study demonstrated that the hypothesized model meets the criteria for good fit, with WeChat: CFI = 0.960, TLI = 0.953, RESEA = 0.066, SRMR = 0.037; Bilibili: CFI = 0.960, TLI = 0.952, RESEA = 0.066, SRMR = 0.036.

Upon confirming the measurement model to be both valid and reliable with an acceptable level of fit, the hypotheses were then analyzed and evaluated through the structural model.

4.3. The structural model

Following the evaluation of the reliability and validity of the measurement model, the structural model with PU as a mediator was further investigated. To gauge the significance of the mediation paths for WeChat and Bilibili, the bias-corrected bootstrap method proposed by Hayes (2009) was adopted.

4.3.1. WeChat

Analysis of direct effects indicated that PEOU, PU, PE and ELM had a significant and positive impact on BI to use WeChat to learn English (PEOU: β = 0.074, p < 0.05; PU: β = 0.294, p < 0.01; PE: β = 0.358, p < 0.001; ELM: β = 0.241, p < 0.001), thereby confirming H1, H2, H3 and H8. Additionally, PE (β = 0.358) had the highest influence on BI, followed by PU (β = 0.294), ELM (β = 0.241), and PEOU (β = 0.074), indicating that these factors are predictors of Chinese undergraduates’ intentions to use WeChat for English learning.

The results indicated that PEOU positively predicted PU of WeChat for learning English (β = 0.125, p < 0.001), thereby affirming H4. Furthermore, PE had a significant positive influence on PU of WeChat for learning English (β = 0.793, p < 0.001), thus validating H5. Nevertheless, ELM was not significantly and positively linked to PU of WeChat for learning English (β = 0.060, p > 0.05), thus not verifying H9 (Table 3). Out of the two factors impacting PU, PE had the strongest effect size (β = 0.793), while PEOU had a lesser effect size (β = 0.125).

Table 3. Direct and indirect effects of the structural model (WeChat)

	Product of coefficient				Bootstrapping 2000 times 95% CI
Path relationship	Point estimate	S.E.	Z	p	Lower 2.5%	Upper 2.5%
Direct effects
PEOU→BI	.074	0.033	2.237	.025	0.010	.148
PU→BI	.294	0.099	2.987	.003	0.066	.445
PE→BI	.358	0.102	3.514	.000	0.208	.666
PEOU→PU	.125	0.030	4.208	.000	0.070	.187
PE→PU	.793	0.043	18.641	.000	0.697	.864
ELM→BI	.241	0.050	4.779	.000	0.151	.360
ELM→PU	.060	0.039	1.531	.126	−0.014	.143
Indirect effects
PEOU→PU→BI	.037	0.016	2.342	.019	0.011	.073
PE→PU→BI	.233	0.076	3.056	.002	0.064	.388
ELM→PU→BI	.018	0.014	1.245	.213	−0.001	.059

PEOU = perceived ease of use; PU = perceived usefulness; PE = perceived enjoyment; BI = behavioral intention to use; ELM = English learning motivation.

The results also showed that the mediating effect of PU on the relationship between PEOU and BI to use WeChat for English learning was significant (β = 0.037, 95% CI: [0.011, 0.073]), thus verifying H6. Furthermore, the mediating effect of PU on the connection between PE and BI to use WeChat for English learning was also confirmed (β = 0.233, 95% CI: [0.064, 0.388]), thus validating H7. However, H10 was not supported as the mediating effect of PU on the relationship between ELM and BI to use WeChat for English learning was not significant, with zero existing between the lower and upper bound in the 95% CI (β = 0.018, 95% CI: [−0.001, 0.059]) (Table 3).

Results in Tables 3 showed that 6 of 7 hypotheses concerning the direct effects of behavioral intention to use WeChat for English learning were validated, and 2 of 3 hypotheses regarding the mediating effects were also confirmed; thus, 8 out of 10 hypotheses were verified.

4.3.2. Bilibili

The findings showed that PEOU and PU positively predicted BI to use Bilibili for English learning (PEOU: β = 0.115, p < 0.01; PU: β = 0.489, p < 0.001), thus affirming H1 and H2. In contrast, PE had no significant and positive effect on BI to use Bilibili (β = 0.120, p > 0.05), thus rejecting H3. ELM was found to positively impact BI to use Bilibili (β = 0.268, p < 0.001), thus verifying H8. What’s more, PU had the greatest positive effect on BI (β = 0.489), ELM (β = 0.268) and PEOU (β = 0.115) being the next two, demonstrating that these factors are predictors of Chinese undergraduates’ adoption intentions to use Bilibili to learn English. However, PE did not affect BI to use Bilibili.

The results indicated that PEOU positively affected PU of Bilibili for English learning (β = 0.199, p < 0.001), thereby confirming H4. Moreover, PE had a positive and significant effect on PU (β = 0.760, p < 0.001), thus validating H5. On the other hand, ELM did not have a positive and significant influence on PU (β = 0.031, p > 0.05), therefore rejecting H9 (Table 4). Of the two factors influencing PU, PE had the largest effect size (β = 0.760), while PEOU had a smaller effect (β = 0.199).

Table 4. Direct and indirect effects of the structural model (Bilibili)

	Product of coefficient				Bootstrapping 2000 times 95% CI
Path relationship	Point estimate	S.E.	Z	p	Lower 2.5%	Upper 2.5%
Direct effects
PEOU→BI	.115	0.039	2.938	.003	0.040	.191
PU→BI	.489	0.083	5.866	.000	0.338	.659
PE→BI	.120	0.090	1.328	.184	−0.067	.291
PEOU→PU	.199	0.042	4.717	.000	0.125	.289
PE→PU	.760	0.041	18.683	.000	0.677	.840
ELM→BI	.268	0.046	5.823	.000	0.178	.353
ELM→PU	.031	0.029	1.096	.273	−0.027	.075
Indirect effects
PEOU→PU→BI	.097	0.029	3.403	.001	0.050	.166
PE→PU→BI	.371	0.064	5.770	.000	0.261	.517
ELM→PU→BI	.015	0.014	0.104	.270	−0.012	.042

PEOU = perceived ease of use; PU = perceived usefulness; PE = perceived enjoyment; BI = behavioral intention to use; ELM = English learning motivation.

The results demonstrated that the mediating effect of PU on the relationship between PEOU and BI to use Bilibili to learn English was significant (β = 0.097, 95% CI: [0.050, 0.166]), thereby validating H6. Similarly, PU was found to have a significant mediating effect on the association between PE and BI to use Bilibili for English learning (β = 0.371, 95% CI: [0.261, 0.517]), thereby affirming H7. However, the mediating effect of PU on the relationship between ELM and BI to use Bilibili for English learning was not significant, as the 95% confidence interval showed zero to be between the lower and upper bound (β = 0.015, 95% CI: [−0.012, 0.042]), thus failing to support H10 (Table 4).

The results in Table 4 indicated that 5 of 7 hypotheses about the direct effects were validated, as well as 2 of 3 hypotheses about the mediating effects, thus verifying 7 of 10 hypotheses in total regarding the intentional use of Bilibili for English learning.

5. Discussion

This study was conducted to investigate the factors that had an impact on Chinese undergraduates’ behavioral intention to use Chinese indigenous social media platforms like WeChat and Bilibili for English learning. The findings suggested that influence of relevant factors on learners’ intentional use of these platforms for English learning could be described by TAM and English learning motivation.

The results showed that PEOU had a positive effect on Chinese undergraduates’ intention to use WeChat and Bilibili for English learning. This finding is in line with previous studies on language learning technologies, such as online technology (Balouchi & Samad, 2021), e-learning (Humida et al., 2022), internet-based technology (Huang et al., 2020). This has substantial implications for educators and developers, as they can increase learners’ intention to use these platforms by creating user-friendly interfaces and providing training and support to help them become familiar with the features and functionalities.

This study revealed that Chinese undergraduates’ intentional use of WeChat and Bilibili for English learning is positively impacted by PU of these platforms. Other studies have also found similar results regarding English mobile learning systems (Chang et al., 2013), teaching blogs (Chen et al., 2015), cloud services (Huang, 2016), and e-learning (Humida et al., 2022). When learners recognize the advantages of these platforms, they develop positive beliefs and motivation to incorporate them into their language learning practices. To improve learners’ perception of these platforms, educators and developers should emphasize their value and benefits. Providing clear information and guidance on how these technologies can support language learning objectives is essential. By highlighting potential positive outcomes such as improved language proficiency, increased engagement, and enhanced learning experiences, educators and developers can further boost learners’ intention to use these platforms.

The influence of PE on the behavioral intention of Chinese undergraduates to use WeChat and Bilibili for English learning was found to be mixed. PE had a significant and positive effect on the intention to use WeChat, yet it had no significant effect on the intention to use Bilibili. Research has highlighted the importance of PE in pre-service teachers’ technology adoption in their teaching (Teo & Noyes, 2011). Tamilmani et al. (2022) suggested that PE is an intrinsic driver of new technology adoption, as evidenced by customers’ satisfaction with a new system. Mubuke et al. (2017) further demonstrated that when learners find mobile learning systems enjoyable, it can increase their intention to use it for learning purposes. This was the case with WeChat, where PE had a positive influence on the intention to use it for English learning. However, the same was not true for Bilibili, as Chinese undergraduates using it for language learning seemed to prioritize avoiding distractions and managing their time rather than seeking pleasure from the platform. These findings suggest that while it is important to incorporate enjoyable elements into language learning technologies, they should be balanced with educational objectives. Educators and developers should ensure that the enjoyable elements are in line with the primary goal of language learning, and support learners in achieving their language learning goals.

This study concluded that Chinese undergraduates who use WeChat and Bilibili for English learning experienced a positive effect of PEOU on PU. This result is in keeping with the existing literature on technology acceptance, which has always established a correlation between PEOU and PU in a variety of technological areas (Winarno & Putra, 2020, Li, Chau, and Lou 2021; Marso, 2022; An et al., 2023; Lee et al., 2022). When a technology is perceived as easy to use, it reduces the cognitive effort needed and increases people’s confidence in using the technology, thus positively affecting its PU. This has important implications for educators and developers when designing user-friendly and useful language learning technologies. By focusing on ease of use, educators can positively influence students’ perceptions of usefulness, thus promoting the acceptance and adoption of these tools.

Examining WeChat and Bilibili as social media platforms for English learning, the study found that PE had a positive effect on learners’ perception of the platforms’ usefulness. This is in line with previous research in language learning technologies, such as e-learning (Li et al., 2021) and mobile learning (Pratama, 2021), which have demonstrated the importance of considering learners’ enjoyment in shaping their perception of technology usefulness. This is because PE can generate positive emotions and satisfaction with the learning process, as well as increase intrinsic motivation and engagement. Therefore, it is essential to incorporate elements that enhance enjoyment, such as gamification and interactive content, to foster learners’ positive attitudes and intention to use these platforms for English learning.

This study revealed the mediating effect of PU on the relationship between PEOU and behavioral intention, as well as between PE and behavioral intention, among Chinese undergraduates who use WeChat and Bilibili for English language learning. This is consistent with earlier studies, such as the meta-analysis by Venkatesh et al. (2003), which showed a mediating effect of PU on the connection between PEOU and usage intention. Additionally, studies by Hsu (2017) on language learning with augmented reality and Huynh and Le Thi (2014) on e-learning systems have also highlighted PU as a mediator between PEOU and usage intention or acceptance. It is evident that when students perceive these platforms as easy to use, it boosts their perception of usefulness, which then affects their intention to use them for English learning purposes. Other investigations conducted in different technological contexts, such as Humida etal., (2022) exploration of e-learning systems and Yang et al., (2022) study of mobile-assisted language learning, have further asserted the positive effect of PE on PU, which eventually shapes users’ intention to use the respective systems. Therefore, it is anticipated that PE will have a positive influence on PU, eventually influencing Chinese undergraduates’ behavioral intention to use WeChat and Bilibili for English learning.

By illuminating Chinese undergraduates’ English learning motivation, this study found that the ELM had an impact on students’ behavioral intention to employ WeChat and Bilibili for English learning. This finding is in contrast to Sun and Gao (2020), which suggested that intrinsic motivation of English learning did not have a direct influence on students’ behavioral intention in mobile-assisted language learning, but did have a positive influence through the two intervening variables, perceived usefulness and task technology fit. It is likely that Chinese undergraduates’ motivation to learn English is a key factor in their intention to use CISMPs for language learning. Fostering intrinsic motivation to learn English is pivotal for enhancing students’ motivation and intention to utilize platforms like WeChat and Bilibili. Further research specifically focusing on the relationship between English learning motivation and behavioral intention in the context of social media platforms would provide valuable insights and contribute to a deeper understanding of the interplay between motivation and technology acceptance in language learning.

The findings of this study showed that there was no direct link between English learning motivation and PU of WeChat and Bilibili for language learning. This result is in line with Hsu’s (2017) research, which investigated the opinions of high school students in Southern Taiwan and found no significant correlation between intrinsic or extrinsic motivation of English learning and the perceived usefulness of computer-assisted language learning. It is possible that in Asian countries where English teaching and learning focuses more on exam-oriented approaches, learners may prioritize tools that can help them get better scores. Since the CISMPs (WeChat and Bilibili) in this study were not specifically designed for exam preparation, learners’ motivation may have been affected if they saw the tools as not being beneficial for scoring well. On the other hand, those with intrinsic motivation may still search for effective ways to learn English regardless of the perceived usefulness of CISMPs.

This study demonstrated that PU does not act as a mediator between ELM and behavioral intention of Chinese undergraduates to use WeChat and Bilibili for English learning. The relationship between ELM, PU, and behavioral intention to use CISMPs for English learning has yet to be fully explored. The results of this study suggested that the impact of motivation on behavioral intention was not mediated by PU, and that other elements may be involved. Further research into the interplay between ELM, PU, and behavioral intention in the context of social media platforms could provide a better understanding of the driving forces behind the adoption and utilization of these platforms for language learning.

This study highlighted that English learning motivation was a factor in the adoption of social media platforms for language learning. Even though it had no indirect effect on the intentional use of Chinese indigenous social media platforms such as WeChat and Bilibili, it did have a direct influence. This study extended the classic TAM by including individual motivational elements such as English learning motivation. It is obvious that a lack of motivation among university students to learn English in China is a major problem, so educators must take steps to motivate students to learn English and be open to using modern technological tools to support their learning. This will help them to keep up with the digital and global times.

This study, which centered on Chinese undergraduates and Chinese indigenous social media platforms, is also relevant to other countries and can be tested in different educational contexts. Every country has its own digital tools, apps, social media tools, etc., and the acceptance of these tools will decide the financial and successful incorporation of these tools into L2 learning.

6. Limitations and future work

This study provides valuable insights; however, it is not without limitations. Firstly, this study was conducted using a quantitative approach and was based solely on self-reported surveys for data collection. Consequently, it is recommended that future studies should incorporate a triangulation of multiple data collection techniques, including interviews, focus-group interview, and observations, and should collect data from additional sources, for example technology developers, educators, and peers.

Additionally, this study extended TAM by introducing a novel dimension of language learning motivation. However, the behavioral intention to adopt CISMPs in English learning may be affected by other individual factors such as psychological states and personality traits. Consequently, future research should enhance TAM by considering individual factors beyond English learning motivation to gain a deeper insight into the factors that affect students’ intention to use social media platforms for English learning.

Finally, given the context, like the role of English in Chinese universities, it would be beneficial to replicate the study in other settings, such as primary schools, middle schools, high schools, and even international contexts, to assess the reliability of the results.

7. Conclusion

This study investigated the main factors influencing Chinese undergraduates’ behavioral intention to use Chinese indigenous social media platforms such as WeChat and Bilibili for English learning. 834 questionnaire responses were collected from a university in eastern China. The results showed that PEOU, PU, PE, and ELM had a positive effect on undergraduates’ intention to use WeChat for English learning. Furthermore, PU mediated the relationship between PEOU and BI, as well as between PE and BI, but not ELM and BI to use WeChat for English learning. Thus, 8 out of 10 hypotheses about intentional use of WeChat to learn English were confirmed.

The results also indicated that PEOU, PU, and ELM but not PE had a positive effect on undergraduates’ intention to use Bilibili for English learning. Additionally, PU mediated the relationship between PEOU and BI, as well as between PE and BI, but not ELM and BI to use Bilibili for English learning. Therefore, 7 out of 10 hypotheses about intentional use of Bilibili to learn English were affirmed.

Despite the limited utilization of social media platforms by Chinese undergraduates for English language learning, we are confident that their effective utilization could lead to the emergence of novel approaches to English learning and result in a transformation of English learning environment. It is necessary to encourage university students to study English and make use of these platforms, while teachers should recommend suitable social media platforms and other digital tools, and inspire students to learn English using emerging technologies. Furthermore, those responsible for developing social media platforms or other emerging technologies should take into consideration factors such as ease of use, usefulness, and enjoyment when designing these tools.

Ethics statement

This study was carried out in accordance with the recommendations of The Ethics Committee of Qufu Normal University with written informed consent from all subjects.

Acknowledgments

The author(s) would like to thank all students for their time and willingness to participate in the survey.

Disclosure statement

The author(s) declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.

Data availability statement

The datasets generated for this study are available on request to the corresponding author.

Additional information

Funding

Thestudy was supported by the Education and Teaching Research Project under Grant (2020JXY028).

Notes on contributors

Cunying Fan

Cunying Fan has a strong interest in the application of digital technology in higher eudcation and has published a number of papers on this topic. For example, she developed a questionnaire to assess the digital skills of Chinese university students and looked into the use of web 2.0 tools in L2 learning. She is also in charge of some academic projects, such as the Innovative Model of College English Teaching Based on the Cultivation of Digital Empathy.

Appendix

Measurement items in the questionnaire

PEOU

(a) The Chinese indigenous social media platforms (WeChat, Bilibili) are easy to use when learning English; (b) The Chinese indigenous social media platforms (WeChat, Bilibili) are easy to learn when learning English; (c) The Chinese indigenous social media platforms (WeChat, Bilibili) are easy to access when learning English; (d) The Chinese indigenous social media platforms (WeChat, Bilibili) are convenient when learning English.

PU

(a)The Chinese indigenous social media platforms (WeChat, Bilibili) enhance my English learning; (b) The Chinese indigenous social media platforms (WeChat, Bilibili) make English learning more efficient; (c) The Chinese indigenous social media platforms (WeChat, Bilibili) make English learning easier; (d) The Chinese indigenous social media platforms (WeChat, Bilibili) keep me motivated and active as I can access English learning resources anytime and anywhere; (e) The Chinese indigenous social media platforms (WeChat, Bilibili) increase my English learning productivity; (f) Overall, I find the Chinese indigenous social media platforms (WeChat, Bilibili) are useful for my English learning.

PE

(a) I find using the Chinese indigenous social media platforms (WeChat, Bilibili) to be enjoyable for fulfilling my needs to learn English; (b) The actual process of using the Chinese indigenous social media platforms (WeChat, Bilibili) for English learning is pleasant; (c) I have fun using the Chinese indigenous social media platforms (WeChat, Bilibili) to learn English; (d) When I learn English via the Chinese indigenous social media platforms (WeChat, Bilibili), time passes quickly.

ELM

(a) I enjoy learning English very much; (b) I put a lot of effort into English language learning; (c) I think learning English is fun; (d) I spend a lot of time on learning English; (e) Learning English is important to me.

BI

(a) I will recommend the Chinese indigenous social media platforms (WeChat, Bilibili) for English learning to others; (b) I think using the Chinese indigenous social media platforms (WeChat, Bilibili) to learn English is a good idea; (c) I think it is necessary to learn English with the Chinese indigenous social media platforms (WeChat, Bilibili); (d) I plan to continue to use the Chinese indigenous social media platforms (WeChat, Bilibili) for my English learning in the future.