February 2026 – Volume 29, Number 4
https://doi.org/10.55593/ej.29116a4
Falentinus Ndruru
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
<falentinusndruru
gmail.com>
Utami Widiati
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
<utami.widiati.fsum.ac.id>
Yazid Basthomi
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
<ybasthomium.ac.id>
Impiani Zagoto
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
English Language Education, Universitas Nias Raya, Indonesia
<zimpianigmail.com>
Mala Rovikasari
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
<mala.rovikasari.2402219students.um.ac.id>
Sri Wahyuningsih
Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia
Universitas Islam Negeri Sunan Kudus, Kudus, Indonesia
<sri.wahyuningsih.2402219students.um.ac.id>
Abstract
As digital tools reshape academic writing, digital multimodal composing (DMC) is increasingly relevant in EFL contexts, yet links between digital multimodal composing competence, cognitive, and affective factors remain underdeveloped. This study investigated the direct and indirect relationships among digital multimodal composing competence (DMCC), academic writing self-efficacy (AWSE), cognitive engagement (CE), and writing enjoyment (WE) in higher education. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 215 students across 18 universities were analyzed. Results revealed that DMCC strongly predicted AWSE and also significantly predicted CE and WE. AWSE significantly influenced CE and WE, while CE did not significantly predict WE. These findings highlight the mediating role of self-efficacy in linking digital competence to both cognitive engagement and writing enjoyment, suggesting that competence alone is insufficient without confidence. Pedagogically, scaffolded multimodal tasks with reflective guidance can strengthen competence and confidence, leading to greater cognitive engagement and enjoyment in writing. Future research should examine additional mediators and adopt longitudinal or cross-cultural designs to validate and extend these findings.
Keywords: Cognitive engagement, Digital multimodal composing, self-efficacy, writing enjoyment.
Modeling Digital Multimodal Composing Competence, Self-Efficacy, Cognitive Engagement, and Enjoyment in Writing
The rapid integration of digital technologies has reshaped writing pedagogy, shifting it from predominantly linear, text-based practices toward increasingly multimodal forms of communication. As a result, digital multimodal composing competence (DMCC), the ability to integrate linguistic, visual, audio, and interactive resources into cohesive texts, has become an important literacy skill in contemporary EFL writing contexts (Kress, 2017; Yu et al., 2024). Previous research has shown that DMC can support various aspects of writing development, including fluency, creativity, and rhetorical awareness (Hafner & Ho, 2020; Jiang et al., 2021; Maghsoudi et al., 2022). However, much of this research has focused primarily on performance outcomes and technical skills, while the affective dimensions of digital writing, particularly writing enjoyment, have received comparatively less attention (Jiang & Hafner, 2025; Ndruru et al., 2025a). Consequently, there remains limited understanding of how learners experience multimodal writing emotionally and how such experiences relate to sustained engagement and long-term writing enjoyment.
Writing enjoyment is increasingly recognized as an important affective resource that supports persistence, resilience, and continued engagement in demanding learning tasks (Dewaele et al., 2018; Fredrickson, 2001; MacIntyre & Mercer, 2014). This issue is especially salient in EFL contexts, where learners often face linguistic and cognitive challenges that can intensify anxiety and reduce motivation (Sun & Wang, 2020; Vincent et al., 2023). Although recent research in applied linguistics has called for a shift from deficit-oriented approaches that focus on reducing negative emotions toward pedagogical designs that promote positive emotions such as enjoyment (Dewaele & Alfawzan, 2018), empirical work has largely examined writing enjoyment in traditional, monomodal contexts. As a result, the affective demands of digital multimodal composing and their implications for learners’ emotional engagement remain insufficiently explored (Ardi et al., 2024; Dewaele & Alfawzan, 2018). Taken together, these gaps highlight the need to examine how learners’ digital composing competence relates to writing enjoyment and the psychological factors that may help explain this relationship. Against this backdrop, the purpose of this study is to examine the relationship between digital multimodal composing competence and writing enjoyment among EFL university students, with particular attention to the mediating roles of academic writing self-efficacy and cognitive engagement. By focusing on these relationships within the Indonesian higher education context, this study addresses a setting in which digital literacy is increasingly emphasized, yet challenges related to multimodal competence, technological access, and writing anxiety persist (Direktorat Jenderal Pendidikan Tinggi, Riset, dan Teknologi, 2024; Inggarwati et al., 2022; Yanuar, 2023). This study also aims to contribute to a clearer understanding of how digital competence and psychological resources jointly shape learners’ engagement and enjoyment in EFL academic writing.
Literature Review
While writing enjoyment has gained increasing attention in applied linguistics, its relationship with digital multimodal composing competence in academic writing remains limited. This literature review examines four interconnected strands of knowledge that inform the present study, particularly research on DMC in EFL academic writing, academic writing self-efficacy, cognitive engagement in EFL writing tasks, and writing enjoyment. Together, these areas provide a foundation for understanding how digital writing competence interacts with psychological mechanisms to shape students’ affective engagement in writing.
Digital Multimodal Composing Competence in EFL Writing
Digital multimodal composing (DMC) competence refers to the ability to orchestrate linguistic, visual, audio, and interactive resources into coherent multimodal texts (Zhang & Yu, 2023). In post-secondary EFL academic writing contexts, such multimodal composing typically takes the form of tasks such as academic slide presentations, digital storytelling projects, interactive infographics, poster presentations, and video-based argumentative or expository presentations, which require learners to integrate written language with visual, audio, and design elements to communicate disciplinary content (Yu et al., 2024; Zhang & Yu, 2023). Empirical work in DMC composition has primarily documented cognitive and rhetorical benefits, enhanced fluency, audience awareness, creativity, and genre control in EFL academic writing contexts (Jiang et al., 2021; Maghsoudi et al., 2022; Pham & Li, 2023).
Furthermore, research increasingly suggests that the benefits of digital multimodal composing depend not simply on the presence of multimodality, but on how multimodal tasks are designed and how effectively learners are supported in managing multimodal demands. Studies show that explicitly scaffolded multimodal writing tasks tend to deepen engagement and support learner autonomy (Sun & Wang, 2020; Yu et al., 2024), whereas tasks involving high technical and rhetorical complexity may lead to superficial engagement when such support is insufficient (Xu, 2021). At the same time, well-supported implementations have been shown to foster higher-order writing outcomes, including authorial stance, writer identity, and learner investment, particularly when multimodal tasks are aligned with disciplinary expectations (Mills, 2016; Zuo, 2024). Together, these findings show that although DMC holds clear pedagogical potential, current research does not yet clearly explain how learners’ engagement and writing development are shaped by their perceived ability to manage multimodal demands across different contexts (Cheung, 2023; Jiang & Hafner, 2025; Yu et al., 2024). Complementing this instructional perspective, Ndruru et al. (2025a) further suggest that DMCC is best understood as a learner-level construct, shaped by students’ perceptions of their ability to manage multimodal academic writing demands rather than by task features alone.
Despite these advances, much of the DMC literature still foregrounds performance outcomes, text quality, genre mastery, and digital skill (e.g., Ndruru et al., 2025b), while affective dimensions are sometimes inferred rather than directly measured. Although self-reported measures are commonly used and valuable for capturing learners’ perceptions, existing research has rarely modeled how digital multimodal composing competence relates to positive affective outcomes through underlying psychological processes. As a result, the mechanisms through which learners’ perceived multimodal competence may shape writing enjoyment remain insufficiently understood. These limitations motivate the present study to examine the association between DMCC and writing enjoyment and to investigate the mediating roles of psychological aspects in this context.
Academic Writing Self-Efficacy in EFL Writing
Academic writing self-efficacy, defined as learners’ beliefs about their capability to perform writing tasks successfully, is a central construct in social cognitive theory (Bandura, 1997) and a robust predictor of persistence and achievement in L2 writing (Bruning et al., 2013). In EFL contexts characterized by unfamiliar genres, linguistic constraints, and high cognitive demands, self-efficacy plays a crucial role in shaping how learners approach writing tasks, including the strategies they employ, the effort they sustain, and their willingness to persist when difficulties arise (Hwang, 2020; Sun & Wang, 2020). Writing self-efficacy has been conceptualized as a multidimensional construct encompassing ideation, linguistic conventions, and self-regulation (Bruning et al., 2013), highlighting its close connection to cognitive control and task management during academic writing. Consistent with this view, writing self-efficacy has also been shown to predict writing enjoyment in EFL contexts (Ardi et al., 2024; Teng & Wang, 2023).
In DMC contexts, academic writing self-efficacy becomes particularly relevant because learners must manage multiple semiotic resources and make ongoing design decisions. Abdelhalim (2024) suggests that learners’ confidence in ideation and self-regulation may be more responsive to multimodal task demands than confidence in linguistic accuracy alone. Zhan & Teng (2025) further indicate that writing self-efficacy is not a fixed trait but a malleable psychological resource shaped by instructional experiences and learners’ regulatory engagement, with clear links to writing performance. These findings collectively suggest that self-efficacy may play an important role in shaping how learners engage with and respond to the demands of multimodal academic writing.
Despite this body of research, academic writing self-efficacy is still most often examined as a static predictor rather than as a psychological mechanism through which other competencies influence learners’ affective engagement. Few studies have explicitly modeled how self-efficacy mediates the relationship between digital multimodal composing competence and writing enjoyment. Although self-report instruments remain the primary and appropriate means of capturing learners’ confidence and task perceptions when carefully validated, existing research has rarely used such measures to explain how perceived multimodal competence translates into positive emotional experiences during academic writing. The present study addresses this gap by positioning academic writing self-efficacy as a mediating mechanism that helps explain how digital multimodal composing competence contributes to writing enjoyment among EFL university students.
Cognitive Engagement in EFL Writing
Cognitive engagement refers to learners’ mental effort, strategy use, and persistence, reflecting the depth of their investment in learning activities (Fredricks et al., 2004). In writing, cognitive engagement involves processes such as planning, monitoring, revising, and reflecting, which are central to effective composing because they enable learners to regulate ideas, language use, and text development over time (Pearson, 2024). In digital multimodal composing (DMC), these cognitive demands often increase, as learners must not only plan written content but also make design decisions, align different modes with purposes and audiences, and integrate multiple sources and media in coherent ways. Importantly, how demanding these processes become depends on instructional and assessment practices, rather than on multimodality itself, since appropriate support can either reduce or amplify cognitive load (Jiang et al., 2021).
In technology-mediated EFL writing contexts, both task design and the platforms used have been shown to influence the extent and quality of learners’ cognitive engagement (Giessler, 2024; Shen et al., 2023), and this influence becomes especially visible in multimodal tasks. Research in explicitly multimodal settings shows that well-scaffolded tasks can support multiple forms of engagement, including cognitive, behavioral, affective, and agentive engagement, whereas limited scaffolding may result in shallow engagement or cognitive overload (Ajabshir, 2024; Yanuar, 2023). At the same time, studies in technology-supported language learning suggest that learners’ perceived ability to manage task demands plays a key role in sustaining cognitive engagement, particularly when tasks require coordination across modes and processes (Wu, 2023). Overall, these findings indicate a close relationship between learners’ perceived DMCC and the extent to which they invest cognitively in multimodal writing. However, existing research has not yet clearly explained how this cognitive engagement connects to learners’ affective experiences. Moreover, cognitive engagement is still often operationalized narrowly as effort or persistence, even though engagement may fluctuate across stages of the writing process and interact with learners’ emotional responses. For these reasons, the present study examines cognitive engagement as a mediating variable linking DMCC to writing enjoyment, alongside academic writing self-efficacy.
Writing Enjoyment in the EFL Context
Writing enjoyment has received increasing attention in applied linguistics, particularly within positive psychology perspectives that associate positive affect with persistence and sustained engagement in language learning (Fredrickson, 2001; MacIntyre & Mercer, 2014). In L2 writing research, enjoyment has been linked to creativity and willingness to revise; however, it has received far less systematic attention than negative emotions such as anxiety and boredom, which have traditionally dominated affective inquiry (Dewaele & Li, 2020). From a control-value perspective, enjoyment is understood as an affective response that emerges when learners perceive writing tasks as both manageable and meaningful, a condition that is especially relevant in cognitively demanding academic writing contexts (Pekrun, 2024). This theoretical framing suggests that enjoyment is unlikely to function as a stable trait or a primary driver of writing development; instead, it is better viewed as a context-sensitive outcome shaped by learners’ perceptions of control and value.
Empirical evidence largely supports this conditional view. Large-scale and longitudinal studies indicate that writing enjoyment may relate positively to writing outcomes, but its effects are often weaker and less stable than those of negative emotions, particularly boredom and, in some contexts, anxiety (Guan et al., 2023; Li et al., 2023). Spring et al. (2019) further suggest that the sources of enjoyment, such as perceived task relevance, purpose, and value, may be more consequential for learning than enjoyment itself. The findings integrally caution against treating enjoyment as a direct or sufficient explanation for writing development and instead highlight the importance of examining the conditions under which enjoyment emerges and fluctuates during writing activity.
At the same time, classroom-based and process-oriented studies show that writing enjoyment is responsive to instructional and psychological conditions. Task design, sequencing, and feedback practices have been shown to shape learners’ enjoyment of writing activities, even when gains in linguistic performance are modest (Tabari et al., 2024; Woolley & Sharif, 2021). Research on emotional regulation during writing further indicates that enjoyment develops dynamically through learners’ management of cognitive and emotional demands, particularly in collaborative and digitally mediated contexts (Zhang et al., 2021). Modeling studies also suggest that enjoyment is often influenced indirectly by psychological factors such as self-efficacy and perceived task control, rather than exerting direct effects on achievement (Ardi et al., 2024). Collectively, this body of research positions writing enjoyment as a secondary but meaningful affective outcome, shaped by learners’ perceptions of competence, control, and engagement. However, its relationship with learners’ DMCC and cognitive engagement, particularly in digital multimodal academic writing, has rarely been examined within integrated models. Addressing this gap provides the rationale for the present study’s focus on writing enjoyment as an affective outcome mediated by self-efficacy and cognitive engagement.
Across the literature, four constructs emerge as central to understanding learners’ affective experiences in EFL academic writing: DMCC, academic writing self-efficacy, cognitive engagement, and writing enjoyment. Existing research has shown that DMCC is associated with cognitive and rhetorical aspects of writing performance, that self-efficacy relates to learners’ persistence and confidence in managing writing demands, and that cognitive engagement supports sustained effort and regulatory processes during writing. At the same time, writing enjoyment has been described as a context-sensitive affective outcome linked to continued participation in writing rather than as a direct driver of performance. Despite these complementary insights, most studies have examined these constructs separately, leaving unresolved how learners’ perceived multimodal competence connects to affective writing experiences through underlying psychological processes. This lack of integration points to the need for research that brings these constructs together within a coherent explanatory framework.
Hypothesis Development
Building on the preceding literature, this study develops a set of hypotheses to explain how DMCC relates to writing enjoyment through learners’ psychological resources. Prior research has shown that perceived DMCC is associated with cognitive and rhetorical aspects of academic writing performance (Jiang et al., 2021; Maghsoudi et al., 2022; Pham & Li, 2023), suggesting that learners who feel capable of managing multimodal writing demands may also experience more positive affect during writing. Research in EFL writing further indicates that perceived competence in managing writing tasks is closely linked to academic writing self-efficacy and cognitive engagement, both of which support persistence, strategic effort, and sustained involvement in demanding writing tasks (Bruning et al., 2013; Fredricks et al., 2004; Hwang, 2020). In multimodal writing contexts, where learners must coordinate multiple modes and make ongoing design decisions, higher DMCC has been shown to support stronger self-efficacy and deeper cognitive engagement (Abdelhalim, 2024; Jiang et al., 2021). In turn, academic writing self-efficacy and cognitive engagement have each been associated with more positive writing experiences, including enjoyment (Ardi et al., 2024; Teng & Wang, 2023). In addition, studies suggest that self-efficacy and cognitive engagement are interrelated, as learners who feel confident in managing writing tasks are more likely to sustain cognitive effort when facing complex demands (Sun & Wang, 2020). This line of reasoning supports a model in which DMCC influences writing enjoyment both directly and indirectly through academic writing self-efficacy and cognitive engagement. Based on this rationale, six hypotheses are proposed, as illustrated in Figure 1.
H1: Students’ digital multimodal composing competence positively predicts their writing enjoyment.
H2: Students’ digital multimodal composing competence positively predicts their academic writing self-efficacy.
H3: Students’ digital multimodal composing competence positively predicts their cognitive engagement.
H4: Students’ academic writing self-efficacy positively predicts their writing enjoyment.
H5: Students’ cognitive engagement positively predicts their writing enjoyment.
H6: Students’ academic writing self-efficacy positively predicts their cognitive engagement.
Figure 1. The conceptual model of writing enjoyment in digital multimodal composing
Methodology
Research Design
This study employed a quantitative, theory-extending design to examine how digital multimodal composing competence (DMCC) influences writing enjoyment (WE) directly and indirectly through academic writing self-efficacy (AWSE) and cognitive engagement (CE). Because these are latent constructs with both direct and mediated relations, a structural equation modeling (SEM) approach was appropriate (Kline, 2016). We adopted variance-based Partial Least Squares SEM (PLS-SEM) rather than covariance-based SEM because the study is prediction-oriented, the indicators are Likert-type and not assumed to be normally distributed, and the model includes multiple endogenous and mediated paths. PLS-SEM is also well suited to reflective constructs, providing robust procedures for evaluating indicator reliability, internal consistency, and convergent and discriminant validity (Hair et al., 2021; Henseler et al., 2015; Nitzl et al., 2016). This makes it methodologically consistent with our objectives and data characteristics.
Participants and Context
The participants were undergraduate students enrolled in English Language Education and English Language and Literature programs at 18 Indonesian universities. At the time of data collection, all participants were taking or had recently completed academic writing courses (e.g., paragraph writing, essay writing, argumentative writing, or thesis writing). Although these courses are not inherently multimodal, they increasingly incorporate digital and multimodal tasks, such as academic slide presentations, poster presentations, digital storytelling projects, or short video-based presentations, as part of academic writing instruction. This population was selected because the study’s focal constructs, digital multimodal composing competence, academic writing self-efficacy, cognitive engagement, and writing enjoyment, presuppose sustained experience with academic writing in English and emerging exposure to multimodal composing. A purposeful cluster sampling strategy was therefore adopted to ensure alignment between participants’ instructional experiences and the constructs under investigation (Cohen et al., 2018; Etikan et al., 2016). Limiting participation to English-related programs helped ensure that respondents could meaningfully interpret the survey items and reflect on their experiences with digital multimodal academic writing, thereby supporting construct validity.
Cluster sampling through academic writing classes across multiple universities further ensured that participants were homogeneous in disciplinary background (English majors engaged in writing instruction) while still being diverse in institutional context and semester level, which strengthens the contextual robustness of the findings. Although purposive sampling does not allow for statistical generalization to all EFL learners, it is particularly well suited to theory-testing and model-building studies where the priority is to obtain data from respondents most likely to have meaningful engagement with the constructs under investigation (Palinkas et al., 2015). To be included, respondents had to (i) be enrolled in an undergraduate English language-related program, (ii) be currently taking or have recently completed an academic writing course, and (iii) provide informed consent. Students outside these criteria or who submitted incomplete surveys were excluded. After screening, 215 valid responses were retained, which meets recommended guidelines for PLS-SEM sample adequacy (Hair et al., 2021). Demographic data were collected to contextualize the sample, including gender, age, academic major, semester, prior exposure to DMC tasks, access to technological tools, and frequency of digital tool use. Table 1 summarizes these characteristics.
Table 1. Demographic details of 215 participants involved in the study
| Demographic aspect | Category | Number | Percentage (%) |
| Gender | Female | 125 | 58.1 |
| Male | 90 | 41.9 | |
| Age | 18 | 8 | 3.7 |
| 19 | 25 | 11.6 | |
| 20 | 61 | 28.3 | |
| 21 | 44 | 20.5 | |
| 22 | 41 | 19.1 | |
| 23 | 22 | 10.2 | |
| 24 | 3 | 1.3 | |
| > 24 | 11 | 3.7 | |
| Major/study program | English Language Education | 168 | 78.1 |
| English Language and Literature | 47 | 21.9 | |
| Semester | 2nd | 40 | 18.6 |
| 4th | 76 | 35.3 | |
| 6th | 74 | 34.4 | |
| 8th | 25 | 11.6 | |
| Access to technological tools | Laptop | 151 | 70.2 |
| Handphone/Smartphone | 187 | 86.9 | |
| PC | 20 | 9.3 | |
| Tablet | 29 | 13.4 | |
| Experience in digital multimodal composing tasks | Academic slide presentation | 185 | 86 |
| Digital storytelling | 57 | 23.7 | |
| Interactive infographics | 87 | 38.1 | |
| Making video essays/presentations | 119 | 55.3 | |
| Poster presentation | 75 | 34.8 | |
| Video-based argumentations | 33 | 15.3 | |
| Frequency of using digital tools for academic writing | Never | – | – |
| Seldom/rarely | 3 | 1.4 | |
| Sometimes | 56 | 26 | |
| Frequently | 80 | 37.2 | |
| Always | 76 | 35.3 |
The demographic profile indicates that participants represented both early and advanced stages of undergraduate study and reported substantial access to digital tools commonly used in academic writing, particularly smartphones and laptops. A large proportion of students also reported experience with multimodal academic tasks such as slide presentations, video-based assignments, and infographics, alongside frequent use of digital tools for writing. These characteristics provide important contextual information for interpreting the findings and confirm that the participants were actively engaged in digital composing practices relevant to the constructs examined in this study.
Instrumentation
Data were collected using a structured questionnaire measuring four latent constructs: digital multimodal composing competence (DMCC), academic writing self-efficacy (AWSE), cognitive engagement (CE), and writing enjoyment (WE). Each construct was operationalized with instruments that have been widely validated in applied linguistics and educational psychology, ensuring both conceptual alignment and psychometric rigor. All items were rated on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), a format well suited to capturing attitudinal data in L2 research (Dörnyei & Taguchi, 2022).
DMCC was assessed with fifteen items adapted from Zhang and Yu (2023), reflecting learners’ ability to integrate linguistic, visual, audio, and interactive resources in academic writing. The scale has demonstrated construct validity in multimodal literacy studies across contexts (Jiang & Hafner, 2025). AWSE was measured with seven items adapted from Bruning et al.’s (2013) Self-Efficacy for Writing Scale and contextualized for EFL settings in prior work (e.g., Ardi et al., 2024). This instrument captures students’ confidence in managing the demands of academic writing, a factor consistently linked to persistence and achievement (Sun & Wang, 2020). CE was measured using seven items adapted from Parsons et al. (2023), conceptualizing engagement as effort, persistence, and strategic investment in writing tasks. This builds on the multidimensional model of engagement (Fredricks et al., 2004) and has been shown to predict both cognitive and affective outcomes, including in digital learning environments (Teng & Wang, 2023). Finally, WE was assessed using the nine-item English Writing Enjoyment Scale (Jin, 2023), which offers a validated operationalization of enjoyment in L2 writing and addresses earlier reliance on proxy measures (Dewaele & Alfawzan, 2018).
To ensure cultural appropriateness, all instruments were adapted to the Indonesian EFL context following cross-cultural adaptation guidelines (Beaton et al., 2000). Two senior professors with expertise in teaching academic writing, second language acquisition (SLA), TEFL, and media-technology integration in ELT independently reviewed all items for content relevance, clarity, and contextual fit. Their feedback was then discussed and reconciled, and revisions were made when both experts agreed that an item could be misinterpreted, was overly technical, or insufficiently contextualized. These revisions primarily involved simplifying wording (e.g., simplifying overly technical or abstract wording, clarifying item phrasing to reduce potential ambiguity), clarifying task references (e.g., specifying academic writing or multimodal composing contexts), and adjusting examples (e.g., slide presentations, video-based assignments, and multimodal academic tasks) to reflect students’ instructional experiences, rather than altering the underlying constructs. No items were added or removed, and the conceptual integrity of each construct was retained. A pilot test with thirty undergraduates confirmed the clarity and reliability of the instrument. The Cronbach’s α values for the instruments indicated strong internal consistency (DMCC = .89, AWSE = .87, CE = .85, WE = .91), all exceeding the commonly used .70 benchmark (Field, 2018).
Data Collection
Data were collected over a two-week period in late April 2025 through an online questionnaire administered via Google Forms. An online survey was chosen because it facilitated efficient access to geographically dispersed participants across Indonesian universities, reduced administrative costs, and ensured standardized administration compared to paper-based formats (Creswell & Creswell, 2018). Distribution was conducted through academic WhatsApp groups managed by writing instructors, a strategy shown to increase participation in educational research by leveraging existing academic networks (Bryman, 2016). The questionnaire required approximately 15-20 minutes to complete, a length deemed appropriate for undergraduate respondents while allowing sufficient depth of information.
Before accessing the items, students were provided with an information sheet outlining the study’s objectives, confidentiality safeguards, and their right to decline participation. Informed consent was obtained electronically in accordance with institutional ethical requirements, ensuring that participation was fully voluntary and data were handled responsibly. A total of 217 responses were received, of which two were excluded during screening: one from a master’s student in English Language Education and one from a student majoring in Islamic Economics. The final dataset, therefore, comprised 215 valid responses from undergraduate students in English-related programs, all meeting the inclusion criteria specified earlier.
Ethical standards were observed throughout the study in accordance with institutional and disciplinary guidelines for educational research. Participation was voluntary, informed consent was obtained from all participants, and anonymity and confidentiality were ensured through secure and responsible data management procedures. At the same time, the data collection process carried inherent limitations. Participation was subject to self-selection and required digital access and literacy, which may have underrepresented students with limited technological resources. As with all self-report surveys, responses may also reflect social desirability or recall bias (Podsakoff et al., 2003). Because we were aware of these limitations, we exercise caution when interpreting the generalizability of the findings.
Data Analysis
Data analysis was conducted using SmartPLS 4.1, following the widely recommended two-step procedure of first evaluating the measurement model and then the structural model (Hair & Alamer, 2022). This approach ensures that the constructs are reliable and valid before testing their hypothesized interrelations. For the measurement model, reflective indicators were assessed in terms of indicator reliability, internal consistency, and validity. Indicators with loadings above .70 were considered acceptable, while internal consistency was evaluated with composite reliability (CR ≥ .70). Convergent validity was established through the average variance extracted (AVE ≥ .50), and discriminant validity was assessed with the heterotrait-monotrait (HTMT) ratio, using the conservative threshold of .85 (Henseler et al., 2015). Together, these checks confirmed that DMCC, AWSE, CE, and WE were measured both reliably and distinctively.
For the structural model, hypothesized relationships were estimated using nonparametric bootstrapping with 5,000 subsamples, which provides robust standard errors and bias-corrected confidence intervals (Chin, 1998; Hair et al., 2021). Model explanatory power was evaluated with R² values, while effect sizes (f²) assessed the incremental contribution of each predictor. Beyond explanation, predictive relevance was examined using Q² via the PLSpredict algorithm, following recommendations to test not only explanatory adequacy but also practical predictive validity (Shmueli et al., 2019). By integrating measurement validation, structural testing, and predictive assessment, the analysis provides robust evidence for evaluating both the theoretical relationships and the practical relevance of the proposed model, consistent with best practices in PLS-SEM research.
Findings
Measurement Model Assessment
The measurement model was assessed using four criteria: indicator reliability, internal consistency, convergent validity, and discriminant validity. All item loadings exceeded the recommended threshold of 0.70, ranging from 0.703 to 0.911 across the constructs, indicating satisfactory indicator reliability. Then, Composite reliability (CR) values ranged from 0.931 (AWSE) to 0.961 (WE), well above the accepted 0.70 threshold, confirming strong internal consistency. Convergent validity was established with Average Variance Extracted (AVE) values between 0.606 and 0.742, exceeding the recommended 0.50 benchmark (Hair et al., 2021).
Table 2. Indicator Loading, Composite Reliability (CR), Average Variance Extracted (AVE) Measurement
| Construct | Items | Indicator Loading | Composite Reliability (CR) | Average Variance Extracted (AVE) |
| Academic Writing Self-efficacy | AWSE1 | 0.821 | 0.931 | 0.660 |
| AWSE2 | 0.813 | |||
| AWSE3 | 0.798 | |||
| AWSE4 | 0.836 | |||
| AWSE5 | 0.859 | |||
| AWSE6 | 0.827 | |||
| AWSE7 | 0.726 | |||
| Cognitive Engagement | CE1 | 0.852 | 0.953 | 0.742 |
| CE2 | 0.884 | |||
| CE3 | 0.844 | |||
| CE4 | 0.904 | |||
| CE5 | 0.882 | |||
| CE6 | 0.848 | |||
| CE7 | 0.814 | |||
| Digital Multimodal Composing Competence |
DMCC1 | 0.703 | 0.958 | 0.606 |
| DMCC2 | 0.767 | |||
| DMCC3 | 0.704 | |||
| DMCC4 | 0.722 | |||
| DMCC5 | 0.771 | |||
| DMCC6 | 0.838 | |||
| DMCC7 | 0.738 | |||
| DMCC8 | 0.802 | |||
| DMCC9 | 0.772 | |||
| DMCC10 | 0.755 | |||
| DMCC11 | 0.784 | |||
| DMCC12 | 0.840 | |||
| DMCC13 | 0.849 | |||
| DMCC14 | 0.797 | |||
| DMCC15 | 0.810 | |||
| Writing Enjoyment | WE1 | 0.835 | 0.961 | 0.735 |
| WE2 | 0.801 | |||
| WE3 | 0.860 | |||
| WE4 | 0.878 | |||
| WE5 | 0.855 | |||
| WE6 | 0.866 | |||
| WE7 | 0.846 | |||
| WE8 | 0.857 | |||
| WE9 | 0.911 |
In addition to the measurement model, discriminant validity was confirmed using the Heterotrait-Monotrait (HTMT) ratio of correlations. All HTMT values fell between 0.661 and 0.871, below the 0.90 threshold (Henseler et al., 2015), indicating that the constructs are empirically distinct. Together, these results demonstrate that the measurement model achieved satisfactory reliability and validity.
Table 3. Discriminant Validity Using Heterotrait-Monotrait Ratio (HTMT)
| AWSE | CE | DMCC | WE | |
| AWSE | ||||
| CE | 0.801 | |||
| DMCC | 0.807 | 0.786 | ||
| WE | 0.871 | 0.661 | 0.764 |
Structural Model Assessment
Multicollinearity. To ensure the integrity of the path estimates, multicollinearity was first assessed using the Variance Inflation Factor (VIF). Values ranged from 1.000 to 2.865, well below the recommended ceiling of 5.0 (Hair et al., 2021), confirming that multicollinearity was not a concern in this model.
Table 4. Multicollinearity Using Variance Influence Factor (VIF)
| WE | AWSE | CE | |
| AWSE | 2.862 | – | – |
| CE | 2.736 | – | – |
| DMCC | 2.865 | 1.000 | 2.371 |
Path Analysis. The hypothesized structural paths were tested using bootstrapping with 5,000 resamples. Figure 2 provides a visual summary of the structural model, displaying standardized path coefficients and highlighting both significant and nonsignificant relationships. Following Hair et al. (2021), coefficients of 0.10–0.19 are considered small, 0.20–0.29 moderate, and ≥0.30 strong.
Figure 2. Result of Path Analysis Measurement
Table 5 presents the full set of path coefficients, significance levels, and effect sizes. Results indicated that Digital Multimodal Composing Competence (DMCC) had a strong positive effect on Academic Writing Self-Efficacy (AWSE), a moderate-to-strong effect on Cognitive Engagement (CE) and a moderate effect on Writing Enjoyment (WE). AWSE significantly predicted both WE and CE, confirming its role as a key mediator. In contrast, the direct path from CE to WE was negative and nonsignificant.
Table 5. Path Coefficients, Significance, and Effect Size Interpretation for Hypothesis Testing
| Hypothesis | β | Standard deviation | T Statistics |
P values | Significance | Effect size interpretation | |
| H1 | DMCC -> WE | 0.311 | 0.080 | 3.871 | 0.000 | Yes | Moderate |
| H2 | DMCC -> AWSE | 0.760 | 0.036 | 21.153 | 0.000 | Yes | Strong |
| H3 | DMCC -> CE | 0.425 | 0.073 | 5.806 | 0.000 | Yes | Moderate to strong |
| H4 | AWSE -> WE | 0.635 | 0.079 | 8.023 | 0.000 | Yes | Strong |
| H5 | CE -> WE | -0.080 | 0.077 | 1.041 | 0.298 | No | None |
| H6 | AWSE -> CE | 0.424 | 0.073 | 5.801 | 0.000 | Yes | Moderate |
Coefficient of Determination Using R-squared (R2). The explanatory power of the model was assessed using the coefficient of determination (R²). Table 6 presents the R² values for all endogenous constructs. The model explained 0.578 of the variance in AWSE, 0.635 in CE, and 0.694 in WE. According to Hair and Alamer’s (2022) classification, 0-0.10 = weak, 0.11-0.30 = modest, 0.30-0.50 = moderate, and >0.50 = strong, all three values fall into the strong range.
Table 6. Coefficient Determination Using R-squared (R2)
| R-square | R-square adjusted | Interpretation | |
| AWSE | 0.578 | 0.576 | Strong |
| CE | 0.635 | 0.631 | Strong |
| WE | 0.694 | 0.689 | Strong |
These results suggest that the structural model of digital multimodal composing competence (DMCC) has strong explanatory capability for predicting learners’ academic writing self-efficacy, cognitive engagement, and writing enjoyment.
Predictive Relevance using Q-squared (Q2). In addition to explanatory power, the model’s predictive relevance was evaluated using Q-squared (Q2) obtained through PLSPredict procedures. As proposed by Hair and Alamer (2022), the predictive relevance value (Q-squared) is categorized as small (0), medium (0.25), and large (0.50). The Q-squared values in Table 7 were 0.570 for AWSE, 0.553 for CE, and 0.532 for WE, confirming that the model demonstrates strong predictive accuracy for all key outcomes.
Table 7. Predictive Relevance Using Q-squared (Q2)
| Q-squared predict | Interpretation | |
| AWSE | 0.570 | Large |
| CE | 0.553 | Large |
| WE | 0.532 | Large |
These results support the model’s utility in forecasting how learners’ digital competence and beliefs translate into writing-related engagement and affective outcomes in EFL academic contexts.
Effect Size. To assess the practical significance of the relationships among constructs, effect sizes (f²) were calculated. Following Hair et al. (2014), f² values of 0.02, 0.15, and 0.35 are interpreted as small, medium, and large effects, respectively. Table 8 presents the relationship between DMCC and AWSE, which demonstrated a large effect (f² = 1.371), and AWSE also had a large effect on WE (f² = 0.461), highlighting their strong influence in the model. Moderate effects were found for DMCC CE (f² = 0.209) and AWSE CE (f² = 0.207), while the effect from DMCC to WE was small (f² = 0.110). The path from CE to WE had a negligible effect (f² = 0.008), in line with its statistical insignificance. These findings emphasize the importance of self-efficacy as a central mediator between digital multimodal competence and positive writing outcomes.
Table 8. Effect Size Using F-Squared (F2)
| f-square | Interpretation | |
| DMCC -> WE | 0.110 | Small |
| DMCC -> AWSE | 1.371 | Large |
| DMCC -> CE | 0.209 | Medium |
| AWSE -> WE | 0.461 | Large |
| CE -> WE | 0.008 | Negligible |
| AWSE -> CE | 0.207 | Medium |
Overall, the results support five of the six hypothesized relationships. Digital multimodal composing competence positively predicted academic writing self-efficacy, cognitive engagement, and writing enjoyment. Academic writing self-efficacy also showed strong positive effects on both cognitive engagement and writing enjoyment, whereas the direct path from cognitive engagement to writing enjoyment was not statistically significant. Together, these findings indicate that the proposed structural model accounts for meaningful variance in writing enjoyment and related psychological constructs.
Discussion
The following section interprets the results of the structural model by examining each hypothesized relationship. Emphasis is placed on how DMCC, self-efficacy, and engagement interact to influence writing enjoyment in the EFL context. This discussion integrates theoretical perspectives and prior research to illuminate the cognitive and affective dimensions of learners’ academic writing experiences.
This study found that DMCC significantly predicts WE, supporting Hypothesis 1. This finding aligns with Fredrickson’s (2001) broaden-and-build theory, which explains how competence and autonomy promote positive emotions such as enjoyment. It also echoes multimodal writing studies (Hafner & Ho, 2020; Jiang et al., 2021; Yu et al., 2024) showing that digital multimodal tasks enhance satisfaction by providing creative freedom and semiotic diversity. At the same time, it extends research that has focused more on performance than affective outcomes (Maghsoudi et al., 2022; Zhang & Peng, 2025). While Xu (2021) cautioned that DMC may overwhelm learners with limited digital skills, our results suggest that competence transforms multimodal writing into a rewarding experience. In Indonesia, where digital proficiency is uneven, and writing anxiety persists (Inggarwati et al., 2022; Yanuar, 2023), these findings imply that DMCC can help reduce negative emotions and sustain engagement. By fostering enjoyment, DMC may also indirectly support persistence and long-term writing outcomes.
Hypothesis 2 was also supported, with DMCC strongly predicting AWSE. This result reflects Bandura’s (1997) social cognitive theory, where mastery experiences build belief in one’s capabilities. Prior studies show that digital tools scaffold ideation and revision, boosting confidence in writing (Abdelhalim, 2024; Sun & Wang, 2020). Our findings extend Bruning et al.’s (2013) model by suggesting that multimodal tasks strengthen self-efficacy across ideation, conventions, and self-regulation simultaneously. For example, multimodal planning supports idea generation, while coordinating text and visuals reinforces conventions and develops regulation skills. In the Indonesian context, where digital confidence is uneven (Belladina et al., 2024), this highlights the potential of carefully scaffolded DMC tasks, from guided captioning to open-ended multimodal essays, to progressively enhance AWSE. Higher self-efficacy is not only valuable for psychological well-being but also a well-documented predictor of improved writing achievement.
The results also supported Hypothesis 3, showing that DMCC significantly predicts CE. This supports earlier findings that digital environments foster sustained attention and strategic effort (Giessler, 2024; Shen et al., 2023) and affirms the multiliteracies framework (Cope & Kalantzis, 2009). Multimodal writing tasks require planning across modes, textual, visual, and auditory, placing demands on learners’ metacognitive monitoring. This extends research by Fredricks et al. (2004) and Zhong and Zhan (2024), who found that competence fosters deeper investment, and complements Zhang and Yu’s (2023) work highlighting planning and cohesion as cognitively demanding subskills of DMC. Mills (2016) also showed that scaffolded multimodal tasks foster agency and metacognitive awareness. Taken together, these findings suggest that DMCC promotes meaningful opportunities for sustained CE. For practice, this means multimodal writing can serve not only as a creative outlet but also as a platform for cultivating executive functioning and resilience in academic writing, which are vital for sustained progress and outcomes.
Hypothesis 4 was strongly supported: AWSE significantly predicted WE. This is consistent with control-value theory (Pekrun, 2024), which posits that enjoyment arises from both competence and control, and with studies linking self-efficacy to positive writing emotions (Ardi et al., 2024; MacIntyre & Mercer, 2014). The strength of this path suggests that enjoyment is not simply a byproduct of appealing task design but is rooted in students’ confidence. In Indonesian contexts, where self-efficacy challenges are well documented (Belladina et al., 2024; Yanuar, 2023), this finding emphasizes the need for explicit confidence-building strategies such as formative feedback, recognition of progress, and sequenced multimodal modules. Building AWSE not only enhances learners’ enjoyment but also contributes to sustained motivation and eventual improvements in writing performance.
Contrary to expectations, Hypothesis 5 was not supported, as cognitive engagement did not significantly predict writing enjoyment. Although earlier studies have reported reciprocal links between effort and enjoyment (Fredricks et al., 2004; Zhang et al., 2021), this result does not contradict that body of work. Instead, it suggests that cognitive engagement does not always translate into enjoyment in digital multimodal academic writing in the EFL context. From a control-value perspective, enjoyment is more likely to develop when sustained effort is accompanied by a sense that the task is manageable, meaningful, and progressing as intended (Pekrun, 2024). While in DMC contexts, cognitive engagement often involves intensive regulation to cope with task complexity, design choices, and coordination across modes (Jiang et al., 2021). When these demands are not adequately supported through clear guidance or scaffolding, students may continue to invest effort without experiencing enjoyment, reflecting what Xu (2021) describes as strategic compliance rather than intrinsically rewarding engagement. In sum, this finding extends previous research by pointing to a boundary condition in the engagement-enjoyment relationship that cognitive engagement contributes to enjoyment only when learners perceive their effort as effective and leading to visible progress, a condition that may not always be present in complex multimodal writing tasks examined in this study.
The final hypothesis was supported: AWSE significantly predicted CE. This affirms Bandura’s (1997) principle that confidence drives persistence and aligns with evidence that writing self-efficacy predicts strategic effort (Ardi et al., 2024; Teng & Wang, 2023). Our findings extend this link to multimodal contexts, showing that students with higher AWSE were more willing to sustain effort in complex tasks. Self-efficacy thus acts as a motivational engine, enabling learners to maintain CE even under challenging conditions. Positioning AWSE and CE within a DMC framework also responds to calls for integrating strands often studied separately, digital multimodal competence, self-efficacy, and affective engagement (Abdelhalim, 2024; Dewaele & Li, 2020; Jiang & Hafner, 2025). For practice, this means that fostering CE begins with confidence-building. Demonstrations, scaffolded practice, and reflective activities that highlight growth as multimodal writers can empower students to engage more deeply and persistently, leading to stronger academic writing development. These findings complement earlier work on task complexity, digital tools, and engagement strategies in predicting writing performance (Ndruru et al., 2025b), extending the focus from performance outcomes to affective dimensions of academic writing.
The findings of this study offer pedagogical implications for EFL academic writing contexts that incorporate digital multimodal composing. Rather than assuming that greater cognitive engagement naturally leads to positive emotional experiences, the results show that writing enjoyment is more closely associated with learners’ DMCC and academic writing self-efficacy. Although cognitive engagement remains important for sustaining effort in complex digital, multimodal writing tasks, the non-significant relationship between cognitive engagement and writing enjoyment suggests that effort alone does not guarantee positive affect when learners do not feel capable of managing cognitively and emotionally demanding multimodal writing tasks. For instructional practice, this implies that prioritising learners’ perceived multimodal competence and writing self-efficacy is more likely to support positive writing experiences than increasing cognitive challenge alone. From the learners’ perspective, this pattern suggests that sustained effort without enjoyment may reflect limited confidence in managing multimodal academic writing rather than low motivation. While at the curricular level, the findings caution that introducing cognitively demanding multimodal tasks without sufficient attention to learners’ perceived competence and self-efficacy may increase workload without improving affective outcomes.
Several limitations should be acknowledged within this study. First, the cross-sectional design limits causal interpretation, especially in understanding how perceived DMCC and AWSE relate to writing enjoyment over time; longitudinal or experimental research is needed to capture these developmental dynamics. Second, the reliance on self-report measures, while appropriate for examining learners’ perceptions, limits insight into the processes through which engagement and enjoyment emerge during digital multimodal writing tasks. Future research could therefore complement survey data with more nuanced data sources, such as learners’ reflections, writing processes, or multimodal artifacts. Third, cognitive engagement was operationalised primarily in terms of persistence and regulation, which may not fully capture motivational dimensions such as curiosity or autonomy and may help explain the non-significant relationship between cognitive engagement and writing enjoyment. Finally, while the present model focused on four core psychological constructs, future studies could extend this framework by examining how task characteristics, instructional mediation (e.g., feedback), learner developmental differences, and contextual conditions interact with DMCC, self-efficacy, and engagement in shaping writing enjoyment across diverse EFL settings. Addressing these areas would advance both the theoretical model and its practical applications.
Conclusion
This study has examined the relationships among digital multimodal composing competence (DMCC), academic writing self-efficacy (AWSE), cognitive engagement (CE), and writing enjoyment (WE) in Indonesian EFL higher education using PLS-SEM. Five of the six hypotheses were supported, underscoring the central role of DMCC and AWSE in shaping both cognitive and affective writing outcomes. The nonsignificant CE → WE path highlighted that cognitive effort alone is insufficient to generate enjoyment without supportive psychological resources such as self-efficacy and perceived value. This research makes three key contributions. First, it advances theory by integrating digital, cognitive, and affective constructs into a unified explanatory model and by clarifying the mediating role of AWSE in linking DMCC to both CE and WE. Second, it extends writing research beyond a narrow focus on performance by foregrounding writing enjoyment as a meaningful affective outcome shaped by learners’ perceived competence and confidence, rather than by effort alone. Third, it contributes contextual insights by drawing on data from Indonesian higher education, while demonstrating that the proposed structural relationships among DMCC, AWSE, CE, and WE can inform research in other EFL contexts. Although instructional implementation may require contextual adaptation, the model offers a transferable framework for examining how digital multimodal competence and psychological factors interact in EFL academic writing, ultimately supporting the design of writing instruction that fosters learners’ confidence, engagement, and sustained enjoyment in academically demanding tasks.
Acknowledgement
We would like to thank the Indonesian Endowment Fund for Education Agency (LPDP) for granting the doctoral study scholarship for the first author (grant number: LOG-13892/LPDP.3/2024), the fourth author (grant number: LOG-13963/LPDP.3/2024), and the last author (grant number: LOG-20914/LPDP.3/2024), and for supporting the completion of this study.
About the Authors
Falentinus Ndruru is a doctoral student in the English Language Education program, Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia, and holds a master’s degree in linguistics from Universitas Warmadewa, Denpasar, Indonesia. His research interests focus on academic writing, applied linguistics, the integration of technology in English language teaching, and multimodal approaches to writing. ORCID ID: 0009-0008-5576-0417
Utami Widiati is a professor of ELT in the Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia. Her main research interests are curriculum and material development, second language acquisition, and teacher professional development. ORCID ID: 0000-0002-8603-4556
Yazid Basthomi is a Professor of Applied Linguistics in the Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia. During his doctoral studies, he was a Fulbright scholar at the ELI, University of Michigan. Having interests in genre analysis, intercultural education, and digital culture, he is currently the coordinator of the publication division of TEFLIN. ORCID ID: 0000-0003-3314-3334
Impiani Zagoto is an assistant professor at Universitas Nias Raya and holds a master’s degree in English language teaching. Currently, she is pursuing her doctoral degree in English language teaching at Universitas Negeri Malang, East Java, Indonesia. Her research interests are in English language teaching and learning, psychological factors in language learning, writing assessment, and language curriculum. ORCID ID: 0009-0005-8104-5386
Mala Rovikasari is a doctoral student in the English Language Education program, Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia. She is interested in feedback literacy, academic writing, PLS-SEM analysis, and the integration of technology in ELT. ORCID ID: 0000-0002-4625-8010
Sri Wahyuningsih is a lecturer at the Department of English Language Education, Faculty of Tarbiyah, Universitas Islam Negeri Sunan Kudus, Indonesia. She is currently pursuing a doctorate degree at the Department of English, Faculty of Letters, Universitas Negeri Malang, Indonesia. Her research interests include academic writing, teacher professional development, English language teaching, and research identity. ORCID ID: 0000-0001-6913-630X
To Cite this Article
Ndruru, F., Widiati, U., Basthomi, Y., Zagoto, I., Rovikasari, M., & Wahyuningsih, S. (2026). Digital multimodal composing competence and writing enjoyment: Modeling self-efficacy and cognitive engagement among EFL students. Teaching English as a Second Language Electronic Journal (TESL-EJ), 29(4). https://doi.org/10.55593/ej.29116a4
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