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Abstract

Online teaching, widely adopted as an emergency response during crises such as the COVID-19 pandemic, has exposed various challenges and issues within vocational education while simultaneously offering opportunities for its digital transformation. While existing studies primarily focus on online learning from the learners’ viewpoint. In contrast, this study investigates teachers’ willingness to sustain online teaching in vocational education, thereby highlighting the teachers’ viewpoint. Using survey data from 17,009 questionnaire responses collected from vocational education teachers, this study employs structural equation modeling to capture the practical challenges of online teaching and learning in this context. The study extends the Technology Acceptance Model (TAM) by integrating context-specific variables. The findings demonstrate that perceived usefulness and perceived resources significantly and directly influence teachers’ willingness to sustain online teaching in vocational education. Additionally, perceived ease of use, perceived learner engagement, and perceived behavioral control exert significant indirect effects. Theoretically, this study extends the TAM by contextualizing it within vocational education. Practically, it proposes two key recommendations: (1) it advocates for the establishment of a comprehensive, accessible repository of digital teaching resources tailored to vocational contexts; (2) it underscores the importance of fostering multistakeholder collaborations to cultivate vocational education teachers’ digital pedagogical competence.

Keywords

online teaching willingness to sustain online teaching structural equation modeling

Introduction

The digital transformation of education has accelerated significantly due to the impact of the COVID-19 pandemic, marking a notable breakthrough, particularly in vocational education. Online teaching has emerged as a crucial alternative to traditional face-to-face instruction during times of crisis, such as epidemics and wars, a shift whose importance cannot be overstated (Kovalchuk et al., 2023). According to UNESCO's report, the COVID-19 pandemic has spurred the widespread adoption and implementation of online education, particularly in higher education (UNESCO, 2021). Similarly, the World Bank Group (2021) analyzed the effects of implementing distance learning for K-12 learners during the pandemic. Moreover, the World Economic Forum (2020) emphasized that the COVID-19 pandemic has fundamentally reshaped global education. These developments create new opportunities to advance the digital transformation of vocational education.

As in-person instruction gradually resumes, it is essential to explore how emergency measures introduced during the pandemic can evolve into sustainable digital strategies that enhance the quality and resilience of vocational education (Han et al., 2020). However, these opportunities for transformation are accompanied by significant challenges. The OECD (2021) observed that while the pandemic accelerated the adoption of online teaching and learning in vocational education, it also exposed the limitations of such an approach. Notably, online instruction cannot compensate for the lack of hands-on training and internships central to vocational education. Furthermore, many teachers face barriers such as insufficient technical skills, limited instructional quality, and low confidence in delivering online education, all of which hinder the long-term integration of digital teaching practices.

Given the challenges discussed above, the teachers’ willingness to sustain online teaching in vocational education emerges as a pivotal factor. It influences their ability to leverage the opportunities brought by the pandemic and facilitates meaningful progress in the digital transformation of vocational education. This study investigates the influencing factors and examines their impact on teachers’ willingness to sustain online teaching in vocational education from the teachers’ perspective. Based on the findings, targeted strategies are proposed to support the long-term integration of online instruction into vocational education. Specifically, this study concentrates on addressing the following research questions.

What are the influencing factors of teachers’ willingness to sustain online teaching in vocational education postpandemic?

How can teachers’ willingness to sustain online teaching be enhanced based on these influencing factors?

Literature Review

Teachers’ willingness to sustain online teaching is generally conceptualized as a perception-based construct, shaped by their evaluation of past online teaching experiences, and perceived instructional effectiveness. These perceptions subsequently shape their future teaching behaviors. Willingness is a future-oriented behavioral attribute that cannot be directly measured or validated in the present. However, it can be effectively inferred by assessing individuals’ behavioral intentions.

Previous studies have demonstrated that, while behavior is influenced by multiple factors beyond willingness, a stable and significant correlation persists between behavioral intentions and actual behavior across various domains. This finding underscores the critical role of intention as a predictor of future action. Consequently, the study of sustained behavioral engagement, such as teachers’ willingness to sustain online teaching, can reasonably be approached through the lens of behavioral intentions.

Several theoretical models have been widely employed to investigate behavioral intentions and their predictive power, including the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), the Innovation Diffusion Theory. Numerous studies have extended these models or added context-specific variants. Notably, both the TPB and TAM are theoretical extensions of the TRA.

The TRA, initially proposed by Ajzen and Martin (1980), provides a foundational framework for understanding how individual beliefs shape and predict behavioral intentions. The TPB extends upon the TRA by incorporating perceived behavioral control (PBC) as an additional determinant of behavioral intention (Fishbein & Ajzen, 2010). In comparison, the TAM examines user adoption of new technologies through two primary factors (Davis, 1986), which are perceived usefulness (PU) and perceived ease of use (PEU).

Technology Acceptance Model and UTAUT focus more specifically on technology-related acceptance and behavioral intention than TRA and TPB, which are broader in scope and not technology-specific. However, applying the TAM to studies on teaching behavior, particularly in online instruction in vocational education, requires model adaptation and contextual extension. These modifications improve the model's capacity to guide practical interventions to increase teachers’ willingness to sustain online teaching.

For instance, the TAM3 extends upon the original TAM by exploring a wider range of determinants influencing PU and PEU, thereby providing a more comprehensive framework for intervention design (Venkatesh & Bala, 2008). In one meta-analysis, King and He (2006) identified four key dimensions for TAM expansion: prior factors, theory-driven factors, contextual factors, and consequent factors. Similarly, Scherer et al. (2019) employed a meta-analysis of empirical studies examining teachers’ technology acceptance in educational settings and extracted core influencing factors using the TAM as the primary analytical framework. Existing literature that applies or extends the TAM to explore teachers’ willingness to engage in online teaching predominantly clusters into two main categories.

Integration of TAM with Other Models to Construct a Comprehensive Theoretical Framework has Been the Focus of Research

Several studies have combined the TAM with the TPACK to analyze the factors influencing vocational education teachers’ willingness to adopt Web 2.0 technologies for instructional purposes. For instance, Khong et al. (2023) and Prasojo et al. (2020) integrated the TAM with the TPACK to investigate digital technology adoption, specifically addressing teachers’ experiences with online teaching during the COVID-19 pandemic and their intentions to sustain such practices postpandemic. Prasojo et al. (2020), conducted before the pandemic, further examined vocational education teachers’ acceptance of Web 2.0 technologies and willingness to continue incorporating them into online teaching.

Panisoara et al. (2020) integrated the TAM with the SDT and the Job Demands-Resources Model to develop a comprehensive theoretical model. Their study investigated the factors influencing teachers’ willingness to sustain online teaching during the COVID-19 pandemic and how these factors drive these influences. Although such integrative approaches broaden the explanatory power of technology adoption models, they also encounter challenges, including conceptual overlap, model convergence, and theoretical compatibility. These issues may result in models with inherent limitations.

Technology Acceptance Model as the Base Model is Extended by Adding Other Influencing Factors

Another prominent stream of research involves using the TAM as a foundational framework and extending it by incorporating additional influencing factors relevant to specific research contexts. These studies often integrate variables derived from the research objectives or questions into the TAM to investigate their relationships and impacts.

For instance, Bajaj et al. (2021) extended the TAM to investigate teachers’ willingness to sustain online teaching postpandemic, particularly in response to the rapid and widespread adoption of online education during the pandemic. Similarly, Wang et al. (2013) examined factors influencing the willingness to adopt an e-learning platform. The results confirmed that PEU and PU significantly impacted the behavioral intentions (Wang et al., 2013).

Building upon prior research, this study employs the TAM as the core theoretical foundation and further expands it by incorporating contextual factors unique to vocational education. In particular, this study investigates the challenges and constraints vocational education teachers encounter in online teaching. Existing literature suggests these challenges can be classified into three key categories. Firstly, constraints stemming from the teaching environment. Secondly, there are misconceptions regarding effective instructional strategies in online teaching. Thirdly, there are implementation barriers to learner-centered pedagogy (Cox & Prestridge, 2020).

This study incorporates three additional variables into the extended model, as shown in Figure 1. Perceived Resources (PR) refers to vocational education teachers’ perceptions of the quality, accessibility, and usability of online teaching resources. Perceived Behavioral Control refers to the degree to which vocational education teachers perceive online teaching behavior to be easily or difficultly controllable. Perceived Learner Engagement (PLE) refers to teachers’ perceptions of student engagement in online learning and their evaluation of learners’ participation and learning outcomes. Grounded in the theoretical model, this study empirically tests the following hypotheses.

H1: PEU is a significant positive predictor of PU.

H2: PU is a significant positive predictor of teachers’ willingness to sustain online teaching in vocational education.

H3: PEU is a significant positive predictor of the teachers’ willingness to sustain online teaching in vocational education.

Figure 1.

Hypothetical Model for the Study of the Teachers’ Willingness to Continue Online Teaching in Vocational Education.

All three assumptions are derived from the TAM theoretical model (Davis, 1986).

H4: PBC is a significant positive predictor of the teachers’ willingness to sustain online teaching in vocational education.

H5: PBC has a significant positive predictive effect on PU.

H6: PBC has a significant positive predictive effect on PEU.

The primary distinction between the TPB and the TRA lies in including PBC as a fundamental construct (Fishbein & Ajzen, 2010). In this study, PBC primarily refers to vocational education teachers’ perceptions of their ability to manage and control various aspects of the teaching process, encompassing instructional design, methods, and practices. Consequently, this study extends the TAM by integrating PBC as a supplementary predictor variable. The study seeks to investigate whether PBC directly influences the teachers’ willingness to sustain online teaching in vocational education or whether its effect is mediated through the existing TAM constructs, which are PEU and PU.

H7: PLE has a significant positive predictive effect on PBC.

H8: PLE has a significant positive predictive effect on PU.

H9: PLE has a significant positive predictive effect on PEU.

Teachers’ perceptions of learner engagement significantly influence teachers’ expectations of student performance, which in turn can shape their instructional behavior (Wang et al., 2018). In this study, PLE focuses on vocational education teachers’ perceptions of students’ engagement in the online learning environment. Therefore, this study aims to investigate whether the perceptions of students’ engagement indirectly influence the willingness to sustain online teaching through the mediating effects of PBC, PU, and PEU.

H10: PR has a significant positive predictive effect on PLE.

H11: PR has a significant positive predictive effect on PU.

H12: PR has a significant positive predictive effect on PEU.

H13: PR has a significant positive predictive effect on willingness to continue online teaching.

The hypothesized relationships among PR, PEU, and the teachers’ willingness to sustain online teaching in vocational education offer an extended perspective on the TAM from the perspective of prior knowledge and contextual factors. PR is mainly examined within real-world educational environments, which are often constrained by nonideal conditions and resource limitations (Mathieson et al., 2001). Thus, this study investigates perceived resource availability's direct and indirect predictive effects on vocational education teachers’ behavioral intentions to sustain online teaching.

Methodology

This study primarily analyzed the questionnaire data from vocational education teachers, yielding 17,009 valid responses. The questionnaire was administered through the Wenjuanxing (Questionnaire Star) platform to teaching staff from vocational education institutions, encompassing higher vocational colleges and secondary vocational schools across mainland China. Among them, 42.12% were teachers from secondary vocational schools, and 57.88% were teachers from higher vocational colleges.

The primary objective of this study is to develop a structural model for examining the factors influencing teachers’ willingness to sustain online teaching in vocational education. To achieve this, structural equation modeling (SEM) was employed to explore the relationships among key constructs and to uncover underlying mechanisms. Data processing and analysis involved multiple statistical tools. ORACLE was utilized for questionnaire data extraction and transformation. SPSS 25 was utilized for reliability and validity tests, factor analyses, and correlation analyses. SPSSAU was utilized for path analysis and SEM.

To ensure the validity and reliability of the findings, this study utilized a questionnaire adapted from established scales, ensuring alignment with constructs about teachers’ willingness to sustain online teaching in vocational education. The reliability and validity test results for the questionnaire, presented in Table 1, indicate the robustness of the instrument. As shown in Table 1, the cumulative variance contribution rate of extracted principal components for all factors exceeded 50%, indicating that the items captured sufficient information to represent the underlying constructs. The loading coefficients are all greater than 0.6, indicating strong consistency among the variables within the factors. The Kaiser–Meyer–Olkin (KMO) values for individual factors were greater than 0.6, and the overall KMO value was 0.887 (p = 0.000 < 0.05), confirming the adequacy of the sample for factor analysis and supporting construct validity. The Cronbach's alpha coefficients for each category of variables are above 0.6, indicating acceptable internal consistency. The Cronbach's alpha coefficient of the overall questionnaire is 0.676, which falls within the generally accepted threshold for reliability.

Table 1.

Parameters of Reliability and Validity Tests of the Questionnaire and the Basis for the Design of the Questions.

Factors	Items	Loading	CITC	Cronbach's alpha coefficients	KMO	Approximate χ² (p value)	Cumulative contribution rate of variance explained by principal components%	Basis of item design
PBC	PBC_1	0.767	0.592	0.827	0.788	26917.911 (p = 0.0000)	66.934%	Taylor and Todd (1995), Ajzen (2002)
	PBC_2	0.876	0.745
	PBC_3	0.846	0.697
	PBC_4	0.779	0.607
PLE	LE_1	0.920	0.694	0.820	0.500	11194.380 (p = 0.000)	84.722.%	Salas-Rueda et al. (2022), Zheng et al. (2022)
PLE	LE_2	0.920	0.694	0.820	0.500	11194.380 (p = 0.000)	84.722.%	Salas-Rueda et al. (2022), Zheng et al. (2022)
PR	PS_1	0.678	0.519	0.809	0.800	27097.181 (p = 0.000)	57.161%	Mathieson et al. (2001)
	PS_2	0.751	0.591
	PS_3	0.754	0.599
	PS_4	0.828	0.685
	PS_5	0.761	0.597
PEU	PE_1	0.693	0.444	0.725	0.623	17623.456 (p = 0.000)	68.971%	Davis, (1986)
	PE_2	0.897	0.665
	PE_3	0.885	0.626
PU	PU_1	0.858	0.472	0.636	0.500	4294.610 (p = 0.000)	73.620%	Davis, (1986)
PU	PU_2	0.858	0.472	0.636	0.500	4294.610 (p = 0.000)	73.620%	Davis, (1986)

Note: PU, perceived usefulness; PR, perceived resources; PEU, perceived ease of use; PLE, perceived learner engagement; PBC, perceived behavioral control.

PBC: This includes teachers’ participation in online teaching training, adaptation of teaching methods, adaptation of assessment methods, and adaptation of interaction methods.

PLE: This includes improving student engagement in online learning and grasping the state of student learning.

PR: This includes barriers to skills, barriers to teaching experience, and lack of teaching resources.

PEU: This includes student engagement, student satisfaction, and quality of student–teacher interaction.

PU: This includes teachers’ attitudes and expectations about how online teaching and learning can be more effective and achieve teaching and learning goals.

Research Results and Discussions

In order to construct the final structural model, this study first conducted Pearson Correlation Analysis to examine the relationships among the variables in the theoretical model. This step served as an initial verification of the significance of hypothesized path relationships, and the model was refined based on the strength and direction of the correlation coefficients. The SEM was constructed using SPSSAU, which contains five latent variables and seventeen observed variables. After several rounds of iteration and adjustment, the final SEM was obtained, depicting the teachers’ willingness to sustain online teaching in vocational education (as shown in Figure 2).

Figure 2.

Structural Equation Model Diagram of the Teachers’ Willingness to Sustain Online Teaching in Vocational Education (*** means p < 0.001).

By integrating the key indicators presented in Table 2, it can be concluded that the full model exhibits a strong fit and can reliably support the conclusions. In particular, the Root-Mean-Square Error of Approximation is 0.064, less than 0.08. Additionally, the Goodness-of-Fit Index, Comparative Fit Index, and Incremental Fit Index all exceed 0.9, with values of 0.947, 0.944, and 0.944, respectively. These results confirm that the final model demonstrates an acceptable fit and can be effectively used to analyze the influencing factors and underlying mechanisms of vocational education teachers’ willingness to sustain online teaching.

Table 2.

The Fit of the Structural Equation Model of Willingness to Sustain Online Teaching.

Indexes	χ²	df	GFI	RMSEA	RMSEA 90% CI	RMR	CFI	NFI	NNFI
Criteria	-	-	>0.9	<0.10	-	<0.05	>0.9	>0.9	>0.9
Value	6888.607	96	0.947	0.064	0.027–0.068	0.038	0.944	0.943	0.930

Other indexes	TLI	AGFI	IFI	PGFI	PNFI	PCFI	SRMR
Criteria	>0.9	>0.9	>0.9	>0.5	>0.5	>0.5	<0.1
Value	0.930	0.925	0.944	0.669	0.755	0.755	0.042

Given the large sample size and the fact that the variables do not fully meet the assumptions of normality, which significantly affects the chi-square and p-values, the overall model fit remains acceptable and within a reasonable range for large-sample SEM analysis. Based on the above model, this study further analyses the factors and paths that influence teachers’ willingness to sustain online teaching in vocational education. The path coefficients and related indexes between variables are shown in Table 3. It can be found that all standardized errors fall within the expected range, with no negative or outlier values. Additionally, the significance test of the parameter estimates falls within the expected range (absolute value of z > 2, p < 0.05).

Table 3.

Path Coefficients of the Teachers’ Willingness to Sustain Online Teaching in Vocational Education.

X	Y	Nonstandardized path coefficients	SE	z(CR)	Standardized path coefficients
PBC	PEU	0.700	0.014	51.480	0.713
PEU	PU	1.208	0.020	60.119	0.819
PU	Willingness	0.600	0.012	49.395	0.675
PR	PEU	−0.157	0.009	−17.071	−0.195
PR	PLE	0.963	0.015	66.339	0.711
PR	Willingness	0.204	0.014	14.886	0.193
PLE	PBC	0.171	0.006	30.481	0.282
PLE	PEU	0.217	0.008	28.667	0.364

The analysis of the path coefficients reveals the direct influence effects among the latent variables in the model. To further examine the overall effect of these variables on the dependent variable, teachers’ willingness to sustain online teaching in vocational education, this study calculated total effect sizes, as presented in Table 4.

Table 4.

Analysis of Effects of Interactions Between Variables.

	Indirect effect					Total effect
	PLE	PBC	PEU	PU	Willingness	Willingness
PR	0.688**	−0.093**	0.061**	0.088**	−0.270**	−0.132**
PLE		−0.105**	−0.183**	−0.203**		−0.039**
PBC			0.653**	0.258**		0.132**
PEU				0.421**		−0.054**
PU						0.585**

*p < 0.05; **p < 0.01; ***p < 0.001.

Based on Hattie's (2008) interpretation of effect sizes in educational research, 0.1, 0.3, and 0.5 are the intervals dividing small, medium, and large effect sizes (Hattie, 2008; Shen et al., 2019). The above table shows that PU demonstrated a large effect size for willingness to sustain online teaching, indicating it is the most influential predictor. However, PBC and PLE exhibited small to medium effect sizes (ranging between 0.1 and 0.3), suggesting moderate predictive power. Notably, both PR and PLE showed negative total effects, implying that under certain conditions, these factors may inhibit teachers’ willingness to continue online teaching.

For PU, PR's effect size is less than 0.1, meaning it has less influence. The effect size of PBC ranges from 0.1 to 0.3, which showed a medium effect. The effect size of PU ranges from 0.3 to 0.5, which exhibited a significant effect. However, the effect size of PLE on PU is negative, and the absolute size of the effect size ranges from 0.1 to 0.3, which means that it has a negative and medium influence. This shows that PBC is much more influential than PR, perceived learner involvement and barriers to teaching conditions.

Perceived behavioral control exerted the strongest influence on PEU, with a total effect size of 0.653. This result indicates that PBC has a much greater influence than PLE and PR. The critical pathway “PR → PLE → PU → Willingness” emerged clearly when tracking the largest effect sizes along each step, highlighting a key indirect mechanism within the model.

Based on the results of the above analyses, eight hypotheses, H1, H2, H6, H7, H9, H10, and H13, were supported, whereas the remaining hypotheses were not. Two of the unsupported hypotheses need to be further discussed and verified. H3 was not supported, meaning PEU did not directly predict the effect on the teachers’ willingness to sustain online teaching in vocational education but was instead mediated by PU. This finding aligns with prior research on postpandemic technology use in education. For example, Bajaj et al. (2021) found that teachers’ sustained use of online teaching tools after the COVID-19 pandemic was influenced more by PU than ease of use. Similarly, Nikou (2020) reported that teachers’ behavioral intentions to use online conferencing tools were indirectly shaped by the ease of use through PU.

H12 was rejected because the observed effect of PR on PEU was contrary to the direction of the original hypothesis, with a significant negative relationship. Notably, this negative relationship contradicts findings from previous studies, particularly Mathieson et al. (2001), which served as the theoretical foundation for incorporating PR into the extended TAM framework in this study. A potential explanation for this unexpected outcome may stem from the cognitive burden experienced by vocational education teachers. An excessive availability of perceived online teaching resources could complicate instructional design and delivery rather than facilitate it. This counterintuitive finding underscores the necessity for further empirical investigation.

Discussion

Based on the analyses of the questionnaire results, approximately half of the vocational education teachers expressed the willingness to sustain online teaching after the COVID-19 pandemic, as illustrated in Figure 3. Accordingly, 18.10% of vocational education teachers have strong intentions to sustain online teaching, while a quarter remained neutral. Notably, 16.90% indicated unwillingness, and another 6.00% explicitly indicated unwillingness to sustain online teaching in the postpandemic era.

Figure 3.

Percentage of the Teachers’ Willingness to Sustain Online Teaching After the COVID-19 Pandemic in Vocational Education.

A comparative analysis of teacher groups reveals a significant difference in willingness levels between higher vocational colleges and secondary vocational schools (see Figure 4). More than half of the teachers from higher vocational colleges expressed willingness to sustain online teaching, whereas only 40% of the secondary vocational schools’ teachers expressed the same attitude. In contrast, nearly 30% of teachers from secondary vocational schools reported reluctance or strong reluctance to continue online teaching, compared to roughly 20% among their counterparts in higher vocational colleges.

Figure 4.

Comparison of Two Types of Teachers’ Willingness to Sustain Online Teaching in Vocational Education.

These findings indicate that many vocational education teachers remain undecided or hold negative attitudes toward sustaining online teaching, particularly in secondary vocational schools. Consequently, a central issue arising from this study is how to effectively transform neutral or negative perceptions and increase the proportion of teachers willing to sustain online teaching practices in the postpandemic era.

In response to this challenge, and grounded in the findings of the constructed model, the study proposes the following three recommendations to foster sustainable online teaching in vocational education.

Enhancing Teachers’ Willingness by Strengthening PU

To enhance teachers’ willingness to sustain online teaching in vocational education, improving their PU of online teaching is crucial. While both PU and PEU are fundamental constructs in the original TAM, this study reveals that PU alone directly affects teachers’ willingness to continue online teaching, whereas PEU functions indirectly via PU. These findings align with prior research, demonstrating that heightened PU predicts sustained technology adoption in educational contexts. In particular, teachers are more inclined to integrate technology into teaching when they perceive it as instrumental in enhancing learning outcomes and instructional efficiency (Sadaf et al., 2016).

One effective strategy is to support teachers in conducting data-informed assessments within their online teaching practices. This enables them to develop a more systematic and evidence-based understanding of how online instruction enhances teaching effectiveness and what factors influence that contribution.

On the one hand, institutions should establish a structured evaluation framework tailored to the context of vocational education. Teachers can use digital tools to collect, analyze, and interpret data from online teaching activities to assess instructional value and refine their pedagogical approaches. Feng et al. (2023) demonstrated the potential of educational data mining and AI-driven analytics to evaluate, predict, and intervene in online learning, effectively improving teaching outcomes in vocational education.

On the other hand, national-level initiatives, such as the National Vocational College Skills Competition and the Typical Cases of the Achievements of Informatization Construction and Application in Vocational Education, provide concrete examples and benchmarking standards that can guide teachers’ assessments of online teaching effectiveness. These practices offer demonstrative and exemplary references, facilitating effectiveness assessment in a structured and meaningful way. Hence, the study advocates for enhancing online teaching efficacy through teacher-led evaluation of its practical value, supported by digital analytics and exemplary benchmarks from leading vocational institutions.

Improving Perceived Resource Support Through Targeted Infrastructure and Governance

Among all extended variables, PR was the only factor found that demonstrated a significant, direct, and positive effect on teachers’ willingness to sustain online teaching. This finding aligns with prior research, such as Sangeeta and Tandon (2021), which highlights the pivotal role of supportive online teaching environments in shaping and maintaining behavioral intentions.

China has made substantial progress in recent years by developing multitiered digital infrastructure and resource systems to support vocational education. The National Vocational Education Intelligent Teaching Platform integrates exemplifies these efforts. It integrates high-quality, domain-specific digital resources from national, regional, and institutional levels, aiming to strengthen the instructional environment for vocational educators. It comprises four major centers: the Professional and Curriculum Service Centre, the Virtual Simulation and Training Centre, the Teacher Competence Enhancement Centre and the Teaching Material and Resource Centre. These centers offer comprehensive technical, pedagogical, and content support, helping to reduce perceived barriers and enhance resource accessibility for teachers (Ministry of Education of the People's Republic of China, 2022).

Although the National Platform consolidates a wealth of resources, further enhancement through effective governance mechanisms is essential to improve teachers’ ability to locate, access, and utilize these resources efficiently. Governance should prioritize improving the accessibility and usability of digital teaching materials through the following strategies. First, resource governance should focus on dismantling institutional and regional silos by integrating and optimizing resource repositories developed independently by national, provincial, and institutional stakeholders. This integration can be achieved by establishing standardized metadata and resource data protocols, enhancing interoperability. Additionally, clearly defined copyright and sharing mechanisms are necessary to address ownership and distribution issues, fostering broader access and reuse.

Second, effective resource governance should aim to eliminate disciplinary silos to support the development of high-quality, multidimensional, and systematized digital teaching materials for vocational education. Knowledge mapping can transcend rigid subdisciplinary boundaries and facilitate reconstructing a more integrated and application-oriented curriculum system. For instance, Jiang (2023) explored using knowledge maps to enhance the effectiveness of resource recommendations in vocational education.

Third, governance should bridge the disconnect between classroom instruction and practical training by strategically integrating digital resources into vocational education. This embedding digital teaching materials into immersive formats such as virtual reality (VR) and augmented reality and restructuring teaching workflows across classroom and workplace scenarios. Such integration addresses the limitations of traditional online internship models. Research by Ravichandran and Mahapatra (2023) has demonstrated VR technologies’ potential to enhance learning outcomes in vocational education. Ultimately, targeted and systemic governance is essential to improving the accessibility and usability of digital teaching resources, thereby facilitating the broader digital transformation of vocational education.

Enhancing Teachers’ Online Instructional Capacity Through Multistakeholder Collaboration

PLE and PBC reflect vocational education teachers’ attention to learner needs and their competence and confidence in online instruction. Improving these dimensions requires targeted efforts to strengthen teachers’ online instructional competence through coordinated actions involving multiple stakeholders.

This study demonstrates that PLE and PBC indirectly affect teachers’ willingness to sustain online teaching, mediated by other variables. This is consistent with existing research findings that prior teaching experience (PBC) and PLE influence behavioral willingness, albeit to a lesser extent than PU (Mustafa & Garcia, 2021). Thus, developing teachers’ professional capabilities is crucial for sustaining online teaching in vocational education.

In recent years, many countries have widely adopted policy initiatives such as teaching skills competitions, teacher training programs, and dual-teacher (academic and technical) development programs. In the Chinese context, policy analyses highlight the strategic importance of building dual-qualified teaching teams to meet the evolving needs of digital vocational education (Nie, 2022).

Sustainable progress calls for a multistakeholder synergy system to cultivate teaching competence. The government, institutions, enterprises, teachers, and domain experts play integral roles in this system. Governments provide policy direction and funding support. Institutions offer organizational frameworks and platforms for capacity development. Industry partners facilitate real-world integration and training opportunities. Experts contribute through guidance, evaluation, and curriculum innovation. Together, these actors form a synergistic mechanism that supports the development of teachers’ digital and pedagogical skills. This study, therefore, underscores the necessity of multistakeholder collaboration to enhance the instructional quality and sustainability of online teaching in vocational education.

Conclusion

In summary, this study investigates the factors that influence and model the structure of the teachers’ willingness to sustain online teaching in vocational education from the perspective of teachers during the COVID-19 pandemic. Drawing on the actual context and challenges of online teaching in vocational education, the research extends the TAM by incorporating PR, PLE, and PBC. A SEM was developed to understand better and predict teachers’ willingness to sustain online teaching. Based on the findings, the study further explores targeted strategies to enhance teachers’ willingness to sustain online teaching in the postpandemic context. Despite these contributions, two limitations should be noted. Firstly, using cross-sectional data restricts temporal analysis and thus limits the strength of causal inferences regarding the relationships among variables. Secondly, future research could enrich the current model by employing longitudinal data and advanced statistical approaches, such as chain mediation analysis, to uncover deeper pathways influencing teachers’ willingness to sustain online teaching.

Footnotes

Ethical Statement

The present research adhered to the ethical standards for social science research. As the study relied solely on voluntary and anonymous questionnaire data without involving any intervention or identifiable personal information, it did not require formal ethics committee approval under the institutional policy. Therefore, there are no ethical issues or conflicts of interest associated with this research. No ethical approval was required for this work.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Beijing Social Science Foundation (Project No. 23JYB011), under the project titled “Research on the Assessment Framework for Adaptive Problem-Solving Competence in the Era of Intelligent Digitalization.” The authors would like to express their sincere gratitude for the financial and academic support provided by the foundation, which made this study possible.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Xiu Guan

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Teacher Willingness to Sustain Online Teaching in Vocational Education: A Post-Pandemic Survey Study

Abstract

Keywords

Introduction

Literature Review

Integration of TAM with Other Models to Construct a Comprehensive Theoretical Framework has Been the Focus of Research

Technology Acceptance Model as the Base Model is Extended by Adding Other Influencing Factors

Methodology

Research Results and Discussions

Discussion

Enhancing Teachers’ Willingness by Strengthening PU

Improving Perceived Resource Support Through Targeted Infrastructure and Governance

Enhancing Teachers’ Online Instructional Capacity Through Multistakeholder Collaboration

Conclusion

Footnotes

Ethical Statement

Funding

Declaration of Conflicting Interests

ORCID iD

References