The Path From Digital Competence to Adaptive Use: An Empirical Analysis of 9,726 Primary and Secondary School Teachers

Abstract

While digital competence is widely acknowledged as fundamental to educational transformation, the mechanisms impeding its translation into innovative teaching practices remain inadequately understood. This study investigates teachers’ adaptive use of digital tools, characterized by exploratory adaptation and innovative application behaviors. By developing a multiple mediation model incorporating four key constructs: educational contextual norms (ECN), digital tool structure (DTS), technology adoption intention (TAI), and perceived usefulness (PU), this study illuminates the transformation pathway from digital competence to adaptive use. Analysis of data from 9,726 primary and secondary school teachers reveals three transformation pathways: an intrinsically driven pathway relying on internal motivation, an extrinsically driven pathway dependent on external norms, and a multi-driven path demonstrating significant sequential mediation through ECN → DTS → PU. Findings indicate that digital competence facilitates innovation indirectly through a progressive process of interpreting contextual norms, selecting appropriate tools, and developing value recognition, thereby providing insights for fostering teachers’ digital innovation practices in educational settings.

Keywords

digital competence adaptive use chain mediation transformational pathways primary and secondary school teachers

Introduction

As educational digital transformation accelerates, teachers’ digital literacy has emerged as a critical factor in improving instructional quality and fostering classroom innovation. Globally, teachers’ digital competence is widely recognized as a cornerstone of educational digitalization, with a growing emphasis shifting from skill acquisition to innovative teaching practices. According to the UNESCO ICT Competency Framework for Teachers (Version 3), teachers should effectively integrate ICT into their lessons and use technology to promote pedagogical innovation and educational change (UNESCO, 2018). The UNESCO AI Competency Framework for Teachers further suggests that teacher competence should evolve from acquisition to deep application and creation in the AI era (UNESCO, 2024). The OECD emphasizes the need for teachers to enhance their ability to integrate digital skills and pedagogies and to be able to align their teaching practices with the digital environment (OECD, 2025). Similarly, UNICEF points out that teachers’ digital competence is an important prerequisite for promoting online teaching improvement and educational innovation (UNICEF Europe and Central Asia, 2022). China’s 2022 industry standard Digital Literacy for Teachers further emphasizes that teachers should possess the ability to utilize digital technology resources to achieve pedagogical innovation (Ministry of Education of the People’s Republic of China, 2023). In July 2025, the General Office of the Ministry of Education of China launched the Digital Empowerment for Teacher Development Action to promote the reform and development of the teaching force (Ministry of Education of the People’s Republic of China, 2025).

Empirical studies suggest that the relationship between teachers’ digital competence and classroom innovation is not a straightforward linear one (Suárez-Rodríguez et al., 2018). Although some teachers possess certain technical competencies, their actual teaching practices are often constrained by specific knowledge, self-efficacy, and cultural factors, resulting in an insufficiently innovative use (Ertmer & Ottenbreit-Leftwich, 2010; Xie et al., 2023). Based on a large-scale survey of Chinese teachers, Chen et al.(2023) noted that teachers still need to strengthen the application of digital knowledge and innovation. International surveys corroborate this phenomenon: multiple OECD reports indicate that teachers’ use of digital tools predominantly remains confined to administrative tasks and replicating traditional methods (OECD, 2019, 2023). Concurrently, TALIS data reveal that on average, approximately 43% of teachers feel inadequately prepared when using ICT in classrooms, and about 18% urgently require relevant professional development (OECD, 2021). These findings collectively demonstrate that merely mastering digital skills is insufficient to ensure innovative classroom applications.

Further research indicates that the gap between digital competence and classroom innovation is constrained by multiple factors. Scholars have pointed out that teachers’ beliefs and readiness, as well as school-level resources, technical support and cultural values influence teachers’ integration of technology in the classroom (Inan & Lowther, 2010; Li et al., 2025; Wang, 2024). In conclusion, the transformation from teachers’ digital competence to innovative teaching is jointly influenced by their internal willingness and the surrounding educational environment. This study focuses on uncovering the mediating pathways through which teachers convert digital competence into innovative instructional practices—a process that narrows the gap between “possessing capability” and “innovative application” and has become a core priority for both academics and policymakers.

This study takes teacher digital competence (DC) as the starting point and introduces variables such as technology adoption intention (TAI), educational contextual norms (ECN), digital tool structure (DTS), and perceived usefulness (PU) to construct a multiple mediation model. This model explores how teachers’ digital competence translates into adaptive use (AU) through multiple mechanisms. To explain this process, the study draws on the Information Systems Success Model, the Adaptive Structuration Theory, and the Technology Acceptance Model. Specifically, the Information Systems Success Model is used to assess the effectiveness of the digital tool structure; the Adaptive Structuration Theory emphasizes that technology value depends on users’ adaptive choices within their socio-technical contexts, helping reveal key interactive mechanisms in technology application; and the Technology Acceptance Model highlights the mediating role of perceived usefulness and adoption intention in technology adoption. The integration of these three frameworks not only deepens understanding of the transformation mechanisms of teachers’ digital competence but also responds to domestic and international calls for enhancing teachers’ digital literacy and innovative teaching. It provides empirical support for optimizing teacher training and educational digitalization policies.

Theoretical Foundations

Adaptive Structuration Theory

DeSanctis and Poole proposed the Adaptive Structuration Theory (AST) based on group decision support systems in 1994 (DeSanctis & Poole, 1994), emphasizing that the ultimate value of technology is not directly determined by its functional design but rather emerges as a result of continuous reconstruction through the interaction between social structures and technological structures. Norms, expectations, and behavioral patterns within social structures guide users in how they understand and approach technology. Meanwhile, the inherent structures and resources within technology are selected, interpreted, and reconfigured in practice, thereby shaping actual usage patterns.

Existing research indicates that high levels of digital competence do not automatically translate into extensible or innovative applications in the classroom; rather, it often relies on the adaptive selection and restructuring of structural rules and resources. This study proposes that the process by which teachers transform digital competence into innovative classroom applications is activated by Educational Contextual Norms (ECN). ECN encompass official policy recommendations and peer group usage models. These norms alone do not directly alter teacher behavior. Only when teachers possess sufficient digital competence can they effectively interpret external norms like official recommendations and peer practices, transforming them into executable and innovative technology applications. In other words, DC is the prerequisite for ECN to exert influence.

This study operationalizes the “Appropriation” concept from AST as “Adaptive Use” (AU). Its core lies not in whether teachers use technology, but in how they proactively select, combine, and innovate functions between technological structures and instructional needs. Specifically, adaptive use exhibits three characteristics: (1) Structural Selection: Teachers make alternative choices among multiple digital tools or similar functionalities; (2) Structural Reconfiguration: Teachers recombine multiple functions to achieve instructional objectives based on teaching tasks; (3) Structural Innovation: Teachers apply existing tool functions to novel teaching activities beyond their intended purposes. Accordingly, this study defines AU as the degree to which teachers flexibly select, extend, and innovatively utilize digital tools based on their functional understanding.

Information System Success Model

The Information System Success Model (ISSM) posits that information quality, systems quality, and service quality individually or collectively influence willingness to use and user satisfaction, which in turn affect net benefits through their mutual interaction (DeLone & McLean, 2003).

In this study, digital tools refer to instruments that utilize digital technology to collect, acquire, retrieve, transmit, store, and process multimedia resources. These include, but are not limited to, interactive software, online platforms, various applications, and internet services. The quality of the instructional resources produced by these tools constitutes the information quality that teachers prioritize. Meanwhile, the stability of system operation, the interactivity of the interface, the alignment with instructional needs, and functional coherence collectively form the systems quality and service quality of digital tools. Therefore, this study treats the digital tools teachers routinely use as information systems, evaluating their structural characteristics and effectiveness in education through three variables: systems quality, resource quality, and functional coherence. This research posits that the DTS —encompassing the above three variables—influence teachers’ willingness to use digital tools and their perceived value, which in turn affects the active selection and innovative application of these technological tools.

Technology Acceptance Model

Davis proposed the Technology Acceptance Model (TAM) in 1989, which comprises four core elements: perceived usefulness, perceived ease of use, behavioral intention, and actual usage. Perceived usefulness (PU) refers to the extent to which an individual believes using a specific system can enhance their performance. Perceived ease of use (PEOU) denotes the degree to which an individual perceives the system enables unconscious operation. Existing research indicates that PEOU not only directly influences PU but also indirectly affects behavioral intention through attitudes (Unal & Uzun, 2021). TAM emphasizes that individuals’ adoption of information technology is mediated by psychological cognitive variables such as PU, PEOU, and behavioral intention, while social norms serve as external variables directly influencing PU/PEOU.

This study views digital tools structure as an extension of PEOU, positing that whether teachers can interpret norms as actionable teaching strategies depends on DC. Therefore, ECN is not treated as an external variable but incorporated into the mediation model as an “interpretable norm” requiring activation by DC. In other words, teachers can only transform high-level digital competence into exploratory and innovative use when they perceive the technology’s usefulness and ease of use; this perception of usefulness depends on the support and guidance provided by ECN. This study positions ECN between DC and tool perception to emphasize that teachers’ understanding and implementation of norms do not occur automatically but depend on their technical ability to “decode” norms. Therefore, compared to traditional models treating social norms as external variables, the proposed sequence (DC→ECN→DTS, PU→AU) more accurately reflects the psychological mechanisms through which teachers transform norms into practical strategies within educational contexts. Specifically, this sequence highlights that DC is a prerequisite for ECN to function, clarifying the logical relationship between individual competence and contextual norms in driving adaptive use.

Research Hypotheses

TAM consistently highlights the central role of utilization intention in explaining actual technology adoption among individuals. The correlation between behavioral intention and actual use behavior was significant and robust, and this association was particularly significant in the teacher population (Scherer et al., 2019). Additionally, empirical research on the individual distinctions of teachers suggests that teachers’ attitudes toward using technology have an impact on their adoption of new technologies in the classroom (Avci, 2022; Holzmann et al., 2020). Based on this, this study proposes Hypothesis:

TAI positively influences AU.

Teachers’ adoption of technology and tools is driven not only by individual cognition but also significantly influenced by social contexts and normative factors. Peer influence and recommendations from officials proved to be important factors in driving teachers’ use of digital resources and technology tools (Baddar & Khan, 2023; Hazzan-Bishara et al., 2025). Larger-scale studies focused on subject-specific teaching further indicate that social influence is a key predictor of whether teachers introduce innovative technologies into their classrooms (Weinhandl et al., 2025). Therefore, this study defines educational contextual norms as peer influence and official recommendations and proposes hypothesis:

ECN have a positive impact on AU.

The Information System Success Model and related research indicate that the structural quality of a system influences user behavior. In educational settings, this means that systems stability, resource integrity, and functional coherence are critical factors determining whether teachers can and will integrate digital tools into classroom instruction. Specifically, technology compatibility is an important factor in driving teachers’ adoption of technology in teaching and learning (Lawrence & Tar, 2018); the ease of use of the tool significantly affects the behavioral intention towards the teachers’ application of AI (Al Darayseh, 2023). Therefore, this study proposes Hypothesis:

DTS has a positive influence on AU.

PU has been extensively validated as a key factor in predicting users’ adoption intention (Davis & Venkatesh, 1996). Existing research indicates that when teachers decide whether to use technology, they often base their perceptions on whether the technology can help achieve instructional goals (Abel et al., 2022). In educational practice, performance expectancy serves as a key determinant influencing teachers’ continued use of 3D modeling (Branko et al., 2023). Therefore, this study proposes:

PU positively influences AU.

Perceived technological competence is a critical factor influencing the use of instructional and application software by teachers (Dogan et al., 2021). Teachers’ digital competence can both drive technology adoption by increasing intention to use and indirectly influence usage behavior via interactions with the social environment. Mediated by their intentions to use, the higher the level of teachers’ digital competence, the better the quality of online teaching behaviors (Li et al., 2021). When teachers possess adequate technical competence, they are more likely to recognize and adopt recommendations from colleagues and schools, as they can evaluate the value of suggested tools based on instructional needs. Conversely, when technical competence is lacking, teachers may disregard or reject recommendations due to low confidence, even when they exist. Subjective norms can promote technology adoption intention and actual usage by influencing individuals’ attitudes and perceptions (Liu, 2025; Mayantao & Tantiado, 2024). Concurrently, peer collaboration can also have a catalytic effect on teachers’ integration and consistent use of tools in the classroom (Chiu, 2022). AST posits that teachers’ technology use stems from structural choices and innovation. ECN provides teachers with rules to follow and practical examples to emulate. Teachers’ digital competence influences their ability to understand, decode these norms, and translate them into actionable strategies. Thus, ECN mediates between teachers’ digital competence and adaptive use. Based on the above research, this study proposes Hypotheses:

TAI positively mediates the relationship between DC and AU.

ECN positively mediates the relationship between DC and AU.

This study defines the essence of educational contextual norms as peer influence and official recommendations. Compared to the traditional concept of “social influence,” such contextual norms can directly promote teachers’ adaptive use of digital tools at the practical level without requiring the psychological mediating variable of “intention”. Teachers’ digital literacy not only influences their usage intentions but also shapes their recognition and understanding of contextual norms, thereby further affecting their perceptions of digital tool structures. Existing research indicates that under group norm guidance, teachers tend to evaluate the structural advantages of tools more positively. Meanwhile, tool structures (interface quality, technical compatibility, etc.) play a crucial role in promoting teachers’ technology adoption (Weinhandl et al., 2025). High DC enables teachers to more accurately interpret the pedagogical value embedded in ECN, leading to more positive judgments about tool utility and thereby enhancing their PU of digital tools. The TAM model posits that PU is the core cognitive factor driving technology adoption. Related research also indicates that digital competence beliefs enhance teachers’ perceptions of the ease and usefulness of technology (Antonietti et al., 2022) and indirectly increase usage frequency by enhancing perceptions of the usefulness of tools (Cattaneo et al., 2025). Simultaneously, subjective norms can indirectly promote teachers’ behavioral intentions by elevating individual perceived usefulness (Teo, 2011). Based on this, the following hypotheses are proposed:

DC exerts a positive indirect effect on AU through the chained mediating effects of ECN and DTS.

DC exerts a positive indirect effect on AU through the chained mediating effects of ECN and PU.

Building upon the aforementioned assumptions, this study further proposes a more complex chained mediation hypothesis to explore how competence transforms into adaptive usage behavior through multiple pathways. As a crucial contextual social influence variable, ECN not only affect teachers’ behavioral intentions but may also enhance their evaluation of tool value through group identification and peer interaction, thereby indirectly influencing their perceptions of platform structure and resources (Chiu, 2022). Existing research indicates that information quality and system characteristics significantly predict extended use and exploratory use through PU (Saeed & Abdinnour-Helm, 2008). Fathema et al. also note that systems quality, perceived self-efficacy, and facilitating conditions as critical predictors of teachers’ attitudes toward LMS (Fathema et al., 2015), while teachers’ beliefs and attitudes influence their intentions and actual behaviors in using LMS. Teachers with higher DC are better equipped to recognize and interpret ECN, thereby focusing on structural characteristics of digital tools such as systems quality, resource quality, and functional coherence. AST posits that understanding technological structural features influences users’ structured choices and innovative practices. Consequently, positive perceptions of tool structure enhance both teachers’ PU and their intention to adopt new technologies—both psychological mechanisms serving as key predictors of adaptive use. Therefore, this study proposes:

DC exerts a positive indirect effect on AU through the chained mediating effects of ECN, DTS, and PU.

H10

DC exerts a positive indirect effect on AU through the chained mediating effects of ECN, DTS, and TAI.

Based on these hypotheses, this study constructs a structural equation model as depicted in Figure 1.

Figure 1.

Model

Methodology

Participants

This study conducted a questionnaire survey among in-service teachers in China. A total of 11,260 questionnaires were collected. After excluding 1,534 invalid responses—those showing widespread uniformity in answers or exhibiting regular responses—9,726 valid questionnaires remained, yielding an effective utilization rate of 86.4%. Among them, male teachers accounted for 25.8%, while female teachers made up 74.2%. These teachers came from various disciplines including Chinese language, mathematics, history, and art.

Instrument

To ensure the validity of the measurement scale, this study directly adopted established scales developed by scholars including S K. Sharma, Eun-Jung Ko, Cao, Davis, and H. Sun, as detailed in Table 1. Additionally, the Digital Competence section draws from the education industry standard Digital Literacy for Teachers released by China’s Ministry of Education in 2022, measuring teachers’ digital competence across three dimensions: digital technology knowledge and skills, digital technology application skills, and strategies for selecting digital technology resources (Zhou et al., 2020). The questionnaire comprises two main sections: Part One collects basic information; The second part was the core part of the questionnaire, including five scales and assessment items for numerical ability.

Table 1.

Measurement Items

Variables	Measurement items		Sources
DTS	Systems quality	DTS1. I use digital tools whose use is not limited by time and place	(Cao et al., 2021; Davis, 1989; Ko et al., 2021; Sharma et al., 2017)
	Systems quality	DTS2. The digital tools I use have a simple and beautiful interface that is easy to use
	Functional coherence	DTS3. The digital tools I use provide features and services that are available
	Resource quality	DTS4. I use digital tools that assist me in my daily teaching duties
ECN	Authoritative recommendations	ECN1. I use digital tools because they are officially recommended by my school or education department, and they provide quality resources that meet my teaching needs	(Ko et al., 2021; Sun, 2012; Wixom & Todd, 2005)
	Peer influence	ECN2. I use the digital tool because I have seen other teachers use it
	Peer influence	ECN3. Other teachers have shown me the new features and uses for digital tools
PU	Performance expectations	PU1. I think digital tools make the teaching resources more abundant	(Cao et al., 2021; Davis, 1989; Ko et al., 2021)
	Performance expectations	PU2. In the classroom, digital tools have allowed me to be more efficient in my teaching tasks
	Intrinsic motivation	PU3. I found the process of figuring out how to use digital tools interesting
TAI	Acceptance and intention to adopt new technologies	TAI1. If I hear about a new digital tool, I’ll find a way to try it out	(Sun, 2012)
		TAI2. Among my peers, I’m usually the first to try digital tools
		TAI3. I like to try new digital tools
AU	Extensible application	AU1. I was able to easily use the features of the digital tools	(Sun, 2012)
	Innovative applications	AU2. I used a similar function in the digital tool in place of the previous function in the other tool
		AU3. I combine some of the features in the digital tools to accomplish a teaching task
		AU4. I apply some of the functionality in the digital tool to tasks for which that functionality is not available

Results

Common Method Bias

This study incorporated common method variance in the partial least squares model. Results indicate that the mean variance explained by the main construct was 0.724, while the mean variance explained by the method factor was 0.039, with a ratio of approximately 18.58:1 (see Table 2). This suggests that method variance had a relatively minor impact on the findings. Furthermore, this study employed Harman’s single-factor test to further validate common method bias. Results showed that four factors had eigenvalues exceeding 1, with the first factor explaining 43.083% of total variance—below the conventional 50% threshold. This indicates questionnaire data remained unaffected by common method bias.

Table 2.

Common Method Bias Analysis

	Indicator	Substantive Factor	R1²	Method Factor	R2²
	Indicator	Loading (R1)	R1²	Loading (R2)	R2²
DTS	DTS1	0.692	0.479	−0.168	0.028
	DTS2	0.905	0.819	−0.050	0.003
	DTS3	0.908	0.824	0.044	0.002
	DTS4	0.890	0.792	0.130	0.017
ECN	ECN1	0.812	0.659	0.422	0.178
	ECN2	0.749	0.561	−0.412	0.170
	ECN3	0.864	0.746	−0.115	0.013
PU	PU1	0.907	0.823	−0.131	0.017
	PU2	0.929	0.863	−0.037	0.001
	PU3	0.881	0.776	0.169	0.029
TAI	TAI1	0.839	0.704	0.150	0.023
	TAI2	0.825	0.681	−0.156	0.024
	TAI3	0.899	0.808	−0.005	0.000
AU	AU1	0.883	0.780	0.128	0.016
	AU2	0.888	0.789	−0.024	0.001
	AU3	0.897	0.805	0.184	0.034
	AU4	0.586	0.343	−0.461	0.213
DC	DC1	0.914	0.835	−0.027	0.001
	DC2	0.931	0.867	−0.048	0.002
	DC3	0.720	0.518	0.090	0.008
Average	—	—	0.724	—	0.039

Descriptive Statistics and Correlation Analysis

The correlation analysis found two significant positive correlations between the DTS, ECN, PU, TAI, and AU, as shown in Table 3.

Table 3.

Descriptive Statistics and Correlation Analysis

	Average	Standard deviation	DTS	ECN	PU	TAI
DTS	1.8600	0.67286
ECN	2.0515	0.71850	0.666**
PU	1.7251	0.66584	0.703**	0.602**
TAI	2.3223	0.74208	0.420**	0.366**	0.423**
AU	2.0333	0.68727	0.664**	0.611**	0.735**	0.493**

Reliability and Validity

Both Cronbach’s alpha and CR values for the questionnaire scale exceeded 0.7, indicating satisfactory reliability. In this study, factor loadings and AVE values were both greater than 0.5, indicating that the latent variables possess excellent convergent validity, as detailed in Table 4.

Table 4.

Construct Reliability and Validity

Construct	Indicator	Factor loading	Cronbach’s alpha	CR	AVE
DTS	DTS1	0.683	0.872	0.914	0.728
	DTS2	0.904
	DTS3	0.910
	DTS4	0.894
ECN	ECN1	0.857	0.738	0.845	0.647
	ECN2	0.698
	ECN3	0.849
PU	PU1	0.904	0.891	0.932	0.821
	PU2	0.928
	PU3	0.886
TAI	TAI1	0.870	0.816	0.889	0.728
	TAI2	0.790
	TAI3	0.896
DC	DC1	0.753	0.817	0.859	0.672
	DC2	0.783
	DC3	0.914
AU	AU1	0.890	0.834	0.890	0.677
	AU2	0.891
	AU3	0.905
	AU4	0.552

A KMO value greater than 0.7 is generally considered acceptable. The KMO value for the scales in this study exceeded 0.9, indicating good construct validity for the questionnaire. See Table 5 for details.

Table 5.

KMO and Bartlett’s Test

KMO quantity of sample suitability		.930
Bartlett’s test of sphericity	Approx. Chi-square	128938.897
	df	190
	Sig	.000

After executing the algorithm and performing 5,000 bootstrap samples in PLS-SEM, the HTMT ratios and their 95% confidence intervals were obtained (see Table 6). All HTMT values were below 0.90, with the upper limit of the 95% confidence interval at 0.891—below 1. This indicates that the six latent variables in this study exhibit good discriminant validity.

Table 6.

HTMT - Confidence Intervals

	ECN	TAI	PU	DTS	DC
TAI	0.489 [0.465, 0.514]
PU	0.768 [0.751, 0.785]	0.499 [0.477, 0.521]
DTS	0.865 [0.851, 0.880]	0.500 [0.478, 0.522]	0.817 [0.802, 0.832]
DC	0.112 [0.088, 0.138]	0.136 [0.112, 0.160]	0.063 [0.053, 0.078]	0.064 [0.054, 0.080]
AU	0.814 [0.795, 0.832]	0.612 [0.590, 0.632]	0.880 [0.869, 0.891]	0.810 [0.794, 0.825]	0.114 [0.092, 0.138]

Hypotheses Testing

In this study, the partial least squares (PLS) method based on the analysis of principal components was used to construct a structural model to validate the hypothesized paths to the mechanism of action that transforms teachers’ digital competence into adaptive use (Figure 2).

Figure 2.

The measurement model with path coefficients

Discussion

Direct Effects: Perceived Usefulness is the Strongest Driver of Teachers’ Adaptive Use

The direct effect test aims to clarify the direct driving role of Technology Adoption Intention (TAI), Educational Contextual Norms (ECN), Digital Tool Structure (DTS), and Perceived Usefulness (PU) on teachers’ Adaptive Use (AU). The overall conclusion shows that all four types of variables have a significant positive impact on adaptive use, among which perceived usefulness has the strongest driving effect, as presented in Table 7.

Table 7.

Direct Variable Relationships

Path	Original sample	Sample mean	Standard deviation	T statistics	p values	Intervals
Path	Original sample	Sample mean	Standard deviation	T statistics	p values	2.5%	97.5%
H1 TAI -> AU	0.164	0.164	0.008	21.489	.000	0.150	0.179
H2 ECN -> AU	0.160	0.160	0.011	13.983	.000	0.138	0.183
H3 DTS -> AU	0.176	0.176	0.013	13.509	.000	0.150	0.202
H4 PU -> AU	0.476	0.476	0.012	38.680	.000	0.452	0.499

Empirical results indicate that TAI exerts a significant positive influence on AU (β = 0.164, p < .001), supporting Hypothesis H1. This finding indicates that teachers’ intention to actively experiment with new technologies serves as a crucial endogenous driver for their innovative teaching practices. Consistent with existing research emphasizing the pivotal role of behavioral intentions in technology adoption, this result further validates the central position of intention in teachers’ technology adoption processes. ECN exerted a significant positive effect on AU (β = 0.160, p < .001), validating Hypothesis H2. This indicates that recommendations and influences from peers, schools, and educational authorities directly promote teachers’ adaptive use.

Furthermore, DTS exhibited a significant positive influence on AU (β = 0.176, p < .001), validating Hypothesis H3. This research suggests that tool usability, functional completeness, and resource availability are necessary prerequisites for instructors to effectively integrate technology into classroom education. This conclusion aligns with Rahimi, who found that richer teaching resources available to teachers increase the likelihood of upgrading problem-solving methods and engaging in high-level innovative practices in the classroom (Rahimi, 2024). Similarly, Wu et al. discovered that accessing school-related resources significantly enhances teachers’ digital competence (Wu et al., 2022), further underscoring the critical role of resources and tool quality in AU.

Among all variables, PU showed the strongest direct effect (β = 0.476, p < .001), validating Hypothesis H4. This indicates that teachers’ perceptions and recognition of digital tools’ potential to enhance teaching effectiveness and work efficiency are key drivers of adaptive use. This finding aligns with existing research conclusions. Perceptions about the usefulness and compatibility of technology integration significantly play a role in their application intentions and behaviors (Alshalawi, 2019); perceived usefulness significantly predicted teachers’ actual use of emerging technologies across diverse educational contexts (Akiry, 2021; Gumbi et al., 2024; Sudaryanto et al., 2023).

Direct Mediation: Digital Competence Indirectly Promotes Adaptive Use through Technology Adoption Intention and Educational Contextual Norms

The indirect effect test focuses on the intrinsic pathways through which digital competence (DC) influences adaptive use (AU) via mediating variables. The overall findings indicate that digital competence can indirectly promote adaptive use through single mediation paths such as technology adoption intention (TAI) and educational contextual norms (ECN), as well as through multiple chain mediation pathways, with all mediating paths being statistically significant, as presented in Table 8.

Table 8.

Indirect Variable Relationships

Path	Original sample	Sample mean	Standard deviation	T statistics	p values	Intervals
Path	Original sample	Sample mean	Standard deviation	T statistics	p values	2.5%	97.5%
H5 DC-> TAI -> AU	0.019	0.019	0.002	11.414	.000	0.016	0.022
H6 DC-> ECN -> AU	0.017	0.017	0.002	8.928	.000	0.014	0.021
H7 DC-> ECN -> DTS -> AU	0.014	0.014	0.002	8.939	.000	0.011	0.017
H8 DC-> ECN -> PU -> AU	0.014	0.014	0.001	9.679	.000	0.011	0.017
H9 DC-> ECN -> DTS -> PU -> AU	0.020	0.020	0.002	10.858	.000	0.017	0.024
H10 DC-> ECN -> DTS -> TAI -> AU	0.006	0.006	0.001	9.643	.000	0.005	0.007

Empirical results indicate that DC exerts a significant positive effect on AU through the mediating role of TAI (β = 0.019, p < .001), confirming Hypothesis H5. This finding aligns with conclusions from existing literature. Rahimi and Tafazoli found that teachers’ digital competence is significantly correlated with positive attitudes toward technology integration (Rahimi & Tafazoli, 2022). Nikou et al. state that digital literacy directly affects the intention and efficiency of using digital technology (Nikou & Aavakare, 2021). This result further supports the notion that teachers’ use of technology depends on their existing attitudes and beliefs about technology and their current level of knowledge and competence (Ertmer et al., 2012). Our findings indicate that stronger digital competence among teachers can stimulate their intrinsic motivation and exploratory desire, leading to greater proactivity and persistence in adaptive use rather than relying solely on external directives or pressure.

The results demonstrate that DC exerts a significant positive influence on AU via the mediating role of ECN (β = 0.017, p < .001), thereby confirming Hypothesis H6. This outcome corresponds with established research. Petko et al. showed that educational technology integration is influenced by individual teacher readiness, including teachers’ perceptions of the usefulness of the technology and confidence in their skills for teaching and learning, which, in turn, is influenced by the school’s overall readiness (Petko et al., 2018). This finding suggests that the more digitally competent a teacher is, the more likely he or she is to perceive and respond to normative expectations from the school organization and professional community, and thus exhibit higher levels of technology use behaviors. This study further reveals the mechanism of digital competence, indicating that teachers’ digital competence may affect their perception of educational contextual norms, thus strengthening the internalization of technology usage norms within the educational environment.

Chain Mediation: Educational Contextual Norms Exert Dominant Influence through Digital Tool Structure and Perceived Usefulness

This finding validates the innovation of our model based on Adaptive Structuration Theory, namely that teachers’ digital competence (DC) serves as the prerequisite for activating educational contextual norms (ECN). Only when teachers possess high levels of DC can they effectively ‘decode’ ECN, which subsequently influences their perception of the tool structure (DTS) and perceived usefulness (PU). Specifically, DC impacts ECN and indirectly influences AU via two different chain mediation pathways. First, DC affects DTS through ECN, promoting adaptive use behavior among teachers and supporting Hypothesis H7. Second, DC enhances teachers’ PU via ECN, indirectly enhancing AU and supporting Hypothesis H8. This suggests that teachers with higher levels of digital competence are more likely to recognize and respond to social expectations from their peers and educational authorities, making it easier to select a digital tool with greater ease of use or perceive its value in their teaching practice, resulting in greater levels of adaptive use. This finding aligns with existing research. Teachers’ digital skills play a crucial role in integrating digital resources (Guillén-Gámez et al., 2023); their competence beliefs and value beliefs are significantly related to their perceived teaching competence and integration (Cheng et al., 2021; Song & Zhou, 2021). Further, Zhang et al. revealed that the social environment indirectly influenced technology application behavior through teacher efficacy and outcome expectations (Zhang et al., 2021). Concurrently, the ease of use of digital tools influences teachers’ intention to adopt technologies and, consequently, actual use (Gumbi et al., 2024).

However, although the indirect effects of the chained mediating paths were statistically significant (β = 0.014, p < .001), the overall effect sizes were small. This suggests that the digital tool structure has limited influence in promoting adaptive use and may require synergistic or interactive effects from other latent variables to generate stronger driving forces. Further analysis revealed that after digital competence significantly influenced the structural quality and functional coupling of digital tools via educational contextual norms, its effect pathways diverged into two mediating mechanisms: first, DTS indirectly promoted AU by enhancing PU (β = 0.020, p < .001); second, DTS enhances TAI, thereby further driving AU (β = 0.006, p < .001). Hypotheses H9 and H10 are supported. This finding is consistent with previous findings. Teacher attitudes, self-efficacy, digital competencies, and digital tools have an impact on their ICT integration, either directly or indirectly (Peng et al., 2023). Related studies also indicate that teacher education can enhance teachers’ recognition of technology value and attitude through role models, thereby boosting their self-efficacy in TPACK application (Baran et al., 2019; Tondeur et al., 2017). Preservice teachers’ TPACK level is closely linked to their perceptions of the usefulness of instructional technology (Joo et al., 2018). Concurrently, technological knowledge reserves and institutional support emerge as crucial mediating variables predicting the alignment between TPACK and ISTE standards (Nelson et al., 2019).

Notably, the effect sizes of the two mediating pathways differ significantly: the indirect effect via the “Technology adoption intention” pathway is only 30% of that via the “Perceived usefulness” pathway (0.006/0.020). This implies that the primary role of educational contextual norms lies in guiding and facilitating teachers’ exposure to high-quality digital tools. Once exposed, the direct recognition of practical value derived from the tools’ inherent quality features and user experience (perceived usefulness) becomes the more central and direct psychological mechanism driving teachers’ adaptive use.

Main Effect: Teachers’ Digital Competence Primarily Influences Adaptive Use through Indirect Mechanisms

This study reveals the underlying mechanisms through which teachers’ digital competence impacts their adaptive use. Findings indicate that teachers’ adaptive use is driven by a composite mechanism involving digital tool structure, educational contextual norms, perceived usefulness, and technology adoption intention. Among these, the chained mediating pathway, “ECN → DTS → PU” (β = 0.020), emerges as the dominant mechanism with the strongest effect. This implies that teachers with high digital competence are more acutely adept at identifying and responding to external social recommendations, thereby accessing high-quality tools. The tools’ inherent ease of use and functional performance directly trigger “perceived usefulness,” which significantly promotes their innovative application in teaching practices. All indirect effects were statistically significant (p <.001), and bootstrap sample means showed high consistency with original values, indicating the robustness and reliability of multiple mediating pathways within the model. This further supports the validity of the study’s conclusions.

Transformation Pathway Analysis and Implications

This study, through structural equation modeling analysis, reveals three typical pathways for the transformation of teachers’ digital competence into adaptive use. Among them, the intrinsically driven pathway highlights the pivotal role of teachers’ internal motivation and intention to adopt competency conversion; the extrinsically driven pathway emphasizes the external forces of educational contextual norms and external support in promoting competency application; while the multi-driven pathway reveals the chain mechanism formed by the interaction of multiple factors, as illustrated in Figure 3.

Figure 3.

Conversion pathways

Intrinsically Driven Pathway: Competence Transformation Driven by Intrinsic Motivation

This study reveals a typical intrinsically driven pathway for the transformation of teachers’ digital competence into adaptive use. This pathway follows a mediation model of “digital competence -> technology adoption intention/perceived usefulness -> adaptive use” highlighting the central role of the intrinsic motivation system in the competence transformation process. Specifically, teachers’ digital competence stimulates their proactive willingness to explore new technologies while reinforcing their recognition of the pedagogical value of tools, thereby promoting innovative applications in the classroom.

This finding strongly aligns with conclusions from existing research. Studies by Chien et al. (2014) and Habibi et al. (2023) both indicate that teachers’ technological beliefs and usage intentions are better predictors of their classroom integration behaviors than technical knowledge alone. Meanwhile, Chiriacescu et al. (2023) confirm that perceived usefulness and enjoyment are crucial psychological mechanisms linking teacher competence to usage attitudes. Mumtaz (2000) also pointed out that teachers often need to confirm the practical utility of technology before genuinely integrating it into their teaching practice.

Based on the characteristics of this pathway, it is recommended that relevant departments and schools establish a professional development system centered on value recognition for teachers. Specifically, through case-based teaching, reflective practice, and workshops, efforts should focus on enhancing teachers’ self-efficacy, deepening their understanding of the educational value of technology, and providing ongoing practical opportunities in real classroom scenarios, thereby constructing a transformation chain from competence to practice. Student feedback and classroom observations should also serve as crucial positive feedback mechanisms, providing teachers with grounds for continuous improvement. As Durak (2021) noted, in teacher education aimed at effective technology integration, explicitly encouraging teachers’ own beliefs and focusing on developing their confidence in technology integration practices may prove beneficial.

Extrinsically Driven Pathway: Behavior Transformation Guided by External Support

The study also identifies a significant extrinsically driven pathway, revealing that teachers’ digital competence is effectively transformed into adaptive use through external factors such as educational contextual norms, digital tool structure, and organizational support. This pathway underscores the crucial role of the external environment in facilitating competence transformation.

The transformation of teachers’ digital competence into extended classroom practice and innovative application depends not only on individual intention but is significantly influenced by peer experience sharing and recommendations from administrators and educational authorities. When colleagues reduce uncertainty about tool usage through classroom demonstrations or experience sharing, teachers demonstrate greater propensity for imitation and experimentation. Similarly, when school administrators or authoritative bodies promote tools through training or policy documents, this top-down normative influence further reinforces teachers’ adoption intentions, thereby driving the transformation of digital competence toward adaptive use. Furthermore, school leadership support and organizational culture development prove equally critical within the externally driven pathway. Principals’ technological leadership can indirectly enhance teachers’ professional digital competence by cultivating a school-wide digital culture (Rasdiana et al., 2024). Organizational cultural environments—encompassing organizational atmosphere, institutional culture, leadership style, and human-machine relationships—also exert substantial influence on teachers’ behavioral patterns (Lv et al., 2022).

Consequently, schools and educational departments should enhance guidance mechanisms through peer demonstrations and authoritative recommendations while promoting observation exchanges and developing professional learning communities. Simultaneously, sustained institutional and training support should be provided to foster a stable, positive educational ecosystem, thereby facilitating teachers’ successful transition from digital competence mastery to educational practice innovation.

Multi-Driven Pathway: Multi-Layered Chain-Based Integration and Transformation

Transcending single-driver models, this study also identifies a more complex multi-driven pathway. This pathway exhibits typical chain reaction characteristics: digital competence first increases sensitivity to contextual norms, then enhances the perception of tool ease of use, subsequently strengthens perceived usefulness and adoption intention, and ultimately leads to deep adaptive use.

Existing research similarly substantiates this multi-factor driving mechanism. Teachers’ technology integration is influenced by four dimensions: school, students, teachers, and technology (Martin et al., 2025). Raygan and Moradkhani similarly found through empirical research that EFL teachers’ technology integration depends not only on their TPACK and attitudes but also on the indirect influence of school climate (Raygan & Moradkhani, 2022). School climate further promotes technology integration by affecting teacher attitudes. This indicates that internal and external factors at different levels often interact to jointly shape teachers’ technology use behaviors.

Based on the characteristics of the multi-driven pathway, it is recommended to establish an institutional and organizational collaborative framework. On one hand, an adaptive use-oriented teacher evaluation system should be developed, incorporating innovative applications of digital technology into assessment indicators. This system should include evaluation criteria covering the breadth of tool usage, depth of teaching integration, and dimensions of innovative practice, linked to professional promotion and performance reward mechanisms. On the other hand, regional digital education resource sharing platforms should be established to provide certified high-quality tool resources and serve as support hubs for teacher case exchange and professional development, thereby forming a virtuous cycle of evaluation, resources, and development.

Conclusions

This study systematically reveals the transformation mechanism of teachers’ digital competence into adaptive use by constructing a multiple-chain mediation model that includes educational contextual norms, digital tool structure, and perceived usefulness. The research identifies three typical transformation pathways: the intrinsically driven pathway highlights the core role of internal motivation, the extrinsically driven pathway emphasizes the crucial influence of the external environment, and the multi-driven pathway demonstrates the chain reaction characteristics of multiple elements. Empirical results indicate that teachers, influenced by peer and authoritative recommendations, are more inclined to adopt digital tools with well-designed structures and high-quality resources. This process enhances their recognition of the tools’ pedagogical value and the perceived usefulness of the technology, ultimately promoting exploration and innovation in teaching practice.

This study extends traditional technology acceptance models by demonstrating that educational contextual norms function as mediating variables activated through teachers’ digital competence. These findings suggest that effective digital transformation in schools requires developing comprehensive support systems-including tool recommendation mechanisms, professional learning communities, and institutional incentives-to create environments where technological integration can flourish. However, this study has several limitations that should be acknowledged. First, as the research sample was primarily drawn from China, the cross-cultural generalizability of the findings requires further verification. Second, the cross-sectional research design presents challenges in fully elucidating the dynamic causal relationships among variables. Additionally, the measurement of digital tool structure relied mainly on teachers’ subjective perceptions; future research could benefit from incorporating objective technical indicators to enable more comprehensive evaluation.

Footnotes

ORCID iDs

Yating Li

Xiao Zhang

Min Chen

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the National Natural Science Foundation of China [grant number 62277020, 62307018], the Industry-University Cooperation and Collaborative Education Program of the Ministry of Education of China [grant number 241202377042256], and the Fundamental Research Funds for the Central Universities [grant number CCNU25JCPT024].

Declaration of Conflicting Interests

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

Data Availability Statement

Data will be made available on reasonable request.*

References

Abel

V. R.

Tondeur

Sang

(2022). Teacher perceptions about ICT integration into classroom instruction. Education Sciences, 12(9), 609. https://doi.org/10.3390/educsci12090609

Akiry

(2021). Factors influencing faculty members’ behavioral intentions to use flipped classrooms in Saudi Arabia at Taif university. (Doctoral dissertation). Northern Illinois University.

Al Darayseh

(2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132

Alshalawi

A. S.

(2019). An investigation of the adoption of social media applications by faculty members at a prominent university in the Kingdom of Saudi Arabia. (Doctoral dissertation). Florida Institute of Technology.

Antonietti

Cattaneo

Amenduni

(2022). Can teachers’ digital competence influence technology acceptance in vocational education? Computers in Human Behavior, 132, 107266. https://doi.org/10.1016/j.chb.2022.107266

Avci

(2022). Examining the factors affecting teachers’ use of digital learning resources with UTAUT2. Malaysian Online Journal of Educational Technology, 10(3), 200–214. https://doi.org/10.52380/mojet.2022.10.3.399

Baddar

Khan

M. A.

(2023). Teachers’ intention to use digital resources in classroom teaching: The role of teacher competence, peer influence, and perceived image. Higher Learning Research Communications, 13(2), 26–41. https://doi.org/10.18870/hlrc.v13i2.1397

Baran

Canbazoglu Bilici

Albayrak Sari

Tondeur

(2019). Investigating the impact of teacher education strategies on preservice teachers’ TPACK. British Journal of Educational Technology, 50(1), 357–370. https://doi.org/10.1111/bjet.12565

Branko

Šorgo

Helm

Weinhandl

Lang

(2023). Exploring factors affecting elementary school teachers’ adoption of 3D printers in teaching. TechTrends, 67(6), 990–1006. https://doi.org/10.1007/s11528-023-00909-y

10.

Cao

Chu

Wang

(2021). The impact of online live teaching platform quality on college students’ learning ability: The chain mediating effect of knowledge sharing and satisfaction. Library and Information Service, 65(04), 61–72. https://doi.org/10.13266/j.issn.0252-3116.2021.04.007

11.

Cattaneo

A. A.

Antonietti

Rauseo

(2025). How do vocational teachers use technology? The role of perceived digital competence and perceived usefulness in technology use across different teaching profiles. Vocations and Learning, 18(1), 5. https://doi.org/10.1007/s12186-025-09359-4

12.

Chen

Zhou

Man

(2023). Investigating teachers’ information literacy and its differences in individuals and schools: A large-scale evaluation in China. Education and Information Technologies, 28(3), 3145–3172. https://doi.org/10.1007/s10639-022-11271-6

13.

Cheng

S. L.

Chen

S. B.

Chang

J. C.

(2021). Examining the multiplicative relationships between teachers’ competence, value and pedagogical beliefs about technology integration. British Journal of Educational Technology, 52(2), 734–750. https://doi.org/10.1111/bjet.13052

14.

Chien

S. P.

H. K.

Hsu

Y. S.

(2014). An investigation of teachers’ beliefs and their use of technology-based assessments. Computers in Human Behavior, 31, 198–210. https://doi.org/10.1016/j.chb.2013.10.037

15.

Chiriacescu

F. S.

Chiriacescu

Grecu

A. E.

Miron

Panisoara

I. O.

Lazar

I. M.

(2023). Secondary teachers’ competencies and attitude: A mediated multi-group model based on usefulness and enjoyment to examine the differences between key dimensions of STEM teaching practice. PLoS One, 18(1), Article e0279986. https://doi.org/10.1371/journal.pone.0279986

16.

Chiu

T. K.

(2022). School learning support for teacher technology integration from a self-determination theory perspective. Educational Technology Research & Development, 70(3), 931–949. https://doi.org/10.1007/s11423-022-10096-x

17.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

18.

Davis

F. D.

Venkatesh

(1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040

19.

DeLone

W. H.

McLean

E. R.

(2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748

20.

DeSanctis

Poole

M. S.

(1994). Capturing the complexity in advanced technology use: Adaptive structuration theory. Organization Science, 5(2), 121–147. https://doi.org/10.1287/orsc.5.2.121

21.

Dogan

N. A.

Celik

(2021). Teachers’ skills to integrate technology in education: Two path models explaining instructional and application software use. Education and Information Technologies, 26(1), 1311–1332. https://doi.org/10.1007/s10639-020-10310-4

22.

Durak

H. Y.

(2021). Modeling of relations between K-12 teachers’ TPACK levels and their technology integration self-efficacy, technology literacy levels, attitudes toward technology and usage objectives of social networks. Interactive Learning Environments, 29(7), 1136–1162. https://doi.org/10.1080/10494820.2019.1619591

23.

Ertmer

P. A.

Ottenbreit-Leftwich

A. T.

(2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551

24.

Ertmer

P. A.

Ottenbreit-Leftwich

A. T.

Sadik

Sendurur

(2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001

25.

Fathema

Shannon

Ross

(2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning and Teaching, 11(2), 210–233.

26.

Guillén-Gámez

F. D.

Ruiz-Palmero

García

M. G.

(2023). Digital competence of teachers in the use of ICT for research work: Development of an instrument from a PLS-SEM approach. Education and Information Technologies, 28(12), 16509–16529. https://doi.org/10.1007/s10639-023-11895-2

27.

Gumbi

N. M.

Sibaya

Chibisa

(2024). Exploring pre-service teachers’ perspectives on the integration of digital game-based learning for sustainable STEM education. Sustainability, 16(3), 1314. https://doi.org/10.3390/su16031314

28.

Habibi

Riady

Samed Al-Adwan

Awni Albelbisi

(2023). Beliefs and knowledge for pre-service teachers’ technology integration during teaching practice: An extended theory of planned behavior. Computers in the Schools, 40(2), 107–132. https://doi.org/10.1080/07380569.2022.2124752

29.

Hazzan-Bishara

Kol

Levy

(2025). The factors affecting teachers’ adoption of AI technologies: A unified model of external and internal determinants. Education and Information Technologies, 30(11), 1–27. https://doi.org/10.1007/s10639-025-13393-z

30.

Holzmann

Schwarz

E. J.

Audretsch

D. B.

(2020). Understanding the determinants of novel technology adoption among teachers: The case of 3D printing. The Journal of Technology Transfer, 45(1), 259–275. https://doi.org/10.1007/s10961-018-9693-1

31.

Inan

F. A.

Lowther

D. L.

(2010). Factors affecting technology integration in K-12 classrooms: A path model. Educational Technology Research & Development, 58(2), 137–154. https://doi.org/10.1007/s11423-009-9132-y

32.

Joo

Y. J.

Park

Lim

(2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Educational Technology & Society, 21(3), 48–59.

33.

E. J.

Kim

A. H.

Kim

S. S.

(2021). Toward the understanding of the appropriation of ICT-based smart-work and its impact on performance in organizations. Technological Forecasting and Social Change, 171, 120994. https://doi.org/10.1016/j.techfore.2021.120994

34.

Lawrence

J. E.

Tar

U. A.

(2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79–105. https://doi.org/10.1080/09523987.2018.1439712

35.

Vale

Tan

Blannin

(2025). Factors influencing the use of digital technologies in primary mathematics teaching: Voices from Chinese educators. Education and Information Technologies, 30(9), 12573–12608. https://doi.org/10.1007/s10639-024-13309-3

36.

Gao

Chen

(2021). A moderated mediation model of the relationship between primary and secondary school teachers’ digital competence and online teaching behavior. Frontiers in Education, 6, 744950. https://doi.org/10.3389/feduc.2021.744950

37.

Liu

(2025). Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: The mediating role of confidence and AI readiness. BMC Psychology, 13(1), 311. https://doi.org/10.1186/s40359-025-02620-4

38.

Shouse

R. C.

(2022). Impact of organizational culture, occupational commitment and industry-academy cooperation on vocational education in China: Cross-sectional hierarchical linear modeling analysis. PLoS One, 17(2), e0264345. https://doi.org/10.1371/journal.pone.0264345

39.

Martin

Gezer

Ceviker

(2025). Exploring multidimensional factors influencing teachers’ technology integration in K–12 education: A systematic review. Journal of Research on Technology in Education, 1–35. https://doi.org/10.1080/15391523.2025.2534951

40.

Mayantao

Tantiado

R. C.

(2024). Teachers’ utilization of digital tools and confidence in technology. International Journal of Multiple Research Approaches, 7(05), 1945–1952. https://doi.org/10.47191/ijmra/v7-i05-16

41.

Ministry of Education of the People’s Republic of China . (2023). Digital literacy for teachers. (educational industry standard of the People’s Republic of China, JY/T 0466-2022).

42.

Ministry of Education of the People’s Republic of China . (2025). Notice on organizing and implementing the action for empowering teachers’ development through digitalization.

43.

Mumtaz

(2000). Factors affecting teachers’ use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319–342. https://doi.org/10.1080/14759390000200096

44.

Nelson

M. J.

Voithofer

Cheng

S. L.

(2019). Mediating factors that influence the technology integration practices of teacher educators. Computers & Education, 128, 330–344. https://doi.org/10.1016/j.compedu.2018.09.023

45.

Nikou

Aavakare

(2021). An assessment of the interplay between literacy and digital technology in higher education. Education and Information Technologies, 26(4), 3893–3915. https://doi.org/10.1007/s10639-021-10451-0

46.

OECD . (2019). OECD skills outlook 2019: Thriving in a digital world.

47.

OECD . (2021). Supporting teachers’ use of ICT in upper secondary classrooms during and after the COVID-19 pandemic (Teaching in focus no. 41).

48.

OECD . (2023). OECD digital education outlook 2023: Towards an effective digital education ecosystem.

49.

OECD . (2025). Preparing teachers for digital education: Continuing professional learning on digital skills and pedagogies (OECD education policy perspectives no. 122).

50.

Peng

Abdul Razak

Hajar Halili

(2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PLoS One, 18(8), e0286112. https://doi.org/10.1371/journal.pone.0286112

51.

Petko

Prasse

Cantieni

(2018). The interplay of school readiness and teacher readiness for educational technology integration: A structural equation model. Computers in the Schools, 35(1), 1–18. https://doi.org/10.1080/07380569.2018.1428007

52.

Rahimi

A. R.

(2024). Beyond digital competence and language teaching skills: The bi-level factors associated with EFL teachers’ 21st-century digital competence to cultivate 21st-century digital skills. Education and Information Technologies, 29(8), 9061–9089. https://doi.org/10.1007/s10639-023-12171-z

53.

Rahimi

A. R.

Tafazoli

(2022). The role of university teachers’ 21st-century digital competence in their attitudes toward ICT integration in higher education: Extending the theory of planned behavior. The Jalt Call Journal, 18(2), 238–263. https://doi.org/10.29140/jaltcall.v18n2.632

54.

Rasdiana Wiyono

B. B.

Imron

Rahma

Arifah

Azhari

Elfira Sibula

Maharmawan

M. A.

(2024). Elevating teachers’ professional digital competence: synergies of principals’ instructional e-supervision, technology leadership and digital culture for educational excellence in digital-savvy era. Education Sciences, 14(3), 266. https://doi.org/10.3390/educsci14030266

55.

Raygan

Moradkhani

(2022). Factors influencing technology integration in an EFL context: Investigating EFL teachers’ attitudes, TPACK level, and educational climate. Computer Assisted Language Learning, 35(8), 1789–1810. https://doi.org/10.1080/09588221.2020.1839106

56.

Saeed

K. A.

Abdinnour-Helm

(2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45(6), 376–386. https://doi.org/10.1016/j.im.2008.06.002

57.

Scherer

Siddiq

Tondeur

(2019). The technology acceptance model (TAM): A meta-analytic structural equation modelling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009

58.

Sharma

S. K.

Gaur

Saddikuti

Rastogi

(2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behaviour & Information Technology, 36(10), 1053–1066. https://doi.org/10.1080/0144929x.2017.1340973

59.

Song

Zhou

(2021). STEM teachers’ preparation, teaching beliefs, and perceived teaching competence: A multi-group structural equation approach. Journal of Science Education and Technology, 30(3), 394–407. https://doi.org/10.1007/s10956-020-09881-1

60.

Suárez-Rodríguez

Almerich

Orellana

Díaz-García

(2018). A basic model of integration of ICT by teachers: Competence and use. Educational Technology Research & Development, 66(5), 1165–1187. https://doi.org/10.1007/s11423-018-9591-0

61.

Sudaryanto

M. R.

Hendrawan

M. A.

Andrian

(2023). The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption. E3S Web of Conferences, 388, 04055, 04055. https://doi.org/10.1051/e3sconf/202338804055

62.

Sun

(2012). Understanding user revisions when using information system features: Adaptive system use and triggers. MIS Quarterly, 36(2), 453–478. https://doi.org/10.2307/41703463

63.

Teo

(2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008

64.

Tondeur

Scherer

Siddiq

Baran

(2017). A comprehensive investigation of TPACK within pre-service teachers’ ICT profiles: Mind the gap. Australasian Journal of Educational Technology, 33(3), 46–60. https://doi.org/10.14742/ajet.3504

65.

Unal

Uzun

A. M.

(2021). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619–637. https://doi.org/10.1111/bjet.13046

66.

UNESCO . (2018). ICT competency framework for teachers (version 3).

67.

UNESCO . (2024). AI competency framework for teachers.

68.

UNICEF Europe and Central Asia . (2022). Educators’ digital competence framework.

69.

Wang

(2024). In-service teachers’ perceptions of technology integration in English as a foreign language classrooms in China: A multiple-case study. ECNU Review of Education, 7(2), 333–356. https://doi.org/10.1177/20965311231193692

70.

Weinhandl

Helm

Anđić

Grosse

C. S.

(2025). Decoding digital integration: Exploring factors influencing mathematics teachers’ technology adoption. Education and Information Technologies, 1–37. https://doi.org/10.1007/s10639-025-13740-0

71.

Wixom

B. H.

Todd

P. A.

(2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042

72.

Zhou

Chen

(2022). Factors associated with teachers’ competence to develop students’ information literacy: A multilevel approach. Computers & Education , 176,104360. https://doi.org/10.1016/j.compedu.2021.104360

73.

Xie

Nelson

M. J.

Cheng

S. L.

Jiang

(2023). Examining changes in teachers’ perceptions of external and internal barriers in their integration of educational digital resources in K-12 classrooms. Journal of Research on Technology in Education, 55(2), 281–306. https://doi.org/10.1080/15391523.2021.1951404

74.

Zhang

Chen

Liu

(2021). Investigating the influencing factors of teachers’ information and communications technology-integrated teaching behaviors toward “learner-centered” reform using structural equation modeling. Sustainability, 13(22), 12614. https://doi.org/10.3390/su132212614

75.

Zhou

Wang

Liang

X. F.

Chen

(2020). Development and application of information literacy assessment tool for primary and secondary school teachers. In 2020 Ninth International Conference of Educational Innovation through Technology (EITT) (pp. 70–75). IEEE. https://doi.org/10.1109/EITT50754.2020.00019