INTERNATIONAL JOURNAL OF EDUCATION AND HUMANITIES
e-ISSN: 2829-8675 .
-ISSN: 2830-4578 Volume.
Issue.
November .
: 160-176 DOI: https://doi.
org/10.
56314/ijoleh.
The Influence of Digital Literacy.
Self-Efficacy, and Teaching Commitment on Instructional Innovation of Junior High School Teachers Nurhas Sasmiranda1*.
Anwar Ramli2.
Muhammad Hasan3.
Najamuddin4 1,2,3,4 Universitas Negeri Makassar.
Indonesia Correspondence* Email:
nurhassasmiranda2@gmail.
Received : 20 October 2025 Accepted : 07 November 2025 Published : 10 November 2025 Copyright .
2025 Author.
Nurhas Sasmiranda.
Anwar Ramli.
Muhammad Hasan.
Najamuddin This work is licensed under a Creative Commons AttributionShareAlike 4.
0 International License.
Abstract This study aims to analyze the influence of digital literacy, self-efficacy, and teaching commitment on instructional innovation among junior high school teachers in Anggeraja District.
The research The population and sample consisted of all teachers from SMPN 1 and SMPN 3 Anggeraja, totaling 73 Data were collected through the distribution of 73 questionnaires.
To test the research hypothesis.
Partial Least SquaresAe Structural Equation Modeling (PLS-SEM) was used with the SmartPLS version 4.
9 application.
The results showed that digital literacy had a positive and significant effect on teachers' teaching commitment .
oefficient = 0.
p = 0.
Teachers with high digital literacy tend to demonstrate stronger dedication, responsibility, and consistency in their professional duties.
Furthermore, digital literacy significantly influenced instructional innovation .
oefficient = 0.
, where digitally competent teachers were more capable of creating creative and adaptive teaching methods.
Meanwhile, self-efficacy also had a positive and significant effect on teaching commitment .
oefficient = 0.
p = 0.
indicating that confident teachers are more consistent and enthusiastic in fulfilling their professional roles.
However, self-efficacy did not significantly affect innovation .
oefficient = 0.
p = 0.
, suggesting that confidence alone is insufficient to drive innovation without external support and strong commitment.
Indirectly, digital literacy also influences instructional innovation Published By : CV.
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Page through teaching commitment as a mediator.
This implies that teaching commitment strengthens the transformation of digital capabilities into innovative teaching practices.
On the other hand, selfefficacy did not significantly affect innovation through commitment .
= 0.
, indicating the need for additional factors such as creativity and collaborative culture for self-confidence to effectively foster innovation.
Keywords:
Digital Literacy.
Self-Efficacy.
Teaching Commitment.
Instructional Innovation.
Junior High School Teachers INTRODUCTION Technology in education enhances and advances teaching, learning, and instruction in modern life.
Students' attitudes toward learning have been observed to improve with the use of information and communication technology (Wismawan et al.
, 2.
Education is the primary foundation of national development, and in today's technological era, teachers act as facilitators capable of addressing various challenges and changes.
The use of technology has improved the quality of learning and students' attitudes toward learning (Lazar et al.
, 2.
The quality of education in Indonesia is highly dependent on components of the education system, such as the quality of teachers, programs, and facilities.
Improving teacher quality is crucial to supporting sustainable educational development because teacher quality is a vital part of the teaching and learning process, which is the goal of any educational organization (Sulastri et al.
, 2.
One of the causes of the low quality of education in Indonesia is the inability of teachers to demonstrate innovative work behavior, which has an impact on the low performance of schools in achieving learning objectives through various methods and means (Hermaini & Nurdin, 2.
Without innovation, organizations struggle to grow and fail to achieve their goals.
Learning innovation is conscious discovery that creates something new to support the achievement of learning goals, particularly in the development of science and technology, including digital literacy, which requires inspiring teachers to possess basic digital skills.
Improving the quality and professionalism of teachers is done through workshops, seminars, training, and certification (Latiana, 2.
Professional teachers are a key prerequisite for quality education.
The commitment of certified teachers reflects responsibility, self-efficacy, and the utilization of innovation and technology in digital literacy.
This commitment includes an understanding of professional duties and responsibilities.
However, many Published By : CV.
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Page teachers in the field still teach beyond their competencies, and learning is not designed systematically, comprehensively, and collaboratively with students.
Lack of understanding by teachers has an impact on hindering promotions due to the minimal research components carried out (Kristiawan et , 2.
Digital literacy enables the transformation of activities through the use of technology.
Teachers need to have digital awareness in their lives, work, and According to Meyers et al.
, .
Digital literacy competencies encompass critical thinking skills, which are crucial given the abundance of information available online and the ease of content creation.
This literacy also supports the development of teachers' thinking skills in carrying out their duties.
In digital literacy, teachers must be able to use, analyze, create, and reflect on various digital devices as a form of expression and communication (Sholichah & Pahlevi, 2.
Digital literacy encompasses individual competencies in accessing, applying, assessing, analyzing, and processing digital data to create new knowledge (Liu et al.
, 2.
This understanding also includes the safe use of digital tools to obtain information from the internet (BiezA, 2.
Teachers need to understand digital literacy because they are required to create, collaborate, and share content responsibly (Zainuddin et al.
Teachers with low digital skills are at risk of spreading unreliable Therefore, the ability to find and interpret credible sources of information is crucial amidst the rapid growth of technology.
Teachers' innovative behavior is very important in achieving educational goals, especially through collaboration with schools, colleges, and industry (Khairunnisa & Ashila, 2.
Teachers are required to actively seek out and create new, interesting learning methods and improve existing deficiencies (Isnain & Nurwidawati, 2.
Data analysis, such as through learning analytics, helps teachers understand student learning behavior and make informed teaching decisions (Yin et al.
, 2.
Self-efficacy, the belief in one's own abilities, significantly influences innovative behavior.
Teachers with high self-efficacy are more open to new approaches, motivate students to think critically, and create creative learning environments.
They are also more resilient to challenges and motivated in their work (Firdausiah & Etikariena, 2.
Innovative behavior encourages positive changes from traditional to modern and improves the quality of learning (Ramdayana & Prasetyono, 2.
Strong self-efficacy strengthens innovation and achievement of results (Wijaya et al.
, 2.
, while teachers with low efficacy tend to be negative, less enthusiastic, and easily angered (Kusumawati, 2.
Self-efficacy refers to an individualAos belief, particularly that of teachers, in their own ability to plan, implement, and manage the teaching and learning process effectively to achieve desired outcomes.
For teachers, self-efficacy serves as a determining factor in the implementation of instructional innovation, as those with a high level of self-efficacy are more likely to experiment with new strategies, integrate digital technologies, and maintain their teaching Published By : CV.
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Page commitment despite challenges.
In the era of digital-based educational transformation, self-efficacy not only reflects confidence in pedagogical competence but also the ability to adapt to digital literacy that supports instructional innovation.
Teachers with high levels of self-efficacy tend to be more open to using innovative learning methods, trying new approaches to teaching, and striving to improve the quality of education.
They are also more likely to create a classroom environment that supports student creativity, motivates active participation, and inspires students to think critically and develop new ideas.
High levels of innovative behavior enable teachers to develop more effective and engaging learning methods, stimulate student creativity, and provide opportunities for students to think critically, explore new ideas, and develop problem-solving skills.
Individuals with high levels of innovative behavior will believe and trust in themselves to organize all their motivation and abilities when carrying out a series of tasks or when facing certain obstacles or problems to achieve their desired goals, thereby improving performance within the organization (Firdausiah & Etikariena, 2.
Teacher commitment reflects sincerity in teaching, developing a career, and carrying out duties professionally.
(Hassan et al.
, 2.
Committed teachers tend to teach effectively and build synergy with students so that learning is easy to understand (Arifin, 2.
In the digital era, teachers are required to master technology and apply it innovatively in learning.
However, many teachers still lack technological skills and are unable to optimally integrate digital tools.
This challenge requires ongoing training and professional Furthermore, the gap in technology access among students must be addressed to ensure inclusive learning.
Teachers must also ensure that technology is used in a relevant manner and does not diminish social In its use, teachers need to teach digital ethics, maintain security, and protect student privacy.
Learning innovation requires mastery of technological skills, collaboration, creativity, an understanding of student characteristics, and the ability to manage adaptive and personalized learning.
However, observations at junior high schools in Anggeraja District indicate that teachers' ability to develop learning innovations remains low due to limited digital literacy, low selfefficacy, and a lack of commitment to the profession.
Other obstacles, such as minimal institutional support, resistance to change, and limited time and resources, also exacerbate the situation.
Although technological facilities in schools are considered adequate, teachers' ability to utilize them innovatively remains limited.
Yet, professional teachers with high commitment and selfefficacy are key to creating quality education.
In line with the view Cahyaningrum et al.
, .
that teacher career commitment is influenced by self-efficacy, so this study aims to analyze the influence of digital literacy, self- Published By : CV.
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Page efficacy, and teaching commitment on teacher learning innovation at Junior High Schools in Anggeraja District.
METHOD
The research method in this document uses a quantitative approach with an associative research type to test the relationship between digital literacy variables (XCA), self-efficacy (XCC), teacher commitment (Z), and learning innovation (Y).
The study was conducted on Junior High School (SMP) teachers in Anggeraja District.
Enrekang Regency, during AprilAeMay 2025.
The study population consisted of 92 teachers, and because the number was less than 100, a saturated sampling technique was used, namely the entire population was sampled.
Data collection was carried out through a closed questionnaire with a five-point Likert scale and documentation to complete contextual data.
The research instrument was tested using a validity test .
roduct-moment correlatio.
and a reliability test (Spearman Brown formul.
, assisted by SPSS software version 26.
In this study.
SPSS was employed to perform descriptive statistical analysis, as well as validity, reliability, and preliminary correlation tests among variables, while SmartPLS was utilized to analyze causal relationships between latent variables through Structural Equation Modeling (SEM-PLS) in order to examine both direct and indirect effects among the research The collected data were analyzed descriptively and inferentially.
Descriptive analysis was used to describe the characteristics of respondents and each research variable based on the average score.
Meanwhile, inferential analysis was conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) through the SmartPLS 3.
9 application to test the The analysis stages included testing the outer model .
onvergent validity, discriminant validity, and composite reliabilit.
and the inner model .
oodness of fit.
R-square, predictive relevance, and influence between Hypothesis testing was conducted using the bootstrapping technique, where the relationship between variables is declared significant if the t-statistic value Ou 1.
65 at a significance level of 0.
RESULTS AND DISCUSSIONS
Descriptive Analysis of Research Variables This study analyzed four main variables: Digital Literacy (X.
Self-Efficacy (X.
Teacher Commitment (Z), and Learning Innovation (Y).
The results of the descriptive analysis showed that all variables were in the high category.
Digital Literacy (X.
obtained an average score of 4.
255, reflecting teachers' ability to use digital technology effectively in learning.
Self-Efficacy (X.
had an average 009, indicating teachers' confidence in their professional abilities.
Teacher Commitment (Z) had an average score of 4.
119, indicating high loyalty and responsibility towards the profession.
Meanwhile.
Learning Innovation (Y) Published By : CV.
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180, indicating that teachers have a strong tendency to innovate in the teaching and learning process.
Partial Least Squares Analysis (PLS-SEM) PLS-SEM analysis was conducted to test the validity, reliability, and relationships between variables in the structural model.
Figure 1.
PLS-SEM Model Specifications Measurement Model (Outer Mode.
Convergent Validity Test Convergent validity testing is conducted by assessing the loading factor of each indicator against its construct.
An indicator is considered valid if its loading factor is greater than 0.
70, indicating that the construct can explain more than 50% of the indicator's variance.
Figure 2.
PLS-SEM Algorithm Estimation Results Table 1.
PLS-SEM Outer Loadings Values Digital Literacy (X.
X1.
X1.
X1.
X1.
X1.
X2.
X2.
X2.
Self Efficacy (X.
Teacher Commitment (Z) Innovation Learner (Y) Published By : CV.
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Page X2.
Table 1 shows the outer loading value of each indicator on its construct based on the initial PLS-SEM estimate, which describes the indicator's contribution in representing the latent construct.
In the Digital Literacy construct (X.
with five indicators, the outer loading value ranges from 0.
584Ae0.
indicators (X1.
1 and X1.
70 are still retained because they are in the range of 0.
40Ae0.
70, and the AVE value still meets convergent validity (Ou 0.
The Self-Efficacy construct (X.
has four indicators with a value of Ou 0.
indicating strong representation, while Teacher Commitment (Z) shows high internal consistency with a value of Ou 0.
Meanwhile.
Learning Innovation (Y) has three indicators above 0.
86 and one indicator (Y.
with a value of 0.
which can still be retained if the AVE is Ou 0.
50 and is substantially relevant.
Thus, all constructs meet convergent validity and are worthy of proceeding to the AVE test stage.
Table 2.
Average Variance Extracted (AVE) Value Average Variance Extracted (AVE) Digital Literacy (X.
Self-Efficacy (X.
Teacher Commitment (Z) Learning Innovation (Y) Source: Data Processing Results .
Based on Table 2, the Average Variance Extracted (AVE) value for each variable shows results above the minimum threshold of 0.
50, indicating good convergence between the construct and its indicators.
This means that the latent variables are able to explain more than 50% of the variance in their indicators, thus all constructs meet the criteria for convergent validity.
Table 3.
Convergent Validity Test Results Variables Indicator AVE Convergent Loading Cut Validity Factor Value Digital Literacy (X.
Social Media (X.
X1.
X1.
X1.
X1.
X1.
X2.
X2.
X2.
X2.
Valid Valid Published By : CV.
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(Z)
Learning Innovation (Y) Valid Valid Source: Data Processing Results .
Based on the results of the initial measurement model analysis using PLSSEM, the outer loading and AVE values obtained indicate that all constructs have met convergent validity, with the criteria of loading factors > 0.
70 and AVE > 0.
50 (Hair et al.
, 2.
Although some indicators, such as X1.
X1.
, and Y.
, are below 0.
70, all are maintained because the AVE value of each construct exceeds 0.
50, namely Digital Literacy .
Self-Efficacy .
Teacher Commitment .
, and Learning Innovation .
Thus, the indicators in each construct can represent the latent variables adequately.
Discriminant Validity Test A discriminant validity test is conducted to ensure that each concept of each latent variable is distinct from the other variables.
The model has good discriminant validity if the squared AVE value of each exogenous construct exceeds the correlation between that construct and the other constructs.
The results of the discriminant validity test are as follows:
Table 4.
Fornell-Larcker Criterion Value Construct Digital Literacy (X.
Self-Efficacy (X.
Teacher Commitment (Z) InnovationLearning (Y) Literacy Digital (X.
SelfEfficacy (X.
Commitment Teacher (Z) Innovation Learning (Y) Source: Data Processing Results .
Based on Table 4.
the results of the analysis with the Fornell-Larcker criteria, the discriminant validity of this model has been met, indicating that each construct is clearly different from the other constructs.
The AVE root value for Digital Literacy .
Self-Efficacy .
Teacher Commitment .
, and Learning Innovation .
is greater than the correlation between related constructs, for example the correlation between Digital Literacy and Learning Innovation is 0.
839 which is still below the AVE root of construct Y.
This confirms that each construct is unique and there is no overlapping meaning between In addition, discriminant validity is also supported by the results of cross-loading, where the indicator value for the construct is higher than for other constructs, so the model is declared discriminantly valid.
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Page Table 5.
Cross-Loading Value Digital Literacy Self-Effect Teacher Learning (X.
(X.
Commitment Innovation (Y) (Z) X1.
X1.
X1.
X1.
X1.
X2.
X2.
X2.
X2.
Source: Data Processing Results .
Table 4.
8 shows the results of the analysis of cross-loading values between indicators against their constructs to assess discriminant validity.
An indicator is declared valid if the loading value on the original construct is higher than on other constructs.
The results show that all indicators have the highest loading on the appropriate construct, such as X1.
4 with a value of 0.
836 on Digital Literacy compared to lower loadings on other constructs, and a similar pattern is seen in indicators X2.
1AeZ.
3, and Y1.
1AeY1.
Thus, all indicators are proven to have good discriminant validity, indicating that the measurement model can represent the constructs accurately and consistently.
Composite Reliability Construct reliability can be assessed from the Cronbach's alpha and composite reliability values of each construct.
The recommended composite reliability and Cronbach's alpha values are greater than 0.
Table 6.
Construct Reliability Value Cronbach's Alpha Composite Reliability Digital Literacy (X.
Self-Efficacy (X.
Teacher Commitment (Z) Learning Innovation (Y) Source: Data Processing Results .
Construct reliability was tested using Cronbach's Alpha and Composite Reliability with a minimum threshold of 0.
70 (Hair et al.
, 2.
The analysis results showed that all constructs met these criteria, namely Digital Literacy ( = 0.
CR = 0.
Self-Efficacy ( = 0.
CR = 0.
Teacher Commitment ( = Published By : CV.
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, and Learning Innovation ( = 0.
CR = 0.
Thus, all constructs had high internal consistency and good reliability, so the research instrument was declared reliable for use in the next analysis stage.
Structural Model (Inner Mode.
Coefficient of Determination (R Squar.
The coefficient of determination (R Squar.
indicates the extent to which the model is able to explain the variance of the dependent variable and can be influenced by the number of variables in the model.
An R Square value of 67 or more is categorized as good, 0.
33 is classified as moderate, and 0.
19 is considered weak, which means the higher the R Square value, the stronger the influence of the exogenous variable on the endogenous variable.
Table 7.
Coefficient of Determination (R Squar.
Value R Square R Square Adjusted Learning Innovation (Y) Teacher Commitment (Z) Source: Data Processing Results .
The coefficient of determination (R Squar.
measures the ability of independent variables to explain dependent variables, with values between 0Ae the closer to 1 means the better the model.
Based on the analysis results, the R Square value for Learning Innovation (Y) is 0.
746, and the Adjusted R Square is 735, indicating that 74.
6% of Y variability is explained by Digital Literacy (X.
Self-Efficacy (X.
, and Teacher Commitment (Z).
Meanwhile.
Teacher Commitment (Z) has an R Square of 0.
527 and an Adjusted R Square of 0.
meaning that 52.
7% of its variability is explained by X1 and X2.
These results confirm that the model has good and stable predictive ability.
Path Coefficient The path coefficient measures the extent to which the independent variable influences the dependent variable in a structural model.
This coefficient indicates the direction of the relationship, whether positive or .
If the path coefficient value is positive, then the influence of one variable on another variable is in the same direction.
If the path coefficient value is negative, then the influence of one variable on another variable is in the opposite direction.
Table 8.
Path Coefficient Results Self-Efficacy (X.
-> Learning Innovation (Y) Self-Efficacy (X.
-> Teacher Commitment (Z) Teacher Commitment Original Sample (O) Sample Mean
(M)
Standard Deviation (STDEV) T Statistics (O/STDEV) Values 2,582 Published By : CV.
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Page (Z) -> Learning Innovation (Y) Digital Literacy (X.
-> Learning Innovation (Y) Digital Literacy (X.
-> Teacher Commitment (Z) Source: Data Processing Results .
2,762 5,362 4,647 Table 8 shows that Digital Literacy (X.
and Self-Efficacy (X.
have an important role in shaping Teacher Commitment (Z) and Learning Innovation (Y).
Digital Literacy has a positive and significant effect on Teacher Commitment ( = 0.
p = 0.
and Learning Innovation ( = 0.
p = 0.
, indicating that the higher the teacher's ability to utilize technology, the higher their commitment and innovation in teaching.
Self-efficacy also has a significant positive effect on Teacher Commitment ( = 0.
p = 0.
, but does not have a significant effect on Learning Innovation .
= 0.
, indicating that teacher self-confidence has not directly driven innovation without the support of other factors.
In addition.
Teacher Commitment is proven to have a significant positive effect on Learning Innovation ( = 0.
p = 0.
indicating that professional commitment is an important driver for teachers in creating innovative and quality learning.
Hypothesis Testing The bootstrapping procedure produces a t-statistic value to test the hypothesis by comparing the p-value and t-statistic.
The hypothesis is accepted (H.
and H0 is rejected if the t-statistic is > 1.
96 or the p-value is < 0.
05 at a 5% significance level ( = 0.
Thus, the hypothesis acceptance criteria are determined based on the t-statistic value exceeding the t-table or the p-value being less than 0.
If the p-value < 0.
05 then it has a significant effect.
If the p-value > 0.
05 then it has no significant effect.
Figure 3.
PLS-SEM Bootstrapping Estimation Results Published By : CV.
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Page Table 9.
Bootstrapping Results Original Sample T Statistics (O) (O/STDEV) 4,647 P Values Digital Literacy (X.
-> Teacher Commitment (Z) Digital Literacy (X.
-> 5,362 Learning Innovation (Y) Self-Efficacy (X.
-> Teacher 0.
2,582
Commitment (Z) Self-Efficacy (X.
-> Learning 0.
Innovation (Y) Teacher Commitment (Z) -> 0.
2,762
Learning Innovation (Y) Source: Data Processing Results .
The results of the analysis show that Digital Literacy has a positive and significant effect on Teacher Commitment ( = 0.
t = 4.
p = 0.
and Learning Innovation ( = 0.
t = 5.
p = 0.
, indicating that teachers with high digital literacy are more committed, creative, and adaptive to learning technology.
Self-Efficacy also has a significant positive effect on Teacher Commitment ( = 0.
t = 2.
p = 0.
, but does not have a significant effect on Learning Innovation ( = 0.
t = 0.
p = 0.
indicating that teacher self-confidence does not directly encourage innovation without other supporting factors.
Meanwhile.
Teacher Commitment has a significant positive effect on Learning Innovation ( = 0.
t = 2.
p = 0.
confirming that professional commitment encourages teachers to continue to innovate in improving the quality of learning.
DISCUSSION
The Influence of Digital Literacy on Teachers' Teaching Commitment The research results show that digital literacy has a positive and significant impact on teachers' commitment to teaching.
Teachers' ability to utilize technology not only increases learning effectiveness but also strengthens their sense of professional responsibility.
This aligns with the findings of Simanjuntak & Murniarti, .
as well as Feng & Sumettikoon, .
, that digital literacy contributes to increased self-efficacy and professional loyalty.
Teachers with digital competence tend to be more committed, creative, and adaptive in facing the demands of modern learning.
The Influence of Digital Literacy on Learning Innovation Digital literacy has also been shown to significantly influence learning Teachers who master digital skills can create engaging, collaborative, and technology-based learning strategies.
These results are consistent with research.
Feng & Sumettikoon, .
which emphasizes that digital literacy encourages educators to innovate through the use of interactive media and online platforms.
However, obstacles such as limited infrastructure Published By : CV.
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Page and minimal training still need to be addressed through policy support and ongoing competency development programs.
The Influence of Self-Efficacy on Teacher Commitment This study also found that self-efficacy has a positive effect on teachers' commitment to teaching.
Teachers with high confidence in their abilities tend to be more diligent, responsible, and consistent in teaching.
This finding aligns with research Arviv Elyashiv & Rozenberg, .
, which shows that self-efficacy increases professional responsibility and dedication to work.
Therefore, increasing self-efficacy needs to be integrated into teacher training and professional development programs.
The Influence of Self-Efficacy on Learning Innovation Contrary to the initial hypothesis, self-efficacy did not significantly influence learning innovation.
Although teachers believe in their abilities, this belief is not enough to encourage innovative behavior without environmental support, facilities, and peer collaboration.
This finding is supported bySun et al.
which emphasizes that work engagement and organizational culture are more important determinants of the emergence of innovation than selfefficacy alone.
The Influence of Teacher Commitment on Learning Innovation Teacher commitment has been shown to have a positive and significant impact on learning innovation.
Teachers who are highly dedicated to their profession tend to be more open to change and willing to implement new learning approaches.
These results align with research.
Ahakwa, .
as well as Muyoz-Fernyndez et al.
, .
, which emphasizes that professional commitment strengthens innovative behavior in educational practice.
Thus, increasing commitment is an important prerequisite for creating sustainable The Influence of Digital Literacy on Learning Innovation through Teacher Commitment Mediation analysis shows that teacher commitment is a significant mediator between digital literacy and learning innovation.
This means that increased digital skills encourage teacher dedication, which then transforms into innovation in teaching practice.
This finding is consistent with studies Yulin & Danso, .
, which identifies commitment as the link between digital literacy and pedagogical readiness for innovation.
Therefore, teacher development strategies must combine strengthening digital literacy with fostering professional The Influence of Self-Efficacy on Learning Innovation through Teacher Commitment Research findings indicate that teacher commitment does not significantly mediate the relationship between self-efficacy and learning Although self-efficacy plays a significant role in psychological Published By : CV.
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Page readiness, without the support of creativity and a collaborative environment, its influence on innovation remains limited.
Dewanto et al.
, .
research shows that creativity is a stronger mediator than commitment in linking self-efficacy to Therefore, teacher professional development needs to emphasize the integration of self-efficacy, creativity, and peer collaboration to optimally realize innovation.
The limitations of this study lie in its scope and methodology.
The research was conducted only among junior high school teachers in the Anggeraja District with a limited sample size, making it difficult to generalize the findings to other regions or educational levels.
Furthermore, the use of a quantitative approach through closed-ended questionnaires does not fully capture the depth of teachersAo perceptions and experiences regarding instructional The analysis using PLS-SEM was also cross-sectional in nature, which limits the ability to explain changes in relationships among variables over time.
External factors such as school culture, policy support, and technological environment were also not fully considered as moderating variables that might influence the research outcomes.
CONCLUSION AND RECOMMENDATION
The results of the study indicate that digital literacy has a positive and significant effect on teaching commitment and learning innovation, where teachers with high digital skills are more dedicated, creative, and adaptive in Self-efficacy also has a positive effect on teaching commitment, but not significantly on learning innovation, indicating that self-confidence needs to be accompanied by institutional and collaborative support to produce real Furthermore, teacher commitment plays a crucial role in transforming digital literacy into sustainable learning innovation, while selfefficacy through commitment has not shown a significant effect.
Overall, digital literacy is a key factor driving teacher commitment and innovation in the digital education era.
It is recommended that schools and education offices strengthen digital literacy training and teacher self-efficacy development programs through ongoing mentoring and professional collaboration.
Furthermore, technological infrastructure and an innovative culture are needed so that teacher commitment can develop into creative and sustainable learning practices.
REFERENCES