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Exploring the factors aecting
elementary mathematics teachers’
innovative behavior: an integration
of social cognitive theory
Kai Li
1, Tommy Tanu Wijaya
2*, Xiaoying Chen
2,4* & Muhammad Syahril Harahap
3
Teacher innovative behavior is one of the vital factors, aecting student engagement, addresses
diverse needs, promotes critical thinking, fosters lifelong learning, and contributes to educational
research and development. By encouraging and supporting teacher innovation, we may can ensure
that education remains relevant, eective, and impactful in preparing students for the future. Teacher
innovative behavior is also needed to improve the mathematics skills of elementary school students,
and it is important to determine the predictors that signicantly aecting Teacher innovative behavior.
Therefore, this study aimed to develop a model that predicted possible factors aecting mathematics
teachers’ innovative behavior based on Social Cognitive Theory (SCT). Data were collected from
132 elementary school teachers in China to verify the model, and the analysis was conducted using
a structural equation modelling approach. Theoretically, 10 of the 15 hypotheses were found to
be signicant. The results showed that facilitating conditions and self-ecacy signicantly aect
mathematics teachers’ innovative behavior. Meanwhile, Technological, Pedagogical and Content
Knowledge (TPACK) knowledge, Social Inuences, Rewards, Work engagement and anxiety did not
show any eect. The contribution developed a model and provided new knowledge about the factors
aecting elementary school teachers’ innovative behavior. Practically, this could be used to improve
teachers’ innovative behavior.
Teacher innovative behavior is crucial for the sustainability of education systems and the overall development
of students. In today’s rapidly changing world, where new technologies, pedagogical approaches, and societal
needs emerge, teachers need to adapt and innovate to meet the evolving demands of education. Enhancing
innovative behavior has emerged as a signicant area of focus in the twenty-rst century1. is behavior is widely
acknowledged to yield positive outcomes, beneting both teacher performance during instruction and student
capabilities2,3. As Docherty4 argued, the introduction of teacher innovative behavior can greatly optimize the
learning process, fostering an environment that is conducive to heightened student engagement. Furthermore,
scholarly literatures indicates that embracing innovative behavior empowers teachers to stay informed about the
evolving teaching challenges within the dynamic educational landscape5,6.
Teachers’ innovative behavior encompasses the generation of creative ideas to revolutionize teaching styles
and instructional models7,8. e current Chinese government has issued many new goals that focus on the ability
to innovate and foster this concept9,10. Many studies show that the use of various kinds of technology-based learn-
ing tools11–14, innovative learning models15,16, STEM education17–19 and other innovations continue to increase.
e innovation ability of teachers may not maximal and their behavior still needs attention and improvement.
erefore, dierent studies should be carried out to encourage innovative behavior in mathematics teachers.
is innovative behavior may be more dicult to improve due to neoliberal reasons, and the strong eects
of standardization. Mathematics teachers encounter two primary challenges that impede their ability to foster
creativity and novelty in the design of teaching and learning activities. Firstly, they oen rely on established
teaching habits and methods that hinder their willingness to explore alternative approaches. Secondly, some
perceive themselves as lacking inherent creativity, further inhibiting their condence in innovative practices.
is study shows instances where teachers incorporate innovations into instructional activities. However, these
OPEN
1Teacher Education Collage, Chongqing University of Education, Chongqing, China. 2School of Mathematical
Sciences, Beijing Normal University, Beijing, China. 3Institut Pendidikan Tapanuli Selatan, Padangsidimpuan,
Indonesia. 4Education Bureau of Jinwan District, Zhuhai, China. *email: 202139130001@mail.bnu.edu.cn;
chenxy@bnu.edu.cn
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innovations are oen dictated by administrative obligations and school standards rather than self-generated
creative endeavors. Several barriers such as the standardization of teaching and learning activities focused on
individual students’ mathematical abilities and learning outcomes, leading to a decrease in innovation.
Given the following context, it is crucial to identify the factors that inuence innovative behavior and explore
ways to enhance the innovative behavior of elementary mathematics teachers. Previous results established that
Social Cognitive eory (SCT) encompasses internal and external factors impacting individual behavior. Numer-
ous studies have used SCT to construct models for comprehending individual behavior20,21. In alignment with
previous results, this study employs the concept to investigate the environmental and internal factors that poten-
tially inuence the innovative behavior of mathematics teachers. e initial hypothesis is examined and analyzed
utilizing the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.
e ndings are useful for closing the study gap regarding factors increasing teachers’ innovative behav-
ior. is study focused on answering the question of which predictors signicantly aect teacher innovative
behavior, especially at the elementary school level under the Social Cognitive eory. e strongest predictor
that inuences teacher innovative behavior, especially at the elementary school level, is the level of support and
encouragement received from school leadership and administrators. e ndings are expected to contribute
both practically and theoretically to teachers, and school principals.
Literature review
Mathematics teachers’ innovative behavior
Innovative behavior is dened as the creation or innovation conducted to improve performance in the work
environment22,23. According to Hunter24 in the context of mathematics teaching, there are 3 main indicators for
measuring innovative behavior. First, developing an innovative learning environment that benets all students
and the second indicator is innovative tasks, supporting pedagogical practices. e third is the use of new learn-
ing media, aligning with mathematics teaching activities. According to Wei etal.25 there were 5 main indicators
of teaching innovation in mathematics classes, namely interactive discussions, open-ended activities, mathemat-
ics problem-solving, multilevel teaching, and independent teaching. In conclusion, innovative behavior is the
innovative ideas of mathematics teachers at the elementary school level to innovate with their teaching styles
and models to improve student mathematics outcomes.
Several studies succeeded in proving that Innovative behavior is one of the signicant components of teacher-
teaching success26,27. erefore, Innovative behavior plays an important role in improving student performance
and school progress which is the concern of a mathematics teacher28,29. However, this study shows that teachers’
innovative behavior is still low and needs attention2,30. erefore, studies are needed to theoretically, practically,
and signicantly increase teachers’ innovative behavior.
In the context of education, the teaching approaches employed by teachers exhibit signicant exibility and
adaptability in response to the diverse circumstances and conditions encountered in the classroom31,32. Teachers
possess the capacity to innovate by incorporating various learning models and media to eectively accomplish the
objectives of mathematics education. However, they may be inclined to maintain a risk-averse mindset, hesitant
to adopt new teaching methods that might not yield immediate success in enhancing teaching performance.
According to Bandura33, the process of innovation is beset with challenges, gradual in nature, yields unpredictable
outcomes, and entails relatively low success rates. ese factors constitute the underpinnings for the low nature
of teacher innovative behavior. e Chinese government persists in its dedication to discerning the determinants
capable of exerting an impact on the variable. Subsequently, appropriate policies and programs will be designed
to enhance teacher innovative behavior.
e Chinese government places a strong emphasis on innovation and creativity34. It recognizes the signi-
cance of innovation in the education world and states that China needs to cultivate a culture of innovation.
Furthermore, there is no institution more eective at fostering innovation and creativity than schools. e
Ministry of Education (MOE) has also issued numerous policies to support teaching innovation35. Based on this
background, this study shows that the determinants inuencing the innovative behavior exhibited by teachers
within educational institutions are signicant.
Furthermore, mathematics at the elementary school level is an important stage that focuses on basic knowl-
edge, aecting students’ abilities at the secondary school level. In the new curriculum issued in 2022, China
divides mathematics material into algebra, geometry, statistics and mathematical applications in daily life. e
government also emphasizes the objectives of teaching and learning the subject at the elementary school level on
Knowledge and skills, mathematical thinking, problem-solving, and emotional attitudes. Subsequently, the four
aspects are divided into more detailed learning activity objectives. At the elementary school level, mathematics
material is quite complex and teachers need innovative ideas to teach eectively. Innovative behavior may be
important in increasing student creativity, problem-solving skills, and critical thinking8. Considering the factors
aecting mathematics, the concept may accelerate and encourage innovative behavior of a mathematics teacher
appropriate to the learning objectives in the learning curriculum issued by the Chinese government.
Social cognitive theory
To overcome the issues related to teachers’ innovative behavior, Social Cognitive eory (SCT) is one of the
theories used to analyze the factors inuencing individual behavior. SCT, as elaborated by Bandura36 explained
that individual behavior was aected by two primary factors, namely internal and environmental. is theory
has been widely used and applied in various elds, particularly in education37–39. Previous studies suggested the
need to develop and modify environmental and internal factors related to innovative behavior20. In terms of envi-
ronmental factors, social inuences and facilitating conditions were explored in previous studies. For individual
internal factors, technology literacy, stress, and individual engagement are associated with innovative behavior.
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Based on the context, this study divides environmental factors into facilitating conditions, social inuences and
rewards appropriate to predictors that are oen used in previous results.
Social inuences in the context of this studies are dened as people around elementary mathematics teachers
who believe that innovative behavior can improve teaching performance and positively aect students. Wu20
found that Social inuences is a vital predictor of innovative teaching in China e role of teachers is to con-
tinuously learn and develop their teaching skills. In the twenty-rst century, TPACK knowledge, proposed by
Mishra40 is believed to be a comprehensive framework, guiding teachers in teaching and serving as a foundation
for instructional innovation. e support from people around teachers can enhance their enthusiasm to continue
learning and mastering the Technological Pedagogical Mathematical Knowledge (TPMK) ability. Additionally,
engagement has been proven to be positively aected by social inuence41. Engagement among elementary school
teachers is likely to improve signicantly when enhanced support is received from their peers and colleagues.
Having a strong team and support network can foster an environment conducive to innovation in their teach-
ing practices. e assistance and encouragement may also lead to increased recognition and emotional support,
which can play a vital role in motivating teachers to persevere and continue their innovative eorts within the
school setting. Guo42 substantiated the powerful impact of social support in eectively reducing individuals’
anxiety levels. erefore, when teachers embark on innovative approaches, their primary concern oen revolves
around the fear of potential negative consequences on students’ learning outcomes. Social inuence can reduce
the anxiety of elementary school teachers in innovating their teaching practices. Kuriawan5 emphasized that
support, direction, and feedback from people and the environment are needed to improve teachers’ innovative
behavior. e initial hypothesis is formulated as follows:
H1 Social inuence has a positive eect on the TPACK ability mathematics teachers at elementary school
mathematics teachers.
H2 Social inuence positively aects the work engagement of mathematics teachers at the elementary school
level.
H3 Social inuence positively aects the self-ecacy of mathematics teachers at the elementary school level.
H4 Social inuence has a negative eect on the anxiety of mathematics teachers at the elementary school level.
H5 Social inuence has a positive eect on mathematics teachers’ innovative behavior at the elementary school
level.
Furthermore, elementary mathematics teachers’ innovative behavior may be aected by the rewards obtained
by teachers. Moreover, rewards are always believed to work to improve individual performance and behavior43,
including in the context of education. Teachers may be motivated to seek rewards, signicantly aecting work
engagement44. Moreover, teachers may feel valued when they successfully innovate in classroom practices, espe-
cially when their innovations lead to improved student learning outcomes. With rewards given to teachers for
their innovations, the anxiety associated with innovation among teachers may decrease. e previous result
predicted that rewards are related to individual behavior45,46. erefore, the reward factor may be able to encour-
age mathematics teachers to innovative behavior. Based on the literature review, the initial hypothesis is that:
H6 Rewards have a positive eect on the TPACK ability of mathematics teachers at the elementary school level.
H7 Rewards have a positive eect on the work engagement of mathematics teachers at the elementary school
level.
H8 Rewards have a positive eect on mathematics teacher self-ecacy at the elementary school level.
H9 Rewards have a negative eect on the anxiety of mathematics teachers at the elementary school level.
H10 Rewards positively aect mathematics teachers’ innovative behavior at the elementary school level.
e last Factor environmental is Facilitating conditions. FC are predicted as the main key and directly aect
mathematics teachers’ innovative behavior. A teacher can innovate in teaching and learning activities with sup-
portive school facilities. Wijaya47 found that Facilitating conditions is the signicant factor aecting mathematics
Teachers’ Behavior. Based on the literature review, the initial hypothesis is that:
H11 Facilitating conditions have a positive eect on mathematics teachers’ innovative behavior at the elemen-
tary school level.
Regarding internal factors, TPACK ability, work engagement, self-ecacy, and anxiety are believed to aect
mathematics teachers’ innovative behavior. TPACK ability was rst introduced by Shulman48, and mathematics
teachers need technological, pedagogical and strong mathematical knowledge before innovating in learning.
Teachers should be able to combine learning models and technology-based media49, specically on algebra
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and geometry problems. is is predicted to have a strong relationship with mathematics teachers’ innovative
behavior in teaching and learning activities. Based on the literature review, the initial hypothesis is that:
H12 TPACK ability has a positive eect on mathematics teachers’ innovative behavior at the elementary school
level.
Previous study has widely used work engagement to analyze professionalism and performance in teaching1,4,50.
e concept can be interpreted as the individual level of seriousness to give eort in work matters. Several stud-
ies have proven that teacher work engagement is a signicant predictor aecting job performance, job satisfac-
tion, and commitment, as well as increasing creativity and innovation in teaching methods aecting teaching
performance1. Based on the literature review, the initial hypothesis is that:
H13 Work engagement has a positive eect on mathematics teachers’ innovative behavior at the elementary
school level.
Self-ecacy refers to teachers’ personal beliefs in the ability to eectively perform behaviors that contribute
to the improvement of their teaching performance. It has been widely used in the educational context in previous
studies related to the behavior of a teacher or student8. e concept signicantly aects mathematics teachers’
innovative behavior 3,8. Based on the literature review, the initial hypothesis is that:
H14 Self-ecacy has a positive eect on mathematics teachers’ innovative behavior at the elementary school
level.
In the context of this study, anxiety is dened as the tendency of teachers to be uneasy and nervous about
innovating by teaching mathematics at the elementary school level. Many studies support that anxiety has a
negative eect on a person’s innovation51–53. e many tasks and amnesty of the school and the fear of their
innovations failing to improve students’ mathematics ability may have a signicant negative eect on innovative
behavior. Based on the literature review, the initial hypothesis is that:
H15 Anxiety has a negative eect on mathematics teachers’ innovative behavior at the elementary school level.
Based on the description of the literature review, predictors aecting elementary mathematics teachers inno-
vative behavior consist of seven independent, four intermediate and one dependent variable, resulting in 15
initial hypotheses, as seen in Fig.1.
Methodology
is study determines the factors aecting mathematics teachers’ innovative behavior at the elementary school
level. To achieve this goal, quantitative methods are used by distributing questionnaires and processing the data
with PLS-SEM techniques for hypothesis testing.
Participants
is study collected questionnaire data from 132 elementary mathematics teachers on the factors that aect
teachers’ innovative behavior. About 61.36% and 38.64% of participants were female and male elementary math-
ematics teachers. From the study, 75.76% of elementary mathematics teachers had an undergraduate education
level while 24.24% had a graduate education level. Furthermore, 57.58% of school locations are in rural areas
and 42.42% are in urban areas. e respondents in this study, who have been part of the teaching experiment,
are distributed in a balanced manner. About 34.85% possess teaching experiences of under ve years, while
33.33% have accumulated 6–15years of teaching experience. Additionally, 31.81% boast a considerable teach-
ing experience of more than 15years, and Table1 shows the main demographic respondents. e structural
equations model sample size was better if not less than 10054. erefore, this study reached the recommended
sample respondent provisions.
Instrument and data collection
e entire questionnaire used was adopted from a previous study and supported by a strong literature review
(see appendix). e instrument was checked and validated by 2 doctoral students and 1 post-doctoral expert in
innovative behavior. is study used the Social Cognitive eory as the basis for developing all the items in this
instrument. e questionnaire was divided into two parts. e rst part contained sociodemographics (gender,
level of education, school location, teaching experiences), while the second consisted of 22 questionnaires derived
from 8 constructs. It was designed with a 5-point Likert scale from 1 = strongly disagree to 5 = strongly agree.
First, two postdoctoral fellows designed and modied the instrument taken from previous study. Subsequently,
the initial questionnaire was given to 2 professors in the eld of mathematics and psychology education. A total
of 5 Chinese elementary mathematics teachers were involved in lling out and reviewing the questionnaire to
ensure the questionnaire was understandable.
e population in this study were elementary school mathematics teachers from Sichuan province. is study
randomly selected 150 schools and administered an online questionnaire. Online questionnaires were considered
more eective and ecient for elementary mathematics teachers in China. e utilization ensured that the work
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time of elementary mathematics teachers remained uninterrupted. ese questionnaires were conveniently lled
out by the teachers to accommodate their schedules accordingly. Moreover, the implementation facilitated a more
comprehensive data collection process, as they were eortlessly disseminated through various platforms and
social media channels. e condentiality of the questionnaire responses was strictly maintained, with the data
solely used for study purposes. Human Ethics Approval for the interviews was obtained from the Chongqing
University of Education Human Ethics Committee on the 2 February 2023 (Approval number: 202302024). All
Figure1. Framework model.
Table 1. Respondent demographic data.
Demographic Type N Percentage
Gender Male 51 38.64
Female 81 61.36
Level of education Undergraduate 100 75.76
Graduate 32 24.24
School location Urban 56 42.42
Rural 76 57.58
Teaching experiences
Less than 5years 46 34.85
between 6 and 15years 44 33.33
Over 15years 42 31.81
Tot a l 132 100
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of the procedures were performed in accordance with the Declaration of Helsinki and relevant policies in China.
Before their participation, All participants agreed to participate voluntarily, with informed consent when they
lled in the survey and were able to withdraw from the study freely at any time.the distribution of the question-
naires took place between March and May 2023. Ultimately, valid data were collected from 132 elementary
mathematics teachers. e data were condential and participation was anonymous with- out any potential risk
to the integrity of the subjects.
Data analysis
Data analysis used SPSS and SMART-PLS3. SPSS soware is used for descriptive statistics data processing,
which is a key step in the initial process and data screening, specically in quantitative study. e second step,
SMART-PLS 3 is the main soware in PLS-SEM (variance-based SEM) analysis oen use to design new study
models55–57. is study uses PLS_SEM instead of CB_SEM because it is more practical where there is no need to
determine the normality of the data58. It can also analyze study models with relatively small samples, including
many indicators and path relationships59. Furthermore, PLS-SEM is more exible for identifying the relationship
between measurement items and each construct compared to CB-SEM60,61.
PLS-SEM is a nonparametric algorithm computation used to determine the value of each latent variable62. e
analysis steps are to enter data information, measure the construct, analyze discriminant validity and determine
each relationship between construct variables63,64. In study with the PLS-SEM approach, Hair etal.65 recom-
mended paying attention to several factors. Analyzing the signicance level should be below 0.05 since the
relationship between variables is declared signicant. e model has good enough explanatory power when R2
values are not less than 0.25.
In Partial Least Squares Structural Equation Modeling (PLS-SEM), the evaluation of measurement and struc-
tural models follows specic criteria to guarantee the credibility and accuracy of the model.
To begin, the measurement model undergoes rigorous scrutiny. is includes assessing the reliability of the
measurement scales, typically done using metrics such as composite reliability (CR) and Cronbach’s alpha. Fur-
thermore, the convergent validity of indicators is examined through the average variance extracted (AVE) and
factor loadings. An AVE above 0.5 indicates that the observed variables adequately represent the latent construct.
Discriminant validity is then conrmed by comparing the square root of AVE with inter-construct correlations,
ensuring that dierent constructs are distinct from one another.
Moving to the evaluation of structural models, path coecients, indicating the strength and direction of rela-
tionships between latent constructs, are scrutinized. Bootstrapping techniques aid in estimating the signicance
of these coecients. Examining eect sizes, such as R2 values, claries the proportion of variance explained in
endogenous constructs by their exogenous counterparts.
Results
Measurement model evaluation
In PLS-SEM, validity and reliability tests on each construct are veried using the CFA technique66,67. As seen in
Table2, all item loadings exceed the minimum criterion of 0.7, hence the construct has a good agreement. e
CR value should be more than the 0.7 limits since each construct has good inner consistency68. In this study, the
CR value ranges from 0.897 to 0.944, indicating the absence of a problem with inner consistency. Furthermore,
the Ave value should be above the 0.5 thresholds for the construct to have good convergent validity68. e low-
est AVE value is 0.744 and it is considered to have reached the minimum criteria. Finally, the Cronbach alpha
ranged from 0.807 to 0.896, exceeding the 0.6 threshold recommended by Hair68.
Common method bias
According to the recommendation of Kock69, a collinearity test was required in PLS-SEM to determine when
the data collected had bias problems. A multicollinearity test was carried out by analyzing the variable ination
factor (VIF) values70,71. is study found that the VIF value was not more than 3.3, as shown in Table2. erefore,
there was no multicollinearity problem.
Discriminant validity was analyzed using the Fornell-lacker test72, and Table3 indicated that this study had
a good discriminant validity where the AVEs in each construct were greater than others.
Structural model Evaluation
Model t
Model t in Smart PLS can be seen from the SRMR, d-ULS, and d_G values 73. e dierence that exists between
the observed correlation and the matric model can be seen in the SRMR value. A good SRMR value is less than
0.08 and this study has 0.04 (Table4). Furthermore, the dierence in the covariance matrix and the empirical
covariance matrix can be observed in d-Uls and d_G, which are listed using the composite factor model. In
conclusion, this study meets the requirements of a good t model.
Structural model
e structural model was evaluated by examining the signicance of the path coecients using the bootstrapping
technique with 5000 resamples 74,75. e hypothesis was tested using tailed testing because the type of testing
was the directional method. In addition, the complete structural model can be seen in Table5. Social inuence
was found to have a signicant eect on TPACK knowledge (H1: β = 0.480, p < 0.001), work engagement (H2:
β = 0.550, p < 0.001), self-ecacy (H3: β = 0.537, p < 0.001), anxiety (H4: β = − 0.242, p < 0.05). e reward had a
signicant eect on TPACK knowledge (H6: β = 0.414, p < 0.001), work engagement (H7: β = 0.332, p < 0.001),
self-ecacy (H8: β = 0.318, p < 0.001), anxiety (H9: β = − 0.225, p < 0.05). Meanwhile, facilitating conditions
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signicantly aect mathematics teachers’ innovative behavior (H11: β = 0.332, p < 0.05). Social inuence, TPACK
knowledge, anxiety, and work engagement did not signicantly aect mathematics teachers’ innovative behav-
ior. Self-ecacy also aected mathematics teachers’ innovative behavior signicantly (H14: β = 0.207, p < 0.05).
While exploring the indirect eects within our analysis, particularly as detailed in Table6, e analysis indi-
cates that Social Inuence signicantly aects Innovative Behavior through Self Ecacy, with a relatively high
t-statistic and the lowest p-value among the paths evaluated.
Table 2. Results for reliability, convergent validity, and multicollinearity test.
Construct Indicator Outer loadings Cronbach’s Alpha Composite reliability Average variance extracted
(AVE) VIF
Anxiety
AN1 0.913 0.896 0.935 0.827 2.111
AN2 0.910 2.278
AN3 0.906 2.140
Work engagement
EN1 0.894 0.867 0.919 0.791 1.606
EN2 0.898 1.963
EN3 0.876 1.846
Facilitating conditions
FC1 0.866 0.870 0.920 0.794 2.032
FC2 0.928 2.477
FC3 0.878 2.360
Innovative behavior IB1 0.947 0.882 0.944 0.895 2.386
IB2 0.945 1.844
Reward
RW1 0.863 0.835 0.901 0.752 1.844
RW2 0.856 2.366
RW3 0.881 2.374
Self-ecacy
SE1 0.903 0.885 0.929 0.813 2.300
SE2 0.889 2.740
SE3 0.913 2.758
Social inuence
SI1 0.893 0.827 0.897 0.744 2.816
SI2 0.888 2.581
SI3 0.804 2.649
TPACK
TPACK1 0.901 0.878 0.925 0.804 2.649
TPACK2 0.891 3.027
TPACK3 0.897 2.320
Table 3. Results of the Fornell-Larcker test for assessing discriminant validity. All bolded loadings in the
diagonal dimension are the square root values of AVE.
Anxiety Work engagement Facilitating
conditions Innovative behavior Reward Self-ecacy So cial inuences TPACK
Anxiety 0.910
Work engagement − 0.483 0.889
Facilitating condi-
tions − 0.542 0.848 0.927
Innovative behavior − 0.460 0.801 0.839 0.946
Reward − 0.448 0.768 0.821 0.767 0.867
Self-ecacy − 0.504 0.854 0.847 0.805 0.744 0.901
Social inuence − 0.445 0.813 0.859 0.780 0.794 0.790 0.862
TPACK − 0.470 0.865 0.841 0.790 0.794 0.839 0.808 0.897
Table 4. Results of model t.
Saturated model
SRMR 0.044
d_ULS 0.636
d_G 0.857
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Figure2 showed the P-value and explanatory power (R2). e model explained most of the variance in all
endogent models such as TPACK knowledge (71.6%), work engagement (70.2%), self-ecacy (66.1%), anxiety
(22.2%) and mathematics teacher innovative behavior (75.5%). It had a strong explanation model for the exist-
ing available variables. Moreover, the model was proven to have stability and robustness. e signicance of the
path can be seen in Fig.2.
Discussion
is study develops and tests a model to predict factors aecting mathematics teachers’ innovative behavior.
e major contribution is to modify Social cognitive theory with variables that have a strong relationship with
Elementary Mathematics Teachers’ Innovative Behavior. From the results of data processing obtained from
respondents, this study has empirical ndings such as:
Empirical tests reported 10 out of 15 initial hypotheses to be signicant. Facilitating conditions and self-
ecacy were found to have signicant direct eects on elementary mathematics teachers’ innovative behavior.
Interesting ndings are facilitating conditions found as a predictor with the rst largest eect on mathematics
teachers’ innovative behavior. is diers from the previous results, where information literacy is the biggest
factor aecting innovative behavior20. erefore, elementary mathematics teachers in schools need complete
facilities to enhance innovative learning. Respondents were almost 50% of teachers working in rural area schools.
In the context of education in rural areas in China, a notable issue persists where numerous classrooms lack
adequate facilities, compelling teachers to resort to traditional learning methods. is poses challenges when
attempting to introduce innovative approaches to education. Mathematics teachers, in particular, may per-
ceive that having complete and sucient facilities enhances their eectiveness in implementing novel teaching
techniques within the classroom. Moreover, favorable facilitating conditions can also bolster their condence
in making signicant advancements in the instructional models employed to teach mathematical concepts.
Consequently, the identication of the concept as the primary factor exerting the most substantial inuence
carries important implications. Schools and government can investigate further what teachers need to support
their innovative behavior. Subsequently, providing facilities such as technology-based learning media and full
classrooms with technology-based facilities may change and modify teaching methods. Providing training and
guidance to mathematics teachers on improving a teacher’s innovative behavior might be considered.
Table 5. Results of the initial hypothesis test.
Direct eect β M STDEV T Statistics P values
Anxiety—> Innovative behavior 0.010 0.003 0.049 0.197 0.844
Work engagement—> Innovative behavior 0.127 0.116 0.121 1.055 0.292
Facilitating conditions—> Innovative behavior 0.332 0.329 0.146 2.274 0.023
Reward—> Anxiety − 0.255 − 0.253 0.125 2.043 0.042
Reward—> Work engagement 0.332 0.328 0.080 4.150 0.000
Reward—> Innovative behavior 0.141 0.135 0.101 1.406 0.142
Reward—> Self ecac y 0.318 0.310 0.094 3.379 0.001
Reward—> TPACK 0.414 0.409 0.068 6.084 0.000
Self-ecacy—> Innovative behavior 0.207 0.205 0.103 2.003 0.046
Social inuences—> Anxiety − 0.242 − 0.245 0.106 2.293 0.022
Social inuences—> Work engagement 0.550 0.555 0.077 7.152 0.000
Social inuences—> Innovative behavior 0.069 0.067 0.100 0.685 0.481
Social inuences—> Self ecacy 0.537 0.548 0.099 5.417 0.000
Social inuences—> TPACK 0.480 0.486 0.067 7.196 0.000
TPACK—> Innovative behavior 0.064 0.072 0.128 0.497 0.620
Table 6. indirect eect on Innovative behavior.
Indirect Eect Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Va lu es
Reward—> Anxiety—> Innovative behavior − 0.002 − 0.000 0.014 0.175 0.861
Social inuence—> Anxiety—> Innovative behavior − 0.002 − 0.001 0.012 0.195 0.846
Reward—> Self ecac y—> Innovative behavior 0.066 0.065 0.041 1.605 0.109
Social inuence—> Self Ecacy—> Innovative behavior 0.111 0.104 0.059 1.899 0.058
Reward—> TPACK—> Innovative b ehavior 0.026 0.033 0.052 0.504 0.614
Social inuence—> TPACK—> Innovative b ehavior 0.030 0.035 0.059 0.513 0.608
Reward—> Work engagement—> Innovative behavior 0.042 0.037 0.041 1.039 0.299
Social inuence—> Work engagement—> Innovative
behavior 0.070 0.058 0.064 1.088 0.277
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e study revealed that direct social inuences do not signicantly impact the innovative behaviors of math-
ematics teachers. However, it was found that these social inuences have a substantial indirect eect on such
behaviors by enhancing teachers’ self-ecacy. is nding is consistent with prior research, which also identied
only indirect eects of social inuences on the variable of innovative behavior20. In the specic cultural context
of China, where interpersonal relationships are highly valued12, the advocacy for innovative teaching methods by
respected individuals exerts a notable inuence on mathematics educators. is motivates them to explore and
adopt novel pedagogical approaches. is discovery is of great practical signicance, underscoring the crucial
roles that schools, teachers, and governmental entities play in fostering and supporting innovation within the
realm of mathematics education.
e unique nding is that rewards signicantly aect mathematics teachers’ innovative behavior. Teachers
in China oen have high pressure, chasing learning materials to be mastered by students76–78. is may reduce
mathematics teachers’ innovative behavior. Elementary Mathematics Teachers assert that incentives such as
awards or recognition from schools exert a signicant impact on their motivation to innovate teaching methods.
is nding provides valuable information for schools and decision-makers to reward and recognize teachers
with the courage to innovate in classroom teaching and learning activities. In addition, the learning innovation
Figure2. Final model with R2 value and path coecients (β).
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competition may be one of the facilities to reward elementary mathematics teachers who have dared to innovate
the learning models used in the classroom.
Based on the Social cognitive theory36, self-ecacy has a signicant eect on mathematics teachers’ innovative
behavior. is is appropriate to previous studies where self-ecacy has a strong eect on teacher behavior8,79.
Individuals with high self-ecacy may can do better than they think. Reinforcement of the concept is one of the
right ways for elementary mathematics teachers to innovative behavior. Schools and teachers can pay attention
to these aspects.
Meanwhile, elementary mathematics teachers do not consider that TPACK knowledge can signicantly
encourage innovative behavior. Even though mathematics teachers master TPACK knowledge, it is very dif-
cult to innovate learning without adequate condition facilities and support from the people. Achievement of
the goal to enhance the innovative behavior of elementary mathematics teachers can only be realized when the
environment aligns with its objectives and provides mutual support to one another.
Anxiety has absolutely no relationship with mathematics teachers’ innovative behavior. is interpretation
holds on the condition that the environment extends its support, adequate facilities are accessible, the mathemat-
ics teachers possess robust self-ecacy to foster educational innovations, and they are unburdened by anxieties
when implementing novel teaching and learning practices in the classroom.
Conclusion
In conclusion, when teachers’ innovative behavior is one of the aspects to be improved in the twenty-rst century,
this study provides empirical evidence by investigating the factors with a signicant eect and nding the most
inuential factors on elementary mathematics teachers’ innovative behavior. ese results found that facilitating
conditions and self-ecacy signicantly aect elementary mathematics teachers innovative behavior. Meanwhile,
facilitating conditions are the most signicant factor aecting mathematics teachers’ innovative behavior. Social
Inuence signicantly aects Innovative Behavior through Self Ecacy, as indicated by its p-value below 0.1,
representing the most substantial indirect eect with the highest t-statistic and lowest p-value among the evalu-
ated paths. is study contributes and can be used according to the gap in the innovative behavior of elementary
mathematics teachers. Schools and decision-makers can also use the results to improve mathematics teachers’
innovative behavior in their respective schools.
Contribution and implications
e ndings contribute theoretically and practically to the study of innovative behavior. eoretically, the results
add to the literature related to the innovative behavior of mathematics teachers at the elementary school level,
where instructional innovation is crucial and has a positive impact on students’ abilities. It explores the key to
success to improve elementary mathematics teachers innovative behavior based on social cognitive theory when
mathematics teachers innovative behavior is needed and highlighted at this time. Based on the literature review,
study on innovative behavior is very limited, specically in the context of mathematics teachers. is study
provides new knowledge where facilitating conditions and self-ecacy are signicant factors for elementary
mathematics teachers innovative behavior.
Besides oering theoretical implications, this study also presents practical applications for educational institu-
tions. It sheds light on the determinants of innovative behavior among mathematics teachers, thereby enabling
decision-makers and school principals to gain a deeper understanding, oer informed feedback, and develop
strategies to foster instructional innovation. Additionally, this research can serve as a valuable resource for
local and national education authorities in the development, modication, and renement of teacher training
programs.
Limitations
Even though this study provides new knowledge, several limitations need to be considered. First, the respond-
ents are small and limited to teachers at the elementary school level hence generalizing the ndings and model
should be carried out carefully. is study supports future analyses to retest the result with a larger sample and
at dierent levels, such as secondary school or university. Second, it uses a qualitative approach needed for more
objective results and in-depth discussion. ird, certain potential predictors, such as teachers’ literacy skills,
wellbeing, and other relevant factors, could be incorporated and re-evaluated to establish an improved model
with enhanced explanatory power. is study believes that innovative behavior is closely related to individual
psychology. erefore, experts in the eld of psychology can continue this study.
Data availability
e raw data supporting the conclusions of this research will be made available upon request by the author of
this publication.
Appendix
Detail questionnaires
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Var iable English version
Facilitating conditions
Schools provide facilities that support teachers to innovate in math learn-
ing
e government and schools oen hold training on innovations in math
learning
I can readily access curriculum resources focused on innovative
approaches to math learning
Social inuences
When I have diculties innovating in math learning, other math teachers
are ready to help
e school supports teachers to innovate in math learning
People around me believe that I can innovate in math learning
Reward
e school gives rewards to teachers who can innovate in math learning
My math learning innovations are appreciated by others
I am very happy that the school gives appreciation and gis when I suc-
cessfully innovate in mathematics learning
TPACK
I have the mathematical knowledge, pedagogical knowledge and techno-
logical knowledge to innovate in mathematics
I can choose new learning media and learning tools that are suitable for
the mathematics topic I am teaching
I can combine technology-based learning media and learning methods to
teach mathematics
Work engagement
I am very serious about innovating my way of teaching mathematics
I am willing to sacrice my time to innovate new ways of teaching math
I am always hungry to learn new knowledge, new learning models, and
new learning media therefore I can innovate when teaching math
Self ecacy
I am condent that my learning innovations can eectively improve my
students’ skills
I believe I can innovate my teaching methods to achieve learning objec-
tives
I am condent that my students will like my math learning innovations
Anxiety
I am anxious when I have to make innovations in math learning
I am afraid that my math learning innovations will not be successful
I am afraid that math learning innovations are a waste of time
Innovative behavior I oen innovate my math learning by using ICT
I like to use new methods and learning models in math lessons
Received: 16 September 2023; Accepted: 21 January 2024
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Author contributions
“Conceptualization, T.T.W. and K.L.; methodology, X.C.; soware, T.T.W.; validation, X.C and M.S.H.; formal
analysis, M.S.H; investigation, K.L.; resources, X.C.; data curation, K.L.; writing—original dra preparation, all
authors; writing—review and editing, all authors; visualization, M.S.H; supervision, K.L.; project administration,
K.L.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.”
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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Vol:.(1234567890)
Scientic Reports | (2024) 14:2108 | https://doi.org/10.1038/s41598-024-52604-4
www.nature.com/scientificreports/
Funding
is study was supported by Chongqing Education Commission Science and Technology Research Project (Grant
No. KJQN202101605)；Research on Humanities and Social Sciences of Chongqing Municipal Education Com-
mission (Grant No.23SKGH369) and Supported by Research Program of Chongqing University of Education
(Grant No. KY202301C).
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to T.T.W.orX.C.
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