The mediating role of meaning in work in promoting teachers’ technology integration

Teachers’ integration of technology has been a critical focus for both teachers and researchers over the past three decades. This emphasis has intensified due to the COVID-19 pandemic, where technology integration has become a key factor in the success of classroom teaching and learning processes. Despite this attention, previous studies have shown limited exploration of the relationship between teachers’ technology integration and meaning in work as an internal variable. Therefore, using AMOS-structural equation modeling (SEM) analysis, this study aimed to develop a conceptual model examining the mediating role of meaning in work in the relationship between digital leadership, self-efficacy, and teachers’ technology integration. The study involved 200 junior high school teachers from Balikpapan City, East Kalimantan Province, a region in eastern Indonesia projected to become the new capital. A total of four variables were analyzed in this study: meaning in work, digital leadership, self-efficacy, and teachers’ technology integration (Z, X, and Y, respectively). The results showed that (1) digital leadership affected meaning in work and teachers’ technology integration, (2) self-efficacy affected meaning in work and teachers’ technology integration, (3) meaning in work affected teachers’ technology integration, and (4) meaning in work could mediate the relationship between digital leadership and self-efficacy in teachers’ technology integration. These findings contribute to a deeper understanding of the relationships among digital leadership, self-efficacy, and meaning in work, and their collective impact on teachers’ technology integration. Furthermore, the study highlights the significant role of meaning in work as a mediator in these relationships, providing a foundation for the development of digital leadership strategies and training programs aimed at improving technology integration in education.

1 Introduction

Teachers’ integration of technology has been a critical focus for educators and researchers over the past three decades (Nelson et al., 2019). This focus has intensified in the past three years due to the widespread adoption of online learning in response to the COVID-19 pandemic (Chiu, 2022; Gomez et al., 2022).

Furthermore, the transition from traditional in-person to online platforms requires teachers to employ a wide range of digital tools and resources to enhance the effectiveness of the instructional method (Eickelmann and Drossel, 2020).

A previous study highlighted the need for teachers to develop digital teaching skills and become proficient technology users (Botham, 2018).

Technology is considered a critical tool for ensuring the continuity of education (AlAjmi, 2022), playing a crucial role in bridging the gap between teachers and students (Khatoony and Nezhadmehr, 2020).

The integration of technology has been proven to enhance the quality of the educational process, particularly during the pandemic (Alqahtani and Rajkhan, 2020; König et al., 2020a). This improvement is especially evident in enhancing competency (Ahmed and Opoku, 2022; Hasin and Nasir, 2021) and increasing student satisfaction with the learning experience (Nasir, 2020).

A previous report (Chiu, 2022) suggested that educational technology could support the learning process by engaging students in interactive activities. In line with these results, school curricula integrate ICT more thoroughly, allowing students to use digital resources for creative and innovative problem-solving.

Furthermore, the development of artificial intelligence (AI) strengthens the importance of technology integration in learning (Akgun and Greenhow, 2022; Al-Sharafi et al., 2023; Chen et al., 2020; Narvaez Rojas et al., 2021). AI facilitates personalized learning by analyzing student data and adjusting materials and teaching methods according to individual needs (Ouyang and Jiao, 2021). This reduces teachers’ administrative burden, such as grading and lesson planning, leading to increased focus on direct student interactions. With the increasing application of AI technology, teachers can eliminate repetitive and tedious tasks and respond to students promptly, thereby advancing adaptive and personalized teaching processes (Chan and Zary, 2019). Several studies have shown that it can also provide innovative teaching aids, such as virtual assistants and adaptive platforms, which enrich the learning experience and improve teaching effectiveness. However, challenges, such as the need for teachers training in using new technology and ensuring equitable access for all students, must be addressed to maximize the potential benefits. AI overuse can also reduce essential human interaction, such as emotional guidance and teachers’ motivation.

Additionally, there is a risk of a digital divide where students from economically disadvantaged backgrounds may not have adequate access to advanced technology, widening the educational gap. Data privacy is a major issue as AI is associated with collecting and analyzing students’ sensitive personal data (Chen et al., 2020; Nguyen et al., 2023), which can pose a risk of security breaches if not managed carefully. Due to the role of integrating ICT in affecting the success of modern teaching processes and providing meaningful learner experiences, it is important to conduct studies on the antecedents and consequences of this variable.

Several studies showed that teachers’ technology integration is affected by and requires support from various components. Adarkwah (2021) underscored the essential role of curriculum, training and development, organizational aspects, leadership, and school culture in achieving effective technology integration. Meanwhile, other studies categorized factors affecting this variable into two categories, such as internal and external (Cheng et al., 2020). The external factors include school principals, pedagogical, technical, and administrative support, and the availability of digital learning resources (Cheng et al., 2020; Chiu, 2017). The internal factors focus more on psychological aspects related to technology, such as teachers’ attitudes and beliefs (Farjon et al., 2019), interests, and concerns about using innovations (Chiu and Churchill, 2016; Hsu, 2016). Previous reports also examined the factors affecting teachers’ intentions to adopt technology in the classroom. The results showed that self-efficacy, attitudes, and perceived ease of use significantly predicted intention (Li et al., 2016).

Due to the wide variety of results related to integrating educational technology in schools, several variables affect the development of studies on this phenomenon. This study explored the variables affecting teachers’ technology integration by sampling teachers in junior high schools in Balikpapan City, East Kalimantan Province, Indonesia. Furthermore, as a developing country, Indonesia has its own unique complexity and challenges. Aksal and Gazi (2015) stated that technology integration in developing countries still faces significant challenges. Technology adoption differed between developed and developing countries, with developed countries being more advanced in infrastructure.

The environmental and contextual differences and strategies used in developed countries cannot be directly applied in developing areas. Rural areas or the size of the community or district where the schools were located could also affect integration.

This study considered the digital leadership of school principals and self-efficacy as independent variables that affect technology integration in education. In addition, effective digital leadership by school principals was posited to positively impact teachers’ technology integration during teaching and learning. Principals who possess skills and knowledge in technology usage and can facilitate its use by teachers are expected to create an environment in schools that encourages adoption (AlAjmi, 2022). Appropriate support and guidance can increase the motivation and confidence among educators in using technology, thereby enhancing the effectiveness of learning and the overall learning experience of students (AlAjmi, 2022). Previous studies found that the digital leadership of school principals was grouped into three categories: technology use, managerial skills, and individual skills. To realize digital transformation in K-12 education, principals must first demonstrate their digital leadership and actively support the development of a digital learning culture in schools (Karakose et al., 2021).

In addition, a period-based analysis revealed that technology management, with a focus on virtual teams, was the dominant theme during the first period. The emphasis shifted to technology in the second period, while the third period focused on COVID-19, virtual reality, and digital technology as the central themes. The thematic evolution analysis indicated that virtual leadership was a significant study theme in the first and second periods, while virtual teams became prominent in the second. Electronic and technology leadership were key themes in the second and third periods. Digital leadership, COVID-19, and virtual reality emerged as important focus areas during the third period. Technology leadership, consistently present across all three periods, was well-developed (Karakose et al., 2022).

Additionally, meaning in work was used as a mediating variable in this relationship. Based on observation, there are no studies on the mediating role of the variable.

According to Lee (2015) and Suyatno et al. (2021), meaning in work comprises four important attributes: the meaning of work, experiencing positive emotions at the workplace, meaningful purposes and goals of work, and work as a part of life toward meaningful existence. Meaning in work was predicted to mediate the relationship between the independent and dependent variables based on the aforementioned attributes. This study was conducted because Indonesia was a developing country with a different technological development context than Western countries. Access to technology, teachers’ readiness, and institutional support greatly determined the integration of teachers’ technology in learning. Therefore, this study aims to test the conceptual model of the meaning of the work variable in mediating the relationship between digital leadership and self-efficacy variables with teachers’ technology integration. First, this study discussed the problems and then described the method used: selecting the appropriate approaches, data collection, and data analysis techniques. The following section presents the data analysis results using a structural equation model. The last section describes the discussion, conclusions, and several recommendations for further studies. The results of this study were expected to offer a framework for management at the macro (government through the Ministry of Education and Culture), meso (educational agencies), and micro levels (school principals) in preparing for teachers’ technology integration in education, leading to the successful implementation of innovations.

2 Methods

2.1 Study design

Using PLS-structural equation modeling (SEM) model analysis, we used a cross-sectional study design to develop a conceptual model by identifying antecedent and consequent variables related to meaning in work for teachers (Collier, 2020). Furthermore, the study procedures comprised four key variables: meaning in work, digital leadership and self-efficacy, and teachers’ technology integration, represented as variables Z, X, and Y, respectively.

2.1.1 Population and respondents

The study population included all junior high school teachers, both public and private, in Balikpapan City, East Kalimantan Province, both public and private. The participants included 1,449 teachers—1,053 women and 396 men—distributed across nine subdistricts.

A convenience sampling technique (Emerson, 2021) was used to select a total of 200 teachers for the study, as shown in Table 1.

Table 1

Table 1. Demographics of respondents.

Table 1 shows that there were more female participants than males. The majority of the participants held a bachelor’s degree, making up 80% of the group. In terms of teaching experience, the majority had been teaching for 1–5 years. Additionally, the largest age group was young teachers between 21 and 30 years old, accounting for 41% of the population. Regarding the subjects taught, religious studies were the most common at 21%, followed by natural sciences at 14%, and English at 11.5%. Overall, the demographics reflected a range of differences in gender, education level, teaching experience, and subject areas.

2.2 Data collection instruments

The data collection process was conducted using four questionnaires: (a) a digital leadership questionnaire adopted from Bass and Riggio (2018), (b) a self-efficacy questionnaire adopted from Lam et al. (2010), (c) a meaning-in-work questionnaire adapted from Steger et al. (2012), and (d) a teachers’ technology integration questionnaire adopted from Allen and Meyer (1990). Furthermore, each variable was developed into several indicators, as shown in Table 2.

Table 2

Table 2. Variable indicators.

The questionnaires were created using a Likert scale with scores ranging from 1 to 5, and the data collected were in the form of intervals. Scores 1, 2, 3, 4, and 5 represented “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree,” respectively. For negative statements, the scores were reversed, with 5, 4, 3, 2, and 1 representing “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree,” respectively.

This study instrument was adapted from the theory developed by previous studies. Based on this theory, the indicators of each variable were adapted, as shown in Table 2. Each indicator was developed into questionnaire statement items with a proportional number of items from each indicator. Furthermore, the study used the expert judgment validity test using the services of 2 people with expertise in the field being studied. After being approved and declared suitable for use, the instrument was developed into a finished instrument. This study conducted an outer model evaluation to test the validity and reliability of indicators from each latent variable. In smartPLS, there were two types of validity and reliability tests: construct reliability and validity and discriminant validity.

2.2.1 Construct reliability and validity

Validity and reliability testing using SmartPLS followed certain criteria: An indicator was considered valid when the Average Variance Extracted (AVE) value was ≥0.50, and both the loading factor and rho-A values were ≥ 0.70 (Hair and Alamer, 2022). Additionally, an indicator was considered reliable when the composite reliability and Cronbach’s alpha reliability values were ≥ 0.70. Based on the analysis results, this study obtained the validity and reliability results of indicators for latent variables, as shown in Tables 3, 4.

Table 3

Table 3. Construct reliability and validity results.

Table 4

Table 4. Loading factor results.

2.3 Discriminant validity

In SMART-PLS, discriminant validity could be assessed using the Fornell-Larcker criterion and the heterotrait-monotrait ratio (HTMT). Discriminant validity ensures that the measurement tool accurately measures the intended construct rather than overlapping with others. According to the Fornell-Larcker criterion, discriminant validity is considered good when the square root of the AVE for each construct is greater than the correlation between that construct and others. In this study, the Fornell-Larcker criterion values met the required criteria, as shown in Table 5.

Table 5

Table 5. Fornell-Larcker criterion.

Discriminant validity in this study was also reinforced by HTMT values, which had fulfilled the criteria, HTMT ≤0.90, as shown in Table 6.

Table 6

Table 6. Heterotrait-monotrait ratio (HTMT).

Table 6 shows that all indicators of each latent variable met the criteria for validity and reliability in the measurement model, both in terms of construct reliability and validity, as well as discriminant validity. Therefore, this ensured that each indicator was accurate, consistent, and capable of precisely representing the results.

2.4 Data analysis

This study used SEM for data analysis using AMOS software (Collier, 2020). Furthermore, SEM analysis was used to examine the relationship between digital leadership and self-efficacy as exogenous variables, and meaning in work and teacher’s technology integration as mediating and endogenous variables. AMOS was selected for SEM analysis because it allowed for the analysis of multi-group data and the availability of SEM diagram tools to evaluate the outer model, inner moderation, and first-order and second-order models. The initial AMOS-SEM model tested in this study is presented in Figure 1.

Figure 1

Figure 1. The results of the AMOS-SEM model tested.

The hypotheses tested in this study are presented below:

H1: Digital leadership affects the meaning in work for teachers.

H2: Digital leadership affects teachers’ technology integration.

H3: Self-efficacy affects meaning in work.

H4: Self-efficacy affects teachers’ technology integration.

H5: Meaning in work affects teachers’ technology integration.

H6: Digital leadership affects teachers’ technology integration through meaning in work.

H7: Self-efficacy affects teachers’ technology integration through meaning in work.

3 Findings

The study results showed the outcome of several data analyses, which consisted of normality tests, Cronbach alpha values, measurement model analysis, structural models, and correlation tests.

3.1 Normality test

This study conducted a data normality test by examining the univariate and multivariate normality indices. Univariate distribution was evaluated based on outliers, skewness, and kurtosis values, while multivariate distribution was assessed through multivariate normality and the presence of outliers. In addition, data normality was determined by the critical ratio (CR) values for skewness and kurtosis. Data were considered normal, both univariately and multivariately, if the c.r. kurtosis value was <3 at a 5% significance level (Ulmann, 2021). As shown in Table 7, the CR values for skewness and kurtosis were < 3, indicating that the data met the criteria for univariate normality.

Table 7

Table 7. Results of the data normality test.

3.2 Cronbach alpha

Cronbach’s alpha, α, indicated the questionnaire’s reliability. The α value must be at least 0.7 or more to be accepted (Hair et al., 2012), as shown in Table 8.

Table 8

Table 8. Cronbach’s alpha level of reliability.

In this study, all questionnaire items from each variable met Cronbach’s alpha reliability criteria because the α value was more than 0.6, as shown in Table 9.

Table 9

Table 9. Cronbach’s alpha results in the study.

3.3 Measurement model

The measurement model represented the relationship between the values of observed indicator variables and unobserved latent variables through confirmatory factor analysis (CFA), which assesses the indicator variable’s validity. The indicator variables presented in Table 2 passed the validity test with CFA analysis in AMOS. In addition, the validity of the indicators was determined using the CFA test, with a criterion of CR value >1.96 and a probability (p-value) of <0.05. The validity results for each indicator in this study are presented in Table 10.

Table 10

Table 10. CR value and probability in the CFA test.

A CR value greater than 1.96 and a probability less than 0.05, as shown in Table 10, indicated that each indicator variable met the validity requirements and adequately reflected the study variables. Additionally, validity was assessed by examining the standardized loading estimate values (loading factors), with values greater than 0.5 considered acceptable, as presented in Table 11. The results demonstrated that the indicators for each research variable exceeded the required loading factor threshold, confirming the validity of each indicator.

Table 11

Table 11. Standardized loading estimate value.

Apart from validity testing, instrument reliability testing was also conducted using construct reliability and variance-extracted tests for each latent variable. The accepted threshold value (cut-off) for construct reliability testing was >0.70, while the recommended threshold value (cut-off) for variance extracted testing was >0.50. Based on Table 12, all instruments in this study met the reliability requirements from both construct reliability and variance extracted tests.

Table 12

Table 12. Construct reliability and variance extracted values.

3.4 Structural model

The structural model illustrated the relationships between latent variables, showing their position as either exogenous, intervening, or endogenous. The validity of the structural model was measured by the value of the GOF (goodness of fit) or model fit test by observing the achievement of conformity criteria indices and their cut-off values. These indices included GFI, AGFI, CMIN/DF, TLI, CFI, and RMSEA. GFI and AGFI served as references to depict the level of model fit within a range of values from 0 (poor fit) to 1.0 (perfect fit). In addition, GFI and AGFI values approaching 1.0 showed that the tested model had a good fit (Arbuckle, 2014). The results obtained in this study, with a GFI value of 0.888 and AGFI of 0.840, showed that the model could be considered a good fit. CMIN/DF and TLI served as indicators to measure the level of model fit in the study, with criteria of CMIN/DF ≤ 2.0 and TLI ≥ 0.95 (Fogarty et al., 2020). The analysis results of this study indicated a CMIN/DF value of 2.248 and a TLI value of 0.917, thereby meeting the acceptance criteria for the model. The next two criteria determining the level of model acceptance were (a) CFI with a criterion value ranging from 0 to 1, where the closer the value was to 1, the higher the level of model acceptance, and (b) RMSEA with a criterion of ≤0.08. In this study, CFI was obtained as 0.933 and RMSEA as 0.081, both of which showed a high level of model acceptance, as presented in Table 13.

Table 13

Table 13. Goodness of fit indices.

3.5 Correlation/hypothesis test

Hypothesis testing in SEM using AMOS was conducted by examining the CR and p-values in the output regression weights table. A hypothesis was considered accepted if the CR value exceeded 1.96 and the p-value was less than 0.05. The strength of the relationships between variables was determined by the estimated values. The results of the hypothesis testing are presented in Table 14.

Table 14

Table 14. Regression weight output.

Based on Table 14, all proposed hypotheses were accepted because the CR value was >1.96 and p < 0.05, with the following description:

a. Hypothesis 1, which posited that digital leadership affected teachers’ sense of meaning in work, was accepted and had a significant correlation because the CR value was 2.078 and p < 0.005.

b. Hypothesis 2, which posited that digital leadership affected teachers’ technology integration, was accepted and had a significant correlation because the CR value was 3.316 and p < 0.005.

c. Hypothesis 3, which posited that self-efficacy affected meaning in work, was accepted and had a significant correlation because the CR value obtained was 4.147 and p < 0.005.

d. Hypothesis 4, which posited that self-efficacy affected teachers’ technology integration, was accepted and had a significant correlation because the CR value obtained was 3.487 and p < 0.005.

e. Hypothesis 5, which posited that meaning in work affected teachers’ technology integration, was accepted and had a significant correlation because the CR value obtained was 3.081 and p < 0.005.

f. Hypothesis 6, which posited that digital leadership affected teachers’ technology integration through meaning in work, was declared acceptable and correlated significantly because the CR value >1.96 and p < 0.005.

g. Hypothesis 7, which posited that self-efficacy affected teachers’ technology integration through meaning in work, was declared accepted and had a significant correlation because the values obtained were CR > 1.96 and p < 0.005.

4 Discussion

The results obtained provided answers to the seven developed hypotheses and were summarized below:

4.1 Digital leadership affects meaning in work and teachers’ technology integration

The results showed that school principals’ digital leadership significantly affected teachers’ sense of meaning in work and technology integration. Furthermore, school principals who could affect, guide, and motivate teachers to develop and implement education and teaching in the digital era had an impact on improving teachers’ performance in fulfilling the learning needs of Generation Z. The characteristics of Generation Z, who were closely connected to technology, required a more modern approach to the learning process. Therefore, teachers must become more tech-savvy and accustomed to integrating technology into teaching. This demand was easier to fulfill with support from the school, primarily through the digital leadership of the school principal. Several studies showed that organizational support, including support from the school principal, positively impacted the meaning of work (Akgunduz et al., 2018). Digital leadership also provided teachers with a real experience in using technology during teaching. This experience ultimately led to the emergence of meaning in work for teachers. Meaning in work was affected by experiences and the happiness experienced. Other studies also showed that digital leadership was correlated with improved work performance and employee well-being because employees performed tasks more easily with technology (Artuz and Bayraktar, 2021; Zeike et al., 2019).

This was also applicable to the field of education, where digital leadership significantly affected teachers’ happiness and work motivation due to the feeling of being assisted by technology facilities, openness, and flexibility in the teaching process supported by school principals. The results reinforced the idea that school principals’ digital leadership significantly impacted teachers’ technology integration. Encouragement from principals who understood the need for technology in the learning process affected the use of technology. This was supported by previous studies that identified various components, such as the curriculum, teachers’ training and competence development, organization, leadership, and school culture, as important factors for the success of technology integration (Adarkwah, 2021). According to previous studies, support from school principals, the availability of digital learning resources, and pedagogical, technical, and administrative support had significant influence (Cheng et al., 2020). School principals with digital leadership could build a digital culture in schools (Karakose et al., 2021). Therefore, digital leadership was an external factor and a focus on innovation in teaching by teachers, which ultimately impacted improving student achievement. Several studies showed that during the pandemic, the use of digital learning technology enhanced the quality of education (König et al., 2020b), specifically in improving competence and students’ satisfaction with the learning experience (Hasin and Nasir, 2021).

4.2 Self-efficacy affects meaning in work and teachers’ technology integration

The results showed that self-efficacy significantly affected the sense of meaning in work and teachers’ technology integration. Furthermore, self-efficacy, showing that teachers had belief and confidence in carrying out tasks, achieving goals, and solving problems, impacted the sense of meaning in work. This variable was closely related to respondents’ engagement in improving students’ achievement and school development. Higher levels of self-efficacy were associated with greater engagement and an increased likelihood of finding meaning in work (Van Wingerden and Van Der Stoep, 2018). Based on the results, engagement, as a consequence of self-efficacy, was an indicator to determine whether teachers found meaning in their work. Teachers’ self-efficacy could be used to determine the level of belief that the work was the center of their lives and provide a measure of the psychological identification with the profession. The stronger one’s belief in the value of their work, the more difficult it became for them to disengage from it, thereby imbuing the profession with greater significance.

In this study, self-efficacy also served as a driver to enhance teachers’ competence in the 21st century by integrating technology into the learning process. Furthermore, it was a crucial construct of incompetence due to the role in predicting the ability to adapt to technology-based teaching methods (König et al., 2020b). Teachers with high self-efficacy were more likely to adapt to technological advancements and master teaching technology as professionals during challenges. The level of pedagogical confidence and belief in the value of technology use could be a barrier to using technological tools in the classroom. This showed that self-efficacy significantly and positively affected technology integration, as reported in previous studies (Farjon et al., 2019). Similar results were also obtained by Clipa et al. (2023), which correlated with teachers’ behavior, self-efficacy, and IT skills.

4.3 Meaning in work affects teachers’ technology integration

Hypothesis 5, meaning in work affected teachers’ technology integration, was accepted and had a significant correlation as the CR value was 3.081 and p < 0.005. Meaning in work refers to individuals’ beliefs, motivation, and sense of purpose regarding their role in a job. In addition, it significantly impacted teachers’ technology integration in the educational context.

Teachers who perceived technology as having clear and relevant meaning in their work were more likely to be motivated to adopt and integrate it into their teaching practices. Meaning in work could reinforce perceptions of the value and relevance of technology in achieving learning goals, thereby enhancing the desire and engagement for its use as an effective learning tool.

The presence of meaning in work also helped overcome barriers that occurred in teachers’ technology adoption. When teachers felt that using technology contributed positively to achieving learning goals or enhancing students’ learning experiences, it alleviated their uncertainties or concerns (Fishman et al., 2016). Therefore, meaning in work could help create a supportive environment and motivate teachers to integrate technology into teaching.

4.4 Digital leadership affects teachers’ technology integration through meaningful in-work

Hypothesis 6, which posited that digital leadership affected teachers’ technology integration through meaning in work, was accepted and had a significant correlation as the CR value >1.96 and p < 0.005. In the context of technology integration by teachers, effective digital leadership had a positive impact through the moderating variable of meaning in work. When school principals created a work environment that provided clear meaning and purpose regarding using technology in learning, it strengthened the relationship between digital leadership and teachers’ technology integration (Sunu, 2022). Meaning in work can also drive teachers’ motivation and engagement in adopting and integrating innovations into their teaching practices (Schmitz et al., 2023). When educators felt that using technology could help achieve larger learning goals or add value to students’ learning experiences, it provided motivation (Suyatno et al., 2020).

The moderating variable of meaning in work could further strengthen the positive effect of digital leadership on teachers’ technology integration by reducing barriers to adoption. School principals who help teachers recognize the value and relevance of technology in their roles can help alleviate uncertainty or concerns (Richardson et al., 2021). Consequently, educators became more receptive to adopting and integrating technology into their teaching practices.

4.5 Self-efficacy affects teachers’ technology integration through meaning in work

Hypothesis 7, which posited that self-efficacy affected teachers’ technology integration through meaning in work, was accepted and had a significant correlation as the CR value >1.96 and p < 0.005. In the context of technology integration by teachers, teachers’ self-efficacy in their ability to use technology had a significant effect. The moderating variable “meaning in work” strengthened the relationship between self-efficacy and technology integration. When educators had high self-confidence in their ability to use technology, the presence of meaning in work reinforced their motivation to integrate technology into teaching practices (winter et al., 2021). When teachers felt that technology could help achieve larger learning goals or provide added value to students’ learning experiences, self-confidence for adoption was often further strengthened.

The moderating variable of meaning in work also helped reduce barriers to technology integration among teachers. When teachers perceived that technology had clear relevance to their work, it helped them overcome uncertainty or concerns (Kimmons et al., 2020). With strong support from the work environment, which provided a clear sense of meaning and purpose related to using technology in learning, educators were more confident and motivated to face challenges and overcome barriers during the adoption process.

5 Conclusion

This study aimed to test a conceptual model examining the mediating role of the meaning of the work variable in the relationship between digital leadership and self-efficacy with teachers’ technology integration. The results indicated that digital leadership, self-efficacy, and the meaning in work significantly influenced teachers’ technology integration. The results also showed that digital leadership not only affected the level of meaning in teachers’ work but also directly influenced technology integration, while self-efficacy had a direct effect on both aspects. In addition, the meaning in work directly impacted teachers’ technology integration and acted as a mediator between digital leadership and self-efficacy in shaping technology integration.

The study findings had specific benefits: First, for teachers, understanding the mediating role of meaning in work could help them recognize the importance of feeling purposeful and valued, which, in turn, enhanced their technology integration.

Training programs or activities that foster a sense of meaning in work could boost their motivation and effectiveness. Second, educational staff could design more effective training programs by considering the role of digital leadership and meaning in work. Such programs would integrate components that promote digital leadership and strengthen meaning in work, ultimately supporting technology integration. Third, policymakers could use these insights to develop policies that promote strong digital leadership, reinforce teachers’ sense of meaning in their work, and enhance self-efficacy through training and resource support.

Furthermore, recognizing that meaning in work influences technology integration suggests that policies could be directed toward allocating resources to programs focusing on technology training and increasing teacher motivation and job satisfaction. Finally, the government could develop more effective national or regional strategies to improve school technology integration. This would include creating action plans that incorporate digital leadership, capacity building for teachers, and enhancing a sense of meaning in work.

This study offers two key recommendations. First, the results showed that digital leadership influences both the meaning of teachers’ work and their ability to integrate technology. Therefore, principals should continuously develop and strengthen their digital leadership capacities. Consequently, the meaning of teachers’ work and their ability to integrate technology into learning could improve. Second, the results showed that self-efficacy plays a role in shaping the meaning of teachers’ work and technology integration. Schools should create supportive conditions and programs to increase teachers’ self-efficacy.

The current study focused solely on junior high school teachers in Balikpapan City, East Kalimantan Province, Indonesia. Consequently, the results cannot be generalized to other areas. Further quantitative studies involving a larger and more diverse sample of teachers across Indonesia are recommended. Furthermore, the data were collected during the COVID-19 pandemic, when online learning was rapidly implemented due to the outbreak. Many teachers were required to integrate technology into their teaching, despite lacking adequate preparation and training. In a developing country like Indonesia, challenges to technology integration in education persist, largely due to high costs and institutional unpreparedness. Therefore, future studies should be conducted under normal circumstances, allowing teachers to integrate technology into teaching without the constraints imposed by emergency situations such as the COVID-19 pandemic.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Universitas Ahmad Dahlan Yogyakarta Indonesia. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

DP: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. SS: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. WW: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. LP: Resources, Writing – review & editing, Supervision. AM: Writing – review & editing, Visualization, Validation, Resources, Formal analysis, Data curation, Conceptualization. NN: Writing – review & editing, Resources, Data curation.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Ministry of Education, Culture, Research, and Technology (grant no. 009/PDUPT/LPPM UAD/IV/2023).

Acknowledgments

The authors are grateful to the Ministry of Education, Culture, Research, and Technology (Kemendikbud-ristek), Universitas Ahmad Dahlan, Universitas Nahdlatul Ulama Surakarta, and STKIP Muhammadiyah Kuningan for supporting this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Educ. Inf. Technol. 26, 1665–1685. doi: 10.1007/s10639-020-10331-z

Crossref Full Text | Google Scholar

Ahmed, V., and Opoku, A. (2022). Technology supported learning and pedagogy in times of crisis: the case of COVID-19 pandemic. Educ. Information Technol. 27, 365–405.

Google Scholar

Akgun, S., and Greenhow, C. (2022). Artificial intelligence in education: addressing ethical challenges in K-12 settings. AI Ethics 2, 431–440. doi: 10.1007/s43681-021-00096-7

Crossref Full Text | Google Scholar

Akgunduz, Y., Alkan, C., and Gök, Ö. A. (2018). Perceived organizational support, employee creativity and proactive personality: the mediating effect of meaning of work. J. Hosp. Tour. Manag. 34, 105–114. doi: 10.1016/j.jhtm.2018.01.004

Crossref Full Text | Google Scholar

Aksal, F. A., and Gazi, Z. A. (2015). Examination on ICT integration into special education schools for developing countries. Turkish Online J. Educ. Technol. 14, 70–72.

Google Scholar

AlAjmi, M. K. (2022). The impact of digital leadership on teachers’ technology integration during the COVID-19 pandemic in Kuwait. Int. J. Educ. Res. 112, 1–10. doi: 10.1016/j.ijer.2022.101928

Crossref Full Text | Google Scholar

Allen, N. J., and Meyer, J. P. (1990). The measurement and antecedents of affective, continuance and normative commitment to the organization. J. Occup. Psychol. 63, 1–18.

Google Scholar

Alqahtani, A. Y., and Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19 pandemic: a comprehensive analysis of e-learning managerial perspectives. Education Sciences 10, 1–16. doi: 10.3390/educsci10090216

Crossref Full Text | Google Scholar

Al-Sharafi, M. A., Al-Emran, M., Arpaci, I., Iahad, N. A., AlQudah, A. A., Iranmanesh, M., et al. (2023). Generation Z use of artificial intelligence products and its impact on environmental sustainability: a cross-cultural comparison. Comput. Hum. Behav. 143:107708. doi: 10.1016/j.chb.2023.107708

Crossref Full Text | Google Scholar

Arbuckle, J. L. (2014). Amos 23.0 User’s Guide. Chicago: IBM SPSS. Ibm.

Google Scholar

Artuz, S. D., and Bayraktar, O. (2021). The effect of relation between digital leadership practice and learning organization on the perception of individual performance. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi 20, 97–120. doi: 10.46928/iticusbe.761479

Crossref Full Text | Google Scholar

Bass, B. M., and Riggio, R. E. (2018). The transformational model of leadership. Leading Organizations 2, 76–86.

Google Scholar

Botham, K. A. (2018). The perceived impact on academics’ teaching practice of engaging with a higher education institution’ s CPD scheme. Innovations in education and teaching international. Taylor & Francis, 55:164–175.

Google Scholar

Chan, K. S., and Zary, N. (2019). Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med. Educ. 5:e13930.

Google Scholar

Chen, X., Xie, H., Zou, D., and Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers Educ. 1:100002. doi: 10.1016/j.caeai.2020.100002

Crossref Full Text | Google Scholar

Cheng, S.-L., Lu, L., Xie, K., and Vongkulluksn, V. W. (2020). Understanding teacher technology integration from expectancy-value perspectives. Teach. Teach. Educ. 91:103062.

Google Scholar

Chiu, T. K. F. (2017). Introducing electronic textbooks as daily-use technology in schools: a top-down adoption process. Br. J. Educ. Technol. 48, 524–537.

Google Scholar

Chiu, T. K. F. (2022). School learning support for teacher technology integration from a self-determination theory perspective. Educ. Technol. Res. Dev. 70, 931–949. doi: 10.1007/s11423-022-10096-x

Crossref Full Text | Google Scholar

Chiu, T. K. F., and Churchill, D. (2016). Adoption of mobile devices in teaching: changes in teacher beliefs, attitudes and anxiety. Interact. Learn. Environ. 24, 317–327. doi: 10.1080/10494820.2015.1113709

Crossref Full Text | Google Scholar

Clipa, O., Delibas, C.-S., and Mâță, L. (2023). Teachers’ self-efficacy and attitudes towards the use of information Technology in Classrooms. Educ. Sci. 13:1001.

Google Scholar

Collier, J. E. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques : Routledge.

Google Scholar

Emerson, R. W. (2021). Convenience sampling revisited: embracing its limitations through thoughtful study design. J. Visual Impairment & Blindness 115, 76–77.

Google Scholar

Eickelmann, B., and Drossel, K. (2020). “SCHULE AUF DISTANZ. Perspektiven und Empfehlungen für den neuen Schulalltag” in Eine repräsentative Befragung von Lehrkräften in Deutschland (Düsseldorf: Vodafone Stiftung Deutschland).

Google Scholar

Farjon, D., Smits, A., and Voogt, J. (2019). Technology integration of pre-service teachers explained by attitudes and beliefs, competency, access, and experience. Comput. Educ. 130, 81–93. doi: 10.1016/j.compedu.2018.11.010

Crossref Full Text | Google Scholar

Fishman, B., Dede, C., and Means, B. (2016). “Teaching and technology: new tools for new times” in Handbook of Research on Teaching, vol. 5. (Casemate group), 1269–1334.

Google Scholar

Fogarty, H., Townsend, L., Ni Cheallaigh, C., Bergin, C., Martin-Loeches, I., Browne, P., et al. (2020). COVID19 coagulopathy in Caucasian patients. Br. J. Haematol. 189, 1044–1049. doi: 10.1111/bjh.16749

Crossref Full Text | Google Scholar

Gomez, F. C., Trespalacios, J., Hsu, Y. C., and Yang, D. (2022). Exploring teachers’ technology integration self-efficacy through the 2017 ISTE standards. TechTrends 66, 159–171. doi: 10.1007/s11528-021-00639-z

Crossref Full Text | Google Scholar

Hair, J., and Alamer, A. (2022). Partial least squares structural equation Modeling (PLS-SEM) in second language and education research: guidelines using an applied example. Res. Methods Appl. Ling. 1:27. doi: 10.1016/j.rmal.2022.100027

Crossref Full Text | Google Scholar

Hair, J. F., Sarstedt, M., Pieper, T. M., and Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning 45, 320–340.

Google Scholar

Hasin, I., and Nasir, M. M. K. (2021). The effectiveness of the use of information and communication technology (ICT) in rural secondary schools in Malaysia. J. Educ. E-Learn. Res. 8, 59–64. doi: 10.20448/JOURNAL.509.2021.81.59.64

Crossref Full Text | Google Scholar

Hsu, P. S. (2016). Examining current beliefs, practices and barriers about technology integration: a case study. TechTrends 60, 30–40. doi: 10.1007/s11528-015-0014-3

Crossref Full Text | Google Scholar

Karakose, T., Kocabas, I., Yirci, R., Papadakis, S., Ozdemir, T. Y., and Demirkol, M. (2022). The development and evolution of digital leadership: a bibliometric mapping approach-based study. Sustainability (Switzerland) 14, 1–26. doi: 10.3390/su142316171

Crossref Full Text | Google Scholar

Karakose, T., Polat, H., and Papadakis, S. (2021). Examining teachers’ perspectives on school principals’ digital leadership roles and technology capabilities during the covid-19 pandemic. Sustainability (Switzerland) 13, 1–20. doi: 10.3390/su132313448

Crossref Full Text | Google Scholar

Khatoony, S., and Nezhadmehr, M. (2020). EFL teachers’ challenges in integration of technology for online classrooms during coronavirus (COVID-19) pandemic in Iran. Asian J. English Lang. Pedagogy 8, 89–104. doi: 10.37134/ajelp.vol8.2.7.2020

Crossref Full Text | Google Scholar

Kimmons, R., Graham, C. R., and West, R. E. (2020). The PICRAT model for technology integration in teacher preparation. Contemp. Issues Technol. Teach. Educ. 20, 176–198.

Google Scholar

König, J., Bremerich-vos, A., Buchholtz, C., Glutsch, N., König, J., Bremerich-vos, A., et al. (2020a). General pedagogical knowledge, pedagogical adaptivity in written lesson plans, and instructional practice among preservice teachers teachers. J. Curric. Stud. 52, 800–822. doi: 10.1080/00220272.2020.1752804

Crossref Full Text | Google Scholar

König, J., Jäger-Biela, D. J., and Glutsch, N. (2020b). Adapting to online teaching during COVID-19 school closure: teacher education and teacher competence effects among early career teachers in Germany. Eur. J. Teach. Educ. 43, 608–622. doi: 10.1080/02619768.2020.1809650

Crossref Full Text | Google Scholar

Lam, S. F., Cheng, R. W. Y., and Choy, H. C. (2010). School support and teacher motivation to implement project-based learning. Learn. Instr. 20, 487–497. doi: 10.1016/j.learninstruc.2009.07.003

Crossref Full Text | Google Scholar

Lee, S. (2015). A concept analysis of “meaning in work” and its implications for nursing. J. Adv. Nurs. 71, 2258–2267. doi: 10.1111/jan.12695

Crossref Full Text | Google Scholar

Li, K., Li, Y., and Franklin, T. (2016). Preservice teachers’ intention to adopt technology in their future classrooms. J. Educ. Comput. Res. 54, 946–966.

Google Scholar

Narvaez Rojas, C., Alomia Peñafiel, G. A., Loaiza Buitrago, D. F., and Tavera Romero, C. A. (2021). Society 5.0: a Japanese concept for a superintelligent society. Sustainability (Switzerland) 13, 1–16. doi: 10.3390/su13126567

Crossref Full Text | Google Scholar

Nasir, M. K. M. (2020). The influence of social presence on students’ satisfaction toward online course. Open Praxis 12:485. doi: 10.5944/openpraxis.12.4.1141

Crossref Full Text | Google Scholar

Nelson, M. J., Voithofer, R., and Cheng, S.-L. (2019). Mediating factors that influence the technology integration practices of teacher educators. Comput. Educ. 128, 330–344.

Google Scholar

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., and Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Educ. Inf. Technol. 28, 4221–4241. doi: 10.1007/s10639-022-11316-w

Crossref Full Text | Google Scholar

Ouyang, F., and Jiao, P. (2021). Artificial intelligence in education: the three paradigms. Comput. Educ. 2:20. doi: 10.1016/j.caeai.2021.100020

Crossref Full Text | Google Scholar

Richardson, J. W., Watts, J. L. D., and Sterrett, W. L. (2021). Challenges of being a digitally savvy principal. J. Educ. Adm. 59, 318–334.

Google Scholar

Schmitz, M.-L., Antonietti, C., Consoli, T., Cattaneo, A., Gonon, P., and Petko, D. (2023). Transformational leadership for technology integration in schools: empowering teachers to use technology in a more demanding way. Comput. Educ. 204:104880.

Google Scholar

Steger, M. F., Dik, B. J., and Duffy, R. D. (2012). Measuring meaningful work: the work and meaning inventory (WAMI). J. Career Assess. 20, 1–16. doi: 10.1177/1069072711436160

Crossref Full Text | Google Scholar

Sunu, I. G. K. A. (2022). The impact of digital leadership on teachers’ acceptance and use of digital technologies. Mimbar Ilmu 27, 311–320. doi: 10.23887/mi.v27i2.52832

Crossref Full Text | Google Scholar

Suyatno, S., Hasanah, E., Wantini, W., Pambudi, D. I., and Supardi, S. (2021). Meaning in work of indonesian teachers: a phenomenological research. Qual. Rep. 26, 3189–3213. doi: 10.46743/2160-3715/2021.4970

Crossref Full Text | Google Scholar

Suyatno, S., Pambudi, D. I., Amurdawati, G., Wantini, W., and Ningrum, M. V. R. (2020). Factors affecting the meaning in life of teachers. Pedagogika 139, 52–72. doi: 10.15823/p.2020.139.3

Crossref Full Text | Google Scholar

Ulmann, K. (2021). Sustainable development in West and East Slavic countries: a multivariate statistical approach. Zeszyty Studenckie Wydziału Ekonomicznego Uniwersytetu Gdańskiego “NASZE STUDIA” 91–103.

Google Scholar

Van Wingerden, J., and Van Der Stoep, J. (2018). The motivational potential of meaningful work: relationships with strengths use, work engagement, and performance. PLoS One 13, 1–11. doi: 10.1371/journal.pone.0197599

Crossref Full Text | Google Scholar

Winter, E., Costello, A., O’Brien, M., and Hickey, G. (2021). Teachers’ use of technology and the impact of Covid-19. Ir. Educ. Stud. 40, 235–246.

Google Scholar

Zeike, S., Bradbury, K., Lindert, L., and Pfaff, H. (2019). Digital leadership skills and associations with psychological well-being. Int. J. Environ. Res. Public Health 16, 1–12. doi: 10.3390/ijerph16142628

Crossref Full Text | Google Scholar