According to the theoretical approaches, requirements for participation in professional development include, among other things, teachers’ motivation and their prior knowledge (Clarke and Hollingsworth, 2002; Lipowsky and Rzejak, 2017). These aspects are particularly relevant in the present study and will be examined more closely. When it comes to willingness and requirements for participation in professional development, various aspects have been empirically studied – for example, in relation to years of teaching experience (Hauk et al., 2022), perceived practical relevance (Richter et al., 2019), satisfaction with previous training quality (Richter et al., 2018), or specific interest in the topic (Richter et al., 2019). Other motivating factors include career benefits (Cramer et al., 2019), the desire for social interaction, or personal development, with the latter two being positively correlated with beliefs in the effectiveness of training and perceived benefits (Hauk et al., 2022; Rzejak et al., 2014). Additionally, factors beyond personal disposition, such as travel time to the training location, the role of the school staff, and information availability, can also play a role (Richter and Vigerske, 2011).

Beyond individual interests and motivational factors related to professional development participation and topic selection, broader connections can be found in relation to school-type-specific requirements. For instance, Rotermund et al. (2017) found that primary and middle school teachers were more likely to participate in training on topics such as English as a Second Language or classroom management, whereas there were no significant differences in participation in digitalization-related topics compared to high school teachers. Some studies also indicate a slight trend that older teachers more frequently select training programs related to digitalization (Annemann et al., 2025; Rotermund et al., 2017). International comparisons show that, particularly in Germany, there are deficiencies in the participation in digitalization-related professional development and that German teachers tend to rate their digital competencies lower (Annemann et al., 2025).

Regarding the effectiveness of professional development, both international and German-speaking research have identified a number of overarching aspects that contribute to successful or effective training. However, the question of what constitutes a “successful” training is complex and depends on the criteria used – such as short- or long-term learning outcomes, changes in behavioral routines, or satisfaction with the training (Fussangel et al., 2016). It is generally assumed that the benefits of time-limited or “one-shot” professional development events are relatively low (Garet et al., 2001; Lipowsky and Rzejak, 2017), and the long-term effectiveness of teacher training is also questionable and insufficiently studied (Kraft et al., 2018; Wolf and Peele, 2019). Furthermore, professional development is associated with teachers’ motivational regulation and self-efficacy (DePiper et al., 2021; Liu et al., 2025; Trautner et al., 2025).

Motivational regulation refers to individuals’ deliberate efforts to manage their motivation in order to engage with and complete learning activities and is defined as “activities through which individuals purposefully act to initiate, maintain, or supplement their willingness to start, to provide work toward, or to complete a particular activity or goal” (Wolters, 2003, p. 191). Theoretically, motivational regulation is grounded in self-determination theory, which emphasizes processes of internalization and integration that support self-regulated forms of motivation (Ryan and Deci, 2000; Zhang and Liu, 2019), and in models of self-regulated learning that conceptualize motivational regulation as a dynamic process involving motivational monitoring and strategic control (Boekaerts, 1997; Kwok et al., 2022; Pintrich, 2004). Empirical research shows that motivational regulation is positively associated with learning engagement and performance across various learning environments (Mekler et al., 2017; Zhang and Liu, 2019). In professional learning contexts, motivational regulation has been found to depend on motivational beliefs, particularly the perceived professional relevance of learning activities, which promotes active motivation regulation and sustained learning engagement (Erol and Kurt, 2017; Zhang and Liu, 2019).

The concept of self-efficacy refers to an individual’s belief in their ability to cope with various challenges in everyday life and in professional contexts (see section 3.4; Bandura, 1997). In addition to mastery experiences, both accumulated experience and the acquisition of competencies can positively influence a person’s self-efficacy beliefs (Schüle et al., 2017). From the perspective of the offer-and-use model (Lipowsky and Rzejak, 2017), however, (digital) self-efficacy expectations should more appropriately be understood as a predictor of teachers’ engagement and learning within professional development contexts. A similar reasoning holds for the relationship between training and job satisfaction, which tends to be positive (Yoon and Kim, 2022). Looking at potential changes following digitalization-focused training, it becomes evident that in addition to increased digital self-efficacy, participation in digital or digitalization-focused training also positively impacts the integration of media in the classroom (Bremer and Antony, 2017; Gerick et al., 2017; Pan and Franklin, 2011; Yurkofsky et al., 2019).

Various terms (e.g., digital competencies, digital literacy, media literacy), definitions, and frameworks exist to describe digital competence, each emphasizing different aspects. A widely used definition is provided by Hobbs (2010), who describes “digital and media literacy” as the “full range of cognitive, emotional and social competencies that includes the use of texts, tools and technologies; the skills of critical thinking and analysis; the practice of message composition and creativity; the ability to engage in reflection and ethical thinking; as well as active participation through teamwork and collaboration” (p. 17).

In teacher education research, digital competencies can be conceptualized within broader frameworks of professional competence. According to Baumert and Kunter (2006), teachers’ professional competence comprises professional knowledge, beliefs and values, motivational orientations, and self-regulation. While this model is not specific to digital contexts, its components provide a useful structure for understanding that technology integration depends not only on knowledge but also on teachers’ beliefs and motivational readiness.

Another established framework regarding teacher competencies is the TPACK model, which focuses primarily on the didactic use of media (Harris and Hofer, 2011). It distinguishes between pedagogical content knowledge (knowledge of teaching methods for specific content), technological pedagogical knowledge (knowledge of how technology supports learning processes), and technological content knowledge (knowledge of how subject content can be represented, conveyed, and worked on using digital technologies) (see Mishra and Koehler, 2006; Shulman, 1986). The TPACK model also forms the basis for measurement instruments used and refined in numerous studies (Schmidt et al., 2009; Voogt et al., 2013). In the present study, the TPACK model is used to assess teachers’ digitalization-related knowledge (competencies), as it provides an established framework for capturing pedagogical, technological, and content-related dimensions of technology-integrated teaching.

In STEM education, subject-specific requirements gain particular importance. Digitalization affects not only instructional tools but also the methods and approaches of scientific inquiry. The DiKoLAN framework (e.g., von Kotzebue et al., 2021) specifies science-specific digital competence areas such as data acquisition, data processing, and simulation/modelling, and explicitly relates them to the components of TPACK. This highlights that scientific knowledge production in digitalized laboratory and field contexts requires the integration of technological, pedagogical, and subject-specific understanding. Related operationalizations such as MaSter-Bio (Mahler and Arnold, 2022) assess prospective biology teachers’ academic self-concept regarding technological pedagogical content knowledge (TPACK), thereby indicating the need to consider subject-specific refinement for biology teaching beyond general technology skills.

Empirical research generally shows differences in self-assessed digital competencies by age, gender, and teaching subject. Female teachers tend to rate their competencies lower (Grande-de-Prado et al., 2020; Jiménez-Hernández et al., 2020; Palacios Rodríguez et al., 2023), as do older teachers (Jiménez-Hernández et al., 2020; Palacios Rodríguez et al., 2023; Zhao et al., 2021). In contrast, STEM teachers report significantly higher self-assessments of digital competence compared to teachers of other subjects (Ghomi and Redecker, 2019), although differences exist within STEM disciplines as well (Vieira et al., 2023).

Building on the constructs of digital competence and attitudes toward ICT integration discussed above, teachers’ digital self-efficacy represents a key requirement for successful teaching in the digital context (Peng et al., 2024; Ulfert-Blank and Schmidt, 2022). Digital self-efficacy refers to teachers’ confidence in their own ability to use digital technologies meaningfully and successfully in the planning, implementation, and reflection of technology-supported teaching and learning processes (Garzon and Garzon, 2023). Rooted in social cognitive theory, Bandura (1997) defined self-efficacy beliefs as “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” (p. 3).

Teachers’ self-efficacy refers to teachers’ confidence in their own ability to plan, implement, and reflect on teaching and learning processes in order to achieve specific educational goals (Yang and Du, 2024). Teachers with high self-efficacy exhibit greater job satisfaction, engagement, professional commitment, and resilience to stress (Aloe et al., 2014; Bach, 2022; Lazarides and Warner, 2020; Zee and Koomen, 2016) and are less susceptible to burnout (Aloe et al., 2014; Daniilidou et al., 2020; Zee and Koomen, 2016). Building on the general understanding of self-efficacy, digital self-efficacy is defined as teachers’ confidence in their own abilities to use digital technologies meaningfully and successfully during the planning, implementation, and reflection of technology-supported teaching and learning processes (Garzon and Garzon, 2023; Yang and Du, 2024).

Empirically, digital self-efficacy has been linked to several aspects of teachers’ instructional practice. Empirical findings suggest that the frequency of media use in the classroom correlates with teachers’ digital self-efficacy expectations (Hatlevik and Hatlevik, 2018; Li et al., 2018; Paetsch et al., 2023; Peng et al., 2024). Moreover, a positive relationship exists between digital self-efficacy and teachers’ digital competencies (Paetsch et al., 2023; Peng et al., 2024; Ulfert-Blank and Schmidt, 2022; Wang and Chu, 2023; Zeng et al., 2022). Moreover, Zeng et al. (2022) conducted a meta-analysis which found a moderate positive correlation between teachers’ information technology integration self-efficacy and TPACK. While constructs such as TPACK primarily reflect teachers’ perceived knowledge regarding the integration of digital tools, digital self-efficacy emphasizes the belief in one’s capability to apply such knowledge effectively in practice. Thus, self-efficacy serves as a motivational bridge between competence and behavior.

Digital self-efficacy plays a crucial role in the professional development of STEM teachers, as it significantly influences their willingness and ability to integrate technology into teaching and to design effective, technology-enhanced learning environments (Ertmer and Ottenbreit-Leftwich, 2010; Liu et al., 2025). However, teachers often lack professional knowledge of digital technologies and design thinking, as these are rarely part of their initial training (Eriksson et al., 2018). Accordingly, studies highlight the importance of professional development aimed at strengthening digital competencies in order to enhance teachers’ self-efficacy in digital teaching and improve curriculum quality (Garzon and Garzon, 2023; Sehar and Alwi, 2023; Shi et al., 2025). Liu et al. (2025) further demonstrate in their meta-analysis that professional development has a moderately positive effect on the self-efficacy of STEM teachers. Teachers participating in targeted and practice-oriented training report higher levels of digital and technological self-efficacy, especially when programs are hands-on and application-based. Longer and more intensive training phases produce stronger effects, whereas very large group formats tend to be less effective. Non-traditional approaches such as workshops or communities of practice have proven particularly successful, as they foster collaborative learning and immediate application. Overall, the literature emphasizes that continuous, practice-based professional development is crucial for strengthening STEM teachers’ digital self-efficacy and enabling them to integrate technology effectively into their teaching practice.

As in other areas of self-efficacy (Martin et al., 2008; Tschannen-Moran and McMaster, 2009; Zhang et al., 2021), digital self-efficacy expectations are also related to participation in media-related professional development (Drossel and Eickelmann, 2017; Kao et al., 2011), although the direction of the effect is not always clear – self-efficacy is modeled both as a predictor and as a dependent variable. Professional development that targets both digital competencies and self-efficacy can therefore enhance teachers’ confidence to implement technology-supported instruction. And given the established link between self-efficacy and teachers’ willingness to engage in professional development, digital self-efficacy is assumed to play a key role in shaping participation patterns and perceived outcomes of media-related training.

Compared to digital competence and self-efficacy, other areas such as interest in ICT (Information and Communication Technologies) are less researched. ICT interest is conceptualized as a relatively enduring predisposition to engage with ICT-related topics, tasks, or activities, typically accompanied by positive affect and value-related evaluations (Goldhammer et al., 2016; Krapp, 2005). Examining ICT interest is relevant because it is closely linked to learning motivation, and higher levels of interest can foster the development of competencies in the respective domain (Eccles and Wigfield, 2002; Krapp, 2005). Empirical studies have shown positive, though mostly modest, correlations between ICT interest and media-related competencies (Christoph et al., 2015; Fraillon et al., 2014; Juhaňák et al., 2019; Rubach and Lazarides, 2019). Only limited findings exist for teachers, suggesting that (prospective) female teachers report lower ICT interest (Besa et al., 2021; Fraillon et al., 2014).

Beyond general interest, teachers’ responses to the instructional use of digital media can be described as ICT-response beliefs, referring to their evaluations of the pedagogical usefulness of digital technologies in classroom practice. Such beliefs are associated with teachers’ actual use of digital media and their willingness to integrate technology into teaching (Ertmer and Ottenbreit-Leftwich, 2010; Scherer et al., 2019), and more positive beliefs correspond to more frequent and pedagogically meaningful technology integration (Petko, 2012).

While ICT interest reflects a motivational disposition and ICT-response beliefs refer to instructional evaluations, technology acceptance addresses more general beliefs about the usefulness and usability of digital technologies. According to general attitude–behavior models, such evaluations influence intentions and, consequently, behavior (Fishbein and Ajzen, 1975). In technology-related contexts, these beliefs are commonly specified within the Technology Acceptance Model (TAM; Davis et al., 1989), which identifies perceived usefulness and perceived ease of use as key determinants of technology use and has been widely applied in educational research to explain teachers’ use of digital media (Mailizar et al., 2021; Weng et al., 2018; Wong et al., 2012).

Empirical studies examining teachers’ integration provide mixed evidence regarding the role of sociodemographic characteristics. Studies generally show no significant gender differences in technology acceptance among teachers (Cavas et al., 2009; Islahi and Nasrin, 2019; Pamuk and Peker, 2009; Semerci, 2018). In studies involving other population groups, men tend to show slightly more positive attitudes, though the effect sizes are small (Cai et al., 2017; Siddiq and Scherer, 2019). Regarding age, Cavas et al. (2009) found that teachers aged 20–35 had significantly more positive attitudes toward ICT integration than those in the 36–49 or 50 + age groups. Studies examining moderating effects of age or professional status (pre-service vs. in-service teachers) on TAM variables reported no to small effects (Martín-García et al., 2019; Sánchez-Mena et al., 2017; Scherer et al., 2019). As for differences based on subject area, no empirically confirmed statements can be made. Barak (2014) compared attitudes toward ICT integration in two cohorts of STEM teachers (2006 vs. 2012) without comparing them to other subject groups.

In the present study, these evaluative orientations toward digital technologies are differentiated into two related but distinct constructs: ICT-response beliefs, referring to teachers’ evaluations of the pedagogical usefulness of digital media in classroom practice, and technology acceptance and competence beliefs, reflecting more general beliefs about the usefulness of digital technologies and one’s capability to work with them.

A total of N = 1031 (prospective) teachers from the German state of North Rhine-Westphalia participated in the current study. On average, they were 42 years old (SD = 11.46 years; age_min = 22 years, age_max = 65 years). 68.7% of the teachers were female. While 988 teachers (95.8%) stated that they had completed their traineeship, 14 (1.4%) were currently completing their traineeship or had not completed it. A total of 32 teachers (3.1%) reported a lateral or side entry. The average professional experience of the teachers surveyed was around 16 years (SD = 11.60 years; professional experience_min = 0 years, professional experience_max = 41 years). A summary of the sample composition can be found in Table 1.

The majority of teachers teach at an elementary school (37.9%). In addition, large numbers of teachers work at higher track secondary schools (“Gymnasium” = 24.3%, “Gesamtschule” = 18.6%). 16% teach at lower secondary schools (“Realschule” = 4.8%, ‘Hauptschule’ = 8.1%, “Sekundarschule” = 3.1%). A further 2.6% of teachers work at a vocational college.

While 479 teachers did not study a STEM subject (46.5%) and 485 teachers studied one STEM subject (47.0%), 62 teachers stated that they studied two STEM subjects (6.0%). Three and two teachers surveyed reported studying three and four STEM subjects, respectively. For the following analyses, the teachers are differentiated into the groups “No STEM subject”, “One STEM subject” and “Min. two STEM subjects”. For further analysis steps, the teachers were also asked about their participation in various professional development programs within the last 2 years. According to their answers, the teachers were divided into the groups “Professional development related to digitalization” (n = 777; 78.7%), “Professional development not related to digitalization” (n = 132; 13.4%) and “No Professional development” (n = 78; 7.9%). This study was conducted using a cross-sectional study design. At one measurement point in 2024, a questionnaire study was conducted in online format with teachers from the region of East Westphalia (North Rhine-Westphalia) in Germany. The teachers were recruited for participation via the Bielefeld school network, and the survey was conducted using the survey software SoSci Survey.

A short scale on general self-efficacy (Beierlein et al., 2012) adapted to the use of digital media was applied to measure the digital self-efficacy of the participating teachers. This scale also comprised three items. Two subscales of the questionnaire by Stinken-Rösner (2021) and Stinken-Rösner et al. (2023) were used to measure the teachers’ digitalization-related knowledge. Teachers’ technological and pedagogical knowledge (TPK) was assessed using four items, while five items were included to record technological and pedagogical content knowledge (TPACK). In addition, the teachers’ ICT-response beliefs were measured using seven items from the Schmidt and Reintjes (2020) scale. To check the factor structure of these four scales, a principal component analysis was performed. The factor analysis was conducted exclusively with the four scales on Digital Self-Efficacy, TPACK, TPK, and ICT-Response Beliefs, which can be justified from the perspective of construct validity. Only these four scales were examined using factor analysis because, both theoretically and based on an item analysis, they represent the same (or closely related) constructs, and their internal structure must be examined to ensure construct validity. While the four analyzed scales focus on competence beliefs and professional knowledge in the context of digital teaching, motivational regulation refers to learning- or work-related self-regulation processes, and technology acceptance concerns attitudes and behavioral intentions toward technologies. Although these constructs are theoretically related, they do not belong to the same latent structure. Therefore, they were not included in the factor analysis. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.79, and Bartlett’s test of Sphericity was significant (p < 0.001). Solely factors with an eigenvalue ≥ 1 were included. The exploratory factor analysis indicated a four-factor structure in line with theory, explaining 65.20% of the total variance (Table 2).

The teachers’ motivational regulation with regard to teaching was assessed using three items of the Intrinsic Regulation subscale from the Motivational Regulation in Learning Scale (SMR-L, Thomas and Müller, 2016), adapted to the context. The short scale for measuring technology readiness (Neyer et al., 2012) was used to assess teachers’ technology acceptance and technology competence beliefs.

All items/scales were measured using a five-point rating scale (1 = strongly disagree to 5 = strongly agree). The internal consistencies, measured using Cronbach’s alpha, were in a satisfactory range for all (sub)scales (Taber, 2018; Table 3).

Several univariate and multivariate analyses of variance (ANOVA, MANOVA) were conducted to compare teachers with and without a STEM subject in terms of their motivational regulation, digital self-efficacy, digitalization-related knowledge, ICT-response beliefs, technology acceptance and technology competence beliefs in the classroom (research question 1). ANOVAs and MANOVAs were also used to compare teachers who participated in different training formats with and without a focus on digitalization and teachers who did not participate in training courses with regard to the variables mentioned (research question 2).

In addition to testing for normal distribution of the variables using Shapiro–Wilk tests, Levene tests were carried out to test variance homogeneity. These requirements were found to be fulfilled. Subsequent Bonferroni-corrected posthoc tests were used to examine differences between the described groups of teachers with regard to their subject composition and participation in professional development. Chi-square procedures were used to examine the distribution of these teachers among the different groups described in terms of their gender, school type and subjects taught (research question 2).

Bivariate correlation analyses were performed to investigate the relationships between the variables examined (motivational regulation, digital self-efficacy, digitalization-related knowledge and ICT-response beliefs, technology acceptance and competence beliefs, age, professional experience).

The univariate analyses of variance revealed no significant differences between teachers with different numbers of STEM subjects in terms of motivational regulation and digital self-efficacy. Teachers in all groups reported a rather high level of motivational regulation in relation to teaching and a high level of digital self-efficacy.

With regard to digitalization-related knowledge, no significant differences were found for the teachers’ technological-pedagogical knowledge. Significant differences with small effect sizes were evident for the teachers’ technological-pedagogical content knowledge (TPACK) (question 1b). The subsequent Bonferroni-corrected posthoc tests showed that teachers who do not teach a STEM subject differ significantly from those who teach a STEM subject (p < 0.05, M_diff = 0.16, 95%-CI[0.00, 0.31]).

No significant differences were found in the comparison of teachers with regard to their ICT-response beliefs and their technology acceptance and competence beliefs (see Table 4).

The results show that, for the most part, there are no significant differences between teachers with and without STEM subjects in terms of their digitalization-related skills and beliefs. Only with respect to their self-assessed TPACK do teachers without STEM subjects report a significantly lower level.

With regard to age and professional experience (question 2a), the univariate analyses of variance showed significant differences between the teachers who took part in different training formats or did not attend any training [age: F(2,983) = 10.57, p < 0.001, η² = 0.02; professional experience: F(2,982) = 9.50, p < 0.001, η² = 0.02]. The subsequent posthoc tests showed that the teachers who took part in professional development related to digitalization were significantly younger than the teachers who took part in professional development not related to digitalization (p < 0.001, M_diff = 0.16, 95%-CI[0.00, 0.31]) and than the teachers who did not take part in professional development (p < 0.05, M_diff = 0.16, 95%-CI[0.00, 0.31]). In terms of professional experience, the posthoc test only revealed a significant difference between teachers who attended training with and without a digital focus. Teachers who took part in training with a focus on digitalization reported significantly less professional experience in comparison (p < 0.001, M_diff = 0.16, 95%-CI[0.00, 0.31]). Regarding teachers’ gender, school type, and subject taught the chi-square-tests revealed significant effects [gender: χ²(6,943) = 13.92, p < 0.05; school type: χ²(12,983) = 495,04, p < 0.001; subjects: χ²(4,987) = 10.71, p < 0.05]. The gender distribution across the groups with regard to training participation showed that the proportion of female teachers who took part in training courses related to digitalization was relatively higher than in the other training groups. Concerning the type of school, it can be seen that primary school teachers took part in training courses related to digitalization relatively frequently compared to the other training groups, while teachers at lower secondary schools attended a relatively high number of training courses without a focus on digitalization. The chi-square tests for the distribution of teachers with different subjects showed that teachers with a STEM subject took part in training courses with a focus on digitalization relatively frequently comparing the training groups.

The univariate analysis of variance also revealed significant differences between the groups of teachers described for motivational regulation [F(2,983) = 12.03, p < 0.001, η² = 0.02] (question 2b). Here, the posthoc analyses showed that the teachers who participated in professional development with and without reference to digitalization reported significantly higher motivational regulation than the teachers who did not participate in professional development (p < 0.001, M_diff = 0.52, 95%-CI[0.27, 0.78]; p < 0.001, M_diff = 0.53, 95%-CI[0.22, 0.84]). With regard to digitalization-related knowledge (question 2c), the multivariate analysis of variance did not reveal any significant differences for the TPK, while the teachers differed significantly with regard to their TPACK [F(2,972) = 28.68, p < 0.001, η² = 0.06]. The following post-hoc analyses showed that the teachers who took part in training courses without reference to digitalization differed significantly from both the teachers who took part in training courses with reference to digitalization and those who did not take part in training courses (p < 0.001, M_diff = 0.40, 95%-CI[0.17, 0.62]; p < 0.01, M_diff = 0.40, 95%-CI[0.06, 0.74]). Teachers who took part in digitalization training also differed from those who did not (p < 0.001, M_diff = 0.79, 95%-CI[0.51, 1.07]). The latter reported the lowest TPACK. In addition, significant differences were found with regard to the teachers’ digital self-efficacy (question 2d) [F(2,969) = 7.83, p < 0.001, η² = 0.02]. According to the posthoc tests, these findings indicate significant differences in the comparison of teachers who participated in training courses with and without a focus on digitalization (p < 0.001, M_diff = 0.37, 95%-CI[0.13, 0.60]). Teachers who attended training courses without reference to digital media reported the lowest digital self-efficacy.

No significant differences were found between the groups of teachers described for their ICT-response beliefs [F(2,978) = 2.59, p = ns] or for their acceptance of technology and conviction in technological competence for teaching [F(2,978) = 1.80, p = ns] (question 2e and f; Table 5).

The correlation analysis revealed several significant relationships among the key variables. Digital self-efficacy was positively and significantly associated with teachers’ reported TPK and TPACK scores, as well as with their ICT-related beliefs, technology acceptance, and competence beliefs. Furthermore, it was correlated with both teachers’ age and professional experience, indicating that these personal and career-related factors may play a role in shaping digital self-efficacy. Similarly, TPACK showed significant associations with ICT-response beliefs, technology acceptance, and competence beliefs, highlighting the interconnectedness of teachers’ technological knowledge, attitudes, and self-perceptions. ICT-response beliefs were significantly linked to teachers’ age and professional experience, suggesting that longer teaching experience and older age may influence how educators respond to ICT-related challenges. However, no significant correlations were found between motivational regulation and teachers’ ICT-related beliefs, indicating that motivational aspects may not directly influence these specific concerns. Overall, the results suggest that digital self-efficacy and TPACK are closely tied to a range of personal, professional, and attitudinal factors, while motivational regulation appears to operate independently of ICT-related worries. These findings are further illustrated in Table 6.