Assessing Digital Technologies’ Adoption in Romanian Secondary Schools

Abstract

Digital transformation is reshaping educational methods and necessitates new institutional, teaching, and learning approaches. In Romania’s secondary school system, this process is particularly complex due to infrastructural disparities, uneven digital competencies, and limited policy guidance. This study investigates the factors influencing Romanian secondary school teachers’ adoption of digital technologies, addressing a gap in the literature regarding localized teacher behavior within under-digitalized educational environments. Using the Technology Acceptance Model (TAM), this research explores perceived usefulness, perceived ease of use, behavioral intention, and perceived quality as determinants of digital adoption. The analysis was conducted on a sample of 430 teachers utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The key findings reveal that behavioral intention strongly predicts actual usage (β = 0.791, p < 0.001), and perceived ease of use exerts a greater influence than perceived usefulness. Furthermore, perceived quality plays a modest but significant mediating role in enhancing digital engagement. These results suggest that user-friendly, intuitive technologies and targeted professional training are essential for sustained adoption. This study contributes to the field by offering a context-specific understanding of technology’s acceptance in Eastern European education and by extending the TAM framework through the integration of perceived quality. It provides actionable insights for policymakers and school leaders seeking to promote sustainable and inclusive digital transformation in schools.

1. Introduction

The widespread digital transformation process has profoundly reshaped how secondary schools operate, how technology integrates into teaching and learning, and how students are prepared to navigate the complexities of the modern world. This transformation goes far beyond simply implementing digital tools. It necessitates a fundamental rethinking of instructional practices, a redefinition of the roles and responsibilities of educational stakeholders, and a continuous alignment with emerging pedagogical paradigms [1,2]. In this evolving academic landscape, teachers play a crucial role. Their ability to acquire and apply advanced pedagogical and digital competencies is vital for maximizing the benefits of technological integration. As Falloon [3] highlights, sustained professional development remains central to meaningfully embedding digital tools into instructional design. Educators who successfully harness technology to foster critical thinking and personalize learning environments significantly enhance student engagement and academic progress [4].

Students, the primary beneficiaries of digital transformation, increasingly depend on digital content, interactive platforms, and online resources that offer more flexible and individualized learning experiences [5]. However, this shift also presents ongoing challenges related to unequal access to digital infrastructure, which disproportionately impacts disadvantaged or marginalized communities [6]. Addressing these disparities requires a coordinated approach that includes targeted investments in teacher training, equitable policy-making, and a robust technological infrastructure [7].

The extensive process of digital transformation has profoundly changed how secondary schools operate, how technology is integrated into teaching and learning, and how students are prepared to navigate the complexities of the modern world. This transformation extends far beyond simply implementing digital tools. It requires a fundamental rethinking of instructional practices, a redefinition of the roles and responsibilities of educational actors, and an ongoing alignment with emerging pedagogical paradigms.

In this evolving educational landscape, teachers play a pivotal role. Their ability to acquire and apply advanced pedagogical and digital skills is crucial for maximizing the benefits of technological integration. At the same time, this transition is influenced by various barriers, such as unequal access to infrastructure, different levels of digital competence, and gaps in institutional support. Therefore, understanding how educators perceive and adopt digital technologies is essential for guiding future policy and practice.

This study addresses the following research question: What are the key factors influencing secondary school teachers’ adoption of digital technologies, and how do these factors interact within the framework of TAM? To answer this, we first explore the teachers’ perceptions of the usefulness and ease of use of digital tools. Next, we analyze their behavioral intentions and actual usage patterns. Finally, we assess how these perceptions influence perceived quality and a broader adoption of technology in educational settings.

The novelty of this research lies in its application of an extended TAM framework to the under-explored context of Romanian secondary education, utilizing empirical data from 430 teachers. By concentrating on behavioral intention, perceived quality, and usage patterns, this study provides original insights into how systemic and individual factors jointly shape digital adoption. Moreover, employing PLS-SEM offers a robust analytical foundation, delivering theoretical refinement and practical implications for digitally transforming schools in emerging education systems.

This paper unfolds in six main sections. Following this introduction, the literature review synthesizes recent research on digital education and Technology Acceptance Models. The materials and methods section details the research design, sampling strategy, and statistical approach. The results section presents the key findings, which are then interpreted and contextualized in the discussion. The article concludes by outlining implications for practice and directions for future research.

2. Literature Review

2.1. Digital Transformation in Education

In today’s rapidly evolving digital landscape, schools face increasing pressure to effectively redefine their educational paradigms to integrate digital technologies into teaching and learning. Successful adoption of these tools goes far beyond simple implementation; it involves a complex interplay of internal and external factors that shape how educators and students perceive and use technology. Almaiah et al. [8] emphasize that attitudes and behaviors surrounding digital adoption are heavily influenced by the perceived quality of the technology, institutional support, and individual technical readiness.

Cultural context further shapes how technology is understood and used in educational settings. In systems where traditional values such as teacher authority and face-to-face instruction prevail, digital tools are often seen as supplementary rather than transformative [9]. This indicates that meaningful integration requires more than just technical proficiency; it necessitates ongoing cultural and pedagogical adaptation to ensure that digital learning environments align with local educational values.

Digitalization presents significant opportunities for innovation in teaching methods, enhanced school administration, and the development of digital skills crucial for engagement in the global knowledge economy. As Simonette et al. [10] point out, a digitally enriched educational environment cultivates transferable skills like collaboration, critical thinking, and technological literacy that students increasingly need.

2.2. Teachers’ Digital Competencies and Institutional Support

School leadership contributes to this transformation. Yuliandari et al. [2] argue that school principals who actively promote a digital culture significantly influence the successful integration of educational technologies. When educational leaders articulate a clear vision and encourage innovation, they create a supportive organizational climate that empowers teachers to experiment with and refine digital teaching strategies [11,12]. In this context, teachers are not merely content deliverers—they become facilitators of digital literacy and role models for meaningful technology use [13]. Research shows that the likelihood of effective classroom implementation rises dramatically when teachers demonstrate confidence, enthusiasm, and skill in using digital tools [14]. However, successful digital leadership also depends on broader institutional variables, including school size, available infrastructure, and the digital expertise of educators [15]. These factors can either support or constrain the transformation process, underscoring the need for context-sensitive approaches to technology’s integration.

2.3. Barriers and Enablers of Educational Innovation

The COVID-19 pandemic acted as a catalyst, accelerating the shift to digital education and highlighting systemic gaps in digital competence across the teaching workforce. Beyond basic familiarity with digital platforms, effective teaching in a digital age requires a nuanced understanding of how technology enhances learning, promotes engagement, and supports assessment practices [16,17,18,19,20].

Curriculum reform has become a key priority in this environment. Integrating transferable competencies—such as creativity, critical thinking, digital literacy, and problem-solving—aligns the educational process with the demands of the digital era [21]. Demartini et al. [22] emphasize that incorporating data analytics and learning technologies into educational systems can significantly enhance decision-making processes, streamline resource allocation, and support the ongoing tracking of student progress. These digital tools improve institutional efficiency and contribute meaningfully to better educational outcomes.

Digital education emerges as a powerful force for democratizing access to learning. Tools such as open educational resources and mobile learning platforms, particularly when thoughtfully integrated into culturally inclusive curricula, offer a viable path toward addressing persistent disparities in educational access [23,24,25,26]. These technologies create alternative networks for learning that transcend physical and social limitations in areas where conventional schooling remains inaccessible, whether due to geographic isolation, economic hardship, or displacement [27,28].

However, genuine digital transformation involves more than just adopting new tools. It must be rooted in pedagogical adaptability, institutional resilience, and a coherent alignment between educational goals and technological capabilities. When institutions function without a clear strategic vision or the required infrastructure to support change, digital initiatives often stumble. Sustainable transformation hinges on cultivating a culture that values innovation and invests in continuous professional development for educators [8,29].

Despite its potential to reshape education, digitalization in secondary schools still faces significant barriers. Yuliandari et al. [2] note that many institutions struggle to balance integrating technology with maintaining meaningful human connections. Budget constraints clash with educators’ reluctance to adopt new methods, and uneven policy support often hinders progress [30]. Addressing these issues requires a collaborative approach that unites policymakers, schools, and private sector partners to develop inclusive, adaptable, and forward-looking digital strategies [31].

Empowering teachers with digital capabilities requires more than just training; it involves developing a mindset that embraces technology to create inclusive, reflective, and future-focused learning experiences. As Kane [32] argues, advancing education in the digital age necessitates collaboration across sectors, bringing together researchers, policymakers, and practitioners to co-design human-centered educational models where technology enhances rather than overshadows the learning process.

3. Materials and Methods

3.1. Research Design and Hypotheses

The modern educational landscape has been significantly reshaped by digital technologies, making their adoption by students and teachers a central focus of current research. Davis [33] originally developed the Technology Acceptance Model (TAM), which is often utilized in studies on user behavior in digital environments and provides a solid theoretical framework for exploring the motivations behind the adoption process. Perceived utility and ease of use shape users’ attitudes toward technology. However, experiential factors such as previous interactions with technology or psychological comfort with its use heavily influence these elements [34]. More recently, researchers have shown that the extent of one’s digital experience directly impacts one’s ability to assess the effectiveness of technology in educational contexts [35,36]. TAM has proven to be a valuable analytical tool for identifying the key factors that affect teachers’ decisions to adopt AI-based solutions in their instructional practices [8].

To address the research question (what are the key factors influencing Romanian secondary school teachers’ adoption of digital technologies, and how these factors interact within the framework of the Technology Acceptance Model), this study employs a quantitative, theory-driven methodology based on the TAM framework. The research aims to investigate how perceived usefulness (PU), perceived ease of use (PEU), behavioral intention, and quality perceptions influence the actual adoption of digital technologies in Romanian secondary schools.

This research employs a structured questionnaire aimed at secondary school teachers to gather empirical data pertinent to each variable in the model. The instrument was specifically crafted to assess TAM-related constructs and their subdimensions, ensuring that every aspect of the research question is comprehensively addressed. The methods for data collection and analysis are outlined in the following sections, which include variable operationalization, sampling design, and the statistical techniques used.

Perceived utility (PU) and perceived ease of use (PEU) are the two core characteristics of TAM [37]. Through PU, educators assess a technology’s potential to enhance their professional growth and the effectiveness of their instruction. Simultaneously, PEU reflects their perception of the technology’s user-friendliness, intuitiveness, and the minimal effort needed to integrate it into daily tasks. These fundamental concepts have evolved, with subsequent studies introducing additional explanatory elements such as adaptability, responsiveness, and creative levels [38,39]. These enhancements have strengthened TAM’s ability to represent the dynamics of technology adoption in modern learning environments. Building on this theoretical framework, we propose the following hypothesis:

Hypothesis H1.

Perceived usefulness (PU) and perceived ease of use (PEU) positively influence the behavioral intention to adopt digital educational technologies.

Examining the factors influencing PU and PEU is crucial for effectively applying TAM in educational contexts. Speed and innovation are particularly significant among the various qualities that define effective educational technologies. Speed enables educators and learners to access results effortlessly in high-pressure learning environments, where quick adaptation and sustained cognitive effort are essential. This immediacy reduces reaction times and facilitates timely, informed decisions. Conversely, innovation represents the transformative potential of emerging technologies, often challenging traditional teaching methods. This disruptive quality tends to spark educators’ curiosity and promote greater engagement in creative and exploratory learning practices when aligned with evolving pedagogical goals.

However, adopting technology in education goes beyond mere technical efficiency. Teachers’ attitudes and intentions are crucial in shaping how digital tools are integrated into their teaching practices. Research consistently demonstrates that when educators have positive perceptions of educational technologies, they are significantly more likely to include them in their instructional routines [40]. Their satisfaction—whether during or after using a tool—greatly influences their willingness to continue its use. Positive experiences foster repeated usage, build confidence, and promote the long-term integration of digital solutions into the educational process. This perspective supports a second hypothesis:

Hypothesis H2.

The behavioral intention to use digital technologies in education positively impacts actual usage.

This study proposes an expanded version of the traditional TAM, introducing additional dimensions and perceived quality as essential factors influencing the adoption of digital tools in educational settings. Teachers no longer evaluate digital tools solely based on functionality; they increasingly consider how meaningfully these tools enhance the learning process. Technologies that demonstrably improve student engagement, teaching efficiency, and learning outcomes are significantly more appealing for long-term adoption.

This focus on perceived quality enhances the TAM framework, emphasizing the need for an integrated approach that balances technical efficiency with genuine pedagogical value. Educators often rely on past experiences where technology significantly improved educational quality to inform their future adoption decisions. Therefore, we propose a third hypothesis:

Hypothesis H3.

The perception of increased quality resulting from using digital technologies is influenced by behavioral intention and significantly influences digital adoption.

As digitalization reshapes education, exploring the psychological, technical, and contextual variables that influence teachers’ engagement with technology becomes increasingly vital. With TAM now expanded to include informativeness, innovation, convenience, and customization, researchers and practitioners have access to more refined intervention tools. These insights can help inform policies and training programs that support the effective, long-term integration of digital technologies in educational institutions. Figure 1 presents the research model.

3.2. Selected Variables

The model utilized in this study is structured around a set of latent variables: perceived usefulness (PU), perceived ease of use (PEU), behavioral intention, actual usage, and perceptions of quality and performance. These are supported by demographic variables—specifically, gender and age—and a carefully curated selection of exogenous variables, each corresponding to the measurement items that operationalize the latent constructs. For example, PU is reflected in innovation, convenience, cost, and informativeness, while PEU is measured by customization, flexibility, speed, and enjoyment. These observed variables form the foundation for the measurement model, which defines how each latent construct is represented in empirical data.

This study’s exogenous variables were operationalized as questionnaire items to reflect the underlying latent constructs. The instrument was administered to a sample of 430 secondary school teachers from the Southwest Oltenia region of Romania between February and April 2025. The sampling process employed a stratified approach based on two key demographic variables, gender and age, ensuring representation across relevant population segments. The sample was established with a confidence level of 95% and a margin of error of 4.32%, guaranteeing a statistically reliable foundation for inference.

Each exogenous variable was translated into multiple reflective items integrated within the questionnaire, consistent with the theoretical model, and measured using Likert-type scales. These scales captured respondents’ levels of agreement or perceived relevance on a five-point continuum, ranging from strong disagreement to strong agreement. The structure of the questionnaire is presented in

Table 1. Questionnaire design.

Latent Variables	Exogenous Variables
Demographic variables	Gender
	Age
PU	Innovation
	Convenience
	Cost
	Informativeness
PEU	Customization
	Flexibility
	Speed
	Enjoyment
Behavioral intention	Attitude toward use
	Intention to use
Actual usage	Extent of use
Perception of quality and performance	Quality
	Performance

Source: author’s construction based on [33,34,35,36,37,38,39,40].

.

Table A1. Questionnaire items.

Endogenous Variables	Items
Innovation	I find innovative technologies important in education.	1—Strongly disagree; 2—Disagree; 3—Neutral; 4—Agree; 5—Strongly agree
Convenience	Using technology in education is convenient for me.
Cost	The cost of using educational technology is reasonable.
Informativeness	Educational technology provides valuable and relevant information.
Customization	Educational technology can be adapted to my learning needs.
Flexibility	I can use educational technology at my own pace and time.
Speed	Educational technology allows quick access to learning materials.
Enjoyment	Using technology in education is enjoyable.
Attitude toward use	How would you rate your overall attitude toward using technology in education?	1—Very negative; 2—Somewhat negative; 3—Neutral; 4—Somewhat positive; 5—Very positive
Intention to use	How likely are you to use educational technology in the future?	1—Very unlikely; 2—Unlikely; 3—Neutral; 4—Likely; 5—Very likely
Extent of use	To what extent do you currently use technology in education?	1—Never (minimal); 2—Rarely; 3—Sometimes; 4—Often; 5—Always (maximal extent)
Quality	How would you rate the quality of educational technology you have used?	1—Very poor; 2—Poor; 3—Average; 4—Good; 5—Excellent (very high)
Performance	How well does educational technology meet your learning needs?	1—Very poorly; 2—Poorly; 3—Moderately; 4—Well; 5—Very well (very high)

Source: author’s construction based on [57,59,60,61,62].

from Appendix A includes the questionnaire items and associated Likert scales.

The principal component analysis reveals no significant standard method bias, as the first component explains only 44.89% of the total variance, which falls below the 50% threshold, typically indicating substantial standard method variance. The variance distribution across multiple components (with the second component at 8.27% and subsequent components gradually decreasing) suggests that no single factor dominates the covariance structure, thereby supporting the validity of the measurement model. This variance decomposition pattern indicates that respondents did not consistently skew their responses using comparable rating patterns for different constructs [41].

3.3. Methods

The research methodology involves applying structural equation modeling (SEM) with the SmartPLS v3.0 software suite. This method reflects the complexity of the investigated phenomena and the need for a flexible, theory-driven approach to capture the dynamic relationships between multiple latent variables and their corresponding indicators.

Within an SEM model, the formula used is [42]

$${\eta }_i={\alpha }_{\eta }+B{\eta }_i+\Gamma {\xi }_i+{\zeta }_i$$

(1)

• $\eta ,\xi$—endogenous and exogenous latent variables vectors;
• ${\rm B}$—matrix of regression coefficients relating the latent endogenous variables to each other;
• $\Gamma$—matrix of regression coefficients relating the endogenous variables to exogenous variables;
• $\zeta$—disturbance;
• $i$—cases in the sample.

The latent variables are linked to observable variables, as follows:

$$y={\Lambda }_y\eta +\epsilon ,$$

(2)

$$x={\Lambda }_x\xi +\delta ,$$

(3)

• Λ_y, Λ_x—matrices of factor loadings;
• ε, δ—vectors of uniqueness.

This analytical framework, grounded in PLS-SEM and implemented through SmartPLS, provides a nuanced understanding of the mechanisms driving digital technology’s adoption in Romanian secondary schools. It bridges the gap between theoretical constructs and empirical validation, offering explanatory insights and practical relevance for educators, policymakers, and designers of digital learning environments.

4. Results

4.1. SEM Model Design

The structural equation model (SEM) analyzed through partial least squares (PLS) investigates the relationships between latent constructs such as perceived usefulness (PU), perceived ease of use (PEU), behavioral intention, actual usage, and teachers’ perceptions of quality and performance. This model includes exogenous demographic variables (gender, age) and endogenous factors like innovation, convenience, cost, and customization, which influence user adoption and engagement (Figure 2).

4.2. Validity and Reliability

The model demonstrates strong predictive validity, as evidenced by the R² values. Actual usage (R² = 0.801) and behavioral intention (R² = 0.800) exhibit a high explanatory power, indicating that the predictors account for a substantial portion of the variance in these constructs. However, teachers’ perception of quality shows a weaker yet significant explained variance (R² = 0.200). The effect sizes (f²) reveal the relative impact of each predictor. Behavioral intention exerts a powerful influence on actual usage (f² = 2.513), while perceived ease of use significantly affects behavioral intention (f² = 1.096). Perceived usefulness has a moderate effect (f² = 0.354), and teachers’ perception of quality shows a more minor yet meaningful contribution (f² = 0.151) (

Table 2. R squared.

	Original Sample	Standard Deviation	t Statistics	p Values
Actual usage	0.801	0.014	55.865	0.000
Behavioral intention	0.800	0.017	47.751	0.000
Teachers’ perception of quality	0.200	0.040	5.053	0.000

Source: developed by authors using SmartPLS v3.0.

).

These findings emphasize the significant role of behavioral intention in influencing actual usage, reinforcing the TAM framework. The considerable impact of perceived ease of use highlights its importance in shaping user attitudes.

The variance inflation factor (VIF) values for all indicators remain below the critical threshold of 5, with the highest being flexibility (VIF = 2.437) (

Table 3. Variance inflation factor.

Variable	VIF
Attitude_towards_use	1.821
Convenience	1.936
Cost	1.758
Customization	2.247
Enjoyment	1.954
Extent_of_use	1.000
Flexibility	2.437
Informativeness	1.568
Innovation	2.053
Intention_to_use	1.821
Quality	1.000
Speed	2.262

Source: developed by authors using SmartPLS v3.0.

).

This result suggests that multicollinearity does not distort the regression estimates. Internal consistency and convergent validity were assessed using Cronbach’s alpha, composite reliability, and average variance extracted (AVE). All constructs exceeded the recommended thresholds (Cronbach’s α > 0.7, AVE > 0.5), affirming reliability (

Table 4. Reliability and validity assessment.

	Cronbach’s Alpha	Composite Reliability	Average Variance Extracted (AVE)
Behavioral intention	0.803	0.910	0.835
Perceived ease of use	0.876	0.914	0.728
Perceived usefulness	0.829	0.886	0.661
Teachers’ perception of quality	1.000	1.000	1.000
Actual usage	1.000	1.000	1.000

Source: developed by authors using SmartPLS v3.0.

).

The high AVE values (above 0.66) support convergent validity, ensuring that each construct captures a distinct variance.

Since the square root of each construct’s AVE is greater than its correlations with other components, the Fornell–Larcker criteria support discriminant validity. For example, perceived usefulness and ease of use exhibit a lower within-construct correlation (

Table 5. Discriminant validity.

Fornell-Larcker Criterion	Behavioral Intention	Perceived Ease of Use	Perceived Usefulness	Teachers’ Perception of Quality	Behavioral Intention
Actual usage	1.000
Behavioral intention	0.878	0.914
Perceived ease of use	0.881	0.854	0.883
Perceived usefulness	0.780	0.763	0.658	0.813
Teachers’ perception of quality	0.548	0.448	0.475	0.436	1.000

Source: developed by authors using SmartPLS v3.0.

).

This analysis confirms that the constructs are empirically distinct, further supporting the model’s validity. The standardized root mean square residual (SRMR = 0.072) is below the 0.08 cutoff, indicating a good fit. The normed fit index (NFI = 0.914) also suggests a good fit.

4.3. The Main Effects

All the hypothesized paths are statistically significant (p < 0.001). Behavioral intention strongly predicts actual usage, while perceived ease of use has a greater impact on shaping behavioral intention than perceived usefulness. Teachers’ perception of quality has a minor but important effect on actual usage (

Table 6. Path coefficients.

		Coefficients Path (c)	Standard Deviation	t Statistics	p Values	Hypotheses Validation
H1	Perceived ease of use → Behavioral intention	0.791	0.027	22.988	0.000	H1 validated
	Perceived usefulness → Behavioral intention	0.448	0.030	11.795	0.000
H2	Behavioral intention → Actual usage	0.622	0.020	39.986	0.000	H2 validated
H3	Behavioral intention → Teachers’ perception of quality	0.353	0.045	9.998	0.000	H3 validated
	Teachers’ perception of quality → Actual usage	0.194	0.048	9.748	0.000

Source: developed by authors using SmartPLS v3.0.

).

The structural equation model validates the essential dynamics driving the adoption of digital educational tools and provides strong empirical support for the three proposed linkages.

Hypothesis H1 posits that perceived usefulness (PU) and perceived ease of use (PEU) positively influence behavioral intention. The results strongly validate this assertion, with PEU demonstrating a more significant effect (β = 0.622, p < 0.001) compared to PU (β = 0.353, p < 0.001). This finding aligns with established technology acceptance literature, reinforcing that users prioritize intuitive, frictionless experiences when adopting new tools. The substantial path coefficients highlight the cognitive and practical considerations that educators evaluate before committing to digital solutions.

Hypothesis H2, which asserts that behavioral intention translates into actual usage, receives unequivocal confirmation (β = 0.791, p < 0.001). The high R² value (0.801) further substantiates that intention is the primary proximal determinant of real-world engagement. This strong connection suggests that once educators develop a favorable attitude toward digital tools, they are very likely to integrate them into their teaching practices. The effect size (f² = 2.513) highlights the practical significance of this relationship, overshadowing other predictors in the model.

Hypothesis H3 examines the dual role of behavioral intention in shaping perceptions of quality and subsequently influencing actual adoption. The analysis reveals a significant yet moderate effect of intention on perceived quality (β = 0.448, p < 0.001), which in turn has a minor but meaningful impact on usage (β = 0.194, p < 0.001). Although the effect sizes are more modest than those seen in direct intention-to-use pathways, they confirm that quality perceptions serve as a secondary reinforcing mechanism. Educators who recognize tangible improvements in pedagogical outcomes from digital tools demonstrate a slightly higher sustained engagement.

Together, these findings support the theoretical foundations of the Technology Acceptance Model and broaden its relevance to educational contexts. The emphasis on ease of use over usefulness in influencing intention indicates that training and interface design may be key factors for adoption. Meanwhile, the confirmed link from intention to perception of quality to usage suggests that showcasing measurable benefits could further strengthen digital integration in teaching practices. Future research could investigate contextual factors, such as institutional support or subject-specific requirements, to enhance these predictive relationships.

5. Discussion

Beyond integrating new technology into existing classroom practices, the digital revolution in education requires a fundamental shift in our understanding of teaching, educational frameworks, and professional responsibilities. While state-of-the-art resources and infrastructure are essential, a teacher’s ability to integrate technology into their lessons in relevant, flexible, and sensitive ways that align with actual classroom dynamics makes a difference [43].

5.1. Hypothesis H1 Validation

The results from our structural equation modeling reinforce established theories surrounding digital technology’s adoption in education and provide meaningful extensions to them. By confirming Hypothesis H1, which identifies both perceived usefulness (PU) and perceived ease of use (PEU) as significant drivers of behavioral intention, our findings echo the foundational premises of the TAM as outlined by Davis [33] and later developed by Venkatesh and Davis [38]. However, what stands out in our data is the notable impact of ease of use, which surpasses the influence of perceived usefulness. This insight adds depth to previous discussions, supporting Kirinic et al.’s [43] argument that the real catalyst for adoption lies in teachers’ ability to seamlessly integrate technology into their pedagogical routines rather than in the technology’s availability or theoretical advantages. The greater influence of PEU suggests that professional development efforts should primarily focus on minimizing technological friction before introducing advanced features or specialized applications.

5.2. Hypothesis H2 Validation

Hypothesis H2 also finds strong support in our model, confirming the strong link between behavioral intention and actual use. This result closely aligns with Scherer and Teo’s [44] observation that individual characteristics, such as a teacher’s comfort with technology and professional experience, contribute to determining whether digital tools enhance the learning process or remain underutilized. The strength of the relationship in our model, marked by a high path coefficient (β = 0.878), even surpasses what is typically reported in TAM literature. This finding may reflect a post-pandemic shift in educational norms, where digital technologies have become more deeply embedded in daily teaching practices. The findings resonate with those of Arruda [45,46] and Arruda and Kerres [47], who also document increased technology use; our data suggest that behavioral intention continues to serve as the primary gateway to actual adoption, despite this growth.

5.3. Hypothesis H3 Validation

The partial confirmation of Hypothesis H3, which posits that behavioral intention influences perceptions of quality and thereby affects usage, adds further complexity to Bonfield et al.’s [48] human-centered framework. Although our model captures this mediating effect, its relatively modest strength (β = 0.194) lends weight to Kerres’ [49] argument about the persistent disconnect between policy and practice. In other words, teachers may indeed express intentions to use technology and recognize improvements in perceived quality. Still, institutional or structural constraints often prevent these intentions from fully translating into classroom realities. This weaker mediation effect, especially compared to the direct impact of intention, mirrors Olofsson et al.’s [50] emphasis on the importance of the broader institutional environment in shaping successful digital integration.

5.4. General Findings

Our model’s emphasis on ease of use as the primary influence affirms the relevance of TAM. It extends critiques such as those put forward by Marangunic and Granic [51], who underscore the model’s limitations. The dominant role of PEU in our findings supports their contention that factors like digital anxiety remain underexplored and reinforces Koehler et al.’s [52] advocacy for supportive environments that encourage experimentation. The substantial impact of ease of use in our analysis implies that reducing cognitive and technical barriers enables teachers to explore new pedagogical possibilities more freely and confidently.

Interestingly, the relatively subdued role of perceived quality in influencing actual use diverges from what Vuorikari et al. [53] describe in their digital competence framework. Our findings suggest that teachers sometimes adopt digital tools for practical, immediate needs before assessing their pedagogical value. This pattern aligns with Arruda’s [46] observations regarding gaps in developing critical digital skills, where adoption precedes a deep evaluation or reflection on instructional effectiveness.

Our findings align with broader conceptual frameworks such as Hinings et al.’s [54] evolutionary view of digital transformation and Kraus et al.’s [55] focus on the role of institutional culture. The strong link between intention and actual usage confirms that when digital tools meet teachers’ needs and contexts, meaningful adoption becomes feasible, reflecting insights from Benavides et al. [56]. At the same time, the lesser influence of quality perceptions calls for caution, consistent with Stone’s [57] reminder that not all adoption is inherently beneficial or pedagogically sound. Popkewitz’s [58] early critique remains relevant: technology’s adoption rarely unfolds as a neutral or value-free process. Our model, in which ease of use takes precedence over considerations of usefulness or quality, indicates that systemic or institutional pressures may drive adoption more than thoughtful pedagogical reflection. This tension—between clear empirical pathways and more critical, value-laden perspectives—highlights the importance of adopting balanced approaches that consider quantitative evidence along with the complex human realities of teaching and learning [59].

Although the connection between perceived quality and actual usage was statistically significant, it was noticeably weaker than the effect of behavioral intention. These findings suggest that while teachers may acknowledge improvements in instructional quality due to digital tools, this acknowledgment alone does not strongly encourage continued usage. Several possible explanations arise. First, institutional factors such as rigid curricula, limited classroom autonomy, or a lack of time for reflection may hinder teachers from fully integrating and assessing the long-term pedagogical value of these tools [16,46]. Second, in under-resourced educational systems like Romania’s, usage is often driven more by immediate functional needs and technical simplicity than by an appreciation of quality or innovation.

These findings suggest that perceived quality serves more as a sustaining factor than as an initiating one. In practice, this means that unless teachers already possess the intention and ability to utilize a tool, perceived benefits alone will not suffice to ensure continued use. As a result, we recommend a two-phase strategy for professional development and policy design. First, initial training should emphasize ease of use, streamline user interaction, and alleviate digital anxiety. Only after basic usage becomes routine should programs introduce more advanced features and pedagogical applications [52]. Second, technology adoption efforts should not cease at rollout. Teacher support programs, including mentoring, peer exchange, and on-demand technical assistance, are crucial for reinforcing adoption and creating the reflective space needed to recognize and appreciate quality gains [60].

The digital transformation of secondary education paves the way for more flexible and relevant instruction tailored to the demands of the modern world. Creating an equitable educational system capable of meeting the challenges of the twenty-first century necessitates the collective commitment of all stakeholders, including educators, students, decision-makers, and educational leaders [3,57,61,62,63,64]. Long-term training programs, collaboration among teachers, and psychological support to navigate the uncertainties of change can make a significant impact [65,66,67,68,69].

5.5. Theoretical Implications

This study advances technology acceptance literature by revealing context-specific nuances that the original TAM framework could not anticipate. The stark priority of ease over usefulness in educational settings compels us to reconsider assumptions about professional adoption patterns—teachers are not neutral evaluators weighing features but time-pressed practitioners seeking survival solutions. This reality check aligns with emerging sociological perspectives that frame technology’s adoption as emotional labor rather than rational choice, particularly in under-resourced environments.

The discovery that quality judgments follow rather than precede adoption challenges conventional innovation diffusion theories. It indicates that educators operate in a try-then-judge mode, contrary to the evaluate-then-adopt logic that dominates business contexts. This finding invites a theoretical refinement that considers teachers’ unique pressures: mandated adoptions, ethical responsibilities toward vulnerable learners, and the irreversibility of classroom experiments.

This paper also methodologically contributes by demonstrating that established business frameworks require significant adaptations when studying mission-driven fields such as education. Variables like perceived usefulness assume entirely different dimensions when the end user is not a consumer but a professional balancing institutional demands with student welfare. This tension between efficiency and care, which is barely visible in corporate TAM studies, emerges as a critical factor needing theoretical accommodation in educational technology research.

5.6. Practical Implications

These findings offer clear guidance for reshaping digital integration strategies in education. For school administrators, the results highlight the importance of investing in user-friendly platforms with clean interfaces and minimal learning curves. What matters most is not flashy features but how easily teachers can integrate tools into their daily workflow. Curriculum designers should view professional development as ongoing apprenticeships rather than one-time workshops, creating safe spaces where educators can troubleshoot real classroom scenarios. The research also suggests that mentorship programs pairing tech-savvy teachers with hesitant colleagues could be more effective than traditional training, leveraging social influence to build confidence.

In Romania, the integration of digital technologies into secondary education remains inconsistent due to ongoing and complex systemic challenges. One of the most significant barriers is the pronounced disparity between urban and rural areas regarding internet connectivity. While urban schools typically enjoy stable broadband access, many rural institutions continue to function with slow, unreliable, or nonexistent internet connections, severely limiting their ability to develop digital learning environments.

Another significant issue is the technology provided in schools. Many institutions, especially those in economically disadvantaged areas, lack sufficient digital infrastructure, such as updated computers, smartboards, projectors, and enough devices for students. Even when basic equipment is available, it is often outdated, poorly maintained, or shared among too many users, which diminishes its pedagogical effectiveness. Adding to these challenges is the limited availability of dedicated IT personnel who can offer technical support and maintain digital systems, leaving many schools without the expertise necessary to ensure long-term digital integration.

Additionally, national policies supporting digital education are often fragmented and applied inconsistently across regions, leading to disparities in resource allocation and implementation timelines. Teachers frequently report inadequate access to structured, ongoing professional development programs focused on digital pedagogy. Many feel unprepared to navigate the technical and instructional demands of digital tools, contributing to low confidence and a reluctance to experiment with new technologies in the classroom.

This complex empirical reality provides an essential context for our findings, particularly regarding the dominant influence of perceived ease of use. In environments where digital adoption is neither standardized nor adequately supported, educators tend to prefer intuitive and low-effort solutions over tools that are perceived as innovative but challenging to implement. Thus, the findings of this study reflect not only individual-level attitudes but also deeper systemic barriers that affect digital adoption behaviors in Romanian secondary education. Tackling these challenges will require coordinated, policy-driven reforms that invest in infrastructure, training, and equitable digital access.

6. Conclusions

The digital transformation of education signifies a profound paradigm shift that extends beyond merely introducing new tools into classrooms. This study underscores that the meaningful integration of technology is a human-centered process shaped by the intricate interplay among individual beliefs, institutional environments, and pedagogical values. The journey toward effective adoption exposes deeper tensions in contemporary education—between innovation and tradition, efficiency and depth, standardization and personalization.

A key insight from this research is the distinct and influential role of behavioral intention in predicting actual technology use. Teachers’ willingness and motivation to adopt digital tools serve as the primary engine for implementation. This intention is strongly influenced by how intuitive and easy to use the technology appears, suggesting that reducing usability barriers is essential for accelerating digital adoption. Without a positive and proactive attitude toward usage, even the most advanced technologies are unlikely to be integrated effectively.

In contrast, perceived quality plays a complementary and reinforcing role. It does not initiate adoption but instead helps sustain and deepen it. When teachers observe tangible improvements in teaching performance, student engagement, or instructional outcomes due to the use of digital tools, they are more likely to continue using them. Thus, perceived quality enhances long-term integration but cannot substitute for the initial behavioral intention to engage with technology.

These findings highlight that effective technology integration requires a dual strategy: encouraging motivation through a user-friendly design and establishing trust through a clear educational value. Technological solutions alone are inadequate if they do not align with institutional priorities and lack sufficient infrastructure and professional development support.

This study emphasizes the need for more nuanced evaluation frameworks that extend beyond binary questions of adoption or resistance. Educators, policymakers, and system leaders must collaborate to create ecosystems where innovation supports pedagogical goals instead of driving them. In the Romanian context—and similar systems facing structural limitations—this entails investing in both usability and quality, while empowering teachers as agents of change.

Ultimately, the evolution of digital education requires both vision and discernment—the vision to harness technology’s transformative potential and the discernment to ensure it upholds our highest educational values. This balance serves as the foundation for creating inclusive, adaptive, and human-centered educational systems.

Limitations and Further Research

While this study enhances our understanding of technology’s adoption in education, several limitations must be considered when interpreting the results. The reliance on self-reported measures introduces a potential response biases, and the cross-sectional design restricts our ability to establish causal relationships or track adoption patterns over time. However, representative of specific educational contexts, the sample may not fully capture the diversity of technological infrastructures and cultural attitudes toward digital integration across various regions.

Conceptually, this study’s focus on individual cognitive and behavioral factors overlooks important contextual variables. Institutional factors such as administrative support, resource allocation, and policy environments were not included in the model, nor were emotional dimensions like digital anxiety or technostress, which may significantly impact sustainable adoption.

Future research should pursue longitudinal designs to examine how adoption dynamics evolve as technologies mature and become institutionalized. Multilevel analyses could better disentangle the complex interactions among individual, institutional, and systemic factors shaping technology’s integration. Studies employing critical pedagogical perspectives are also needed to explore how power dynamics and hidden curricula influence adoption decisions. Cross-cultural comparative research would help determine whether the observed patterns reflect universal principles or context-specific phenomena.

The most promising path forward lies in mixed-methods approaches that combine quantitative modeling with qualitative insights to bridge the gap between adoption behaviors and their actual pedagogical impact. Rather than simply asking whether educators adopt technology, future studies should explore how adoption transforms teaching and learning practices—a question requiring both methodological sophistication and theoretical nuance. These directions would provide a more comprehensive understanding of technology’s integration, while addressing the current study’s limitations.