Toward Sustainable Technology Use in Education: Psychological Pathways and Professional Status Effects in the TAM Framework

Abstract

The sustainable integration of technology into educational practices is pivotal for modern teaching and learning. Grounded in the Technology Acceptance Model (TAM), this study explores the psychological and contextual factors that influence technology acceptance among pre-service and in-service teachers. Employing a nonexperimental, cross-sectional design, data were collected from 347 participants to examine the relationships between perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. Results indicate that pre-service teachers demonstrate stronger openness to technology adoption, driven primarily by attitudinal factors, whereas in-service teachers’ acceptance is more closely linked to perceived utility and usability. This study advances the TAM by integrating a dual serial mediation model and testing the moderating role of professional status, thereby offering a nuanced understanding of sustainable digital engagement across career stages. Our findings underscore the importance of fostering positive perceptions and providing differentiated support throughout teachers’ professional trajectories to achieve long-term, meaningful technology adoption in education.

1. Introduction

The integration of digital technologies into education has become a pivotal component of modern teaching and learning processes. Despite notable advancements in educational hardware and software, ensuring the effective and sustainable adoption of technology by educators remains a persistent challenge. In particular, understanding the psychological and contextual factors that facilitate or hinder technology acceptance among teachers is critical for promoting innovation, adaptability, and sustainability in educational environments.

In line with the global imperative of digital transformation that is aligned with sustainable education, this study repositions the Technology Acceptance Model (TAM) as a process-based framework for understanding long-term, meaningful technology integration. Unlike traditional TAM applications that focus on short-term behavioural prediction, our approach emphasises sustainable psychological adoption, defined as internalised, consistent, and context-sensitive engagement with digital systems.

The Technology Acceptance Model (TAM), developed by Davis [1], has served as a foundational framework for investigating users’ behavioural intentions and the actual use of technological systems. According to TAM, perceived usefulness and perceived ease of use are central determinants of users’ attitudes toward technology and their subsequent behavioural intentions, ultimately influencing actual system use. While TAM and its extensions (TAM2, TAM3) have been widely applied across educational contexts, gaps remain in understanding how sustainable patterns of technology adoption emerge among teachers at different stages of professional development.

Specifically, there has been limited research exploring how professional status (pre-service versus in-service teachers) may moderate the psychological pathways proposed by the TAM, particularly within the broader framework of educational sustainability. Given that pre-service teachers and in-service teachers may differ in their technological experience, motivational orientations, and institutional support, examining these differences can provide valuable insights into fostering long-term engagement with digital technologies. This study aligns conceptually with UNESCO’s vision of Education for Sustainable Development (ESD), which promotes the integration of sustainability principles into the structure, content, and delivery of education [2]. In this context, we adopt a multidimensional understanding of digital sustainability that extends beyond mere technological access or proficiency. Specifically, we define sustainable adoption as “the long-term, equitable integration of technology balancing pedagogical efficacy, environmental impact, and institutional adaptability.” This definition echoes the goals of ESD by acknowledging the interplay between instructional quality, ecological responsibility, and organisational flexibility, three pillars essential for resilient and future-oriented educational systems.

Addressing this gap, the present study seeks to investigate the psychological mechanisms underlying technology acceptance among pre-service and in-service teachers. Grounded in the TAM, we examine the mediating roles of attitude toward use and behavioural intention, as well as the moderating effect of professional status on key model relationships. In doing so, this research aims to contribute to the literature on sustainable digital practices in education by identifying factors that support lasting, meaningful technology integration across teachers’ career stages.

1.1. Core Principles and Components of the Technology Acceptance Model (TAM)

The adoption and integration of information technology in education remains a central theme within information systems research and practice. Despite significant advancements in educational hardware and software, the effective integration of instructional informatics systems continues to present substantial challenges. Identifying and fostering the conditions under which digital educational technologies are embraced by teachers remains a critical research priority.

The Technology Acceptance Model (TAM), developed by Davis (1989) [1], provides a widely recognised framework for analysing the factors that influence users’ acceptance of technology. Building upon the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen [3], the TAM narrows the focus specifically to computer-based technologies, whereas the TRA addresses broader dimensions of human behaviour [4].

According to the TAM, perceived usefulness (PU) and perceived ease of use (PEU) act as key mediators between external system characteristics and actual technology use [5,6]. The model investigates user–device interactions to predict technology adoption behaviours [7] and has been extensively validated through empirical research [8,9,10].

The TAM identifies five central constructs: perceived usefulness (PU)—the belief that using a specific technology enhances job performance; perceived ease of use (PEU)—the degree to which a technology is perceived as effortless to use; attitude toward use (ATU); behavioural intention (BI); and actual system use (ASU). Within TAM, actual system use (ASU) is influenced directly by behavioural intention (BI), which itself is shaped by both attitude toward use (ATU) and perceived usefulness (PU) [6,11,12]. Additionally, attitude toward use is significantly determined by both perceived usefulness and perceived ease of use, as confirmed by prior TAM-based studies [13].

At its core, the Technology Acceptance Model (TAM) posits that individuals’ acceptance of computer-based technologies is primarily influenced by two belief-driven cognitive appraisals: perceived ease of use and perceived usefulness [14,15,16,17,18]. Perceived ease of use is proposed to influence perceived usefulness directly, while external variables impact both constructs [19]. Thus, the TAM places a strong emphasis on the role of user attitudes in predicting engagement with new technologies.

Nevertheless, the TAM has been critiqued for its limited consideration of external factors such as prior experience, facilitating conditions, and perceived enjoyment [20]. In response, extensions of the model, such as TAM2 and TAM3, have been proposed to capture a broader range of influences.

The TAM has been extensively applied in educational contexts, particularly for the adoption of learning management systems [15,19,21]. Empirical studies have explored system-specific factors like perceived convenience [22], system availability and user experience [8], and technical support and computer self-efficacy [23] in shaping technology acceptance among teachers. Other research [21] confirms that perceived usefulness and perceived ease of use positively predict attitudes and behaviours toward e-learning systems. TAM2 [24] extends the original TAM by incorporating social influence processes, such as subjective norms, voluntariness, and image and cognitive instrumental processes like job relevance, output quality, and result demonstrability [25,26,27]. Research has shown that subjective norms significantly impact behavioural intention, particularly under mandatory system use conditions, independently of perceptions of usefulness and ease of use [6,20,28].

Further refinements led to TAM3 [29], which explains perceived ease of use through the mechanisms of “anchors” and “adjustments.” Anchors, representing initial frames of reference such as computer playfulness [30,31], perceptions of external control [32], computer self-efficacy [33], and computer anxiety [14,29,34], influence users’ initial evaluations. Over time, as users gain more experience, their perceptions are increasingly shaped by various adjustments, [35] particularly those related to objective usability and perceived enjoyment.

Although TAM3 offers a more comprehensive view of technology acceptance, its complexity has been cited as a potential limitation to practical applications, particularly in educational settings [14,29].

Overall, the TAM provides a valuable framework for understanding technology adoption processes in education. Both pre-service and in-service teachers are influenced by their perceptions of the ease of use and usefulness of digital learning technologies, which, in turn, shape their attitudes and behaviours toward system adoption. The extended models, TAM2 and TAM3, highlight the importance of incorporating social and cognitive factors alongside technological features. However, the increasing complexity of these models underscores the need for continued research aimed at refining technology adoption frameworks for practical use in teacher-training programs.

While the Technology Acceptance Model remains a dominant framework in educational technology research, it is increasingly being critiqued for its limitations in accounting for contextual, institutional, and pedagogical dynamics. Several scholars have pointed out the tendency of TAM-based studies to focus on behavioural prediction at the expense of exploring deeper psychological processes or environmental constraints [36,37]. Moreover, inconsistencies in the model’s predictive power across educational settings raise questions about its universal applicability [38,39]. Our study contributes to addressing these gaps by proposing a reconceptualisation of the TAM, not through added variables but through an interpretive shift, emphasising the developmental, career-sensitive, and motivational trajectories that shape teachers’ sustained engagement with technology. This repositioning allows for a more flexible, context-aware application of the TAM, which is particularly relevant for institutions seeking to implement long-term digital strategies grounded in psychological readiness and professional-identity formation.

1.2. Key Elements for Shaping a Sustainable Attitude Toward Technology Acceptance in Pre-Service and In-Service Teachers

The concept of sustainability has been extensively discussed in the academic literature and is often framed through the Triple Bottom Line (TBL) model developed by Elkington, which conceptualises sustainability across three interconnected dimensions: people, planet, and profits [40]. This framework highlights the integration of social, environmental, and economic considerations into sustainable development initiatives.

In the educational domain, sustainable education is defined as the deliberate incorporation of sustainability-related components into discipline-specific curricula [41,42]. Sustainable education also intersects with the notion of sustainable digital transformation, focusing on the strategies and digital tools adopted by educational institutions to advance sustainability objectives. In parallel, sustainable digital learning practices emphasise the integration of environmentally responsible and pedagogically sound digital technologies and instructional designs into higher education teaching and learning environments [43]. Recent research further emphasises that sustainable digital engagement requires not only environmental awareness but also pedagogical coherence and institutional innovation [44]. Thus, sustainable education is not solely concerned with curricular reform but also with the continuous integration of technologies that support the professional development of educators and the operational efficiency of educational institutions.

Several studies have indicated that teachers’ positive attitudes toward computer technology play a pivotal role in successful technology integration. Teachers with favourable perceptions and openness toward technology are more likely to adopt efficient instructional strategies and maintain a focus on enhancing the educational process through digital tools [45,46]. Furthermore, frequent computer use has been associated with the development of more positive attitudes toward technology, creating a virtuous cycle that fosters deeper integration of digital resources into daily teaching practices [47].

Research focusing on pre-service teachers has revealed that perceived usefulness, attitude toward computer use, and computer self-efficacy directly influence behavioural intention toward using technology [48,49,50]. Facilitating conditions have also been shown to positively affect both perceived usefulness and perceived ease of use among pre-service teachers. Complementary findings [51] confirm that perceived usefulness, attitude toward computer use, and technology self-efficacy are significant predictors of pre-service teachers’ behavioural intentions to integrate technology into teaching. Additionally, positive attitudes, subjective norms, and perceived behavioural control have been identified as critical determinants of pre-service teachers’ intentions to use technology-assisted learning environments [52]. As future educators, pre-service teachers demonstrate a strong propensity to embrace technologies aligned with sustainable education objectives, particularly those that enhance learning efficiency and contribute to environmental sustainability. This view is supported by emerging research that frames technology integration as both a pedagogical and sustainability challenge, calling for transformative practices aligned with global sustainability goals [53].

Among in-service teachers, similar predictors have been found to influence technology acceptance. Positive attitudes, perceived usefulness, and perceived ease of use of educational technologies are positively correlated with the intention to adopt digital tools, consistent with the propositions of the Technology Acceptance Model (TAM) [54]. Furthermore, in-service teachers’ self-efficacy, perceived usefulness, and perceived ease of use have been shown to significantly predict their intention to integrate technology into teaching practices [55]. Leveraging their practical experience, in-service teachers are well-positioned to implement sustainable digital practices, thereby contributing to the long-term sustainability and resilience of educational systems.

While traditional applications of the Technology Acceptance Model (TAM) primarily focus on short-term behavioural intentions and system use, our study repositions core TAM constructs—perceived usefulness (PU), perceived ease of use (PEU), attitude toward use (ATU), and behavioural intention (BI)—as psychological pathways that support sustainable digital engagement in education. Rather than introducing new variables, we extend their interpretive scope by framing them as proxies for continuity, adaptability, and internal motivation—dimensions highlighted in recent research on digital transformation and sustainability [44,56]. This approach enables a deeper understanding of how long-term, meaningful integration of technology can emerge from enduring attitudinal and motivational orientations. By preserving the TAM’s conceptual parsimony while shifting the focus toward sustainability-aligned dispositions, our model responds to current demands for frameworks that reflect both psychological agency and systemic educational change.

Reframing the TAM Through a Sustainability Lens: Toward Educational Transformation

Recent critiques of the Technology Acceptance Model (TAM) in educational research have underscored its limited engagement with broader pedagogical, institutional, and sustainability-oriented frameworks [44,57]. To address this gap, we propose a conceptual reframing of the TAM that aligns its psychological mechanisms with the core dimensions of UNESCO’s Education for Sustainable Development (ESD). Rather than interpreting perceived usefulness (PU) and perceived ease of use (PEU) solely as cognitive evaluations of efficiency or simplicity, we reconceptualise these variables as indicators of deeper sustainability dispositions, such as pedagogical contribution, digital resilience, and equitable access [58,59].

Within this expanded lens, PU reflects not only functional gains but also the perceived relevance of technology for fostering inclusive and environmentally conscious teaching practices. PEU represents not just usability, but adaptability and readiness to navigate digital complexity. Moreover, attitude toward use (ATU) and behavioural intention (BI) are reframed as motivational anchors for long-term engagement with educational technology systems, rather than mere precursors to isolated behaviour [2].

This interpretive shift allows the TAM to function not just as a predictive framework but as a pathway toward sustainable digital transformation. By preserving its structural parsimony while expanding the meaning of its constructs, our model supports the development of teachers’ sustained psychological commitment to digital innovation in alignment with the values of ESD, such as critical thinking, ethical responsibility, and institutional transformation [57,60].

1.3. Conceptual Model

The present study is grounded in the Technology Acceptance Model (TAM) and aims to explore the psychological and contextual mechanisms that influence teachers’ intention to use and the actual use of a digital system, with a particular emphasis on developing sustainable attitudes toward technology-supported teaching and learning. By integrating the core constructs of the TAM—perceived usefulness (PU), perceived ease of use (PEU), attitude toward use (ATU), behavioural intention to use (BI), and actual system use (ASU) —the model seeks to offer insights into how educators form long-term, constructive engagement with digital tools.

In line with the foundational assumptions of the TAM [1,24], the model proposes that behavioural intention (BI) is influenced by perceived usefulness (PU), perceived ease of use (PEU), and attitude toward use (ATU). These psychological drivers are essential not only for facilitating short-term acceptance but also for nurturing sustainable, long-term use of educational technologies. Furthermore, the model hypothesises that the relationship between perceived usefulness (PU) and actual system use (ASU) is serially mediated by attitude toward use (ATU) and behavioural intention (BI), indicating that sustainable engagement with technology is supported by both positive beliefs and strong behavioural intentions. A similar serial mediation is expected for perceived ease of use (PEU) and actual system use (ASU), emphasising the role of usability perceptions in cultivating durable and self-sustaining digital practices.

Beyond these core pathways, the model incorporates teachers’ professional status (pre-service versus in-service) as a moderator of the relationships between perceived usefulness and attitude toward use and between perceived ease of use and attitude toward use. Recognising that different stages of professional development may influence technology acceptance, the moderating role of professional status reflects findings from previous research indicating systematic differences in attitudes, perceptions of usefulness, and motivational orientations between pre-service and in-service teachers [7,9,15,55]. These differences suggest that professional status can significantly affect how sustainable attitudes toward technology-supported teaching and learning are developed and maintained.

The theoretical innovation of the current model lies not in the introduction of new constructs but in its structural reinterpretation of the TAM as a process-based, sustainability-sensitive framework. By combining two sequential mediators—attitude and intention—with a contextual moderator—teacher status—we model digital technology acceptance not as a static act of compliance but as a layered, context-dependent psychological process. This structure reflects a shift from “acceptance as prediction” to “acceptance as a sustainability pathway”, in which the interdependence of motivational, affective, and contextual factors becomes central. This approach responds to increasing demands in educational research to account for professional diversity, career-stage differentiation, and the long-term consequences of digital innovation in pedagogy [61,62]. Moreover, by avoiding conceptual inflation and building upon well-validated constructs, our model retains parsimony while enabling richer explanatory power. The result is a theoretically grounded yet practically adaptable framework for analysing sustainable digital engagement across teacher populations.

Overall, this conceptual model extends the traditional TAM framework by integrating both serial mediation and contextual moderation mechanisms, thereby reinterpreting technology acceptance as a dynamic and career-sensitive sustainability pathway. Rather than proposing additional constructs, the model enhances the TAM’s explanatory scope by examining how attitudinal and motivational processes unfold over time and across professional stages. This dual-layered structure captures not only the cognitive–affective–behavioural chain (PU/PEU → ATU → BI → ASU) but also the differentiated psychological profiles of pre-service versus in-service teachers, whose trajectories toward sustainable technology use may diverge. In doing so, the model reflects a shift from acceptance as a static decision to acceptance as a transformational process, consistent with contemporary perspectives on educational innovation, digital resilience, and long-term capacity building in teacher development.

Building upon this conceptual framework and its emphasis on sustainable engagement with educational technology, the following hypotheses were formulated:

H1. 

Perceived usefulness (PU), perceived ease of use (PEU), and attitude toward use (ATU) will significantly predict behavioural intention (BI) to use the system.

H2. 

The relationship between perceived usefulness (PU) and actual system use (ASU) is serially mediated by attitude toward use (ATU) and behavioural intention (BI).

H3. 

The relationship between perceived ease of use (PEU) and actual system use (ASU) is serially mediated by attitude toward use (ATU) and behavioural intention (BI).

H4. 

The relationship between perceived usefulness (PU) and attitude toward use (ATU) is moderated by teachers’ status (pre-service vs. in-service), such that the relationship differs depending on whether the teacher is pre-service or in-service.

H5. 

The relationship between perceived ease of use (PEU) and attitude toward use (ATU) is moderated by teachers’ status, such that the relationship differs depending on whether the teacher is pre-service or in-service.

2. Materials and Methods

2.1. Research Design

The present study employed a non-experimental, cross-sectional research design to examine psychological and contextual factors associated with technology use behaviours among teachers. A quantitative methodology was adopted, involving the collection and analysis of survey data from a sample of 347 participants. The study aimed to identify the direct, mediated, and moderated relationships among key variables drawn from the Technology Acceptance Model (TAM).

The measurement instruments—Attitude Toward Use (ATU), Behavioural Intention to Use (BI), Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Actual System Use (ASU)—were originally developed by Davis [1] and extended by Venkatesh et al. [27]. Prior to data collection, the instruments were translated and culturally adapted for the Romanian educational context using a multistep process that included translation, back-translation, expert review, and cognitive debriefing. A pilot study was conducted to verify the reliability and validity of the adapted measures. Data analysis was conducted using IBM SPSS Statistics (Version 20), and mediation and moderation analyses were performed with Hayes’s PROCESS macro (Version 4.1). Missing data were minimal and handled through listwise deletion to maintain data integrity.

2.2. Participants

The final sample included 347 participants, composed of both pre-service and in-service teachers. Participants were recruited through convenience sampling, selecting individuals who were readily accessible and willing to participate. Although this method facilitated efficient data collection, it presents a limitation regarding the external validity and generalisability of the findings, which is acknowledged when interpreting the results. While the sample provided sufficient variability in terms of age and professional status, it was demographically homogeneous in other respects, particularly gender and national context, which may limit the broader applicability of the findings.

Of the participants, 35.4% identified as men and 64.6% as women. The average age was 31.81 years (SD = 10.49). Regarding professional status, 68.3% of the respondents were enrolled in teacher education programs as pre-service teachers, and 31.7% were active in-service teachers. The diversity in age and professional experience among participants provided a suitable context for testing hypotheses related to the moderating role of professional status.

2.3. Measurements

Validated instruments based on the Technology Acceptance Model were used to measure Attitude Toward Use (ATU), Behavioural Intention to Use (BI), Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Actual System Use (ASU). Perceived Usefulness (PU) and Perceived Ease of Use (PEU) were measured with items developed by Davis (1989) [1], assessing participants’ cognitive appraisals of a system’s utility and usability. Attitude Toward Use (ATU) was measured with the scale adapted by Venkatesh et al. [27] from the original conceptualisation by Davis [1], capturing affective reactions toward technology use. Behavioural Intention (BI) was assessed with items formulated by Davis [1] and refined by Venkatesh and Davis [24], reflecting motivational intent. Actual System Use (ASU) was operationalised following Davis [1] and McFarland and Hamilton [10], focusing on the frequency and intensity of system use through self-report measures.

All instruments were translated and culturally adapted following a rigorous multistep procedure that involved forward translation by bilingual experts, back-translation into the original language, expert panel reviews, and cognitive interviews with a pilot sample. For example, the original item “Using the system improves my teaching performance” was adapted as “Utilizarea sistemului îmi îmbunătățește performanța didactică”, preserving both semantic accuracy and professional relevance. Similarly, “I find the system easy to use” was rendered as “Consider că sistemul este ușor de utilizat”, and “I intend to use this system frequently in my teaching” became “Intenționez să folosesc frecvent acest sistem în activitatea mea.” These adaptations were carefully reviewed by educational psychology experts to ensure that each item reflected both linguistic precision and contextual applicability to Romanian teacher education. A pilot study with 42 pre-service and in-service teachers tested the reliability, clarity, and cultural appropriateness of the translated scales. Based on participant feedback, minor linguistic adjustments were made to optimise item relevance and comprehension.

Reliability analyses conducted both during the pilot phase and in the final dataset indicated acceptable to excellent internal consistency for all scales. The Cronbach’s alpha coefficients were 0.90 for ATU, 0.77 for BI, 0.76 for PU, 0.81 for PEU, and 0.87 for ASU. These results confirmed the psychometric soundness of the translated instruments for use in the Romanian educational context.

While our study employed validated instruments rooted in the Technology Acceptance Model, it is important to acknowledge that none of the original scales directly measured sustainability-related values, behaviours, or competencies. Rather than introducing separate variables, our approach interprets TAM constructs as proxies for sustainable digital engagement. For instance, perceived usefulness is contextualised as alignment with pedagogical goals and long-term instructional improvement, elements consistent with the “people” and “planet” dimensions of the sustainability framework. We recognise this as a limitation of the current operationalisation and suggest that future research incorporate validated measures from the ESD domain, such as the Sustainability Consciousness Questionnaire [63] or UNESCO’s ESD Key Competencies Framework [2], to capture more explicitly the behavioural and affective dimensions of sustainable educational practices.

2.4. Procedure

Participants completed the instruments online, using a Google Drive document. Prior to this, all of them signed an informed consent form for participation. The study was conducted in accordance with the Declaration of Helsinki, and its protocol was approved by the Ethics Committee of the Faculty of Psychology and Educational Sciences, “Alexandru Ioan Cuza” University of Iasi, Romania, under opinion number 819, dated 28 March 2025.

2.5. Data Analysis

Data analysis was performed using IBM SPSS Statistics, Version 20. After preliminary screening, which included identifying and excluding incomplete or inconsistent responses, a total of 347 valid questionnaires were retained. Missing data were minimal and addressed through listwise deletion.

To test the study’s hypotheses, stepwise multiple regression analyses were used to examine direct effects among perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. Serial mediation models were tested using Hayes’s PROCESS macro (Model 6) to examine whether attitude toward use and behavioural intention sequentially mediated the relationships between perceived usefulness and actual system use and between perceived ease of use and actual system use. Moderation analyses were conducted using PROCESS Model 1 to test whether professional status (pre-service vs. in-service) moderated the effects of perceived usefulness and perceived ease of use on attitude toward use. All analyses employed a 95% confidence interval, and indirect effects were estimated using a bootstrapping procedure with 5000 resamples, as recommended by Hayes [64].

3. Results

To ensure the appropriateness of subsequent statistical analyses, an assessment of data normality was conducted. Following the guidance provided by Chan et al. [65], z-scores for skewness and kurtosis within the ±2 range are considered acceptable indicators of normal distribution in large samples (N > 300). As presented in

Table 1. Descriptive statistics of the study variables.

	Actual System Use (ASU)	Behavioural Intention (BI)	Attitude Toward Use (ATU)	Perceived Usefulness (PU)	Perceived Ease of Use (PEU)
N	347	347	347	347	347
Mean	42.00	29.51	15.62	18.97	19.10
Std. Deviation	8.41	5.61	2.90	3.24	4.23
Skewness	0.077	0.091	0.041	0.121	0.104
Std. Error of Skewness	0.131	0.131	0.131	0.131	0.131
Kurtosis	0.209	0.135	0.134	0.552	−0.150
Std. Error of Kurtosis	0.261	0.261	0.261	0.261	0.261

, the obtained skewness and kurtosis statistics fell within these recommended limits, suggesting that the data reasonably satisfy the assumption of normality.

To determine the minimum required sample size for testing the study hypothesis, an a priori power analysis was conducted using G*Power (version 3.1.9.7). Based on the parameters for detecting a medium effect size in a linear multiple regression analysis with a significance threshold of α = 0.05 and a statistical power of 0.80, the analysis indicated that a sample of at least 85 participants would be necessary. Given that the present study included 347 participants, the sample size was more than sufficient to ensure adequate statistical power.

A Pearson correlation analysis was conducted to examine the relationships among the variables included in the conceptual model. As shown in

Table 2. Pearson correlations between conceptual model variables.

Variables	1	2	3	4	5
1. Actual System Use (ASU	1	0.464 **	0.530 **	0.183 **	0.316 **
2. Behavioural Intention (BI)		1	0.451 **	0.367 **	0.392 **
3. Attitude Toward Use (ATU)			1	0.370 **	0.433 **
4. Perceived Usefulness (PU)				1	−0.056
5. Perceived Ease of Use (PEU)					1

N = 347; ** p < 0.01.

, Actual System Use (ASU) was significantly and positively associated with Behavioural Intention (BI; r = 0.46, p < 0.01), and Attitude Toward Use (ATU; r = 0.53, p < 0.01). Moreover, ASU was also moderately correlated with Perceived Ease of Use (PEU; r = 0.32, p < 0.01), while its association with Perceived Usefulness (PU) was weaker but still significant (r = 0.18, p < 0.01).

Behavioural Intention (BI) showed significant positive correlations with all other variables: ATU (r = 0.45, p < 0.01), PU (r = 0.37, p < 0.01), and PEU (r = 0.39, p < 0.01). ATU was also positively correlated with PU (r = 0.37, p < 0.01) and PEU (r = 0.43, p < 0.01). Interestingly, PEU was not significantly associated with PU (r = −0.06; ns), suggesting that, in this context, perceived ease of use may not directly influence perceptions of usefulness.

These results support the theorised associations in the Technology Acceptance Model (TAM), particularly the strong links between attitude, intention, and actual use.

The correlation analysis revealed significant and conceptually relevant associations among the primary variables included in the model. In light of these preliminary findings, a stepwise multiple regression analysis was subsequently performed to examine the extent to which the examined predictors—Attitude Toward Use (ATU), Perceived Ease of Use (PEU), and Perceived Usefulness (PU)—both individually and in combination explain variance in Behavioural Intention (BI).

In Step 1, Attitude Toward Use (ATU) was entered into the model and was found to significantly predict BI (β = 0.45; t (345) = 9.39; p < 0.01), accounting for 20% of the variance in the dependent variable (R² = 0.20; F (1, 345) = 88.25; p < 0.001). The squared semi-partial correlation (sr² = 0.45) indicates a substantial unique contribution of ATU to the prediction of BI.

In Step 2, the inclusion of Perceived Ease of Use (PEU) led to a significant increase in the explained variance (ΔR² = 0.05, F change (1, 344) = 21.80, p < 0.001). Both ATU (β = 0.38, p < 0.01) and PEU (β = 0.24, p < 0.01) remained significant predictors. The model explained 25% of the variance in BI (R² = 0.25), with unique contributions of sr² = 0.31 for ATU and sr² = 0.22 for PEU.

In Step 3, Perceived Usefulness (PU) was added as a final predictor. This addition significantly improved the model (ΔR² = 0.08, F change (1, 343) = 40.46, p < 0.001), resulting in a final model that explained 33% of the variance in BI (R² = 0.33, adjusted R² = 0.32). All three predictors remained significant, with PU (β = 0.31, t = 6.36, sr² = 0.28, p < 0.01), PEU (β = 0.33, t = 6.41, sr² = 0.28, p < 0.01), and ATU (β = 0.19, t = 3.56, sr² = 0.16, p < 0.01) each contributing uniquely to the prediction of behavioural intention.

These findings, summarised in

Table 3. Stepwise regression predicting Behavioural Intention.

Variable	β	t	sr2	R	R2	∆R2
Step 1				0.45 **	0.20 **	0.20 **
Attitude Toward Use (ATU)	0.45 **	9.39 **	0.45
Step 2				0.50 **	0.25 **	0.05 **
Attitude Toward Use (ATU)	0.38 **	6.69 **	0.31
Perceived Ease of Use (PEU)	0.24 **	4.66 **	0.22
Step 3				0.56 **	0.33 **	0.08 **
Attitude Toward Use (ATU)	0.19 **	3.56 **	0.16
Perceived Ease of Use (PEU)	0.33 **	6.41 **	0.28
Perceived Usefulness (PU)	0.31 **	6.36 **	0.28

N = 347; ** p < 0.01.

, underscore the collective and individual importance of attitudinal and perceptual factors in explaining the intention to use, consistent with the assumptions of the Technology Acceptance Model (TAM). Notably, PU and PEU emerged as the strongest unique predictors once all three constructs were considered simultaneously.

To gain a deeper understanding of the underlying mechanisms linking the main predictors to actual system use, a serial mediation analysis was conducted. This approach aimed to explore whether the effects of perceived usefulness and perceived ease of use on system use operate through attitudinal and intentional pathways, as suggested by the conceptual model.

To examine whether the effect of perceived usefulness (PU) on actual system use (ASU) was transmitted through behavioural intention (BI) and attitude toward use (ATU), a serial mediation analysis was performed using the PROCESS macro (Model 6) [64], with 5000 bootstrap samples and 95% bias-corrected confidence intervals (CIs).

The model accounted for 35% of the variance in ASU (R² = 0.35, F (3, 343) = 61.59, p < 0.001). PU significantly predicted BI (b = 0.63, SE = 0.09, t = 7.33, p < 0.001), and both PU (b = 0.21, SE = 0.04, t = 4.72, p < 0.001) and BI (b = 0.19, SE = 0.03, t = 7.27, p < 0.001) significantly predicted ATU. Furthermore, ATU (b = 1.23, SE = 0.15, t = 8.42, p < 0.001) and BI (b = 0.46, SE = 0.08, t = 6.06, p < 0.001) were significant positive predictors of ASU.

The total effect of PU on ASU was significant (b = 0.47, SE = 0.14, t = 3.45, p < 0.001), whereas the direct effect became non-significant when the mediators were included (b = −0.22, SE = 0.13, t = −1.78, p = 0.076), indicating full mediation.

The total indirect effect was statistically significant (b = 0.70, SE = 0.10, 95% CI [0.51, 0.90]). All three specific indirect pathways were also significant:

-

PU → BI → ASU (b = 0.29, SE = 0.06, 95% CI [0.18, 0.43]);

-

PU → BI → ATU → ASU (b = 0.15, SE = 0.03, 95% CI [0.09, 0.22]);

-

PU → ATU → ASU (b = 0.26, SE = 0.07, 95% CI [0.14, 0.41]).

The completely standardised indirect effect was β = 0.27 (95% CI [0.20, 0.34]), confirming the presence of a meaningful mediation mechanism. Taken together, the results support a serial mediation model in which PU influences ASU through its effects on both intention and attitude. The complete model is illustrated in Figure 1 below.

To further explore the mechanisms underlying actual system use, a second serial mediation analysis was conducted, this time examining the indirect effect of perceived ease of use (PEU) on system use via attitude toward use (ATU) and behavioural intention (BI). The analysis was carried out using the PROCESS macro (Model 6) [64], based on 5000 bootstrap samples and 95% bias-corrected confidence intervals.

The model explained 34.5% of the variance in ASU (R² = 0.35, F (3, 343) = 60.32, p < 0.001). PEU significantly predicted BI (b = 0.52, SE = 0.07, t = 7.91, p < 0.001), and both PEU (b = 0.21, SE = 0.03, t = 6.09, p < 0.001) and BI (b = 0.17, SE = 0.03, t = 6.70, p < 0.001) were significant predictors of ATU. In turn, ATU (b = 1.13, SE = 0.15, t = 7.55, p < 0.001) and BI (b = 0.41, SE = 0.08, t = 5.40, p < 0.001) positively predicted ASU. However, the direct effect of PEU on ASU became non-significant after including the mediators (b = 0.08, SE = 0.10, t = 0.81, p = 0.417), suggesting full mediation.

The total indirect effect of PEU on ASU was statistically significant (b = 0.55, SE = 0.08, 95% CI [0.41, 0.71]). All three specific pathways were significant:

-

PEU → BI → ASU (b = 0.21, 95% CI [0.13, 0.31]);

-

PEU → BI → ATU → ASU (b = 0.10, 95% CI [0.06, 0.16]);

-

PEU → ATU → ASU (b = 0.23, 95% CI [0.14, 0.36]).

These results support the hypothesis that the effect of perceived ease of use on actual system use is fully mediated through both behavioural intention and attitude. The structure and strength of the mediating paths are presented in Figure 2.

Building upon the previous mediation analyses, we extended the investigation by examining whether the associations between central constructs vary according to contextual characteristics. Specifically, we explored whether teacher status shapes the strength of the relationships between perceived usefulness, perceived ease of use, and attitude toward use. To this end, two separate moderation analyses were conducted.

The first analysis tested whether the relationship between perceived usefulness (PU) and attitude toward use (ATU) differs by teacher status (pre-service vs. in-service), using Model 1 of the PROCESS macro [64]. Both PU and teacher status were mean-centred prior to analysis.

The overall model was statistically significant (F (3, 343) = 22.86, p < 0.001) and explained approximately 16.4% of the variance in ATU (R² = 0.164). PU was a significant positive predictor of ATU (b = 0.34, SE = 0.04, t = 7.72, p < 0.001, 95% CI [0.25, 0.42]). Teacher status also showed a significant main effect on ATU (b = 0.69, SE = 0.31, t = 2.25, p = 0.025, 95% CI [0.09, 1.29]). The full model coefficients are presented in

Table 4. Moderation model: PU, teacher status, and Attitude Toward Use.

Predictor	b	SE	t	p	95% CI
Intercept	15.61 **	0.14	108.59 **	<0.001	[15.33, 15.90]
Perceived Usefulness (PU)	0.34 **	0.04	7.72 **	<0.001	[0.25, 0.42]
Teacher Status (centred)	0.69 *	0.31	2.25 *	0.025	[0.09, 1.29]
PU × Teacher Status Interaction	−0.23 *	0.09	−2.55 *	0.011	[−0.40, −0.05]

N = 347; * p < 0.05, ** p < 0.01.

.

Notably, the interaction term between PU and teacher status was significant (b = −0.23, SE = 0.09, t = −2.55, p = 0.011, 95% CI [−0.40, −0.05]), indicating that the effect of PU on ATU varied as a function of teacher status.

A simple slopes analysis revealed that the association between PU and ATU was stronger among pre-service teachers (−1 SD; b = 0.41, SE = 0.05, t = 7.45, p < 0.001, 95% CI [0.30, 0.52]), than among in-service teachers (+1 SD; b = 0.18, SE = 0.07, t = 2.58, p = 0.010, 95% CI [0.04, 0.32]). This interaction is illustrated in Figure 3.

The second moderation analysis examined whether the relationship between perceived ease of use (PEU) and attitude toward use (ATU) varies as a function of teacher status. As in the previous model, both PEU and teacher status were mean-centred prior to analysis, and the analysis was conducted using Model 1 of the PROCESS macro [64].

The overall model was statistically significant (F (3, 343) = 27.64, p < 0.001), explaining 19.9% of the variance in ATU (R² = 0.199). PEU was a significant positive predictor of ATU (b = 0.30, SE = 0.04, t = 8.21, p < 0.001, 95% CI [0.23, 0.37]). Teacher status also contributed significantly to the model (b = 0.65, SE = 0.29, t = 2.26, p = 0.024, 95% CI [0.08, 1.21]). The full set of coefficients is presented in

Table 5. Moderation model: PEU, teacher status, and Attitude Toward Use.

Predictor	b	SE	t	p	95% CI
Intercept	15.63 **	0.14	110.77 **	<0.001	[15.35, 15.90]
Perceived Ease of Use (PEU)	0.30 **	0.04	8.21 **	<0.001	[0.23, 0.37]
Teacher Status (centred)	0.65 *	0.29	2.26 *	0.024	[0.08, 1.21]
PEU × Teacher Status Interaction	0.02	0.07	0.33	0.740	[−0.12, −0.17]

N = 347; * p < 0.05, ** p < 0.01.

.

However, the interaction between PEU and teacher status was not significant (b = 0.02, SE = 0.07, t = 0.33, p = 0.740, 95% CI [−0.12, 0.17]), indicating that the strength of the association between PEU and ATU did not significantly differ between pre-service and in-service teachers.

Simple slopes analysis confirmed that PEU was positively associated with ATU for both pre-service teachers (−1 SD; b = 0.29, SE = 0.05, t = 6.30, p < 0.001, 95% CI [0.20, 0.38]), and in-service teachers (+1 SD; b = 0.32, SE = 0.06, t = 5.52, p < 0.001, 95% CI [0.20, 0.43]). This pattern is visualised in Figure 4.

4. Discussion

The present findings align with a well-established body of research validating the Technology Acceptance Model (TAM) in educational settings. However, beyond replicating existing associations [1,27,45], our study advances a process-oriented interpretation of the TAM by demonstrating how sustained digital engagement is shaped by interdependent psychological mechanisms—notably, the sequential interplay between perceived usefulness, ease of use, attitude, and intention. By confirming both serial mediation and contextual moderation effects, the results illustrate that technology acceptance is not a single decision point but rather a trajectory of internalisation built upon individual dispositions and a career-stage context. This layered interpretation reinforces our theoretical repositioning of the TAM as a framework capable of capturing the conditions for sustainable technology use in education. Prior research has consistently emphasised the mediating function of attitude and intention in shaping technology-related behaviours among teachers and students [38,55].

In addition to these general pathways of psychological engagement, the study also revealed meaningful differences based on teachers’ professional status, particularly in how perceived usefulness shapes attitude formation. Another noteworthy contribution of this study is the identification of a significant moderating effect of professional status on the link between perceived usefulness and attitude toward use. Pre-service teachers demonstrated a stronger association between PU and ATU, suggesting that they may be more receptive to the pedagogical value of technology. This finding aligns with literature showing that novice teachers, whose professional identities are still forming, tend to be more open to experimentation and innovation in teaching [66,67]. In contrast, in-service teachers may rely on established routines, which can reduce their responsiveness to perceived usefulness [66,67]. These differentiated cognitive–emotional patterns reinforce the need for targeted interventions at early career stages, where openness to technology can be more easily transformed into sustained digital engagement.

Importantly, this study extends the traditional application of the TAM by integrating serial mediation and moderation analyses, offering a more comprehensive understanding of the psychological mechanisms underlying technology adoption. First, the use of serial mediation models confirms a multistep motivational pathway, demonstrating that system perceptions are translated into actual use behaviours through a sequential process involving both intention and attitude. While prior studies have primarily tested direct effects or simple mediation models [20], the present findings highlight that BI and ATU jointly mediate the effects of PU and PEU on ASU, thereby enriching the understanding of the layered nature of technology acceptance processes in educational settings.

The introduction of teacher professional status (pre-service versus in-service) as a contextual moderator represents a novel contribution. Whereas earlier research has predominantly employed descriptive comparisons between pre-service and in-service teachers [48,68], this study statistically tested differential moderating effects. The finding that perceived usefulness exerts a stronger influence on attitudes toward use among pre-service teachers suggests that individuals at the early stages of professional-identity formation may be particularly receptive to instrumental beliefs regarding technology [49]. This difference can be further explained by research on professional-identity development. As noted by Ertmer and Ottenbreit-Leftwich (2010) [62], pre-service teachers are still shaping their beliefs about what it means to teach effectively and are, therefore, more open to integrating tools perceived as useful into their emerging pedagogical frameworks. Their limited exposure to rigid classroom routines allows for greater cognitive flexibility and openness to change. In contrast, in-service teachers often operate within established patterns and institutional constraints that may reduce their sensitivity to perceived usefulness as a motivational trigger. This distinction helps clarify why the PU → ATU pathway is more pronounced in early-career educators. In contrast, the relative stability of in-service teachers’ attitudes may reflect more established beliefs, practical constraints, or distinct motivational orientations, consistent with prior observations of resistance to technology integration among experienced educators [58].

Notably, the absence of a significant moderation effect for the relationship between PEU and ATU indicates that perceived ease of use constitutes a universally important determinant of attitudes across both professional groups. This finding supports user-centred design principles in educational technology development, emphasising the need to reduce the cognitive load and to enhance system accessibility to facilitate adoption among diverse user groups [69].

From a broader perspective, these findings contribute meaningfully to the discourse on educational sustainability. By illustrating how attitudinal and intentional processes differ according to career stage, the results advocate for differentiated support strategies that address the evolving needs and psychological profiles of pre-service and in-service teachers. This perspective aligns with contemporary calls to move beyond “one-size-fits-all” approaches in digital professional development and toward more personalised, sustainable capacity-building frameworks [70].

Moreover, the findings have significant implications for fostering sustainable digital practices in education. Sustainability in this context encompasses not only environmental concerns but also the continuity, adaptability, and relevance of educational practices in a rapidly changing technological and institutional landscape [71]. The demonstrated mediating role of attitude and intention underscores that sustainable engagement with digital systems requires nurturing positive psychological climates where teachers perceive digital tools as both professionally valuable and personally meaningful.

Career-stage differentiation further highlights the importance of embedding sustainability values, such as adaptability, innovation, and long-term impact, within teacher education programs. For pre-service teachers, the strong influence of perceived usefulness on attitudes suggests a critical developmental window for instilling sustainable digital dispositions. Early cultivation of these attitudes may serve as behavioural anchors for future innovation and lifelong learning in teaching practices.

Conversely, promoting sustainable technology integration among in-service teachers necessitates strategies focused on contextual support, collaborative meaning-making, and alignment with practical realities. Given the weaker PU → ATU linkage in this group, interventions should emphasise not only the instrumental benefits of technology but also its alignment with experienced educators’ professional values and practices. The universal importance of perceived ease of use across groups further underscores that reducing barriers to usability remains a fundamental principle for sustainable educational change.

The outcomes of this study may be further understood through the lens of the Triple Bottom Line (TBL), which frames sustainability in terms of environmental, social, and economic dimensions. At the environmental level, increased digital adoption has the potential to reduce reliance on printed teaching materials, lower paper consumption, and minimise the need to commute to physical training sessions, thereby supporting institutional efforts to reduce their ecological footprint. On the social dimension, teachers who perceive digital tools as useful and easy to use are more likely to report higher levels of job satisfaction, stronger motivation, and a greater sense of professional relevance, factors that are essential for sustainable engagement and long-term retention in the profession. From an economic perspective, professional-development strategies that are tailored to the specific needs of teachers can help reduce overall institutional costs. For example, emphasising perceived usefulness in training programs designed for pre-service teachers and prioritising usability for educators at all levels of experience can lead to more efficient structuring of content and delivery formats while maintaining pedagogical relevance. These concrete examples illustrate how the psychological drivers of technology acceptance can align with broader sustainability objectives, contributing to a more holistic and actionable model for digital transformation in education [44].

Finally, these insights contribute to the broader framework of social sustainability in education. Sustained digital engagement among teachers emerges not from compulsion but from motivated alignment between personal values, perceived relevance, and professional identity. Thus, investing in teachers’ perceptions, attitudes, and digital competencies is a strategic imperative for building resilient, adaptable educational systems capable of thriving in an increasingly complex digital future. The implications of these findings extend beyond theoretical modelling and into the design of teacher-education programs. For in-service teachers, effective professional-development initiatives should incorporate curriculum-aligned technology training based on the TPACK framework [72], ensuring pedagogical coherence and subject relevance. By contrast, pre-service teachers, who exhibit greater attitudinal openness, may benefit more from training modules that emphasise system usability and experiential learning with digital tools. These differentiated strategies align with the goal of fostering sustainable, career-stage-responsive technology adoption in education.

While our model retains the structural simplicity of the TAM, it extends its interpretive power by reconceptualising acceptance as a dynamic and career-sensitive psychological process. This shift, from prediction to transformation, offers novel insights into how sustained digital practices are shaped not only by individual cognition but also by professional context and developmental stage. The integration of dual serial mediation and contextual moderation provides a nuanced understanding of how technology acceptance evolves as a sustainability trajectory rather than a one-time behavioural decision.

Given the growing complexity of extended TAM models, applying them in institutional contexts can be challenging. To enhance practical usability, we suggest a simplified version that clusters the original constructs into three core components: value, usability, and readiness to adopt. “Value” reflects perceived usefulness (PU) and its alignment with teachers’ pedagogical goals. “Usability” corresponds to perceived ease of use (PEU), emphasising intuitive design and accessibility. “Readiness to adopt” encompasses both attitude toward use (ATU) and behavioural intention (BI), capturing motivational and affective preparedness. This streamlined model retains TAM’s explanatory power while facilitating its integration into teacher-training programs, professional-development tools, and institutional policy frameworks. It can serve as a conceptual bridge between theoretical rigor and actionable implementation strategies in education systems undergoing digital transformation.

5. Limitations and Future Directions

Despite its contributions, this study is not without limitations. First, the research design was cross-sectional and correlational in nature, which limits the ability to draw causal inferences about the relationships among variables. Longitudinal studies would be valuable in capturing how attitudes, intentions, and usage behaviours evolve over time, particularly in the context of sustained digital engagement.

Second, the study relied entirely on self-reported data, including the measure of actual system use (ASU). Although established and validated instruments were used, self-report measures are inherently susceptible to biases such as social desirability, overestimation, or recall errors. Future studies could address this limitation by triangulating self-reported data with objective usage metrics, such as log data from learning management systems (LMSs), platform analytics, or digital trace data. The integration of behavioural analytics would enhance the validity of system use measurements, mitigate the impact of social desirability bias, and enable a more comprehensive understanding of technology engagement patterns. In doing so, future research can improve the robustness of findings and offer deeper insights into actual usage behaviour [38].

While this study focused on core constructs within the Technology Acceptance Model, it did not include broader psychological variables such as technostress, role ambiguity, or institutional trust, which may also play a role in shaping technology-related behaviours. Moreover, contextual elements like access to digital infrastructure, school leadership support, and professional-development culture were not assessed, although they likely interact with psychological pathways. Future research should consider integrating such factors to offer a more comprehensive picture of technology adoption and sustainability in diverse educational environments.

Third, participants were recruited using convenience sampling from one national context, potentially limiting the generalisability of the results. This limitation is compounded by the demographic profile of the sample, which consisted predominantly of female participants (64.6%) and was drawn exclusively from Romania. Such demographic and cultural homogeneity may affect how participants perceive and respond to technology-related constructs. Prior research highlights that gender and cultural context can moderate technology acceptance mechanisms, potentially shaping constructs like perceived usefulness or behavioural intention in culturally specific ways [67]. Therefore, generalising the findings to more diverse educational contexts should be done with caution. Cultural, institutional, and infrastructural differences may shape technology acceptance processes in meaningful ways. Thus, replicating the study in diverse educational settings and countries could provide broader insights into the generalisability of the proposed model. Critically, we contend that analysing TAM mechanisms within the Romanian educational context offers a unique contribution, as this region remains underrepresented in international educational technology literature. The specific institutional, historical, and policy environment of Romanian teacher education may shape psychological responses to digital innovation in ways that differ from those in Western systems. As such, this study may serve as a valuable baseline for future cross-cultural research examining how contextual factors influence the sustainability of technology adoption in education.

At the same time, we emphasise that the study was also designed to explore the cultural specificity of how TAM mechanisms manifest within the Romanian educational context. Most TAM-based studies have been conducted in high-income or Anglophone educational systems, often overlooking culturally distinct environments. By applying the model in Romania, we aim to contribute to the diversification of the TAM evidence base and to highlight how cultural factors may shape the attitudinal and motivational dynamics of technology acceptance. While our findings are not intended to be universally generalised, they offer contextually rich insights and may inform comparative studies that examine the role of national educational systems in shaping sustainable digital practices.

In addition to professional status, future research should also consider examining other contextual variables that may shape teachers’ psychological engagement with technology. Factors such as institutional support, digital literacy training, and teaching specialisation could interact meaningfully with core TAM constructs and influence long-term technology adoption. Accounting for such moderators may enhance the explanatory depth of future models and support the development of more context-sensitive and career-stage-responsive interventions.

Building on these directions, future research could further explore how sustainable technology use develops over time and across different educational systems. Longitudinal and cross-cultural investigations would not only enhance the generalisability of the present findings, but also offer insight into how institutional policies, cultural values, and the evolution of professional identity influence durable patterns of digital engagement. By embracing this broader scope, future studies may help integrate psychological, organisational, and pedagogical dimensions of sustainability into a more comprehensive understanding of educational technology adoption.

In addition to these methodological considerations, future research would benefit from adopting longitudinal designs to explore how teachers’ attitudes and usage behaviours evolve over time, particularly in response to institutional interventions or systemic changes. Relying solely on self-reported data to assess technology use may obscure actual behavioural patterns; thus, future studies should incorporate objective metrics such as LMS usage logs, digital platform analytics, or observational data. Moreover, expanding the current model to include contextual variables, such as institutional support structures, levels of digital literacy, type and quality of teacher training, and subject specialisation, would offer a more ecologically valid understanding of technology adoption. These elements are likely to interact meaningfully with psychological drivers and could inform the design of differentiated context-responsive strategies for sustainable technology integration in education.

6. Conclusions

This study provides important theoretical and practical insights into the psychological mechanisms underpinning sustainable technology integration in education. By extending the Technology Acceptance Model (TAM) with serial mediation and moderation analyses, the research demonstrates that perceived usefulness, perceived ease of use, attitude toward technology use, and behavioural intention interact in complex, meaningful ways to influence actual system use. These constructs, situated within an educational sustainability framework, help illuminate how digital practices can be adopted and maintained across diverse teaching populations.

A central contribution of this study lies in the identification of professional status as a salient moderator of the technology acceptance process. The finding that perceived usefulness exerts a stronger influence on attitudes among pre-service teachers suggests that formative career stages represent critical periods for shaping lasting digital dispositions. Meanwhile, the consistent impact of perceived ease of use across both pre-service and in-service groups highlights the enduring value of intuitive, accessible technologies in fostering adoption regardless of professional experience.

Importantly, the originality of our contribution lies in how we apply and expand the TAM as a process-based model of sustainable engagement. Rather than introducing new variables, we clarify how classical constructs, when embedded within a framework sensitive to career-stage and psychological dynamics, can reflect sustainability values such as continuity, relevance, and adaptability. This interpretative layer strengthens the coherence between the conceptual model and the article’s broader educational sustainability goals, aligning psychological acceptance pathways with systemic and long-term digital transformation in teaching practice.

From a sustainability perspective, these findings underscore that long-term digital transformation in education depends not only on technological infrastructure but also on cultivating psychological readiness, agency, and contextual alignment [53,73]. Sustainable integration requires continuous support systems, adaptive professional-development strategies, and technology designs responsive to the evolving needs of educators. Moreover, by fostering positive attitudinal and motivational conditions, institutions can encourage resilient, future-oriented teaching practices that align with the principles of sustainability: promoting equity, long-term capacity building, and responsible innovation.

In relation to the study’s hypotheses, our findings provide nuanced support across all proposed relationships. H1 was confirmed: perceived usefulness (PU), perceived ease of use (PEU), and attitude toward use (ATU) were all significant predictors of behavioural intention (BI), reinforcing the core assumptions of the TAM framework. H2 and H3 were also supported, as both PU and PEU influenced actual system use (ASU) through serial mediation pathways involving ATU and BI. These results underscore the importance of attitudinal and motivational mechanisms in sustaining digital engagement. Regarding contextual effects, H4 was supported: professional status moderated the PU → ATU relationship, with stronger effects among pre-service teachers. This finding highlights the need for targeted interventions that strengthen perceived usefulness during the early stages of teacher preparation. In contrast, H5 was not supported, indicating that PEU influences attitude formation equally across career stages. This pattern suggests that usability remains a universal driver of positive perceptions and sustainable technology adoption among teachers.

These findings align with a growing body of research that highlights the critical role of attitudinal and motivational factors in shaping sustainable patterns of technology use in educational settings [55,73]. Rather than viewing digital engagement as a product of mere exposure or access, these studies underscore the importance of psychological readiness, perceived relevance, and internal commitment in fostering meaningful integration of technology into teaching practice. Building on this foundation, the present study contributes novel empirical evidence by employing a dual serial mediation model that captures the dynamic interplay between perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. This approach reveals that long-term, sustained adoption of educational technology is not simply a matter of functionality or policy enforcement but emerges through a sequential process in which educators internalise the perceived value of digital tools and translate that value into intentional and consistent behaviour. As such, our model advances a more process-oriented and psychologically grounded understanding of sustainable technology use, one that integrates both individual agency and systemic relevance within the broader landscape of educational transformation.

Positioned beyond a mere validation effort, this study introduces a reframing of technology acceptance as a context-sensitive, sustainability-oriented process. By highlighting both the sequential psychological mechanisms and their variation across career stages, the findings offer a nuanced perspective that supports long-term, equitable, and adaptable digital integration in education. This approach aligns theoretical rigor with actionable insights for teacher education and policy, contributing to the ongoing dialogue on sustainable innovation in teaching and learning.

Taken together, these refinements position the present study not only as a validation of the TAM framework but also as a conceptual contribution that expands its utility in sustainability-oriented educational contexts. By introducing a practical, streamlined version of TAM, cantered on value, usability, and readiness, we offer an accessible framework for teacher training and institutional policy. Furthermore, by recommending the inclusion of contextual variables and objective behavioural data in future research, we respond to ongoing calls for more ecologically valid, system-aware approaches to digital adoption. Ultimately, sustainable technology use in education depends on more than functional access; it requires theoretical clarity, contextual responsiveness, and strategic integration into the evolving professional landscape of teaching.