Educational podcasts and student engagement in Ecuadorian higher education: an SEM-based evaluation of the technology acceptance model

The integration of digital technologies has reshaped educational environments, positioning podcasts as an accessible and flexible resource that supports learning in asynchronous and multitasking contexts. In Ecuador, their adoption is challenged by the digital divide and socioeconomic inequalities, yet they also represent an opportunity to promote educational equity. This study examined the acceptance of podcasts in higher education through the Technology Acceptance Model and its relationship with student engagement. A Structural Equation Modeling approach was applied using data collected from university students, incorporating constructs such as perceived usefulness, ease of use, attitude, intention, and user experience. The model demonstrated a strong fit (CFI = 0.957; RMSEA = 0.065), confirming that perceived usefulness and ease of use significantly influence both attitude and intention to adopt. The findings suggest that podcasts constitute a strategic pedagogical tool to foster engagement, autonomy, and meaningful learning in digital higher education contexts.

1 Introduction

The rapid advancement of digital technologies has profoundly transformed higher education, giving rise to new pedagogical models aligned with the principles of Education 4.0, where flexibility, autonomy, and learner-centered approaches play a central role. Within this context, podcasts have emerged as an accessible and scalable digital resource that supports asynchronous learning and facilitates knowledge acquisition in multitasking environments. Their low technical requirements and compatibility with mobile devices make them particularly relevant in contemporary higher education systems seeking to enhance learning opportunities beyond traditional classroom boundaries.

Recent research highlights that podcasts can positively influence learning outcomes by improving content comprehension, knowledge retention, and students’ engagement with academic materials. These attributes are especially valuable for students who balance academic responsibilities with work or personal commitments, as podcasts enable self-paced learning and greater control over study routines. Despite their growing use, empirical evidence on the factors that determine students’ acceptance of podcasts as formal educational tools remains limited, particularly in non-Western and emerging educational contexts.

The Technology Acceptance Model (TAM) has been widely employed to explain individuals’ adoption of digital technologies in educational settings, emphasizing the role of perceived usefulness and perceived ease of use in shaping attitudes and behavioral intentions. However, existing studies often examine technology acceptance in isolation, without sufficiently addressing how acceptance relates to student engagement, a key dimension of effective learning in Education 4.0 environments. Moreover, limited attention has been given to experiential factors, such as user experience, that may play a decisive role in shaping students’ sustained use of podcast-based learning resources.

These gaps are particularly evident in Latin American higher education systems, where digital innovation coexists with persistent challenges related to the digital divide and socioeconomic inequalities. In this context, podcasts represent both an opportunity and a challenge: while they offer a low-cost and flexible learning alternative, their successful integration depends on students’ perceptions, experiences, and engagement with the technology.

Against this backdrop, this study investigates the acceptance of educational podcasts in higher education by applying a Structural Equation Modeling (SEM) approach grounded in the Technology Acceptance Model and extended to incorporate user experience and student engagement. By empirically examining the relationships among these constructs, the study seeks to contribute to a more nuanced understanding of how podcasts can support engagement, autonomy, and meaningful learning in advanced digital educational environments.

2 Theoretical framework

2.1 Technology acceptance model (TAM) in digital higher education

The Technology Acceptance Model (TAM) has been widely adopted as a theoretical framework to explain individuals’ acceptance and use of digital technologies in educational contexts. Grounded in the work of Davis (1989), the model posits that perceived usefulness (PU) and perceived ease of use (PEOU) are the primary determinants shaping users’ attitudes toward a technology, which in turn influence behavioral intention and actual use. In higher education, TAM has proven particularly effective in explaining students’ adoption of learning management systems, mobile learning applications, and other digital educational tools (Wang et al., 2023).

In the context of podcast-based learning, PU is associated with students’ perceptions of improved comprehension, academic performance, and study efficiency, whereas PEOU reflects the extent to which podcasts are perceived as intuitive, accessible, and compatible with students’ learning routines. Empirical studies consistently show that when students perceive educational technologies as both useful and easy to use, they are more likely to develop favorable attitudes and sustained intentions to adopt them (Xu and Deng, 2024; Kakhki et al., 2025). However, the increasing complexity of digital learning environments has raised questions regarding the sufficiency of the original TAM to fully capture technology acceptance processes in Education 4.0 contexts (Mastour et al., 2025).

2.2 Extending TAM: engagement-oriented and experiential perspectives

Recent scholarship argues that traditional TAM-based models tend to focus primarily on adoption intentions, often neglecting learning-related outcomes such as student engagement. At the same time, studies on student engagement frequently examine motivational and behavioral dimensions without explicitly modeling the technological acceptance mechanisms that precede them (Lu et al., 2023). Moreover, a substantial portion of the existing research relies on descriptive or regression-based approaches, with limited use of Structural Equation Modeling (SEM) to test complex, multivariate relationships among acceptance, experience, and engagement constructs.

This fragmentation in the literature reveals an important theoretical gap: the lack of integrative models that simultaneously examine technology acceptance, experiential factors, and student engagement within a single analytical framework. Additionally, most empirical evidence originates from developed countries, limiting the generalizability of findings to contexts characterized by digital inequalities and heterogeneous access to technology, such as higher education systems in Latin America (Ruiz-Herrera et al., 2023; Adnan et al., 2025).

2.3 User experience as a mediating construct in podcast acceptance

To address these limitations, the present study incorporates User Experience (UX) as a complementary construct within the TAM framework. UX is conceptualized as users’ overall evaluation of their interaction with a technology, encompassing both technical dimensions (e.g., usability, accessibility, interface clarity) and perceptual–affective dimensions (e.g., enjoyment, engagement, perceived dynamism) (Bonfanti et al., 2023). In educational technologies, positive user experiences have been shown to enhance satisfaction, reduce technology-related anxiety, and foster continued use (Wang et al., 2024; Dong and Itoh, 2025).

The inclusion of UX is theoretically justified for three main reasons. First, UX integrates multiple experiential dimensions that are particularly relevant in asynchronous learning technologies, such as podcasts, where sustained use depends not only on functional utility but also on perceived enjoyment and learning flow. Second, UX captures students’ holistic interaction with the technology, thereby complementing PU and PEOU without overlapping conceptually with them. Third, incorporating UX allows for a parsimonious extension of TAM, avoiding the inclusion of multiple redundant variables that could unnecessarily inflate the model and compromise interpretability.

Rather than introducing a wide array of psychological or contextual factors, the selection of UX as a mediating construct enables a focused examination of how technological perceptions translate into behavioral intention and engagement, aligning with calls for more theoretically coherent and empirically tractable TAM extensions (Walczak et al., 2022; Buawangpong et al., 2025).

2.4 Student engagement in podcast-based learning

Student engagement, defined as the cognitive, emotional, and behavioral involvement in learning activities, represents a critical outcome in Education 4.0 environments. Digital technologies that promote autonomy, flexibility, and experiential learning have been shown to foster deeper engagement and more meaningful learning experiences (He and Li, 2023). Podcasts, by allowing students to control the pace, timing, and context of learning, are well positioned to support engagement, particularly among learners balancing academic and non-academic responsibilities (Kakhki et al., 2025).

From a TAM perspective, engagement can be understood as a downstream outcome of technology acceptance processes. When students perceive podcasts as useful and easy to use, and when their interaction with the technology results in a positive user experience, they are more likely to engage actively and persistently with learning content. However, empirical evidence linking TAM constructs directly to engagement outcomes remains limited, underscoring the need for integrated analytical models that connect acceptance mechanisms with educational impact.

Despite the growing literature on podcasts, technology acceptance, and student engagement, several gaps persist. First, most studies analyze TAM without explicitly incorporating engagement as an outcome variable. Second, engagement-focused studies often lack a rigorous acceptance framework and rarely employ SEM to model complex relationships. Third, User Experience has not been systematically examined as a mediating construct in podcast-based learning acceptance models. Finally, empirical research from emerging and developing educational contexts remains underrepresented.

Taken together, these gaps highlight the need for an integrative analytical approach that simultaneously examines technology acceptance, user experience, and student engagement. In this regard, the present study offers a theoretically coherent and empirically robust contribution by applying a Structural Equation Modeling framework to capture the interplay between technological perceptions, experiential interaction, and engagement outcomes in advanced digital learning environments.

3 Materials and methods

This study adopted a quantitative research approach at the descriptive, correlational, and explanatory levels, employing a cross-sectional design. This methodological strategy was selected to examine the structural relationships between technology acceptance constructs and student engagement in the context of educational podcast use. The application of Structural Equation Modeling (SEM) was particularly appropriate, as it enables the simultaneous estimation of measurement and structural models, allowing for a rigorous assessment of latent variables and their interrelationships within an extended Technology Acceptance Model (TAM) framework.

The study population consisted of undergraduate students enrolled at the Technical University of Ambato (Ecuador). A probabilistic sampling strategy was employed using simple random sampling (SRS) to ensure representativeness across the institution. A total of 376 students participated in the study, drawn proportionally from the university’s 10 faculties. This sample size exceeds commonly recommended thresholds for covariance-based SEM, thereby ensuring adequate statistical power and stability of parameter estimates.

Participation was voluntary and anonymous, and ethical principles related to informed consent and confidentiality were strictly observed throughout the data collection process.

Data were collected through a structured questionnaire composed of 24 items, measured on a five-point Likert scale ranging from strongly disagree to strongly agree. The instrument was developed based on validated scales from prior studies on technology acceptance and adapted to the context of podcast-based learning in higher education.

The questionnaire measured five latent constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), Intention to Use (IU), and User Experience (UX). Each construct was operationalized using five items, with the exception of User Experience, which was measured using four items reflecting students’ experiential interaction with educational podcasts.

User Experience (UX) was incorporated as a complementary construct extending the traditional TAM framework. UX was conceptualized as students’ holistic evaluation of their interaction with podcast-based learning, integrating both technical dimensions (e.g., system integration, ease of access) and experiential–affective dimensions (e.g., perceived enhancement of learning, digital habit formation, and preference for interactive environments).

The inclusion of UX is theoretically justified for three main reasons. First, podcast-based learning is inherently asynchronous and self-directed, making experiential interaction a critical determinant of sustained use beyond initial adoption. Second, UX captures affective and interactional dimensions that are not fully explained by perceived usefulness or ease of use, thereby complementing rather than overlapping with core TAM constructs. Third, incorporating UX allows for a parsimonious extension of TAM, avoiding the inclusion of multiple redundant psychological or contextual variables that could inflate the model and reduce interpretability.

Items measuring UX were adapted from established research on technology acceptance and user interaction in digital environments, ensuring conceptual consistency with prior empirical work while remaining contextually appropriate for educational podcasts.

The instrument was subjected to internal consistency analysis using Cronbach’s alpha. The reliability coefficients obtained for each construct were as follows: Perceived Usefulness (α = 0.944), Perceived Ease of Use (α = 0.939), Attitude Toward Use (α = 0.951), User Experience (α = 0.914), and Intention to Use (α = 0.952). The overall reliability coefficient for the questionnaire was α = 0.977, indicating excellent internal consistency and strong acceptance of the measurement instrument, in accordance with established methodological standards (Oviedo and Campo-Arias, 2005; Reidl-Martínez, 2013).

Although UX exhibited slightly greater variability compared to other constructs, its reliability coefficient exceeded recommended thresholds, supporting its inclusion as a valid and stable construct within the model.

Data analysis was conducted using covariance-based Structural Equation Modeling (SEM) following a two-stage approach. First, a Confirmatory Factor Analysis (CFA) was performed to evaluate the measurement model, assessing factor loadings and construct reliability. Second, the structural model was estimated to test the hypothesized relationships among TAM constructs, User Experience, and intention to use podcasts.

Model fit was evaluated using a combination of absolute and incremental fit indices, including the Chi-square statistic, Root Mean Square Error of Approximation (RMSEA), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI/TLI), and Comparative Fit Index (CFI). These indices were selected in accordance with best practices for SEM in educational research and interpreted using established cutoff criteria.

To enhance methodological rigor and transparency, all analytical decisions—including construct selection, item specification, and model evaluation criteria—were guided by theoretical coherence and empirical adequacy rather than by purely statistical optimization. The potential heterogeneity observed in User Experience indicators is acknowledged as a methodological limitation and interpreted as reflective of differences in students’ digital habits and interaction patterns, rather than as evidence of construct misspecification.

4 Results

The findings of the confirmatory factor analysis indicate that the construct Perceived Usefulness (PU) achieved very high standardized loadings (0.77–0.94), demonstrating strong convergent validity. In particular, the items related to content comprehension (0.93) and productive study (0.94) emerged as the most representative, suggesting that students recognize podcasts as an effective resource to optimize content assimilation and enhance academic performance. These results reinforce the relevance of TAM in educational contexts by confirming that perceived usefulness constitutes a key determinant of technology acceptance.

For Perceived Ease of Use (PEOU), the results also reflect a solid performance, with loadings ranging between 0.82 and 0.89. Items related to ease of use (0.89) and intuitive interface (0.89) stood out as the primary indicators of the construct. This consistency reveals that students value simplicity and accessibility as fundamental attributes for incorporating podcasts into their study practices, which is consistent with the literature emphasizing the role of ease of use as a direct facilitator of technological adoption.

Attitude Toward Use (ATU) presented homogeneous and high results (0.88–0.90), consolidating its mediating role within the model. The importance of perceiving podcasts as a preferred format (0.90) and as an enjoyable class resource (0.90) is particularly notable, confirming that a positive experiential component significantly strengthens students’ willingness to engage with this technology. Similarly, Intention to Use (IU) also reached high values (0.86–0.92), with emphasis on the consideration of podcasts as an official resource (0.92) and as part of a study strategy (0.90). These results suggest that, beyond occasional use, there is a clear willingness to integrate podcasts into formal academic planning, underscoring their institutional potential in higher education.

Finally, the construct User Experience (UX) exhibited a more heterogeneous performance, with loadings ranging from 0.63 to 0.81. While items related to technological integration (0.80) and enhanced learning (0.81) maintained acceptable levels, others, such as those associated with digital habits (0.69) and interactive preference (0.63), reflected lower consistency. These findings suggest the need to refine this construct in future studies, either through the redefinition of items or the inclusion of new indicators that better capture the comprehensive experience of students using educational podcasts.

The results obtained from the structural equation modeling analysis indicate that the model demonstrates a statistically significant fit (Table 1). The Chi-square value (χ² = 710.342, df = 220, p < 0.001) shows, as is often the case with large samples, the sensitivity of this test. In this regard, the χ²/df ratio = 3.2 falls within the acceptable range, close to the recommended threshold of ≤3, suggesting a reasonably parsimonious fit between the data and the model.

Table 1

Table 1. Confirmatory factor analysis: summary of the measurement model, validity, and reliability.

The incremental fit indices reached satisfactory values, with CFI = 0.957, IFI = 0.958, TLI = 0.951, and NFI = 0.946, all above the 0.90 threshold and close to or exceeding 0.95. These results confirm the robustness and consistency of the proposed model compared to the null model. They further suggest that the model adequately represents the latent structure underlying the acceptance of podcasts as an educational resource.

Regarding RMSEA = 0.065, this value is below the 0.08 threshold, indicating an acceptable fit, and is close to the optimal range recommended in the literature (<0.06). This indicator, together with the comparative indices, supports the overall validity of the model and the relevance of the constructs included in explaining technological adoption in the educational context.

Hypotheses

The model examines four hypotheses that explore the correlations among the latent variables:

H1: Attitude Toward Use (AU) is significantly related to Intention to Use (IU).

H2: Perceived Ease of Use (PEU) is significantly related to Intention to Use (IU).

H3: Perceived Usefulness (PU) is significantly related to Intention to Use (IU).

H4: User Experience (UE) is significantly related to Intention to Use (IU).

To better understand the relationships among the latent variables, it is necessary to examine their covariances, the details of which are presented in Table 2.

Table 2

Table 2. Relationships between the latent constructs.

The verification of the hypotheses can be summarized as follows (Table 3).

Table 3

Table 3. Hypotheses verification.

All hypotheses were found to be highly significant (p < 0.01), each explaining more than 50% of the variance, with User Experience accounting for the highest proportion at 82.8% (Figure 1).

Figure 1

Figure 1. Conceptual model: graphical description with estimated parameters.

Overall, the satisfactory fit indices and high standardized loadings provide strong evidence of construct validity and structural consistency within the proposed TAM-based model.

5 Discussion

The results of this study provide robust empirical support for the Technology Acceptance Model (TAM) as an explanatory framework for understanding the adoption of educational podcasts in higher education, while also extending its explanatory scope through the inclusion of User Experience (UX). Consistent with TAM theory, perceived usefulness (PU) and perceived ease of use (PEOU) emerged as central determinants shaping students’ attitudes and intentions to adopt podcasts. However, beyond confirming established relationships, the findings offer nuanced insights into how these constructs operate within asynchronous and multitasking learning environments.

The strong standardized loadings observed for perceived usefulness—particularly those related to topic comprehension and productive study—indicate that students perceive podcasts as a resource that directly enhances learning effectiveness rather than as a peripheral or supplementary tool. This reinforces previous evidence suggesting that perceived learning improvement is a primary driver of technology acceptance (Xu and Deng, 2024; Kakhki et al., 2025). Importantly, this study extends prior work by Mastour et al. (2025) and Lu et al. (2023) by demonstrating that, in the context of podcasts, usefulness is not only associated with content quality or institutional support but also with the technology’s capacity to be seamlessly integrated into students’ daily routines. The asynchronous and multitasking nature of podcasts appears to amplify their perceived academic value, highlighting a contextual dimension of usefulness that has received limited attention in earlier TAM-based studies.

Regarding perceived ease of use, the findings confirm that simplicity, intuitive interfaces, and low cognitive effort are key conditions for adoption, consistent with Prasetyo et al. (2025) and Song et al. (2023). However, the results suggest that ease of use extends beyond technical operability. In this study, PEOU is closely linked to students’ sense of autonomy and control over their learning processes. This interpretation aligns with Buawangpong et al. (2025), who emphasize that clear and accessible technological environments reduce anxiety and foster confidence. Thus, PEOU functions not merely as a usability indicator but as a psychological enabler that supports self-directed learning, particularly relevant in digital higher education contexts.

Attitude toward use plays a pivotal mediating role in the model, confirming its theoretical position within TAM. The strong association between attitude and constructs related to enjoyment and learning dynamism underscores the importance of affective and motivational dimensions in technology acceptance. This finding is consistent with Doo (2023) and Ibrahim et al. (2024), who stress the role of intrinsic motivation in shaping favorable adoption behaviors. A distinctive contribution of this study lies in demonstrating that attitudes toward podcast use are also socially and institutionally embedded. Faculty endorsement and institutional legitimacy significantly reinforce positive attitudes, corroborating the arguments of Park et al. (2024) and Desmaryani et al. (2024). This suggests that acceptance is not solely an individual decision but a collective process shaped by trust, academic norms, and pedagogical signaling within the educational ecosystem (Bîlbîie et al., 2024).

The results related to intention to use further reinforce this interpretation. Students expressed not only a willingness to continue using podcasts as a personal learning resource but also an openness to their formal integration into institutional learning strategies. This supports prior findings that adoption intentions are strengthened when technologies are perceived as aligned with curricular objectives and academic expectations (Zou and Huang, 2023; Jiao and Cao, 2024). Moreover, the positioning of podcasts at the intersection of educational and experiential engagement aligns with studies suggesting that entertainment-related elements can enhance rather than undermine learning commitment (Brar et al., 2022; Förster, 2024; Puiu and Udriștioiu, 2024). In this sense, engagement is not diluted by enjoyment but reinforced through meaningful and contextually relevant learning experiences.

A critical and theoretically significant finding concerns the construct of User Experience. While UX demonstrated acceptable reliability, its indicators showed greater variability compared to other constructs, particularly those related to digital habits and interactive preferences. Rather than indicating construct weakness, this variability reflects the heterogeneity of students’ prior technological experiences and interaction styles. This result highlights an important theoretical implication: UX in educational podcasts is not yet a fully stabilized construct and may depend heavily on design maturity, platform features, and personalization capabilities. Compared to the more consolidated findings of Bonfanti et al. (2023), Dong and Itoh (2025), and Alturkustani et al. (2025), the present results suggest that current podcast platforms still lack sufficient interactivity and adaptive design to fully leverage experiential engagement.

From a theoretical perspective, these findings justify the inclusion of UX as a complementary construct within TAM, particularly in asynchronous learning technologies. UX captures experiential dimensions that are not fully explained by usefulness or ease of use, without inflating the model with redundant variables. Practically, the results point to the need for educational institutions and content designers to move beyond content delivery and invest in interaction design, personalization, and engagement-oriented features. Incorporating elements such as adaptive recommendations, interactive prompts, or gamified components may strengthen UX and, consequently, reinforce sustained adoption and engagement.

Overall, this study advances the literature by demonstrating that the acceptance of educational podcasts is a multidimensional phenomenon that integrates technological perceptions, affective responses, and experiential interactions. By situating TAM within the broader context of user experience and student engagement, the findings contribute to a more comprehensive understanding of how digital tools can support meaningful learning in Education 4.0 environments.

6 Conclusion

The findings of this study confirm that the Technology Acceptance Model (TAM) provides a solid and theoretically coherent framework for explaining the acceptance of educational podcasts in higher education. The results demonstrate that perceived usefulness and perceived ease of use remain the central drivers shaping students’ attitudes and intentions to adopt this technology. Importantly, podcasts are not perceived merely as supplementary learning aids, but as strategic pedagogical tools that enhance content comprehension, support productive study practices, and align with the asynchronous and multitasking demands characteristic of contemporary digital learning environments. Overall, the satisfactory fit indices and high standardized loadings provide strong evidence of construct validity and structural consistency within the proposed TAM-based model.

Beyond validating the core structure of TAM, the study highlights the relevance of contextual and social dynamics in shaping technology acceptance. Attitude toward use emerges as a key mediating construct influenced not only by individual perceptions but also by institutional trust and faculty endorsement. This finding underscores that acceptance processes are embedded within an educational ecosystem, where pedagogical legitimacy and institutional support play a decisive role in fostering favorable dispositions toward digital tools.

The results related to intention to use further suggest that podcasts possess strong potential for sustained adoption and institutional integration. Their positioning at the intersection of educational and experiential engagement indicates that affective and motivational dimensions—rather than detracting from learning—can enhance student commitment and engagement when aligned with academic objectives.

At the same time, the analysis reveals that user experience represents a critical yet underdeveloped dimension. Variability in user experience indicators points to limitations in personalization and interactivity, suggesting that current podcast platforms have not fully leveraged experiential design features. This finding not only justifies the inclusion of user experience as a complementary construct within TAM but also signals a priority area for pedagogical innovation and future research.

Nevertheless, the cross-sectional nature of the study limits the ability to infer causal relationships among the examined constructs. While the SEM results provide robust evidence of structural associations, future longitudinal or experimental research designs are recommended to capture changes in technology acceptance and student engagement over time.

Overall, the study contributes empirical evidence that both reinforces and extends TAM by demonstrating that technology acceptance in higher education—particularly within the Ecuadorian higher education context—is a multidimensional phenomenon shaped by the interaction of cognitive evaluations, motivational responses, experiential perceptions, and contextual factors. These insights support the strategic role of educational podcasts within Education 4.0 environments and provide a foundation for designing more engaging, inclusive, and effective digital learning experiences. Future studies should replicate the proposed model across different institutional and cultural contexts to further assess the external validity of the findings.

Data availability statement

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

Author contributions

DM-U: Conceptualization, Investigation, Project administration, Writing – original draft. RY-V: Investigation, Writing – original draft. LM-F: Formal analysis, Methodology, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

The authors would like to thank the Dirección de Investigación y Desarrollo-DIDE of the Universidad Técnica de Ambato. This article is derived from the research project entitled “Innovación en la Comunicación de la Universidad Técnica de Ambato,” approved with Resolution No. UTA-CONIN-2023-0373-R by the Dirección de Investigación y Desarrollo-DIDE of the Universidad Técnica de Ambato, Ecuador.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript.

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