1 Introduction

In recent decades, the field of Information and Communications Technology (ICT, Martínez-Navalón et al., 2023) has experienced significant growth and development, generating a marked interest in e-learning (Saadé & Kira, 2007). This phenomenon is reflected in the notable increase in scientific literature and official research into this learning modality in numerous countries (Al-Ruheel et al., 2022) in the wake of the COVID-19 pandemic in 2019, a situation which led to the need to adapt educational methods to cope with the new social reality (Martínez-Gómez et al., 2022).

Previous research on ICT has identified the benefits associated with this approach to learning. It has been shown to enhance flexible thinking and reduce resistance to change, fostering a greater willingness to explore new routines (Barak, 2018). Even in face-to-face educational programmes, ICT has become indispensable both outside and inside the classroom, and B-learning is no exception (Flores-Alarcia & Arco-Bravo, 2012).

Within the classroom, not surprisingly there is a whole range of online resources used both by teachers as methodological sources and by students as learning tools (Huang et al., 2022). In addition, the growing trend towards cooperative group work has boosted the spontaneous use of ICT, which enables simultaneous interaction (Dail & Vásquez, 2018).

In the university environment, there has been an increase in the teaching load outside the classroom with heavy reliance on the use of ICT (Flores-Alarcia & Arco-Bravo, 2012). An example of this is the virtual delivery of practical assignments or portfolios, involving the use of ICT for their presentation via the specific platforms offered by each university (Abdullah et al., 2016).

However, not everyone has the same proficiency and attitude towards the use of ICT. In this sense, a close relationship has been established between the level of competence and attitudes towards ICT. Empirical evidence has shown that experience with both computer and internet use generates positive attitudes towards these technologies. It is considered that their use can enhance performance and improve the user’s self-efficacy and enjoyment (Abdullah et al., 2016). However, it has also been observed that these experiences can have a negative impact, including anxiety and stress relating to the use of ICT (Fernández-Batanero et al., 2021). This is an important consideration, especially in light of the urgent need for implementation of ICT in education. All of the above also applies not only to students, who are often considered to be ‘digital natives’ (Barak, 2018), but also to teachers, who have been forced to adopt digital training as a fundamental working tool (Fernández-Batanero et al., 2021). It is therefore particularly relevant to assess the perceived ease of use of ICT among future teachers, understood as “the degree to which the user expects the target system to be effortless” (Saadé & Kira, 2007 p 1192).

To assess this variable, Davis et al. (1989) developed the theoretical model known as the Technology Acceptance Model (TAM), which determines a person’s willingness to use ICT. This model is made up of both personal attitudes towards ICT use and perceived ease of use. Davis (1989) designed two Likert-type scales to assess these constructs and validated them for a Canadian study sample. These scales were called Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), with the current study applying the latter scale. Both scales yielded a unidimensional model, each consisting of six items and with adequate reliability (α = 0.98 and 0.94 respectively).

However, not all subsequent studies have applied the same set of items in the scale. Some opted for the original six-item scale proposed by Davis (1989), while others reduced the scale to four or five items and in some cases introduced new items and eliminated some of the original items (Hess et al., 2014). This has led to controversy regarding the number and accuracy of the items used to assess PU and PEOU of ICT according to the scales proposed by Davis (1989).

Moreover, in studies subsequent to the validation of PU and PEOU by Davis (1989), the unidimensionality of the scales has been assumed without testing their possible multidimensionality. Similarly, the original study accepted the unidimensionality of the scale, focusing on ease of use and excluding items that considered difficulty. However, this approach is controversial given that the lack of perception of a situation as being easy does not necessarily imply that it is perceived to be difficult. In light of the above, it is necessary to carry out studies to test the scale and its factorial structure. In this context and in relation to our specific objective, this study assesses the characteristics of PEOU among future teachers in Spain due to its close relationship with task efficacy (Tor-Carroggio et al., 2019), especially in the case of both students and teachers in the educational field in the near future.

It is also essential to bear in mind that although more than 316 studies have been conducted to assess the reliability of PEOU, some of these studies have pointed out that the reliability of the scale may vary depending on the language in which it is applied (Hess et al., 2014; Straub et al., 1997). For this reason, after an exhaustive analysis which revealed that not all the studies use the same number of items in the scale, the conclusion was reached that it is essential to examine the psychometric properties of PEOU and its factorial structure in Spanish. This analysis allows a real understanding of the applicability of the scale in the context of B-learning, which in turn will contribute significantly to our understanding of PEOU in this specific educational environment.

Furthermore, in addition to examining the possible variability of the scale according to language, it is also relevant to determine whether this variability may be related to the gender of the subjects. While no previous studies were found demonstrating factorial invariance in this regard, some studies have found gender differences for PEOU in relation to ICT, although the results vary. For example, the study by Ramírez-Correa et al. (2015) found that women scored higher on this variable compared to men. However, Ong and Lai (2006) found that men scored higher for PEOU of ICT and also for PU and self-efficacy. The study by Lu and Chiou (2010) with Asian university students examined similar variables, finding that men scored higher compared to women for PEOU and user-friendliness of the e-learning system used. They also found that men considered the platform easy to use to share content and collaborate in class with classmates.

These differences underline the importance of conducting more detailed research to determine whether there is a variation in PEOU of ICT according to gender, given that apart from the above findings to our knowledge there are no studies assessing factorial invariance across gender in the specific case of PEOU. It is therefore essential to conduct this analysis in order to better understand how gender differences may influence PEOU of ICT and thus develop more inclusive and effective educational strategies.

Finally, it is important to note that these scales have been widely used to assess various learning systems, such as learning management (De Smet et al., 2012), smartphones (Park et al., 2012), iPads (Wu et al., 2013), the computer mouse (Tzafilkou & Protogeros, 2020), educational wikis (Liu, 2010), Moodle (Hsu & Chang, 2013), e-portfolios (Abdullah et al., 2016) and social networks (Al-Ammary et al., 2014), among others. However, their use in ICT assessment has not been sufficiently widespread in general terms, despite the wide range of applications that these technologies represent (Susanto & Aljoza, 2015). This constitutes a further reason to perform an in-depth analysis of the psychometric properties of PEOU of ICT in this specific case.

1.1 The current study

A number of studies have validated PEOU, with their findings including a variety of items. Spanish studies of PEOU of ICT (Martínez-Navalón et al., 2023; Ramírez-Correa et al., 2015) do not assess the psychometric properties of PEOU. For this reason, and due to the different factors commented previously, the general objective of this study is to assess the psychometric properties of PEOU among the Spanish adult population, and also to observe its factorial structure.

Two studies were carried out for this purpose. The specific objective of the first study was a) to calculate the factorial structure using an exploratory factor analysis (EFA).

The second study had the following specific objectives: b) to determine the reliability of PEOU; c) to carry out a classical item analysis; d) to test the PEOU model and check its factorial structure via confirmatory factor analysis; and e) to analyse factorial invariance across genders.

Our starting point consisted of the items of the first study based on the original version of the scale (Davis et al., 1989), as this was the most complete version. Based on this, we then checked its factorial structure and whether it has adequate psychometric properties with a Spanish sample.

2 Study 1: Exploratory factor analysis (EFA)

2.1 Participants

For the EFA the sample consisted of a total of 474 students (Mage = 21.17; SD = 4.03). Of these, 124 were male (26.2%) and 350 were female (73.8%).

All the participants were enrolled in the 1st to 4th years of the Bachelor’s Degree in Early Childhood Education Teaching or the Bachelor’s Degree in Primary Education Teaching at the University of Alicante, with the criteria for exclusion of participants being omission of answers.

In terms of reliability, the results were as follows: α = 0.88 for the Difficulty subscale, α = 0.82 for the Ease subscale and α = 0.88 for the PEOU Total. These values indicate high internal consistency of the responses by the participants regarding PEOU of ICT in the educational context.

2.2 Instruments

Perceived Ease of Use (PEOU): In this study, the initial PEOU scale designed by Davis (1989) was used, consisting of 14 items assessing perceived ease and difficulty with regard to the use of ICT. The participants responded to these items using a 7-point scale (1 = Strongly Agree; 7 = Strongly Disagree). The items addressed various ICT-related concepts and could vary depending on the context of the study. For example, one of the items could be: “I find it easy to remember how to perform tasks using ICT.”

The reliability of the scale was adequate for its individual factors and for the total scale, with values of α = 0.92, 0.83 and 0.91 respectively. These results indicate a high degree of consistency in the participants’ responses regarding PEOU of ICT.

2.3 Procedure

Firstly, an interview was conducted with the faculty executive team and the teaching staff to establish the objectives of the research and to request permission and collaboration in the study. Subsequently, the scale was administered virtually during class time, guaranteeing the anonymity and voluntary participation of the participants. The questionnaire took approximately 10 min to complete.

2.4 Data analysis

An exploratory factor analysis (EFA) of principal axes with varimax rotation was carried out to analyse the internal structure of PEOU. Only items with values equal to or greater than 0.40 were selected for analysis. In addition, sampling adequacy was assessed using the KMO index and Bartlett’s test of sphericity. Scores were considered very good when the values were equal to or above 0.80, following the criteria of Kaiser and Rice (1974). The analyses were conducted using the SPSS 24 software.

2.5 Results

2.5.1 Exploratory factor analysis (EFA)

Table 1 shows the results of the EFA for PEOU. In terms of sampling adequacy, the KMO index had a value of 0.92 and Bartlett’s test of sphericity yielded 5749.125, with significance levels of p < 0.001.

Table 1 Exploratory factor analysis of PEOU
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All the items had saturation levels of greater than 0.40. Therefore, all the items of the initial version by Davis (1989) were retained. This 14-item version was called the “Revised Version of the PEOU of ICT Scale” (RV-T-PEOUS). As shown in Table 1, two factors were identified that explained 52.10% of the total variance.

The first factor, called ‘Difficulty’, explained 40.30% of the variance, with factor loadings ranging from 0.58 to 0.82. This factor is composed of eight items (1, 2, 3, 4, 5, 7, 9 and 10) and reflects the difficulty and frustration a person feels when making mistakes while using ICT (e.g. “I frequently make mistakes when using ICT”).

The second factor was called ‘Ease’ and explained 11.80% of the variance. It is composed of six items (6, 8, 11, 12, 13 and 14) and the factor loadings ranged from 0.41 to 0.80. This factor assesses the ease with which the subject uses ICT (e.g. “It is easy for me to remember how to perform tasks using ICT”).

3 Study 2: Confirmatory factor analysis classical item analysis, correlations between each factor and the total scale and factorial invariance across gender

3.1 Method

3.1.1 Participants

For this study, the sample consisted of a total of 796 students between 17 and 51 years of age (Mage = 21.25; SD = 3.99), in the 1st to 4th year of the Bachelor’s Degree in Early Childhood Education Teaching or the Bachelor’s Degree in Primary Education Teaching at the University of Alicante. 22% of the participants were male (175) and 78% were female (621). The vast majority of the participants (96.98%) were of Spanish nationality, while 1.88% were Irish and 1.13% were German. The participants who omitted responses were excluded from the analysis.

3.1.2 Instruments

Perceived ease of Use (PEOU)

The instrument used was the same as in Study 1.

3.1.3 Procedure

The procedure for requesting collaboration and consent and administering the scale was similar to that described in Study 1.

3.1.4 Data analysis

Several Confirmatory Factor Analyses (CFA) were carried out to assess the structure of the PEOU scale. Several models were tested: Model 0 was a unidimensional model, Model 1 had two uncorrelated factors, Model 2 had two correlated factors and Model 3 had two correlated factors and two correlated items. The goodness-of-fit indices used included the RMSEA (Root Mean Square Error of Approximation); <0.08 reasonable and < 0.06 excellent), GFI (Goodness of Fit Index; ≥ 0.95 [Ruiz et al., 2010]), CFI (Comparative Fit Index; acceptable around 0.90) and TLI (Tucker Lewis Index; >0.90 good fit) [Brown, 2006; Hu & Bentler, 1999], complying with the criteria for determination of a good model fit.

Classical item analyses were conducted to assess internal consistency and content validity. Means, standard deviations, skewness, kurtosis, factor loadings and item-scale correlations were also calculated. In addition, Cronbach’s alpha coefficients were calculated by removing the item.

Pearson correlation coefficients were calculated to assess the relationships between the total score of the PEOU scale and the scores of the ‘Ease’ and ‘Difficulty’ factors. These coefficients provide information about the internal consistency of the scale and how the items are related to each other. Cronbach’s alpha and Omega coefficients were calculated to assess the internal consistency of the ‘Ease’ and ‘Difficulty’ dimensions. Acceptable values for scale reliability are usually ≥ 0.70 according to Nunnally (1978) and ≥ 0.70 for Omega (McDonald, 2013; Ventura-León, 2018). These values indicate the internal consistency of the items in each dimension of the scale.

Finally, a multi-group CFA was performed to test for invariance. Starting from a non-restrictive model (M0 = baseline), it was then nested to others with greater restrictions (M1 = metric, M2 = scalar and M3 = strict). The criteria for testing the level of invariance are met when Δχ2 is not significant, ΔCFI = >-0.01 and ΔRMSEA ≤-0.01 (Asparaouhov & Muthén, 2014; Chen, 2007). When testing for measurement invariance, especially strict invariance, latent means are compared to estimate the differences (Dimitrov, 2010).

Differences in PEOU of ICT between men and women were assessed using the Student’s t-test and the magnitude of the differences was quantified by calculating Cohen’s d. The rating of the ease subscale items was reversed to ensure that all the ratings were in the same direction for their comparison.

The SPSS 24 and AMOS 24 software packages were used to carry out these analyses.

3.2 Results

3.2.1 Confirmatory factor analysis (CFA)

In the CFA of the PEOU scale, several models were evaluated to determine the most appropriate structure (see Table 2; Fig. 1). In this case, a 14-item model with no factors (Model 0), a one-factor model (Model 1), an uncorrelated two-factor model (Model 2), a correlated two-factor model (Model 3) and a correlated two-factor model with additional correlation of items 1 and 2 (Model 4) were tested.

Table 2 Fit indices for the different PEOU models evaluated
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Fig. 1
figure 1

Factorial structure of the RV-T-PEOUS

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The results of the CFA showed that Model 4, consisting of two correlated factors with a specific correlation between items 1 and 2, had the best fit to the data according to several goodness-of-fit indices. These indices included a chi-square value χ² = 336.261, CFI = 0.95, TLI = 0.94 and RMSEA = 0.06 with a 90% confidence interval between 0.059 and 0.073. These values indicate a good fit of the model to the data, confirming the structural validity of the RV-T-PEOUS.

The final structure of the scale consisted of 14 items grouped into two correlated factors: Factor I, ‘Difficulty’; and Factor II, ‘Ease’. In addition, it included a specific correlation between items 1 and 2 to improve the fit of the model to the data collected in the study. The results allow a detailed understanding of PEOU of ICT among the study population and validate the structure of the revised scale in the Spanish context.

3.2.2 Classical item analysis

Table 3 shows the descriptive statistics for each item of the RV-T-PEOUS. The mean scores (M) ranged from 1.71 (item 14) to 3.00 (item 9), indicating a variety of participant responses. The standard deviations (SD) ranged from 1.13 (item 13) to 1.57 (item 3), reflecting a dispersion of responses around the mean.

Table 3 Analysis of RV-T-PEOUS items
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The item-scale correlation coefficients, which indicate the strength of the relationship between each item and the total scale score, ranged from 0.55** to 0.85**, suggesting a good correlation between the items and the global measure of PEOU. The corrected item-scale correlations, which adjust the correlation to take into account the effect of the item in question, ranged from 0.44 to 0.73, indicating adequate consistency in the participants’ responses.

The item-test correlations, which represent the relationship between each item and the total score excluding that particular item, ranged from 0.48** to 0.80**, indicating a significant contribution by each item to the general construct of PEOU. The corrected item-test correlations, which adjust these correlations to take into account the effects of the respective items, ranged from 0.40 to 0.75, confirming the unique contribution by each item to the total measurement.

The internal consistency of the RV-T-PEOUS was high, with Cronbach’s alpha (α) values of 0.89 for Factor I (Difficulty), 0.81 for Factor II (Ease) and 0.89 for the total scale. In addition, the Omega coefficient values were equally high (0.90 for Difficulty, 0.83 for Ease and 0.93 for the total), indicating excellent internal consistency.

Taken together, these results suggest that the RV-T-PEOUS is a reliable and valid measure for assessment of PEOU of ICT among the study population.

3.2.3 Correlations between each factor and the scale total

The correlations between the dimensions of the RV-T-PEOUS and the scale total were positive and strong, ranging from 0.91 to 0.42 (see Table 4). These results indicate that the individual factors (Difficulty and Ease) are positively related to the total measure of PEOU. That is, when participants reported greater ease or less difficulty in a specific area, they also tended to perceive greater ease of use in general.

Table 4 Correlation coefficients for the different dimensions of the RV-T-PEUS and the scale total
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Furthermore, the correlation coefficients between the scale factors were significant, showing a moderate relationship (0.42) between the Difficulty and Ease dimensions. This finding suggests that although the factors are interrelated they represent different aspects of the user’s experience with ICT. Perceived difficulty and perceived ease are not simply opposites, but rather each factor contributes uniquely to the overall assessment of PEOU.

3.2.4 Factorial invariance across gender

Multi-group CFA was performed to test the measurement invariance of the RV-T-PEOUS (see Table 5). Firstly, it was found that for both males and females the χ2/d.f. values were below 3, the RMSEA values were below 0.08 and the CFI values were 0.93 for males and 0.96 for females. Next, the configuration, baseline or free invariance model (M0) was tested, which proposed that the scale would have a bifactor structure in the two groups and allowed the factor loadings and intercepts to be freely estimated. The indices obtained were (χ2/d.f. = 2.846; CFI = 0.95; RMSEA = 0.048). The metric invariance model (M1) was then tested, which restricted the factor loadings to make them equal between males and females. The indices showed that the model fitted well and when compared to M0, the ΔRMSEA was 0.001 (≤ 0.015), the ΔCFI was 0.001 (≤ 0.01), and the Δχ2 was not significant (p = 0.170). The scalar invariance test (M2), in which the intercepts and factor loadings were restricted to make them equal in the two groups, showed a good fit when compared to M1 and no significant changes Δχ2(p = 0.077), ΔCFI (0.001); ΔRMSEA (0.001). Finally, the strict invariance model (M3), in which the error variances were restricted in addition to the factor loadings and intercepts, also fitted correctly in its comparison with M2 in ΔRMSEA which was 0.010, and in ΔCFI = 0.002, but Δχ2 (p < 0.001) was significant contrary to expectations.

Table 5 Fit indices for the PEOU model according to gender
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4 Discussion

The aim of this study was to examine the psychometric properties of the Spanish version of the PEOU scale (Davis, 1989) in a sample of Spanish future teachers. Two studies were carried out. The first study consisted of an EFA which yielded a two-factor solution composed of eight items for the first factor and six items for the second factor. These data coincide with the initial scale found in the study by Davis (1989), but not with the unidimensional structure finally chosen by the author, nor with the studies that have subsequently been carried out with the scale. Specifically, the study of Davis (1989) and subsequent studies that have tested the validity of the PEOU, opted for the items that make up the ease of use factor and the difficulty items were not subjected to the same analysis. We understand that based on the optimization of their results. The difference in our study was to approach the theoretical concept from the statistical point of view and from the comprehensibility of the items in Spanish language. In fact, based on the second study, according to the CFA of the models tested, this structure for the Spanish version resulted in the most adequate goodness-of-fit indices (Brown, 2006; Hu & Bentler, 1999). Thus, the Spanish version of the PEOU (RV-T-PEOUS) respected the 14 initial items of the study by Davis (1989), labelling the first factor as ‘Difficulty’ and the second factor as ‘Ease’.

The first factor, ‘Difficulty’, reflects the degree to which ICT use is expected to be complex and reflects a negative attitude towards PEOU. On the other hand, the second factor, ‘Ease’, reflects the degree to which individuals expect the use of ICT to be effortless and reflects a positive outlook towards PEOU. Due to the findings from his study with a Canadian population, Davis (1989) opted to eliminate the items of the ‘Difficulty’ factor. However, in our study, despite the good fit of the data with the Davis (1989) model, the data have an even better fit with the two-factor model when measuring PEOU among the Spanish population. In fact, we consider it is appropriate to assess both ease and difficulty, since the fact that an individual does not find the use of ICT easy — as assessed in this study — does not mean that he/she necessarily considers it difficult, or vice versa. Thus, despite the fact they are opposites, the situation and the person’s thinking do not have to be dichotomous, i.e. it does not have to be entirely black and white, but rather there are many possible shades of grey on the scale (Vicent et al., 2019). Furthermore, this multidimensional approach is relevant and provides further insight into the construct of PEOU in the context of ICT. The bifactor structure of the RV-T-PEOUS with its two factors ‘Difficulty’ and ‘Ease’ offers a more detailed and accurate picture of how users perceive the ease of use of ICT. Indeed, the distinction between the ‘Difficulty’ and ‘Ease’ factors can help researchers and practitioners to understand users’ perceptions of the ease of use of technologies in more detail, which in turn can aid the design of educational interventions and strategies such as B-learning to improve the adoption and effective use of ICT in education and other fields.

Moreover, the assessment of factorial invariance across gender is an important step to ensure the equivalence of the scale between men and women. In this case, the results indicated a good fit of the items to the bifactor proposal of the RV-T-PEOUS for students of the Bachelor’s Degrees in Early Childhood Education and Primary Education. They indicate that when the elements of the factor structure remain invariant across gender, the goodness-of-fit indices for configural, metric and scalar invariance were adequate. Only in the case of strict invariance was the Δ χ2 significant and therefore unsatisfactory, and so partial invariance is assumed in this case (Dimitrov, 2010) since strict invariance tests are very restrictive (Bentler, 2004). These data suggest that while there are differences in responses between men and women, the overall patterns of responses to the scale items are comparable and can be interpreted similarly in both groups. This is essential in order to be able to make meaningful comparisons between genders in future research.

Furthermore and as mentioned in the introduction, although there are no previous studies that perform this analysis, there are studies showing differences between genders with varying results, with women scoring higher in PEOU of ICT than men in some cases (Ramírez-Correa et al., 2015) and the opposite holding true in other cases (Ong & Lai, 2006). As can be seen, in our case the results are not in line with these studies. More specifically, the findings of the present study demonstrate for practical purposes that there is no difference between genders in the assessment with the RV-T-PEOUS. These results are valuable as they show that the scale is equally valid and reliable for both genders in this specific context. However, it is important to note that the factorial invariance may vary in different contexts and populations. It is therefore essential to conduct invariance analyses for different samples and contexts to ensure that the scale is applicable and valid across different demographic and cultural groups.

In this respect, it is important to note that differences between the results found in this study and others may be due to various factors, such as the specific characteristics of the study sample, differences in the educational or cultural context and the particularities of the measurements used. PEOU of ICT may be influenced by a variety of individual and contextual factors, which can lead to variations in the responses of different groups of people.

Ultimately, these results support the usefulness of the RV-T-PEOUS as a reliable and valid tool to assess PEOU of ICT in the Spanish educational environment, thus providing a solid basis for future research in this field.

Several limitations of the study should be noted. One of the main limitations is the generalisability of the results to other populations, especially those of different ages, languages and cultures. PEOU of ICT can vary significantly depending on age, cultural background and previous experiences with technology. Therefore, it is essential to replicate this study in different demographic, different age ranges, and cultural groups to assess the applicability of the RV-T-PEOUS in various contexts. In fact, future studies could assess the link between ICT usability and socio-economic status, previous ICT experience and the distinction between urban and rural environments by analysing the mediating role of these variables, and establish models that establish the influence of psychoeducational variables and check their influence on the ease of use of ICTs. Longitudinal monitoring of participants’ PEOU of ICT could provide a more complete and accurate picture of how these perceptions may change or remain stable over time. Also, given that this study focused on university students, it would be valuable to conduct similar research in other educational contexts, such as educators, secondary schools or higher technical schools, in order to better understand the perceptions of students at different educational levels.

Despite the limitations of the study, it is evident that the RV-T-PEOUS is a valid and reliable instrument for assessment of PEOU of ICT among the Spanish adult population. Given the growing importance of ICT in all aspects of modern life (Martínez-Navalón et al., 2023), having a reliable tool to assess how people perceive and use these technologies is essential and becomes indispensable in the case of B-learning.

Similarly, this instrument may be valuable not only for academic research but also for educational practice and professional development (Al-Ruheel et al., 2022; Flores-Alarcia & Arco-Bravo, 2012; Saadé & Kira, 2007). Teacher training in the effective use of ICT is essential to prepare students for the ever-changing digital world. Educators need to understand not only how to use these technologies, but also how to facilitate their use by students in a way that is perceived as easy and useful.

In short, this study fosters research to raise awareness of these aspects as it provides a valid and reliable instrument for assessment of PEOU of ICT in a Spanish sample, allowing an improvement of scientific knowledge in the educational, artistic-visual and psychological fields.

5 Conclusion

This study not only contributes to scientific knowledge in the educational and psychological fields, it also has important practical implications for teacher training and continuous development of effective teaching strategies in the digital age. Understanding how people perceive and use ICT is essential to adapt education to the changing needs of learners and to promote responsible and meaningful use of technology in society.

This study provides a valid and reliable tool to measure PEOU of ICT in the Spanish educational context. Its bifactor structure and the factorial invariance across gender make the RV-T-PEOUS a versatile and robust tool for future research in the field of education and technology. These results provide a solid basis to better understand and improve the implementation and adoption of ICT in educational settings, enabling the design of more effective interventions and pedagogical strategies tailored to users’ needs and perceptions.

To conclude, while this study provides a solid basis for the assessment of PEOU in the specific context studied, further research is needed to expand our understanding of how PEOU of ICT varies across different population groups and cultural contexts, and in this sense the RV-T-PEOUS can facilitate this process.