Introduction

In the contemporary landscape, rapid advancements in multimedia technologies are significantly transforming educational practices worldwide (Shunkov et al., 2022). This technological shift is especially pronounced in China’s Early Childhood Education (ECE) sector (Nisak et al., 2022), where professionals educators play a pivotal role. These roles emphasise their contributions that extend beyond conventional teaching to a wide range of nurturing, mentoring, and developmental responsibilities. The emergence of the educator aligns with directives from China’s central government in 2019 (Central People’s Government of the People’s Republic of China, 2019). However, as multimedia tools become increasingly embedded in educational arenas, educators are challenged with leveraging their capabilities to optimize their effectiveness (Sudarsana, 2018).

Using multimedia tools extends beyond mere access; it encompasses acceptance and skilful implementation of such technologies. In this context, the Technology Acceptance Model (TAM) offers a comprehensive framework to decipher the variables affecting technology utilization (Davis et al., 1989). A noticeable void exists in the current literature regarding the influence of multimedia on educator effectiveness in China, signalling a research opportunity.

Addressing this gap, our study delves into how multimedia applications, grounded in TAM principles, impact educator effectiveness in Guangdong Province, China. The research makes multiple contributions. Primarily, it shapes educational strategies and methodologies by shedding light on factors influencing educator effectiveness in multimedia applications. Furthermore, employing TAM to analyse multimedia dynamics as used by educators, this study introduces a fresh perspective, especially considering the specific cultural and regional background.

This research endeavours to elucidate multimedia utilization and its repercussions on educators’ effectiveness. We aim to explore the interplay of TAM variables, including Multimedia of Perceived Usefulness (PU), Perceived Ease of Use (PEU), and their bearing on Educators’ Effectiveness (EE) in Guangdong Province, China. To realize this, a quantitative survey method is adopted, targeting educators from the province’s ECE domain.

Our study’s paramount contribution lies in the novel application of TAM to ECE in China. The insights promise to demystify the determinants fuelling the successful integration of multimedia technologies in education, potentially amplifying educator effectiveness and enriching ECE quality (Livingstone et al., 2019). Consequently, this inquiry stands at the juncture of technology acceptance, education, and productivity, heralding both theoretical innovations and tangible enhancements in the domain.

Literature review

Conceptualization and role of educators in early childhood education

China’s evolving ECE landscape, particularly in Guangdong Province, has led to the emergence of educators as a distinctive role that unifies educational and caregiving responsibilities (Kong, 2023; Zhao et al., 2022). These professionals cultivate children’s holistic development and facilitate a vital communication bridge between homes and educational institutions (Arvola et al., 2021). Nevertheless, Guangdong’s flourishing ECE sector must also grapple with maintaining quality standards and addressing the scarcity of adequately trained educators (Oon et al., 2019).

The educator role originated from the unique societal and policy context in China. The pivotal turning point came in 2019 when the Chinese central government decreed the provision of care services for infants and toddlers under three years of age (Central People’s Government of the People’s Republic of China, 2019). Consequently, the scope of the role expanded beyond traditional educational duties to incorporate professional caregiving services. This broader remit led to reconsidering the term ECE educator and represents a more refined classification within ECE, acknowledging these professionals’ distinct and substantial contributions during children’s critical early developmental stages.

The emergence of this role has its challenges. The scarcity of qualified educators and escalating demand for ECE services have heightened concerns regarding the quality of care and education in some contexts. Moreover, the inconsistent availability of ongoing professional development opportunities, critical for enhancing educators’ competencies and practices, compounds these challenges (Logan et al., 2020).

Efforts towards improving ECE are evident in China’s ongoing policy initiatives, encompassing national and regional strategies to enhance early childcare services (Central People’s Government of the People’s Republic of China, 2019; Guangdong Provincial People’s Government, 2020). As the integration of new media technology becomes more commonplace in ECE settings, educators are poised at the intersection of numerous opportunities and challenges within this rapidly evolving educational milieu.

Multimedia technologies in early childhood education: adoption and integration

Multimedia technologies, a critical facet of modern education, encompass interactive, digital, and combined media to enhance teaching and learning experiences (Neo and Neo, 2004). This includes digital tools, such as tablets, software applications, video and audio tools, interactive whiteboards, and online platforms. They enable a combination of text, graphics, sound, animation, and video to create engaging, multisensory learning environments (Kamran, 2019).

In today’s technological environment, these multimedia technologies have markedly influenced global ECE practices, including those in China. Recognizing the pivotal role technology plays in education, the Chinese government has been proactively fostering its integration, catalysing technology adoption within ECE environments (Adarkwah, 2021).

Many digital tools and resources have been ingeniously woven into China’s ECE curricula to enhance children’s learning experiences and facilitate the acquisition of essential 21st-century competencies (Weng and Li, 2018). In addition, the proliferation of multimedia technologies has diversified communication and collaboration channels among educators, children, and parents, enabling remote and flexible learning paradigms (Anderson and Rivera Vargas, 2020). Government initiatives, such as the “Internet Plus” education strategy and the “National Outline for Medium and Long-term Education Reform and Development (2010–2020)”, illustrate China’s commitment to encouraging technology use in educational settings, including ECE (Li et al., 2016).

However, the extent of multimedia technology integration within Chinese ECE contexts varies and is influenced by factors such as geographical location, funding availability, and access to resources (Luo et al., 2023). While urban ECE institutions equipped with advanced digital resources are typically at the vanguard of this shift, rural institutions grapple with infrastructural, financial, and access-related challenges (Hu et al., 2021).

Given this scenario, there is a critical need for professional development and training programs that equip educators with the necessary skills to efficiently integrate multimedia technologies into their pedagogical practices. Thus, understanding the factors contributing to successful technology adoption in Chinese ECE settings and the specific roles and experiences of educators within technology-enhanced ECE environments is a crucial avenue for future research.

Implications of multimedia technologies in early childhood education: opportunities and challenges

Multimedia technologies’ integration within ECE offers notable benefits but, at the same time, poses some challenges. A thorough understanding of both aspects is instrumental to enhancing the practical application of technology in ECE settings.

Multimedia technologies stand to significantly improve children’s learning experiences (Naluwooza et al., 2023). They foster learner engagement, enable personalized learning experiences, and expand access to diverse learning resources (Ismoilovich and Ravshanbekovich, 2023). With multimedia-enhanced learning activities, children can develop critical 21st-century skills such as creativity, problem solving, and critical thinking (Alzubi, 2023). Furthermore, multimedia technologies offer educators refined methods for assessing learner progress and addressing individual needs. They enhance stakeholder communication and collaboration and provide the groundwork for flexible, remote learning environments. Finally, they can foster continuity between home and school learning environments (Sonnenschein et al., 2021).

However, incorporating multimedia technologies into ECE settings is challenging (Lindeman et al., 2021). For example, excessive screen time poses potential risks to children’s physical health and social development (Kaimara et al., 2022). Organizations such as the American Academy of Paediatrics advise limited screen time for young children and underscore the importance of educator and parental supervision (Przybylski, 2019).

Educators may also need help integrating technology into their practices due to technical skill gaps, inadequate training, or resource limitations (Hu et al., 2021). These challenges necessitate ongoing professional development and adequate support for educators.

The digital divide—a disparity in access to digital resources—can intensify existing inequalities in educational opportunities among children from different socioeconomic backgrounds (Reddick et al., 2020). Thus, ensuring equitable access to multimedia technologies is critical to delivering inclusive, high-quality ECE experiences.

Hence, despite the manifold benefits multimedia technologies offer for ECE, it is vital to navigate the associated challenges to ensure meaningful, effective, and equitable technology integration. Understanding educators’ experiences within technology-enhanced ECE environments and identifying the factors influencing successful technology adoption are critical steps towards optimizing multimedia technology use in early childhood education.

The intersection of multimedia efficacy and educator efficiency: bridging prior research with current inquiry

The multimedia integration in ECE is not just a matter of technological innovation; it represents a significant shift in pedagogical practices and educator roles. This nexus between technology and education offers a compelling backdrop to understand the evolving role of educators and the factors that affect their effectiveness.

Mertala (2019) highlighted the nuanced roles of teachers beyond imparting education. They also attend to students’ emotional, physical, and social needs and their role in society. Teachers’ beliefs and attitudes become paramount when introducing technology, especially multimedia tools, into the educational sphere. Mertala suggests that there is a critical role that educators’ beliefs play in shaping their approach to technology. However, while Mertala brings to light the importance of beliefs, there is a gap in understanding how these beliefs directly influence the effectiveness of educators in utilizing multimedia in their teaching.

Drawing insights from Latini et al. (2020), the choice of medium for reading, whether print or digital, has been shown to affect comprehension processes. Their study suggests that participants exhibited more integrative processing with printed than digital materials. This raises critical questions about the potential challenges educators might face while leveraging multimedia resources. If comprehension is affected by the medium, then understanding how this affects the effectiveness of educators in imparting knowledge remains to be fully explored.

Li et al. (2019) extensively analysed multimedia learning trends over two decades. While they identified prevailing themes and trends, such as the importance of cognitive load and animation in multimedia learning, their practical application and impact on educator effectiveness are underresearched. Knowing the trends is essential, but how they align with the day-to-day practices of educators and their efficacy in diverse educational settings is an area our study seeks to explore further.

Gong (2022) discussed the confluence of multimedia technology and children’s drama education. The study highlighted the promising potential of human–computer interaction technologies in preschool drama education. However, while the tools and methodologies are advancing, understanding the nuances of how educators adapt to and effectively implement these tools in their curriculum is a dimension that needs to be deeply examined by Gong.

Last, Coskun and Cagiltay (2022) used eye-tracking metrics to understand learners’ cognitive processes in animated multimedia settings. Their insights provide a nuanced understanding of the relationship between design, attention, and learning outcomes. However, how educators can harness this understanding to improve their effectiveness, especially when animations and simulations become standard tools, remains an area with potential for further inquiry.

Our research aims to fill the gaps identified above by examining the direct impact of multimedia tools and methodologies on the effectiveness of educators. We seek to understand how these multimedia advancements, while promising on the surface, translate into real-world effectiveness in educational contexts, especially in Guangdong Province. Our inquiry aims to provide a more grounded perspective that juxtaposes the promise of multimedia with the practical realities and challenges faced by educators.

Theoretical framework and hypothesis development

Overview of the Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), developed by Davis et al. (1989), is a seminal theoretical framework in information systems (Granić, 2023). Crafted to predict and understand user acceptance and utilization of information technology, TAM pivots around two principal determinants: Perceived Usefulness (PU) and Perceived Ease of Use (PEU) (Warsono et al., 2023).

As shown in Fig. 1, both PU and PEU are directly linked to an individual’s Behavioural Intention to Use (BIU), a system within the TAM framework. When educators perceive multimedia technology to be advantageous (PU) and user-friendly (PEU), their intention to integrate and employ that technology (BIU) increases. This intention ultimately materializes as Actual System Use, representing the integration and use of multimedia tools in their teaching methods.

Fig. 1
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Technology Acceptance Model.

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In the context of our research, which probes the effects of multimedia on the effectiveness of educators, TAM serves as an instrumental analytical framework. Utilizing TAM, we can systematically analyse how educators evaluate multimedia technologies regarding their perceived advantages and user accessibility. Furthermore, by integrating specific external variables pertinent to the educational realm—such as institutional guidelines, pedagogical training, or curriculum directives—we can direct our insights towards the factors influencing the acceptance and adaptation of multimedia tools by educators.

Building on these theoretical foundations, our research model and hypotheses will explore the complex interplay between perceived ease of use, perceived usefulness, and the diverse external variables that influence an educator’s decision to incorporate multimedia technologies into educational strategies.

Applying TAM to this study

The TAM has become instrumental in probing the determinants driving technology adoption, especially within educational landscapes. It is highly pertinent when integrating multimedia technology into teaching paradigms. At the heart of this examination lies the quest to discern educators’ perspectives on the benefits and ease of using such multimedia tools. Based on the solid foundations of TAM, the present study seeks to debunk the hidden correlations between educators’ adoption of multimedia technology and their subsequent effectiveness.

Two pivotal independent variables underscore this inquiry. First, the PU serves as a barometer measuring the extent of educators’ conviction that multimedia tools can bolster the quality of their pedagogical endeavours. This translates into gauging the level of agreement among educators that multimedia-rich content can curate a learning experience that is in-depth and interactive for students. Complementing this is the second variable, PEU, which focuses on the anticipations of educators regarding how seamlessly multimedia technology can be woven into their teaching fabric. The underlying contemplation is whether educators perceive these technologies as intuitive additions to their teaching arsenal, bereft of any substantial impediments.

By synthesizing these several strands, the focal point that emerges is the dependent variable of Educators’ Effectiveness (EE). This encapsulates the tangible, positive repercussions observed when multimedia instruments are deployed in instructional settings. Effectiveness is broadly conceived, ranging from a palpable surge in student engagement to discernible strides in learning outcomes or even the clarity of feedback on delivered content.

Based on the above, we formulate the following hypotheses. Figure 2 shows the variables and their hypothesized relationships.

Fig. 2
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Research Model.

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Hypothesis 1 (H1): The PU of multimedia tools significantly affects EE.

Hypothesis 2 (H2): The PEU of multimedia tools significantly affects EE.

Hypothesis 3 (H3): PEU significantly affects the PU of multimedia tools among educators.

Methodology

Questionnaire design and measurement items

This study leverages a quantitative research design and employs a meticulously crafted questionnaire to extract insights from educators, parents, and other pivotal stakeholders within ECE in Guangdong Province, China. The focus is to elucidate participants’ demographics and perceptions of multimedia technology’s PU and PEU and its influence on the effectiveness of educators in ECE.

Our questionnaire is influenced by the foundational works of Davis et al. (1989) and Seligman (2001) for the PU and PEU dimensions. Notably, while Seligman’s original research revolved around computer-based patient records (CBPR), we adapted his items, replacing “CBPR” with “multimedia technology in ECE” to better fit our study context. A 5-point Likert scale was used for all items covered by the variables, with 1 indicating strong disagreement and 5 indicating strong agreement.

Below Table 1 presents the dimensions, their corresponding items, the item number, and the originating sources:

Table 1 Questionnaire Items.
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Deploying this questionnaire on Questionnaire Star (https://www.wjx.cn/) aligns with our commitment to accessibility and participant data protection. This online format guarantees a streamlined data collection process, reaching a wider sample base. Furthermore, all collected data are safeguarded through strict measures, ensuring participant confidentiality. The gathered data will be statistically analysed to identify pertinent patterns and relationships.

Sampling technique and sample size

A cluster sampling technique was employed to ensure that the study sample adequately represented the target population of educators in Guangdong Province. The process involves grouping participants based on their roles and geographical locations and ensures the inclusion of diverse perspectives while accounting for the potential variation in experiences with technology-enhanced ECE across different settings.

Utilizing Cochran’s (1977) method for determining sample size in survey research, it was possible to determine the ideal sample size for this study.

$${{{\mathrm{n}}}}_0 = \left( {{{{\mathrm{Z}}}}^2 \times {{{\mathrm{p}}}} \times {{{\mathrm{q}}}}} \right)/{{{\mathrm{E}}}}^2$$

where: n0 = required sample size; Z = Z score (1.96 for a 95% confidence level); p = estimated proportion of the population with the characteristic of interest (0.5, if unknown); q = complementary proportion (1-p); E = margin of error (e.g., 0.05 for a 5% margin of error)

As reported by Shen (2022) and cited by the official website of the National People’s Congress of the PRC (2022), there are ~90,000 educators employed in over 5400 early childhood education institutions within Guangdong Province.

To initiate the process, the complementary proportion (q) is computed:

$${{{\mathrm{q}}}} = 1 - {{{\mathrm{p}}}} = 1 - 0.5 = 0.5$$

Subsequently, the values are input into Cochran’s formula:

$${{{\mathrm{n}}}}_0 = \left( {1.96^2 \times 0.5 \times 0.5} \right)/0.05^2$$
$${{{\mathrm{n}}}}_0 = \left( {3.8416 \times 0.25} \right)/0.0025$$
$${{{\mathrm{n}}}}_0 \approx 384.16$$

Consequently, with a margin of error of 5%, the estimated sample size for this study is ~384 participants.

Using Cochran’s formula and the stratified random sampling technique will enhance the study’s internal and external validity, ensuring that the findings can be generalized to the broader population of educators within Guangdong Province.

Based on the data needs described above, we employed a systematic and purposive sampling strategy to select participating institutions and educators within our data sampling pool. Based on information from the Infant Care and Early Development Industry Association of Guangdong Province China (2023), 225 ECE member institutions are located across various cities in Guangdong Province, China.

Our first step involved the systematic sampling of institutions. We selected every fifth institution from the Infant Care and Early Development Industry Association list to ensure broad coverage. Given our population size of 225 institutions, this systematic selection yielded a sample of 45 institutions.

Following the selection of institutions, we employed purposive sampling to select educators within these chosen establishments. Our goal is to achieve a target sample size of 384 educators. Therefore, we aim to disseminate approximately ten questionnaires per institution, adjusting the exact number slightly based on the total number of educators available at each institution.

Data analysis

Utilizing the SPSSAU tool (The SPSSAU Project, 2023), this study used descriptive and inferential statistical methodologies. Descriptive statistics offered insight into data features, encompassing central tendencies and variability metrics. The questionnaire’s reliability, construct validity, and item analysis were assessed.

Structural equation modelling (SEM) was then employed through the abovementioned analysis tool to decipher relationships between observable variables and underlying constructs, such as the influence of Innovative Behaviour on Professional Community and Shared Leadership. Through these methods, the study empirically addressed the research hypotheses.

Results and analysis

From August 2 to August 11, 2023, we distributed questionnaires to 450 educators affiliated with institutions that are members of the Infant Care and Early Development Industry Association. We received 400 valid responses; the response rate was high at 89%, representing substantial participation from the targeted educators.

Participants’ demographic information

This section provides a detailed breakdown of the demographic data of the participants who took part in the survey, as shown in Table 2. A total of 400 respondents participated, and their demographic information spans five main categories: gender, age, education level, years of experience in their current role, and the location of their ECE institution.

Table 2 Participants’ demographic information.
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From Table 2, most participants were female, representing 70.75% of the total respondents. Most participants were between the ages of 25 and 45, with the largest group being those aged 35–45. The predominant education level was a vocational/technical diploma, held by 48.75% of participants. Most respondents had 1–3 years of experience in their current role, and a significant majority, 90.25%, were associated with ECE institutions located in urban areas.

Reliability and validity

In our study, the reliability of the questionnaire was measured using Cronbach’s alpha for the 24 items, as shown in Table 3. Based on a sample of 400 respondents, the calculated Cronbach’s alpha was 0.958. Generally, in social science research a Cronbach’s alpha value above 0.7 is acceptable, suggesting that the questionnaire items have good internal consistency.

Table 3 Cronbach Alpha.
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As shown in Table 4, the KMO statistic was calculated to be 0.966. A KMO value close to 1 suggests that patterns of correlations are relatively compact, and, hence, factor analysis should yield distinct and reliable factors. Specifically, KMO values greater than 0.8 are considered significant, indicating that the dataset is suitable for factor analysis. Moreover, Bartlett’s Test data also support this view, as shown in Table 4.

Table 4 KMO and Bartlett’s Test.
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Items analysis

The data in Table 5 compare the means (M) and standard deviations (SD) for PU, perceived PEU, and EE between the low and high groups. All items for PU, PEU, and EE consistently show statistically significant differences between the low and high groups, as evidenced by p values that are all <0.01. The asterisks also reinforce this, which denote significance at the 0.01 level.

Table 5 Item Analysis.
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For the PU items, the low group’s mean values range between 2.58 and 2.82, whereas the high group’s mean values are between 4.32 and 4.51. The t values (or CR values) for these comparisons are all significantly large, ranging from 10.090 to 13.023, further affirming the robustness of the difference between the two groups. Similarly, for the PEU items, the low group’s means are between 2.72 and 2.94, while the high group’s means span from 4.39 to 4.52. Their t values (CR) fluctuate from 10.412 to 12.476, emphasizing the marked distinction in perceived ease of use between the two groups. Last, concerning EE items, the low group’s means are from 2.49 to 2.85, while the high group’s means are more elevated, ranging from 4.37 to 4.58. The t values for these items vary between 10.803 and 14.269, with the latter being the highest t value in the entire table, indicating the most significant difference observed between the low and high groups for item EE-1.

Hence, the evident difference in mean scores across all items for PU, PEU, and EE between the low and high groups suggests a marked difference in the perceptions of usefulness, ease of use, and effectiveness of multimedia tools between these two categories. The consistently significant p values solidify this observation, underscoring that these differences are statistically significant and not due to random chance.

Average

Figure 3 shows the average scores for each item, giving us an overall picture of respondents’ attitudes and values towards the question.

Fig. 3
figure 3

Average.

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For the PU measures, the average responses range between 3.795 and 3.875. This suggests that participants, on average, leaned towards agreeing that the multimedia tools were helpful, as these scores are closer to 4 on a 5-point scale. The slight variations within this range are subtle, with PU-4 having the highest mean value of 3.875, indicating that this aspect of perceived usefulness had the highest agreement among respondents. Regarding the PEU domain, the average responses span from 3.845 to 3.935, which implies that participants typically found multimedia tools relatively easy to use. The highest average value is for PEU-3 at 3.935, which might indicate a specific feature or aspect of the multimedia tool that was particularly intuitive for the respondents. For EE, the mean values fluctuate between 3.853 and 3.947. These values again tilt towards the higher end of the scale, signifying that, on average, participants felt that multimedia tools enhanced the effectiveness of educators. Within this domain, EE-7 registers the highest mean value of 3.947, suggesting that participants most recognized or valued this specific dimension of effectiveness.

SEM analysis

Table 6 presents various metrics assessing the fit of a statistical model.

Table 6 Model Fit Indicators.
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A central focus is on the chi-square statistic (χ2 = 394.034) with degrees of freedom (df) of 249. Although the p value is significant at 0.000, caution should be exercised when interpreting this result, as chi-square is known to be sensitive to sample size. A more informative indicator might be the chi-square to degrees of freedom ratio (χ2/df). This ratio stands at 1.582, well below the recommended threshold of 3, suggesting an acceptable fit of the model.

Several goodness-of-fit indices support this observation: the goodness-of-fit index (GFI = 0.927), comparative fit index (CFI = 0.983), normed fit index (NFI = 0.955), and nonnormed fit index (NNFI = 0.981) all exceed the desired threshold of 0.9. Moreover, the root mean square error of approximation (RMSEA) is 0.038. Values below 0.05 frequently signify a strong alignment with the data, although values up to 0.08 are acceptable. This observation further reinforces the proposition of a model that fits well. The evaluation above is additionally supported by the RMSEA 90% Confidence Interval, which spans from 0.031 to 0.045, falling within the permitted range.

In addition, the standardized root mean square residual (SRMR) of 0.024 further supports the adequacy of the model’s fit, as values below 0.1 are typically considered favourable. Other indices, such as the AGFI (0.912), IFI (0.983), PGFI (0.769), PNFI (0.862), and PCFI (0.887), further reinforce the robustness of the model’s fit to the observed data.

The presented fit indicators consistently signal a satisfactory model fit to the data, making it a robust foundation for drawing subsequent inferences.

Table 7 comprehensively delineates the predictive relationships between various constructs. Examining these relationships offers a panorama of the associations and their strengths, highlighting the constructs’ ability to predict various outcomes.

Table 7 Model Regression Coefficient Summary Table.
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The nonstandardized regression coefficients and standard errors present the raw associations between the predictor and the outcome variables. Meanwhile, the z (critical ratio value) and p values provide statistical indicators for the significance of these relationships. The standardized regression coefficients provide insight into the relative strength of the relationships, adjusting for the scales of the variables.

A closer look at the relationships suggests that PU, PEU, and EE are significant predictors for their respective outcome variables, as evidenced by p values consistently being less than 0.001.

For instance, the association between PU and EE reveals a nonstandardized coefficient of 0.227, with a standardized value of 0.242, underscoring a moderate yet significant relationship. Similarly, PEU’s influence on PU and EE stands out, particularly with a substantial effect on PU, as shown by a standardized coefficient of 0.506. Such findings emphasize the central role these predictors play in determining the outcomes.

Moreover, the predictors’ relationships with their respective measures, such as PU’s association with PU-1 to PU-6 and EE’s influence on EE-1 to EE-12, are all statistically robust. This is evident from the significant critical ratio values and the consistently significant p values.

In essence, the table provides a comprehensive view of the regression relationships, underscoring the robustness and significance of the predictors in explaining the variances in their corresponding outcomes. This analytical exposition aids in understanding the crucial pathways and associations in the studied context, offering valuable insights for scholars and practitioners alike.

Figure 4 illustrates the research model, visually mapping the intricate relationships between constructs reinforced by the standardized regression coefficients and offering a concise graphical summary of the statistical findings presented in the Model Regression Coefficient Summary Table.

Fig. 4
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Research Model with Data Testing.

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Conclusion

Our study aimed to explore the potential relationships between multimedia tools and PU and PEU and the resultant effects on EE. Drawing on the data and subsequent analyses, we arrive at the following conclusions concerning the hypotheses.

PU of multimedia tools significantly affects EE

Our data robustly support this hypothesis, showing a significant positive regression coefficient of 0.227 (p < 0.001, standardized coefficient: 0.242). This reveals a clear connection between the perceived value of multimedia tools and the resulting effectiveness of educators. In essence, when multimedia tools are perceived as valuable and relevant, educators are more likely to integrate them effectively into their teaching and care methods, leading to enhanced outcomes in early childhood education. This highlights the inherent need for developers and policy-makers to ensure that multimedia tools are not only technologically advanced but also cater directly to the practical needs of educators.

PEU of multimedia tools significantly affects EE

The hypothesis is strongly supported by our findings, reflected by a significant regression coefficient of 0.493 (p < 0.001, standardized coefficient: 0.487). The implications of this are profound. If educators find multimedia tools cumbersome or nonintuitive, even the most advanced features can be underutilized, undermining potential educational benefits. The ease with which these professionals can navigate and apply multimedia tools directly impacts their ability to harness their full potential, directly influencing the quality of the education and care provided.

PEU significantly affects the PU of multimedia tools among educators

The data offer a solid endorsement for this hypothesis, with a regression coefficient of 0.545 (p < 0.001, standardized coefficient: 0.506). This suggests a symbiotic relationship between ease of use and perceived value. If a multimedia tool is user-friendly, its perceived utility among educators increases, making it more likely to be integrated into their daily routines. This intertwining of utility and usability underscores the importance of holistic tool design, where functionality and user experience are both prioritised.

Overall, the conclusions drawn from our hypotheses provide a compelling narrative about the importance of both perceived usefulness and ease of use in the context of multimedia tools for educators. It is not merely about creating technologically sophisticated tools; it is about ensuring they align with the practical needs and comfort levels of educators. As the landscape of early childhood education in China continues to evolve, these insights offer critical guidance for both tech developers and educational policy-makers, emphasizing the need for tools that are both potent and accessible.

Discussion

The intricate connection between technology, specifically multimedia tools, and education has been extensively examined in academic research. With the ever-evolving landscape of digital learning, it is imperative to understand the factors that influence the successful adoption and effectiveness of these tools. Our study, rooted in this context, offers several insights that warrant discussion.

First, our findings align with the broader literature that emphasizes the role of PU in technology adoption. The significant effect of PU on EE aligns with the tenets of the TAM, which posits that the perceived usefulness of technology is a primary determinant of its acceptance and use. This result underscores the importance for developers and educators alike to ensure that multimedia tools incorporate advanced features and are genuinely helpful in the intended context.

Our observation on the role of PEU sheds light on a critical aspect of technology implementation in educational contexts. The positive influence of PEU on educators’ effectiveness is a testament to the age-old adage: simplicity is the ultimate sophistication. It is about more than having a tool with many features; its potential benefits remain unrealized if it is not user-friendly. The influence of PEU on PU further cements the notion that tools perceived as easy to use are also deemed more practical. This interconnectedness suggests that usability and utility are not mutually exclusive but intertwined dimensions that educational technology developers must address concurrently.

In addition, the robust effect size of the relationship between PEU and PU is noteworthy. While usefulness is paramount, the ease with which educators can harness this usefulness is equally critical. This has significant implications for training and professional development programs. As institutions introduce new multimedia tools, they must ensure that support mechanisms are in place to make the transition smooth for educators.

However, our study is not without its limitations. Factors such as cultural nuances, institutional peculiarities, or regional specifics might affect the observed relationships. It is also relevant to highlight that our survey was conducted exclusively in Guangdong Province. As such, the findings may not be generalizable to other provinces in China, suggesting the need for caution when interpreting the outcomes.

In conclusion, our investigation stresses the significance of the association between perceived usefulness and ease of use in determining the effectiveness of multimedia tools for educators. As we navigate deeper into the digital age, these revelations serve not just as scholarly reflections but as essential guideposts for stakeholders straddling technology and education, mapping out the trajectory of digital education.

Directions for future studies

Given the geographical limitation of our study in Guangdong Province, future research could explore similar dynamics in other provinces of China to ascertain the generalizability of our findings. Cross-provincial comparisons might identify regional variances in the adoption and effectiveness of multimedia tools. In addition, longitudinal studies could be conducted to track changes in perceptions and usage patterns over time, offering insights into the evolving nature of digital learning. There is also a potential avenue to delve deeper into specific multimedia tool features and their direct impact on educators’ teaching methodologies and student outcomes. Ultimately, as technology continues to permeate educational settings, it is imperative for research to stay abreast of these developments, ensuring that tools are both relevant and effective in the ever-changing educational landscape.