New data-driven model accelerates educational technology in developing countries

While models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) have been instrumental in forecasting tech adoption in developed nations, their transferability to the educational systems of LMICs is limited. These frameworks often overlook the infrastructural fragility, cultural complexities, and pedagogical particularities of emerging economies. Even with extensions like TAM2 and UTAUT2, core variables like gender, age, and experience, largely designed for workplace settings in Western contexts, do not adequately capture the realities of schools in sub-Saharan Africa, Southeast Asia, or Latin America.

In the global race to achieve the United Nations' Sustainable Development Goal 4 (SDG4), aimed at ensuring inclusive and equitable quality education by 2030, developing nations face the toughest road ahead. Limited infrastructure, scarce resources, and deep-rooted socioeconomic divides continue to impede their educational progress. Now, a newly published peer-reviewed study offers a major step forward. Titled “Toward Sustainable Education: A Contextualized Model for Educational Technology Adoption for Developing Countries” and published in Sustainability, the paper proposes a tailored adoption framework known as ETADC that seeks to guide policymakers, educators, and institutions in low- and middle-income countries (LMICs) through a more informed, cost-effective, and locally responsive integration of educational technologies.

Developed by researchers from Central China Normal University, the ETADC model stands apart from conventional technology adoption models like TAM and UTAUT by embedding educational specificity and regional constraints directly into its structure. The model introduces six interlinked variables, performance expectancy, effort expectancy, social influence, facilitating conditions, price value, and actual acceptance/use, to holistically account for how educational technologies succeed or fail across developing contexts. Using meta-analysis and structural equation modeling across 30 high-impact studies involving 8,934 subjects, the authors affirm the model’s robustness and predictive power, marking a significant advancement in educational planning science.

Why do conventional EdTech adoption models fall short in the Global South?

While models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) have been instrumental in forecasting tech adoption in developed nations, their transferability to the educational systems of LMICs is limited. These frameworks often overlook the infrastructural fragility, cultural complexities, and pedagogical particularities of emerging economies. Even with extensions like TAM2 and UTAUT2, core variables like gender, age, and experience, largely designed for workplace settings in Western contexts, do not adequately capture the realities of schools in sub-Saharan Africa, Southeast Asia, or Latin America.

The ETADC model addresses this gap by redefining the pathway to technology acceptance in educational contexts. It highlights performance expectancy (whether the technology demonstrably enhances learning outcomes) and effort expectancy (how easy it is for teachers and students to use) as primary influencers. But it also adds unique emphasis to price value, a metric reflecting cost-effectiveness in low-budget systems, and social influence, which captures the role of peer endorsement and institutional pressure. Facilitating conditions, like reliable internet or available training, are shown to indirectly influence adoption by improving perceived ease of use.

The inclusion of the TPACK framework, Technological Pedagogical Content Knowledge, is another innovation. TPACK ensures that adoption isn’t just about having devices, but about knowing how to integrate them into actual teaching strategies. This emphasis on practical capability is essential in environments where digital literacy cannot be assumed and teacher training is sporadic.

How was the ETADC model developed and validated for general use?

To build ETADC, the researchers began by reviewing over 100 peer-reviewed studies across nine SSCI-ranked educational technology journals, eventually selecting 70 for in-depth analysis. The majority (85.7%) applied variants of TAM or UTAUT, reaffirming the foundational relevance but also the need for contextual recalibration. The model’s architecture was then constructed by synthesizing these established theories with firsthand data on teacher and student experiences from developing countries.

A rigorous validation followed. The team conducted a two-stage meta-analytic structural equation modeling (MASEM) using R Studio 4.4.0. Data were pooled from 30 of the most statistically complete studies. Goodness-of-fit indices confirmed the model's reliability: RMSEA stood at 0.0387, SRMR at 0.0476, CFI at 0.9916, and TLI at 0.9578, all indicating strong alignment with observed data. The model explained 52% of the variance in acceptance and use of EdTech and 53% in perceived effort expectancy, significantly outperforming its predecessors.

Path coefficients also supported the model’s predictive integrity. For example, effort expectancy (β = 0.29), facilitating conditions (β = 0.37), and price value (β = 0.24) were all statistically significant predictors of EdTech acceptance. Notably, the strongest influence on ease of use came from social influence (β = 0.44), underscoring the vital role of peer networks and cultural perception in LMIC environments.

Can this model guide real-world EdTech adoption across the developing world?

To test the model’s practical relevance, the researchers applied the ETADC framework to assess 11 educational technologies, including Quizlet, Duolingo, Google Classroom, and Nearpod, across five dimensions: performance expectancy, price value, facilitating conditions, effort expectancy, and social influence. These evaluations were based on user feedback, market ratings, download sizes, and compatibility across devices.

The exercise revealed important insights. Technologies like Duolingo and Canva scored highly due to their ease of use, educational alignment, and favorable user reviews. Others, like Tinkercad and Nearpod, underperformed in categories such as download accessibility or teacher endorsement. This diagnostic ability makes the ETADC model not only a theoretical framework but also a practical evaluation tool that institutions can adopt to benchmark EdTech products before investment.

Moreover, the study recommends that ministries of education, NGOs, and international development bodies use ETADC to screen technologies against local constraints. The model supports the design of policy interventions such as subsidies for high-impact tools or targeted training programs to bridge digital literacy gaps. For educational technology developers, it offers a blueprint for product design that better aligns with the operational realities of under-resourced classrooms.