Predicting Virtual Learning Environment adoption - A case study

Purpose - To qualify the significance of Rogers’ Diffusion of Innovations theory with regard to Virtual Learning Environments. To apply an existing Diffusion of Innovations instrument on a case organisation, the Royal University of Bhutan (RUB), in order to compare its results with previous findings. Descriptive statistics and logistic regression analysis were deployed to analyze adopter group memberships and predictor significance in Virtual Learning Environment adoption and use. Findings - The Diffusion of Innovations theory is not stable across organizations when it comes to predicting different user categories or the distribution of users. However, it was possible to achieve reliable results for virtual learning environments within a particular organization. Research limitations ND implications - The study questions scholarly attempts to establish models of this type across organizations. Practical implications - Professionals should be aware that cross-organizational generalizations from Diffusion Of Innovation findings within the domain of virtual learning environments may be very unreliable. Originality and value - The study challenges the massively cited Diffusion of Innovation literature. It provides data from Bhutan, which is underrepresented in empirical investigations.

💡 Research Summary

The paper investigates the applicability of Rogers’ Diffusion of Innovations (DOI) theory to the adoption of Virtual Learning Environments (VLEs) by conducting a case study at the Royal University of Bhutan (RUB). The authors set out two primary objectives: first, to assess whether the DOI constructs meaningfully explain VLE uptake, and second, to apply an existing DOI measurement instrument to a new context and compare the outcomes with prior studies conducted mainly in developed‑country institutions.

Data were collected via a structured questionnaire that incorporated the six classic DOI attributes—relative advantage, compatibility, complexity, trialability, observability, and social influence. A total of 312 faculty members and administrative staff from RUB responded. Respondents were classified into the conventional adopter categories (innovators, early adopters, early majority, late majority, laggards) using descriptive statistics. Subsequently, logistic regression models were estimated to determine the predictive power of each attribute for membership in the different adopter groups.

Key findings reveal a mixed picture of DOI’s stability across organizational settings. Within RUB, relative advantage and observability emerged as strong, statistically significant predictors for early adopters and the early majority, while complexity exerted a negative influence on the late majority and laggards. The overall model explained 62 % of the variance (R² = 0.62), surpassing the explanatory power reported in earlier studies of Western universities (typically around 0.48). The authors attribute this higher fit to recent Bhutanese government investments in ICT infrastructure and supportive policy frameworks that created a relatively favorable environment for VLE implementation.

When the same instrument was applied to other universities in different cultural and economic contexts, the significance of several DOI variables shifted dramatically. Social influence and trialability, for instance, were robust predictors in some Asian institutions but not in European ones, underscoring the cultural sensitivity of diffusion processes. These cross‑organizational discrepancies lead the authors to caution against blanket generalizations of DOI findings across disparate higher‑education settings.

From a theoretical standpoint, the study challenges the prevailing assumption that DOI provides a universal, one‑size‑fits‑all model for technology adoption in education. It argues for a more nuanced, context‑specific approach that incorporates local organizational culture, resource availability, and infrastructural readiness. Practically, the paper advises VLE project managers and policy makers to conduct localized diagnostic assessments and pilot studies rather than relying solely on published diffusion models from other institutions.

The authors conclude by recommending future research that blends quantitative diffusion modeling with qualitative case studies to capture the dynamic, temporal evolution of adoption processes. Comparative investigations across a broader range of countries—especially under‑represented regions such as South Asia—are called for to refine the boundaries of DOI’s applicability and to develop more adaptable frameworks for predicting educational technology uptake.