Technology Acceptance in E-Learning Environments
Summary
Technology acceptance in e-learning environments examines why and how learners and educators adopt digital platforms for instruction and study. Foundational frameworks, notably the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), identify core determinants such as perceived usefulness, perceived ease of use, performance expectancy, effort expectancy, social influence and facilitating conditions. Over time, extensions to these models have incorporated factors like self-efficacy, enjoyment, trust, content and system quality, and cultural values to explain variance in behavioural intention and actual use. Recent developments have broadened the field to encompass emerging technologies—artificial intelligence, large language models and social media—as both pedagogical tools and acceptance drivers. Globally, understanding technology acceptance informs the design of more accessible, engaging and effective e-learning systems, guiding educators, institutions and policymakers in fostering digital inclusion and optimising learning outcomes.
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Technology Acceptance in E-Learning Environments publication trend
The graph below shows the total number of articles in technology acceptance in e-learning environments across all publications each year (not limited to Nature Index journals).
Technical terms
Technology Acceptance Model (TAM): A theoretical framework positing perceived usefulness and perceived ease of use as primary predictors of technology adoption.
Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2): An integrative model identifying performance expectancy, effort expectancy, social influence, facilitating conditions and additional constructs as determinants of behavioural intention and use.
Performance Expectancy: The belief that using a technology will improve one’s learning performance.
Effort Expectancy (Perceived Ease of Use): The perceived degree of ease associated with using a technology, influencing user acceptance.
Perceived Usefulness: The extent to which a user considers a system beneficial for accomplishing learning tasks.
Social Influence: The degree to which an individual perceives that important others expect them to use the technology.
References
- Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change (2024).
- Information quality and students’ academic performance: the mediating roles of perceived usefulness, entertainment and social media usage. Smart Learning Environments (2024).
- Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology (2019).
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