The adoption of a social learning system: Intrinsic value in the UTAUT model
Bibliographic record
Abstract
Abstract The purpose of this study is to identify the determinants of the intention to use and the effective use of a learning management system that integrates social learning tools. Data were collected through an online questionnaire and analyzed using structural equation modeling techniques. As our theoretical lens, we adapted the original unified theory of acceptance and the use of technology model by extending it with intrinsic value construct. As such, this research allowed for the first time testing an extended version of the unified theory of acceptance and the use of technology model in a social learning context. Our results show that facilitating conditions and intrinsic value variables explained the behavioral intention to use a learning management system that integrates social media technology and that facilitating conditions variable predicted use behavior. Our research findings suggest fostering both students’ enjoyment and interest in using social learning technologies for education and offering them facilitating conditions to strengthen technology adoption. Practitioner Notes What is already known about this topic Universities know that students are accustomed to use social media for personal purposes. Teachers are trying to integrate social media tools into learning management systems. There is little knowledge about what makes students’ willing to use social media tools for learning. The unified theory of acceptance and use of technology is an approved model that explains the intention to use and the effective use of technology. What this paper adds The paper identifies the determinants that make students’ willing to use a learning management system in which a social media tool is embedded. Apart from the main determinants of the unified theory of acceptance and the use of technology model, intrinsic value—defined as the feeling of both enjoyment and interest from performing an activity—explains behavioral intention and use behavior toward social media systems. Implications for practice and/or policy The paper concludes with advices for decision makers in universities who want to integrate social learning tools in learning management systems: They have to pay attention to not only the social media system’s functionalities, but also to how the system can be enjoyable and interesting to use. They have to think about offering better facilitating conditions to students—like user manuals, an online FAQ, discussion forums, training sessions, or personal human support—to strengthen the adoption of social learning systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".