Effect of Cultural Values on Students' Adoption of Social Media for Collaborative Learning
Bibliographic record
Abstract
ABSTRACT Background Collaborative learning, which emphasises cooperative group techniques, intersects with the evolving role of social media as a tool. Understanding how cultural values influence these dynamics is crucial for effectively integrating and utilising social media into collaborative learning environments. Objective This research aims to advance knowledge in collaborative learning by introducing a multidimensional approach to understanding the impact of espoused cultural values (ECV) on technology acceptance in the Indian context, using the unified theory of technology acceptance and usage (UTAUT) for collaborative learning. Methods The study employed a multivariate data analysis approach using raw data collected through a convenience sampling technique from 250 engineering students in Rajasthan, India. The study investigated the influence of ECV treated as a higher‐order construct, on effort expectancy (EE), performance expectancy (PE), social influence (SI), facilitating conditions (FC) and students' intentions to use Facebook for collaborative learning. The analysis was performed using the partial least squares structural equation modelling (PLS‐SEM) method with SmartPLS v3.2.9. Results The PLS‐SEM analysis demonstrated significant impacts of ECV on EE, PE, FC and SI. To provide better insights, the lower‐order constructs of ECV (i.e., uncertainty avoidance, power distance, masculinity/femininity and individualism/collectivism) that influenced the intent to use were also analysed. This research contributes to the understanding of factors that influence the adoption of collaborative learning tools and guides the development of tailored strategies for technology adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".