Understanding the Usage of Social Networks Apps Among Organizations and Its Impact on Team Performance: Empirical Study
Bibliographic record
Abstract
Nowadays in the digital era the development of applications influences the process of group collaboration. And social networks apps usage among enterprises and institutions is growing enormously. Most of the researches on social networks technologies usage are focused on the individual viewpoint, while others are from the organizational viewpoint. Nevertheless, not many research works have examined the actual influence of social networks apps usage via smart devices on team’s performance. The need of communication in group teamwork while doing tasks or projects, forces the team to plan their meetings to finish tasks given. Therefore, using the quantitative approach, this research aims to reveal the predictors that drive the employees to use social networks technologies, and the consequences of using these technologies in team performance. This research model was validated using Partial Least Squares approach with on 110 respondents. The results disclosed that perceived ease of use, perceived compatibility, perceived usefulness, and perceived interactivity of the social network applications, drive the employees to use these applications when performing their group projects. In addition, collaborative working is also a significant predictor for the employees to use social networks applications. Also, the use of social networks technologies also showed a positive impact on team performance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".