Improving Job Performance Through Social Media: The Mediating Role of Transactive Memory Capability
Bibliographic record
Abstract
This research examines the effects of socialmedia use on job performance, transactivememory capability (TMC) and the role of transactive memory capability as a mediator between job performance and social media use. The study is conducted on the teaching faculty member in the North of India's public universities. A snowball sampling has been employed, and 608 respondents who met the study's selection criteria have been identified. The hypothesis and numerous interactions between variables of this study were tested using Structural Equation Modeling. It has been found that social media has a significant and positive impact on job performance. This study has also indicated a partial mediating role of TMC in the relationship between social media use and job performance. The study adds to the empirical literature by demonstrating the positive effects of social media use by the teaching faculty on TMC development and job performance. It highlights that social media can be considered a legitimate communication tool to increase workplace connectivity. Faculties should understand how social media generates transactive memory capability so that they can use it more effectively. It also fills the gap by considering TMC among teaching faculties working together to store, retrieve and share data through social media in Indian Public universities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".