The effect of social media agility to strengthen the business relationship: Evidence from pharmaceutical firms in Thailand
Bibliographic record
Abstract
The firms and the industries still using the ways and means of promotion and publicizing the products have still not achieved the level of success as the modern means of social media is providing in creating a huge demand for their products. This study involves the modern means of communication agility promises success, development, and significance of the product in an efficient manner. Social media plays a vital role in creating a demand for the products by explaining the need of the product and a very effective source of creating a link among industries, firms, management, employees and especially the individual ones who come to know the skills and expertise. The impact of social media in strengthening the relationship with the customer has been very keenly observed and the findings show that such research has detailed that the use of social media has influenced the internal and the external capabilities. The data gathered in this study shows that the firms and the industries using the approach of social media have a revolutionary impact on building and strengthening the relationship between the customer and the management, employees. This study shows a strong bond between a firm and the customer using social media.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".