The Role of Social Media on Establishing Brand Value: A Content Analysis on Banks in Turkey
Bibliographic record
Abstract
There is an increasing use of social media on a global scale and it has been forcing organizations to restructure and adjust their marketing activities. In a relationship-driven economy, the goal of achieving a sustainable competitive advantage makes it crucial to adapt to the ever-changing trends in the market. Social media contributes to this goal since it has a considerable impact on creating and shaping brand value for organizations. The social networks help organizations enhance the development of strong brands not only through promoting their products and services but also providing them the platform to build strong and reliable relationships with their customers. This paper aims to investigate the role of social media on brands by examining the active role of banks on social networks. For this purpose, the “tweets” of the five Turkish banks with the highest brand values in the banking sector have been analyzed by content analysis method. The sample banks are drawn from the Banker’s annual Top 500 Banking Brands 2014 report. Brand value is used as the selection criteria of the sample banks and “Twitter” social network is considered as the primary social media outlet. The data is composed of the “tweets” and gathered from the official Twitter accounts of the banks with the highest brand values in Turkey. The “retweets” and the texts sent by other Twitter users are excluded. The findings indicate that the sample banks are active users of social media. These banks do not only use Twitter but also other social networks as well as their official websites. In addition, the paper displays specific purposes the banks have for using social media sites.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".