Employee’s (Happy) Branding Corporate’s ‘Social’ Reputation: Can You Put a Price on That?
Bibliographic record
Abstract
The purpose of this study is to investigate the relationship between Corporate ‘Social’ Reputation (CSR) and employees’ increasing usage of Social Media (SM) and related technologies in promoting and strengthening their company’s branding strategies. This study draws on the (Rokka, Karlsson, & Tienari, 2014) conceptualization of corporate reputation management in SM as balancing acts, which take place in relation to different, contradictory, and sometimes paradoxical priorities related to branding and managing employees. The research method of this study was based on the quality content analysis and primarily relied on the recent research articles, and surveys. The findings contribute to the existing discussion on the role of SM, particularly on the employees’ growing usage of Facebook, Twitter and Instagram in building CSR bottom line. Future research is discussed regarding the motivation that drives employees to become brand evangelists. Key implications for researchers, practitioners and policy makers are highlighted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".