Employees as influencers: measuring employee brand equity in a social media age
Bibliographic record
Abstract
Purpose The practice of frontline employees articulating their brand voice and posting work-related content on social media has emerged; however, employee brand equity (EBE) research has yet to be linked to employees’ social media activity. This paper aims to take a methods-based approach to better understand employees’ roles as influencers. As such, its objective is to operationalize and apply the three EBE dimensions – brand consistent behavior, brand endorsement and brand allegiance – using Instagram data. Design/methodology/approach This qualitative research uses a case study of employee influencers at SoulCycle, a leading North American fitness company and examines 100 Instagram images and 100 captions from these influential employees to assess the three EBE dimensions. Findings Brand consistent behavior (what employees do) was the most important EBE dimension indicating that employees’ social media activities align with their employer’s values. Brand allegiance (what employees intend to do in the future) whereby employees self-identify with their employer on social media, followed. Brand endorsement (what employees say) was the least influential of the three EBE dimensions, which may indicate a higher level of perceived authenticity from a consumer perspective. Originality/value This research makes three contributions. First, it presents a novel measure of EBE using public Instagram data. Second, it represents a unique expansion and an evolution of King et al. ’s (2012) model. Third, it considers employees’ work-related content on social media to understand employees’ role as influencers and their co-creation of EBE, which is currently an under-represented perspective in the internal branding literature.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".