Decoding the employee influencer on social media: applying Taylor’s six segment message strategy wheel
Bibliographic record
Abstract
Purpose The paper aims to examine how employees influence their employer’s brand by applying Taylor’s (1999) six segment message strategy wheel in an employee influencer context. Design/methodology/approach The research uses a content analysis of employees’ public social media posts – including captions and images – to analyze the message strategies employees use to promote their employers. Findings While ego and social were popular message strategies in both the images and captions, the findings evidence the varying message strategies employees use in text-based versus image-based messages. Four “imagined audiences” of employee influencers are identified: current customers, prospective customers, current employees and prospective employees. Research limitations/implications The research provides insight into how employees act as influencers in building their employer brand on social media. Practical implications A unique measurement tool is developed that can be used by companies and future researchers to decode employees’ online communications. Originality/value This research contributes to theory and practice in the following important ways. First, the research provides a modernization of an existing framework from an offline setting to an applied industry context in an online setting. Second, this research focuses on a subtype of social media influencer, the employee influencer, which is an underdeveloped area of research. Third, a unique measurement tool to analyze text-based and image-based social media data is developed that can be used by companies and future researchers to decode employees’ online communications.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".