The brand personality dimensions of business-to-business firms: a content analysis of employer reviews on social media
Bibliographic record
Abstract
Purpose: The purpose of this paper is to explore the brand personalities that employees are creating of their employer brands, in particular business-to-business (B-to-B) brands, when describing these brands on social media. We examine how the brand personalities, based on written online reviews, differ between high- and low-ranked, and high- and low-rated brands.Methodology/Approach: 6,300 written employee reviews from a social media platform, Glassdoor, are used for content analysis in DICTION, to determine the brand personality dimensions they communicate (J. L). An independent B-to-B brand ranking data source, Brandwatch, is used as a reference to various brands’ level of ranking, while an ANOVA test is used to determine whether there is a difference in the brand personality trait means when comparing high and low-ranked, and high- and low-rated brands.Findings: Our findings suggest that a strong social media presence does not equate to a strong employer brand personality perception among employees, since there are no significant differences between B-to-B firms based on their rankings.Research Implications: Extant literature has mostly explored the impact of either critical reviews or favourable customer ratings and reviews on company performance, with very little research focusing on the B-to-B context. In addition, research employing DICTION for the purposes of content analysis of reviews is sparse. The methodology used in this study could thus be employed to further compare and contrast the reviews from a single company, dividing top and low starred reviews to compare discrepancies.Practical Implications: The results of this study show how online shared employee experiences of employer brands contribute to the formation of a distinct employer brand personality. From a managerial viewpoint, engaging with current and past employees and being cognizant of the online narratives that they share on social media, may be an early indicator of where the firm is lacking (or showing strength) in its’ employee engagement. This would offer a way for firms to both understand their employer brand personality as well as gauge how they compare to top employers in a specific sector or industry.Originality/Value/Contribution: The study attempts to grow the literature of employee brand engagement in a B-to-B context, by recognizing the important role that employees play in engaging with their employer brand online. Two main contributions are offered. The first contribution relates to the finding that employees perceive highly-rated B-to-B brands as being more competent, exciting, sincere and sophisticated than low-rated B-to-B brands. Second, the methodology used in this study proves to be a novel and accurate way of comparing employee reviews and perceived employer brand personality, with the employer-created intended brand image.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".