Can emojis and B2B mix? The effects of emojis and emoji–text interactions on B2B social media engagement
Bibliographic record
Abstract
Purpose Social media engagement is becoming increasingly critical for business-to-business (B2B) marketing. Yet, creating compelling brand posts that stimulate engagement remains a challenge for B2B marketers. As an increasingly popular linguistic element, emojis have been rapidly incorporated into B2B social media posts. However, there is little clarity around if, when, why and how emojis influence stakeholder responses to B2B social media posts. This study aims to address this gap by investigating emojis’ effects on B2B social media engagement. Design/methodology/approach This study draws on the B2B communication model, fluency theory and extant research on emojis’ communicative effects and examines: how emoji use in social media posts influence B2B social media engagement; and how different emoji–text integrations moderate such effects. A field study was conducted to analyze 64,547 tweets from 82 B2B brands in 19 industries. Findings The results of this study reveal an inverted U-shaped relationship between emoji count in B2B social media posts and engagement. Moreover, the ways in which emojis are integrated with the text can affect engagement and moderate such relationship. The inverted U-shaped relationship is weakened when emojis are placed inside the text or used as text substitutions. Originality/value This study reveals the role that emojis play in driving B2B social media engagement. Besides, this study presents a pioneer investigation of the nuanced effects of emoji–text interactions in the B2B context.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".