How B2B social media content strategies generate engagement across different social media platforms
Bibliographic record
Abstract
Social media (SM) has become an essential means of engaging key stakeholders for business-to-business (B2B) firms. As a result, academic research has paid increasing attention to the factors that drive SM engagement in business markets. Although studies have examined the types of SM content that generate higher engagement in B2B contexts, current research lacks deeper insight into message strategies across different social media platforms (SMPs). We address this knowledge gap by examining whether the use of SM content strategies by B2B firms varies across platforms and whether their effectiveness in generating engagement varies across SMPs. In doing so, we build a new SM content strategy framework focused on messaging functions that capture a broad set of message stakeholders. Using advanced Random Forest modeling, we analyze 1700 SM messages and related engagement data from 18 of the largest Canadian B2B companies based on their revenues. We advance current research knowledge by demonstrating how different message strategies across SMPs achieve higher engagement levels. The findings offer concrete insights for practitioners to effectively engage diverse stakeholders across different SMPs. • The engagement of stakeholders in social media is a strategic issue for B2B firms. • No research has studied the effectiveness of content strategies across SM platforms. • Study creates a new broadened functional SM content strategy conceptualization. • We analyze 1700 SM messages and their engagement data with random forest modeling. • We find that both content strategy use and effectiveness differ across SM platforms.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".