Do Social Media Efforts Matter for a Firm's Bottom Line? Evidence from Facebook
Bibliographic record
Abstract
Despite the increasing attention paid to the business value of social media, it is still not clear how they affect firm performance. This study theorizes and empirically examines how firms’ social media efforts—in terms of intensity, richness, and responsiveness—influence consumer behavior (engagement and attention) and firm performance. Using detailed data collected from the Facebook pages of 63 firms over the 2010-2012 period, we find that the richness and responsiveness of a firm’s social media efforts are significantly associated with the firm’s market performance, captured by abnormal returns and Tobin’s q. Interestingly, the intensity of a firm’s social media efforts is not significantly associated with firm performance. We also find that not only do consumer engagement and attention directly impact firm performance, but they also mediate the relationship between a firm’s social media efforts and firm performance. Unlike prior studies that examine the impact of third-party or consumer-initiated social media, such as blogs and consumer ratings, our study focuses on estimating financial returns to firms’ own efforts on firm-initiated social media, thereby assessing the business value of social media directly.
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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 | 0.001 | 0.010 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".