To talk or not?: An analysis of firm‐initiated social media communication's impact on firm value preservation during a massive disruption across multiple firms and industries
Bibliographic record
Abstract
Abstract We examine the role of firm‐initiated social media communication using Twitter in mitigating the negative impact of large‐scale disruptions, such as the Covid‐19 pandemic, on the shareholder value of firms. We develop our hypotheses using signaling theory and test them using data collected from Twitter and Bloomberg®. Our data set consists of 121,988 firm‐generated tweets from 467 S&P 500 firms collected in March 2020 at the time of the lockdown announcement in the United States. We find that frequent and relevant communication reduces latency and increases the observability of messages, preserving a firm's shareholder value. We also find that a positive outlook and extent of interest from stakeholders results in preserving shareholder value. On average, firms lost about 1.08% of their market value per day (about 9.72% during the 9‐day period around the lockdown announcement). Our study contributes to the extant literature in three ways: (1) adds to the literature on disruptions–shareholder value by considering large‐scale disruptions such as the Covid‐19 pandemic, (2) highlights informational and communication elements of risk management strategy, and (3) adds to the growing body of literature on Twitter by considering firm‐generated tweets. The results of our study are of importance to managers as well. For instance, firms tweeted about 57 times per week, and each additional tweet could preserve about $5.85 million of a firm's market valuation, on average. Also, it is not enough that the firms took appropriate actions during a large‐scale disruption; they also need to communicate their actions and its implications to their stakeholders effectively. These results can help managers devise their Twitter communication strategy during large‐scale disruptions.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".