How to react as a company towards negative consumer UGC to avoid a bigger company crisis
Bibliographic record
Abstract
More and more companies are faced with negative online messages about their business, messages that can be widely spread online and eventually cause financial loss for a company. As in the case of the Canadian musician who made a song about United Airlines. This company had, according to him, destroyed his guitar and the customer service did not meet his expectations. This song became a hit on Youtube and resulted in a public relations embarrassment of the company. It is important for a company to understand the online etiquette and respond properly towards negative user generated content (UGC), content that is added by users. By properly responding to negative UGC, a larger company crisis can be prevented. Coombs crisis communication theory is highly appreciated in science. The theory provides scientifically proven rules how a company can deal with crisis communication, but it makes no distinction between hedonic and utilitarian products. The question arises whether online responding to both types of products requires a different approach. In addition, can the Coombs strategies be used in an online environment or are there more appropriate ways to respond as a company towards negative UGC? In one week, three focus groups were performed among different consumers to study this topic. In addition there were eleven in depth interviews executed with public relations consultants of a public relations agency in Amsterdam, The Netherlands. Important findings were that the Coombs strategies are suitable for use in an online environment, but there are a few remarks. For example, a relatively mild and cost neutral solution already fits for a utilitarian product, while for a hedonistic product also a compensation is required, which is a more expensive strategy. A negative UGC message about a utilitarian product can meet an instrumental approach while for the hedonic product more empathy is asked of the concerned company. In the hedonic case it is not only about the intrinsic value of the product, but rather the extrinsic value. Sense plays an important role. Subsequently the research provides areas of consideration when it comes to respond towards negative UGC as a company. The choice of focus group research has shown a positive one. After the consumers first wrote down their own vision on paper, their opinion was sometimes updated during the focus groups. This type of research held the participants focused and they worked well together to provide a good research result. By discussing the topic and the focus group consumer insights with public relations professionals, this study research results became only richer. By adding their professional vision a complete image outlined about how companies can react towards negative UGC.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".