Mastering the digital dialogue: How restaurant managers tackle positive and negative eWOM in the UAE restaurant industry
Bibliographic record
Abstract
This study examines how restaurant managers strategically respond to online reviews through electronic word-of-mouth (eWOM), distinguishing between approaches for positive and negative feedback. Using a multi-method research design, the study first conducted a content analysis of 671 eWOM responses, categorizing key strategies. This was followed by in-depth interviews with 11 managers actively engaging in eWOM to validate the findings. Results indicate that managers use rapport-building and marketing strategies for positive reviews, leveraging them to enhance customer relationships and attract new patrons. Engaging with both positive and negative reviews is crucial for customer loyalty and brand perception. While responses to positive reviews reinforce strengths, addressing negative feedback helps mitigate reputational risks. This research contributes to eWOM strategy literature by highlighting managers’ objectives for response strategies, filling a critical gap in the restaurant industry, and providing practical insights for optimizing online engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".