How to Respond to Customer Complaints -from the Perspective of Argument Strength
Bibliographic record
Abstract
In this era of rapid network technology development, more and more people are sharing or receiving complaints about products or companies via online platforms. Related research finds that negative electronic word of mouth is perceived as credible and will have an adverse impact on companies. The purpose of this study is to explore how company response strategies to negative reviews affect corporate image and purchase intention. We aim to provide appropriate processing mechanisms to help companies reduce the damage of negative word-of-mouth. This study used an experimental design method, manipulating the experimental situation so that the subjects had a simulated personal experience with a company. A questionnaire was provided to collect subjects’ opinions. There were 180 valid subjects. We utilized variance analysis to verify the hypotheses. This study had three primary findings: (1) Different response strategies to negative reviews will have different effects on corporate image and purchase intention. Among them, the accommodative strategy is significantly better than the other strategies – defensiveness and no action – for enhancing corporate image and purchase intention. (2) The impact of the response strategy on purchase intention will be moderated by the strength of the reviews’ arguments, especially for accommodative strategies; however, this moderating effect does not occurred with respect to the impact of response strategies on corporate image. (3) Corporate image has a positive impact on purchase intention. Managerial implications for marketing managers are also discussed.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".