Crowdsourced views on consumer misbehaviour in service encounters: know your rights!
Bibliographic record
Abstract
Reports of consumer misbehaviour in service encounters shared on social media can present challenges for companies, subjecting them to public scrutiny and placing reputation at risk as online communities appraise the harm caused by violations of the rights of others. Rights are essential to service encounters because they provide a framework for ensuring that both the provider and the service recipient are treated fairly and respectfully. Also, the failure to respond promptly and appropriately can damage a firm’s reputation or expose the company to legal action for failing to address the behaviour. This article aims to demonstrate how companies can research how consumers invoke rights to make sense of consumer misbehaviour in service encounters. We report the results of a study that uses text analytic methods to analyse rights-based themes in a sample of over eight thousand comments gathered from a Karen-memefocused subreddit. The study finds that rights feature prominently in Karen discourse on Reddit. Moreover, the comments often implicitly or explicitly refer to human rights principles in the context of mask-wearing, property and health, the right to complain, and free speech. Interestingly, companies’ right to refuse service was the most prevalent theme. Many people seem to empathise with service providers and company owners who suffer the wrath of Karen’s dysfunctional actions. The discovery of folk knowledge of rights principles in online conversations opens new avenues for research on how consumers evaluate and respond to different aspects of service encounters. With such insights, companies can develop better strategies for addressing and preventing consumer misbehaviours by upholding employee and consumer rights and well-being.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".