Buffer bots: The role of virtual service agents in mitigating negative effects when service fails
Bibliographic record
Abstract
Abstract In recent years, marketers have placed increased reliance upon artificial intelligence (AI) and, subsequently, the use of virtual agents in customer service contexts is on the rise. Despite such service digitalization, service can still fail. While there is an increasing literature on the effect of virtual agents in service settings, questions remain as to how customers react to service failure that results from interactions with virtual service agents. To this end, we deconstruct the effect of virtual agent service failure across two studies: one involving a process service failure and another involving an outcome service failure. We specifically manipulate the type of service agent that causes the service failure (human vs. virtual agent) and the magnitude of the failure (small vs. large). Results show that firms can leverage virtual service agents to mitigate or buffer the negative effects of service failure. From a managerial perspective, our findings suggest that firms could engage virtual service agents in situations where there may be a risk of outcome service failure—particularly in settings where relatively large magnitude failures may be experienced. In such a setting, we find that virtual service agents can mitigate the negative effects of service failure, more so than when the failure results from an interaction with a human service agent.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".