How Much Compensation Should a Firm Offer for a Flawed Service? An Examination of the Nonlinear Effects of Compensation on Satisfaction
Bibliographic record
Abstract
This research examines the nonlinear effects of compensation on customer satisfaction in order to determine the optimal compensation after a flawed service. As our core contribution, we argue that the nature of this nonlinear effect depends on the way customers handle a flawed service. Building on the Service-Dominant (S-D) logic, this research introduces two specific failure handling tactics—when customers reject versus accept a flawed value proposition—that affect the shape of the nonlinear function of compensation on satisfaction. Our key hypotheses are tested with two experiments that manipulate 11 compensation levels (0–200%) and the two failure handling tactics (rejection vs. acceptance). Consistent with our logic, both studies reveal an S-shaped curve progression for service rejection and a concave shape for service acceptance. For service rejection, the highest incremental effect of compensation on satisfaction lies in between 60% and 120%. For service acceptance, the highest return in satisfaction is obtained with the first dollars invested in partial compensation. As a major managerial takeaway, firms can use these findings to determine the compensation level that provides the best satisfaction return.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".