Supplier selection in the aftermath of a supply disruption and guilt: Once bitten, twice (not so) shy
Bibliographic record
Abstract
ABSTRACT The supply management literature assumes that supplier selection is devoid of emotions and unaffected by the history and experience associated with a previously‐selected supplier. In this paper, we relax these assumptions. Specifically, we consider the following sourcing opportunity: a sourcing professional had (alternatively, had not) recommended a critical‐component supplier that originated an avoidable (alternatively, unavoidable) supply disruption (aka, the “disrupted supplier”). In the aftermath of this supply disruption, the sourcing professional is asked to recommend a supplier for a new‐to‐beoutsourced critical component (i.e., one unrelated to the component whose flow was interrupted), taking into consideration the influence of guilt as an emotional reaction to the supply disruption. Analyses of data from 286 sourcing professionals participating in a scenario‐based, roleplaying experiment reveal that sourcing professionals experience higher levels of guilt when (a) they (versus their predecessor) had been responsible for selecting a disrupted supplier and (b) they deem the supply disruption to be controllable (versus uncontrollable) by the disrupted supplier. Guilt‐laden sourcing professionals are then more likely to recommend a riskier albeit more advantageous supplier for a new‐to‐be‐outsourced critical component. Our results provide the first evidence that prior supplier selection decisions gone awry influence future supplier selection decisions through the emotion of guilt. Moreover, they demonstrate that supply disruptions in one context have carryover effects on future sourcing decisions in unrelated contexts—an insight that is absent from the literature on supply disruptions.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".