Asymmetric investments in exchange relationships, perceived supplier shirking and cross-functional information sharing as a moderator
Bibliographic record
Abstract
Purpose With asymmetric investments in exchange (i.e. sourcing) relationships, both sourcing firms and suppliers invest but one party invests more than the other. This paper aims to examine the associations between asymmetric (i.e. unequal) investments in exchange relationships and the tendency of the strategic supplier base to shirk as perceived by the sourcing firm, as well as the moderation effects of cross-functional information sharing within a sourcing firm on these associations. Design/methodology/approach The authors analyzed survey data from 500 US middle-market manufacturers via ordinary least squares (OLS) estimation. Besides appropriate controls, the authors also employed the heteroskedasticity-based instrumental variable approach to ensure that analytical inferences are not influenced by endogeneity. Findings On average, when a sourcing firm invests more than its strategic supplier base into their exchange relationships, the perceived tendency of the strategic supplier base to shirk decreases. This negative association is more pronounced when a sourcing firm facilitates cross-functional information sharing. Conversely, when the strategic supplier base invests more than the sourcing firm into their exchange relationships, the perceived tendency of the strategic supply base to shirk is not detected unless the sourcing firm facilitates cross-functional information sharing. Originality/value Prior research reveals that investments by a sourcing firm or by suppliers influence supplier shirking. This paper provides new evidence as to how and why asymmetric investments in exchange relationships relate to the perceived tendency of the strategic supplier base to shirk and new evidence as to how and why cross-functional information sharing safeguards against this tendency when investments in exchange relationships are unequal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".