Does a Supplier's Operational Competence Translate into Financial Performance? An Empirical Analysis of Supplier–Customer Relationships
Bibliographic record
Abstract
ABSTRACT We conduct an empirical investigation of how a supplier's operational competence, as reflected by outcomes in the areas of quality, cost, delivery, flexibility, and new product development, translates into financial gains from a key customer. In contrast to previous research directed at the firm level, this study focuses on the supplier–customer relationship level. Using survey data from 158 suppliers in the manufacturing industry, we perform structural equation modeling to map out the paths from operational competence to financial performance—via dependencies and cooperative behaviors between suppliers and their customers. This study is the first scholarly attempt to examine the link between suppliers’ operational competencies and financial performance in interorganizational relationships. It is also an early investigation into operational competence as a source of bi‐lateral dependence. Our findings show that the supplier's operational competences increase its customer's dependence by enhancing the value of its products/services. However, the resulting increase in the supplier's power is not leveraged to shape relationship behaviors or capture value from its customer. In contrast, the customer's existing power as a major buyer plays an important role in shaping cooperative behaviors and affecting the supplier's financial performance from the customer relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".