Business‐to‐business e‐procurement: success factors and challenges to implementation
Bibliographic record
Abstract
Purpose The paper seeks to pursue the understanding of current business‐to‐business e‐procurement practices by describing the success factors and challenges to its implementation in the corporate setting. Design/methodology/approach Members of the Institute for Supply Management and the Council of Logistics Management were asked to respond to a survey questionnaire. Factor analysis was used to analyze data from valid responses received from 185 firms. Findings Factor analysis resulted in three e‐procurement success factors (SF):supplier and contract management; end‐user behavior and e‐procurement business processes; and information and e‐procurement infrastructure. Three challenge‐to‐implementation factors (CIF) also emerged: lack of system integration and standardization issues; immaturity of e‐procurement‐based market services and end‐user resistance; and maverick buying and difficulty in integrating e‐commerce with other systems. Research limitations/implications A representative sampling design should be used in the future to be able to make claims for generalizable results. Practical implications E‐procurement is a very important initiative with significant cost savings potential for firms. This study's findings can guide various stages of corporate implementation efforts. Originality/value This study fulfills the need for solid empirical findings on this very important topic that has a direct impact on a firm's bottom line. E‐procurement is still in the early stages of marketplace deployment and guidance is still needed on how to do it right.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".