Evolving B2B e‐commerce adaptation for SME suppliers
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore a central issue in industrial marketing, namely the buyer‐seller relationship, by focusing on how its development influences and is influenced by the use of B2B e‐commerce strategies. More specifically, the paper aims at identifying what kinds of B2B electronic interactions are imposed by influential buyers; exploring the link between these electronic interactions and the buyer‐seller relationship; and seeing how influential buyers and SME suppliers adapt their own strategies in this online environment. Design/methodology/approach The multi‐case study methodology was used to allow for rich data collection and analysis and to support the discovery of patterns. Findings The results indicate that large buyers use specific e‐commerce processes and tools for the different relationships they have with their SME suppliers. The latter must adapt to these requirements to attain the next relationship level or risk forfeiting their established position. When a supplier reaches the new level, other requirements arise, forcing it to continuously adapt its e‐commerce strategy. Research limitations/implications The model proposed in this paper can serve as a tool to align B2B e‐commerce strategies and buyer‐seller relationship levels. Practical implications Some SME suppliers have developed a competitive advantage by going through this cycle faster than their competitors. The following relationship stages were observed: pre‐relationship, spot relationship and contractual relationship. Interestingly, there was no collaboration stage in the relationships studied. Originality/value The paper contributes to an understanding of the link between electronic interactions and the buyer‐seller relationship. Its information is particularly relevant to organizations that transact or plan on transacting electronically with clients or suppliers in a B2B setting.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".