Managing business‐to‐business relationships throughout the e‐commerce procurement life cycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract E‐commerce technologies provide effective and efficient ways in which corporate buyers can gather information rapidly about available P/S (products and services), evaluate and negotiate with suppliers, implement order fulfillment over communications links, and access post‐sales services. From the supplier side, marketing, sales, and service information is also readily gathered from customers. Building and maintaining customer relationships is the key to success in e‐commerce and, unless service is maintained, customer loss may result, more than offsetting any cost efficiencies due to introducing e‐commerce technology. Since the core of e‐commerce is information and communications, support for managing customer relationships is available to those who know how to use it. Discusses how technology can be used to encourage and facilitate customer‐business relationships. Shows through a customer relationship life cycle model how the management of related procurement functions in customer companies can adjust to take advantage of these relationships.
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.
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 | 0.004 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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 it