E-Business Process Negotiation: Formal Requirements for Strategy Support
Bibliographic record
Abstract
Inter-organisational processes constructed and operated in a business-to-business e-commerce context (e-business processes) have received much focus in the recent literature on workflows. However, the problem of supporting e-business process negotiation (eBPN) remains under-explored. This paper reports on a novel investigation into this area. Though the research is still in its initial stages, significant findings can already be reported. In this paper, an analysis and formal representation of a set of electronic negotiation (e-negotiation) system requirements for the eBPN domain is documented. This contribution should eventually lead to a generic framework for more objective and efficient evaluation of e-negotiation systems, or their sub-systems, vis--vis this problem domain. The set of requirements pertains to general negotiation strategy support, as opposed to process-specific strategy support. Despite this limitation of scope, a major shortcoming in current e-negotiation systems is identified using the formalised requirements framework : the lack of a holistic approach. Also noteworthy is the use of other e-negotiation evaluation frameworks (e.g. the Montreal Taxonomy) in the derivation of the requirements set. Motivated by the above findings, further work on a new and more holistic eBPN approach called SEPNA is currently underway and this is briefly discussed here.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".