CHOOSING THE RIGHT B2B INFORMATION SYSTEM: MANAGING A PORTFOLIO OF CHOICES
Bibliographic record
Abstract
In this paper we propose that firm choices related to b2b systems are determined by two fundamental sets of decisions that firms need to make when committing to a b2b system. The first of these choices relate to the level of control and influence a firm needs to exercise over a b2b system – including financial ownership of the system, deciding who can access and use the system, and how the transactions are to be carried out using the system. We refer to this set of choices as the governance choices related to the b2b system. The second set of choices relate to the extent of use that the b2b system will be subjected to – including the extent to which the system will be integrated with internal processes, as well as the scope or diversity of transactions the b2b system can handle. We refer to this set of choices as the intensity of use of b2b systems. Based on data collected from large US manufacturers, and using governance and use intensity as the principal dimensions, we develop a matrix identifying four distinct modes of b2b system participation. We also identify a number of contextual factors that can influence b2b system participation modes. Finally, we identify a set of outcomes that each of the b2b system participation modes can provide. Our framework can help the practitioners answer two key questions. First, given a specific transaction context, which b2b system participation mode best suits the context? Second, having chosen a specific b2b system participation mode what outcomes can the firms expect? 129
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| 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".