Business-To-Business Ecommerce Of Information Systems: Two Cases Of Asp-To-Sme Erental
Bibliographic record
Abstract
Enterprises today can “eRent” Information Systems (ISs), through the Internet, from Application Service Providers (ASPs). This emerging IS “eRental” concept is a special case of Business-to-Business eCommerce, where the product is an IS application and the business units engaged in commerce are an enterprise and an ASP. For small or medium-sized enterprises (SMEs), IS eRental might be an appealing solution to complex and costly IT acquisition and implementation. It is yet too early to assess what the future holds for ASP and how far-reaching ASP implications could be for IS delivery and management in the future economy. It is possible however, to focus on and learn from ASP case studies. This paper briefly describes Net-POS and Silverbyte, two Israeli software vendors for the hospitality industry whose members, mostly SMEs, confront with great difficulty the high cost of owning, maintaining, and managing the state-of-the-art IS infrastructure required in the Internet era. These vendors have recently entered the ASP arena by adding an IS eRental option to their for-sale IS offerings. The case studies are followed by a discussion of the new ASP concept as well as of possible directions for research on ASP-to-SME eRental.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".