E-Business for SME Development
Bibliographic record
Abstract
In recent years, information communication technologies (ICT) have become accessible throughout the world, and appear to overcome both distance and time (Cairncross, 1997). ICT affects not only individual citizens, but also companies of all sizes and across all sectors. Of all the benefits of ICT use, the most important appears to be the improvement of competitive capacity by removing geographical and time barriers. SMEs that use ICT hope to increase their market share, achieve growth and overtake their competitors. But does better competitive capacity also have an impact on local and regional development (LRD)? And does LRD have an impact on the economy of a country and the limit of a nation? The ICT/regional development challenge is a sizeable one, since the two elements evolve at very different speeds-ICT evolves quickly, while regional development is achieved slowly. Regional development at both the economic and social levels extends over a given area, within a well-identified space. The people living within the region have shared interests, goals and needs. They share, cooperate and collaborate between themselves and with the region’s stakeholders and businesses. The principal concepts underlying their actions are autonomy, partnership, solidarity, a sense of belonging, inter-personal and intercompany networks, creativity, innovation and so on. Regions, like businesses, must live with competitors both near and far. If we look at all these elements, ICT in general and e-business in particular1may well be powerful factors in local and regional development, playing an innovative role in an economic sector where both time and distance have been redefined. E-business can also generate new economic activities, new products and new services. It constitutes a vital part of the new economy, which is composed of high technology, multimedia and dot.com firms. Throughout the industrialized world, a number of ICT and e-business initiatives have been proposed to support and accelerate LRD (EuroCom, 2002). For example, some governments have proposed projects that will enable them to follow the pack, while in other countries projects have emerged from the bottom up, through regional and local economic portals or electronic markets.Request access from your librarian to read this chapter's full text.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.124 | 0.082 |
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".