A Model for Offshore Information Systems Outsourcing Provider Selection in Developing Countries
Bibliographic record
Abstract
Information systems outsourcing has become one of the most important aspects of strategic management. It is becoming the rule, rather than the exception in today’s globalized business environment. Many countries and regions have done numerous reforms to become the best offshore information systems outsourcing destinations in the world. For a long time, developing countries remained outside of this competition, but recently the trend has been changing. More developing countries are positioning themselves as viable alternatives to the traditional offshore destinations like India, Ireland and Czech Republic. Selecting an outsourcing provider is a daunting, delicate and very important task for organizations, and can be the cause of an outsourcing failure if not properly addressed. This paper proposes a model based on PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) for selecting the best offshore information systems outsourcing provider among a list of developing countries. A case study of 8 African countries is presented in the last part of the paper to illustrate the model. Our model differs from others as it deals with the provider selection problem at country level while most of the existing models deal with the problem at firm level. Another particularity of this model is the set of criteria chosen to analyze the suitability of a country for being the right offshore information systems outsourcing destination.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".