A Systematic Literature Review on IT Outsourcing Decision and Future Research Directions
Bibliographic record
Abstract
During the recent decades, some academic research on the subject of information technology outsourcing (ITO) decision has appeared in different outlets, which may impede the use of such resources and as a result, repetition of research by various researchers is very likely. The purpose of this paper is then to conduct a systematic literature review (SLR) pertaining to research on ITO decision. Then, this review intends to 1) classify ITO decision literature, 2) provide a list of factors affecting ITO decision, and 3) identify ITO strategies. To this end, 91 ITO articles published between 2000 and 2018 in 51 unique journals were reviewed. The results yielded three kinds of descriptive, relational, and comparative ITO decision studies. The determinants of ITO decisions are classified into technological, organizational, environmental and user adoption factors. Furthermore, the trend of studied ITO strategies in the reviewed literature is analyzed, and future sourcing varietals are proposed. Finally, some insights and future research directions are proposed based on the review results.
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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".