Small and Medium Enterprises (SMEs) in the Cloud in Developing Countries: A Synthesis of the Literature and Future Research Directions
Bibliographic record
Abstract
Research in cloud computing is undergoing rapid growth since its evolution less than a decade ago. This paper contributes to the understanding of this growing research area and by this, considers the potential for cloud computing in small and medium enterprises (SMEs) in developing countries (DCs). The current state of research is assessed in a review of 95 research articles drawn from journals which are both peer-reviewed and academic. To do this, a framework is developed to categorise and analyse the research according to a socio-technical spectrum, identifying levels of analysis and differentiating research activity according to a lifecycle model that incorporates the requirement, needs and desires, adoption, use and adaptation and impact of SMEs in the cloud. The highlights of research in the area to date is an unbalanced use of quantitative approaches and lack of in depth use of case studies to form the basis of theorising in the area. Some gaps are also identified pointing to the fact that issues concerning the extent of impact of cloud computing in SMEs have been ignored, whilst adoption is widely covered. To support in correcting these disparities in the literature, this paper identifies key research gaps relating to conceptual approaches, methodologies, issues addressed and finally provides pointers for future research directions.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".